The Federal Republic of Nigeria National Food Consumption and Micronutrient Survey 2021 Preliminary Report September 2022 Federal Ministry of Health Federal Ministry of Agriculture and Rural Development Federal Ministry of Finance, Budget, and National Planning International Institute of Tropical Agriculture September 2022 i ii The Federal Republic of Nigeria National Food Consumption and Micronutrient Survey 2021 Preliminary Report September 2022 iii Published by the International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria The International Institute of Tropical Agriculture (IITA) is a not-for-profit institution that generates agricultural innovations to meet Africa’s most pressing challenges of hunger, malnutrition, poverty, and natural resource degradation. Working with various partners across sub-Saharan Africa, we improve livelihoods, enhance food and nutrition security, increase employment, and preserve natural resource integrity. IITA is a member of CGIAR, a global agriculture research partnership for a food secure future. International address: IITA, Grosvenor House, 125 High Street Croydon CR0 9XP, UK Headquarters: PMB 5320, Oyo Road Ibadan, Oyo State ISBN 978-978-131-406-3 Recommended citation: Federal Government of Nigeria (FGN) and the International Institute of Tropical Agriculture (IITA). 2022. National Food Consumption and Micronutrient Survey 2021. Preliminary Report. Abuja and Ibadan, Nigeria: FGN and IITA. 288 pp. Printed in Nigeria by IITA iv Contents NFCMS Collaborating Institutions ........................................................................................................xiii Acronyms and Abbreviations ............................................................................................................... xiv Foreword ............................................................................................................................................ xvi Preface ................................................................................................................................................xvii Acknowledgments ............................................................................................................................... xix Members of the NFCMS Steering Committee (SC) ................................................................ xx Members of the NFCMS Technical Advisory Committee (TAC) .............................................. xxi Members of the NFCMS Implementation Working Group (IWG) ...........................................xxiii Key Survey Personnel ...........................................................................................................xxiv Executive Summary............................................................................................................................ xxv Key Findings ..................................................................................................................................... xxviii Background ............................................................................................................................................1 Introduction .............................................................................................................................................2 Objectives ...............................................................................................................................................3 Survey Design ........................................................................................................................................4 Study area .................................................................................................................................4 Survey design, target populations, and reporting domains ........................................................4 Sampling method ......................................................................................................................5 Questionnaires and sample collection ......................................................................................6 Survey Implementation .........................................................................................................................10 Pre-survey activities and Adaptation of INDDEX24 Mobile Application ..................................10 Eligibility Criteria, Recruitment of Respondents, and Consent Procedures ............................10 Recruitment, training, and selection of field teams ................................................................. 11 Pilot Survey .............................................................................................................................14 Survey implementation (Field Work) ....................................................................................................15 Sensitization ...........................................................................................................................15 Survey components, order of field operations, and information collected by each component .......15 Deployment of field teams and administration of survey questionnaire to selected respondents .......16 Data quality management and processing ...........................................................................................19 Sampling weights, non-response adjustment, and data analysis .........................................................20 Sampling Weights ...................................................................................................................20 Base weights .................................................................................................................20 Non-response adjustment ........................................................................................................21 Data analyses ..........................................................................................................................23 Household in sample key findings ........................................................................................................26 Total number of households and persons listed in the selected EAs by type of building structure ...................................................................................................26 Distribution of Sampled children .....................................................................................26 Distribution of sampled non-pregnant women and women of reproductive age ............27 Distribution of Sampled Adolescents ..............................................................................27 Distribution of children aged 6-59 months .....................................................................28 Sex Distribution of household heads ..............................................................................28 Female-headed households ..........................................................................................29 Income-generating activities of household heads ..........................................................29 Wealth Index (Wealth Quintiles) .....................................................................................31 Water .......................................................................................................................................31 Households’ drinking water from an improved water source ..........................................31 Other Sources of water ...................................................................................................32 v Households Drinking Water from an Unimproved Water Sources ..................................32 Distribution of households by source of drinking water ..................................................33 Sanitation ........................................................................................................................37 Food insecurity.........................................................................................................................38 Data Validation ...............................................................................................................39 Results Moderate Food Insecurity ..................................................................................40 Coping Strategies in the last seven days .......................................................................42 Food security and coping strategies ...............................................................................43 Households by Coping Index Group ...............................................................................44 Production of animal source foods ................................................................................45 Production of vegetables ...............................................................................................46 Access to land and trees or bushes that bear fruits .......................................................47 Financial Inclusion ..........................................................................................................48 Dietary Intake .......................................................................................................................................50 Overview of dietary intake data presented in this report .........................................................50 Characteristics of Respondents for the Dietary Intake ............................................................50 Pregnancy Stage and Lactation status ....................................................................................53 Pregnancy Stage by Trimester ......................................................................................53 Lactation Status of WRA .................................................................................................53 Infant and Young Child Feeding Practices ...............................................................................54 Characteristics of Respondent for the Sampled Children ...............................................58 Ever Breastfed ...............................................................................................................58 Continued Breastfeeding ...............................................................................................58 Bottle Feeding ................................................................................................................59 Biofortification coverage...........................................................................................................60 Yellow Cassava .............................................................................................................61 Orange-fleshed sweet potato .........................................................................................62 Orange Maize .................................................................................................................64 Fortification Coverage ..............................................................................................................65 Overview of coverage and fortification indicators among non-pregnant WRA ...............68 Vegetable oil ...................................................................................................................69 Wheat Flour ...................................................................................................................74 Maize Flour .....................................................................................................................79 Semolina Flour ...............................................................................................................83 Sugar ..............................................................................................................................87 Salt .................................................................................................................................91 Bouillon ...........................................................................................................................95 Fortification Status of the Food Samples Collected from the Respondents’ Households ........98 Food sample analysis .....................................................................................................98 Fortification status of the food vehicles collected from the households of the selected respondents .......................................................................................99 Anthropometry ....................................................................................................................................102 Anthropometry of children (aged 6-59 months) ....................................................................102 Anthropometry of adolescent girls (aged 10-14 years) ......................................................... 110 Anthropometry of WRA (aged 15-49 years) .......................................................................... 114 Intervention coverage, health status, and anaemia risk factors ......................................................... 119 Intervention coverage, health status, and anaemia risk factors among children (aged 6-59 months) .......................................................................................................... 119 Intervention coverage among children (aged 6-59 months) ......................................... 119 Self-reported morbidity prevalence and anaemia risk factors among children (aged 6-59 months) ..........................................................................................................133 vi Intervention coverage, health status, and anaemia risk factors among adolescent girls (aged 10-14 years) ...........................................................................................................141 Intervention coverage among adolescent girls (aged 10-14 years) ..............................141 Self-reported morbidity prevalence and anaemia risk factors among adolescent girls (aged 10-14 years) ...........................................................................................145 Other anaemia risk factors among adolescent girls (aged 10-14 years) ......................147 Intervention coverage, health status, and anaemia risk factors among WRA (15-49 years old) ..............................................................................................................149 Intervention coverage among WRA (aged 15-49 years old) .........................................149 Self-reported morbidity prevalence among WRA (aged 15-49 years) ..........................154 Other anaemia risk factor among WRA (aged 15-49 years) .........................................158 Intervention coverage, health status, and anaemia risk factors among pregnant women (15-49 years old) .................................................................................................160 Intervention coverage among pregnant women (15-49 years old) ...............................160 Self-reported morbidity prevalence ...............................................................................167 Other anaemia risk factors among pregnant WRA (aged 15-49 years) ........................170 Malaria, plasma glucose, H. pylori, helminths, Hba1c, haemoglobin genotype .................................171 Malaria, plasma glucose, H. pylori, helminths, Hba1c ...........................................................171 Prevalence of malaria, H. pylori, and helminths among children (aged 6-59 months) .......172 Prevalence of malaria and H. pylori among adolescent girls (aged 10-14 years) ........175 Prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated glycated haemoglobin (HbA1c) among WRA (aged 15-49 years) ...........................176 Prevalence of malaria, H. pylori, and helminths among pregnant women (aged 15-49 years) ..................................................................................................179 Haemoglobin genotype (Blood disorders)..............................................................................180 Anaemia .............................................................................................................................................184 Prevalence of anaemia among children (aged 6-59 months) ................................................185 Prevalence of anaemia among adolescent girls (aged 10-14 years) .....................................191 Prevalence of anaemia among WRA (aged 15-49 years)......................................................194 Prevalence of anaemia among pregnant women (aged 15-49 years) ...................................198 Conclusions ........................................................................................................................................202 Citations..............................................................................................................................................203 Annexes..............................................................................................................................................204 Annex 1. Coverage and response rates calculated using unweighted data ..........................204 Annex 2. Anthropometry and Biomarker component – Scope of the preliminary report ........212 Annex 3. Anthropometry Data Quality Report ......................................................................213 Annex 4. Infant and Young Child Feeding Practices ..............................................................221 Annex 5. Biofortification Coverage ........................................................................................223 Annex 6. Fortification Coverage.............................................................................................232 Annex 7. Summary of Food Samples Collected and Analyzed..............................................240 Annex 8. Household listing form ............................................................................................241 Annex 9. Household Questionnaire .......................................................................................245 Annex 10. Diet Questionnaire (for first visit) ..........................................................................261 Annex 11. Questionnaire for Children ....................................................................................271 Annex 12. Biomarker Questionnaire (Q) ................................................................................280 Annex 13. Adolescent girls (10-14 years old) and WRA (15-49 years old) ............................285 Annex 14. Pregnant women (15-49 years old) ......................................................................287 vii Tables 1. Sampling target groups by survey components .............................................................................5 2. Reporting domain by target groups and survey components .........................................................5 3. Adjusted sample size per EA, geopolitical zone, and at national level by sampling target group ......6 4. Biomarker measurements and analysis method/matrix by target group ........................................8 5. Adjusted sample size per EA for Phase 2 data collection ............................................................17 6. Example of response rates, corresponding adjustment factors, and final non-response adjusted weight for each weighting class in years for WRA .........................................................22 7. Variables to be considered for forming the adjustment cells for each target group .....................23 8. Reporting domain and disaggregation level of household in sample component .......................23 9. Number of respondents against those sampled ...........................................................................25 10. Total number of households and persons listed in the selected EAs by type of building structure ....26 11. Distribution of children aged 6-59 months in listed households ...................................................26 12. Distribution of non-pregnant and pregnant WRA in listed households .........................................27 13. Distribution of Adolescents ...........................................................................................................27 14. Distribution of sampled children (aged 6-59 months) in listed households ..................................28 15. Distribution of Households in Sample by Sex of Head of Household ..........................................28 16. Distribution of Household in Sample by Level of Education of Head of Household .....................29 17. Percentage Distribution of Households by Wealth Index Quintile7 ...................................................................................29 18. Percentage of head of households with income-generating activities .........................................30 19. Percentage distribution by main work of head of household for income ......................................30 20. Household Wealth Index ..............................................................................................................31 21. Percentage of household heads for which water was piped into the premises or neighbour ......32 22. Percentage of Houses that Drank from Water Sources from Other Sources ...............................33 23. Percent distribution of household according to main source of drinking water ............................35 24. Use of Sanitation facilities ............................................................................................................38 25. Results of estimating the Rasch Model on the FIES data collected in the NFCMS of Nigeria 2020 .............................................................................................................................39 26. Percentage of households in the sample experiencing food Insecurity .......................................41 27. Percentage of Households that did not Have Food or Money to Buy Food in Preceding seven Days .............................................................................................................42 28. Coping Strategies Index Score ....................................................................................................44 29. Percentage Distribution of Households by Coping Index Group ..................................................45 30. Percentage of households that Produce animal sourced foods ...................................................46 31. Percentage of households in sample that have land for gardening ............................................47 32. Percentage of households in sample that have trees or bushes that produced fruits ..................48 33. Percentage of Households that Have Accounts with Financial Institution ....................................49 34. Key sections of the diet questionnaire and results reported ........................................................50 35. Characteristics of Respondents for the Diet Component .............................................................52 36. WRA who reported having breastfed a child during the previous day or night .............................54 37. Lactating status among non-pregnant WRA who breastfed a child yesterday .............................54 38. IYCF indicators reported for infants and young children aged 6-23 month ..................................56 39. Characteristics of respondents for the sampled children .............................................................58 40. Percentage of children who were ever breastfed .........................................................................58 41. Percentage of children with continued breastfeeding ..................................................................59 42. Percentage of children fed from a bottle with a nipple .................................................................59 43. Biofortification indicators reported in the NFCMS ........................................................................60 44. Fortification coverage indicators reported in the NFCMS using data collected in the diet questionnaire ...............................................................................................................67 45. Percentage of Non-Pregnant Women of Reproductive Age (WRA) whose Households Consumed VegeOil (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile ............................................................................................................72 46. Percentage of Non-Pregnant Women Whose Households Consumed Wheat Flour (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile ......................................................................................................................77 viii 47. Percentage of Non-Pregnant Women Whose Households Consumed Maize Flour (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile ......81 48. Percentage of Non-Pregnant Women Whose Households Consumed Semolina Flour (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile ....85 49. Percentage of non-Pregnant Women Whose Households Consumed Sugar (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile .................89 50. Percentage of Non-Pregnant Women Whose Households Consumed Salt (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile .........93 51. Percentage of Non-Pregnant Women Whose Households Consumed Bouillon (purchased, branded, and labelled as fortified) by Residence, Zone, and Wealth Quintile .........96 52. Food vehicle samples collected and analysed .............................................................................98 53. Minimum National Industrial Requirements (NIS)-Expected Value in the Mandatory Vehicles ....99 54. Descriptive statistics of Fortificant contents (at any level) of the Food samples collected from the households of Non-pregnant Women at repeat interview ............................................101 55. Malnutrition status of children (aged 6-59 months), Nigeria 2021 ..............................................106 56. Severe malnutrition status of children (aged 6-59 months), Nigeria 2021 .................................108 57. Prevalence of stunting, thinness, normal weight, overweight, and obesity in adolescent girls (aged 10-14 years), Nigeria 2021 ................................................................. 112 58. Prevalence of thinness, normal weight, overweight, and obesity in WRA (aged 15-49 years), Nigeria 2021 .............................................................................................. 117 59. Vitamin A supplementation among children aged 6-59 months, Nigeria 2021 ...........................121 60. Coverage of nutrition counselling on specific key messages in the past six months among children (aged 6-59 months) whose caregivers1 reported receiving some form of nutrition counselling, Nigeria 2021 ...........................................................................................................124 61. Use of micronutrient powder or any sprinkles with iron in the past six months among children (aged 6-59 months), Nigeria 2021 ................................................................................126 62. Deworming in the past six months among children (6-59 months), Nigeria 2021 ......................129 63. Background characteristics of children (aged 6 to 11 months) who received some form of drug for intestinal worms in the last six months ...................................................130 64. Use of therapeutic feeds in the past 12 months and the day before the interviews among children aged 6-59 months, Nigeria 2021 ......................................................................131 65. Use of therapeutic feeds in the past 12 months and the day before the interviews among children with wasting (aged 6-59 months), Nigeria 2021 ...........................................................132 66. Prevalence of diarrhoea1 and blood in stool among children (aged 6-59 months), Nigeria 2021 ....135 67. Most common1 diarrhoea treatment among children (aged 6-59 months), Nigeria 2021 ...........137 68. Prevalence of fever, cough, and difficulty breathing among children (aged 6-59 months), Nigeria 2021 ...............................................................................................................................139 69. Prevalence of pica among children (aged 6-59 months), Nigeria 2021 .....................................140 70. Use of multivitamin, iron or iron/folic acid tablets, and deworming treatment among adolescent girls (aged 10-14 years), Nigeria 2021 ....................................................................143 71. Prevalence of self-reported illness and hospitalization/clinic visits in the past two weeks among adolescent girls (aged 10-14 years), Nigeria 2021 ........................................................146 72. Prevalence of pica, smoking, and diagnosis of anaemia in the past six months among adolescent girls (aged 10-14 years), Nigeria 2021 ....................................................................148 73. Use of multivitamin, iron or iron/folic acid tablets, and deworming treatment among WRA (aged 15-49 years), Nigeria 2021 ..............................................................................................151 74. Prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among WRA (aged 15-49 years), Nigeria 2021 .........................................................................156 75. Prevalence of pica, smoking, and diagnosis of anaemia in the past six months among WRA (15-49 years), Nigeria 2021 .......................................................................................................159 76. WHO recommendations on antenatal care for a positive pregnancy experience ......................161 77. Prevalence of at least one antenatal care visit among pregnant women, Nigeria 2021 ............162 78. Percentage of pregnant women (aged 15 to 49 years) who consumed a tablet or syrup containing iron at least once in the last seven days and those who consumed a tablet or syrup containing iron and/or folic acid the day before the interview, Nigeria 2021 ................164 ix 79. Percentage of pregnant women (aged 15-49 years) who had spoken to a health worker or community volunteer about what foods to eat during pregnancy and about breastfeeding their newborn .............................................................................................................................166 80. Prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among pregnant women (aged 15-49 years), Nigeria 2021 .......................................................168 81. Prevalence of smoking among pregnant women, Nigeria 2021 .................................................170 82. Blood analysis done in the field and laboratory for respective respondents ..............................171 83. Prevalence of malaria, H. pylori, and helminths among children (aged 6-59 months), Nigeria 2021 ...............................................................................................................................174 84. Prevalence of malaria and H. pylori among adolescent girls (aged 10-14 years), Nigeria 2021 ..... 175 85. Prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated HbA1c among WRA (aged 15-49 years), Nigeria 2021 .........................................................................177 86. Prevalence of malaria, H. pylori, and helminths among pregnant women (aged 15-49 years) Nigeria 2021 ...............................................................................................................................179 87. Prevalence of haemoglobin genotype (HbAA, HbAS) and prevalence of inherited blood disorders (HbSS) among children (aged 6-59 months), Nigeria 2021 .............................181 88. Prevalence of haemoglobin genotype (HbAA, HbAS) and prevalence of inherited blood disorders (HbSS) among WRA (aged 15-49 years), Nigeria 2021 ...................................183 89. Anaemia cut-offs for the respective target groups......................................................................184 90. Prevalence of anaemia among children (aged 6-59 months), Nigeria 2021 ..............................187 91. Anaemia among children (aged 6-59 months) by infection-related characteristics, haemoglobin genotype, and supplement use, Nigeria 2021 ......................................................189 92. Prevalence of anaemia among adolescent girls (aged 10-14 years), Nigeria 2021 ...................191 93. Anaemia among adolescent girls (aged 10-14 years) by infection-related characteristics and supplement use, Nigeria 2021 ............................................................................................192 94. Prevalence of anaemia among WRA (aged 15-49 years), Nigeria 2021 ...................................195 95. Anaemia among WRA (aged 15-49 years) by infection-related characteristics, haemoglobin genotype, and supplement use, Nigeria 2021 ......................................................196 96. Prevalence of anaemia among pregnant women (aged 15-49 years), Nigeria 2021 .................199 97. Anaemia among pregnant women (aged 15-49 years) by infection-related characteristics and supplement use Nigeria, 2021 ....................................................................200 98. Cluster (Sampled EAs for the Survey) Coverage Rate by Zone and Sector (Rural-Urban) .......204 99. National Response Rates of Target Groups/Sector by Modules ................................................205 100. Response Rate by Sector (Rural-Urban), Target Groups and Specific Modules ........................206 101. Zonal Response Rates of Target Groups by Modules ................................................................208 102. Response Rates by Age Group and Zone .................................................................................. 211 103. Infant and young child feeding practices ....................................................................................221 104. Consumed yellow cassava or any food products made from it in the past 30 days ...................223 105. Frequency of consumption of yellow cassava or any food products made from it in the past 30 days among consumers ......................................................................................225 106. Consumed OFSP or any food products made from it in the past 30 days .................................226 107. Frequency of consumption of OFSP or any food products made from it in the past 30 days among consumers ......................................................................................................................228 108. Consumed orange maize or any food products made from it in the past 30 days .....................229 109. Frequency of consumption of orange maize or any food products made from it in the past 30 days among consumers .............................................................................................................231 110. Type, source, and brand of vegeoil obtained for the household, by target group .......................232 111. Type, source, and brand of wheat flour obtained for the household, by target group .................234 112. Type, source, and brand of maize flour obtained for the household by target group ..................235 113. Type, source, and brand of semolina flour obtained for the household by target group .............236 114. Type, source, and brand of sugar obtained for the household by target group1 ........................237 115. Type, source, and brand of salt obtained for the household by target group1 ............................238 116. Type, source, and brand of bouillon obtained for the household by target group1 .....................239 117. Summary of the food samples collected, processed and distributed for laboratory analyses ....240 118. MicroChem Lab., South Africa ....................................................................................................240 119. Intertek Lab., Germany ...............................................................................................................240 120. BATO Lab., Lagos ......................................................................................................................240 121. FIIRO, Oshodi Lagos ..................................................................................................................240 x Figures 1. Geopolitical zones in Nigeria ..........................................................................................................4 2. Survey components, order of field operations, and information collected by each component ...15 3. Screen plot of the principal components’ analysis conducted on the residuals obtained after estimating the Rasch Model .................................................................................................40 4. Calibration of the FIES measurement scale obtained with the data collected in the NFCMS, Nigeria, and the Global FIES Reference Scale ............................................................................40 5. Pregnancy Stage by Trimester .....................................................................................................53 6. Percentage of respondents that consumed selected biofortified foods the previous 30 days .....61 7. Percentage of respondents that consumed yellow cassava (or any food products made from it) the previous 30 days at national level and by residence, zone, and wealth quintile ....................61 8. Frequency of consumption of yellow cassava (or any food products made from it) in the previous 30 days among consumers ..................................................................................62 9. Percentage of Respondents that Consumed Orange-Fleshed Sweet Potatoes (or any food products made from it) in the Previous 30 Days at National Level and by Residence, Zone and Wealth Quintile ..............................................................................63 10. Frequency of consumption of orange-fleshed sweet potato (or any food products made from it) in the previous 30 days among consumers ........................63 11. Percentage of respondents that consumed orange maize (or any food products made from it) in the previous 30 days at national level and by residence, zone, and wealth quintile ................................................................................64 12. Frequency of consumption of orange maize (or any food products made from it) in the previous 30 days among consumers ..................................................................................65 13. Coverage of Selected Food Vehicles among Households of the sampled Non-Pregnant Women at National Level .............................................................................................................68 14. Percentage of Non-Pregnant Women Whose Households Consumed Vegetable Oil (purchased, branded, labelled as fortified and fortified) at National Level ...................................70 15. Main type of vegetable oil used in the household among consumers ..........................................73 16. Brand of vegetable oil obtained the last time among consumers .................................................74 17. Percentage of Non-Pregnant Women Whose Households Consumed Wheat Flour (purchased, branded, labelled as fortified and fortified) at National Level ...................................75 18. Main types of wheat flour used in the household among consumers ..........................................78 19. Brand of wheat flour obtained the last time among consumers ...................................................78 20. Percentage of Non-Pregnant Women Whose Households Consumed Maize Flour (purchased, branded, labelled as fortified and fortified) at National Level ...................................80 21. Main type of maize flour used in the household among consumers ............................................82 22. Brand of maize flour obtained the last time among consumers ...................................................82 23. Percentage of Non-Pregnant Women Whose Households Consumed Semolina Flour (purchased, branded, labelled as fortified and fortified) at National Level ...................................83 24. Main type of semolina flour used in the household among consumers ......................................86 25. Brand of semolina flour obtained the last time among consumers ..............................................86 26. Percentage of Non-Pregnant Women Whose Households Consumed Sugar (purchased, branded, labelled as fortified and fortified) at National Level ...................................88 27. Main type of sugar used in the household among consumers .....................................................90 28. Brand of sugar obtained the last time among consumers ............................................................90 29. Percentage of Non-Pregnant Women Whose Households Consumed Salt (purchased, branded, labelled as fortified and fortified) at National Level ...................................92 30. Main types of salt used in the household among consumers ......................................................94 31. Brands of salt obtained the last time among consumers .............................................................94 32. Percentage of Non-Pregnant Women Whose Households Consumed Bouillon (purchased, branded, labelled as fortified and fortified) at National Level ...................................95 33. Main types of bouillon used in the household among consumers ................................................97 34. Brand of bouillon obtained the last time among consumers ........................................................97 35. Fortification status of food vehicle samples collected from non-pregnant women at the repeat interview ................................................................................................................100 xi 36. Anthropometric status for children (aged 6-59 months), Nigeria 2021 .......................................105 37. Prevalence of thinness, normal weight, overweight, and obesity among adolescent girls (aged 10-14 years), Nigeria 2021 .................................................................... 111 38. Prevalence of thinness, normal weight, overweight, and obesity among WRA (aged 15-49 years), Nigeria 2021 .............................................................................................. 116 39. Coverage of nutrition-specific interventions among children (aged 6-59 months), Nigeria 2021 ...............................................................................................................................120 40. Source of verification among children (6-59 months) who received a Vitamin A dose in the past six months, Nigeria 2021 ..........................................................................................122 41. Prevalence of self-reported morbidity (reported by caregiver), and anaemia risk among children (aged 6-59 months), Nigeria 2021 ................................................................................133 42. Reported treatments for diarrhoea in children (aged 6-59 months), Nigeria 2021 .....................136 43. Coverage of nutrition-specific interventions among adolescent girls, Nigeria 2021 ...................141 44. Frequency of use of multivitamins in the past seven days among adolescent girls (aged 10-14 years), Nigeria 2021 ..............................................................................................144 45. Frequency of use of any iron/folic acid tablets in the past seven days among adolescent girls (aged 10-14 years), Nigeria 2021 ........................................................144 46. Overall prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among adolescent girls (aged 10-14 years), Nigeria 2021 ........................................................145 47. Prevalence of anaemia risk factors (pica and smoking) and diagnosis of anaemia in the past six months among adolescent girls (aged 10-14 years), Nigeria 2021 .....................147 48. Coverage of nutrition-specific interventions among WRA (aged 15-49 years), Nigeria 2021 ....149 49. Frequency of use of multivitamins in the past seven days among WRA, Nigeria 2021 .............153 50. Frequency of use of any iron/folic acid tablets in the past seven days among WRA, Nigeria 2021 ........................................................................................................153 51. Overall prevalence of self-reported illness and hospitalization/clinic visits in the past two weeks among WRA (aged 15-49 years), Nigeria 2021 ......................................154 52. Prevalence of anaemia risk (pica and smoking) and diagnosis of anaemia in the past six months among WRA (aged 15-49 years), Nigeria 2021 .....................................158 53. Overall prevalence of any nutrition-related interventions – antenatal care, supplementation, and nutrition counselling - among pregnant women (aged 15-49 years), Nigeria 2021 .............160 54. Timing of the first antenatal care visit by month of pregnancy among pregnant women, Nigeria 2021 ...............................................................................................................................163 55. Adequacy of number of antenatal care visits by the length of pregnancy among pregnant women, Nigeria 2021 ......................................................................................163 56. Frequency of use of iron tablet or syrup in the past seven days among pregnant women, Nigeria 2021 ......................................................................................165 57. Overall prevalence of self-reported illness (fever, malaria, diarrhoea, and cough) and hospitalization/clinic visits in the last two weeks among pregnant women, Nigeria 2021 ..........167 58. Overall prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated HbA1c among children 6-59 months, adolescent girls, WRA, and pregnant women, respectively, Nigeria 2021. .........................................................................................................173 59. Prevalence of haemoglobin genotype and prevalence of inherited blood disorders by target group at national level (linked to Tables 88 and 89), Nigeria 2021 .............................181 60. Overall prevalence of any, mild, moderate, and severe anaemia by target group, Nigeria 2021 ...............................................................................................................................186 xii NFCMS Collaborating Institutions The 2021 National Food Consumption and Micronutrient Survey was implemented by the International Institute of Tropical Agriculture (IITA), in collaboration with the following organizations: Federal Ministry of Health (FMOH) Federal Ministry of Agriculture and Rural Development (FMARD) Federal Ministry of Finance, Budget, and National Planning (FMFB&NP) National Population Commission, Nigeria (NPC) National Bureau of Statistics, Nigeria (NBS) United Nations Children’s Fund (UNICEF) Tufts University- International Dietary Data Expansion Project (INDDEX) FHI360 Solutions-Intake Center for Dietary Assessment Oxford Policy Management (OPM) Food and Agriculture Organization of the United Nations (FAO) University of Ibadan University of Calabar University of Wisconsin-Madison, USA Cornell University, USA Gates Foundation World Bank Group Donor Support and Disclaimer This project is supported by the Federal Ministry of Health and World Bank Group under the cooperative agreement # NG-ND, FMOH-127145-CS-CDS/01, and the Gates Foundation, United Nations Children’s Fund, and Foreign, Commonwealth & Development Office under the cooperative agreement # NIWPCA2020240/PD2020200, United States Agency for International Development, Nutrition International under the cooperative agreement # 10-2097-INTINS-01, and Federal Ministry of Agriculture and Rural Development to the International Institute of Tropical Agriculture. The findings and conclusions of this report are those of the authors and do not necessarily represent the official position of the funding agencies. Contact Information Information about the NFCMS 2021 may be obtained from the headquarters of the Federal Ministries of Health and Agriculture and Rural Development, Nutrition Divisions, Abuja, Nigeria. Additional information about the NFCMS 2021 may be obtained from IITA, Oyo Road, Ibadan, Nigeria (telephone: +234-803-4035281; fax: +44-208-7113786; email: iita@cgiar.org; internet: www.iita.org). xiii Acronyms and Abbreviations ANC Antenatal Care ASF Animal Source Foods CAPI Computer-Assisted Personal Interviews CI Confidence Interval CM Community Mobilizers DHS Demographic and Health Survey EAs Enumeration Areas FAO Food and Agriculture Organization of the United Nations FCDB Food Composition Database FCDO Foreign, Commonwealth & Development Office FCT Federal Capital Territory FGN Federal Government of Nigeria FIES Food Insecurity Experience Scale FMARD Federal Ministry of Agriculture and Rural Development FMFB&NP Federal Ministry of Finance, Budget, and National Planning FMOH Federal Ministry of Health FRIL Food, Recipe, and Ingredient Listing GAIN Global Alliance for Improved Nutrition GLP Good Laboratory Practice HH Household HIV Human Immunodeficiency Virus HPLC High-Performance Liquid Chromatography IFA Iron Folic Acid IFPRI International Food Policy Research Institute IITA International Institute of Tropical Agriculture IMCI Integrated Management of Childhood Illness INDDEX International Dietary Data Expansion Project IYCF Infant and Young Child Feeding LGAs Local Government Areas LSMS Living Standards Measurement Survey MNCHW Maternal Neonatal and Child Health Weeks MNDC National Micronutrient Deficiency Control MOS Measure of Size MRDR Modified Relative Dose Response NBS National Bureau of Statistics NC North Central NCD Non-Communicable Diseases NDHS Nigeria Demographic and Health Survey NE North East NFCMS National Food Consumption and Micronutrient Survey NFPN National Policy on Food and Nutrition NHREC National Health Research Ethics Committee of Nigeria NPC National Population Commission NPHCDA National Primary Health Care Development Agency NW North West OPM Oxford Policy Management ORS Oral Rehydration Salt PPS Probability Proportional to Size xiv PSEM-CF Portion Size Estimation Methods Conversion Factors PSEMs Portion Size Estimation Methods PSUs Primary Sampling Units RDT Rapid Diagnostic Test Kit SC Steering Committee SCD Sickle Cell Disease SE South East SES Socioeconomic Status SMARD State Ministry of Agriculture and Rural Development SMOH State Ministry of Health SPHCDA State Primary Health Care Development Agency SS South South SW South West TAC Technical Advisory Committee ToT Training of Trainers UNICEF United Nations Children’s Fund USA United States of America USAID United States Agency for International Development VA Vitamin A VAD Vitamin A Deficiency WASH Water, Sanitation and Hygiene WHO World Health Organization WRA Women of Reproductive Age xv Foreword Malnutrition has been identified as a major constraint to development. The proportion of individuals and households that are both malnourished and food insecure has been on the increase in Nigeria; and children, women, adolescent girls, and the elderly are the most affected. The Federal Government of Nigeria, in collaboration with other stakeholders, implemented the National Food Consumption and Micronutrient Survey (NFCMS) as one of its landmark steps towards addressing the burden of malnutrition and its associated consequences, and ensuring the availability of very reliable data for decision making. The dearth of food consumption and nutrition data poses great challenges to addressing questions that policy makers need to answer in tackling issues of undernutrition, micronutrient deficiencies, overweight and obesity, and diet-related chronic non-communicable diseases (DR-NCDs), and in improving the food systems to deliver healthy diets to the population. Some data are available from a variety of sources to help identify dietary trends in adults, infants, young children, women, and households experiencing poverty, but the picture is fragmented and incomplete making it difficult for policy makers to take an informed decision towards addressing malnutrition in the country. The National Food Consumption and Micronutrient Survey (2021), which is the third nationally- representative survey of its kind in Nigeria, was conducted to assess the micronutrient status and dietary intake of Women of Reproductive Age (15–49 years), including pregnant and lactating women and children aged 6 –59 months. The study also assessed the micronutrient status of non- pregnant adolescent girls aged 10–14 years and identified key factors associated with poor nutrition in these populations. The information generated will provide a foundation for the formulation of evidence-informed policies and programmes and in monitoring progress, going forward. The outcome of the Survey will enhance the deliverables of the National Multisectoral Plan of Action for Food and Nutrition (NMPFAN 2021 – 2025) as well as priority actions identified in the Nigeria Food Systems Transformation Pathways both of which are consistent with the policy thrust of the present administration as encapsulated in the National Development Plan (2021-2025) and Nigeria Agenda (2050). A very high consultative process was adopted in the implementation of the Survey. All stakeholders in food and nutrition sectors including representatives of government, Organised Private Sector, Civil Society Organisations, Academia, local NGOs, Development Partners, and International Donor Agencies were involved in its implementation. This preliminary report presents a first look at selected findings from the (NFCMS) 2021 and covers sample households’ socioeconomic and demographic characteristics, diet questionnaire, anthropometry, biomarker questionnaires, food sample analysis, and biomarker indices analysed in-country. The report does not include findings from the 24-hour dietary recall and biomarker indices currently undergoing independent analysis and whose result will be presented in a final report in July 2023. Prince Clem Ikanade Agba Honourable Minister of State, Budget, and National Planning xvi Preface Nigeria is undergoing rapid urbanization with a rapidly growing population. Nigeria continues to face high rates of chronic undernutrition, micronutrient deficiency, overweight and obesity, and associated diet-related non-communicable diseases (a.k.a. triple burden of malnutrition). There is a significant rise in the incidence of diet-related non-communicable diseases (NCDs) and the prevalence of overweight and obesity, as well as Type 2 diabetes in adults. The Global Panel estimates that the number of people with Type 2 diabetes in the country would double by 2030. The complexity in food systems (e.g., due to urbanization) implies that any attempt at improving the multiple burden of malnutrition would require a systemic approach to identifying risk factors and designing evidence-informed policies and interventions that account for spatial and socio-cultural issues. When defining and understanding the scope and magnitude of food and nutritional issues and their causes, there is a need for high quality, timely, and complete data. In addition, reliable data is also required to examine the use and targeting of resources and determine the impact and cost-effectiveness of intervention programmes. Nutrition data can be used to bolster social accountability and are necessary to assess progress on national and global nutrition targets. The lack of current data on food consumption and micronutrients from a representative sample remain a major constraint to understanding nutrient and dietary gaps in Nigeria. The National Food Consumption and Micronutrient Survey (NFCMS) 2021 is the third nationally representative survey of its kind conducted in Nigeria, following those implemented in 1968 and 2001. It provides up-to- date information on micronutrient status, anthropometry, and dietary intake indicators. The NFCMS 2021 is a cross-sectional population-based survey with the sample stratified by geopolitical zone. Sampling within each zone followed a two-stage random selection strategy with enumeration areas (EAs) as the sampling units for the first stage. A total of 390 EAs were selected with probability proportional to size (PPS) using systematic sampling. The second stage was a complete listing of households carried out in each of the 390 selected EAs, followed by a line listing of all eligible respondents per target group in each sampled EA. The target groups were women of reproductive age (WRA) aged 15-49 years, children (aged 6-59 months), pregnant women, and non-pregnant adolescent girls (aged 10-14 years). A representative sample of 14 820 respondents was selected for the survey. The NFCMS 2021 collected information on types and amounts of foods consumed in the last 24 hours, height/length, weight, age, and biological samples, precisely blood, urine and stool and analyzed locally and internationally for haemoglobin genotype, HbA1c, status of iron and inflammation, vitamin A, folate, zinc, iodine, vitamin B1, vitamin B2, vitamin B12, malaria, H. pylori, haemoglobin, plasma glucose, and helminths. In addition to presenting national estimates, the report provides estimates of key indicators for both rural and urban areas, and the country’s six geopolitical zones. Estimates are not provided at the state level. The NFCMS 2021 is unique in several ways. For the first time, the survey was implemented using computer-assisted personal interviewing (CAPI), allowing more rapid provision of data than in previous surveys. The survey tools and design used can serve as a model for the use of the application in other African country’s food consumption surveys, especially the INDDEX24 Mobile Application used to capture dietary intake data. Nigeria is the first country to use this innovative dietary intake assessment tool in a large-scale survey. Some of the dietary data of interest include the use of fortifiable vehicles to assess the impact of large-scale fortification programme, and consumption of biofortified crops, which will be used to measure the impact of these programmes. xvii Different databases were also created (Food, Recipe, and Ingredient List Database; Conversion Factors Database; Recipe and Yield Factors database). This is an important resource for Nigeria in dietary intake assessment, especially when using the novel INDDEX 24 Mobile Application. Also, the Nigerian Food Composition Database that document all commonly consumed foods and beverages with their nutrient values, was revised after 26 years. These are intended to be used as reference, as well as adoption by other African countries, especially in West Africa. National food composition table/database is a resource every county needs to assess dietary intake for strategic interventions. The International Institute of Tropical Agriculture (IITA) is a not-for-profit institution that generates agricultural innovations to meet Africa’s most pressing challenges of hunger, malnutrition, poverty, and natural resource degradation. Working with various partners across sub-Saharan Africa, IITA improves livelihoods, enhances food and nutrition security, increases employment, and preserves natural resource integrity. IITA is a member of CGIAR, a global agriculture research partnership for a food secure future. We believe that with these data and evidence, Nigeria is better placed to improve the nutrition outcomes of its population, especially women and children. The quality, volume and diversity of the data collected in the NFCMNS has set Nigeria apart in the African continent and globally because it will spark a global conversation about how to invest in agriculture, nutrition, and food systems to ensure a future in which all children get good quality food they need to thrive, not just to survive. And it is not just talk, the dialogue among like-minded investors will lead to action and action will bring results and impact. Dr Nteranya Sanginga Director General, International Institute of Tropical Agriculture xviii Acknowledgments The Federal Ministry of Health (FMOH) and the Federal Ministry of Agriculture and Rural Development (FMARD) wishes to acknowledge the following organizations that participated and contributed immensely to the success of the survey which implementation took place between January to September 2021. Special thanks to the Lead implementing Agency, the International Institute of Tropical Agriculture IITA, which oversaw the technical and implementing aspect of the survey, under the charismatic lead investigator and overall survey coordinator, Dr Bussie Maziya Dixon. The lead Biomarker Dr Mercy Lung’aho, the lead Dietary intake, Dr Olapeju Phorbee and Mr Samuel Ofodile, database manager, Dr Isiaka Olarewaju and Dr Kolapo Usman as consultant statisticians. Special appreciation goes to the Bill and Melinda Gates Foundation (BMGF), Global Alliance for Improve Nutrition (GAIN), FMOH through the World Bank supported Accelerated Nutrition Result in Nigeria (ANRIN) project, FMARD, Foreign Commonwealth and Development Office (FCDO), United States Agency for International Development (USAID), Nutrition International (NI), for their financial and technical support. Worthy of note is the effective collaboration and support from UNICEF as the financial custodian of the survey, and WHO for their support in shipping and clearing of samples to China. Furthermore, we wish to appreciate the contributions of Prof Rasaki Sanusi, University of Ibadan. Prof Henrietta Ene-Obong, University of Calabar, who served as co-principal investigators and additional resource persons during the training of field teams. The Federal Ministry of Health and the Federal Ministry of Agriculture also extends its appreciation to Dr Victor Ajieroh of BMGF, Dr Adeyinka Onabolu senior adviser to the Honourable Minister of Agriculture and Dr Chris Isokpunwu senior adviser to the Honourable Minister of Health for their valuable contributions to the survey. The FMOH and FMARD, also appreciate the technical advisors in the survey namely CDC Atlanta USA which provided technical support in the designing and planning of the survey through December 2020, FHI Solution- Intake, Tufts University-INNDEX, Cornel University, University of Wisconsin, National Bureau of Statistics, National Population Commission, Oxford Policy Management, the Zonal Coordinators, Assistant Zonal Coordinators, supervisors, interviewers, and the respondents. We sincerely appreciate the sustained leadership provided by the Honorable Minister of Health, Dr Osagie Ehanire, Honourable Minister of Agriculture Dr Mohammed Mahmood Abubakar and the Honourable Minster of Budget and National Planning Zainab Shamsuna Ahmed, throughout the National Food Consumption and Micronutrient Survey (NFCMS). Similarly, we extend appreciation to the Permanent Secretaries of Health, Agriculture and Budget as well as the Honourable Minister for State for Health, Ministry of Agriculture and MBNP, for their immense contributions to the success of the survey. Finally, the overall effective coordination efforts of the Director Nutrition FMOH Dr Binyerem Ukaire, Director Social Development MBNP Dr Faniran Sanjo, Deputy Director FMARD Dr Rasaq Oyeleke, and Deputy Director MBNP Mrs. Nelson Chito towards the successful implementation of the survey is highly commendable. Dr. Salma Ibrahim Anas MBBS, MWACP, FMCPH Director: Family Health Department xix Members of the NFCMS Steering Committee (SC) S/N Institution Name 1 Ministry of Finance, Budget, and National Planning Mr. Kayode Obasa* 2 Ministry of Finance, Budget, and National Planning Mrs. Falilat Abdulraheem* 3 Ministry of Finance, Budget, and National Planning Mr. Bolaji A Saadu* 4 Ministry of Finance, Budget, and National Planning Mr. Omotayo Adeyemi 5 Federal Ministry of Agriculture and Rural Development Mrs. Karima Babangida* 6 Federal Ministry of Agriculture and Rural Development Eng. A, G. Abubakar 7 Federal Ministry of Health Dr. Adebimpe Adebiyi* 8 Federal Ministry of Health Dr. Salma Anas Kolo 9 Federal Ministry of Health Dr Binyerem Ukaire 10 National Bureau of Statistics Dr. Isiaka Olarewaju* 11 National Bureau of Statistics Mr. Imeh Udoabah 12 National Population Commission Mr. Kolapo Usman* 13 National Population Commission Mrs. Titilayo Tawa 14 Bill & Melinda Gates Foundation Dr. Victor Ajieroh 15 Aliko Dangote Foundation Dr. Francis Aminu 16 European Union Dr. Anthony Ayeke 17 World Bank Dr. Ritgak Asabe Sarah Dimka 18 United States Agency for International Development Dr. Laurel Rushton 19 United States Agency for International Development Mr. Ebenezer Oluloto 20 Foreign, Commonwealth & Development Office (FCDO) Ms. Melkamnesh Alemu* 21 Foreign, Commonwealth & Development Office (FCDO) Mr. Diego Moroso 22 International Institute of Tropical Agriculture Dr. Kenton Dashiell 23 United Nations Children’s Fund Dr. Simeon Nanama* 24 United Nations Children’s Fund Mr. Zakaria Fusheini* 25 United Nations Children’s Fund Ms. Nemat Hajeebhoy 26 Global Alliance for Improved Nutrition Dr. Michael Ojo 27 FMARD/GAIN Dr. Adeyinka Onabolu * Served at various times xx Members of the NFCMS Technical Advisory Committee (TAC) S/N Institution Name 1 National Population Commission Unogu S.M.* 2 National Population Commission Mr. Lukman Esuola* 3 National Population Commission Mr. Kolapo Usman* 4 National Population Commission Mrs. Titilayo Tawa 5 Federal Ministry of Health Dr. Chris Isokpunwu* 6 Federal Ministry of Health Mr. Dominic Elue* 7 Federal Ministry of Health Dr Binyerem Ukaire 8 Federal Ministry of Health Dr. Maria Odey 9 Federal Ministry of Agriculture & Rural Development Mrs. Zainab Towobola* 10 Federal Ministry of Agriculture & Rural Development Dr. Rasaq Oyeleke 11 Ministry of Finance, Budget, and National Planning Mrs. Rosaline Gabriel* 12 Ministry of Finance, Budget, and National Planning Mrs. Nelson Chito 13 University of Calabar Prof. Henrietta Ene-Obong 14 University of Ibadan Prof. Rasaki Ajani Sanusi 15 Ahmadu Bello University Dr. Muyiwa Owolabi 16 Usman Danfodiyo University, Sokoto Dr. Shafa’atu Giwa Ibrahim 17 Usman Danfodiyo University, Sokoto Prof. Rabi’u Umar Aliyu 18 Aliko Dangote Foundation Dr. Francis Aminu 19 Bill & Melinda Gates Foundation Dr. Victor Ajieroh 20 World Bank Sangeeta Carol Pinto* 21 World Bank Dr. Ritgak Asabe Sarah Dimka 22 World Bank Eubert Rufurunesu Vushoma 23 FHI Solutions-Intake Center for Dietary Assessment Dr. Megan Deitchler 24 FHI Solutions-Intake Center for Dietary Assessment Dr. Elaine Ferguson* 25 FHI Solutions-Intake Center for Dietary Assessment Dr. Marieke Vossenaar 26 FHI Solutions-Intake Center for Dietary Assessment Dr. Joanne Arsenault 27 International Dietary Data Expansion (INDDEX) Dr. Jerome Some 28 John Hopkins University Dr. Rebecca Heidikamp 29 FAO Dr. Olutayo Adeyemi* 30 FAO Ms. Nkeiruka Enwelum* 31 UNICEF Dr. Simeon Nanama* 32 UNICEF Mr. Henry Mark Edward* 33 UNICEF Mr. Zakaria Fusheini * 34 UNICEF Mr. Celestine Ekwuluo* 35 UNICEF Mr. Abraham Bangamsi Mahama* 36 UNICEF Mrs Hanifa Namusoke 37 UNICEF Mr Edward Kutondo 38 FCDO Ms. Melkamnesh Alemu* 39 FCDO Mr. Diego Morosso xxi S/N Institution Name 40 IITA Dr. Busie Maziya-Dixon 41 IITA Dr. Olapeju Phorbee 41 IITA Dr. Mercy Gloria Lungaho 42 IITA Mr. Samuel Ofodile 43 Nutrition International Mrs Titilola Abolade 44 IFPRI Dr. George Mavrotas* 45 NPHCDA Mr. Inuwa Yau* 46 Nigeria Centre for Disease Control Dr. Ayoola Olufemi* 47 National Bureau of Statistics Mr. Kola Ogundiya* 48 National Bureau of Statistics Mr. Bishop Ohioma* 49 National Bureau of Statistics Dr Isiaka Olarewaju* 50 National Bureau of Statistics Mr. Imeh Udoabah 51 OPM Femi Adegoke 52 OPM Babatunde Akano 53 FMARD/GAIN Dr Adeyinka Onabolu xxii Members of the NFCMS Implementation Working Group (IWG) S/N Institution Name 1 National Population Commission Mrs. Titilayo Tawa 2 Federal Ministry of Health Dr. Binyerem Ukaire 3 Federal Ministry of Health Mr. Dominic Elue 4 Federal Ministry of Health Dr. Maria Odey 5 Federal Ministry of Agriculture & Rural Development Dr. Rasaq Oyeleke 6 Ministry of Finance, Budget, and National Planning Mrs. Nelson Chito 7 University of Calabar Prof. Henrietta Ene-Obong 8 University of Ibadan Prof. Rasaki Ajani Sanusi 9 University of Ibadan Dr. Olutayo Adeyemi 10 Bill & Melinda Gates Foundation Dr. Victor Ajieroh 11 World Bank Dr. Ritgak Asabe Sarah Dimka 12 World Bank Eubert Rufurunesu Vushoma 13 FHI Solutions-Intake Center for Dietary Assessment Dr. Megan Deitchler 14 FHI Solutions-Intake Center for Dietary Assessment Dr. Mario Chen 15 FHI Solutions-Intake Center for Dietary Assessment Dr. Marieke Vossenaar 16 FHI Solutions-Intake Center for Dietary Assessment Dr. Joanne Arsenault 17 Tufts University- International Dietary Data Expansion Project (INDDEX) Dr. Jerome Some 18 John Hopkins University Dr. Rebecca Heidikamp 19 UNICEF Hanifa Namusoke 20 International Institute of Tropical Agriculture Dr. Busie Maziya-Dixon (Chairperson) 21 International Institute of Tropical Agriculture Dr. Olapeju Phorbee 22 International Institute of Tropical Agriculture Dr. Mercy Gloria Lungaho 23 International Institute of Tropical Agriculture Mr. Samuel Ofodile 24 International Institute of Tropical Agriculture Dr. Isiaka Olarewaju 25 International Institute of Tropical Agriculture Dr. Kolapo Usman 26 Nutrition International Titilola Abolade 27 National Bureau of Statistics Mr. Imeh Udoabah 28 Oxford Policy Management (OPM) Dr. Femi Adegoke 29 Oxford Policy Management (OPM) Mr. Babatunde Akano 30 FMARD/GAIN Dr. Adeyinka Onabolu xxiii Key Survey Personnel Lead Implementing Agency International Institute of Tropical Agriculture Institution Name Role IITA Dr Busie Maziya-Dixon Principal Investigator and Overall Survey Coordinator Dr Mercy Lun’gaho Biomarker Lead Dr Olapeju Phorbee Dietary Intake Lead Dr Isiaka Olarewaju Statistician Dr Usman Kolapo Statistician Mr Sam Ofodile Database Manager University of Ibadan Prof. Rasaki Ajani Sanusi Co-Principal Investigator University of Calabar Prof. Henrietta Ene-Obong Co-Principal Investigator OPM Dr. Femi Adegoke Project Manager Mr. Babatunde Akano Database Manager FHI Solutions-Intake Center for Dietary Dr Megan Deitchler Technical Advisor-Dietary Intake Assessment Dr Marieke Vossenaar Technical Advisor-Dietary Intake Dr Joanne Arsenault Technical Advisor-Dietary Intake Dr Mario Chen Technical Advisor-Dietary Intake Tufts University- INDDEX Dr Jennifer Coates Technical Advisor-Dietary Intake Dr Jerome Some Technical Advisor-Dietary Intake Dr Winnie Fay Bell Technical Advisor-Dietary Intake Dr Sarah Wafa Technical Advisor-Dietary Intake University of Wisconsin-Madison Prof. Sherry Tanumihardjo Technical Advisor-Biomarker Cornell University Prof. Saurabh Mehta Technical Advisor-Biomarker National Bureau of Statistics (NBS) Mr. Udoabah Imeh Technical Advisor-Survey Design Mrs Florence Oke Technical Advisor-Survey Design National Population Commission (NPC) Mrs Titilayo Ahmed Sensitization and mobilization Ahmadu Bello University Dr. Muyiwa Owolabi Zonal Coordinator (NW) Federal University of Agriculture- Dr. Catherine Oladoyinbo Zonal Coordinator (SW) Abeokuta University of Uyo Dr. Yetunde Alozie Zonal Coordinator (SS) Michael Okpara University of Dr. Patricia Ukegbu Zonal Coordinator (SE) Agriculture Independent Consultant Mrs. Ladi Williams Zonal Coordinator (NE) Dr. Dolapo Duro Zonal Coordinator (NC) Federal Ministry of Health Mr. Gabriel Odugbo Ikwulono Assistant Zonal Coordinator (NC) Kano State Ministry of Health Mr. Abdullahi Yusuf Koki Assistant Zonal Coordinator (NE) Jigawa State Ministry of Health Mr. Shafi’u Dahiru Gumel Assistant Zonal Coordinator (NW) Federal Medical Centre, Abia State Mr. Ejiofor Agbo Assistant Zonal Coordinator (SE) Rivers State University Dr. Holy Brown Assistant Zonal Coordinator (SS) University College Hospital, Ibadan Dr. Fisayo Ogah Assistant Zonal Coordinator (SW) xxiv Executive Summary The National Food Consumption and Micronutrient Survey (NFCMS) is a cross-sectional population-based survey. The primary objective of the survey is to assess the micronutrient status, anthropometry, and dietary intake of women of reproductive age (WRA), aged 15-49 years, including pregnant and lactating women, and children (aged 6-59 months) as well as the micronutrient status of non-pregnant adolescent girls (aged 10-14 years) and identify key factors associated with poor nutrition in these populations. The information generated will provide a foundation for the formulation of evidence-informed policies and programmes. In the short- to medium-term, the information will provide a baseline from which to monitor changes over time. The NFCMS 2021 collected information on four distinct components: (1) socioeconomic and demographic information of household in sample; (2) dietary intake – types and amounts of foods consumed in the last 24 hours; (3) anthropometry – height/length, weight, age; and (4) micronutrient status through a series of biomarkers such as haemoglobin genotype, HbA1c, status of iron and inflammation, vitamin A, folate, zinc, iodine, vitamin B1, vitamin B2, vitamin B12, malaria, H. pylori, haemoglobin, plasma glucose, and helminths, which were analysed from blood, urine, and stool samples. Analyses of the biological samples were carried out in both local and international laboratories that adopted rigorous quality control measures. For dietary intake, the results are presented separately for children aged 6-23 and aged 24-59 months at the national level and by location. For WRA, including pregnant women, data were disaggregated by geopolitical zone and by location at the national level. In addition, lactating women, because of their higher energy and nutrient requirements, are presented separately. This preliminary report presents selected findings from the NFCMS 2021 and covers respondent’s household socioeconomic and demographic characteristics, including information collected from household listing, diet questionnaire, anthropometry, biomarker questionnaire, food sample analysis, and biomarker indices analysed in-country. The report does not include findings from the 24-hr dietary recall, and biomarker indices currently being analysed in international laboratories. Presented below are selected key findings. A total of 9107 households were enumerated, with 3990 in urban areas and 5177 in rural areas. The number of households in sample varied among the geopolitical zone with the smallest (1328) in the South East and the largest (1687) in the North West. At the national level, only a small proportion of the household heads were females (11.8 percent in urban and 9.7 percent in rural areas). The proportion of female-headed households was highest in the South East compared to the North West, which had the least. Distribution of household by type also showed that the proportion of female-headed households was higher among those with primary or no formal education as compared with those with higher education. Results obtained for water, sanitation, and hygiene (WASH) show that in Nigeria, 62 percent of households drink water from an improved water source located within the premises. The most common main source of drinking water is the borehole (42.6 percent). The use of borehole is prevalent in urban (46.3 percent) and rural (39.9 percent) areas. At national level, 26.5 percent of the households used improved toilets that were not shared with any other households while 29.5 percent households used improved toilets shared with at least one other household. The proportion of households that shared improved toilets was high in the urban areas compared to rural areas with only about 18 percent. One in every five (21.5 percent) households in Nigeria use an unimproved toilet facility, while 23.5 percent of households did not have a toilet facility. xxv Our findings on food security indicate that: (1) overall, 79 percent of the sample households were food insecure (57 percent moderately food insecure and 22 percent were severely food insecure) indicating that they went without eating for a whole day because of lack of money or other resources; and (2) the proportion of food insecurity reduced with increase in education attainment. For infant and young child feeding (IYCF) practices and diet, preliminary analysis indicates that: (1) almost all children (97 percent), aged 6-23 months, were ever breastfed and 58 percent of children (aged 12-23 months) received continued breastfeeding (it was more practiced in the rural than in the urban areas); (2) few non-pregnant women reported having consumed biofortified crops or any products made from yellow cassava (3 percent), orange-fleshed sweet potato (5 percent), and orange maize (13 percent) in the 30 days preceding the survey; 3) among the non-pregnant women who reported having consumed yellow cassava, and orange maize, the vast majority reported consuming it on 1 to 9 days in the past 30 days, whereas few consumed it daily. A high proportion of households of sampled non-pregnant women of reproductive age (WRA) consumed vegetable oil (90 percent), sugar (88 percent), salt (99 percent), and bouillon (99 percent) in any form. Fewer households of sampled non-pregnant WRA consumed flours in any form (57 percent for maize flour, 29 percent for semolina flour, and 28 percent for wheat flour). The proportion of households that consumed foods that were obtained through purchases (as opposed to for example gifts or food aid) were similar to those consuming the food in any form for most food vehicles, except for maize flour (57 percent of household consumed it, but only 29 percent purchased it). The proportion of respondents whose households consumed these foods in a branded form (which was used as a proxy for commercially processed and thus amenable to large-scale fortification) was considerably lower for most foods, i.e., vegetable oil (33 percent), sugar (22 percent), wheat flour (13 percent) maize flour (<1 percent), semolina flour (23 percent), salt (47 percent), except for bouillon, which remained high (96 percent). The same proportion of households that consumed branded foods also consumed foods labelled as fortified and confirmed to be fortified (in any amount) based on linking the report brand to secondary market data on fortification quality. Consumption of foods that were branded was lower among respondents from rural households compared to respondents from urban households for most foods, i.e., vegetable oil (20 percent vs. 48 percent), wheat flour (8 percent vs. 27 percent), semolina flour (7 percent vs. 40 percent), sugar (15 percent vs. 31 percent), and salt (37 percent vs. 59 percent) except bouillon (95 percent vs. 97 percent). Based on the analysis of food samples that were collected in a sub-sample of households of the sampled non-pregnant WRA and analysed for micronutrient contents, most samples were fortified at any level for vitamin A in sugar (74 percent), iodine in salt (100 percent), iron and zinc in wheat flour (100 percent each) while iron and zinc in semolina flour was also 100 percent. Conversely, about one third was fortified at any level with vitamin A in vegetable oil (31 percent) and vitamin A in wheat flour (26 percent). The prevalence of stunting, wasting, underweight, and overweight in children (aged 6-59 months) nationally was 33.3, 11.6, 25.3, and 1.5 percent, respectively. Stunting was highest in the North West zone (47.9 percent), wasting was highest in children (aged 6-59 months) in the North East zone (17.2 percent), and underweight was highest in the North West zone (35.5 percent). The prevalence of severe stunting, severe wasting, severe underweight, and obesity in children (aged 6-59 months) nationally was 16.7, 3.0, 9.2, and 0.6 percent, respectively. Severe stunting was xxvi highest in children (aged 6-59 months) in the North West zone (27.3 percent), severe wasting was highest in children (aged 6-59 months) in the North East zone (6.3 percent), severe underweight was highest in children (aged 6-59 months) in the North West zone (13.6 percent), and obesity was the highest in the South East zone (1.7 percent). For adolescent girls and women of reproductive age, results obtained for nutrition status indicates that the percentage of adolescent girls with thinness was 15.1 percent, overweight was 3.1 percent, and obesity was 1.1 percent nationally. Thinness was recorded highest in the North West zone (20.6 percent). For women of reproductive age, the prevalence of thinness, overweight, and obesity was 14.1, 14.8, and 8.2 percent, respectively. Thinness was highest among WRA in the North West zone (21.6 percent), overweight and obesity were highest among WRA in the South East zone at 21.2 and 15.5 percent, respectively. The prevalence of anaemia among children (aged 6-59 months), was 62 percent nationally. Specifically, 31 percent of children (aged 6-59 months) were found to be mildly anaemic, 29 percent were moderately anaemic, and 2 percent were severely anaemic. Anaemia was more prevalent in children in the North West zone (73 percent). Additionally, at the national level, anaemia was present in 41 percent of adolescent girls. The prevalence of mild anaemia was 16 percent, moderate anaemia was 24 percent, and severe anaemia was 1 percent. Severe anaemia was higher in adolescent girls residing in rural areas (1.7 percent). About 55 percent of women of reproductive age suffered from anaemia. The prevalence of mild anaemia was 31 percent, moderate anaemia was 22 percent, and severe anaemia was 1 percent. Anaemia was highest in women of reproductive age in the North East zone (46 percent). Findings on national interventions of interest for all four target groups, in the past six months preceding the survey, indicated that nationally iron/micronutrient powder use among children (aged 6-59 months) was 7 percent, vitamin A supplementation was 25 percent, and the percentage of children whose caregivers received any nutrition counselling was 15 percent. For women of reproductive age, 13 percent took multivitamins, while iron/folic acid usage was 15 percent nationally. From the preliminary findings, it can be concluded that: (1) two in every three households drank water from an improved water source located on premises, and that the most common main source of drinking water was the borehole; (2) there is high level of food insecurity and that the proportion of food insecurity reduced with higher education; (3) consumption of biofortified crops is low; (4) stunting and anaemia are public health problems and that there are zonal differences; and (5) coverage of some national interventions is low. Therefore, the results present opportunities for the formulation of evidence-based policies and programmes and a baseline from which to monitor changes over time. xxvii Key Findings Household in-sample characteristics • A total of 9107 households were enumerated, with 3990 (43.8 percent) in urban areas and 5177 (56.6 percent) in rural areas. Number of households in sample varied among the geopolitical zone with the smallest (1328) in the South East and the largest (1687) in the North West. A total of 34 469 individuals (10 546 children 6-59 months of age, 18 781 non-pregnant WRA aged 15-49 years, 2040 pregnant women, and 3102 non-pregnant adolescent girls) were sampled (Table 15). • Most of the household heads (89.4 percent) were males. Nationally, only a small proportion of the household heads (10.6 percent) were females with 11.8 percent in urban and 9.7 percent in rural areas. The proportion of female-headed households was highest in the South East (15.7 percent) compared to the North West (5 percent), which had the least. Distribution of household by type also showed that the proportion of female-headed households was higher among those with primary (14.8 percent) or no formal education (13.2 percent) as compared with those with higher education (7.6 percent) (Table 15). • In terms of educational qualification among the female heads of household, the results show that 24 percent of female-headed did not have any formal education, 33 percent had primary education, and about 30 percent had secondary education. A small proportion (13 percent) of female heads of household had education beyond secondary (Table 16). • Overall, 94 percent of households’ heads were engaged in income-generating activities. The proportions are similar in urban (93.8 percent) and rural areas (94.1 percent). Analysis by type of activities shows that the agricultural sector took the lead with 36.8 percent, while sales and services-related activities followed with 16.3 percent and 12.6 percent, respectively. However, the pattern of distribution was different among the geopolitical zones. Engagement in agricultural sector was higher in northern zones as compared to the south (Table 18 and 19). • At the national level, 11 percent of households were engaged in the production of animal source foods (6.4 percent own any livestock, herds, other farm animals, or poultry; 1 percent raise rabbit, guinea pigs, grass cutters, snails, fish, or other small animals; 1.5 percent raise fish; and 5 percent catch/harvest fish from the wild). The proportion of HHs involved in the production of animal source foods was very low between rural (14 percent) and urban areas (8 percent) (Table 30). • Overall, 3 out of 10 households indicated that they have land for vegetable gardening. The proportion was higher in rural areas (38 percent) compared to urban areas (16 percent). Among the zones, the households in South East (68 percent) and South South (41 percent) had higher proportions of households who had access to land for vegetable production than in any other geopolitical zones (Table 31). • Results obtained for households in sample that have trees or bushes that produce fruits indicated that 31 percent had trees or bushes that produce fruits. A high proportion of households that have trees or bushes that produce fruits were found in the South East (56 percent), South South (44 percent), and North Central (39 percent) (Table 32). xxviii Water, sanitation, and hygiene • In Nigeria, 62 percent of households drink water from an improved water source located on premises, and the most common main source of drinking water is the borehole (42.6 percent of households) The use of borehole is prevalent in urban (46.3 percent) and rural (39.9 percent) areas. A borehole is a deep, narrow well that tap into naturally occurring underground water (Table 23). • At the national level, 26.5 percent of the households used improved toilets that were not shared with any other households, while 29.5 percent of households used improved toilets shared with at least one other household. The proportion of households that shared improved toilets was high in the urban areas (about 44 percent) than in the rural areas (18 percent) (Table 24). • One in every five households (21 percent) in Nigeria use an unimproved toilet facility. Usage of unimproved toilets is more prevalent in the North West (40 percent). Percentage in other zones ranged between 8.4 in South West and 20.4 in North East. The proportion of households practicing open defecation system was 21.5 percent. The practice was more prominent in the rural areas (34.5 percent) than in the urban areas (7.4 percent). The practice was highest in North Central (44 percent) (Table 24). Food Security and coping strategies • Our findings on food security indicate that: (1) overall, 79 percent of the sample households were food insecure (57 percent moderately food insecure and 22 percent were severely food insecure) indicating that they went without eating for a whole day because of lack of money or other resources; and (2) the proportion of food insecurity reduced with increase in education attainment (Table 26). • Respondents were asked if in the past seven days there were times when their household did not have enough food or money to buy food. A very small proportion (3.5 percent) of households belonged to the group of “none or minimal food insecurity”, 54.3 percent belonged to the “stressed food consumption”, while 42.3 percent were found in the “crisis food consumption” group (Table 27). Infant and Young Child Feeding • Almost all (97 percent) children (aged 6-23 months) were ever breastfed. Similar patterns were observed in urban and rural areas, and for girls and boys (Table 40). • About 58 percent of children (aged 12-23 months) received continued breastfeeding. As expected, the practice of continued breastfeeding decreased with age (84, 55, and 27 percent for children aged 12-15, 16-19, and 20-23 months, respectively). For children aged 12-23 months, continued breastfeeding was more common in rural areas (64 percent) than in urban areas (48 percent). Similar patterns were observed for boys and girls (Table 41). • Twenty (20) percent of children (aged 6-23 months) were bottle-fed. The use of bottles with a nipple decreased with age (28, 18, and 14 percent for children aged 6-11, 12-17, and 18-23 months, respectively). Similar patterns were observed for urban and rural areas, and boys and girls (Table 42). Biofortification Coverage • Few non-pregnant women reported having consumed biofortified crops or any products made from them in the past 30 days. Only three percent consumed yellow cassava, five percent consumed orange-fleshed sweet potato and 13 percent consumed orange maize (Figure 6). xxix • Although consumption of yellow cassava was low across the country, significant differences were observed by zone; consumption was one percent in the North West and eight percent in the North East. No differences were observed by residence (i.e., urban vs rural) and wealth quintile (Figure 7). • Although consumption of orange-fleshed sweet potato was low across the country, significant differences were observed by zone; consumption was 16 percent in the North East and around two percent in all other zones. No differences were observed by residence and wealth quintile (Figure 9). • Although consumption of orange maize was relatively low across the country, significant differences were observed by zone; coverage was 38 percent in the North East and between 4 and 14 percent in all other zones. No differences were observed by residence and wealth quintile (Figure 11). • Among the non-pregnant women who reported having consumed yellow cassava, orange- fleshed sweet potato, and orange maize, the vast majority reported consuming it on 1 to 9 days in the past 30 days (77, 84, and 56 percent for yellow cassava, orange-fleshed sweet potato, and orange maize, respectively), whereas few consumed it daily (2, 0, and 16 percent for yellow cassava, orange-fleshed sweet potato, and orange maize, respectively) (Figures 8, 10, and 12). Fortification coverage • A high proportion of households of sampled non-pregnant women of reproductive age (WRA) consumed vegetable oil (90 percent), sugar (88 percent), salt (99 percent), and bouillon (99 percent) in any form. Fewer households of sampled non-pregnant WRA consumed flours in any form (57 percent for maize flour, 29 percent for semolina flour, and 28 percent for wheat flour). The proportion of households that consumed foods that were obtained through purchases (as opposed to for example gifts or food aid) were similar to those consuming the food in any form for most food vehicles, except for maize flour (57 percent of household consumed it, but only 29 percent purchased it) (Figure 13). • The proportion of respondents whose households consumed these foods in a branded* form (which was used as a proxy for commercially processed and thus amenable to large-scale fortification) was considerably lower for most foods, i.e., vegetable oil (33 percent), sugar (22 percent), wheat flour (13 percent) maize flour (<1 percent), semolina flour (23 percent), salt (47 percent), except for bouillon, which remained high (96 percent). That said, the same proportion of households that consumed branded foods also consumed foods labelled as fortified and confirmed to be fortified (in any amount) based on linking the report brand to secondary market data on fortification quality (Figure 13) • A high proportion of non-pregnant women came from households that either consumed unbranded or unknown brand of all the selected food vehicles except semolina flour and bouillon (Figure 13) • The proportion of non-pregnant women from households that consumed unbranded and unknown oil was higher in the northern zones (65% North central, 56% North East, and 68% North West) compared to the southern zones (South East 23 percent, South South 26 percent and South West 32 percent) (Table 45) *A proportion of fortified foods manufactured by large industries may also have been repackaged without branding, and therefore they were not identified at the household level. xxx • Consumption of foods that were branded was lower among respondents from rural households compared to those respondents from urban households for most foods, i.e., vegetable oil (20 percent vs. 48 percent), wheat flour (8 percent vs. 27 percent), semolina flour (7 percent vs. 40 percent), sugar (15 percent vs. 31 percent), and salt (37 percent vs. 59 percent) except bouillon (95 percent vs. 97 percent) (Tables 45, 49, and 50) Fortification status of household food samples • Based on the analysis of food samples that were collected in a sub-sample of households of the sampled non-pregnant WRA and analysed for micronutrient contents, it was revealed that most samples were fortified at any level for vitamin A in sugar (74 percent), iodine in salt (100 percent), iron and zinc in wheat flour (100 percent each) while iron and zinc in semolina flour was also 100 percent. Conversely, about one third was fortified at any level with vitamin A in vegetable oil (31 percent) and vitamin A in wheat flour (26 percent) (Figure 35). • The measured mean amounts of micronutrients in the fortified samples were 2.6 mg/kg vitamin A in vegetable oil, 3.1mg retinyl palmitate/kg vitamin A in sugar, 60 mg/kg iodine in salt, 0.8 mg retinyl palmitate/kg vitamin A, 53.9 mg/kg iron, and 42.2 mg/kg zinc in wheat flour, and 0.8 mg retinyl palmitate/kg vitamin A, 38.6 mg/kg iron, and 36.0 mg/kg zinc in semolina flour (Table 54) Anthropometry • At the national level, the prevalence of stunting, wasting, underweight, and overweight in children was 33.3, 11.6, 25.3, and 1.5 percent, respectively. Stunting was highest in the North West zone (47.9 percent), wasting was highest in children in the North East zone (17.2 percent), and underweight was highest in the North West zone (35.5 percent) (Figure 36). • The prevalence of severe stunting, severe wasting, severe underweight, and obesity in children at the national level was 16.7, 3, 9.2, and 0.6 percent, respectively. Severe stunting was highest in children in the North West zone (27.3 percent), severe wasting was highest in children in the North East zone (6.3 percent), severe underweight was highest in the North West zone (13.6 percent), and obesity was highest in the South East zone (1.7 percent) (Table 56). • The percentage of adolescent girls with thinness was 15.1 percent, overweight was 3.1 percent, and obesity was 1.1 percent. Thinness was highest in the North West zone (20.6 percent) (Figure 37). • At the country level, the prevalence of thinness, overweight, and obesity among WRA was 14.1, 14.8, and 8.2 percent, respectively. Thinness was highest among WRA in the North West zone (21.6 percent), overweight was highest among WRA in the SE zone (21.2 percent), and obesity was highest among WRA in the South East zone (15.5 percent) (Figure 38). Intervention Coverage • In the six months preceeding the survey, the use of iron/micronutrient powder (seven percent) and therapeutic feeds in the past 12 months (three percent) was low. At the national level, the prevalence of children (aged 6-59 months) receiving a vitamin A capsule in the past six months was 25 percent. The percentage of children receiving deworming treatment in the past six months was 28 percent. The percentage of children (aged 6-59 months) whose caregivers received any nutrition counseling in the past six months was 15 percent (Figure 39). • About 25 percent of adolescent girls reported using deworming treatment in the six months preceeding the survey. The use of iron/folic acid tablets and multivitamins in the six months preceeding the survey was reported among 11 and 9 percent of adolescent girls, respectively (Figure 43). xxxi • In the six months preceeding the survey, the use of deworming treatment was reported in 19 percent of women of reproductive age. Also, at the national level, 13 percent of WRA took multivitamins, while 15 percent used iron/folic acid (Figure 48). • At the national level, 44 percent of pregnant women reported receiving at least one antenatal care visit. About 66 percent of pregnant women took iron/folic acid tablets the day before the interview, while 87 percent reported taking iron/folic acid tablets at least once in the past seven days before the interview. Pregnant women (34 percent) were reported speaking to a health worker or community volunteer about what food to eat during pregnancy. On the other hand, women (32 percent) were reported talking to a health worker or community volunteer about breastfeeding their newborn (Figure 53). Self-reported morbidity and other anaemia risk factors • In the two weeks preceeding the survey, the prevalence of diarrhoea among children (aged 6-59 months) was 35 percent. At the national level, the presence of blood in stool was reported among 8 percent of children (aged 6-59 months), and 14 percent reported having diarrhoea a day before the interview. Fever was reported in 46 percent of children (aged 6-59 months). Furthermore, 37 percent of children (aged 6-59 months) had cough in the two weeks preceeding the survey, while the prevalence of fast, short, rapid breaths or difficulty breathing was 13 percent. Pica in the past seven days preceeding the survey, was reported among 20 percent of children (aged 6-59 months) (Figure 41). • The overall prevalence of self-reported illness (cough, fever, malaria, and diarrhoea) and hospitalization/clinic visits among adolescent girls in the two weeks preceeding the survey at the national level were 32, 29, 20, 16, and 6 percent respectively (Figure 46). The occurrence of self-reported smoking among adolescent girls was low (0.3 percent). Furthermore, the prevalence of pica in the seven days preceeding the survey and clinically diagnosed anaemia in the six months preceeding the survey among adolescent girls was 9 and 4 percent, respectively (Figure 47). • Nationwide, the overall prevalence of self-reported illness (fever, malaria, cough, and diarrhoea) and hospitalization/clinic visits in the two weeks preceeding the survey among women of reproductive age (aged 15-49 years) was 36, 27, 23, 17 and 8 percent respectively (Figure 51). Also, the incidence of smoking among WRA was low (0.5 percent). The prevalence of pica in the past seven days and clinically diagnosed anaemia in the six months preceeding the survey among WRA was 5 and 6 percent, respectively (Figure 52). • The overall prevalence of self-reported illness (fever, malaria, diarrhoea, and cough) and hospitalization/clinic visits in the two weeks preceeding the survey among pregnant women (aged 15-49 years) was 40, 30, 21, 20, and 19 percent, respectively (Figure 57). The occurrence of smoking among pregnant women was 0.4 percent (Table 81). Malaria, H. pylori, Helminth, and Plasma glucose • Malaria: The national prevalence of malaria among children (aged 6-59 months), adolescent girls, women of reproductive age, and pregnant women was 24, 33, 13, and 14 percent, respectively (Figure 58). • H. pylori: The national prevalence of H. pylori among children (aged 6-59 months), adolescent girls, women of reproductive age, and pregnant women was 36, 55, 64, and 59 percent, respectively (Figure 58). xxxii • Helminth: The national prevalence of helminth among children (aged 6-59 months), women of reproductive age, and pregnant women was 11, 6, and 4 percent, respectively (Figure 58). • Elevated plasma glucose (plasma glucose > 200 mmol/L or mg/dL): The national prevalence of elevated plasma glucose among women of reproductive age was 0.2 percent (Figure 58). Anaemia • Children (aged 6-59 months): anaemia was present in 62 percent of children. The prevalence of mild anaemia was 31 percent, moderate anaemia was 29 percent, and severe anaemia was 2 percent (Figure 60). • Adolescent girls (aged 10-14 years): anaemia was present in 41 percent of adolescent girls. The prevalence of mild anaemia was 16 percent, moderate anaemia was 24 percent, and severe anaemia was 1 percent (Figure 60). • Women of Reproductive Age (aged 15-49 years): anaemia was present in 55 percent of WRA. The prevalence of mild anaemia was 31 percent, moderate anaemia was 22 percent, and severe anaemia was 1 percent (Figure 60). • Pregnant women (aged 15-49 years): anaemia was present in 86 percent of pregnant women. The prevalence of mild anaemia was 20 percent, moderate anaemia was 62 percent, and severe anaemia was 4 percent (Figure 60). • Preliminary analysis of the relationship between anaemia, infection, haemoglobin genotype (blood disorders), and the use of micronutrient powder among children showed that 74 percent of children with severe anaemia had malaria. About 78 percent of children with any anaemia had normal haemoglobin genotype. Severity of anaemia was also associated with normal haemoglobin genotype. About 52 percent of children with moderate anaemia and 61 percent with severe anaemia had fever in the two weeks preceeding the survey (Table 91). • Preliminary analysis of the relationship between anaemia, infection, haemoglobin genotype (blood disorders), and use of supplements among women of reproductive age showed that 77 percent of WRA with mild anaemia had normal haemoglobin. Also, 76 percent of WRA with moderate anaemia had normal haemoglobin, while 66 percent of WRA with severe anaemia had normal haemoglobin (Table 95). xxxiii xxxiv Background The last National Food Consumption and Micronutrient Survey (NFCMS) was undertaken about 20 years ago in 2001 (Maziya-Dixon, et al., 2004; Nigeria Food Consumption and Nutrition Survey 2001-2003, IITA, https://hdl.handle.net/10568/100010). The findings of that study likely no longer represent the current micronutrient status or dietary consumption patterns of the Nigerian population. This lack of recent and reliable information presents several challenges, both in terms of reviewing ongoing programmes and in informing the development of new guidance and policies. Updated information on the population’s micronutrient status and dietary intakes is required for informed, evidenced-based decisions about current and future food, nutrition, and agriculture programming and policy making in Nigeria. During a high-level national nutrition data stakeholder workshop in Abuja in July 2017, stakeholders agreed that a national survey to collect information on dietary intake and micronutrient status was needed. Subsequently, in January 2018, a NFCMS methodology workshop was held in Abuja, during which agreements were reached on the scope and level of representativeness for the survey, and key decisions pertaining to the survey governance structure. In this light, UNICEF was nominated as the fund management agency for the survey, and lITA as the lead implementing agency. 1 Introduction The 2021 NFCMS is the third nationally representative survey of its kind conducted in Nigeria, following those implemented in 1968 and 2001. The Federal Government of Nigeria, in collaboration with the International Institute of Tropical Agriculture (IITA), and other stakeholders, implemented this survey. Data collection took place from 17 February to 16 June 2021 for household (HH) listing and HH questionnaire with a one-week break for Easter holidays; and 8 March 2021 to 4 July 2021 for dietary intake, anthropometry, and biomarker, excluding that of the Modified Relative Dose Response (MRDR) with a four-week break during Ramadan. Data collection for MRDR commenced on 17 August 2021 to 17 September 2021. Funding for NFCMS 2021 was provided by the Federal Ministry of Health, Gates Foundation, World Bank Group, Foreign, Commonwealth & Development Office, United Nations Children’s Fund, and Nutrition International. Technical assistance was provided by the National Population Commission, Nigeria (NPC), National Bureau of Statistics, Nigeria (NBS), Tufts University- International Dietary Data Expansion Project (INDDEX), FHI360 Solutions-Intake Center for Dietary Assessment, University of Wisconsin-Madison, USA and Cornell University, USA. This preliminary report presents selected findings from the NFCMS 2021 and covers respondent’s household socioeconomic and demographic characteristics, including information collected during household listing, diet questionnaire, anthropometry, biomarker questionnaire, food sample analysis, and biomarker indices analysed in-country. The report does not include findings from the 24-hr dietary recall, and biomarker indices being analysed outside the country. A comprehensive analysis of the data will be presented in a final report in July 2023. 2 Objectives The primary objective of the survey is to assess the micronutrient status and dietary intake of women of reproductive age (WRA) (aged15-49 years), including pregnant and lactating women and children (aged 6-59 months). The study also assessed the micronutrient status of non-pregnant adolescent girls (aged 10-14 years) and identified key factors associated with poor nutrition in these populations. The information generated will provide a foundation for the formulation of evidence- informed policies and programmes. In the short- to medium-term, the information will provide a baseline from which to monitor changes over time. The specific objectives of the survey include (dietary related objectives in bold): 1. assess the food consumption of children (aged 6-59 months), excluding breastmilk, and WRA to determine their intakes of energy, protein, fat, and selected micronutrients, as well as the amounts of specific nutrient-dense foods relevant for food-related nutrition policies and programmes; 2. determine the adequacy of nutrient intake in children (aged 24-59 months) and WRA to identify populations at risk of inadequate intake; 3. assess infant and young child feeding (IYCF) practices among children (aged 6-23 months) and compare the nutrient density of their complementary feeding diets with recommendations; 4. assess the prevalence, severity, and distribution of specific micronutrient deficiencies and other forms of malnutrition (e.g. stunting, wasting, overweight/obesity) among WRA, adolescent girls, and children (aged 6-59 months); 5. identify key factors (e.g. infection, blood disorders, supplement use) associated with anaemia in WRA and children (aged 6-59 months) to inform strategies to prevent and treat anaemia in these populations; 6. measure the coverage of national interventions to improve micronutrient status and dietary intake in WRA and children (aged 6–59 months), including iron folic acid (IFA) supplements, IYCF counselling, vitamin A supplementation (VAS), biofortification, and food fortification programmes; and 7. assess the prevalence of food insecurity and identify other key factors at individual and HH level (e.g. education, SES) that are associated with micronutrient status and dietary intake in WRA and children (aged 6–59 months), and the micronutrient status in adolescent girls. 3 Survey Design Study area The country’s 2006 Population and Housing Census, which placed its population at 140 431 790, served as the sampling frame. Nigeria is the most populous black nation in the world. Nigeria is comprised of 36 states and the Federal Capital Territory (FCT) (Figure 1) with 774 Local Government Areas (LGAs) and 662 529 enumeration areas (EAs) categorized into six geopolitical zones (North West, North East, North Central, South West, South East and South South). Nigeria has more than 500 ethnic groups with the most populous being Hausa, Yoruba, and Igbo. Figure 1. Geopolitical zones in Nigeria Survey design, target populations, and reporting domains The NFCMS is a cross-sectional population-based survey that collects data on dietary intake, micronutrient status, and anthropometry. The following demographic groups are the focus for the survey: (1) children aged 6-59 months; (2) non-pregnant WRA (aged 15-49 years), including lactating women; (3) pregnant women (aged 15-49 years); and (4) non-pregnant adolescent girls (aged 10-14 years). No dietary data was collected for adolescent girls aged 10-14 years. Table 1 shows the sampling target groups for which data is collected for specific survey components. 4 Table 1. Sampling target groups by survey components Sampling target groups Micronutrient biomarker/ anthropometry Dietary intake Non-pregnant WRA (15-49 years old) √ √ Children (6-59 months old) √ √ Pregnant women (15-49 years old) √ √ Non-pregnant adolescent girls (10-14 years old) √ Not collected The survey was successfully carried out in 364 Primary Sampling Units (PSU) referred to as EAs, after 26 EAs with security challenges during fieldwork were dropped. These areas were in Lagos (1 cluster), Ogun (1 cluster), Sokoto (2 clusters), Kebbi (1 cluster), Zamfara (1 cluster), Yobe (2 clusters), Borno (8 clusters), Anambra (1 cluster), Cross River (1 cluster), and Rivers (2 clusters). More clusters were lost in the NE zone (10) followed by NC (6), NW (4), SS (3), SW (2), and SE (1). The reporting domains and level of disaggregation are presented in Table 2. For dietary intake, the results are presented separately for children aged 6-23 versus 24-59 months at the national level and by location (urban and rural). For WRA, including pregnant women, data was disaggregated by geopolitical zone and by location (urban and rural) at the national level. In addition, lactating women, with higher energy and nutrient requirements are presented separately. For biomarker and anthropometry, results are presented at the national level, geopolitical zone, and by location (urban and rural) for WRA and children (aged 6-59 months); and at national level and by location (urban and rural) for pregnant women (15-49 years old) and non-pregnant adolescent girls (10-14 years old). Table 2. Reporting domain by target groups and survey components Sampling target groups Non-pregnant WRA Children (6-59 Pregnant women Non-pregnant (15-49 years old) months old)* (15-49 years old) adolescent girls (10-14 years old) Reporting domain for dietary National & intake geopolitical region National National No data collected Reporting domain for micronutrient biomarker/ National & National & anthropometry geopolitical region geopolitical region National National Outcomes disaggregated by urban and rural areas National National National National *Dietary data is presented separately for infants and young children aged 6–23 months and children aged 24–59 months. Sampling method The NFCMS is a cross-sectional population-based survey with the sample stratified by geopolitical zone. Sampling within each region follows a two-stage random selection strategy. In the first stage, EAs were selected adopting principles of Probability Proportional to Size (PPS) using systematic sampling. Sixty-five (65) EAs within each region were selected. In the second stage, eligible respondents were randomly selected within the sampled EAs. The sample size estimates for non-pregnant WRA (15-49 years old) and children (6-59 months old) were calculated for key micronutrient biomarker indicators. The sample size calculations for these two sampling groups were based on the combination of an estimated prevalence, required absolute precision (margin of error), and a 95 percent level of confidence, for producing estimates at the geopolitical level, using the following formula: ( ) 5 Where: n is the calculated sample size z is the statistic that defines the level of confidence required p is an estimate of the key indicator to be measured by the survey in the population group of interest, for example, the prevalence of iron deficiency among WRA, expressed as a proportion of that population d is the desired level of precision, or the margin of error to be obtained. Margin of error for a geopolitical region used is ± x 5 percentage points. As statistically computed, z = 1.96, which is the z-statistic for the 95 percent confidence level. If the expected estimate of the key indicator (p) was unknown, the value of 0.5 (or 50 percent) was used because it produces the largest sample size (for a given value of d). For all estimates of sample size, a design effect of 2 was used to account for the sample design, which is the value often used when there is little information from which to make a more informed decision. The calculated sample sizes were further inflated to account for non-response rate by 20 percent (Table 3). To interpret retinol concentrations, the MRDR test was conducted on a sub-sample of respondents. This required the collection of a second venous blood sample – pregnant WRA (aged 15-49 years). A second dietary recall sample and MRDR were randomly selected from respondents of the first dietary recall and biomarker with the numbers varying by population groups. A second 24-hour recall was collected on a non-consecutive day for a randomly selected sub-sample of respondents who completed the first 24-hour dietary recall. The number of repeats corresponded to 38 percent of the sample of children (aged 6-59 months), 25 percent of the sample of non-pregnant WRA, and 33 percent of the sample of pregnant WRA. These data are needed to remove the within-person variation from the data and simulate “usual” intake distributions for the sample. Table 3. Adjusted sample size per EA, geopolitical zone, and at national level by sampling target group1 Sampling target population Respondents Sample size per Total sample size selected per EA geopolitical zone at national level Non-pregnant WRA (15-49 years old) 16 1040 6240 Children (6-59 months old) 16 1040 6240 Pregnant women (15–49 years old) 3 195 1170 Non-pregnant adolescent girls (10-14 years old) 3 195 1170 Total 38 2470 14 820 Questionnaires and sample collection Five questionnaires, excluding the Household Listing Form, were developed for the NFCMS 2021: (1) household; (2) non-pregnant WRA; (3) pregnant WRA; (4) children aged 6–59 months; and (5) adolescent girls aged 10–14 yrs. To help guide the development of questionnaires, the tools and protocols used for the standard Demographic and Health Survey (DHS-7) were adopted. The review process for the questionnaires involved: identifying and justifying information required; defining the priority indicator; providing rationale for why this survey is the right place to measure the indicator; what questions will elicit the information needed for the indicator; and how will the information be reported. For the selection of indicators and questions, the following principles were used as a guide: • if there is no clearly defined indicator, we cannot include questions in the survey; • indicator definitions and questions should be consistent with national and global standard definitions and questions; 6 • use standard procedures, questions, and response questions whenever possible; • indicators and questions already used in Nigeria survey reports, such as the NDHS and LSMS surveys, should be included, where possible; • from global guidance or tools such as IYCF revised in 2021; and • expert advice. Comments were solicited from a group of key stakeholders and development partners after which these were presented to the Technical Advisory Committee (TAC) and Steering Committee (SC) for approval before applying for the ethical clearance. After all questionnaires were finalized in English, they were translated into Hausa, Yoruba, and Igbo; and translated back to English. The survey protocol was reviewed and approved by the National Health Research Ethics Committee of Nigeria (NHREC). At implementation, the questionnaires were disaggregated to three based on the components of the NFCMS: HH Questionnaire, Diet Questionnaire, and Anthropometry/ Biomarker Questionnaire. The HH Listing Form listed all members and visitors of the sample HHs. They are those who live in the HH and/or guests who stayed there last night. Information on relationship to head of HH, sex, and age was collected on each person listed. For children (aged 6-59 months) and WRA, additional information was collected (i.e., date of birth, birth certificate, source of birth certificate for children 6-59 months, and pregnant status for WRA). Data on age and pregnant status were used to identify WRA, adolescent girls, and children (aged 6-59 months) who were eligible for individual interviews. The HH Questionnaire collected information on general characteristics of the head of HH (i.e., ethnicity, religion, education, and employment). It also collected information on the HHs dwelling unit (source of drinking water; type of toilet facilities; materials used for flooring, external walls, and roofing; ownership of various animals and durable goods; area where members of the HH often wash their hands; main way of refuse disposal, presence of a vegetable garden and fruit trees; HH food insecurity; and HH coping strategies). The Diet Questionnaire collected information on respondents’ identity confirmation (name, age, date of birth, completion of HH Questionnaire), socio-demographic characteristics, consumption of biofortified foods (yellow cassava, OFSP, and orange maize), and fortification coverage for selected food vehicles (vegetable oil, wheat flour, maize flour, semolina, sugar, salt, and bouillon) for children (aged 6-59 months) and WRA. In addition, pregnancy and lactation data were collected among WRA and selected IYCF practices among children (aged 6-59 months only). The Diet Questionnaire was followed by a quantitative interactive 24-hour (i24-hr) dietary recall interview collected using the INDDEX24 mobile application. In addition, fortifiable food samples were collected in a 25 percent sub-sample of WRA during the repeat i24-hr dietary recall and tested for levels of fortification (i.e., iodine in salt, vitamin A in edible oil, vitamin A in sugar, and iron in flours). No dietary data was collected for adolescent girls (aged 10-14 years). The Anthropometry and Biomarker Questionnaire collected information on respondent identity confirmation, socio-demographic characteristics, anaemia risk and health status, and micronutrient intervention coverage. In addition, height/length and weight measurements were recorded, and biomarker samples (blood, stool, and urine) were collected for children (aged 6-59 months), adolescent girls (aged 10-14 years), and WRA. Information on the laboratory analysis conducted on the biomarker samples for each target group is presented in Table 4. 7 Table 4. Biomarker measurements and analysis method/matrix by target group Biomarker Children Non-Pregnant measurement/ Analysis method/ matrix (6-59 Adolescents Pregnant status months) (10-14 years) women women (15-49 years) (15-49 years) Presence of Plasmodium falciparum Malaria malaria parasitemia in venous whole blood sample detected using a rapid � � � � diagnostic test kit (RDT) Presence of IgG antibodies specific Helicobacter to Helicobacter pylori (H. pylori) in pylori venous whole blood sample detected � � � � using a rapid qualitative immune assay test RDT Helminths Presence of helminth eggs in stool samples detected using microscopy � x � � Whole venous blood glucose concentration measured using a Plasma glucose HemoCue (Hb-301) instrument. Results converted to equivalent x x x � plasma values using a constant factor of 1.11. Glycated Whole venous blood sample haemoglobin assessed using a Bio-Rad D10 auto- x x x � (HbA1c) analyzer Haemoglobin Whole venous blood assessed genotype (blood using high-performance liquid disorders) chromatography (HPLC) in a � x x � laboratory setting Anaemia measured from whole Haemoglobin venous blood sample using a � � � � HemoCue (Hb-201) instrument Sandwich Elisa assay for Ferritin, serum transferrin receptors Iron status and (sTfR), c-reactive protein (CRP), α1- � � � � markers of acid glycoprotein (AGP) in serum inflammation Sandwich Elisa assay for RBP in serum � � � � Serum retinol and MRDR in serum samples analyzed � � Vitamin A using HPLC and a standardized (20% sub- x x (20% sub- method for 3,4-didehydroretinol and sample) sample) retinol Microbiological assay for serum Folate folate and Red Blood Cells (RBC) folate from whole venous blood x � � � lysate Vitamin B 12 Serum B 12 assessed using Roche E-170 Vitamin B12 “ECLIA” � � � � � Vitamin B1 x x x (20% sub- Erythrocyte transketolase (ETK) sample) activity assay of saline- washed Red Blood Cells (RBC) � Vitamin B2 x x x (20% sub- sample) Serum zinc assessed using Atomic Zinc Absorption � � x � Spectroscopy (AAS) Iodine Urinary iodine using ammonium persulfate x x � � 8 The gold standard to determine vitamin A status is liver biopsy. However, access to this tissue is limited, except under special circumstances. The MRDR test has been validated in animals as a function of liver vitamin A reserves and can be used in infants, children, and women. The MRDR test involves first giving the respondent a single oral dose of vitamin A2 dissolved in an oil and then taking a single blood sample four to six hours later for vitamin A analysis. It is a good indicator of vitamin A liver stores and is less affected by inflammation than serum retinol concentrations. Anthropometry measurements (length or height, weight, and age) were taken from all children. Height and weight were collected for adolescent girls (aged 10-14 years), and WRA (15-49 years old), except for pregnant women. Standard procedures using the World Health Organization (WHO) methodology were utilized1. In addition, the Anthro Survey Analyzer was used to check quality of anthropometry data. The results are shown in Annex 3. For children under 24 months, recumbent length was measured to the nearest 0.1 cm using a wooden length board (ShorrBoard brand). The same device was used to measure standing height to the nearest 0.1 cm for children two years and older, adolescent girls (aged 10-14 years), and WRA (aged 15-49 years). In a few cases among children where length was taken instead of height or vice-versa, the measurement was tared (±0.7 cm) 2 following the WHO recommendations3 before calculating the height/length-for-age Z-score. Electronic scales (SECA brand) were used to measure the weight of consenting respondents. Children not yet able to stand on their own were weighed while being held by their caregiver using the ‘mother-child’ tare function on the scale. All measurements were taken with minimal clothing and with participants not wearing shoes. 1 Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. 2 As the WHO 2008 guidelines explain, this adjustment should be done since in general height is about 0.7 cm less than length and this difference has been considered in developing the WHO growth standards. 3 WHO 2008. Training course on child growth assessment. WHO Child Growth Standards. https://www.who.int/childgrowth/ training/module_b_measuring_growth.pdf 9 Survey Implementation Pre-survey activities and Adaptation of INDDEX24 Mobile Application In preparation for the collection of the quantitative interactive 24-hour (i24-hour) dietary recall data (Gibson and Ferguson 2018), extensive pre-survey work is required to develop the dietary input data required for the tablet application INDDEX24 mobile application (Coates et. al 2017). These methods are well established, validated, and recommended for collecting detailed individual- level food information in the context of national surveys (EFSA J. 2014). The INDDEX24 mobile application was selected for the survey as it was specifically developed for use in large surveys in low income developing countries. It offers the following advantages over paper questionnaire: guides enumerators and respondents through a i24-hour dietary recall interview in a structured manner; contains modifiable instructions to allow adjustments to the interview process; allows for real time data monitoring and checking by on-site supervisors and remote data managers; and provides instant calorie count for foods consumed as a quick data quality check among others. Advanced preparation for the collection of dietary data were conducted through several workshops. Each training workshop was followed by field work. The following resources were developed and used in the development and adaptation of the INDDEX24 mobile application for Nigeria: (1) a list of foods, recipes, and ingredients (FRIL) that are consumed and are likely to be encountered during i24-hr recalls were collected from WRA and young children in urban and rural areas of each of the six geopolitical zones; (2) a database of standard recipes for selected mixed dishes listed for each geopolitical zone, including ingredients and their proportions; (3) standardized portion size estimation methods (PSEMs) for estimating portion sizes of each item listed in the FRIL; (4) a database on PSEM Conversion Factors (PSEM-CF and edible portion that will translate the quantity of each reported item using the assigned PSEM to the equivalent gram weight for the edible portion; (5) a table of tags and descriptors of items in the FRIL for detailed description needed for improved matching in the INDDEX24 mobile application; and (6) a Nigeria food composition database (FCDB) for each item listed in the FRIL detailing their energy and nutrient content. In addition, the following pre-survey activities were conducted for the biomarker and anthropometry component: (a) identification of suitable cold stores and engagement of officials from the State Primary Health Care Development Agency (SPHCDA) (b) assessment of cold stores across the 36 states and the Federal Capital Territory, (c) assessment of local laboratories for biomarker analysis; and (d) development of tools for field data management. Following the completion of the pre-survey activities, a Training of Trainers (ToT) Workshop for zonal coordinators on dietary intake was conducted, this was soon followed by the training of potential field data collectors for all the components (HH listing and questionnaire, dietary intake, anthropometry, and biomarker). In addition, a training was conducted on how to mobilize and sensitize selected communities and respondents about the survey to enhance response. After the training of field teams, a pilot was conducted followed by a debriefing meeting. All data collection tools and procedures were fine-tuned after the pilot, which set the stage for the commencement of training of potential interviewers and supervisors. Eligibility Criteria, Recruitment of Respondents, and Consent Procedures Inclusion in the survey was based on being apparently healthy (showing no signs of illness), aged 15-49 years and pregnant women, children (aged aged 6-59 months), non-pregnant adolescent girls aged (10-14 years), willing to participate by giving consent, and residing in the EA. Pregnant 10 girls aged (10-14 years old) were excluded (pregnancy status was based on self-report). The exclusion criteria included difficulty standing (unsteady or chair-bound) for anthropometry, but interviews and specimen collection were included. Individuals who refuse to participate or are unable to give informed consent or assent were excluded. Participation was voluntary and participants were not paid for being respondents in the survey. Nevertheless, they were given a gift as an incentive. Incentives were given at different occasions during data collection, for example, plates and bowls were given during the pre-training of respondents for the collection of dietary data, results for Hb, H. pylori, malaria, and referral to a primary health care centre; and a plastic bowl after the visit from the biomarker/anthropometry team. In addition, soap was given after the diet interview and again after the repeat interview. Fortified vegetable oil was added as an incentive and was given by the biomarker field teams. Respondents that declined to participate were excluded and not replaced. Participants were informed that all personal information they provide will remain confidential and will only be used to provide for the intended objective. Upon first contact with the respondent (HH head, non-pregnant WRA, pregnant WRA, or caregiver of minors), a general written consent for all survey procedures for all components of the survey was obtained by the HH team. Additional written consent/assent was obtained for each component of the survey (i.e., biomarker, or anthropometry). Adolescent girls (aged 10-14 years) were asked to agree to the anthropometry and biomarker components after permission was granted by their parent or guardian. Interviewers used tablets with an electronic informed consent form to collect consents from potential survey participants. All potential participants were given a printed copy of the consent form. If the respondent is illiterate, a witness was requested by the respondent to sign on behalf of the respondent. Consent was recorded by making a mark on the consent form on the tablet and on a printed copy retained by the participant. Consent processes were conducted in different stages. Written consent to participate in the survey was obtained from each respondent. Several consent forms were used for the survey. Recruitment, training, and selection of field teams All the field teams for dietary intake, biomarker, and anthropometry, except that of HH listing and HH questionnaire, were recruited using the following process: (1) a job description was developed based on roles and responsibilities agreed upon as indicated in the protocol; (2) advertised in print media and IITA website for a period of two weeks, and applications were received by the Human Resource Office; and (3) a committee was drawn from collaborators and partners in the survey (University of Ibadan, University of Calabar, Oxford Policy Management (OPM), and FMOH, and FMARD) to shortlist suitable candidates that were invited to the training workshops. This process was followed for the zonal coordinators, supervisors, interviewers, anthropometrists, laboratorians, and phlebotomists. A total of 540 field staff (295 males and 245 females) were recruited. For the listing and HH questionnaire, and social mobilization field teams, existing personnel of NBS, NPC, FMARD, and FMOH were recruited. A ToT workshop on Dietary Intake Component of the NFCMS was conducted in Abuja on 7-18 December 2020. The overall objective was to train potential zonal coordinators and IITA survey team on data collection using specific survey tools (diet questionnaire and i24-hr dietary recall using the INDDEX24 mobile application) to enable them to co-facilitate the training of supervisors and interviewers. The following topics were covered during the training: interviewing techniques/ 11 skills; i24-hr dietary recall methodology; how to collect dietary data using the INDDEX24 mobile application, how to administer the diet questionnaire; standard procedures for field data collection; coordination of field teams; field quality checks and supervision; Field Planning & Monitoring Application (Planfeld); and communication, among others. Classroom practices were given priority during training after completion of each substantial topic. Participants made two field visits to different communities around Abuja. Each visit was followed by detailed feedback on what went well and what the trainees need to be re-trained on. A total of 18 participants (12 from the zones and 6 Research Associates from IITA) were trained. At the end of the training, based on field and classroom performance, six zonal coordinators were selected, the other six were taken as supervisors, and the remaining six Research Associates became field personnel assisting the zonal coordinator during training of field teams and data collection. A training workshop on dietary intake assessment for potential field teams was held on 11-29 January 2021 in Abuja. A total of 214 participants (47 supervisors and 167 interviewers) composed of 86 males and 128 females, were pre-selected from all over the country and trained. Seven subject matter experts sourced locally and internationally (Tufts University- International Dietary Data Expansion Project (INDDEX) and FHI360 (Solutions-Intake Center for Dietary Assessment) were used as facilitators at the training (physically or virtually). All training sessions were live- streamed and adherence to COVID-19 safety guidelines was enforced. The training methods used included demonstrations, role play, practice time working in pairs, and the provision of daily feedback with corrections. In terms of content, all the aspects of dietary intake data collection, ranging from technical and operational to logistics with field coordination, were adequately covered during the training. Technically, dietary interviews included the collection of interactive 24-hr data and a series of questions related to diet (e.g. infant, and young child feeding practices, consumption of fortified and biofortified foods). Intake and INDDEX prepared training guides/handouts based on their expertise, with inputs from IITA. Alongside training guides, the supportive materials provided included PowerPoint presentations delivered live or pre-recorded, demonstration videos/training guides/handouts on dietary pre-training, use of INDDEX24 mobile Application, interactive 24-hr dietary recall interview, PSEMs and testing dietary scales, and monitoring of playdough density. The playdough is one of the PSEMs used during data collection. At the end of the training exercise, the participants who will collect data were selected based on classroom performance, completion of the diet questionnaire, and 24-hr recall using INDDEX24 mobile application. A 10-day training workshop for field supervisors, laboratorians, and phlebotomist for the biomarker and anthropometry component was conducted on 20-30 January 2021 in Abuja. The anthropometrists and interviewers were trained for five days, and the field supervisors, laboratorians, and phlebotomist for 10 days. A total of 224 participants (148 trainers, field supervisors, laboratorians, and phlebotomists; 21 anthropometrists; and 55 interviewers) were trained. Topics covered during the workshop were: introduction to NFCMS; overview of survey field team members’ roles and responsibilities; what samples are collected and why; laboratory safety and Good Laboratory Practice (GLP); consent, assent, and confidentiality; urine sample collection and handling; stool sample collection and procedure for helminth assessment; venous blood collection and handling of plasma, serum and RBC; laboratory procedures for rapid malaria, Hb, H. pyroli, and plasma glucose; labeling of samples; biohazard waste management; transfer of field forms to the digital platform Computer-Assisted Personal Interviews (CAPI)I and CommCare 12 (an open-source mobile data collection platform that enables non-programmers to build mobile applications for data collection in low-resource communities); field forms, results, and referrals; sample custody and tracking; field anthropometry and biomarker setup, and quality assurance. For anthropometry, the following topics were covered: introduction to NFCMS; overview of roles and responsibilities; anthropometric data collection; components of anthropometry measurements (age, sex, height/length, and weight); procedure and protocols for anthropometric measurements; interview techniques; obtaining consent; introduction to CAPI and how to complete the questionnaire; and security and COVID-19. A nine-day training programme for interviewers in the HH Listing and Socio-economic status component of the NFCMS, followed by a pilot study and debriefing meeting, was held in Abuja. Meanwhile, field practice demonstration sessions were held at designated locations within the FCT. The objective of the workshop was to train interviewers (mappers and listers) for the conduct of mapping, listing, and the administration of HH socio-economic questionnaires. The training exercise was held from 18 to 22 January 2021 and was subsequently followed by field practice demonstration exercises held from 23 to 27 January 2021. A total of 124 participants (78 males and 46 females) drawn from members of staff of NBS and NPC were trained. The information covered during the training included: the importance of HH listing; survey design and methodology, mapping, and HH listing; reading of enumeration area maps and tracing of enumeration area boundaries; listing procedure; how to complete the HH questionnaire; HH food insecurity and coping strategies; data quality control measures; how to synchronize and send completed data to the central server; and roles and responsibilities of field personnel. Trainees were subjected to two short quizzes and an examination to test their knowledge and understanding on the modules taught them during the classroom training sessions. Mock interviews, demonstrations, role playing, discussions, comments, and question and answer sessions were used during the training workshop. A debriefing meeting on the outcome of the pilot survey was also held, which led to some modifications to already-developed questionnaires and menu on the CAPI device. A two-day ToT Workshop on Mobilization and Sensitization for State Officers from the State Ministry of Health (SMOH), NPC, and State Ministry of Agriculture and Rural Development (SMARD) was held from 27 to 28 January 2021 in Abuja. Participants were nutrition desk officers (focal persons) from SMARD, State Nutrition Officers from the Ministry of Health, and State Mobilization (SM) Officers from NPC. Resource persons were from NPC. Participants were trained on the following topics: community mobilization essentials; preparing community mobilizers (CM); effective mobilization; community entry; introduction to CM tools, IEC material and other documents; community mobilization reporting tools; and reporting CM activities, among others. Three participants were drawn from each state, plus the FCT. A total of 107 persons (55 males and 51 females) participated in the training workshop. Training of mobilizers and sensitizers from each of the selected EAs per state were trained by those trained during the ToT. A three-day MRDR Survey Training, Planning Meeting and Pilot for selected biomarker component coordinators (6), field supervisors (6), laboratorians (18), and phlebotomists (18) for the NFCMS was held from 2 to 8 August 2021 in Abuja. A total of 48 persons participated in the MRDR training. Participants were trained on the use of CommCare and MRDR Apps; how to conduct the MRDR survey; and age verification. An interactive session was held to discuss the appropriate oily snack, and foods to avoid on the day of dosing. Review of movement plans, logistics plans, and distribution of field supplies was done zone by zone. Practical demonstrations were also carried out to acquaint trainees with installing the MRDR application, updating their tablets, dosing methodology for MRDR 13 survey, etc. Pilot studies were undertaken within the FCT. The challenges encountered during the pilot were deliberated upon during the debriefing session and noted for improvement of the MRDR survey. Below are the number of persons per component that were used for data collection. Component/section Number of team members NBS-ICT sampling of respondents 36 NBS/NPC HH listing and questionnaire 145 NPC/FMARD/FMOH sensitization and mobilization 485 Anthropometry and Biomarker 156 Dietary intake 184 Total 1006 Pilot Survey After training all field teams, a pilot survey was conducted that included gathering informed consent, data collection and management, and biomarker sample collection in 18 EAs. Through the latter, the intended number of respondents in each target group per EA were selected, resulting in 671 total respondents. Participants were accordingly informed that they were participating in a pilot survey. The pilot was conducted in selected urban and rural communities (18 EAs) close to the training location and surrounding Abuja. This pilot was conducted mainly to test the tools and implementation, including tablets, communications, social mobilization, forms, interview techniques, questionnaires, quality control tools, anthropometry, phlebotomy, lab techniques, etc. Data collected from these respondents were not included in the survey. Information gathered from the pilot survey was used to modify survey collection instruments and field procedures. All changes in the questionnaire after the pilot were agreed upon by the stakeholders and approved by the TAC and SC before approval by the ethics committee. 14 Survey implementation (Field Work) Sensitization Social mobilization and sensitization in the areas surveyed was led in each state by a State Mobilizer from the NPC, and assisted by a state subject matter specialist from FMOH and FMARD. The SM worked with the CM in each of the selected EAs. The CM were selected from the Departments of Health and Agriculture in each LGA. Survey components, order of field operations, and information collected by each component Given the highly technical nature of the survey, the skills required for the different survey components differ markedly. And as such, separate field teams were recruited to undertake the HH listing, dietary assessment, anthropometry, and the collection and handling of biomarker samples. While there were different teams with specialized proficiency and training dedicated to the different survey components, the different forms were linked by HH ID (from the HH line-listing) enabling subsequent alignment and linking of components during analysis of indicators across the different enumeration tools/components. There was also a higher-level supervision and coordination across these teams that provided oversight for the entire survey data collection process. The field teams, the survey component they are responsible for, and information collected by each component during their visit is summarized in Figure 2. Step 1 Sensitization  Sensitization Team  Mobilization Relationship to h Step 2 Line-listing  Line-listing of all ead   sampled EAS LINE- Sex Team*  Random selection LISTING Age in years/months   of respondents Pregnancy status for girls and women+ SES/Housing/Assets Step 3 Household SES  Informed consent HOUSEHOLD Drinking water/toilet facilities Team*  Household Questionnaire QUESTIONNAIRE Food access FIES / coping Step 4 Dietary  Pre-training for dietary Pre-training Team#   24-hour recall 24-hour recall IYC feeding+ Biofortified food consumption Step 5 Dietary Intake  Short diet questionnaire DIET Food fortification coverage Team#  First dietary 24-hour recall QUESTIONNAIRE Lactation status for women Pregnancy status women BIOMARKER Anaemia risk factors Intervention coverage & health status Biomarker  Nutrition Questionnaire QUESTIONNAIRE Step 6 Anthropometry  Anthropometry measurements Weight+  Biological sample collection ANTHROPOMETRY Team Height/length+  MRDR in 20% of the sample SAMPLE Biological samples and measurements COLLECTION Step 7 Dietary Repeat  Short diet questionnaire Intake Team DIET  Repeat dietary 24-hour recall 24-hour recall (>20% QUESTIONNAIRE  Food samples collection subsample)# Food fortification coverage & collection FOOD SAMPLES (Salt, oil, sugar, wheat flour, and semolina)+ * The line-listing and household SES teams are the same interviewers. # The dietary pre-training and dietary repeat intake teams are the same interviewers. + Only collected for relevant respondents. Figure 2. Survey components, order of field operations, and information collected by each component 15 Deployment of field teams and administration of survey questionnaire to selected respondents Five questionnaires were used to collect information on: (1) HH; (2) non-pregnant and lactating WRA; (3) pregnant WRA; (4) children (aged 6-59 months); and (5) adolescent girls (aged 10- 14 years). Each sampled respondent received a minimum of two visits and a maximum of up to five visits. For each component, a maximum of three visits were made if the respondent was not available for the first visit. The teams deployed to the field at different times. The mother or caretaker of adolescent girls (aged 10-14 years) and children (aged 6-59 months) were present during all interviews and sample collections. After the completion of the diet questionnaire, the respondent was invited by the biomarker interviewers to complete the biomarker interview. Sensitization teams: The sensitization team was deployed on 10 February 2021, a week before the HH listing team. Sensitization was conducted a week before the team entered the community. In addition, a jingle was played via the widely listened radio stations in each of the states a week before the teams commenced data collection and until data collection was completed in the state. The jingle was translated to Hausa, Yoruba, and Igbo and to other languages, as needed. Local guides were also available to the teams in each community. Line listing team: The line listing teams was deployed on 17 February 21 and continued after a one-week break during Easter holidays. The teams listed all building structures in the selected EAs and all members of a HH. The listing data was then transmitted to a central server for sampling of respondents. The list of sampled respondents was then sent to the HH teams. Household SES team: The HH listing teams also administered the HH questionnaire after sampling of respondents. The teams deployed on 17 February 21 and continued after a one-week break during Easter holidays. The teams collected information on general characteristics of the head of HHs (i.e., ethnicity, religion, education, and employment). The HH in sample questionnaire also collected information on the HHs’ dwelling unit (i.e., source of drinking water; type of toilet facilities; materials used for flooring, external walls, and roofing; ownership of various animals and durable goods; area where members of the HH often wash their hands; main way of refuse disposal, presence of a vegetable garden and fruit trees; HH food insecurity; and HH coping strategies). Dietary pre-training: After the completion of the HH questionnaire, the sampled respondent was invited to participate in a group dietary pre-training. The interviewers trained the sampled respondents on the process of data collection for the 24-hr dietary recall interview. They also provided all selected respondents with bowls and plates and requested them to serve all foods/ drinks for the selected participant (i.e., either the WRA, or child, or pregnant woman). Dietary intake team: The day after the training was observed as a reference day. The following day, the diet team conducted the diet interview using the short diet questionnaire and first 24-hour dietary recall. For example, if the training of respondents is conducted on Monday, then Tuesday is observed as the reference day, and the diet interview is conducted on Wednesday. Biomarker and anthropometry teams: The biomarker and anthropometry teams moved together in the same EA with the dietary intake team. Immediately after the dietary interview, the respondent is referred to the biomarker and anthropometry teams. The biomarker team administered the biomarker questionnaire and collected anthropometry measurements, blood, and urine samples. 16 Dietary repeat intake team: A random sample (25 percent) of non-pregnant WRA and children (6- 59 months old) from respondents who completed the 24-hour dietary recall was visited for a repeat 24-hour dietary recall interview and collection of food samples on non-consecutive days. Phase 1 data collection commenced on 17 February 2021 for the HH listing and questionnaire field teams, while the dietary intake and biomarker/anthropometry field teams commenced on the week of 8-12 March 2021. At the end of Phase 1, a total of 162 EAs were listed, respondents sampled, and HH interviews conducted in 144 EAs. Three of the zones had collected data on dietary intake and biomarker from 27 EAs each.Challenges encountered during Phase 1 data collection included: (1) size of randomly selected EAs resulting in not meeting required number of respondents; (2) coverage rate of less than 80 percent; (3) poor mobilization in sensitization especially, in urban areas; (4) feedback from reviewers of the dietary interviews was not stepped down to the supervisors and interviewers, resulting in same mistakes occurring through the period; and (5) security-related issues. To address the observed challenges, the following steps were undertaken: (1) sample uptake was increased for the remaining EAs in each zone (Table 5) – children (6-59 months old ) increased by 5, adolescent (10-14 years) increased by 1, WRA increased by 4, and pregnant women increased by 1); (2) revisited EAs where possible; (3) local mobilizers, supervisors and interviewers were re-trained; (4) scheduled appointments; (5) aimed for maximum visits to each respondent (3x); (6) improved incentives for respondents (sachet fortified vegetable oil); (7) played the jingle once a week before the team enters the state and continue until end of data collection in the state; (8) conducted targeted mobilization; and (9) made sure that local guides were from the community. In addition, refresher training after the Ramadan break was conducted focussing on observed mistakes during data collection. Table 5. Adjusted sample size per EA for Phase 2 data collection Respondents Respondents Sampling target population selected per EA in selected per EA in Total sample size at Phase 1 Phase 2 national level Non-pregnant WRA (15-49 years old) 16 20 6240 Children (6-59 months old) 16 21 6240 Pregnant women (15-49 years old) 3 4 1170 Non-pregnant adolescent girls (10-14 years old) 3 4 1170 Total 38 49 14 820 Phase 2 data collection commenced immediately after Easter holidays (12 April 2021 for the HH listing and questionnaire field teams and ended 24 June 2021, while the dietary intake and biomarker/anthropometry field teams commenced 17 May 2021 and ended 04 July 2021). At the end of Phase 2 data collection period, the anthropometry and biomarker component had collected data and biological samples from 12 410 individuals (5469 WRA, 5061 children aged 6-59 months, 880 pregnant women, and 1000 adolescent girls). For dietary intake, a total of 11 713 were interviewed (5435 WRA, 5016 children aged 6-59 months, and 893 pregnant women). In addition, a total of 1152 salt samples, 398 sugar, 340 vegetable oil, 91 semolina flour, and 48 wheat were collected. For biomarker samples, at the end of field work, 5961 urine samples were collected indicating a coverage rate of 86 percent, 10 295 stool samples representing a coverage rate of 75.4 percent, 17 and 11 957 blood samples representing 80.7 percent coverage. More blood samples were collected in the North West zone compared to South East. For the dietary component, from the 364 EAs covered, a total of 11 344 respondents were completely interviewed, which is equivalent to 89 percent national coverage. The North West had the highest coverage at 2081, followed by South West at 1967, SS at 1918, North East at 1857, North Central at 1783, and South East at 1738. No zone had less than 92 percent coverage in complete questionnaire administration based on the number of EAs covered. For food samples collected from the 20 percent sub-samples of non-pregnant WRA at the dietary intake repeat interview, 2031 food samples were collected nationwide (1153 salt, 338 vegetable oil, 400 sugar, 89 semolina flour, and 51 wheat flour). A total of 364 EAs were covered out of the 390 samples. Twenty-six (26) EAs were lost to insecurity. Although total coverage was higher for dietary intake compared to biomarker, the minimum coverage rate of 80 percent was met for all survey components, except for the stool sample. 18 Data quality management and processing Given the magnitude and complexities of the survey, daily monitoring of data collection was undertaken. Key indicators that were measured daily included: • completion rates; • refusals and revisits; and • data inconsistencies such as: • duplicate IDs; ─ out-of-range dates and times; ─ outliers for key continuous variables, etc.; and ─ data mismatch (e.g. some biomarker data do not have the corresponding HH data). A dashboard was designed and used to monitor interviewer’s performance completion rate for the various components and tracking the average frequency of revisits. To ensure data quality control for the dietary intake component, the following actions were undertaken: (1) crosschecking of selected respondents to make sure there are no duplicates or oversampling; (2) summarizing respondents selected in each EA to highlight EAs where there were too few sampled respondents for discussion with the listers; (3) daily monitoring and review of collected data and feedback to zonal coordinators; (4) daily discussion of errors noticed immediately with the supervisors and interviewers; (5) testing of tablets, weighing scales, and play doughs; (6) tracking of interviewers with respect to the time taken to complete an interview since the time taken varies with the number of food items consumed; and (7) conducting random review of collected data in CommCare. For anthropometry, the following information were reviewed real-time daily during data collection: (1) tracking of consent (Did the respondent/guardian give consent?); (2) checking for data completeness based on the respondent’s age, height/length, weight, and relevant comments; (3) logging of errors (Are there any duplicates in the data?); (4) EA summary - done vs. not done (Does the data in the server match what was done in the field?); (5) conducting data quality checks (completeness, sex ratio, age heaping, digit preference for height and weight, implausible z-scores, standard deviation of z-scores, normality of z-scores); (6) tracking performance of equipment and calibrations; and (7) tracking performance of lead anthropometrists and corrective actions. In addition, at least once a week, we interrogated the quality of the anthropometry data through the following questions: (a) do we have all data collected by field teams?; (b) did the right respondent give the correct data?; (c) are ages of children (6-59 months) verified?; and d) are the interviews complete? During data analysis, the Anthro Survey Analyzer was used to check quality of anthropometry data. The results are shown in Annex 3. Collection, testing, and processing of biological specimens are critical parts of the NFCMS. Sample collection, processing, transport, and storage were done with great care so that the laboratory results are accurate, valid, and accurately reflect the micronutrient status of the survey participants. All team members followed universal precautions, which are procedures that must be followed by all team members to prevent exposure to HIV, hepatitis, and other infectious agents that are encountered during all collection, processing, and handling of biological specimens. Proper cold chain logistics were followed throughout the survey. The cold chain followed biological samples from the initial collection until the sample is analyzed. All biomarker team members were trained on cold chain logistic and management to preserve sample quality. 19 Sampling weights, non-response adjustment, and data analysis Sampling Weights The frame used for the sampling of clusters for the survey was derived from the EA list that was developed and maintained by the NPC and used in the last census (2006) in Nigeria. It covers the entire geographic area of Nigeria, and the EA are mutually exclusive and exhaustive of the entire landmass of the country. It is the most comprehensive small area demarcation that guarantees every cluster of being included in a survey with a known probability of inclusion. The 65 EAs for each geographic zone were selected with PPS, using the estimated population of the individual EA as their Measure of Size (MOS). The data collected was weighted appropriately for each target group to account for the probability of selection of the sample at each stage in the sampling process. The weights applied were adjusted for non-response by target group. Base weights Due to the non-proportional allocation of the samples across the six geopolitical zones and target groups, as well as differences in non-responses across sampling units (EA, listed target groups) and indicator level (i.e., anthropometry, malaria, haemoglobin, diet questionnaire etc.), sampling weights are needed for any analysis of the NFCMS data. This will ensure the representativeness of the survey results at the national and domain levels. The first stage of sampling probabilities for each selected PSU (EA) in the h-th stratum (geopolitical zone) are as follows: Sampling Probability 1st stage MOShα = measure of size (MOS) of α-th EA (PSU) of the h-th geopolitical zone (stratum) Estimated PSU population size from the 2006 census frame ah = number of EAs (PSU) to be selected in the h-th geopolitical zone (stratum). These are given in Table 5. = total estimated population size of the h-th geopolitical zone (stratum) The NPC provided the sampling frame with all the information needed to enable the calculation of the first stage sampling probabilities. The second stage sampling probabilities was computed separately for each target group. For a target group (please note that another subscript to refer to the specific target group has not been added for simplicity), the probability of selection are as follows: Sampling Probability 2nd stage 20 b hα = number of sampled individuals in the target group in the α-th EA (PSU) of the h-th geopolitical zone (stratum). This will be 16 for WRA and children (aged 6–59 months), and 3 for non-pregnant adolescent girls and pregnant women. Nhα = total number of eligible individuals in the α-th EA (PSU) of the h-th geopolitical zone (stratum). The final selection probability (πhα) for individuals within a target group in the α-th PSU (EA) of the h-th stratum (geopolitical zone) is given by multiplying the first and second stage selection probabilities - π1hα and π2hα as follows: The final base sampling weight (whα) is the inverse of the final selection probability, given by: whα = 1/πhα. This weight was applied to each participant in a specific target population in the α-th PSU (EA) of the h-th stratum (geopolitical zone). Based on this description, the following information needed to calculate the base weights were obtained: 1) First stage a. Number of PSU (EAs) selected in each zone b. Measure of size (MOS) (e.g. estimated population size of each selected EA) c. Total sum of MOS (i.e., the final cumulative MOS) for the entire population of EAs in each zone 2) Second stage a. Total number of eligible individuals per target group in each selected EA b. Number of eligible individuals selected in each target group per selected EA c. Number of selected individuals in each target group per EA completing the survey The data obtained were carefully documented, maintained electronically, and retained for use at the time of data analysis. This includes sampling unit identifiers (zonal code, state code, EA code, and respondent ID) used for merging with the survey data. Non-response adjustment At the inception of the sampling design, the issue of insecurity and other matters that may hinder access to some clusters were taken into consideration. While the calculated design was to use 60 clusters per zone for the prevailing security and access issues, the number of clusters to be sampled was boosted to 65 from 60 for each zone. This will serve as the reporting domain. A total of 26 out of the 390 EAs (or 6.67 percent) were not accessed, and distributed as follows (NC-6, NE-10, NW-4, SE-1, SS-3, and SW- 2). The highest inaccessible was from NE with 10 EAs; 8 of these are from Borno state and 2 from Yobe State. In NC zone, the six that were not accessed are three each from Benue and Niger states. In NW, four were not accessed (1 from Kebbi, 2 form Sokoto, and 1 from Zamfara states). SE has one EA not accessed (Anambra state). From SS, two EAs were not accessed (one from Rivers and one from Cross river states). The two EAs not covered in SW are one each from Ogun and Lagos states. All these EAs were not covered due to security concerns, except the one in Lagos where the local community refused to participate in the survey despite several advocacy from different stakeholders. The EA was abandoned after several advocacy visits. 21 It is noteworthy that that the cluster coverage rate in NE stood at 85 percent. Thus, 15 percent of the cluster were not covered and 80 percent of these uncovered are from Borno state only. Borno, by 2021 projected population, represent 20 percent of the population of the entire NE combined. The survey was designed to have the least level of analysis at zonal level; thus, the 85 percent coverage achieved could be a good representation of the zone. Moreover, from other similar studies, such as DHS, Borno is not known to exhibit high levels of differential from the other states in the zone. Only 5 out of 13 proportionally allocated to Borno by population size were covered and an attempt to make state level inferences using the covered clusters form Borno may yield to a high-level bias and low-level precision of such result. The adjustment for the non-response at cluster level was done by state and urbanicity (rural or urban). For example, if in the design y, rural clusters were sampled in a state and only x was accessible, the cluster response rate is calculated as Cij, for the ith state and jth urbanicity. Where Cij = Xw/Yw; Xw=sum of sampling weights of the x accessible clusters; and Yw= sum of sampling weights of all the sampled clusters (base Weight) for the ith state and jth urbanicity. i = 1,2,3 …………. 37 and j = 1,2. The cluster non-response adjustment factor is the inverse of Cij (that is, 1/Cij). The base weights were adjusted to account for non-response bias by using a weighting class adjustment. This was done by dividing the original sample into T mutually exclusive and non- overlapping subsets, called adjustment cells (indexed by T within which members are assumed to have similar values) for the response variable of interest and all response probabilities are presumed to be equal. The weighting class adjustment is done by computing the response rate for each adjustment cell and using it to adjust the base weights for participants in the cell. The response rate for cell t is given by The non-response adjustment factors are obtained as the inverse of these response rates, Finally, the non-response adjusted weight was then obtained by multiplying the base weight for each participant i in the weighting class t by the corresponding adjustment factor as follows: Table 6 gives the response rates and corresponding adjustment factors calculated. Table 6. Example of response rates, corresponding adjustment factors, and final non-response adjusted weight for each weighting class in years for WRA Weighting class Weighted response Adjustment factors (Inverse of Final non-response rate (%) weighted response rate) adjusted weight Rural 15–24 y 84 1.19 99.96 25–34 y 42 2.38 99.96 35–49 y 90 1.11 99.90 Urban 15–24 y 92 1.09 100.28 25–34 y 60 1.67 100.20 35–49 y 75 1.33 99.75 22 Table 7 lists the variables to be considered for forming the adjustment cells for each target group. Table 7. Variables to be considered for forming the adjustment cells for each target group Sampling target groups Variables considered for forming adjustment cells Categories Non-pregnant WRA (aged 15-49 years) Age 15-24, 25-34, 35-49 y Urbanicity Rural, urban Children (aged 6-59 months) Age 6-11, 12-23, 24-59 mo Urbanicity Rural, urban Pregnant women (aged 15-49 years) Age 15-24, 25-34, 35-49 y Urbanicity Rural, urban Adolescent girls (aged 10-14 years) Urbanicity Rural, urban It should be noted that further disaggregating the weighting classes used for the non-response adjustment by the reporting domain of the target groups (i.e., for WRA and children) was not conducted. This was discussed extensively, and it was generally agreed to uphold the calculation of non-response as indicated in the protocol (Table 7). This specifies that the adjustment should take into consideration urbanicity (rural/urban), age group for each of the target groups at the national level, and apply to each cell nationwide, assuming that each of the cell (e.g. children 6 to12 months, from rural or WRA-age-15-23-urban or WRA-age-24-34-rural) are likely to be more homogeneous even at the national level. The response rate was calculated and applied at the individual modules (i.e., malaria test, diet, genotype, etc.) as presented in Annex 1. Further breaking this to zonal level might be unstable. Although calibration of weights to population estimates is a standard step in weight calculation for population surveys, this was not conducted due to lack of projections of population estimates for the target groups. Data analyses There are four components of the dataset: HH, Dietary, Anthropometry, and Biomarker. Sampling weights and non-response adjustment factors were applied and merged with final survey data. The HH ID and Personal ID were the unique link to various data sets. Out of 86 314 persons listed, 34 469 were the target population in 9107 HHs. Hence, total number of HH questionnaires completed was 9107. All the HHs gave consent to the survey, thereby, yielding a response rate of 100 percent. The HHs in sample data were mainly processed and analyzed using SPSS statistical software (version 21). A section of the analysis (food security) was done using “R” statistical package. Various NFCMS indicators were produced and cross-tabulated with nominal variables such as place of residence (urban/rural), type of HH (sex of HH head), level of education of HH head, as well as the wealth quintile group of the HH. In all cases, reports are provided at national level and at geopolitical zonal level. Table 8. Reporting domain and disaggregation level of household in sample component National Residence Household type Education of household head Geopolitical zone Wealth quintile Rural Male-headed None North Central Poor Urban Female-headed Primary North East Second Secondary North West Middle Technical / Vocational cert. South East Fourth Higher / University/ College South South Richest Others (Specify) South West Missing 23 Of the 12 805 individuals sampled for the diet component, 11 713 completed the diet questionnaire. The final sample used for analysis is 11 255 respondents (5281 non-pregnant women aged 15- 49 years, 1006 pregnant women aged 15-49 years, and 4968 children aged 6-59 months). Non- pregnant WRA were subdivided by lactation status, which was defined as having breastfed a child aged <12 months the previous day or night. Children aged 6-59 months were subdivided by age groups (6-23 months and 24-59 months) to account for potential breastfeeding in the younger children. All diet data were analyzed using the SAS statistical software (v9.4). Frequencies and Chi-square tests were obtained using SAS Procedure Surveyfreq using the survey design variables for EA and geopolitical zone, with the final sample weights adjusted for non-response. For all target groups, data are presented by urbanicity (urban vs. rural). For non-pregnant women (aged 15-49 years), data are presented per geopolitical zone (North Central, North East, North West, South East, South South, South West) and by wealth quintile. For children (aged 6-23 and 24-59 months), data are presented by sex (male vs. female). The total number of respondents for each analysis used as the denominator for percentages are reported in the tables. The survey’s micronutrient component has essentially two parts – the anthropometry and the biomarker aspects of the study. Data collection, analysis, and reporting of these aspects of the survey follow international standards.4 5 As presented in Annex 2, the Anthropometry aspect included 4912 children (aged 6-59 months),1006 adolescent girls, and 5239 WRA, totalling 11 157 respondents out of the 12 873 individuals sampled for the NFCMS. A total of 31 individuals were excluded from the analysis due to lack of signed consent. The Biomarker aspect included the biomarker questionnaire and biological measurement. a) Biomarker questionnaire: The biomarker questionnaire collected information on intervention coverage, self-reported morbidity, and anaemia risk factors that targeted 4916 children (6-59 months), 1002 adolescent girls, 5239 WRA, and 863 pregnant women. A total of 12 020 respondents out of the 12 873 individuals were sampled for the NFCMS. Fourteen (14) individuals were excluded from the analysis due to lack of signed consent (n=11) and ineligible interviews (n=3). Details of the questionnaire are also summarized in Annex 12. b) Biological samples and measurements: During the survey, whole venous blood, urine, and stool samples were collected from various target groups in the field. The preliminary report presents measurements from the field and local labs. Annex 2 summarizes the measurements included in the preliminary report, and Table 9 shows the number of respondents against those sampled. To highlight insights from data in the analysis, children were subdivided by age groups (6-11 months, 12-23 months, 24-35 months, 36-47 months, and 48-59 months). Data for adolescent girls was assessed per age (10,11,12,13, and 14 years), as well as for pregnant and non-pregnant women (15-19 years, 20-29 years, 30-39 years, and 40-49 years). 4 Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. 5 Centers for Disease Control and Prevention, World Health Organization, Nutrition International, UNICEF. Micronutrient survey manual. Geneva: World Health Organization; 2020. Licence: CC BY-NCSA 3.0 IGO. 24 Table 9. Number of respondents against those sampled Respondent (number sampled) Malaria Plasma glucose H. pylori Helminths HbA1c Haemoglobin Haemoglobin genotype Children (aged 6-59 months); 4678 No 4672 4240 No 4674 4548 (5576) Adolescents (aged 10-14 years); 996 No 984 No No 999 No (1206) Women of Reproductive Age, (aged 15-49 years); 5159 5109 5161 4669 5309 5272 5137 (6091) Pregnant women (aged 15-49 years); 959 No 959 846 No 847 No (1138) Total (n/N) 11 792/ 5109/ 11 776/ 9755/ 5309/ 11 792/ 9658/ Respondents/ Number 14 011 6091 14 011 12 805 6091 14 011 11 667 samples The anthropometry indices were built using the Stata Software (Version 14.0) “zanthro” command available from the WHO6. A summary of the data quality assessment from the Anthro Survey Analyzer is presented in Annex 3. All biomarker data were analyzed using Stata statistical Software (Version 16). Frequencies and proportions were obtained using two-way tabulations. To adjust for the survey design, variables cluster (enumeration area), strata (regional zone), and weight were specified using the syntax below: svyset EA_Code [pw = component_weight], strata(zone) where EA_code is the unique ID that identified the enumeration area, component weight refers to the standardized weight by biomarker component and zone is the geopolitical zone in the country. For children (aged 6-59 months), data were grouped by age, sex, residence, zone, wealth quintile, and caregiver’s education. For adolescent girls (aged 10-14 years), data were grouped by age, residence, and wealth quintile. For WRA (aged 15-49 years), data were grouped by age, residence, zone, wealth quintile, and educational attainment. For pregnant women, data were grouped by age, residence, wealth quintile, and educational attainment. Differences across groups were assessed using Chi-square tests where significance was determined at 5 percent level. The total number of respondents for each analysis was used as the denominator for percentages reported in the tables. The data tables have accompanying notes for clarification as needed. 6 Vidmar, S. I., Cole, T. J., & Pan, H. (2013). Standardizing Anthropometric Measures in Children and Adolescents with Functions for Egen: Update. The Stata Journal: Promoting Communications on Statistics and Stata, 13(2), 366–378. https://doi. org/10.1177/1536867X1301300211 25 Household in sample key findings The results presented are for those HHs with sampled respondents. There was a comprehensive listing of all HHs conducted in 390 EAs to produce the sampling frame for the survey, which included children under five years, pregnant women, non-pregnant WRA, and non-pregnant adolescent girls. The exercise involved listing all HH members living in the residential building structures in the selected EAs. A total of 86 314 individuals were listed from 18 791 HHs. From this, a sample of 9107 HHs was selected for inclusion in the sample and included a total of 34 469 individuals from the four target groups. The main respondents in each of the HHs gave consent to the survey, thereby yielding a response rate of 100 percent. The results are presented as frequency distribution tables or as means with confidence intervals (95 percent CI). Total number of households and persons listed in the selected EAs by type of building structure Table 10 presents the number of HHs and persons listed by use of building structures. Other HHs listed were contained in building structures for both residential and commercial purposes. Table 10. Total number of households and persons listed in the selected EAs by type of building structure Number of Households Listed Number of Persons Listed N % N % National 18 791 100.0 86 314 100.0 Residential only 17 675 94.1 81 628 94.6 Residential/commercial 1026 5.5 4291 5.0 Residential/Religious 68 0.4 311 0.4 Residential/Institutional 22 0.1 84 0.1 Distribution of Sampled children Table 11 presents the distribution of the individual children (aged 6-59 months) in the sampled HHs. It is noteworthy that almost the same proportion of males and females were sampled across the children age groups as male and female children constitutes about 50 percent in each category. Table 11. Distribution of children aged 6-59 months in listed households 6-23 months 24-59 months Total Characteristics N % N % N % National 3527 100.0 7019 100.0 10 546 100.0 Sex Male 1757 49.8 3527 50.2 5284 50.1 Female 1770 50.2 3492 49.8 5262 49.9 Residence Urban 1406 39.9 2807 40.0 4213 39.9 Rural 2121 60.1 4212 60.0 6350 60.1 Geopolitical Zone North Central 517 14.7 1081 15.4 1,598 15.2 North East 889 25.2 1601 22.8 2,490 23.6 North West 850 24.1 1783 25.4 2,633 25.0 South East 327 9.3 711 10.1 1,038 9.8 South-South 482 13.7 964 13.7 1,446 13.7 South West 462 13.1 879 12.5 1,341 12.7 26 The percentage of listed HHs in the urban areas varied from 27.3 percent in North West to 83.1 percent in the South West. For the target population, the percentage from urban areas varied from 38.6 percent (pregnant women) to 48.9 percent (non-pregnant WRA) Distribution of sampled non-pregnant women and women of reproductive age The distribution of non-pregnant women of non-reproductive age in listed HHs shows that a little above half of the sampled respondents were found in the rural areas (Table 12). The distribution of sampled non-pregnant WRA were virtually close in all the geopolitical zones, except South East. However, greater proportion of pregnant WRA were more distributed in the rural areas. Table 12. Distribution of non-pregnant and pregnant WRA in listed households Non-Pregnant WRA Pregnant WRA Characteristics N % N % National 18 781 100.0 2,040 100.0 Residence Urban 9185 48.9 787 38.6 Rural 9596 51.1 1253 61.4 Geopolitical Zone North Central 3160 16.8 298 14.6 North East 3604 19.2 483 23.7 North West 3823 20.4 517 25.3 South East 2177 11.6 191 9.4 South-South 3065 16.3 293 14.4 South West 2952 15.7 258 12.6 Distribution of Sampled Adolescents Table 13 presents the distribution of the adolescents in the sample HHs. About 53 percent of the sampled adolescents were from rural areas. North West and North East have close to one-fourth of sample adolescents. On a general note, about 60 percent of the listed adolescents were in the north geopolitical zones. Table 13. Distribution of Adolescents Adolescents Characteristics N % National 3102 100.0 Residence Urban 1457 47.0 Rural 1645 53.0 Geopolitical Zone North Central 461 14.9 North East 703 22.7 North West 702 22.6 South East 349 11.3 South-South 462 14.9 South West 425 13.7 27 Distribution of children aged 6-59 months Table 14 presents the distribution of sampled children (aged 6-59 months). The table shows that the children were evenly distributed by sex. Table 14. Distribution of sampled children (aged 6-59 months) in listed households Children aged 6-59 Months Characteristics N % National 10 546 100.0 Sex Male 5284 50.1 Female 5262 49.9 Residence Urban 4213 39.9 Rural 6333 60.1 Geopolitical Zone North Central 1598 15.2 North East 2490 23.6 North West 2633 25.0 South East 1038 9.8 South-South 1446 13.7 South West 1341 12.7 Sex Distribution of household heads Table 15 presents sex distribution by age. About 89 percent of the HHs were male-headed. Results also show that male-headed HHs in the rural area is higher than in the urban areas. Table 15. Distribution of Households in Sample by Sex of Head of Household Households in Sample Male-headed Female-headed Characteristics N % % National 9107 89.4 10.6 Residence Urban 3990 88.2 11.8 Rural 5117 90.3 9.7 Level of Education None 1562 86.8 13.2 Primary 2150 85.2 14.8 Secondary 3421 91.2 8.8 Technical /Voc certificate 376 92.7 7.3 Higher/University/College 1169 92.4 7.6 Others (Specify) 380 97.2 2.8 Missing 48 97.8 2.2 Geopolitical Zone North Central 1390 84.4 15.6 North East 1458 92.8 7.2 North West 1687 95.0 5.0 South East 1328 84.3 15.7 South-South 1591 85.0 15.0 South West 1653 89.4 10.6 28 Female-headed households Table 16 presents sex distribution of HHs in sample by level of education of head of HH. The result reveals that more than half of female HH heads either had primary or did not have any formal education. Table 16. Distribution of Household in Sample by Level of Education of Head of Household Type of Household Level of school completed by household head Male-headed Female-headed All None 18.7 24.2 19.3 Primary 22.3 32.7 23.4 Secondary 36.3 29.8 35.6 Technical/Vocational certificate 4.3 2.9 4.2 Higher/University/College 13.0 9.1 12.6 Others(Specify) 4.8 1.2 4.4 Missing 0.6 0.1 0.5 Total 100.0 100.0 100.0 Table 17 presents distribution of HHs in sample by Wealth Quintile Index. The results show that about half (51.4 percent) of female-headed HHs were in the middle and fourth quintiles, unlike the male- headed HHs, which were almost evenly distributed. Table 17. Percentage Distribution of Households by Wealth Index Quintile7 Type of Household Wealth Index Quintiles Male-headed Female-headed Overall Poorest 20.4 14.7 20.0 Second 20.3 15.9 20.0 Middle 19.6 25.0 20.0 Fourth 19.5 26.4 20.0 Richest 20.2 18.0 20.0 Total 100.0 100.0 100.0 Income-generating activities of household heads As reported by Carletto et al. (2007), income-generating activities include a full range of agricultural and non-agricultural activities carried out by rural HHs. This allows an understanding of the relationship between the various economic activities that take place in the rural and urban spaces, and of their implications for economic growth, poverty reduction and food security. About 94 percent of HH heads were engaged in various income-generating activities. The proportion of HH heads engaged was almost the same in urban (93.9 percent) and rural areas (94.1 percent). Male HH head were more engaged compared with their female counterparts. Also, majority of HHs were into income-generating activities, irrespective of educational levels. Except for SS, the proportion of HH heads that were engaged were over 90 percent in all the geopolitical zones Table 18). 7 Refer to Wealth Index (Wealth Quintiles) on page 31 29 Table 18. Percentage of head of households with income-generating activities Disaggregation Total Households in Sample (N) % National 9107 94.0 Residence Urban 3990 93.9 Rural 5117 94.1 Household Type Male-headed 8090 94.9 Female-headed 1017 85.9 Level of Education None 1562 90.9 Primary 2150 93.9 Secondary 3421 95.4 Technical /Voc certificate 376 95.3 Higher/University/College 1169 93.8 Others (Specify) 380 96.4 Missing 48 84.3 Geopolitical Zone North Central 1390 94.1 North East 1458 98.1 North West 1687 94.0 South East 1328 90.5 South-South 1591 88.4 South West 1653 97.0 Table 19 presents the distribution of income-generating activities by type in the six geopolitical zones. Results obtained indicate that nationally, the agricultural sector took the lead with 36.8 percent, while sales and related activities followed with 16.3 percent. Service-related activities constituted 12.6 percent of the economic activities engaged in. The pattern of distribution was, however, different among the geopolitical zones. Engagement in agricultural sector was higher in northern zones as compared to the south (Table 19). Table 19. Percentage distribution by main work of head of household for income Geopolitical Zone Main work of household head for income North North North South South South Central East West East South West National Agricultural, Animal Husbandry, and Forestry Workers; Fishermen; and Hunters 48.8 52.9 42.0 29.1 28.7 19.9 36.8 Sales and Related Workers 6.9 14.2 22.9 19.4 14.5 16.0 16.3 Service Workers 10.2 12.5 8.9 13.6 15.4 16.7 12.6 Professional, Technical, and Related Workers 6.2 3.5 4.1 7.1 4.5 15.2 6.9 Not working and didn’t work in last 12 months 5.9 1.9 5.7 9.4 11.3 3.0 5.8 Transportation and Material Moving Workers 3.7 2.3 4.7 6.9 6.2 7.1 5.2 Others(Specify) 6.7 1.9 1.2 4.1 5.4 10.4 4.9 Production, Construction, and Extraction Workers 3.2 2.2 1.8 4.7 5.3 4.5 3.4 Office and Administrative Support Workers 3.2 3.1 4.4 0.8 3.3 2.4 3.1 Administrative and Managerial Workers 3.7 4.1 2.8 1.8 2.3 1.2 2.6 Installations, Maintenance, and Repair Workers 1.6 1.4 1.3 2.9 2.7 3.6 2.2 Missing 0.0 0.0 0.4 0.1 0.3 0.1 0.2 Total 100 100 100 100 100 100 100 30 Wealth Index (Wealth Quintiles) The Wealth Index, presented as quintiles was constructed using the asset approach, whereby all HH possessions are included, as much as possible. These quintiles are derived from a series of questions about HH construction materials, water sources and sanitation access, and ownership of various items, which form a wealth index score. The wealth index quintiles divide the population into five equally large groups, based on their wealth rank. The five broad categories are poor, second, middle, fourth, and richest quintiles. Results shown in Table 20 indicate that about two-third of the listed HHs in rural areas were in the poor and second quintile categories. However, about 64 percent of the HHs in urban area were in the fourth and richest quintile categories. Similarly, HHs in the North East and North West have higher proportions of HHs in poor quintile categories compared with HHs in the southern part of the country. Table 20. Household Wealth Index Total Households Percentage Disaggregation in Sample (N) Poor Second Middle Fourth Richest National 9107 20.0 20.0 20.0 20.0 20.0 Residence Urban 3990 2.7 6.8 18.6 30.8 41.0 Rural 5117 30.9 28.3 20.8 13.2 6.8 Geopolitical Zone North Central 1390 17.4 20.4 25.6 20.7 16.0 North East 1458 38.4 20.1 16.8 13.2 11.5 North West 1687 29.1 32.5 18.2 11.3 8.8 South East 1328 8.3 9.8 24.5 23.9 33.5 South-South 1591 4.2 11.8 21.5 29.5 33.0 South West 1653 4.8 9.0 18.1 32.9 35.2 Weights were applied based on number of households in sample and household size. Water Households’ drinking water from an improved water source Improved water sources include piped water, tube-well, borehole, and protected well. Other sources are rainwater, protected spring, and bottled water. Table 21 presents proportion of HHs’ drinking water from water piped into dwelling unit or compound. Results show that nationally, 1.1 percent of HHs had water piped into dwelling unit or compound. The results indicate that the proportion for the urban areas (1.8 percent) was three times more than the HHs in the rural areas (0.6 percent). It is noteworthy that most of the HHs that had water piped into dwelling unit or compound had HH heads with higher educational attainment. However, the proportion was ridiculously low in all the zones; as low as 0.2 percent in the South East zone. On the other hand, the ratio increased with wealth quintile groups, ranging from 0.2 percent for the poorest to 2.7 percent for the richest quintile. 31 Table 21. Percentage of household heads for which water was piped into the premises or neighbour Disaggregation Total Households in Sample (N) % National 9107 1.1 Residence Urban 3990 1.8 Rural 5117 0.6 Household Type Male-headed 8090 1.1 Female-headed 1017 0.9 Level of Education None 1562 0.8 Primary 2150 0.8 Secondary 3421 0.8 Technical /Voc certificate 376 2.2 Higher / University/ College 1169 2.5 Others (Specify) 380 1.1 Missing 48 0.0 Geopolitical Zone North Central 1390 1.1 North East 1458 0.9 North West 1687 1.7 South East 1328 0.2 South-South 1591 0.6 South West 1653 1.1 Wealth Quintile Poor 1523 0.2 Second 1476 0.6 Middle 1734 0.7 Fourth 2071 0.9 Richest 2303 2.7 Other Sources of water Other sources of water explored in this study include water from improved sources for which collection time did not exceed 30 minutes for a round-trip (including queuing). Results show that education and wealth status have no major implication in the proportion of HHs that had access to such sources of water. Households Drinking Water from an Unimproved Water Sources Unimproved water sources include unimproved well, unprotected spring, water kiosk, tanker truck, and cart with water tank/drum. Other sources are sachet/pure water, river, stream, pond, and lake. The percentage of HHs that drank water from unimproved sources were smaller compared to those that drank from improved water sources. About 36 percent of HHs drank water from unimproved water sources in the country. A greater proportion (40.2 percent) of HHs in rural area as against 36.1 percent in urban areas were affected. The proportion of female-headed HHs (34.7 percent) was close to that of male-headed HHs (36.1 percent). The percentage varied among the geopolitical zones, ranging from 28.3 percent in South East to 43.3 percent in the North East. The practice of drinking water from unprotected sources was more pronounced among the HHs in the poor and second quintile categories of wealth. 32 Table 22. Percentage of Houses that Drank from Water Sources from Other Sources Total Households in Disaggregation Sample Water sources not Water from unimproved water (N) exceeding 30 minutes sources National 9107 63.6 36.0 Residence Urban 3990 69.4 29.8 Rural 5117 59.6 40.2 Household Type Male-headed 8090 63.5 36.1 Female-headed 1017 64.4 34.7 Level of Education of HH Head None 1562 56.1 43.8 Primary 2150 64.3 35.7 Secondary 3421 67.5 32.2 Technical /Voc certificate 376 68.0 30.4 Higher / University/ College 1169 67.6 30.7 Others (Specify) 380 45.8 54.2 Missing 48 65.3 33.7 Geopolitical Zone North Central 1390 60.8 38.4 North East 1458 56.7 43.3 North West 1687 66.1 33.7 South East 1328 71.3 28.5 South-South 1591 65.8 33.7 South West 1653 62.0 37.2 Wealth Quintile Poor 1523 44.0 56.0 Second 1476 58.6 41.4 Middle 1734 73.7 26.2 Fourth 2071 75.8 23.7 Richest 2303 63.6 34.8 Distribution of households by source of drinking water Table 23 presents the distribution of HHs based on main sources of drinking water. At the national level, only about 62.3 percent of the HHs have improved sources of drinking water. The table reveals that the use of piped water was low in the country and across all geopolitical zones. Some degree of sourcing drinking water was observed with public pipe/standpipe (5 percent). Drinking water from this public tap was more common in urban (7.4 percent) than in rural areas (4.4 percent). Also, it is more common in the northern parts of Nigeria than in the southern zones. The most common main source of drinking water is the borehole (about 43 percent). The use is both prevalent in rural and urban areas among the male-headed and female-headed HHs, as well as educated and none-educated HHs. However, it is more common in the southern zones of the country. The use of protected well was also used among the HHs (12 percent). It was used by both male-headed and female-headed HHs and found among HHs with no or little education. Protected well was more prevalent in North Central, North West and South West. Unprotected well was the most common source of drinking water among the unprotected sources. About 12 percent of HHs practiced the use of unprotected well for drinking water. Its use was prevalent in rural (19.2 percent) than in the urban areas (1.7 percent). Sachet water, known as pure water in Nigeria, was also commonly used. In the country, about 10.7 percent of HHs drink sachet water. Its prevalence was higher in urban (23.2 percent) than in rural areas (2.1 percent). 33 It is also most common in the southern part of the country: South East (14 percent); South South (15 percent); and South West, the most prevalent zone (32 percent). River, stream, pond, and lake constitute the other sources of drinking water. About 10 percent employed this source for drinking water in Nigeria. HHs that used this source were mainly found in rural areas (17.1 percent). It was employed by both male-headed and female-headed HHs who had primary (14.6 percent) or no formal education (13.6 percent). Analysis by zones shows that use of water from river, pond, and lake were more prevalent among HHs in North Central (23.6 percent) and in South South (15.8 percent). 34 35 Table 23. Percent distribution of household according to main source of drinking water Main Source of Drinking Water Improved sources Unimproved sources Piped water (01) (02) (03) (04) (05) (06) (08) (10) (14) (07) (09) (11) (12) (13) (15) (16) (98) Total 0.4 0.5 0.2 5.6 42.6 12.0 0.2 0.4 0.5 12.1 1.1 1.0 0.8 1.3 10.7 10.8 0.0 100 62.3 37.7 9,107 Residence Urban 0.8 0.6 0.4 7.4 46.3 10.6 0.1 0.2 0.9 1.7 0.4 1.7 1.1 2.9 23.2 1.6 0.0 100 67.4 32.6 3,990 Rural 0.1 0.3 0.1 4.4 39.9 12.9 0.2 0.6 0.1 19.2 1.6 0.4 0.6 0.2 2.1 17.1 0.0 100 58.7 41.3 5,117 Household Type Male-headed 0.4 0.5 0.2 5.7 42.4 12.0 0.2 0.5 0.4 12.9 1.1 0.9 0.8 1.3 10.5 10.3 0.0 100 62.2 37.8 8,090 Female-headed 0.0 0.5 0.4 4.9 44.2 11.9 0.1 0.3 0.9 4.6 1.5 1.5 0.7 1.3 12.5 14.8 0.0 100 63.1 36.9 1,017 Education of HH head None 0.3 0.3 0.2 5.3 34.2 13.2 0.0 0.5 0.1 23.1 2.4 0.3 1.7 2.3 2.4 13.6 0.0 100 54.2 45.8 1,562 Primary 0.1 0.4 0.3 4.2 43.0 13.7 0.2 0.5 0.0 12.2 1.8 1.1 0.7 0.8 6.3 14.6 0.0 100 62.5 37.5 2,150 Secondary 0.3 0.2 0.3 5.9 46.6 12.2 0.2 0.3 0.3 5.0 0.5 1.2 0.4 1.2 15.1 10.4 0.0 100 66.3 33.7 3,421 Tech /Voc certificate 1.8 0.2 0.2 7.6 45.8 9.0 0.3 0.7 1.6 10.3 0.0 1.4 1.0 0.5 15.0 4.6 0.0 100 67.3 32.7 376 Higher /Univ/College 1.0 1.5 0.1 6.7 46.6 8.8 0.0 0.6 2.0 3.0 0.3 1.1 1.0 1.2 21.3 4.7 0.0 100 67.2 32.8 1,169 Others 0.0 1.1 0.0 7.3 29.3 6.7 0.4 0.0 0.0 48.0 0.2 0.6 0.3 0.9 0.1 5.0 0.0 100 44.8 55.2 380 Missing 0.0 0.0 0.0 8.4 39.4 17.4 0.0 0.0 1.0 7.1 0.0 0.0 0.0 1.2 20.1 5.4 0.0 100 66.3 33.7 48 Into dwelling Into yard/plot To neighbour Public tap/ stand- pipe Tube-well/ borehole Protected well Protected spring Rainwater collection Bottled water Unprotected well Unprotected spring Tanker truck Cart with tank/ drum Water Kiosk Sachet/Pure water River/ stream, pond/ lake/ dam/canal/irrigation Other Total % using improved sources % using unimproved sources No of households 36 Percent distribution of household according to main source of drinking water (continued) Main Source of Drinking Water Improved sources Unimproved sources Piped water (01) (02) (03) (04) (05) (06) (08) (10) (14) (07) (09) (11) (12) (13) (15) (16) (98) Total 0.4 0.5 0.2 5.6 42.6 12.0 0.2 0.4 0.5 12.1 1.1 1.0 0.8 1.3 10.7 10.8 0.0 100 62.3 37.7 9,107 Geopolitical Zone North Central 0.5 0.3 0.2 5.0 33.9 19.0 0.4 0.1 0.8 8.0 2.0 0.6 0.6 0.0 4.8 23.6 0.0 100 60.3 39.7 1,390 North East 0.6 0.3 0.1 11.8 35.7 4.6 0.0 0.1 0.1 16.8 1.6 1.3 2.3 7.7 4.7 12.4 0.0 100 53.1 46.9 1,458 North West 0.4 1.1 0.2 6.9 40.5 14.4 0.1 0.0 0.2 29.4 0.5 1.2 1.3 0.5 2.3 0.9 0.0 100 63.7 36.3 1,687 South East 0.0 0.2 0.0 1.0 61.4 1.8 0.4 2.8 0.2 1.1 3.7 3.3 0.3 0.0 12.8 11.0 0.0 100 67.9 32.1 1,328 South South 0.1 0.3 0.2 3.8 58.1 2.6 0.0 0.6 0.5 3.2 0.4 0.0 0.0 0.0 14.3 15.8 0.0 100 66.3 33.7 1,591 South West 0.5 0.2 0.5 3.7 35.1 21.4 0.1 0.2 0.9 1.7 0.2 0.2 0.0 0.0 26.2 9.2 0.0 100 62.5 37.5 1,653 Wealth Quintile Poor 0.1 0.0 0.1 4.4 26.6 11.1 0.1 0.5 0.0 29.8 2.5 0.0 1.1 0.5 0.1 23.1 0.0 100 42.9 57.1 1,523 Second 0.0 0.5 0.1 5.7 36.2 14.6 0.2 0.5 0.0 21.5 2.0 0.1 0.7 1.6 0.6 15.7 0.0 100 57.8 42.2 1,476 Middle 0.3 0.4 0.1 7.2 47.2 15.9 0.2 0.6 0.0 8.5 0.9 0.9 1.0 1.8 4.1 10.9 0.0 100 71.9 28.1 1,734 Fourth 0.3 0.3 0.3 6.0 54.6 11.3 0.2 0.4 0.4 3.3 0.5 1.7 0.7 1.3 13.0 5.8 0.0 100 73.8 26.2 2,071 Richest 1.1 1.1 0.5 4.8 45.8 7.6 0.1 0.2 1.7 0.7 0.0 1.8 0.6 1.0 32.2 0.9 0.0 100 62.8 37.2 2,303 Into dwelling Into yard/plot To neighbor Public tap/ stand-pipe Tube-well/ borehole Protected well Protected spring Rainwater collection Bottled water Unprotected well Unprotected spring Tanker truck Cart with tank/ drum Water Kiosk Sachet/Pure water River/ stream, pond/ lake/ dam/canal/irrigation Other Total % using improved sources % using unimproved sources No of households Sanitation Sanitation refers to public health conditions in relation to clean drinking water and treatment, and disposal of human excreta and sewage. In this study, sanitation is measured by the proportions of HHs that did not share toilets, shared toilets, used unimproved toilets or involved in open defecation. At the national level, only about 26.5 percent of the HHs have improved private toilets, which were not shared with other HHs. About 35 percent of HHs were found in urban areas, while 20.6 percent in the rural areas. The proportion was also higher in the male-headed HHs (26.8 percent) than that of female-headed HHs (23.3 percent). Expectedly, the proportion of HHs using unshared improved toilets increased with the level of education of the HH head. Among the geopolitical zones, South East had the highest proportion (40.7 percent) while North West had the least (23.2 percent). It is also noteworthy that a great percentage used private toilets in the North East (34%). Furthermore, the proportion of HHs using unshared improved toilets increased with the level of wealth quintile group of the HHs. It ranged from 9.1 percent among the poor to 49.1 percent among the richest quintile. At the national level, 28.5 percent of the HHs used improved toilets that were shared with at least one other HH. This was practiced more in urban (44 percent) than in rural areas (17.9 percent). It is more common in the South West (48.5 percent) and South South (43.2 percent) than in the other geopolitical zones. It is noteworthy that sharing improved toilets was prevalent among the fourth quintile group. The use of unimproved toilets and open defecation were more common in rural areas than urban areas. Use of unimproved toilets and open defecation were pronounced among uneducated HH heads. Usage of unimproved toilets was highest in North West (40.0 percent) while the use of open defecation was highest in North Central (44 percent). The practice of open defecation was more prevalent among the poor and second quintile categories (See Table 24). 37 Table 24. Use of Sanitation facilities olds % Households Disaggregation Total Households % Households in Sample (N) Toilets not % Households % Househ ed Not using toilet shared Toilets shared Unimprov toilets facilities National 9107 26.5 28.5 21.0 23.5 Residence Urban 3990 35.0 44.0 12.9 7.4 Rural 5117 20.6 17.9 26.5 34.5 Household Type Male-headed 8090 26.8 28.2 21.3 23.0 Female-headed 1017 23.3 31.4 17.7 27.0 Level of Education of Head None 1562 18.5 17.2 25.7 38.3 Primary 2150 22.4 24.6 22.2 30.1 Secondary 3421 25.9 37.6 16.6 19.3 Technical /Voc certificate 376 31.7 36.8 19.0 10.7 Higher / University/ College 1169 50.2 30.9 12.5 6.0 Others (Specify) 380 16.4 10.0 56.5 17.1 Missing 48 16.5 34.6 8.7 37.6 Geopolitical Zone North Central 1390 19.0 22.0 13.8 43.9 North East 1458 33.5 18.4 20.9 27.1 North West 1687 23.2 21.9 40.0 14.0 South East 1328 40.7 22.0 8.5 28.6 South-South 1591 30.5 33.2 20.6 15.1 South West 1653 20.6 48.5 8.4 22.0 Wealth Quintile Poor 1523 9.1 8.0 30.5 52.1 Second 1476 18.1 13.8 33.4 34.1 Middle 1734 24.1 29.6 22.1 23.9 Fourth 2071 28.0 45.5 14.0 11.5 Richest 2303 49.1 41.1 7.9 1.3 Food insecurity Food insecurity is a fundamental element of HHs’ economic and social living conditions, contributing in a fundamental way to the overall well-being of the HHs’ members. What we call food insecurity is a condition of limited or uncertain regular access to adequate food. A focus on HH food insecurity within the NFCMS is justified by the ample existing literature demonstrating that living in food insecure HHs increases the risk of some forms of malnutrition (i.e., stunting in children, micronutrient deficiencies or obesity in adults). In this report, food insecurity is measured with Food Insecurity Experience Scale (FIES) (https:// www.fao.org/in-action/voices-of-the-hungry/fies/en/). It allows estimating the probability that over the 12 months preceding the survey, members of the HH may have experienced various degrees of food insecurity. The measure is obtained by analysing data on self-reported occurrence of conditions (i.e., members of the HH having to skip a meal or eat less than they thought they should, running out of food in the HH, feeling hungry but not able to eat because there was not enough money or other resources for food insufficient food quantity). Using the Rasch Model, the qualitative answers (yes or no) given to the questions included in the FIES module are first tested for validity and then converted in quantitative measures on a continuous scale of severity (Cafiero et. al., 2018). 38 In reporting results, reference is typically made in two categories: moderate food insecurity and severe food insecurity. Moderate food insecurity is revealed by the reporting of experiences associated with reduced quality of food consumption, as well as reduced quantity (e.g. portion sizes are reduced or meals are skipped). Severe food insecurity is revealed by such experiences as feeling hungry but not being able to procure food or not eating for an entire day due to lack of money or other resources. HHs having experienced moderate food insecurity have almost certainly compromised the quality of the food they eat, and likely reduced the normal quantities of food consumed. Severe food insecurity implies having almost certainly reduced the quantity of food consumed and, occasionally, having run out of food in the HH, felt hungry and, at the most extreme, gone for entire days without eating. Data Validation Prior to compilation of results, FIES data collected in the NFCMS have been subject to validation by testing their adherence to the restrictions imposed by the Rasch measurement model to confirm that they can be used to generate valid measures of the severity of food insecurity in the surveyed population. Results confirm that the eight questions included in the standard FIES module can be used to create a proper measurement scale in this application in Nigeria: All items reveal an infit statistics value lower than 1.2 (Table 25). Also, the residuals (obtained as the difference between the actual response given by each HH to each item and the response that would be expected given the estimated model’s parameters) show no sign of a possible additional dimension being captured by the data (Figure 3). Furthermore, the resulting food insecurity measurement scale compares well with the global FIES reference scale; thus, allowing for robust calibration of classifications against the thresholds set up, at the global level, to define moderate and severe food insecurity (Figure 4). Table 25. Results of estimating the Rasch Model on the FIES data collected in the NFCMS of Nigeria 2020 Item: Severity SE. Infit Worry_insuff_food -2.18 0.06 0.99 Ate_unhealty_food -2.27 0.06 1.02 Ate_few_food -1.75 0.05 1.03 Skipped_meal -0.27 0.04 0.92 Ate_less -1.03 0.04 0.85 Ranout_food 1.50 0.04 0.98 Hungry 1.43 0.04 0.89 No_food_whole_day 4.56 0.06 1.03 Note: All infit values are below the threshold value of 1.2, indicating that all eight items can be used to form a valid measurement scale possessing desirable properties that ensure invariance measurement. 39 Figure 3. Screen plot of the principal components’ analysis conducted on the residuals obtained after estimating the Rasch Model Note: The chart shows the percentage of variance captured by the eight principal components obtained from the residuals, ranked in order of decreasing variance. The linear shape of the chart confirms that no principal components dominate in terms of explained variance, and that no residual structure can be detected in the residuals. Therefore, the data contribute to measurement of the single latent trait, interpreted as the severity of food insecurity. Figure 4. Calibration of the FIES measurement scale obtained with the data collected in the NFCMS, Nigeria, and the Global FIES Reference Scale Note: The chart shows the alignment of the severity levels associated with the eight FIES items as obtained from the FIES data collected in Nigeria (vertical axis) against those of the Global FIES reference scale (horizontal axis). Using all eight items as anchoring points, the resulting correlation between the two scales is 96.8 percent. Results Moderate Food Insecurity Tables 26 presents estimates of the percentage of HHs heads that reported their HHs having experienced food insecurity. The estimate is obtained as the average of the probability of being classified as either “moderately” plus “severely” food insecure, computed over the entire sample. 40 Table 26. Percentage of households in the sample experiencing food Insecurity Total No of Moderate + severe Severe HH in Sample % 95% CI % 95% CI National 9107 78.7 78.3 79.1 22.2 21.9 22.5 Residence Rural 3990 78.3 77.7 78.8 22.9 22.5 23.3 Urban 5117 79.0 78.5 79.5 21.6 21.3 22.0 Household type Male-headed 8090 78.4 78.0 78.8 22.0 21.7 22.3 Female-headed 1017 81.0 79.9 82.0 23.8 23.0 24.6 Education of HH head None 1562 78.2 77.4 79.1 22.4 21.7 23.1 Primary 2150 83.2 82.5 83.8 24.8 24.2 25.3 Secondary 3421 80.7 80.1 81.3 22.6 22.2 23.1 Technical / Vocational cert. 376 68.9 66.9 71.0 18.4 17.1 19.8 Higher / University/ College 1169 67.6 66.4 68.8 17.6 16.9 18.4 Others (Specify) 380 82.3 80.7 83.9 21.3 19.9 22.7 Missing 48 74.7 69.8 79.6 17.2 13.7 20.7 Geopolitical zone North Central 1390 73.2 72.1 74.2 19.6 18.9 20.2 North East 1458 85.1 84.3 85.8 25.2 24.5 26.0 North West 1687 67.5 66.5 68.5 18.1 17.5 18.7 South East 1328 79.8 78.9 80.8 23.8 23.1 24.5 South South 1591 85.5 84.8 86.2 22.3 21.7 22.9 South West 1653 81.7 80.9 82.5 24.5 23.8 25.2 Wealth quintile Poor 1523 81.5 80.7 82.4 23.5 22.8 24.2 Second 1476 80.8 80.0 81.7 24.0 23.3 24.7 Middle 1734 83.1 82.4 83.9 24.4 23.8 25.0 Fourth 2071 82.0 81.3 82.7 24.0 23.4 24.6 Richest 2303 69.1 68.3 69.9 16.9 16.4 17.4 Results show that 79 percent of the sample HHs would be classified as food insecure (57 percent are moderately food insecure, while 22 percent are severely food in secure). There was a little difference in the proportions between the urban (78.3 percent) and rural areas (79.0 percent). Also, a little higher proportion was noticed among the female- headed HHs (81.0 percent) than the male-headed HHs (78.4.0 percent). However, the same cannot be said of the pattern with regards to the education of the head of HH where the proportion of food insecurity reduced with higher education. With regards to moderate and severe food insecurity, HHs in North West (67 percent) fared relatively better, while HHs in North East and South South were worst hit with 85.1 and 85.5 percent, respectively. Though the difference was not much, the percentage of HHs categorized as moderately or severely food insecure reduced with wealth quintile position with the richest, having the lowest with 69.1 percent. The pattern of distribution of HHs that were severely food insecure was almost the same with those that were moderately or severely food insecure. Nationally, about 22 percent of the 79 percent moderately or severely food insecure were severely food insecure. They belong to the 23.5 percent among the poor wealth quintile group and 16.9 percent among the richest. 41 Coping Strategies in the last seven days In addition to the FIES question, respondents were also asked whether they had enough food or enough money to buy food seven days before the survey. This question is normally used to collect data to inform the so-called “reduced Coping Strategy Index” (r-CSI), an indicator typically used in the context of repeated surveys conducted for rapid, emergency food security assessments. The results shown in Table 27 indicate that about 41.5 percent of the HHs reported not having food or money to buy food seven days prior the survey. Table 27. Percentage of Households that did not Have Food or Money to Buy Food in Preceding seven Days Disaggregation Total Households in Sample (N) % National 9107 41.5 Residence Urban 3990 40.6 Rural 5117 42.1 Household Type Male-headed 8090 41.1 Female-headed 1017 45.2 Level of Education of Head None 1562 37.6 Primary 2150 47.3 Secondary 3421 44.0 Technical /Voc certificate 376 33.4 Higher / University/ College 1169 31.0 Others (Specify) 380 45.9 Missing 48 37.3 Geopolitical Zone North Central 1390 27.6 North East 1458 39.9 North West 1687 34.7 South East 1328 52.3 South South 1591 62.5 South West 1653 39.8 Wealth Quintile Poor 1821 43.6 Second 1821 43.3 Middle 1822 42.4 Fourth 1820 45.9 Richest 1820 33.0 The disaggregation by place of residence (urban/rural) and by sex of the HH head confirms the results already commented as derived from the FIES scale. That is, there is a slightly higher percentage of HHs reporting difficulties in buying or obtaining food in the rural areas, and among women-headed HHs (even though differences are very small). Also consistent with the FIES-based results, difficulties are reported by a significantly lower percentage of HHs when the HH head has a higher education or when the HH belongs to the highest wealth quintile. The only partly contrasting results concerns the disaggregation by geopolitical zone. Though North Central and North West are confirmed areas with the lowest incidence of reported food access problems, HH from the North East and the South West regions seem to have experienced significantly less difficulty than HHs in the South East and the South South when referring to 42 problems experienced during the seven days prior to the survey. These results may point to a slightly better recent situation in the North East and South West zones as compared to the entire past year, while the situation continued to be problematic in the South East and South South. Food security and coping strategies The Coping Strategies Index (CSI) is one of the tools used for rapid food insecurity assessments in emergency contexts. It is quick and easy to administer, straight-forward to analyze, and rapid enough to provide real-time information. It aims at recording the things that people do when they cannot access enough food and the adjustments HHs make in their consumption and livelihoods when they do not have enough food or money. Coping can be in terms of consumption changes, expenditure reduction, and income expansion. It is an appropriate tool for measuring food security during emergency situations when other methods are not practical or timely. The index is obtained by counting coping strategies that are not equal in severity; thus, needs to be weighted differently, depending on how severe they are by the analysts. In building the Reduced Coping Strategies Index (rCSI), the frequency in which a given strategy is reported during the last seven days is multiplied by a weight that reflects the severity of individual behaviors. Finally, the totals are added. The Coping Strategy Index is a score that ranges from 0 to 56; smaller numbers reflect better food security than larger numbers. A high score means an extensive use of negative coping strategies; hence, increased food insecurity. Factors consider for Coping strategies Severity weight Number of days in a week - Rely on less preferred and less expensive foods 1 Number of days in a week - Borrow food, or rely on help from a friend or relative? 2 Number of days in a week - Limit portion size at mealtimes 1 Number of days in a week - Restrict consumption by adults in order for small children to eat 3 Number of days in a week - Reduce number of meals eaten in a day 1 The HHs are classified into three categories: a. households with CSI = 0 – 3: None/Minimal food insecurity b. households with CSI = 4 – 18: Stressed food consumption c. households with CSI ≥ 19: Crisis food consumption Table 28 presents the average rCSI score in the country, disaggregated by residence, HH type, education, geopolitical zone, and wealth level. The national Coping Strategies Index Score was 18.2. There was little difference in the index score obtained for rural (17.9) and urban areas (18.7), indicating that almost equal proportion of HHs were food insecure across place of residence. Also, there was no significant difference for male- and female-headed HHs. There was no specific pattern to compare the north with the south as the index ranged from 17.4 for South-South and 19.9 for North Central. Though, the richest quintile had the lowest index of 16.9, the difference between the poorest quintile (18.1) was not significant. 43 Table 28. Coping Strategies Index Score Households in Sample Disaggregation (Not Having Food or Money to Buy Food in Preceding 7 Index Score CI Days) National 3944 18.2 18.2 18.2 Residence Urban 1672 18.7 18.7 18.7 Rural 2272 17.9 17.9 17.9 Household Type Male-headed 3473 18.2 18.1 18.2 Female-headed 471 18.6 18.6 18.6 Education of Head of HH None 610 17.9 17.9 18.0 Primary 1058 18.7 18.7 18.7 Secondary 1570 18.0 18.0 18.0 Technical / Vocational certificate 137 18.9 18.9 19.0 Higher / University/ College 374 17.1 17.1 17.1 Others 175 19.0 18.9 19.0 Missing 20 22.7 22.6 22.8 Geopolitical Zone North Central 392 19.9 19.9 19.9 North East 600 16.7 16.6 16.7 North West 574 17.5 17.5 17.6 South East 698 18.9 18.9 18.9 South-South 998 17.4 17.3 17.4 South West 682 19.8 19.7 19.8 Wealth Quintile Poor 810 18.1 18.1 18.1 Second 830 18.8 18.7 18.8 Middle 873 19.1 19.1 19.1 Fourth 816 18.1 18.1 18.1 Richest 613 16.9 16.9 16.9 Households by Coping Index Group Table 29 presents the distribution of HHs based on coping index groups. The HHs were grouped into three different categories: (1) none or minimal food insecurity; (2) stressed food consumption; and (3) crisis food consumption. The result shows that very small proportion (3.4 percent) of HHs belonged to the group of “none or minimal food insecurity”. About 54 percent of the HHS belonged to the “stressed food consumption”, while 42 percent were found in the “crisis food consumption” group. This ratio was almost equal across other nominal variables (i.e., place of residence, sex, and education of head of HH). Though relatively very small percentage belonged to the “none or minimal food insecurity” group across the zones, the pattern varied from one geopolitical zone to the other. 44 Table 29. Percentage Distribution of Households by Coping Index Group Households in Sample Disaggregation (Not Having Food or Money None or Minimal Stressed food Crisis food to Buy Food in Preceding food insecurity consumption consumption seven Days) National 3944 3.4 54.3 42.3 Residence Urban 1672 3.2 52.8 44.0 Rural 2272 3.5 55.3 41.1 Household Type Male-headed 3473 3.4 54.2 42.4 Female-headed 471 3.3 55.3 41.4 Education of Head of HH None 613 3.6 55.8 40.7 Primary 1061 3.1 52.7 44.2 Secondary 1571 3.1 56.1 40.8 Technical / Vocational certificate 137 .9 52.6 46.6 Higher / University/ College 379 6.2 53.2 40.6 Others 163 3.2 48.4 48.4 Missing 20 3.9 40.0 56.1 Geopolitical Zone North Central 392 3.5 48.7 47.8 North East 600 5.9 57.3 36.7 North West 574 5.3 51.5 43.3 South East 698 1.2 56.7 42.1 South-South 998 2.5 59.2 38.3 South West 682 2.0 50.6 47.4 Wealth Quintile Poor 810 3.4 55.3 41.3 Second 830 4.0 47.8 48.2 Middle 873 3.3 51.0 45.7 Fourth 816 2.7 56.8 40.5 Richest 613 3.8 60.8 35.4 Production of animal source foods Production of animal source foods by HHs is expected to engender ready access to nutritious food products needed for growth and development; thereby, reducing food insecurity. Similarly, HHs that own livestock, rear small animals, or farm fish, or engage in fishing are expected to be more food secured than others. The HHs were asked if they own any livestock, herds, other farm animals or poultry. The response was used to determine the proportion of HHs that were involved in the production of animal source foods. Generally, the percentage of HHs involved in the production of animal sourced food was very low at 11.3 percent and disaggregated as follows: 6.4 percent own any livestock, herds, other farm animals, or poultry; 1 percent raise rabbit, guinea pigs, grass cutters, snails, fish, or other small animals; 1.5 percent raise fish; and 5 percent catch/harvest fish from the wild). The proportion of animal production in the rural areas (13.9 percent) was almost double than that of urban areas (7.5 percent) (Table 30). The low proportion was observed among male-headed (11.8 percent) and female-headed (7.2 percent) HHs. It is noteworthy that similar low proportion of HHs produced animal source food irrespective of education, wealth strata, and across different geopolitical zones. Among the geopolitical zones, South West recorded the lowest proportion. 45 Table 30. Percentage of households that Produce animal sourced foods Total Households in Disaggregation Sample % (N) National 9107 11.3 Residence Urban 3990 7.5 Rural 5117 13.9 Household Type Male-headed 8090 11.8 Female-headed 1017 7.2 Level of Education of Head None 1562 11.2 Primary 2150 13.6 Secondary 3421 10.3 Technical /Voc certificate 376 6.7 Higher / University/ College 1169 11.0 Others (Specify) 380 13.3 Missing 48 1.9 Geopolitical Zone North Central 1390 10.6 North East 1458 12.6 North West 1687 11.8 South East 1328 15.3 South-South 1591 13.8 South West 1653 6.3 Wealth Quintile Poor 1821 12.3 Second 1821 14.2 Middle 1822 13.3 Fourth 1820 9.7 Richest 1820 7.6 Production of vegetables Globally, home gardens have been documented as an important supplemental source contributing to food and nutritional security and livelihoods. Home gardening refers to the cultivation of a small portion of land, which may be around the HH or within walking distance from the family home (Odebode, 2006). The most fundamental benefit of home gardens stems from their direct contributions to HH food security by increasing availability, accessibility, and utilization of food products. Therefore, HHs that have a vegetable garden that they use for their own consumption are expected to be more food secured than others. Overall, the result indicates that almost 3 out of 10 sample HHs (29.2 percent) have land for vegetable gardening (Table 31). A higher proportion (38.3 percent) of HHs in rural areas had access to land for gardening compared to only 16.1 percent in urban areas. However, almost the same proportion (29 percent) of male- and female- headed HHs had access. Among the zones, more HHs in South East had land for gardening (67.9 percent). 46 Table 31. Percentage of households in sample that have land for gardening Disaggregation Total Households in Sample (N) % National 9107 29.2 Residence Urban 3990 16.1 Rural 5117 38.3 Household Type Male-headed 8090 29.3 Female-headed 1017 29.0 Level of Education of Head None 1562 20.1 Primary 2150 38.8 Secondary 3421 30.4 Technical /Voc certificate 376 27.2 Higher / University/ College 1169 24.7 Others (Specify) 380 25.3 Missing 48 14.6 Geopolitical Zone North Central 1390 25.5 North East 1458 13.8 North West 1687 21.3 South East 1328 67.9 South-South 1591 41.1 South West 1653 24.7 Wealth Quintile Poor 1821 25.6 Second 1821 32.7 Middle 1822 36.3 Fourth 1820 29.1 Richest 1820 23.1 Access to land and trees or bushes that bear fruits The presence of fruit-bearing trees or bushes for their own consumption is expected to aid HH access to food products that give minerals and vitamins for increased food security. Table 32 presents percentage of HHs that have fruit-bearing trees or bushes for their own consumption. Results obtained for HHs that have fruit-bearing tress or bushes indicated that 31 percent of the sample HHs had trees or bushes that produced fruits. Expectedly the proportion was higher in the rural areas (40.7 percent) compared with those in the urban areas (17.0 percent). Among the geopolitical zones, higher proportion of HHs were found in the South East (56.0 percent) and South South (43.6 percent). South West recorded low percentage (26.4 percent) but North East and North West recorded the lowest with 21.1 and 18.1 percent, respectively. However, with exception of the richest quintile group, the proportion of HHs that had fruit-bearing trees or bushes were mostly evenly distributed among other wealth quintile group. 47 Table 32. Percentage of households in sample that have trees or bushes that produced fruits Disaggregation Total Households in Sample (N) % National 9107 31.0 Residence Urban 3990 17.0 Rural 5117 40.7 Household Type Male-headed 8090 31.0 Female-headed 1017 31.0 Level of Education of Head None 1562 22.9 Primary 2150 40.6 Secondary 3421 32.4 Technical /Voc certificate 376 32.0 Higher / University/ College 1169 25.1 Others (Specify) 380 20.7 Missing 48 29.6 Geopolitical Zone North Central 1390 38.5 North East 1458 21.1 North West 1687 18.8 South East 1328 56.0 South-South 1591 43.6 South West 1653 26.4 Wealth Quintile Poor 1821 31.8 Second 1821 34.4 Middle 1822 36.3 Fourth 1820 30.3 Richest 1820 23.5 Financial Inclusion Financial inclusion emphasizes that HHs have access to useful and affordable financial products and services that meet their needs – transactions, payments, savings and credit – made available and accessible in a responsible and sustainable manner. One good measure of financial inclusion is having accounts with bank or financial institution. It is expected that HHs that have access to credit or financial institutions will have more financial resources to procure nutritious foods when compared to other HHs that do not. Table 33 presents the percentage of HHs having accounts with banks or financial institutions. The results indicated that about six out of 10 HHs in Nigeria were financially inclusive. This means that about 60 percent of HHs had at least one member has an account with a bank or other financial institution. However, more of these HHs were found in urban centres (81 percent) than in rural areas (44 percent). Education seemed to play a key role in the proportion of HHs that had accounts with banks or financial institutions as majority had some degree of education. However, HHs in southern parts of the country had more accounts in banks than their northern counterparts. Moreover, possessing accounts with banks was higher with rich categories of HHs than their poor counterparts. 48 Table 33. Percentage of Households that Have Accounts with Financial Institution Disaggregation Total Households in Sample (N) % National 9107 59.1 Residence Urban 3990 81.5 Rural 5117 43.6 Household Type Male-headed 8090 58.7 Female-headed 1017 62.2 Level of Education of Head None 1562 22.3 Primary 2150 49.7 Secondary 3421 75.1 Technical /Voc certificate 376 83.9 Higher / University/ College 1169 94.6 Others (Specify) 380 17.1 Missing 48 50.5 Geopolitical Zone North Central 1390 60.7 North East 1458 47.7 North West 1687 32.8 South East 1328 74.8 South-South 1591 76.1 South West 1653 78.9 Wealth Quintile Poor 1821 11.1 Second 1821 33.0 Middle 1822 62.2 Fourth 1820 83.6 Richest 1820 95.7 49 Dietary Intake Overview of dietary intake data presented in this report This chapter presents preliminary findings of the data collected from the diet questionnaire on the following parameters: pregnancy and lactation status for women of reproductive age (WRA), select indicators of Infant & Young Child Feeding (IYCF) practices, coverage of biofortified foods, and fortification coverage (selected food vehicle consumption, source, branding and fortification status) (Table 34). In addition, the fortification status of food samples collected from the homes of a sub-sample of non-pregnant WRA are included in this report. Table 34. Key sections of the diet questionnaire and results reported Sections Results reported in this preliminary report Pregnancy and lactation status -Pregnancy status of women of reproductive age -Pregnancy trimester (WRA) -Lactation status -Lactation stage in months Infant & Young Child Feeding -Ever breastfed, 6-23 months1 (IYCF) Practices -Continued breastfeeding, 12–23 months2 -Bottle feeding, 6-23 months3 Biofortification coverage -Consumption of selected biofortified foods (i.e., yellow cassava, orange- fleshed sweet potato & orange maize) in the past 30 days -Frequency of consumption of selected biofortified foods in the past 30 days Fortification coverage -Consumption of selected food vehicles4 (i.e., vegetable oil, wheat flour, maize flour, semolina flour, sugar, salt, and bouillon) -Consumption of selected purchased food vehicles and branded food vehicles as proxy to fortifiable food vehicles - Types, sources, and brands of selected food vehicles used -Consumption of selected food vehicles labelled as fortified and selected food vehicles assumed to be fortified (using secondary data from GAIN5). -Fortification status of selected food samples collected from the homes of a sub-sample of non-pregnant WRA. 1 Ever breastfed is defined as the “percentage of children born in the last 24 months who were ever breastfed”. The WHO IYCF indicator includes children born in the last 24 months, whether living or dead. The NFCMS survey only includes live children aged 6-23 mo. 2 Continued breastfeeding is defined as the percentage of children 12–23 months of age who were fed breast milk during the previous day. 3 Bottle feeding is defined as the percentage of children 0–23 months of age who were fed from a bottle with a nipple during the previous day. The WHO IYCF indicator includes children born in the last 24 months, whether living or dead. The NFCMS survey only includes live children aged 6-23 mo. 4 Food vehicle refers to the food that is selected for the addition of one or more nutrients. 5. Assumed fortification status based on data previously collected by GAIN. Most of the diet indicators included in the 2021 Nigerian NFCMS, precisely usual intakes of foods and nutrients, nutrient adequacy of the diet and infant and young child feeding practices, will be derived from quantitative 24-hour dietary recall data (with repeat interviews in a sub-sample). Since these data are currently being processed and analyzed, these findings are not presented in this preliminary report. Characteristics of Respondents for the Dietary Intake The diet component of this survey targeted non-pregnant WRA (aged 15-49 years), pregnant WRA, and children (aged 6-59 months). The final sample for analysis comprised 4968 children, 5281 non-pregnant women, and 1006 pregnant WRA. The characteristics of the respondents of the diet questionnaire are shown in Table 35. 50 The respondents include 1654 and 3314 children aged 6-23 months and 24-59 months, respectively. Boys and girls in both age groups have a ratio of almost 1:1. Over half of the women (58 percent of non-pregnant and 83 percent of pregnant women) were between 20 and 39 years. Twenty-two (22) percent of the non-pregnant women and 10 percent pregnant women were teenagers (aged 15-19 years). In all the groups, except non-pregnant women, about one-third of the respondents had no education, 13-15 percent had primary education, and 25-32 percent had completed senior secondary school. Less than 10 percent of both groups of children’s respondents reported having education beyond senior secondary. In all the respondent groups, except non-pregnant women, about two-thirds were from the rural areas, while the rest were from the urban sector. 51 52 Table 35. Characteristics of Respondents for the Diet Component Children 6-23 months Children 24-59 months Non-Pregnant Women of Pregnant women Reproductive 15-49 y 15-49 y N1 % [95% CI] 2 N1 % [95% CI] 2 N1 % [95% CI] 2 N1 % [95% CI] 2 Gender Male 46.8 [43.5, 50.1] 50.9 [48.8, 53.1] 1654 3314 Not applicable Female 53.2 [49.9, 56.5] 49.1 [46.9, 51.2] Age 15-19 years 22.4 [20.6, 24.1] 9.7 [7.3, 12.1] 20-29 years 32.6 [31.0, 34.3] 51.1 [46.6, 55.7] Not applicable 5281 1006 30-39 years 25.5 [24.0, 27.0] 32.3 [27.9, 36.6] 40-49 years 19.5 [18.2, 20.8] 6.9 [4.6, 9.2] Highest level of school completed3 None 29.7 [25.3, 34.0] 31.3 [27.8, 34.8] 22.1 [19.1, 25.1] 30.8 [25.4, 36.2] Primary 14.4 [11.3, 17.6] 13.1 [11.2, 15.0] 14.9 [13.4, 16.4] 14.1 [11.1, 17.1] Junior secondary 8.6 [6.7, 10.5] 7.1 [5.9, 8.3] 10.8 [9.6, 12.0] 9.5 [7.4, 11.5] Senior secondary 1654 25.5 [22.5, 28.5] 3314 24.5 [21.4, 27.6] 5281 31.8 [28.9, 34.7] 1006 24.5 [20.7, 28.3] Technical/vocational certificate5 1.2 [0.5, 1.8] 1.4 [0.9, 1.9] 1.8 [1.3, 2.3] 0.9 [0.4, 1.4] Higher / university/ college 8.1 [5.8, 10.4] 7.7 [6.0, 9.4] 18.3 [16.1, 20.5] 19.9 [15.8, 23.9] Non-formal education 12.6 [9.6, 15.5] 14.1 [11.0, 17.3] 0.0 [0.0, 0.1] 0.1 [0.0, 0.2] Residence4 Urban 38.5 [30.8, 46.2] 37.0 [29.9, 44.0] 46.5 [39.8, 53.3] 36.9 [29.3, 44.4] 1654 3314 5281 1006 Rural 61.5 [53.8, 69.2] 63.0 [56.0, 70.1] 53.5 [46.7, 60.2] 63.1 [55.6, 70.7] Wealth quintile4 Lowest 25.2 [20.7, 29.6] 25.6 [21.6,29.6] 20.7 [17.0,24.3] 25.9 [19.7, 32.1] Second 25.8 [21.7, 29.9] 26.1 [22.5,29.8] 23.4 [20.4,26.4] 26.4 [22.0, 30.8] Middle 1648 17.4 [15.1, 19.7] 3301 18.6 [16.0,21.2] 5259 20.2 [17.8,22.6] 1002 18.0 [14.1, 21.9] Fourth 17.1 [14.2, 19.9] 15.6 [13.1,18.0] 18.1 [15.8,20.3] 17.3 [13.0, 21.6] Highest 14.5 [10.6, 18.4] 14.1 [11.0,17.2] 17.7 [14.8,20.6] 12.4 [9.6, 15.2] 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 For children, these data pertain to the respondent (i.e., the sampled child’s caregiver). There were no responses for 29 caregivers of 24-59 m old children, 9 non-pregnant women, and 2 pregnant women. 4 These data pertain to the household of the sampled respondent 5 Technical/vocational certificate are professional or vocational training attended with certificate at post-secondary education. Differences across groups were not tested statistically. Pregnancy Stage and Lactation status This section describes the self-reported pregnancy stages of all pregnant women respondents. Pregnancy stage was assessed because energy and nutrient requirements for pregnant women vary by stage. The details of this population, in terms of their energy and nutrient intakes and adequacy, will be presented in the 24-hour recall report. The pregnancy stages reported by the respondents were categorized in trimesters: first (0-3 months); second (4-6 months); and third (7-9 months), as shown in Figure 5. Nationally, about 20 percent of the sampled pregnant women were in the first trimester of pregnancy, 25 percent were in the second trimester, and 30 percent were in the third trimester. Twenty-five (25) percent of the women do not know the stage of their pregnancy or were not willing to tell, possibly for cultural reasons. Similar patterns were observed in urban and rural areas. Pregnancy Stage by Trimester Figure 5. Pregnancy Stage by Trimester Among pregnant women 15-49y (unweighted sample size = 1006) Data are weighted to account for survey design and non-response Lactation Status of WRA All women, regardless of their pregnancy status or whether they had young children, were asked whether they breastfed a child the previous day or night prior to the interview. Lactation status was assessed because energy and nutrient requirements for women increase during lactation. The details of this population, in terms of their energy and nutrient intakes and adequacy, will be presented in the 24-hour recall report. Table 36 shows the percentage of WRA who reported having breastfed a child the previous day or night. Nationally, about 24 percent of the non-pregnant women and 10 percent of the pregnant women reported breastfeeding a child. Almost 29 percent of non-pregnant women from the rural sector and 18 percent from the urban section reported breastfeeding a child. The proportion of women who breastfed ranged between 14 and 17 percent in the southern zones, and between 19 and 36 percent in the northern zones. Differences in breastfeeding rates likely reflect demographics and of whether the respondent woman has an infant or a young child. 53 Table 36. WRA who reported having breastfed a child during the previous day or night Breastfed a child yesterday during Sample size (N)1 the day or night2 % [95% CI]3 National Pregnant women (aged 15-49 years) 1006 9.9 [7.7, 12.1] Non-pregnant women (aged 15-49 years) 5281 23.9 [21.8, 26.1] Residence, non-pregnant women (aged 15-49 years) Urban 2156 18.0 [14.6, 21.4] Rural 3125 29.1 [26.3, 31.9] Zonal, non-pregnant women (aged 15-49 years) North Central 857 19.4 [15.3, 23.6] North East 830 26.2 [21.4, 31.0] North West 944 35.7 [30.2, 41.2] South East 855 14.9 [11.8, 18.1] South South 888 14.4 [12.1, 16.7] South West 907 17.1 [12.5, 21.6] 1 Unweighted sample size 2 Age of the child was not asked. 3 Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. Energy requirements for lactation vary by breastfeeding stage. Table 37 shows the age of the child being breastfed among non-pregnant women who breastfed a child the day and night before the interview. About 25 and 34 percent breastfed children aged less than 6 months and 6-12 months, respectively, while more than 40 percent breastfed a child in the second year of life. Similar patterns were observed in urban and rural areas. Table 37. Lactating status among non-pregnant WRA who breastfed a child yesterday Lactating stage in months (among non-pregnant women who breastfed a child yesterday during the day or night)2 Sample size (N)1 <6 months 6-11.9 months ≥ 12 months % [95% CI] % [95% CI] % [95% CI] Residence Urban 334 28.6 [24.0, 33.1] 42.1 [36.8, 47.5] 29.3 [23.6, 35.0] Rural 831 22.4 [17.9, 27.0] 29.3 [25.3, 33.2] 48.3 [43.6, 53.0] Zonal North Central 184 29.1 [19.0, 39.1] 33.6 [26.3, 40.9] 37.3 [27.1, 47.6] North East 233 24.7 [19.5, 29.9] 36.3 [29.3, 43.4] 39.0 [29.0, 48.9] North West 342 20.9 [14.6, 27.2] 28.8 [23.3, 34.3] 50.3 [43.7, 56.9] South East 131 36.1 [28.1, 44.1] 39.6 [30.8, 48.5] 24.3 [15.2, 33.3] South South 134 28.2 [17.4, 38.9] 42.0 [30.6, 53.4] 29.8 [19.8, 39.9] South West 141 26.2 [19.2, 33.2] 40.0 [31.2, 48.8] 33.8 [24.9, 42.7] National 1152 24.9 [21.5, 28.2] 34.1 [30.6, 37.5] 41.0 [37.2, 44.9] 1 Unweighted sample size for women who breastfeed a child yesterday during the day or night 2 Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. Infant and Young Child Feeding Practices This survey was designed to assess IYCF practices for children (aged 6-23 months) using the 2021 WHO/UNICEF indicators (WHO/UNICEF 2021) (as summarized in Table 38). Since children under six months were not included in the survey, indicators that relate to this age group cannot 54 be reported (e.g. early initiation of breastfeeding and exclusive breastfeeding under six months). Although some data required to assess the WHO/UNICEF IYCF indicators was collected using the diet questionnaire, most data was collected using quantitative 24-hour dietary recall data (with repeat interviews in a sub-sample). Since these data are currently being processed and analyzed, these findings are not presented in this preliminary report. The findings in this preliminary report include rates of ever breastfeeding, continued breastfeeding, and bottle-feeding practices among children (aged 6-23 months). Data are presented for the age groups that these indicators relate to and are disaggregated into the age groups recommended by the WHO/UNICEF (as shown in Table 38). One limitation of this survey is that the children sampled are aged 6-23 months, while the WHO/UNICEF indicators of ever breastfeeding and bottle feeding are intended for children starting at 0 month. As such, these findings are not directly comparable to other surveys. The diet questionnaire was also designed to assess the diet of children aged 24-59 months. Although beyond the range of the WHO indicators, data for the indicators ever breastfeeding, continued breastfeeding, and bottle-feeding practices are presented for all age groups for which data was collected in the survey in Annex 4. 55 56 Table 38. IYCF indicators reported for infants and young children aged 6-23 month WHO Indicator Definition WHO age group for NFCMS indicator Age group Data collection tool Breastfeeding indicators Ever breastfed Percentage of children born in the last 24 months who Children born in the last Children 6-23 Diet questionnaire were ever breastfed 24 months months of age Early initiation of Percentage of children born in the last 24 months who Children born in the last Not within the scope of this survey breastfeeding were put to the breast within one hour of birth 24 months Exclusively breastfed for Percentage of children born in the last 24 months who Children born in the last Not within the scope of this survey the first two days after were fed exclusively with breast milk for the first two days 24 months birth after birth Exclusive breastfeeding Percentage of infants (0-5 months old) who were fed Infants 0-5 months of Not within the scope of this survey under six months exclusively with breast milk during the previous day age Mixed milk feeding under Percentage of infants 0–5 months old who were fed Infants 0-5 months of Not within the scope of this survey six months formula and/or animal milk in addition to breast milk during age the previous day Continued breastfeeding Percentage of children (aged 12–23 months) who were fed Children 12-23 months Children 12-23 Diet questionnaire 12-23 months breast milk during the previous day of age (12-15, 16-19 months of age and 20-23 months) Complementary feeding indicators Introduction of solid, Percentage of infants (aged 6-8 months) who consumed Infants 6-8 months of Children 6-8 24-hour recall data semisolid or soft foods 6-8 solid, semi-solid or soft foods during the previous day age months of age (if months sample size allows) Minimum dietary diversity Percentage of children (aged 6-23 months) who consumed Children 6-23 months Children 6-23 24-hour recall data 6-23 months foods and beverages from at least five out of eight defined of age (6-11, 12-17 and months of age food groups during the previous day 18-23 months) Minimum meal frequency Percentage of children (aged 6-23 months) who consumed Children 6-23 months Children 6-23 24-hour recall data 6-23 months solid, semi-solid or soft foods (but also including milk feeds of age (6-11, 12-17 and months of age for non-breastfed children) the minimum number of times 18-23 months) or more during the previous day Minimum milk feeding Percentage of non-breastfed children (aged 6-23 months) Children 6-23 months Children 6-23 24-hour recall data frequency for non- who consumed at least two milk feeds during the previous of age (6-11, 12-17 and months of age breastfed children 6-23 day 18-23 months) months Minimum acceptable diet Percentage of children (aged 6-23 months) who consumed Children 6-23 months Children 6-23 24-hour recall data 6-23 months a minimum acceptable diet during the previous day of age (6-11, 12-17 and months of age 18-23 months) 57 Table 38. IYCF indicators reported for infants and young children aged 6-23 month (continued). Egg and/or flesh food Percentage of children (aged 6-23 months) who consumed Children 6-23 months Children 6-23 24-hour recall data consumption 6-23 months egg and/or flesh food during the previous day of age (6-11, 12-17 and months of age 18-23 months) Sweet beverage Percentage of children (aged 6-23 months) who consumed Children 6-23 months Children 6-23 24-hour recall data consumption 6-23 months a sweet beverage during the previous day of age (6-11, 12-17 and months of age 18-23 months) Unhealthy food Percentage of children (aged 6-23 months) who consumed Children 6-23 months Children 6-23 24-hour recall data consumption 6-23 months selected sentinel unhealthy foods during the previous day of age (6-11, 12-17 and months of age 18-23 months) Zero vegetable or fruit Percentage of children (aged 6-23 months) who did not Children 6-23 months Children 6-23 24-hour recall data consumption 6-23 months consume any vegetables or fruits during the previous day of age (6-11, 12-17 and months of age 18-23 months) Other indicators Bottle feeding 0-23 Percentage of children (aged 0-23 months) who were fed Children 0-23 months Children 6-23 Diet questionnaire months from a bottle with a nipple during the previous day of age months of age (6-11, 12-17 and 18-23 months) Infant feeding area graphs Percentage of infants (aged 0-5 months) who were fed Infants 0-5 months of Not within the scope of this survey exclusively with breast milk, breast milk and water only, age breast milk and non-milk liquids, breast milk and animal milk/formula, breast milk and complementary foods, and not breastfed during the previous day 1 These are also presented for children aged 24-59 months (see Annex 4). 2 Taken from: Indicators for assessing infant and young child feeding practices: definitions and measurement methods. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2021. Licence: CC BYNC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo. Characteristics of Respondent for the Sampled Children For most of these children (90-96 percent), the respondent was the child’s mother (Table 39). Over 40 percent of the respondents were between 20 and 39 years of age for both groups of children. Less than 10 percent of the respondents were either teenage or elderly caregivers. Table 39. Characteristics of respondents for the sampled children Children aged 6-23 months Children aged 24-59 months N1 % [95% CI] N1 % [95% CI] Relationship of the respondent to the sampled child Mother 95.7 [94.5, 96.8] 89.8 [88.3, 91.3] Father 0.8 [0.3, 1.3] 1.8 [1.1, 2.4] 1654 3293 Other family member 3.5 [2.5, 4.6] 8.4 [7.1, 9.8] Other - 0.0 [0.0, 0.1] Gender of the respondent Female 94.6 [93.0, 96.2] 91.8 [90.1, 93.5] 1654 3293 Male 5.4 [3.8, 7.0] 8.2 [6.5, 9.9] Age of the respondent 15-19 y 8.4 [6.1, 10.7] 4.6 [3.6, 5.7] 20-29 y 51.9 [48.4, 55.3] 42.2 [39.5, 44.9] 30-39 y 31.2 [28.3, 34.0] 36.4 [33.8, 38.9] 1654 3293 40-49 y 5.7 [4.3, 7.1] 10.2 [8.8, 11.6] 50-59 y 0.8 [0.2, 1.4] 1.9 [1.3, 2.4] 60 y or older 2.1 [1.0, 3.1] 4.8 [3.4, 6.1] 1 Unweighted sample size 2 Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. Ever Breastfed Breastfeeding is recommended for all infants worldwide, except in very few cases, for those with specific medical conditions (WHO/UNICEF 2021). In this survey, almost all (97 percent) children aged 6-23 months were reportedly ever breastfed (Table 40). Similar patterns were observed in urban and rural areas, and for girls and boys. Table 40. Percentage of children who were ever breastfed Children aged 6-11 Children aged 12-17 Children aged 18-23 Children aged 6-23 months months months months N1 % [95% CI]2 N1 % [95% CI]2 N1 % [95% CI]2 N1 % [95% CI]2 Residence (P=0.364) (P=0.902) (P=0.301) (P=0.350) Urban 240 99.6 [99.0, 100.0] 232 97.9 [95.9, 99.8] 230 89.4 [81.5, 97.4] 702 95.7 [93.3, 98.0] Rural 269 99.0 [97.9, 100.0] 372 97.7 [95.9, 99.6] 311 94.2 [89.9, 98.6] 952 97.0 [95.5, 98.5] Sex (P=0.765) (P=0.200) (P=0.141) (P=0.091) Male 235 99.4 [98.4, 100.0] 286 96.8 [94.5, 99.1] 257 88.7 [82.0, 95.5] 778 95.1 [92.8, 97.4] Female 274 99.2 [98.1, 100.0] 318 98.7 [97.0, 100.0] 284 95.3 [90.2, 100.0] 876 97.8 [96.0, 99.5] National 509 99.3 [98.6, 100.0] 604 97.8 [96.4, 99.2] 541 92.3 [88.2, 96.4] 1654 96.5 [95.2, 97.8] 1 Unweighted sample size 2 Data are weighted to account for survey design and non-response. Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). Continued Breastfeeding The WHO Global Strategy for IYCF recommends that children continue to be breastfed for two years or beyond (WHO/UNICEF 2021). As shown in Table 41, 58 percent of children (aged 12-23 months) received continued breastfeeding. As expected, the practice of continued breastfeeding generally decreased with age, with 84 percent of children aged 12-15 months, 55 percent of 58 children aged 16-19 months, and 27 percent of children aged 20-23 months still being breastfed. For children aged 12-15 and 16-19 months, similar patterns were observed in urban and rural areas. For children aged 12-23 months, 48 and 64 percent of children in urban and rural areas, respectively, were breastfed the previous day or night (p<0.05). Similar patterns were observed for boys and girls. Table 41. Percentage of children with continued breastfeeding Children aged 12-15 Children aged 16–19 Children aged 20–23 Children aged 12–23 months months months months N1 % [95% CI]2 N1 % [95% CI]2 N1 % [95% CI]2 N1 % [95% CI]2 Residence (P = 0.008)** (P = 0.186) (P = 0.105) (P = 0.002)** Urban 160 75.0 [66.3, 83.8] 162 47.7 [30.9, 64.5] 140 20.3 [9.8, 30.7] 462 47.8 [38.4, 57.1] Rural 272 88.2 [84.0, 92.5] 215 60.3 [51.8, 68.7] 196 31.3 [22.7, 39.9] 683 64.0 [59.5, 68.5] Sex (P = 0.819) (P = 0.105) (P = 0.589) (P=0.229) Male 205 83.7 [78.4, 89.1] 180 49.4 [38.6, 60.2] 158 24.7 [14.4, 35.0] 543 55.7 [49.3, 62.0] Female 227 84.6 [79.3, 89.8] 197 60.2 [50.5, 69.8] 178 28.8 [19.2, 38.4] 602 60.0 [55.4, 64.7] National 432 84.2 [80.3, 88.1] 377 55.0 [47.0, 63.0] 336 26.9 [20.3, 33.5] 1145 58.0 [53.8, 62.2] 1 Continued breastfeeding is defined as the percentage of children (aged 12-23 months) who were breastfed during the previous day. 2 Unweighted sample size. 3 Data are weighted to account for survey design and non-response. Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). Bottle Feeding The WHO guiding principles recommend avoiding the use of feeding bottles because they are difficult to keep clean and represent a particularly important route for the transmission of pathogens. In addition, bottle feeding may interfere with optimal suckling, as such cup feeding is preferable (WHO/UNICEF 2021). As shown in Table 42, 20 percent of children (aged 6-23 months) used the feeding bottle with a nipple the day before the interview. The use of feeding bottle with a nipple was found to decrease with the age (28, 18, and 14 percent for children aged 6-11 months, 12-17 months, and 18-23 months, respectively). No difference was found in the use of feeding bottle with a nipple between rural and urban residence or between girls and boys for children 6-23 months. Table 42. Percentage of children fed from a bottle with a nipple Children aged 6-11 Children aged 12-17 Children aged 18-23 Children aged 6-23 months months months months N1 % [95% CI]2 N1 % [95% CI]2 N1 % [95% CI]2 N1 % [95% CI]2 Residence (P = 0.271) (P = 0.816) (P = 0.203) (P = 0.949) Urban 240 31.4 [24.2, 38.6] 232 17.0 [9.9, 24.0] 230 10.8 [5.6, 16.0] 702 19.8 [14.7, 24.9] Rural 268 26.0 [19.6, 32.4] 369 18.1 [12.0, 24.2] 311 15.6 [10.3, 20.8] 948 19.6 [16.1, 23.1] Sex (P = 0.387) (P = 0.656) (P = 0.964) (P = 0.746) Male 234 25.5 [18.3, 32.8] 286 18.6 [12.4, 24.8] 257 13.7 [7.9, 19.6] 777 19.2 [15.3, 23.0] Female 274 30.7 [22.8, 38.6] 315 16.9 [11.4, 22.5] 284 13.5 [8.0, 19.1] 873 20.1 [15.9, 24.4] National 508 28.3 [23.4, 33.2] 601 17.7 [13.1, 22.4] 541 13.6 [9.8, 17.5] 1650 19.7 [16.8, 22.6] 1 Bottle feeding is defined as the percentage of children 0–23 months of age who were fed from a bottle with a nipple during the previous day 2 Unweighted sample size. 3 Data are weighted to account for survey design and non-response. Differences between groups were compared using Chi-square test (*signifies P<0.05, ** signifies P<0.01, ***signifies P<0.001). 59 Biofortification coverage Biofortification is a process of breeding staple crops to have higher levels of essential nutrients either through selective conventional breeding or genetic modifications. Biofortification of staple crops represents a major strategy to tackle micronutrient deficiency and enhance the availability of micronutrients among people with poor diets (Meenakshi et al., 2019). The focus of biofortification research is vitamin A, iron, and zinc deficiencies, which are of public health significance. In Nigeria, the staple crops of focus are cassava, maize, and sweet potato biofortified with pro-vitamin A, as well as millet and sorghum with iron and zinc through selective conventional breeding. This survey looks at three Nigerian staple crops that are biofortified with vitamin A, (yellow cassava, orange- fleshed sweet potato, and orange maize). The survey was designed to assess coverage and usual intakes of selected biofortified foods. The diet questionnaire was used to assess consumption in the last 30 days, whereas quantitative 24-hour dietary recall data (with repeat interviews in a sub-sample) will be used to estimate usual intakes. Since 24-hour dietary recall data are currently being processed and analysed, its findings are not presented in this preliminary report. Therefore, this section presents coverage of the three biofortified crops that were assessed in the survey. Coverage in this survey is defined as the proportion of respondents that consumed the biofortified crops and/or foods made from them in the previous 30 days. The key indicators used in the survey are shown in Table 43. The results for non-pregnant women (aged 15-49 years) are presented in the body of the report, while those for all survey target groups are presented in Annex 5. Table 43. Biofortification indicators reported in the NFCMS NFCMS Data collection tool NFCMS survey Indicator definition Data collection tool Survey question Included in the preliminary report Proportion of respondents who consumed biofortified foods (orange maize, orange sweet Diet In the last 30 days, did od] or any food Yes potato, yellow cassava) in the last 30 days questionnaire you eat [fo products made from it? Proportion of respondents who consumed In the last 30 days, biofortified foods (orange maize, orange sweet Diet how many days did you potato, yellow cassava) at least once a day in questionnaire eat [food] or any food Yes the last 30 days products made from it? As shown in Figure 6, few respondents reported having consumed biofortified crops, or any products made from them in the past 30 days. Only 3, 5, and 13 percent of the respondents consumed yellow cassava, orange-fleshed sweet potato orange maize, respectively. Although this was not confirmed by the survey, the higher consumption of orange maize could be explained by better consumer acceptance because of the similarity to the conventional non- biofortified maize that consumers are familiar with. This is unlike cassava and sweetpotato, with completely different colour traits between biofortified and non-biofortified (white) varieties. Differences may also relate to differences in the availability of the biofortified varieties. Efforts need to be made on consumer acceptance and availability of these crops, otherwise, the expected impact of biofortified crops on micronutrient deficiency may not be achieved. . 60 Figure 6. Percentage of respondents that consumed selected biofortified foods the previous 30 days Among non-pregnant women (aged 15-49 years) (unweighted sample size responding was 5273 for yellow cassava, 5275 for orange-fleshed sweet potato, and 5264 for orange maize). Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. Yellow Cassava As shown in Figure 7, only three percent of the respondents consumed yellow cassava (or any food products made from it) in the past 30 days of the interview. Although consumption of yellow cassava was found to be low across the country, significant differences were observed by zones (p<0.05). Only one percent of the respondents in North West and seven percent in North East reported having consumed yellow cassava. Figure 7. Percentage of respondents that consumed yellow cassava (or any food products made from it) the previous 30 days at national level and by residence, zone, and wealth quintile Among non-pregnant women (15-49 years) (unweighted sample size = 5273 respondents) Data for wealth quintile missing for 22 WRA because HH data was not collected. Data are weighted to account for survey design and non-response. *Signifies variable differs across groups (p<0.05) using Chi-square test. 61 Figure 8. Frequency of consumption of yellow cassava (or any food products made from it) in the previous 30 days among consumers Among non-pregnant women aged 15-49 years who consumed yellow cassava (or any food products made from it) in the previous 30 days (unweighted sample size for women = 188) Data are weighted to account for survey design and non-response. Among the respondents who reported having consumed yellow cassava, the vast majority (77 percent) reported consuming it for one to nine days in the past 30 days, whereas less than two percent consumed it daily (Figure 8). As a result of the low frequency of consumption, the impact of biofortified yellow cassava consumption on micronutrient deficiency in Nigeria is likely to be limited. Orange-fleshed sweet potato As shown in Figure 9, only five percent of the respondents consumed orange-fleshed sweet potato or any food products made from it in the past 30 days of the interview. Consumption was low irrespective of residence and wealth quintile. Although consumption was found to be low across zones, significant differences were observed (p<0.05). In the North East, 16 percent of respondents reported consuming orange-fleshed sweet potato, whereas only two percent of respondents in all other zones reported being consumers. The relatively higher percentage reported in the North East is likely due to food aids from government and development organizations in response to insurgence in the zone. Also, Working to Improve Nutrition in Northern Nigeria (WINNN), in collaboration with International Potato Centre (CIP) implemented nutrition sensitive kitchen garden intervention in the North East (Yobe) and West (Jigawa) in which WRA were given orange maize and sweet potato to plant in their kitchen gardens. The nutrition division of the FMARD also deployed orange-fleshed sweet potato to the zone in response to the emergence food insecurity from the insurgence. 62 Figure 9. Percentage of Respondents that Consumed Orange-Fleshed Sweet Potatoes (or any food products made from it) in the Previous 30 Days at National Level and by Residence, Zone and Wealth Quintile Among non-pregnant women (aged 15-49) years (unweighted sample size = 5275 respondents) Data for wealth quintile missing for 22 WRA because HH data was not collected. Data are weighted to account for survey design and non-response. *Signifies variable differs across groups (p<0.05) using Chi-square test. Among the respondents who reported having consumed orange-fleshed sweet potato, the vast majority (84 percent) reported consuming it in one to nine days in the past 30 days, whereas no one (0%) consumed it daily (Figure 10). As a result of the low frequency of consumption, the contribution of biofortified orange-fleshed sweet potato to reduction of micronutrient deficiency in Nigeria is likely to be limited. Figure 10. Frequency of consumption of orange-fleshed sweet potato (or any food products made from it) in the previous 30 days among consumers Among non-pregnant women (aged 5-49 years) who consumed orange-fleshed sweet potato (or any food products made from it) the previous 30 days (unweighted sample size for women = 222) Data are weighted to account for survey design and non-response 63 Orange Maize As shown in Figure 11, 13 percent of the respondents consumed orange maize, or any food products made from it in the past 30 days of the interview. Consumption was low irrespective of residence and wealth quintile. Although consumption was found to be low across zones, significant differences were observed (p<0.05). In the North East, 38 percent of respondents consumed orange maize, whereas consumption ranged between 4 and 14 percent in the other zones. This, again, could be due to government and development organization food support to the zone in response to the insurgency. Figure 11. Percentage of respondents that consumed orange maize (or any food products made from it) in the previous 30 days at national level and by residence, zone, and wealth quintile Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5275 respondents) Data for wealth quintile missing for 32 WRA because HH data was not collected. Data are weighted to account for survey design and non-response. *Signifies variable differs across groups (p<0.05) using Chi-square test. Among the respondents who reported having consumed orange maize, 56 percent reported consuming it in one to nine days in the past 30 days, whereas 16 percent reported consuming it daily (Figure 12). Maize is a staple in Nigeria, especially in the North East, where it is consumed in many forms. With the nutritional benefit of the crop, it has the potential to contribute to the national goal of reducing vitamin A deficiency in Nigeria if consumer awareness and acceptance can be strengthened. 64 Figure 12. Frequency of consumption of orange maize (or any food products made from it) in the previous 30 days among consumers Among non-pregnant women (aged 15-49 years) who consumed orange maize (or any food products made from it) the previous 30 days (unweighted sample size for women = 663) Data are weighted to account for survey design and non-response Fortification Coverage Food fortification is the practice of adding micronutrient(s) to commonly consumed foods during processing to increase their nutritional value. It is carried out at large-scale and endorsed by governments as a public health policy that aims to reduce micronutrient deficiencies within a population. In Nigeria, mandatory fortification of salt with iodine began in 1993, while that of sugar, margarine and edible oil with vitamin A and all flours (wheat, maize, cassava, and semolina) with multiple micronutrients, (vitamin A, iron and zinc) started 2002 (Standard Organizations of Nigeria, 2000a, 2000b, 2000c, 2010, 2015a, 2015b). In addition, voluntary fortification of some other food vehicles is gaining popularity e.g., bouillon. Below are terms used in the NFCMS 2021 as defined by Friesen et al, (2019): • Food vehicle: Refers to the food that is selected for the addition of one or more nutrients; it is usually a staple food or condiment that is widely consumed in any form. • Fortifiable food vehicle: Refers to a food vehicle that is industrially processed and therefore amenable to large-scale food fortification. • Fortified food vehicle: Refers to a food vehicle that has been confirmed by laboratory analyses to contain the added micronutrient(s) (in any amount). For the 2021 Nigerian NFCMS, the definitions for fortifiable food vehicles and fortified foods were adapted to the context of Nigeria. • Fortifiable food vehicles: Two proxies were used to assess the coverage of fortifiable food vehicles: ─ food vehicle that was purchased (i.e., not homemade) ─ food vehicle that was branded (i.e., commercially produced) There are limitations in the use of these proxy indicators. Defining fortifiable as purchased has the limitation that in Nigeria not all purchased foods are produced by large-scale industries. For instance, vegetable oil is produced both at large and cottage level, but the production at the cottage level does not provide an opportunity for fortification. This variable is therefore an overestimation of the true coverage. 65 Defining fortifiable as branded has the limitation that this information is not always available. When the brand of the food vehicle is unknown, it is not possible to determine whether the food is fortifiable. This variable is therefore an underestimation of the true coverage. • Fortified foods: Two proxies were used to assess the coverage of fortified foods: ─ food vehicle that is labelled as fortified based on information provided by the brand manufacturer was used (i.e., fortification logo or statement on the label of the package of the branded product) ─ food vehicle that is fortified (in any amount) based on linking the reported brand used by household of the sampled respondent to a fortification status (fortified or not fortified) based on micronutrient content from laboratory analysis of multiple food samples for the given brand using secondary data from the 2021 Global Alliance for Improved Nutrition (GAIN) market assessment of fortified food vehicles. This was done for all food except bouillon, which was not included in the market assessment as it is not currently required to be fortified in Nigeria. There are limitations in the use of these proxy indicators. When the brand of the food vehicle is unknown, it is not possible to examine the label or link the data to the GAIN database. Also, the label information and database information may not reflect the true fortification status. A brand previously fortified may no longer be fortified, or vice versa. Also, there are micronutrient losses during transportation, shelf storage, retail display, etc. between market and homes. The findings in this preliminary report are coverage of seven food vehicles assessed in the NFCMS, namely vegetable oil, wheat flour, maize flour, semolina flour, sugar, salt, and bouillon. Respondents were asked if their households use any of the food vehicles to prepare food at home. Coverage is defined as the proportion of respondents whose households consumed the food vehicle. The indicators used in the 2021 Nigerian NFCMS are summarized in Table 44. In addition, descriptive data are presented for types, sources, and brands of food vehicles consumed in the household. The results for non-pregnant women aged 15-49 y are presented in the body of the report (n=5381), while those for all survey target groups are presented in Annex 6. 66 Table 44. Fortification coverage indicators reported in the NFCMS using data collected in the diet questionnaire NFCMS survey Indicator definition Survey question Data analysis Food vehicles included Proportion of respondents in Does your The following response vegetable oil, wheat each target group (non-pregnant household use [food categories were created: flour, maize flour, women, pregnant women and vehicle] to prepare -consumed food vehicle semolina flour, sugar, children) whose households foods at home? -did not consume food salt, and bouillon consumed the food vehicle vehicle Proportion of respondents in -The last time your The following response vegetable oil, wheat each target group (non-pregnant household got [food categories were created: flour, maize flour, women, pregnant women and vehicle], how did you - purchased semolina flour, sugar, children) whose households get it? - homemade salt, and bouillon consumed the purchased - donations/gifted food vehicle (this is a proxy for - unknown fortifiable) Proportion of respondents in -The last time your The following response vegetable oil, wheat each target group (non-pregnant household got [food categories were created: flour, maize flour, women, pregnant women and vehicle], what was - branded semolina flour, sugar, children) whose households the brand? - unbranded salt, and bouillon consumed the branded food - unknown vehicle (this is a proxy for fortifiable) Proportion of respondents in - The brand name vegetable oil, wheat each target group (non-pregnant reported was linked to flour, maize flour, women, pregnant women and label information (visual semolina flour, sugar, children) whose households inspection of fortification salt, and bouillon consumed the food vehicle that logo or statement on food was labeled as fortified (this is a label) proxy for fortified) Proportion of respondents in each - The brand name reported vegetable oil, wheat target group (non-pregnant women, was linked to secondary flour, maize flour, pregnant women and children) data on fortification status semolina flour, sugar, whose households consumed the from GAIN 2021 market and salt food vehicle that was confirmed assessments to be fortified (this is a proxy for - Data were disaggregated fortified) as fortified below minimum standard range of fortification and fortified at or above standard. In addition to the variables derived from the diet questionnaire, samples for vegetable oil, wheat flour, semolina flour, sugar and salt were collected from a sub-sample of non-pregnant women. These samples were analyzed in the laboratory and the finding are presented here. 67 Overview of coverage and fortification indicators among non-pregnant WRA Figure 13 shows the overview of the fortification coverage indicators of the seven selected food vehicles. Figure 13. Coverage of Selected Food Vehicles among Households of the sampled Non-Pregnant Women at National Level Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5281) Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. Data is missing for 22 non-pregnant women. *Based on linking reported brand to secondary data from GAIN Market assessment 2021 on fortification status (i.e., fortified or not fortified) by brand based on analysis of multiple food samples per brand. A high proportion of households of sampled non-pregnant women of reproductive age (WRA) consumed vegetable oil (90 percent), sugar (88 percent), salt (99 percent), and bouillon (99 percent) in any form. Fewer households of sampled non-pregnant WRA consumed flours in any form (57 percent for maize flour, 29 percent for semolina flour, and 28 percent for wheat flour). The proportion of households of sampled non-pregnant women of reproductive age that consumed foods that were obtained through purchases (as opposed to for example gifts or food aid) were similar to those consuming the food in any form for most food vehicles, except for maize flour (57 percent of household consumed it, but only 29 percent purchased it). The proportion of respondents whose households consumed these foods in a branded form (which was used as a proxy for commercially processed and thus amenable to large-scale fortification) was considerably lower for most foods, i.e., vegetable oil (33 percent), sugar (22 percent), wheat flour (13 percent) maize flour (<1 percent), semolina flour (23 percent), salt (47 percent), except for bouillon, which remained high (96 percent). That said, the same proportion of households that consumed branded foods also consumed foods labelled as fortified and confirmed to be fortified (in any amount) based on linking the report brand to secondary market data on fortification quality. This suggests that most foods that are labelled as fortified are in fact fortified. For bouillon, the drop in the proportion of women from households that consumed bouillon that was branded and labelled as fortified, is likely because fortification is currently on voluntary basis and therefore only some brands are fortifying and labelling their products as such as a means of increasing market competitiveness. 68 Where there is high coverage of foods that are purchased and branded, there is an opportunity for large-scale fortification to reach a high proportion of the population and where a sharp decline is observed between purchased and branded for most foods (except bouillon), it may be due to either a high proportion of non-pregnant women reported their households consumed unknown and/or unbranded food vehicles (sugar, vegetable oil, salt)or a high proportion of them obtained food vehicle(s) from small/cottage-scale production (maize flour and vegetable oil) with no brands. Vegetable oil Figure 14 presents the coverage indicators for vegetable oil nationally among households of the sampled non-pregnant WRA (15-49 years old). There was a high proportion of households of the sampled non-pregnant women that consumed vegetable oil in any form (90%) and purchased it (81 percent). At the same, only about one-third of households of the sampled women of reproductive age consumed vegetable oil that was branded, labelled as fortified and fortified (in any amount). However, the result for these latter three indicators may be underestimated as about 25% of women could not report the brand of the consumed vegetable oil. These results reveal that fortification of vegetable oil is currently reaching at least 31 percent of households of the sampled respondents and has the potential to reach up to around 60 percent of households if all the branded oil is fortified. However, while 33 percent of women come from households that consumed branded vegetable oil (and 26 percent were unknown), 25 percent consumed unbranded oil and thus would not be reached with large-scale food fortification. 69 Vegetable oil 100 9.7 9.7 9.7 9.7 9.7 90 7.4 7.0 7.0 7.0 80 2.24 70 24.73 24.6 25.9 Doesn't consume Consumes food that is homemade or donated 60 3.6 1.0 Not branded 50 Not fortified 90.3 25.7 23.8 25.8 Not labelled as fortified 40 80.7 Don't know 30 0.4 Fortified below standard Fortified at or above standard 20 Yes 32.9 31.4 30.2 10 0 Consumed food Consumes food Consumes food Consumes food *Consumes food that is purchased that is branded that is labelled as that is fortified fortified Figure 14. Percentage of Non-Pregnant Women Whose Households Consumed Vegetable Oil (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women aged 15-49 years (unweighted sample size = 5281) Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. Data for bouillon is missing for 22 non-pregnant women. *Based on linking reported brand to secondary data from GAIN Market assessment 2021 on fortification status by brand Unbranded vegetable oil could originate from small-scale processors (cottage industries) that take their products to the open market (e.g., unrefined groundnut oil) for sale; this type of oil is truly unbranded. This practice is common with groundnut oil, which is commonly processed by women at cottage-level. According to FAO, 2003 (Mustapha and Suleiman, 2006), the locally processed groundnut oil is about 25 percent of the total vegetable oil produced in Nigeria. Unbranded vegetable oil could also come from downsized and repackaged branded vegetable oil, whose identity would have been lost at the point of purchase. Nigeria has the common practice of downsizing and repackaging vegetable oil from barrels/drums into smaller local measures that low-income earners can afford. When this is done, the brand identity of the oil is lost. These oils may be branded originally, but at the time of purchase, the brand is not disclosed to the consumer. Furthermore, the similarity in the proportion of women from households that consumed food that is branded, labelled as fortified and fortified are indicators that most of the producers of vegetable oils that are branded are infact labelling and fortifying their products. Across residence sectors and zones, even though the proportion of households of the selected respondents that consumed vegetable oil was found high nationally, the proportion was higher among urban dwellers compared to rural (96 percent vs. 85 percent) with the same trend found for the proportion of households of the selected respondents that consumed vegetable oil that is purchased, branded, labelled as fortified and fortified (Table 45). Contrarily, the proportion of households of the sampled respondents that consumed unbranded vegetable oil was slightly higher in rural areas compared to urban (28 percent vs. 21 percent) This may be explained by the fact that this type of oil is often cheaper and therefore may be more affordable in rural areas. 70 % of individuals surveyed Within the zones, the proportion of households of the sampled non-pregnant women of reproductive age that consumed vegetable oil were higher in South South and South West (92 percent) and north central (91 percent) zones compared to other zones (88 percent each). The proportion of households of the sampled respondents that consumed unbranded vegetable oil was higher in the northern zones (16-54 percent), especially North central (54 percent) compared to the southern zones (12-29 percent). This is likely because groundnut oil, which is a very common type of oil made at cottage-scale, is produced more widely in the north and unbranded. Also, groundnut is the base crop grown more in the north (FAO, 2003 in Mustapha and Suleiman, 2006). This may account for lower proportion of households of the sampled respondents that consumed the branded vegetable oil in the north (12-21 percent) compared to those in the south (57-65 percent). Higher proportion of women’s households consuming unknown brands was also found, especially in the northern zones (11-52 percent), which could be traced to the practice of downsizing and repackaging vegetable oil that are cheaper and more affordable by the low- income earners. In general, a higher proportion of households of the respondents that consumed unbranded and unknown oil was found in the northern zones (65 percent North central, 56 percent North East, and 68 percent North West) compared to the southern zones (South East 23 percent, South-South 26 percent and South West 32 percent) (Table 45). The same trend was found with wealth quintile as consumption of branded vegetable oil is more in the rich than the poor HHs. With the high percentages of unknown and unbranded vegetable oil, fortification status of vegetable oil consumed in these HHs could not be truly assessed. This could be a challenge in the evaluation of the impact of fortification programme in Nigeria. 71 72 Table 45. Percentage of Non-Pregnant Women of Reproductive Age (WRA) whose Households Consumed Vegetable Oil (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or product Consumed Consumed food Consumed food is unbranded2, 4, 5 Consumed food that is fortified2, 4 that is labelled as food2, 3 food that is Consumed purchased2, 4 that is branded2, 4 Unknown Unbranded fortified2, 4 At standard Below standard Not fortified N1 % [95%CI] National3 Non-pregnant 90.3 80.7 32.9 25.8 24.7 31.4 30.1 0.2 0.5 WRA 5281 [88.5-92.1] [78.4-83.0] [29.9-35.9] [22.9-28.6] [22.2-27.2] [28.5-34.4] [27.3-32.9] [0.0-0.5] [0.2-0.9] Residence P < 0.0001*** 96.2 87.6 48.0 21.6 21.3 46.0 44.1 0.5 1 Urban 2156 [95.0-97.3] [84.7-90.5] [42.6-53.3] [16.5-26.8] [17.7-24.8] [40.5-51.6] [38.5-49.6] [0.0-1.1] [0.3-1.7] 85.2 74.7 19.8 29.3 27.7 18.7 18.0 Rural 3125 0 0.1 [82.3-88.1] [71.3-78.0] [16.7-22.8] [25.0-33.7] [24.2-31.2] [15.7-21.8] [15.0-20.9] [0.0-0.3] Zone P = 0.1722 94.3 84.5 21.0 11.4 53.5 21.0 19.5 North Central 857 0 0 [91.5-97.1] [79.8, 89.2] [13.1-28.9] [8.6-14.2] [45.7-61.3] [13.1-28.9] [11.7-27.3] 87.6 68.1 15.2 38.3 18.2 15.0 12.9 North East 830 0 0 [83.1-92.1] [62.0-74.2] [6.2-24.2] [31.6-45.0] [12.4-24.1] [6.0-24.0] [5.4-20.5] 88.7 77.0 12.5 52.3 16.1 8.8 7.5 North West 944 0 1.7 [84.3-93.1] [71.3-82.6] [7.3-17.8] [46.3-58.3] [12.5-19.7] [4.2-13.4] [3.2-11.7] [0.6-2.8] 87.9 78.5 61.1 10.6 12.3 60.0 57.7 0.1 South East 855 0 [83.8-92.0] [73.1-83.8] [53.8-68.4] [6.5-14.7] [9.0-15.7] [52.6-67.4] [50.4-65.0] [0.0-0.2] 92.4 90.6 65.1 5.9 20.4 64.5 64.3 0.1 South South 888 0 [87.4-97.3] [85.0-96.1] [56.9-73.3] [4.0-7.8] [15.7-25.0] [56.4-72.6] [56.2-72.5] [0.0-0.2] 91.6 89.1 57.4 3.5 29.0 56.6 55.4 1.3 0.1 South West 907 [87.5-95.7] [85.0-93.1] [50.1-64.7] [2.2-4.9] [23.8-34.2] [49.4-63.7] [48.4-62.4] [0.0-2.8] [0.0-0.4] Wealth quintile6 P < 0.0001*** 80.3 67.4 6.9 39.4 23.8 5.7 5.4 0.1 0.1 Lowest 1081 [75.9-84.7 [62.4-72.4] [4.5-9.2] [33.3-45.4] [19.9-27.7] [3.5-7.9] [3.2-7.5] [0.0-0.3] [0.0-0.4] 87.2 73.8 15.8 34.0 27.1 14.5 14.1 0.2 0.6 Second 1111 [83.7-90.6] [68.9-78.6] [12.5-19.0] [28.5-39.6] [22.2-31.9] [11.3-17.8] [10.9-17.3] [0.0-0.6] [0.0-1.5] 93.2 82.9 37.0 21.0 27.1 35.8 33.7 0.4 0.5 Middle 1100 [90.9-95.6] [79.5-86.4] [32.7-41.3] [16.9-25.0] [22.2-32.1] [31.4-40.1] [29.0-38.3] [0.0-1.2] [0.0-1.2] 95.6 90.3 50.1 15.8 26.9 49.0 47.2 0.4 0.3 Fourth 997 [93.7-97.5] [87.5-93.1] [45.3-54.9] [12.4-19.1] [22.4-31.4] [44.1-53.8] [42.4-51.9] [0.0-0.9] [0.0-0.7] 97.4 93.0 63.2 14.6 18.0 60.6 58.1 0.1 1.1 Highest 970 [96.1-98.7] [90.5-95.4] [57.8-68.6] [8.8-20.3] [14.4-21.5] [54.7-66.5] [52.3-64.0] [0.0-0.2] [0.0-2.3] 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 When the food brand was unknown or an unbranded product was used, it was not possible to link data to label information. 6 Data is missing for 22 non-pregnant women. The proportion of households of the sampled non-pregnant women that consumed groundnut oil and palm olein as main type of vegetable oil was 51 percent and 44 percent respectively (Figure 15). Figure 15. Main type of vegetable oil used in the household among consumers Among non-pregnant women (aged 15-49 years) who used the food vehicle in the HH (unweighted sample size for women = 4749) Data are weighted to account for survey design and non-response. The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the HH. Oil blend is a mixture of seeds processed into oil (e.g. rapeseed and sunflower). 73 As shown in Figure 16, several brands of oil are available in Nigeria. The proportion of households of the sampled non-pregnant women consumed King’s (100 percent vegetable oil) as their main brand of vegetable oil was 22 percent, followed by Power Oil - pure vegetable oil that was reported by 14 percent of the women. Figure 16. Brand of vegetable oil obtained the last time among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HH and the food vehicle was not “homemade” (unweighted sample size for women = 4320) Data are weighted to account for survey design and non-response The brand was classified as “unknown” when the respondent could not report the brand of food vehicle used in the HH. Wheat Flour Wheat flour is commonly used in the baking industry to make bread and other food products (e.g. biscuits, doughnuts, cakes, meat pies). According to Femi (2020), wheat flour is consumed everyday as bread, biscuits, cakes. Over five million tons of the product was consumed in 2020. However, in some households especially northern homes, wheat flour is used to make locally produced pasta (Taliya), fried pastries, and local foods, such as alkubus and guraza.8 This survey assessed the use of wheat flour at home and the results is provided in this preliminary report. Consumption of wheat-based products processed outside the home, usually by vendors is covered in the 24-houre recall section of the questionnaire and the report would be provided in the survey full report. Figure 17 presents the coverage indicators for wheat flour nationally among non-pregnant WRA (15-49 years old). The proportion of households of the sampled non-pregnant women that consumed wheat flour in any form at home was 28 percent and those that purchased it was 25 percent. At the same time, only 13 percent of the households of the sampled women of reproductive age consumed wheat flour that was branded, labelled as fortified and fortified (in any amount). However, the result for these latter three indicators may be underestimated as 10 percent of the households of the respondents could not report the brand of the consumed wheat flour. Also, the remaining 72 percent that did not use it at home does not mean that the households did not consume wheat flour rather they consumed wheat flour products (i.e., bread, confectionaries) that are vendor processed. The survey result of 24-hour dietary recall will give details of consumption of wheat flour products and contribution of its fortification to nutrient intake. 74 Wheat Flour 100 90 Doesn't consume 80 Consumes food that is homemade or donated 70 71.8 71.8 71.8 71.8 71.8 Not branded 60 Not fortified 50 Not labelled as fortified 40 30 Don't know 11..49 1.0 1.0 1.0 4.3 4.3 40..35 20 10.1 10.1 10.1 Fortified below standard 28.2 10 25.3 12.9 12.9 12.6 Fortified at or above standard 0 0.1 Consumed Consumes Consumes Consumes *Consumes Yes food food that is food that is food that is food that is purchased branded labelled as fortified fortified Figure 17. Percentage of Non-Pregnant Women Whose Households Consumed Wheat Flour (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5281) Data are weighted to account for survey design and non-response UFniwgeuigreht e1d. sPaemrpclee nsitzaeg feor oafll Nreospno-nPdreengtsDifferences across groups were not tested sntaatnistti cWalloy.men Whose Households Consumed Wheat Flour (purchased, branded, labelled as fortified aDnatda ifso mrtisfiseindg) foart 2N2 antoion-nparelg Lneanvte wl omen. *Based on linking reported brand to secondary data from GAIN Market assessment 2021 on fortification status by brand These results reveal that fortification of wheat flour is currently reaching at least 13 percent of households of the sampled respondents but has potential to reach up to 28 percent if all the wheat flour consumed at home is known, branded, and fortified. It could also reach much more if wheat flour used in other vendor-prepared forms outside homes (pastries, confectionaries, etc) is fortified. However, while 13 percent of households of the sampled women of reproductive age consumed branded wheat flour (and 10% were unknown), 4 percent consumed unbranded wheat flour and thus could not be reached with large-scale food fortification. Unknown and unbranded wheat flour could come from two sources. One is likely because of retailers downsizing and repackaging the common 50kg bag into local measures with no brand identity. At the point of sales of the repackaged wheat flour, brand identity is lost, and consumers could not tell which brand they buy and use. Also, in Nigeria, wheat flour is mainly processed at large industrial scale, but also at cottage scale in the north where it is locally grown although in small quantity. These products are usually unrefined and can also be processed at home for local dishes such as ‘swallow’, local pasta (Taliya), and guraza. With these findings, only wheat flour with brand information was linked to the fortification secondary data. Furthermore, the similarity in the proportion of households of the sampled individuals that consumed food that is branded, labelled as fortified and fortified are indicators that most of the producers of wheat flours that are branded are in fact labelling and fortifying their products. Across residence sector (Table 46), the proportion of households of the sampled non-pregnant 75 % of respondents surveyed women that consumed wheat flour was higher (40 percent) in urban than those from the rural (18 percent). The same trend was found for the proportion of households of sampled non-pregnant women that consumed wheat flour that was purchased, branded, labelled as fortified and fortified (Table 57). Contrarily, the proportion of households of the non-pregnant women that consumed unbranded wheat flour was higher in the rural than urban. Within the zones, proportion of households of the sampled non-pregnant women that consumed wheat flour was found highest in the North East (44 percent) and North West (41 percent), followed by South West (29 percent). In the other zones, proportion households of the sampled non-pregnant women that consumed wheat flour was between 9 and 13 percent. 76 77 Table 46. Percentage of Non-Pregnant Women Whose Households Consumed Wheat Flour (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or nsumed food Consume food that product is unbranded2, 4, 5 Consumed food Consume food that is fortified2, 4 Consumed food2, 3 Co that is purchased2, 4 is branded2, 4 that is labelled as Unknown Unbranded fortified2, 4 At Below Not standard standard fortified N1 % [95%CI] National3 Non-pregnant women aged 15-49 5281 28.2 25.3 17.2 10.1 12.9 0.1 12.6 years [24.3, 32.2] [21.3, 29.3] [14.0, 20.3] [8.5, 11.7] 0 [10.1, 15.8] [0.0, 0.2] [9.9, 15.4] 0 Residence P<0.001*** Urban 2156 40.1 36.5 27.2 11.5 1.2 0.2 20.6 [33.5, 46.6] [29.8, 43.2] [21.9, 32.4] [9.2, 13.8] 0 2 [16.3, 26.1] [0.0, 0.4] [15.9, 25.4] 0 Rural 3125 18.0 15.6 8.5 8.9 5.7 .6 [14.6, 21.3] [12.1, 19.0] [6.4, 10.6] [6.6, 11.2] 0 [3.9, 7.4] 0 5 [3.9, 7.4] 0 Zone P<0.001*** North Central 857 12.7 8.8 6.8 5.2 3.5 3.5 [9.1, 16.4] [5.6, 12.1] [4.3, 9.4] [2.0, 8.5] 0 [1.7, 5.2] 0 [1.7, 5.2] 0 North East 830 44.3 40.6 25.1 19.1 1.5 21.5 [34.3, 54.2] [30.8, 50.4] [16.6, 33.6] [14.4, 23.7] 0 2 [14.3, 28.7] 0 [14.3, 28.7] 0 North West 944 40.5 38.6 26.0 13.6 21.7 1.3 [30.2, 50.8] [27.9, 49.3] [17.6, 34.4] [9.6, 17.5] 0 [14.0, 29.5] 0 2 [13.9, 28.8] 0 South East 855 9.9 7.7 4.7 4.8 .7 [7.2, 12.6] [5.3, 10.0] [2.9, 6.5] [3.2, 6.3] 0 2.0 [1.1, 3.0] 0 1 [0.9, 2.6] 0 South South 888 9.2 7.1 6.0 2.5 .8 [5.7, 12.8] [3.9, 10.3] [3.1, 8.9] [1.3, 3.6] 0 4 [2.2, 7.5] 0 4.7 [2.1, 7.2] 0 South West 907 28.7 24.6 17.9 8.2 0.6 8.2 [24.3, 33.0] [20.4, 28.8] [14.7, 21.2] [6.1, 10.3] 0 9.0 [6.9, 11.1] [0.1, 1.2] [6.2, 10.3] 0 Wealth quintile P<0.001*** Lowest 1081 19.1 17.1 7.6 10.3 5.6 5.6 [14.3, 24.0] [12.2, 22.0] [5.1, 10.1] [6.7, 13.9] 0 [3.6, 7.5] 0 [3.6, 7.5] 0 Second 1111 25.3 24.0 14.4 10.8 5 0.1 11.4 [19.5, 31.2] [18.2, 29.8] [9.2, 19.7] [7.8, 3.8] 0 11. [6.8, 16.2] [0.0, 0.3] [6.7, 16.1] 0 Middle 1100 30.5 26.9 20.1 9.9 15.2 0.0 15.2 [24.6, 36.5] [20.7, 33.2] [14.6, 25.6] [7.3, 12.5] 0 [9.8, 20.6] [0.0, 0.1] [9.8, 20.6] 0 Fourth 997 33.3 28.7 20.8 11.1 5.0 0.3 14.2 [28.2, 38.5] [23.5, 33.9] [16.6, 25.1] [8.4, 13.7] 0 1 [11.1, 19.0] [0.0, 0.7] [10.5, 17.9] 0 Highest 970 35.3 31.7 25.0 8.4 18.7 0.1 18.0 [28.0, 42.6] [24.3, 39.1] [19.6, 30.4] [4.2, 12.5] 0 [14.2, 23.3] [0.0, 0.3] [13.7, 22.4] 0 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 When the food brand was unknown or an unbranded product was used, it was not possible to link data to label information. 6 Data is missing for 22 non-pregnant women. The proportion of households of sampled non-pregnant women that consumed all-purpose flour as their main type of flour was 59 percent followed by 16 percent and 15 percent of them that reported refined wheat flour and whole wheat flour respectively (Figure 18). Low proportion of households of the sampled non-pregnant women (6 percent) were unable to report the type of wheat flour used in their households. Figure 18. Main types of wheat flour used in the household among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HH (unweighted sample size for women = 1226) Data are weighted to account for survey design and non-response The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the household. The proportion of households of the sampled non-pregnant women that consumed Dangote wheat flour as their main brand was 22 percent while those that reported Bua wheat flour as their main brand was 15 percent (Figure 19). Figure 19. Brand of wheat flour obtained the last time among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HH and the food vehicle was not “homemade” (unweighted sample size for women = 1095) The brand was classified as “unknown” when the respondent could not report the brand of food vehicle used in the HH. 78 Maize Flour Maize is a staple in Nigeria, especially in the north where it is processed for both intermediate and finished diverse local dishes. In intermediate form, maize flour is commonly used in preparing local dishes like ‘swallow’ tuwo masara, pap, etc. Figure 20 presents the coverage indicators for maize flour nationally among non-pregnant WRA (15-49 years old). The proportion of households of the sampled non-pregnant women that consumed maize flour in any form was 57 percent while those that purchased it was 29 percent and homemade 27 percent. At the same time, the proportion of households of the sampled non- pregnant women that consumed branded, labelled as fortified, and fortified at any level was very low, (between 0 and <1 percent) nationally. However, the proportion of households of the sampled respondent that reported that they consumed homemade, unbranded, and unknown was 27 percent, 16 percent and 13 percent respectively thus about all (56%) of the households of the sampled women that consumed maize flour would not be reached with large-scale food fortification with the target micronutrients (vitamin A, iron, and zinc). Across the residence sector, the proportion of households of the sampled non-pregnant women that consumed maize flour as well as consumed homemade maize flour was higher in rural than urban (Table 47). Also, within the zones, the proportion of households of the sampled individuals that consumed maize flour was higher in the north (80 percent) than in the south (≤20 percent). Large scale fortification of maize flour seems very low (almost nil) in Nigeria probably because most of the maize flour are processed either at home or small/cottage-scale, which makes them fall out of the large-scale food fortification programme. However, considering high consumption (81-85 percent) in the north, where maize is a staple, other means of reaching the households with fortified maize flour may need to be considered.. 79 Maize flour 100 Doesn't consume 90 80 42.6 42.6 42.6 42.6 42.6 Consumes food that is homemade or 70 donated 60 Not branded 50 40 27.5 27.1 27.1 27.1 Not fortified 30 57.4 0.7 20 16.1 15.4 Not labelled as fortified 16.1 29.2 1.4 10 0.7 13.5 13.0 13.5 Don't know 0 0.7 0.5 Consumed Consumes foodConsumes foodConsumes food *Consumes Fortified below standard food that is that is branded that is labelled food that is purchased as fortified fortified Figure 20. Percentage of Non-Pregnant Women Whose Households Consumed Maize Flour (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5281 ) Data are weighted to account for survey design and non-response. Unweighted sample size for all respondents Differences across groups were not tested statistically. Data is missing for 22 non-pregnant women. *Based on linking reported brand to secondary data from GAIN Market assessme nt 2021 on fortification status by brand Figure 1. Percentage of Non-Pregnant Women Whose Households Consumed Maize Flour (purchased, branded, labelled as fortified and fortified) at National Level 80 % of individuals surveyed 81 Table 47. Percentage of Non-Pregnant Women Whose Households Consumed Maize Flour (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or product is Consumed food2, 3 Consumed food Consume food that unbranded2, 4, 5 Consumed food Consume food that is fortified2, 4 that is purchased2, 4 is branded2, 4 that is labelled as Unknown Unbranded fortified2, 4 At Below standard standard Not fortified N1 % [95%CI] National3 Non-pregnant women 4 29.2 0.7 13.4 16.1 0.5 0.0 0.7 aged 15-49 years 5281 57. [53.8, 61.0] [26.2, 32.3] [0.0, 1.4] [11.5, 15.3] [13.7, 18.6] [0.0, 1.1] 0 [0.0-0.0] [0.0-1.3] Residence P=0.023* Urban 2156 51.2 30.4 1.1 13.6 16.7 0.8 1.1 [44.6, 57.9] [25.2-35.7] [0.0, 2.5] [10.1, 17.1] [13.8, 19.7] [0.0-2.2] 0 0 [0.0-2.5] Rural 3125 62.7 28.2 0.4 13.3 15.6 0.2 [56.9, 68.5] [24.0-32.4] [0.1, 0.6] [10.7-15.9] [11.7, 19.5] [0.0-0.3] 0 0 0.4 [0.1-0.6] Zone P<0.001*** North Central 857 84.4 40.2 0.2 9.0 32.1 0.1 .0 [78.6, 90.2] [30.8-49.6] [0.0, 0.4] [3.5-14.5] [22.1, 42.1] [0.0-0.4] 0 0 0 [0.0-0.1] North East 830 81.2 32.3 4.0 15.0 14.4 2.6 4.0 [74.7, 87.8] [25.9-38.8] [0.3, 7.7] [11.4-18.7] [9.6, 19.1] [0.0-6.3] 0 0 [0.3-7.7] North West 944 84.8 49.4 5 20.7 [80.0, 89.6] [43.3-55.6] 0 30. [25.7-35.2] [16.9, 24.5] 0.0 0 0 0 South East 855 17.7 12.4 0.1 3.1 10.0 0.1 0.1 [12.4, 22.9] [8.7-16.1] [0.0, 0.2] [1.3-4.8] [6.4, 13.6] [0.0-0.2] 0 [0.0-0.2] 0 South South 888 7.0 5.1 .6 3.6 [4.7, 9.4] [3.0-7.2] 0 1 [0.3-2.9] [2.0, 5.3] 0.0 0 0 0 South West 907 20.8 8.8 0.0 0.8 8.6 [14.7, 26.8] [5.9-11.7] [0.0, 0.1] [0.1-1.4] [5.8, 11.5] 0.0 0 0 0.0 [0.0-0.1] Wealth quintile P<0.001*** Lowest 1081 75.7 32.6 1.0 19.3 13.3 0.6 1.0 [69.5, 81.9] [27.7-37.4] [0.3, 1.8] [15.8-22.9] [9.8, 16.7] [0.0-1.2] 0 0 [0.3-1.8] Second 1111 67.4 32.4 0.3 13.2 20.0 0.3 [60.8, 74.0] [27.0-37.8] [0.0, 0.8] [9.7-16.6] [14.9, 25.1] [0.0-0.7] 0 0 0.3 [0.0-0.8] Middle 1100 52.5 28.5 1.1 12.2 15.9 0.9 1.1 [45.4, 59.6] [22.0-35.0] [0.0, 2.8] [8.4-16.0] [10.7, 21.1] [0.0-2.5] 0 0 [0.0-2.7] Fourth 997 49.6 29.0 0.6 10.8 19.1 0.3 [43.1, 56.1] [23.6-34.4] [0.0, 1.3] [7.6-14.0] [14.5, 23.8] [0.0-0.9] 0 0 0.6 [0.0-1.3] Highest 970 36.6 22.3 0.5 11.2 11.5 0.2 0.0 0.3 [28.4, 44.8] [16.0, 28.7] [0.0, 1.0] [6.0-16.3] [8.2, 14.7] [0.0-0.5] 0 [0.0-0.1] 0.0-0.9] 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 When the food brand was unknown or an unbranded product was used, it was not possible to link data to label information. 6 Data is missing for 22 non-pregnant women. A high proportion of households of the sampled non-pregnant women (92 percent) consumed white maize as their main type of maize. (Figure 21). Figure 21. Main type of maize flour used in the household among consumers Among non-pregnant women (aged 15-49 years) who used the food vehicle in the HH (unweighted sample size for women = 2573) Data are weighted to account for survey design and non-response. The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the HH. As shown in Figure 22, very low proportion of households of the sampled non-pregnant women (<2 percent) were able to report the brand of maize flour that they purchased. About 54 percent reported using unbranded maize flour, while 44 percent were unable to report a brand. Maize flour is not commonly produced on large-scale in Nigeria. However, cottage processing, which is unbranded, is widespread. As a result of the lack of information on brands, it will not be possible to link the brand of maize flour to the likely fortification status for almost all the households of the non-pregnant women. Figure 22. Brand of maize flour obtained the last time among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HH and the food vehicle was not “homemade” (unweighted sample size for women = 1231) Data are weighted to account for survey design and non-response. The brand was classified as “unknown” when the respondent could not report the brand of food vehicle used in the HH. 82 Semolina Flour Semolina flour is a highly industrialized wheat-based flour in Nigeria, it is commonly prepared as ‘swallow’ and consumed with choice soup. ‘Swallow’ is a commonly used term for common staples (cassava, yam, maize, etc.) cooked into thick ‘swallowable’ meal, and eaten with choice soup in Nigeria. Figure 23 presents the coverage indicators for semolina flour nationally among non-pregnant WRA (15-49 years old). The proportion of households of the sampled non-pregnant women that consumed semolina flour in any form at home was 29 percent and those that purchased it was 26 percent. All the same, 23 percent of the households of the sampled individuals consumed semolina flour that was branded, labelled as fortified and fortified (in any amount). Contrarily, the proportion of households of the sampled women of reproductive age that consumed unbranded (<1 percent) and unknown semolina (5 percent) was relatively low. This is likely because all semolina flours are made in factories through an industrialized process on large scale basis with no home- or cottage-level production. Also, they come in 1 or 2 kg-packs that neither needs downsizing nor re- packing, hence there is low percentage of unknown or unbranded products. The few that reported unbranded could be that the respondents did not simply know the brands consumed. Figure 23. Percentage of Non-Pregnant Women Whose Households Consumed Semolina Flour (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5281) Data are weighted to account for survey design and non-response. Unweighted sample size for all respondents. Differences across groups were not tested statistically. Data is missing for 22 non-pregnant women. *Based on linking reported brand to secondary data from GAIN Market assessment 2021 on fortification status by brand 83 Furthermore, the similarity in the proportion of households of the sampled women that consumed semolina flour that is branded, labelled as fortified and fortified are indicators that most of the producers of semolina flours that are branded are in fact labelling and fortifying their products. Across the residence sector, semolina is predominantly an urban dwellers’ food as the proportion of households of the sampled non-pregnant women from urban sector that consumed semolina was almost half (49 percent) compared to those from the rural sector, which was12 percent. It is also consumed more in households of the rich (57 percent) than those of the poor (5 percent) (Table 48). Within the zones, the proportion of households of the sampled non-pregnant women that consumed semolina flour was highest (80 percent) in the South West followed by North Central (36 percent), and low in the other zones (10-24 percent). The high percentage in the South West may be because of easy access to the flour in cities, such as Lagos, where semolina meal (‘swallow’) is readily available in eateries and restaurants. The low coverage in other states could be due to competing ‘swallows’ prepared from root and tubers (i.e., fufu and gari). These results reveal that fortification of semolina is currently reaching 23 percent of households with likely limited potentials to reach more because there are other alternatives to semolina consumption at home. In the north, where the consumption was found low, the common swallow is Tuwo. Also, in the south-south and South East, cassava-based swallows like fufu, Garri are the most common swallows hence, the people are not likely to consider semolina. In terms of cost and affordability, semolina is more expensive and may not be affordable by all. Table 48 shows that consumption was found more among the households of the rich and the urban dwellers. Fortification of these alternative swallows (Tuwo, fufu, garri, and pounded yam) from other crops may be worth considering, which could come from biofortification of the base crops, especially cassava and maize. These are already in place in Nigeria, but the value chain may need to be strengthened for household reach. 84 85 Table 48. Percentage of Non-Pregnant Women Whose Households Consumed Semolina Flour (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or product is Consumed Consumed nsume food Consumed food unbranded2, 4, 5 Consume food that is fortified2, 4 food2, 3 food that is Co purchased2, 4 that is branded2, 4 that is labelled as Unknown Unbranded fortified2, 4 At standard Below standard Not fortified N1 % [95%CI] National3 Non-pregnant women 28.7 26.2 22.8 5.5 0.5 22.7 0.1 22.6 aged15- 49 years 5281 [25.4, 32.1] [23.2, 29.3] [19.9, 25.7] [4.3, 6.7] [0.2, 0.7] [19.8, 25.6] [0.0, 0.2] [19.7, 25.5] 0 Residence P<0.001*** Urban 2156 48.5 45.2 40.5 7.5 0.5 40.3 0.1 40.2 [42.4, 54.6] [39.5, 50.9] [34.8, 46.2] [5.3, 9.6] [0.2, 0.8] [34.6, 46.0] [0.0, 0.2] [34.5, 45.9] 0 Rural 3125 11.5 9.7 7.3 3.7 0.4 7.3 0.1 7.2 [8.5, 14.6] [6.9, 12.5] [5.0, 9.6] [2.6, 4.8] [0.1, 0.8] [5.0, 9.6] [0.0, 0.2] [5.0, 9.5] 0 Zone P<0.001*** North Central 857 36.4 33.4 26.9 8.0 1.5 26.9 0.1 26.8 [28.0, 44.8] [25.0, 41.7] [19.5, 34.3] [4.8, 11.1] [0.3, 2.8] [19.5, 34.3] [0.0, 0.2] [19.5, 34.2] 0 North East 830 17.0 12.7 10.4 6.6 0.4 [4.3, 29.7] [3.4, 22.1] [2.3, 18.4] [1.8, 11.5] 0.0 10.4 [2.3, 18.4] 0 1 [2.3, 18.4] 0 North West 944 10.3 9.7 8.1 2.0 0.2 7.8 7.8 [5.8, 14.8] [5.4, 14.0] [3.9, 12.3] [0.4, 3.7] [0.0, 0.4] [3.9, 11.8] 0 [3.9, 11.8] 0 South East 855 23.5 19.2 15.5 7.8 0.2 15.2 1.3 14.1 [17.6, 29.4] [13.8, 24.6] [11.6, 19.3] [4.5, 11.1] [0.0, 0.5] [11.4, 19.1] [0.4, 2.3] [10.4, 17.8] 0 South South 888 13.6 10.3 8.5 4.9 0.2 8.5 8.5 [9.2, 18.1] [6.1, 14.4] [4.5, 12.5] [3.3, 6.6] [0.0, 0.5] [4.5, 12.5] 0 [4.5, 12.5] 0 South West 907 79.5 77.5 71.2 7.4 0.8 71.2 71.2 [71.5, 87.5] [69.3, 85.7] [63.4, 79.1] [4.9, 9.9] [0.1, 1.4] [63.4, 79.1] 0 [63.3, 79.0] 0 Wealth quintile P<0.001*** Lowest 1081 4.7 3.8 2.8 1.8 0.1 2.8 0.0 2.7 [2.8, 6.5] [2.1, 5.5] [1.4, 4.2] [0.9, 2.7] [0.0, 0.2] [1.4, 4.2] [0.0, 0.2] [1.4, 4.1] 0 Second 1111 11.8 10.3 8.1 3.4 0.3 8.1 0 8.1 [8.4, 15.2] [7.1, 13.5] [5.5, 10.7] [2.1, 4.6] [0.0, 0.6] [5.5, 10.7] [0.0, 0.1] [5.5, 10.7] 0 Middle 1100 33.4 31.2 26.0 6.4 0.9 26.0 0.2 25.8 [28.8, 37.9] [26.6, 35.7] [21.9, 30.2] [4.5, 8.4] [0.2, 1.5] [21.9, 30.2] [0.0, 0.4] [21.7, 30.0] 0 Fourth 997 45.7 40.8 36.4 8.6 0.7 36.3 0.1 36.2 [40.5, 50.9] [35.6, 46.0] [31.4, 41.3] [5.5, 11.8] [0.0, 1.4] [31.4, 41.2] [0.0, 0.3] [31.3, 41.2] 0 Highest 970 57.0 53.2 48.3 8.1 0.4 47.8 0.2 47.6 [50.9, 63.1] [47.2, 59.3] [41.6, 55.0] [5.1, 11.2] [0.0, 0.9] [41.2, 54.4] [0.0, 0.5] [41.0, 54.2] 0 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 When the food brand was unknown or an unbranded product was used, it was not possible to link data to label information. 6 Data is missing for 22 non-pregnant women. The proportion of households of the sampled non-pregnant women that consumed wheat- based type of wheat flour as the main type was over half (65 percent) while wheat-maize based was reported by 27 percent of the women (Figure 24). Processing of semolina flour is highly industrialized and there is no cottage-level processing. Thus, there is not much unbranded flour in the market. Figure 24. Main type of semolina flour used in the household among consumers Among non-pregnant women (aged 15-49 years) who used the food vehicle (unweighted sample size for women = 1578) Data are weighted to account for survey design and non-response The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the HH. Over half of the households of the sampled non-pregnant women (55 percent) consumed mainly Golden penny brand of semolina flour (Figure 25). This was followed by 13 percent of the women that reported Dangote and Honey well each. Low proportion of households of the sampled non- pregnant women (<1 percent) reported consumption of unbranded semolina. This is likely because semolina flour processing is highly industrialized and packaged in sizes that do not need to be downsized or re-packaged. It gets to the consumers in its original packages with the label. Figure 25. Brand of semolina flour obtained the last time among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HH and the food vehicle was not “homemade” (unweighted sample size for women = 1460) Data are weighted to account for survey design and non-response The brand was classified as “unknown” when the respondent could not report the brand of food vehicle used in the HH. 86 Sugar Sugar is one of the essential household food items, highly industrialized, and in the list of mandatory fortifiable vehicles in Nigeria. Figure 26 presents the coverage indicators for sugar nationally among non-pregnant WRA (15-49 years old). There was a high proportion of households of the sampled non-pregnant women that consumed sugar in any form (88 percent) and purchased it (87%). Contrarily, only 22 percent of households of the sampled women consumed sugar that was branded and labelled as fortified while 21 percent fortified (at any level). However, the result for these latter three indicators may be underestimated as over 60% of the women came from households where this information was unknown. These results reveal that fortification reach with sugar is available for about 22% households of the sampled individuals but has the potential to reach over 80% of households of the sampled individuals if all the consumed sugar brands are known and confirmed fortified. However, while 22 percent of the households of the sampled women consumed branded sugar (and 28% were unknown), 38 percent consumed unbranded sugar and thus their reach with large-scale food fortification could not be assessed. High percentage of unbranded and unknown brands of sugar is more likely due to re-packaging in local containers and smaller packs that low-income consumers can afford. Sugars are usually branded because they are industrially produced at large scale. However, at the point of sales, brands are unknown due to repackaging without the label. As a result, it is not possible to link the brand of sugar to the fortification status for over 60% of the respondents. Furthermore, the similarity in the proportion of households of the sampled women that consumed food that is branded, labelled as fortified and fortified are indicators that most of the producers of sugar that are branded are infact labelling and fortifying the products. Across residence sectors and zones, even though the proportion of households of the sampled women that consumed sugar was found high nationally, the proportion was still higher among urban dwellers compared to rural (92 percent vs. 85 percent) with the same trend found for the proportion of households of the sampled women that consumed sugar that was purchased, branded, labelled as fortified and fortified (Table 45). Contrarily, the proportion of households of the sampled women that consumed unknown sugar was higher in rural areas compared to urban (32 percent vs. 23 percent) This may be explained by the fact that rural households are more likely to purchase the down-sized and re-packaged sugar that are cheaper and more affordable. 87 Sugar 100 11.8 11.8 11.8 11.8 11.8 90 0..72 0.2 0.2 0.2 80 Doesn't consume Consumes food that is homemade or 70 38.2 37.2 38.3 donated Not branded 60 Not fortified 50 0.9 Not labelled as fortified 88.2 87.3 40 27.5 27.5 Don't know 27.5 30 Fortified below standard 20 Fortified at or above standard 10 22.3 23.3 21.3 Yes 0 Consumed food Consumes food Consumes food Consumes food *Consumes that is that is branded that is labelled food that is purchased as fortified fortified Figure 26. Percentage of Non-Pregnant Women Whose Households Consumed Sugar (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5281) Data are weighted to account for survey design and non-response. Unweighted sample size for all respondents. Differences across groups were not tested statistically. Data is missing for 22 non-pregnant women. *Based on linking reported brand to secondary data from GAIN Market assessment 2021 on fortification status by brand. 88 % of individuals surveyed 89 Table 49. Percentage of non-Pregnant Women Whose Households Consumed Sugar (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or product Consumed is unbranded2, 4, 5 Consumed food Consumed food that is fortified2, 4 food that is Consume food that is labelled as At Below Consumed food2, 3 purchased2, 4 that is branded2, 4 Unknown Unbranded fortified2, 4 standard standard Not fortified N1 % [95%CI] National3 Non-pregnant 87.3 22.3 27.5 38.2 22.2 3 0.9 WRA 5281 88.2 [86.3, 90.2] [85.2, 89.3] [19.7, 24.9] [25.2, 29.7] [35.4, 41.1] [20.6, 26.0] 0 21. [18.7, 23.9] [0.5, 1.2] Residence P < 0.0001*** Urban 2156 92.2 91.6 30.8 22.9 38.5 30.7 9.2 1.4 [90.3, 94.0] [89.7, 93.6] [26.9, 34.7] [18.9, 26.8] [34.3, 42.7]] [26.8, 34.6] 0 2 [25.2, 33.3] [0.8, 2.1] Rural 3125 84.8 83.5 14.9 31.5 38.0 14.8 14.4 0.4 [81.9, 87.8] [80.5, 86.5] [12.3, 17.5] [28.1, 34.9] [33.5, 42.6] [12.2, 17.4] 0 [11.9, 17.0] [0.1, 0.7] Zone P = 0.0008*** North Central 857 95.8 94.9 17.2 13.4 64.7 17.9 17.0 [93.8, 97.9] [92.7, 97.0] [10.2, 24.2] [9.7, 17.0] [56.2, 73.1] [10.0, 25.8] 0 [10.0, 24.0] 0 North East 830 88.0 86.3 27.0 44.8 16.1 27.0 6.8 [83.9, 92.2] [82.0, 90.6] [20.9, 33.0] [39.5, 50.1] [11.6, 20.7] [20.9, 33.0] 0 2 [20.8, 32.8] 0 North West 944 84.0 82.4 23.1 40.9 19.6 23.1 23.0 0.1 [78.7, 89.2] [76.9, 87.9] [16.9, 29.3] [36.6, 45.3] [16.1, 23.1] [16.9, 29.3] 0 [16.8, 29.2] [0.0, 0.4] South East 855 93.5 93.3 22.8 26.3 44.3 22.1 4.4 [91.5, 95.5] [91.3, 95.3] [17.0, 28.6] [21.4, 31.3] [37.1, 51.5] [16.7, 27.6] 0 17.9 [13.2, 22.6] [2.2, 6.7] South South 888 90.1 90.0 19.9 24.7 45.5 19.9 8.8 1.1 [86.6, 93.6] [86.5, 93.5] [15.9, 23.9] [20.1, 29.3] [40.1, 51.0]] [15.9, 23.9] 0 1 [15.3, 22.3] [0.1, 2.1] South West 907 85.1 85.1 22.5 2.7 59.9 22.4 2.1 [82.0, 88.3] [82.0, 88.3] [18.1, 27.0]] [1.5, 3.9] [55.8, 64.0] [18.0, 26.8] 0 20.4 [15.8, 25.0] [0.7, 3.5] Wealth quintile6 P < 0.001*** Lowest 1081 78.7 77.6 12.4 40.5 25.6 12.4 2.3 0.1 [74.9, 82.5] [73.3, 81.9] [9.1, 15.7] [36.2, 44.8] [21.7, 29.5] [9.1, 15.7] 0 1 [9.0, 15.7] [0.0, 0.3] Second 1111 87.0 85.4 13.6 32.7 40.0 13.6 13.6 [83.3, 90.7] [81.5, 89.3] [10.6, 16.6] [27.2, 38.3] [34.1, 45.9] [10.6, 16.6] 0 [10.6, 16.6] 0 Middle 1100 91.9 91.1 24.9 21.8 45.1 24.8 23.7 1.1 [89.2, 94.6] [88.3, 94.0] [20.0, 29.9] [18.4, 25.3] [39.0, 51.2] [19.8, 29.7] 0 [18.8, 28.6] [0.4, 1.8] Fourth 997 93.8 93.0 28.0 21.7 44.1 27.8 3 1.5 [91.8, 95.8] [90.8, 95.1] [24.1, 31.8] [18.0, 25.3] [39.0, 49.3] [23.9, 31.7] 0 26. [22.4, 30.2] [0.6, 2.4] Highest 970 91.0 90.9 36.7 17.6 36.7 36.6 5 2.0 [88.7, 93.4] [88.5, 93.2] [32.3, 41.1] [13.2, 21.9] [32.2, 41.3] [32.2, 40.9] 0 34. [30.1, 39.0] [0.7, 3.4] 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 When the food brand was unknown or an unbranded product was used, it was not possible to link data to label information. 6 Data is missing for 22 non-pregnant women. A high proportion of households of the sampled non-pregnant women (87 percent) consumed white granulated sugar as their main type of sugar while white cube was reported by 11 percent. (Figure 27). Figure 27. Main type of sugar used in the household among consumers Among non-pregnant women (aged 15-49 years) who used the food vehicle in the HH (unweighted sample size for women = 4715) Data are weighted to account for survey design and non-response. The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the HH. As shown in Figure 28, several brands of sugar are available in Nigeria. However, 20 percent of the households of the sampled women reported consumption of Dangote granulated sugar, as their main brand. Figure 28. Brand of sugar obtained the last time among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HH and the food vehicle was not “homemade” (unweighted sample size for women = 4696) Data are weighted to account for survey design and non-response. The brand was classified as “unknown” when the respondent could not report the brand of food vehicle used in the HH. 90 Salt Figure 29 presents the coverage indicators for salt nationally among non-pregnant WRA (15-49 years old). There was a high proportion of households of the sampled non-pregnant women that consumed salt in any form (99%) and purchased it (85%). Contrarily, less than half (47 percent) of the households of the sampled women consumed salt that was branded while 46 percent labelled as fortified and fortified (at any level). However, the result for these latter three indicators may be underestimated as over 50% of the households of the sampled women did not know the information. These results reveal that fortification reach with salt is available for less than half of households of the sampled women of reproductive age but has the potential to reach over 90% of households if all the consumed salt is known and confirmed fortified. However, while 47 percent of the household of the sampled women of reproductive age that consumed branded salt (and 35% were unknown), 17 percent consumed unbranded salt and thus their reach with large-scale food fortification of salt could not be assessed. The unknown and unbranded salt could have originated from the practice of downsizing and repackaging in local measures. Salt is usually packed in 50-kg or 25-kg branded bags that are downsized, repacked salt in smaller local measures, which are cheaper and more affordable by low-income households in the rural sector. Furthermore, the similarity in the proportion of households of the sampled non-pregnant women that consumed food that is branded, labelled as fortified and fortified are indicators that most of the producers of salt that are branded are in fact labelling and fortifying their products. Across residence sectors and zones, the proportion of households of the sampled non-pregnant women that consumed salt was as high as that found nationally. On the other hand, the proportion of households of the sampled non-pregnant women that consumed branded, labelled as fortified and fortified (at any level) salt was higher in the urban than rural (Table 50). Contrarily, the proportion of households of the sampled non-pregnant women that consumed unknown and unbranded salt was higher in rural areas compared to urban. This may be explained by the fact that this type of salt is often cheaper and therefore may be more affordable in rural areas. Thus, fortification status of over half of the salt consumed could not be assessed. Salt seems an essential commodity in every HH and an opportunistic vehicle for fortification, which Nigeria taps into in its fortification programme since 1993. 91 Figure 29. Percentage of Non-Pregnant Women Whose Households Consumed Salt (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women (15-49 years) (unweighted sample size = 5281) Data are weighted to account for survey design and non-response. Unweighted sample size for all respondents Differences across groups were not tested statistically. Data is missing for 22 non-pregnant women. *Based on linking reported brand to secondary data from GAIN Market assessment 2021 on fortification status by brand 92 93 Table 50. Percentage of Non-Pregnant Women Whose Households Consumed Salt (purchased, branded, labelled as fortified and fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or product Consumed food2, 3 Consumed food that Consumed food is unbranded2, 4, 5 Consumed food that is Consumed food that is fortified 2, 4 is purchased2, 4 that is branded2, 4 labelled as fortified2, 4 Unknown Unbranded At standard Below standard Not fortified N1 % [95%CI] National3 Non-pregnant women 9.3 84.7 47.0 35.3 17.4 46.3 46.7 0.0 aged 15-49 years 5281 9 [99.0, 99.6] [82.6, 86.7] [43.5, 50.1] [32.1, 38.4] [15.1, 16.7] []42.7, 49.8] [43.2, 50.2] [0.0, 0.0] 0 Residence P = 0.0175* Urban 2156 98.9 89.6 58.7 25.4 14.8 58.1 58.4 0.0 [98.3, 99.5] [87.7, 91.5] [52.3, 65.1] [20.9, 29.9] [11.8, 17.9] [51.8, 64.4] [52.0, 64.8] [0.0, 0.0] 0 Rural 3125 99.7 80.4 36.8 43.9 18.9 36.0 36.5 [99.4, 99.9] [77.3, 83.4] [32.5, 41.1] [39.0, 48.7] [15.8, 22.1] [31.6, 40.3] [32.2, 40.8] 0 0 Zone North Central 857 99.4 94.3 54.6 17.8 26.9 52.9 54.4 0.0 [98.9, 100.0] [92.0, 96.5] [43.6, 65.7] [13.3, 22.3] [18.5, 35.4] [41.8, 64.1] [43.4, 65.4] [0.0, 0.1] 0 North East 830 99.0 83.6 37.5 47.4 14.2 36.2 37.3 [98.1, 100.0] [79.4, 87.9] [29.2, 45.8] [39.8, 54.9] [10.3, 18.1] [27.9, 44.4] [29.0, 45.7] 0 0 North West 944 99.0 76.0 20.5 60.6 17.8 20.3 20.3 [98.3, 99.8] [70.8, 81.1] [14.7, 26.3] [53.9, 67.3] [13.8, 21.9] [14.6, 26.1] [14.6, 26.1] 0 0 South East 855 99.9 91.9 78.5 15.0 6.5 76.9 76.6 [99.8, 100.0] [89.5, 94.4] [74.2, 82.7] [12.0, 17.9] [3.4, 9.7] [72.0, 81.7] [71.7, 81.5] 0 0 South South 888 100.0 89.6 71.4 18.2 10.3 71.2 71.2 [100.0, 100.0] [86.1, 93.1] [64.7, 78.2] [13.3, 23.2] [7.0, 13.7] [64.4, 78.0] [64.4, 78.0] 0 0 South West 907 99.2 84.7 61.5 18.2 19.5 61.2 61.4 [98.4, 99.9] [81.4, 88.0] [57.1, 65.8] [14.7, 21.6] [14.7, 24.3] [56.9, 65.6] [57.0, 65.7] 0 0 Wealth quintile P = 0.234 Lowest 1081 99.5 73.2 19.6 61.3 18.5 18.8 19.2 [98.9, 100.0] [69.2, 77.1] [15.4, 23.8] [55.6, 67.1] [14.8, 22.1] [14.7, 22.9] [15.1, 23.4] 0 0 Second 1111 99.7 82.5 34.7 43.3 21.7 33.7 34.3 [99.4, 100.0] [78.3, 86.7] [29.1, 40.2] [37.6, 48.9] [17.0, 24.9] [28.1, 39.3] [28.8, 39.9] 0 0 Middle 1100 99.5 89.7 52.5 26.8 20.1 52.2 52.2 [99.0, 99.9] [86.8, 92.5] [47.0, 58.0] [22.3, 31.4] [15.3, 24.9] [46.7, 57.7] [46.8, 57.7] 0 0 Fourth 997 99.4 90.8 62.6 21.3 15.4 61.7 62.4 [98.7, 100.0] [88.4, 93.3] [57.1, 68.1] [17.6, 25.0] [11.8, 19.0] [56.2, 67.2] [56.9, 67.9] 0 0 Highest 970 98.5 88.7 72.8 18.5 7.1 72.2 72.4 0.0 [97.1, 99.9] [85.8, 91.7] [66.7, 78.8] [13.2, 23.8] [4.4, 9.9] [66.2, 78.2] [66.4, 78.5] [0.0, 0.1] 0 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 When the food brand was unknown or an unbranded product was used, it was not possible to link data to label information. 6 Data is missing for 22 non-pregnant women. The proportion of households of the sampled non-pregnant women that reported fine table salt as the main type of salt consumed was 66 percent while those whose households consumed coarse cooking salt as their main type of salt was 29 percent. (Figure 30). Figure 30. Main types of salt used in the household among consumers Among non-pregnant women (aged 15-49 years) who used the food vehicle in the HH (unweighted sample size for women = 4715) Data are weighted to account for survey design and non-response The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the HH. The proportion of households of the sampled non-pregnant women that consumed Dangote salt as their main brand of salt was 30 percent and those that consumed Mr. Chef was19 percent (Figure 31). However, 20 percent of the households of the sampled non-pregnant women purchased unbranded salt and 25 percent unknown brands. Purchase of unbranded and unknown brands are likely due to re-packaging without label. Also, salt is highly industrialized in production; thus, brands truly exist for them. However, re-packaging denies consumers access to the brand names. As a result of the high use of unbranded and unknown salt, it is not possible to link the brand of salt to the fortification status for almost half of the respondents. Figure 31. Brands of salt obtained the last time among consumers Among non-pregnant women (15-49 years) among respondents who used the food vehicle in the HH and the food vehicle was not “homemade” (unweighted sample size for women = 4620) Data are weighted to account for survey design and non-response. The brand was classified as “unknown” when the respondent could not report the brand of food vehicle used in the HH. 94 Bouillon Bouillons are taste enhancers added to food, to improve their palatability. Commercial bouillons are composed of ingredients such as salt, sugar, flavour enhancers (monosodium glutamate), herbs, spices, pieces of vegetables, dyes and fragrances. (Mejia et al. 2015). Bouillon is primarily used for seasoning soups and stews, and dishes in cube or granular form and commonly used in Nigeria as a flavour enhancer. One of the main ingredients of bouillon is salt, which if iodized, presents a quick reach to households with iodine, a micronutrient of public health significance. Figure 32 presents the coverage indicators for bouillon nationally among non-pregnant WRA (15- 49 years old). There was a high proportion of households of the sampled non-pregnant women that consumed bouillon in any form (98%), purchased it (97%) and consumed branded bouillon (96%). Bouillon processing is industrialized at large scale; thus, there is low percentage of unknown (2%) and unbranded (0%) bouillon products as there is no cottage level production. Additionally, bouillon comes in micro packages that are affordable to all regardless of socio-economic status. Across residence, zones, and wealth quintile, the proportion of households of the sampled non- pregnant women that consumed bouillon is generally high as 100 percent of the households of the sampled individuals consumed and purchased it. (Table 51). The high HH consumption of bouillon could make it a suitable target for fortification in Nigeria. Currently, bouillon is voluntarily fortified by few industries in Nigeria. Despite this, 61 percent of the non-pregnant women from households consumed bouillons that are labelled as fortified with iodine and/or iron. There is no available secondary data to determine bouillon fortification status as it is currently on voluntary basis in Nigeria. Figure 32. Percentage of Non-Pregnant Women Whose Households Consumed Bouillon (purchased, branded, labelled as fortified and fortified) at National Level Among non-pregnant women (aged 15-49 years) (unweighted sample size = 5281) Data are weighted to account for survey design and non-response. Unweighted sample size for all respondents Differences across groups were not tested statistically. Data is missing for 22 non-pregnant women. 95 96 Table 51. Percentage of Non-Pregnant Women Whose Households Consumed Bouillon (purchased, branded, and labelled as fortified) by Residence, Zone, and Wealth Quintile Food brand is unknown, or product Consumed Consumed food that Consumed food that is unbranded 2, 4 Consumed food that is Consumed food food2, 3 is purchased2, 4 is branded2,4 Unknown Unbranded labelled as fortified2, 4 that is fortified N1 % [95%CI] National3 Non-pregnant 6.7 96.1 1.9 WRA 5281 98.1 9 [97.5, 98.8] [95.8, 97.5] [95.2, 97.0] [1.3, 2.4] 0 60.7 [57.2, 64.3] Residence P = 0.4351 P=0.431 P=0.186 P=0.0144 Urban 2156 98.4 97.3 97.0 1.3 65.8 [97.5, 99.3] [96.1, 98.5] [95.8, 98.2] [0.4, 2.21 0 [61.1, 70.5] Rural 3125 97.9 96.1 95.3 2.4 [96.9, 98.8] [94.9, 97.4] [94.0, 96.6] [1.6, 3.2] 0 56.3 [51.2, 61.5] Zone3 P=0.0161 North Central 857 99.6 99.5 99.1 0.4 6.9 [99.1, 100.0] [99.0, 100.0] [98.2, 100.0] [0.0, 1.0] 0 7 [71.9, 81.9] North East 830 95.9 95.2 94.7 1.0 81.9 [93.5, 98.4] [92.6, 97.8] [92.1, 97.4] [0.2, 1.9] 0 [77.0, 86.9] North West 944 98.1 94.3 93.8 3.9 9.0 [96.7, 99.5] [92.2, 96.4] [91.7, 95.9] [2.3, 5.5] 0 4 [41.3, 56.7] No secondary South East 855 96.8 96.7 96.6 0.1 77.7 [94.5, 99.0] [94.4, 99.0] [94.4, 98.9] [0.0, 0.3] 0 [69.8, 85.5] data available South South 888 99.8 99.8 99.4 0.4 64.7 [99.6, 100.0] [99.6, 100.0] [98.9, 99.9] [0.0, 0.9] 0 [53.1, 76.2] South West 907 98.2 97.1 95.8 2.4 34.9 [97.3, 99.2] [95.8, 98.4] [94.1, 97.4] [1.2, 3.7] 0 [28.8, 41.1] Wealth quintile P = 0.0441 Lowest 1081 96.3 93.9 93.0 2.8 0 [94.2, 98.4] [91.7, 96.1] [90.7, 95.2] [1.5, 4.2] 0 50. [43.3, 56.8] Second 1111 98.1 96.5 96.0 2.0 3 [96.8, 99.3] [95.0, 98.1] [94.5, 97.6] [1.0, 3.1] 0 57. [50.7, 64.0] Middle 1100 99.2 98.3 97.3 1.8 3 [98.5, 99.8] [97.3, 99.3] [96.0, 98.6] [0.6, 2.9] 0 63. [57.4, 69.1] Fourth 997 98.6 97.8 97.5 1.1 66.0 [97.8, 99.4] [96.8, 98.9] [96.4, 98.7] [0.2, 1.9] 0 [61.2, 70.9] Highest 970 98.6 97.0 97.0 1.5 69.0 [97.5, 99.7] [94.7, 99.2] [94.8, 99.2] [0.0, 3.6] 0 [64.1, 74.0] 1 Unweighted sample size. 2 Data are weighted to account for survey design and non-response. 3 Differences between groups were compared using Chi-square test (*signifies P<0.05, **signifies P<0.01, ***signifies P<0.001). 4 Differences across groups were not tested statistically 5 Data is missing for 22 non-pregnant women. High proportion of the households of the sampled non-pregnant women (91 percent) consumed cube type of bouillon in their households (Figure 33). Figure 33. Main types of bouillon used in the household among consumers Among non-pregnant women (aged 15-49 years) who used the food vehicle in the HH (unweighted sample size for women = 5178) Data are weighted to account for survey design and non-response. The type was classified as “unknown” when the respondent could not report the type of food vehicle used in the HH. More than half (55 percent) of the non-pregnant women stated that their households use Maggi as the main brand of bouillon, followed by Ajinomoto (10 percent), Onga (10 percent), Knorr (8 percent), and Tasty (7 percent) (Figure 34). Few women were not able to report the brand of bouillon used in their HHs (<1 percent); thus, unbranded and unknown brands are not an issue in this sector. This is so since they are highly industrialized and available in micro packages that all HHs can afford. Figure 34. Brand of bouillon obtained the last time among consumers Among non-pregnant women (aged 15-49 years) among respondents who used the food vehicle in the HHs and the food vehicle was not “homemade” (unweighted sample size for women = 5135) Data are weighted to account for survey design and non-response. 97 Overall, based on the available information on the branded vehicles, more HHs consume food vehicles that are fortified below national standard [wheat flour (13 percent); semolina flour (23 percent); and sugar (22 percent) while all the three flours and sugar are zero percent fortified at standard. Salt iodization took a different turn as 47 percent of HHs consumed brands that are iodized at/above national standard. Fortification Status of the Food Samples Collected from the Respondents’ Households After the data collection, the food samples were processed and analyzed for the parameters shown in Table 52 to determine the levels of fortification. Salt was analyzed for iodine; vegetable oil and sugar were analyzed for vitamin A; and wheat and semolina flours were analyzed for vitamin A, iron, and zinc. All food samples produced are at large scale and are expected to be fortified with vitamin A, except salt, according to Nigerian law. Vitamin A supports the immune system and plays an important role in maintaining the epithelial tissue in the body. Severe vitamin A deficiency VAD can cause eye damage and is the leading cause of childhood blindness. VAD also increases the severity of infections, such as measles and diarrhoeal disease, and slows recovery from illness. In addition to vitamin A fortification, all flours in Nigeria (wheat, semolina, cassava, composite flour) are expected to be fortified with iron and zinc, which are also considered as micronutrients of public health significance. Iron plays an important role in numerous biological systems and iron deficiency is one of the primary causes of anaemia, which has serious health consequences for children (Nigeria: DHS, 2018). A total of 2031 food samples (salt, sugar, vegetable oil, wheat, and semolina flour) were collected from the homes of sub-sampled non-pregnant WRA at the repeat interview. Table 52 shows the food samples collected for analysis and parameters analyzed. Table 52. Food vehicle samples collected and analysed Food vehicles *Total collected Total analysed Micronutrients analysed Vegetable oil 338 229 Vitamin A Sugar 400 273 Vitamin A Salt 1153 1135 Iodine 51 38 Vitamin A Wheat flour 37 Iron 37 Zinc 89 81 Vitamin A Semolina flour 77 Iron 78 Zinc Total 2031 Not all the samples collected were analysed because some quantities were too small for analysis while few missing. Food sample analysis All food samples, by parameters, were sent to the selected laboratories in and outside the country after conducting due diligence of the lab in terms of capacity, facility, and accreditation for the analysis of interest. Annex 7 shows the food samples sent to all the participating laboratories with their quantities and parameters for analysis. 98 All the food sample results, upon receipt, were compiled by labs, units harmonized, and statistically analyzed using SAS for descriptive values and percentages (Table 54). All results were compared with Nigerian standards (shown below in Table 53) to determine levels of fortification, using the following variables: 1. Fortified at or above standard- defined as the proportion of samples whose fortificant content meet the minimum national standard (Table 53). 2. Fortified below standard - defined as the proportion of samples whose fortificant content does not meet the minimum national standard (Table 53). 3. Not fortified- the fortificant content was too small in quantity to be detected from the analysis. This means the food vehicle was not fortified at all. Table 53. Minimum National Industrial Requirements (NIS)-Expected Value in the Mandatory Vehicles S/N Food Vehicles VA (mg retinyl Iron (mg/ palmitate kg-1)) kg) Zinc (mg/kg) Iodine (mg/Kg) 1 *Wheat flour 2.0 40.0 50.0 2 *Semolina Wheat flour 2.0 40.0 50.0 3 *Maize flour 2.0 40.0 50.0 4 *Whole maize meal 2.0 40.0 50.0 5 *Composite (Wheat-Cassava) flour 2.0 40.0 50.0 6 **Vegetable oil 6.0 7 **Sugar 7.5 8 Margarine 7.8 9 ***Salt 15ppm Source: NIS 168 FOOD GRADE (2004) *Values at all levels-factory, market and HH **Values for factory level only ***Value for HH level Fortification status of the food vehicles collected from the households of the selected respondents Overview of the food sample results Based on the analysis of food samples that were collected in a sub-sample of households of the sampled non-pregnant WRA and analysed for micronutrient contents, it was revealed that the majority of samples were fortified at any level for vitamin A in sugar (74 percent), iodine in salt (100 percent), iron and zinc in wheat flour (100 percent each) while iron and zinc in semolina flour was also 100 percent (Figure 35). Conversely, about one third was fortified at any level with vitamin A in vegetable oil (31 percent) and vitamin A in wheat flour (26 percent). 99 120 100 80 60 40 20 0 Wheat Vegetable flour- Wheat Wheat Semolina flour- Semolina Semolina oil n= 71 Vitamin A flour-Iron flour-Zinc flour-Iron flour-Zinc Sugar n= Salt n= 38 n=37 n=37 Vitamin A n=77 n= 78 201 n=1133 n= 56 Fortified at any level 31 26.3 100 100 70.3 100 100 73.6 99.8 Fortified at or above standard 1.3 0 73 32.4 1.2 48.1 20.5 2.6 95.4 Foortified below standard 29.7 26.3 27 67.6 69.1 51.9 79.5 71 4.4 Not fortified 69 73.7 0 0 29.6 0 0 26.4 0.2 Figure 35. Fortification status of food vehicle samples collected from non-pregnant women at the repeat interview The measured mean amounts of micronutrients (Table 54) in the fortified samples were 2.6 mg/kg vitamin A in vegetable oil, 3.1mg retinyl palmitate/kg vitamin A in sugar, 60 mg/kg iodine in salt, 0.8 mg retinyl palmitate/kg vitamin A, 53.9 mg/kg iron, and 42.2 mg/kg zinc in wheat flour, and 0.8 mg retinyl palmitate/kg vitamin A, 38.6 mg/kg iron, and 36.0 mg/kg zinc in semolina flour. 100 PPeerrcceennttaaggeess Table 54. Descriptive statistics of Fortificant contents (at any level) of the Food samples collected from the households of Non-pregnant Women at repeat interview Food vehicles Fortificants N Mean Median SD Min Max Vegetable oil Vitamin A (mg retinyl palmitate kg-1) 71 2.6 2.4 1.76 0.2 11.3 Wheat flour Vitamin A (mg retinyl palmitate kg-1) 38 0.8 0.8 0.23 0.4 1.0 Wheat flour Iron (mg/kg) 37 *53.9 48.9 26.90 19.1 176.0 Wheat flour Zinc (mg/kg) 37 *42.2 38.9 24.10 4.7 109.4 Semolina flour Vitamin A (mg retinyl palmitate kg-1) 56 0.8 0.7 0.40 0.2 2.0 Semolina flour Iron (mg/kg) 77 *38.6 38.1 16.35 8.4 83.2 Semolina flour Zinc (mg/kg) 78 *36.0 39.4 17.58 2.4 87.0 Sugar Vitamin A (mg retinyl palmitate kg-1) 201 3.1 2.7 2.20 0.2 13.6 Salt Iodine (mg/kg) 1133 60.0 53.1 35.02 2.7 251.5 *Intrinsic values inclusive From the mean contents of the fortificants in the food samples shown in Table 54, all the samples fortified with vitamin A are below the minimum standard. This could be due to losses during food vehicle distribution, from factory to homes, especially during transportation, retail display and handling in open markets, as well as in storage. Vitamin A is photo and thermal sensitive thus the need for further studies on vitamin A retention in the food vehicle value chain to be able to determine where losses lie and fully explore the contribution of large-scale food fortification in the reduction of vitamin A deficiency. However, there is no available minimum standard for the vitamin A in vegetable oil and sugar as household level. This is a limitation in this study as the values used are factory level values. Also, for flours, the iron and zinc values may be over quantified as intrinsic iron and zinc were inclusive. Further study may therefore be necessary to quantify actual fortification levels. 101 Anthropometry 9 This chapter10 reports on the anthropometric status11, 12, 13 of children (aged 6-59 months), adolescent girls (aged 10-14 years), and Women of Reproductive Age (WRA, aged15-49 years). Anthropometric measurements are non-invasive, quantitative measurements of the body that provide a valuable assessment of nutrition status in children and adults. Typically, they are used in the pediatric population to evaluate general health status, nutritional adequacy, and growth and developmental pattern. In adults, body measurements can help assess health and dietary status and determine body composition to help determine underlying nutritional status and diagnose obesity.14 The core measurements of anthropometry in the NFCMS were age, length/height, and weight. Anthropometry of children (aged 6-59 months) A key objective of the NFCMS was to assess the prevalence, severity, and distribution of malnutrition in children (aged 6-59 months). In this context, the term malnutrition covers two broad groups of conditions. One is undernutrition, which includes stunting (low length/height-for-age), wasting (low weight-for-length/height), and underweight (low weight-for-age). The other is overweight (weight- for-length/height) and obesity (weight-for-length/height).15 Stunting reflects linear growth retardation caused by long-term, insufficient nutrient intake and repeated infections. Wasting results from acute food shortage and illness, causing recent weight loss or failure to gain weight. Underweight is a composite indicator that can indicate wasting, stunting, or both. Thus, it might be challenging to interpret. However, it is still a useful anthropometric indicator to track individual-level changes in growth over time when collected sequentially, such as through a growth monitoring programme.16 Overweight and its severe form, obesity, are measures of overnutrition, which result from an energy imbalance between calories consumed and calories expended.17 In the NFCMS survey, stunting is defined as the percentage of children (aged 6-59 months) with height-for-age Z-score (HAZ) below −2SD (HAZ <−2SD) from the WHO Child Growth Standards median. Severe stunting is defined as the percentage of children with HAZ <−3SD. Wasting is defined as the percentage of children with weight-for-height Z-score (WHZ) <−2SD. Similarly, severe acute malnutrition (SAM) or severe wasting is defined as the percentage of children with WHZ <−3SD. Underweight is defined as the percentage of children with weight-for-age Z-score (WAZ) <−2SD, and severe underweight is defined as the percentage of children with WAZ <−3SD. Overweight is defined as the percentage of children with weight-for-length/height Z-score (WHZ) above 2SD (WHZ > 2SD) from the WHO Child Growth Standards median, and obesity is defined as the percentage of children with WHZ >3SD.18 9 The premise of the NFCMS aligns with the UNICEF conceptual framework of determinants of undernutrition (2013*). Individual nutritional status measured by indicators such as those of anthropometry and micronutrient biomarkers is determined by two immediate factors - high quality diets and optimal health. Three underlying factors influence these: access to sufficient, safe, and nutritious food; adequate care practices for especially women and children; and access to health services including healthy environments, water, and sanitation. Finally, at a basic level, political, economic, and institutional determinants underpin all of these factors. *UNICEF (United Nations Children’s Fund). 2013. Improving Child Nutrition: The Achievable Imperative for Global Progress. New York: UNICEF. 10 For scope of preliminary report for the anthropometry component, see Annex 2. 11 The anthropometry indices were built using the Stata Software (version 14.0) using the command “zanthro”. Vidmar, S. I., Cole, T. J., & Pan, H. (2013). Standardizing Anthropometric Measures in Children and Adolescents with Functions for Egen: Update. The Stata Journal: Promoting Communications on Statistics and Stata, 13(2), 366–378. https://doi. org/10.1177/1536867X1301300211 12 See Annex 3 for a summary of the data quality assessment from Anthro Survey Analyzer. 13 See Annex 12 for anthropometry questionnaire – CommCare version. 14 Fryar CD, Gu Q, Ogden CL, Flegal KM. Anthropometric Reference Data for Children and Adults: United States, 2011-2014. Vital Health Stat 3. 2016;(39):1-46. 102 Following WHO and UNICEF (2019) guidelines,19 the following implausible values were removed from the analysis: HAZ larger than |6| SD, WHZ larger than |5| SD, and WAZ smaller than -6 and larger than 5 SD. The calculation of WAZ also excluded values of length outside of the ranges 45-110 cm and values of height outside the ranges 65-120 cm. Also, seven height measurements - from children under nine months - were excluded from the analysis.10 The prevalence of stunting (33.3 percent), wasting (11.6 percent), underweight (25.3 percent), and being overweight (1.5 percent) in children (6-59 months) is summarized in Figure 36. The prevalence of child stunting of 33.3 percent means that one out of every three children (aged 6-59 months) in Nigeria was too short compared to a healthy, well-nourished child of the same age and sex. According to global benchmarks using the ‘novel approach,’10, 20 this level of stunting in children is very high (≥30 percent). The prevalence of wasting or global acute malnutrition of 11.6 percent (children were too thin for their height) is classified as high (10-<15 percent), according to global benchmarks using the ‘novel approach.’10, 11 While the overweight prevalence in children of 1.5 percent is classified as very low (<2.5%) using the ‘novel approach.’10, 11 Table 55 presents the malnutrition status of children (aged 6-59 months) as measured by anthropometric indices, stratified by age category, sex, residence, zone, wealth quintile, caregiver’s education, type of toilet facility, and source of drinking water. • Stunting: There was a statistically significant difference in the prevalence of stunting in children (6-59 months) between the age groups (P < 0.001), residence (P < 0.001), zones (P < 0.001), wealth quintile (P < 0.001), caregiver’s educational attainment (P < 0.001) and type of toilet facility (P < 0.001). The prevalence of stunting was lowest in the 6-11-months old age category (16.3 percent) and more than double at 39.9 percent in children in the 24-35-months old age category. The prevalence was higher among children residing in rural (39.6 percent) than in urban areas (20.6 percent). The prevalence was highest in the North West zone (47.9 percent). The prevalence of stunting was highest among children in the lowest quintile (49.1 percent) and in children whose caregivers had no educational attainment (47.1 percent). It was lowest in children whose households used improved toilet facilities (15.1 percent). • Wasting: There was a statistically significant difference in the prevalence of wasting in children (6-59 months) between the age groups (P < 0.001), zones (P = 0.002) and source of drinking water (P = 0.012). The prevalence of wasting was highest in the 6-11-months old age category (26.1 percent). It was also highest in children in the North East zone (17.2 percent). On the other hand, it was lower in children whose households had improved source of drinking water (10.5 percent) than those with unimproved source (14.6 percent). • Underweight: There was a statistically significant difference in the prevalence of underweight in children (aged 6-59 months) between sex (P = 0.035), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), caregiver’s educational attainment (P < 0.001), source of drinking water (P < 0.001), and type of toilet facility (P < 0.001). The prevalence of underweight was higher in males (27 percent) than in females (23.6 percent). It was higher in children residing in rural (29.2 percent) than in urban (17.4 percent) areas. The prevalence of underweight was highest in the North West zone (35.5 percent), in children in the lowest wealth quintile (37.8 percent), and in children whose caregivers had no educational attainment (35.4 percent). 15 World Health Organization (WHO). What is malnutrition? (http://www.who.int/features/qa/malnutrition/en/) 16 https://www.who.int/data/nutrition/nlis/info/malnutrition-in-children 17 https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight 18 Sinha RK, Dua R, Bijalwan V, Rohatgi S, Kumar P. Determinants of Stunting, Wasting, and Underweight in Five High- Burden Pockets of Four Indian States. Indian J Community Med. 2018;43(4):279-283. doi:10.4103/ijcm.IJCM_151_18 19 WHO, UNICEF (2019) Recommendations for data collection, analysis, and reporting on anthropometric indicators in children under 5 years old. 20 de Onis M, Borghi E, Arimond M, et al. Prevalence thresholds for wasting, overweight and stunting in children under 5 years. Public Health Nutr. 2019;22(1):175-179. doi:10.1017/S1368980018002434 103 • The prevalence was higher in children whose households had an unimproved (31.5 percent) versus improved (23.1 percent) source of drinking water. The prevalence was lowest in children whose households had improved toilet facility (14.7 percent). • Overweight: There was no significant variation in the prevalence of overweight in children (aged 6-59 months) across the background characteristics. Table 56 presents the severe malnutrition status of children (aged 6-59 months) stratified by age category, sex, residence, zone, wealth quintile, caregiver’s education, type of toilet facility, and source of drinking water. • Severe stunting: The prevalence of severe stunting in children (aged 6-59 months) nationally was 16.7 percent. There was a statistically significant difference in the prevalence of severe stunting in children (aged 6-59 months) between the age groups (P < 0.001), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), caregiver’s educational attainment (P < 0.001), source of drinking water (P < 0.001), and type of toilet facility (P < 0.001). The prevalence of severe stunting was lowest in the 6-11-months old age category (6.8 percent). It was higher among children residing in rural (20.8 percent) than in urban (8.4%) areas. It was highest in children in the North West zone (27.3 percent), children in the lowest wealth quintile (29.3 percent), and children whose caregivers had no education (27.9 percent). It was higher in children whose households had unimproved (21.0%) than improved (15.1 percent) source of drinking water. The prevalence of severe stunting was lowest in children whose households have improved toilet facility (6.3 percent). • Severe wasting: Overall, the prevalence of severe wasting in children (aged 6-59 months) was three percent. There was a statistically significant difference in the prevalence of severe wasting in children (aged 6-59 months) between the age groups (P < 0.001) and zone (P < 0.001). The prevalence of severe wasting was highest in the 6-11-months old age category (7.2 percent). It was also highest in children in the North East zone (6.3 percent). • Severe underweight: Overall, the prevalence of severe underweight in children (aged 6-59 months) was 9.2 percent. There was a statistically significant difference in the prevalence of severe underweight in children (aged 6-58 months) between the age groups (P = 0.036), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), caregiver’s educational attainment (P < 0.001), source of drinking water (P = 0.005), and type of toilet facility (P = 0.002). The prevalence of severe underweight was highest in the 6-11-months old age category (11.8 percent). It was higher among children residing in rural (11.0 percent) than in urban (5.5 percent) areas. It was highest in children in the North West zone (13.6 percent), children in the lowest wealth quintile (15.4 percent), and children whose caregivers had no educational attainment (15.2 percent). It was higher in children whose households had unimproved source of drinking water (12.2 percent) than those with improved source of drinking water (8.1 percent). The prevalence of severe underweight was lowest in children whose households have improved toilet facility (5.2 percent). • Obesity: Overall, the prevalence of obesity in children (6-59 months old) was 0.6 percent. There was a statistically significant difference in the prevalence of obesity in children (6-59 months old) between the zones (P = 0.019). The prevalence was highest in the South East zone (1.7 percent). 104 105 Figure 36. Anthropometric status for children (aged 6-59 months), Nigeria 2021 Number of children (aged 6-59 months) who responded nationally: (n= 4912) Number of children (aged 6-59 months) who responded by zone: NC (n= 771); NE (n= 883); NW (n= 905); SE (n= 716); SS (n= 833); SW (n= 854) Using 2006 WHO Child Growth Standards: Stunting, (low length/height-for-age), is defined as height-for-age Z-score (HAZ) <−2SD Wasting, (low weight-for length/height), is defined as weight-for-height Z-score (WHZ) <−2SD Underweight, (low weight-for-age), is defined as weight-for-age Z-score (WAZ) <−2SD Overweight, (weight-for-length/height), is defined as weight-for-length/height Z-score (WHZ) > 2SD 106 Table 55. Malnutrition status of children (aged 6-59 months), Nigeria 2021 Background characteristics Stunting1 Wasting2 Underweight3 Overweight4 N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.000***) (P < 0.000***) (P = 0.338) (P = 0.406) 6-11 months 502 16.3 [12.4,21.3] 503 26.1 [20.2,33.1] 517 25.4 [19.7,32.0] 503 1.5 [0.7,3.2] 12-23 months 1132 31.2 [27.2,35.5] 1139 17.5 [14.3,21.4] 1146 26.9 [23.3,30.9] 1139 1.7 [1.0,2.8] 24-35 months 1214 39.9 [35.6,44.3] 1221 10.0 [7.5,13.3] 1221 27.4 [23.8,31.4] 1221 1.7 [0.9,3.0] 36-47 months 1183 36.4 [31.7,41.4] 1180 5.4 [3.7,7.9] 1186 23.5 [20.0,27.5] 1180 1.6 [1.0,2.7] 48-59 months 824 32.7 [27.9,37.9] 820 5.5 [3.8,7.7] 825 22.5 [18.6,27.0] 820 0.6 [0.3,1.5] Sex (P = 0.418) (P = 0.426) (P = 0.035*) (P = 0.590) Male 2428 34.2 [30.4,38.2] 2432 12.1 [10.5,13.9] 2456 27.0 [24.4,29.7] 2432 1.4 [0.9,2.1] Female 2427 32.4 [29.3,35.7] 2431 11 [9.0,13.5] 2439 23.6 [21.1,26.4] 2431 1.6 [1.1,2.4] Residence (P < 0.000***) (P = 0.998) (P < 0.000***) (P = 0.588) Urban 1990 20.6 [16.9,24.8] 1997 11.6 [9.1,14.6] 2005 17.4 [14.6,20.5] 1997 1.3 [0.8,2.2] Rural 2865 39.6 [36.3,42.9] 2866 11.6 [9.8,13.6] 2890 29.2 [26.6,32.0] 2866 1.6 [1.1,2.2] Zone (P < 0.000***) (P = 0.002**) (P < 0.000***) (P = 0.125) North Central 761 29.6 [22.9,37.3] 765 11.1 [6.5,18.3] 768 21.1 [14.8,29.2] 765 1.5 [0.8,2.7] North East 818 35.3 [27.6,43.8] 818 17.2 [14.0,20.9] 829 29.6 [24.4,35.3] 818 1.8 [1.0,3.2] North West 892 47.9 [42.7,53.1] 890 12.4 [10.0,15.2] 899 35.5 [31.6,39.6] 890 1.7 [1.0,3.0] South East 705 14.2 [11.4,17.7] 709 8.8 [6.7,11.3] 714 9.6 [7.5,12.4] 709 2.7 [1.3,5.7] South South 826 20.1 [16.2,24.6] 827 8.0 [5.8,10.8] 831 14.9 [12.1,18.2] 827 1.1 [0.5,2.3] South West 853 19.0 [15.7,22.7] 854 6.8 [5.0,9.1] 854 15.0 [12.8,17.5] 854 0.4 [0.1,1.1] Wealth quintile5 (P < 0.000***) (P = 0.373) (P < 0.000***) (P = 0.329) Lowest 1041 49.1 [44.3,53.9] 1031 13.2 [10.5,16.5] 1043 37.8 [33.1,42.7] 1031 2.1 [1.3,3.4] Second 1001 41.0 [36.9,45.2] 1008 10.4 [8.2,13.2] 1016 28.8 [24.8,33.1] 1008 1.6 [0.9,2.6] Middle 957 27.9 [23.9,32.2] 966 10.6 [8.0,14.0] 972 18.9 [15.2,23.2] 966 1.0 [0.5,2.0] Fourth 955 21.6 [18.3,25.2] 957 10.5 [8.0,13.7] 960 18.2 [15.6,21.0] 957 1.1 [0.5,2.2] Highest 880 11.8 [9.2,15.1] 880 13.3 [9.7,18.0] 883 13.4 [10.8,16.6] 880 1.4 [0.8,2.6] Caregiver’s educational attainment5 (P < 0.000***) (P = 0.217) (P < 0.000***) (P = 0.393) None 1296 47.1 [43.3,51.0] 1295 13.4 [11.1,15.9] 1306 35.4 [31.9,39.1] 1295 1.6 [1.0,2.6] Primary 757 28.9 [24.7,33.5] 756 12.7 [8.9,17.9] 763 21.6 [17.4,26.5] 756 1.3 [0.6,2.7] Secondary 1984 20.5 [17.6,23.8] 1992 10.3 [8.4,12.7] 1997 16.5 [13.7,19.7] 1992 0.9 [0.5,1.6] Tertiary 512 14.8 [11.2,19.2] 516 9.4 [5.7,15.3] 518 14.3 [10.9,18.5] 516 1.6 [0.7,3.2] Source of drinking water5 (P = 0.513) (P = 0.012*) (P < 0.000***) (P = 0.735) Improved 3613 32.9 [30.0,36.1] 3618 10.5 [8.9,12.3] 3636 23.1 [20.8,25.6] 3618 1.5 [1.1,2.1] Unimproved 1222 34.7 [30.0,39.7] 1225 14.6 [11.7,18.0] 1239 31.5 [27.7,35.5] 1225 1.4 [0.8,2.4] Type of toilet facility5 (P < 0.000***) (P = 0.242) (P < 0.000***) (P = 0.973) Improved facility 1481 15.1 [12.7,17.8] 1480 11.5 [8.1,16.1] 1488 14.7 [12.2,17.5] 1480 1.4 [0.8,2.4] Unimproved facility 2098 40.5 [36.8,44.4] 2111 12.6 [10.9,14.5] 2123 30.6 [27.8,33.5] 2111 1.5 [1.0,2.4] Open defecation 1256 36.5 [32.2,41.0] 1252 9.3 [7.3,11.7] 1264 24.6 [19.9,29.9] 1252 1.4 [0.9,2.3] National 48556 33.3 [30.6,36.2] 48637 11.6 [10.1,13.2] 48958 25.3 [23.2,27.5] 48639 1.5 [1.1,2.0] 107 Table 55. Malnutrition status of children (aged 6-59 months), Nigeria 2021 (continued) Using 2006 WHO Child Growth Standards: 1Stunting, (low length/height-for-age), is defined as height-for-age Z-score (HAZ) <−2SD 2Wasting, (low weight-for-length/height), is defined as weight-for-height Z-score (WHZ) <−2SD 3Underweight, (low weight-for-age), is defined as weight-for-age Z-score (WAZ) <−2SD 4Overweight, (weight-for-length/height), is defined as weight-for-length/height Z-score (WHZ) > 2SD Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children 6-59 months who responded nationally: (n= 4912). Number of children 6-59 months who responded by zone: NC (n= 771); NE (n= 833); NW (n= 905); SE (n= 716); SS (n= 833); SW (n= 854) 5Less than (n = 4912) due to relatively fewer respondents for the household and dietary intake questionnaires 6Less than (n = 4912) due to missing data in implausible values or incomplete data 7Less than (n = 4912) due to missing data in implausible values or incomplete data 8Less than (n = 4912) due to missing data in implausible values or incomplete data 9Less than (n = 4912) due to missing data in implausible values or incomplete data 108 Table 56. Severe malnutrition status of children (aged 6-59 months), Nigeria 2021 Background Severe Stunting1 Severe Wasting2 Severe Underweight3 Obesity4 characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.000***) (P < 0.000***) (P = 0.036*) (P = 0.572) 6-11 months 502 6.8 [4.2,10.8] 503 7.2 [3.7,13.9] 517 11.8 [8.1,17.0] 503 0.9 [0.4,2.2] 12-23 months 1132 14.0 [11.4,17.2] 1139 4.7 [3.4,6.7] 1146 9.8 [7.5,12.7] 1139 0.7 [0.4,1.5] 24-35 months 1214 20.6 [16.6,25.3] 1221 3.3 [2.1,5.2] 1221 11.0 [8.7,13.9] 1221 0.7 [0.2,2.1] 36-47 months 1183 19.7 [16.3,23.6] 1180 1.0 [0.5,1.9] 1186 6.3 [4.7,8.5] 1180 0.3 [0.1,0.9] 48-59 months 824 16.4 [13.0,20.4] 820 0.5 [0.2,1.6] 825 8.0 [5.7,11.1] 820 0.4 [0.1,1.3] Sex (P = 0.578) (P = 0.719) (P = 0.787) (P = 0.511) Male 2428 17.1 [14.7,19.9] 2432 3.2 [2.3,4.4] 2456 9.3 [7.8,11.2] 2432 0.6 [0.3,1.3] Female 2427 16.2 [13.6,19.2] 2431 2.9 [1.9,4.3] 2439 9.0 [7.4,10.9] 2431 0.5 [0.3,0.9] Residence (P < 0.000***) (P = 0.469) (P < 0.000***) (P = 0.776) Urban 1990 8.4 [6.4,10.9] 1997 3.4 [2.2,5.3] 2005 5.5 [4.1,7.3] 1997 0.6 [0.3,1.3] Rural 2865 20.8 [18.0,23.8] 2866 2.8 [2.0,3.9] 2890 11.0 [9.4,12.8] 2866 0.5 [0.3,1.1] Zone (P < 0.000***) (P < 0.000***) (P < 0.000***) (P = 0.019*) North Central 761 14.4 [9.8,20.7] 765 4.0 [2.1,7.3] 768 8.4 [5.4,12.9] 765 0.3 [0.1,0.9] North East 818 18.0 [13.4,23.7] 818 6.3 [4.4,8.8] 829 11.9 [9.1,15.5] 818 0.7 [0.3,1.7] North West 892 27.3 [22.5,32.7] 890 2.2 [1.3,3.7] 899 13.6 [11.1,16.5] 890 0.8 [0.3,1.9] South East 705 5.4 [3.7,7.8] 709 1.7 [1.0,3.1] 714 2.5 [1.4,4.2] 709 1.7 [0.9,3.5] South South 826 6.5 [4.2,9.8] 827 1.8 [1.0,3.2] 831 4.5 [2.8,7.0] 827 0.1 [0.0,0.6] South West 853 5.3 [3.8,7.5] 854 1.2 [0.6,2.3] 854 2.4 [1.5,4.0] 854 0.1 [0.0,0.7] Wealth quintile5 (P < 0.000***) (P = 0.740) (P < 0.000***) (P = 0.611) Lowest 1041 29.3 [25.3,33.7] 1031 3.3 [2.2,4.7] 1043 15.4 [12.5,18.8] 1031 0.8 [0.3,2.0] Second 1001 20.1 [16.7,24.0] 1008 2.8 [1.6,4.9] 1016 10.4 [8.1,13.2] 1008 0.3 [0.1,1.0] Middle 957 12.5 [9.7,16.1] 966 3.4 [1.7,6.4] 972 7.4 [5.4,10.1] 966 0.5 [0.2,1.4] Fourth 955 7.8 [5.8,10.5] 957 2.2 [1.2,3.8] 960 3.8 [2.5,5.8] 957 0.5 [0.2,1.3] Highest 880 3.3 [1.7,6.1] 880 3.7 [2.2,6.2] 883 4.5 [2.8,7.1] 880 0.8 [0.4,1.8] Caregiver’s educational attainment5 (P < 0.000***) (P = 0.086) (P < 0.000***) (P = 0.608) None 1296 27.9 [24.5,31.6] 1295 4.0 [2.8,5.6] 1306 15.2 [12.6,18.1] 1295 0.5 [0.2,1.1] Primary 757 13.9 [10.9,17.5] 756 4.2 [2.1,8.1] 763 7.5 [5.0,11.1] 756 0.2 [0.0,0.8] Secondary 1984 7.1 [5.3,9.4] 1992 1.8 [1.1,3.0] 1997 3.7 [2.6,5.1] 1992 0.4 [0.2,0.9] Tertiary 512 5.5 [3.0,9.8] 516 3.8 [2.0,7.4] 518 3.5 [1.8,6.8] 516 0.3 [0.1,1.1] Source of drinking water5 (P = 0.003**) (P = 0.169) (P = 0.005**) (P = 0.937) Improved 3613 15.1 [13.0,17.6] 3618 2.7 [1.9,3.8] 3636 8.1 [6.9,9.5] 3618 0.6 [0.3,1.0] Unimproved 1222 21.0 [17.5,25.0] 1225 3.9 [2.6,5.7] 1239 12.2 [9.6,15.3] 1225 0.6 [0.2,1.4] Type of toilet facility5 (P < 0.000***) (P = 0.362) (P = 0.002**) (P = 0.536) Improved facility 1481 6.3 [4.8,8.3] 1480 3.6 [1.9,6.6] 1488 5.2 [3.4,7.7] 1480 0.8 [0.4,1.4] Unimproved facility 2098 20.9 [17.8,24.3] 2111 3.2 [2.3,4.4] 2123 11.1 [9.5,13.0] 2111 0.6 [0.3,1.3] Open defecation 1256 18.1 [14.6,22.3] 1252 2.0 [1.3,3.2] 1264 9.0 [6.6,12.2] 1252 0.4 [0.2,0.9] National 48556 16.7 [14.6,19.0] 48636 3.0 [2.3,3.9] 48956 9.2 [8.0,10.5] 48636 0.6 [0.3,0.9] 109 Table 56. Severe malnutrition status of children (aged 6-59 months), Nigeria 2021 (continued) Using 2006 WHO Child Growth Standards: 1Severe stunting, (low length/height-for-age), is defined as height-for-age Z-score (HAZ) <−3SD 2Severe wasting (low weight-for-length/height) is defined as weight-for-height Z-score (WHZ) <−3SD 3Severe underweight, (low weight-for-age), is defined as weight-for-age Z-score (WAZ) <−3SD 4Obesity, (weight-for-length/height), is defined as weight-for-length/height Z-score (WHZ) > 3SD Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children 6-59 months who responded nationally: (n= 4912). Number of children 6-59 months who responded by zone: NC (n= 771); NE (n= 833); NW (n= 905); SE (n= 716); SS (n= 833); SW (n= 854) 5Less than (n = 4912) due to relatively fewer respondents for the household and dietary intake questionnaires 6Less than (n = 4912) due to missing data in implausible values or incomplete data Anthropometry of adolescent girls (aged 10-14 years) Adolescent growth and nutrition have been largely overlooked in national studies on food consumption and nutrition status. The NFCMS Is a landmark study in establishing the nutrition status of adolescent girls in Nigeria. BMI-for-age z-scores and height-for-age z-scores were calculated using the respondents’ height, weight, and age. Stunting or short stature among adolescent girls (10-14 years) is defined as height-for-age Z-score (HAZ) <−2SD. Underweight/thinness among adolescent girls (10-14 years) is defined as a BMI-for-age Z-scores (BAZ) <-2SD. Normal weight among adolescent girls (10- 14 years) is defined as (-2SD≤BAZ≤1). Overweight among adolescent girls (10-14 years) is defined as 1SD2SD. According to recommended practice,21,22 BMI-for-age Z-scores outside |5| SD and BMI values <12 and >50 was considered implausible and excluded from the analysis. The prevalence of thinness, normal weight, overweight, and obesity among adolescent girls (10-14 years) is summarized in Figure 37. At the national level, the prevalence of adolescent girls with normal weight was 80.7 percent; that is, most adolescent girls in Nigeria had an expected body weight compared to a healthy adolescent girl of the same age. The prevalence of adolescent girls with thinness was 15.1 percent, overweight was 3.1 percent, and obesity was 1.1 percent. Table 57 presents the prevalence of stunting, thinness, normal weight, overweight, and obesity among adolescent girls (aged 10-14 years) stratified by age, residence, wealth quintile, type of toilet facility, and source of drinking water. • Stunting: The prevalence of stunting among adolescent girls (aged 10-14 years) was 21.3 percent. There was a statistically significant difference in the prevalence of stunting among adolescent girls (10-14 years old) between residence (P = 0.004), wealth quintiles (P = 0.009), and toilet facility (P < 0.001). The prevalence of stunting was higher among adolescent girls residing in rural (25.2 percent) than in urban areas (14.5 percent). It was highest in adolescent girls in the lowest wealth quintile (30.8 percent) and lowest in adolescent girls in households with improved toilet facility (9.5 percent). • Thinness: There was a statistically significant difference in the prevalence of thinness in adolescent girls between the zones (P = 0.046), with the highest prevalence among adolescent girls in the North West zone (20.6 percent). • Normal weight: There was no significant variation in the prevalence of normal weight in adolescent girls (10-14 years old) across the background characteristics. • Overweight: There was a statistically significant difference in the prevalence of overweight among adolescent girls (aged 10-14 years old) between the wealth quintile (P = 0.005) and toilet facility (P < 0.001). The prevalence of overweight was highest among adolescent girls in the highest wealth quintile (4.1 percent) and adolescent girls in households with improved toilet facility (6.3 percent). • Obesity: There was a statistically significant difference in the prevalence of obesity in adolescent girls with toilet facility (P = 0.034). The prevalence was highest among adolescent girls in households with improved toilet facility (2.5 percent). 21 de Onis, M., A. W. Onyango, E. Borghi, A. Siyam, C. Nishida, and J. Siekmann. 2007. “Development of a WHO Growth Reference for School-Aged Children and Adolescents.” Bulletin of the World Health Organization 85 (9): 660-7. 22 Pullum, Thomas W. 2008. An Assessment of the Quality of Data on Health and Nutrition in the DHS Surveys, 1993-2003. Methodological Reports No. 6. Calverton, Maryland, USA: Macro International Inc. 110 111 Figure 37. Prevalence of thinness, normal weight, overweight, and obesity among adolescent girls (aged 10-14 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of adolescent girls who responded nationally: (n=1006) Stunting or short stature among adolescent girls (10-14 years) is defined as height-for-age Z-score (HAZ) <−2SD. Underweight/thinness among adolescent girls (10-14 years) is defined as a BMI-for-age Z-scores (BAZ) <-2SD. Normal weight among adolescent girls is defined as (-2SD≤BAZ≤1). Overweight among adolescent girls (10-14 years) is defined as 1SD2SD. Reference: https://www.who.int/tools/growth-reference-data-for-5to19-years/indicators/bmi-for-age 112 Table 57. Prevalence of stunting, thinness, normal weight, overweight, and obesity in adolescent girls (aged 10-14 years), Nigeria 2021 Background characteristics Stunting1* Thinness2* Normal3* Overweight4* Obesity5* N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.440) (P = 0.221) (P = 0.126) (P = 0.627) (P = 0.083) 10 years 262 19.6 [13.7,27.3] 261 12.6 [7.7,19.9] 261 82.3 [75.2,87.7] 261 2.6 [1.2,5.3] 261 2.5 [1,5.9] 11 years 156 18.4 [11.3,28.6] 155 13.1 [8.1,20.3] 155 83.2 [75.8,88.7] 155 3.5 [1.7,7.2] 155 0.2 [0,1.4] 12 years 192 28 [19.6,38.4] 193 13.9 [8.5,21.7] 193 82.9 [75.2,88.6] 193 2.9 [1.4,6.1] 193 0.3 [0.1,1.3] 13 years 191 22.4 [14.6,32.8] 192 22.2 [15.5,30.9] 192 72.2 [63.4,79.6] 192 4.4 [2.3,8.3] 192 1.2 [0.3,4.2] 14 years 194 17.6 [10.6,27.8] 194 14.1 [8.4,22.9] 194 83 [73.9,89.4] 194 2.1 [1,4.3] 194 0.8 [0.1,5.1] Residence (P = 0.004**) (P = 0.648) (P = 0.629) (P = 0.052) (P = 0.145) Urban 417 14.5 [10.6,19.6] 416 14.2 [10.5,18.8] 416 79.7 [74.6,83.9] 416 4.4 [2.8,6.8] 416 1.8 [0.8,4.3] Rural 578 25.2 [20.3,30.9] 579 15.6 [11.4,21.1] 579 81.3 [75.9,85.8] 579 2.3 [1.5,3.6] 579 0.7 [0.3,1.9] Zone (P < 0.000***) (P = 0.046*) (P = 0.614) (P = 0.001**) (P = 0.170) North Central 148 16.2 [9.5,26.3] 148 17.6 [8.9,31.6] 148 78.6 [65.6,87.6] 148 3.7 [1.6,8.5] 148 0.1 [0,1] North East 164 22.2 [16.1,29.9] 166 16.9 [11.4,24.3] 166 81.3 [73.4,87.3] 166 0.5 [0.1,2.4] 166 1.2 [0.3,4.2] North West 157 35.8 [26.2,46.6] 156 20.6 [13.3,30.5] 156 77.1 [67.2,84.7] 156 1.3 [0.4,3.9] 156 1.1 [0.3,4.3] South East 172 6.8 [3.3,13.3] 171 6.8 [3.3,13.3] 171 84.4 [76,90.2] 171 8.5 [5,14.2] 171 0.3 [0,2.1] South South 179 10.4 [6,17.4] 179 6.5 [3.6,11.2] 179 84.8 [78,89.8] 179 5.6 [3,10.3] 179 3.1 [1,9] South West 175 15.1 [10.4,21.3] 175 12.2 [7.5,19.3] 175 83 [75.7,88.5] 175 4.1 [2,8] 175 0.6 [0.1,4.5] Wealth quintile6 (P = 0.009**) (P = 0.543) (P = 0.577) (P = 0.005**) (P = 0.070) Lowest 210 30.8 [21.6,41.8] 213 14.7 [9.7,21.8] 213 83 [75.9,88.3] 213 1.4 [0.5,3.7] 213 0.8 [0.2,4] Second 190 24.4 [17.8,32.4] 188 15.4 [9.7,23.6] 188 82.5 [74.4,88.4] 188 2.1 [0.8,5.1] 188 0 [0,0] Middle 205 21.3 [12.7,33.5] 205 20.3 [10.8,34.7] 205 77.3 [63.4,87] 205 1.9 [0.7,4.8] 205 0.6 [0.2,1.8] Fourth 193 14.3 [8.2,23.8] 193 11 [6.7,17.6] 193 82.9 [75.7,88.3] 193 4.2 [2.1,8.3] 193 1.9 [0.5,6.1] Highest 193 9.2 [5.3,15.7] 192 13.7 [8.3,21.9] 192 75.9 [67.4,82.8] 192 7.2 [4.4,11.6] 192 3.1 [1.1,8.5] Source of drinking water6 (P = 0.895) (P = 0.663) (P = 0.976) (P = 0.350) (P = 0.675) Improved 753 21.1 [16.9,25.9] 755 15.6 [11.8,20.2] 755 80.6 [75.9,84.6] 755 2.8 [1.9,4.1] 755 1 [0.5,2.1] Unimproved 238 21.6 [15.4,29.5] 236 14 [9.3,20.5] 236 80.7 [74,86.1] 236 3.9 [2.2,6.7] 236 1.4 [0.4,5.2] Type of toilet facility6 (P < 0.000***) (P = 0.058) (P = 0.524) (P < 0.000***) (P = 0.034*) Improved 312 9.5 [5.2,16.7] 311 10 [6.3,15.6] 311 81.2 [75.3,86] 311 6.3 [4,9.7] 311 2.5 [1,5.8] Unimproved 422 27.5 [22.2,33.5] 422 18.5 [13.8,24.3] 422 79.1 [73.4,84] 422 1.5 [0.8,2.7] 422 0.8 [0.3,2.3] Open defecation 257 20.3 [14.3,28] 258 13.2 [7.9,21.2] 258 83.7 [76,89.3] 258 2.9 [1.4,5.7] 258 0.2 [0,1.4] National 9957 21.3 [17.8,25.3] 9958 15.1 [12,18.8] 9959 80.7 [76.9,84] 99510 3.1 [2.2,4.2] 99511 1.1 [0.6,2.2] 113 Table 57. Prevalence of stunting, thinness, normal weight, overweight, and obesity in adolescent girls (aged 10-14 years), Nigeria 2021 (continued) 1Stunting or short stature among adolescent girls (aged 10-14 years) is defined as height-for-age Z-score (HAZ) <−2SD. 2Underweight/thinness among adolescent girls (aged 10-14 years) is defined as a BMI-for-age Z-scores (BAZ) <-2SD. 3Normal weight among adolescent girls is defined as (-2SD≤BAZ≤1). 4Overweight among adolescent girls (aged 10-14 years) is defined as 1SD2SD. *Reference: https://www.who.int/tools/growth-reference-data-for-5to19-years/indicators/bmi-for-age. Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of adolescent girls who responded nationally: (n=999). 6Less than (n = 999) due to relatively fewer respondents for the household questionnaires 7Less than (n = 999) due to missing data in implausible values or incomplete data 8Less than (n = 999) due to missing data in implausible values or incomplete data 9Less than (n = 999) due to missing data in implausible values or incomplete data 10Less than (n = 999) due to missing data in implausible values or incomplete data 11Less than (n = 999) due to missing data in implausible values or incomplete data Anthropometry of WRA (aged 15-49 years) The height, weight, and body composition of women prior to conception have important implications on the subsequent health of the mother during pregnancy, delivery, and post-partum, and for the development of children both pre-and postnatal23. Information on anthropometry of non-pregnant WRA is especially important in low and low-middle income countries (LMIC), where millions of women of childbearing age have anthropometric evidence of an adverse environment, including recent or/and long-term undernutrition (thinness) and where the rate of increase in overweight and obesity may now exceed that in more affluent countries.24 The WHO25 defines thinness as being below the healthy weight range. Thinness in WRA affects women and increases the risk of an intergenerational cycle of malnutrition and child mortality. Thinness can be defined as a body mass index (BMI) of <18.5 kg/m2 for WRA ≥20 years and as BMI-for-age Z-scores (BAZ) <-2SD in WRA <20 years. WHO defines overweight and obesity as abnormal or excessive fat accumulation that may impair health. Overweight and obesity can be defined as 25≤BMI<30 kg/m2, and obesity as a BMI ≥30 kg/ m2 for WRA ≥20 years. For WRA <20 years old, overweight is defined as 1SD2SD. Normal weight is defined as -2SD≤BAZ≤1 for WRA<20 years and 18.5≤BMI<25 kg/m2 for WRA ≥20 years. The prevalence of thinness, normal weight, overweight, and obesity among WRA (aged 15-49 years) nationally and by the zones are summarized in Figure 38. About 63 percent of WRA had normal weight. Overall, the prevalence of thinness, overweight, and obesity among WRA was 14.1, 14.8, and 8.2 percent, respectively. Table 58 presents the prevalence of thinness, normal weight, overweight, and obesity among WRA (15-49 years) stratified by age category, residence, zone, wealth quintile, educational attainment, source of drinking water, and type of toilet facility. • Thinness: There was a statistically significant difference in the prevalence of thinness among WRA among the age groups (P < 0.001), residence (P = 0.002), zones (P < 0.001), wealth quintile (P < 0.001), educational attainment (P < 0.001), source of drinking water (P = 0.009), and type of toilet facility (P < 0.001). The prevalence of thinness was lowest in the 15-19-years old age category (9.9 percent). It was higher among WRA residing in rural (16.0 percent) versus urban (11.1 percent) areas. It was highest among WRA in the North West zone (21.6 percent). The prevalence of thinness was lowest in women in the highest wealth quintile (6.4 percent), women who had attained tertiary education (8.7 percent), and WRA in households with improved toilet facility (7.3 percent). It was higher among WRA in households with unimproved (16.9 percent) versus improved (13.1 percent) source of drinking water. • Normal weight: There was a statistically significant difference in the prevalence of normal weight among WRA among the age groups (P < 0.001), residence (P < 0.001), zone (P = 0.029), wealth quintile (P < 0.001), educational attainment (P < 0.001), type of toilet facility (P < 0.001), and source of drinking water (P = 0.004). The prevalence of normal weight was 23 Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008;371(9608):243–60. 24 Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382(9890) :427–51. 25 https://www.who.int/news-room/fact-sheets/detail/malnutrition 114 lowest in the 20-29-years old age category (4.0 percent). It was higher among WRA residing in rural (65.9 percent) versus urban (58.4 percent) areas. It was highest among WRA in the North Central zone (66.6 percent). The prevalence of normal weight was lowest in women in the highest wealth quintile (53.6 percent), women who had attained tertiary education (50.9 percent), and WRA in households with improved toilet facility (57.6 percent). It was higher among WRA in households with improved (64.4 percent) versus unimproved (58.9 percent) source of drinking water. • Overweight: There was a statistically significant difference in the prevalence of overweight among WRA among the age groups (P < 0.001), residence (P < 0.001), zone (P = 0.029), wealth quintile (P < 0.001), educational attainment (P < 0.001), and type of toilet facility (P < 0.001). The prevalence of overweight was lowest in the 15-19-years old age category (4.1 percent). It was higher among WRA residing in urban (17.9 percent) versus rural (12.8 percent) areas. It was highest among WRA in the SE zone (21.2 percent). The prevalence of overweight was highest in women in the highest wealth quintile (22.0 percent), women who had attained tertiary education (23.0 percent), and WRA in households with improved toilet facility (20.9 percent). • Obesity: There was a statistically significant difference in the prevalence of obesity among WRA among the age groups (P < 0.001), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), educational attainment (P < 0.001), and type of toilet facility (P < 0.001). The prevalence of obesity was highest in the 40-49-years old age category (15.5 percent). It was higher among WRA residing in urban (12.6 percent) compared to rural (5.2 percent) areas. It was highest among WRA in the South East zone (15.5 percent). The prevalence of obesity was highest in women in the highest wealth quintile (18.0 percent), women who had attained tertiary education (17.5 percent), and WRA in households with improved toilet facility (14.3 percent). 115 116 Figure 38. Prevalence of thinness, normal weight, overweight, and obesity among WRA (aged 15-49 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of women of reproductive age who responded nationally: (n= 5351) Number of women of reproductive age who responded by zone: NC (n= 861), NE (n= 839), NW (n= 908), SE (n= 871), SS (n= 861), SW (n= 899) For WRA <20 years old, thinness is defined as BAZ<-2SD, normal weight is defined as -2SD≤BAZ≤1, overweight is defined as 1SD2SD For WRA ≥20 years, thinness is defined as BMI<18.5 kg/m2, normal weight as BMI 18.5-24.9 kg/m2, overweight is defined as BMI 25-29.9 kg/m2, and obesity defined as ≥ 30 kg/m 117 Table 58. Prevalence of thinness, normal weight, overweight, and obesity in WRA (aged 15-49 years), Nigeria 2021 Background characteristics Thinness1 Normal2 Overweight3 Obesity4 N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.000***) (P < 0.000***) (P < 0.000***) (P < 0.000***) 15-19 years 1149 9.9 [7.6,12.8] 1149 84.4 [81.7,86.8] 1149 4.1 [2.9,5.8] 1149 1.6 [0.8,3.0] 20-29 years 1653 11.3 [15.9,21.0] 1653 4.0 [60.5,66.2] 1653 13.2 [11.3,15.3] 1653 5.2 [4.0,6.6] 30-39 years 1503 16.9 [11.6,16.6] 1503 9.6 [50.9,57.7] 1503 19.6 [16.9,22.6] 1503 12.1 [9.6,15.3] 40-49 years 1030 20.7 [9.6,15.0] 1030 13.0 [45.1,52.3] 1030 23.8 [20.7,27.1] 1030 15.5 [13.0,18.4] Residence (P = 0.002**) (P < 0.000***) (P < 0.000***) (P < 0.000***) Urban 2160 11.1 [9.1,13.3] 2160 58.4 [56.1,60.6] 2160 17.9 [15.9,20.2] 2160 12.6 [10.8,14.7] Rural 3175 16.0 [14.1,18.1] 3175 65.9 [63.6,68.2] 3175 12.8 [11.1,14.7] 3175 5.2 [4.2,6.4] Zone (P < 0.000***) (P = 0.029*) (P < 0.000***) (P < 0.000***) North Central 877 8.4 [5.7,12.2] 877 66.6 [63.1,70.0] 877 17.5 [14.9,20.5] 877 7.5 [5.5,10.1] North East 863 20.2 [15.7,25.6] 863 60.2 [56.3,64.0] 863 11.6 [9.3,14.3] 863 8.0 [5.2,12.1] North West 929 21.6 [19.0,24.6] 929 65.6 [61.8,69.1] 929 9.4 [7.0,12.4] 929 3.4 [2.2,5.4] South East 879 6.4 [4.5,8.9] 879 56.9 [52.4,61.3] 879 21.2 [18.0,24.9] 879 15.5 [12.2,19.4] South South 880 7.2 [5.5,9.4] 880 61.8 [56.7,66.6] 880 19.1 [15.5,23.4] 880 11.8 [9.3,15.0] South West 907 9.3 [7.4,11.6] 907 61.8 [57.8,65.5] 907 18.5 [15.4,22.0] 907 10.5 [8.0,13.6] Wealth quintile5 (P < 0.000***) (P < 0.000***) (P < 0.000***) (P < 0.000***) Lowest 1119 25.6 [21.9,29.6] 1119 63.9 [59.7,67.8] 1119 7.8 [6.0,10.2] 1119 2.7 [1.7,4.4] Second 1137 14.2 [11.9,16.9] 1137 70.1 [66.7,73.2] 1137 11.7 [9.6,14.2] 1137 4.0 [2.8,5.6] Middle 1105 13.6 [10.7,17.1] 1105 60.9 [57.1,64.6] 1105 16.5 [13.6,19.8] 1105 9.1 [7.0,11.6] Fourth 1003 8.5 [6.5,11.0] 1003 64.3 [60.4,68.0] 1003 17.8 [15.1,21.0] 1003 9.4 [7.5,11.8] Highest 949 6.4 [4.7,8.8] 949 53.6 [49.8,57.3] 949 22.0 [18.8,25.6] 949 18.0 [15.3,21.0] Educational attainment5 (P < 0.000***) (P < 0.000***) (P < 0.000***) (P < 0.000***) None 1262 23.1 [20.2,26.2] 1262 63.5 [60.4,66.5] 1262 9.8 [7.8,12.2] 1262 3.6 [2.5,5.1] Primary 854 12.2 [9.7,15.2] 854 59.0 [54.4,63.5] 854 18.3 [15.2,21.8] 854 10.5 [8.2,13.5] Secondary 2416 9.5 [7.9,11.4] 2416 66.4 [63.8,68.9] 2416 15.8 [14.0,17.7] 2416 8.3 [6.7,10.2] Tertiary 539 8.7 [5.9,12.6] 539 50.9 [45.0,56.6] 539 23.0 [19.4,27.1] 539 17.5 [14.0,21.6] Source of drinking water5 (P = 0.009**) (P = 0.004**) (P = 0.756) (P = 0.223) Improved 4007 13.1 [11.6,14.7] 4007 64.4 [62.5,66.3] 4007 14.6 [13.2,16.3] 4007 7.8 [6.7,9.2] Unimproved 1307 16.9 [14.3,19.9] 1307 58.9 [55.5,62.2] 1307 15.1 [12.7,17.8] 1307 9.1 [7.4,11.3] Type of toilet facility5 (P < 0.000***) (P < 0.000***) (P < 0.000***) (P < 0.000***) Improved 1598 7.3 [5.8,9.1] 1598 57.6 [54.5,60.6] 1598 20.9 [18.5,23.5] 1598 14.3 [12.4,16.4] Unimproved 2284 17.6 [15.6,19.9] 2284 63.9 [61.6,66.1] 2284 12.5 [10.8,14.4] 2284 5.9 [4.7,7.5] Open defecation 1432 14.9 [11.9,18.4] 1432 68.6 [65.4,71.7] 1432 11.6 [9.6,14.1] 1432 4.9 [3.7,6.5] National 53356 14.1 [12.7,15.5] 53357 63.0 [61.2,64.7] 53358 14.8 [13.5,16.2] 53359 8.2 [7.1,9.3] 118 Table 58. Prevalence of thinness, normal weight, overweight, and obesity in WRA (aged 15-49 years), Nigeria 2021 (continued) For WRA <20 years old, 1thin is defined as BAZ<-2SD, 2normal weight is defined as -2SD≤BAZ≤1, 3overweight is defined as 1SD2SD For WRA ≥20 years, 1thin is defined as BMI<18.5 kg/m2, 2normal weight as BMI 18.5-24.9 kg/m2, 3overweight is defined as BMI 25-29.9 kg/m2, and 4obesity defined as ≥ 30 kg/m2 Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001) Number of WRA who responded nationally: (n= 5351). Number of WRA who responded by zone: NC (n= 881), NE (n= 864), NW (n= 930), SE (n= 882), SS (n= 883), SW (n= 911) 5Less than (n = 5351) due to relatively fewer respondents for the household and dietary intake questionnaires 6Less than (n = 5351) due to missing data in implausible values or incomplete data 7Less than (n = 5351) due to missing data in implausible values or incomplete data 8Less than (n = 5351) due to missing data in implausible values or incomplete data 9Less than (n = 5351) due to missing data in implausible values or incomplete data Intervention coverage, health status, and anaemia risk factors 26 This chapter describes the coverage of nutrition-specific interventions and highlights self-reported morbidity and anaemia risk factors among children aged 6-59 months, adolescent girls (10-14 years old), WRA (15-49 years old), and pregnant women (15-49 years old).27 The results presented are based on a questionnaire28 administered to the survey respondents and their caregivers in the case of children. Nutrition-specific interventions, such as vitamin A, multivitamin A and iron supplementation, nutrition counselling of caregivers, and delivery of therapeutic foods, are essential for addressing undernutrition in vulnerable populations.29 Another intervention to improve nutritional status is deworming treatment. Helminths (commonly referred to as worms) can cause diarrhoea, poor absorption of nutrients, and loss of appetite, increasing vulnerability to micronutrient deficiencies. On the other hand, pica and poor health status exhibited by frequent illness and inflammation are also associated with anaemia.30 The Nigeria National Micronutrient Deficiency Control (MNDC) guidelines31 describe the interventions to address micronutrient deficiencies among children (aged 6-59 months), such as deworming, vitamin A supplementation, use of micronutrient powders for home fortification, etc. Some interventions, including nutrition education of caregivers, are reflected in the National Policy on Food and Nutrition32 (NFPN), which prioritizes both the health system and food-based approaches to MNDC. An objective of the survey was to assess the coverage of these interventions among children (aged 6-59 months). Intervention coverage, health status, and anaemia risk factors among children (aged 6-59 months) Intervention coverage among children (aged 6-59 months) Figure 39 presents the percentage of children (aged 6-59 months) who took deworming drugs, vitamin A supplementation, iron/micronutrient powder, therapeutic feeds, and whose caregivers received any nutrition counselling. Overall, the use of iron/micronutrient powder (7 percent) and therapeutic feeds (percent) was low. The prevalence among children (aged 6-59 months) receiving a vitamin A capsule in the previous six months was 25 percent nationally, while the percentage of children receiving deworming treatment in the past six months was 28 percent nationally. The percentage of children aged 6-59 months whose caregivers received some form of nutrition counselling in the previous six months was 15 percent nationally. 26 The premise of the NFCMS aligns with the UNICEF conceptual framework of determinants of undernutrition (2013*). Individual nutritional status measured by indicators such as those of anthropometry and micronutrient biomarkers is determined by two immediate factors - high quality diets and optimal health. Three underlying factors influence these: access to sufficient, safe, and nutritious food; adequate care practices for especially women and children; and access to health services including healthy environments, water, and sanitation. Finally, at a basic level, political, economic, and institutional determinants underpin all of these factors. *UNICEF (United Nations Children’s Fund). 2013. Improving Child Nutrition: The Achievable Imperative for Global Progress. New York: UNICEF. 27 For scope of preliminary report for the biomarker component, see Annex 2. 28 See Annex 12 for questionnaire (Q) : Q1. Children 6-59 months; Q2. Adolescent girls (10-14 years) and Women of reproductive age (15-49 years); Q3. Pregnant women (15-49 years) 29 Bhutta ZA, Das JK, Rizvi A, Gaffey MF, Walker N, Horton S, Webb P, Lartey A, Black RE; Lancet Nutrition Interventions Review Group, the Maternal and Child Nutrition Study Group. Evidence-based interventions for improvement of maternal and child nutrition: what can be done and at what cost? Lancet. 2013 Aug 3;382(9890):452-477. doi: 10.1016/S0140- 6736(13)60996-4. Epub 2013 Jun 6. Erratum in: Lancet. 2013 Aug 3;382(9890):396. PMID: 23746776. 30 Julia G. Shaw, Jennifer F. Friedman, «Iron Deficiency Anaemia: Focus on Infectious Diseases in Lesser Developed Countries», Anaemia, vol. 2011, Article ID 260380, 10 pages, 2011. https://doi.org/10.1155/2011/260380 31 Federal Ministry of Health (FMOH). 2013. National guidelines on micronutrients deficiencies control in Nigeria. Abuja: Federal Ministry of Health. 32 Ministry of Budget & National Planning. 2016. National Policy on Food and Nutrition in Nigeria. Abuja: Ministry of Budget and National Planning. 119 Figure 39. Coverage of nutrition-specific interventions among children (aged 6-59 months), Nigeria 2021 Data are weighted to account for survey design and non-response Number of children 6-59 months who responded nationally: (n= 4916) Coverage of vitamin A supplementation among children aged 6-59 months In countries where vitamin A deficiency (VAD) is a public health problem, the WHO recommends giving children (aged 6-59 months) two consecutive high-dose supplements of vitamin A per year.33 In Nigeria, vitamin A is delivered routinely to children (aged 6-59 months) as stipulated in the IMCI strategy at frontline health facilities during bi-annual Maternal Neonatal and Child Health Weeks (MNCHW) and National Immunization Plus Days by trained healthcare workers.34 Table 59 presents the percentage of children (aged 6-59 months) that received a vitamin A capsule in the last six months, stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. There was a statistically significant difference in the prevalence of children (aged 6-59 months) who received a vitamin A capsule in the last six months among the age groups (P = 0.017), residences (P < 0.001), zones (P <0.001), wealth quintiles (P < 0.001) and educational attainment of caregiver (P < 0.001). The percentage of children (aged 6-59 months) who received a vitamin A capsule in the last six months was highest within the 6-11 months age category (35 percent). It was higher in children residing in urban areas (36 percent) compared to those in the rural areas (20 percent). It was lowest in the North West zone (7 percent) and among children in the lowest wealth quintile (13 percent). It was highest among children whose caregivers had attained tertiary education (41 percent). Figure 40 presents the source of verification for caregivers’ statements on vitamin A supplementation in the last six months in children (aged 6-59 months). Most of the data collected (87 percent) were verified through mothers’ recall. 33 Dalmiya, N., & Palmer, A. (2007). Vitamin A supplementation: a decade of progress. UNICEF. 34 Aghaji, A.E., Duke, R. & Aghaji, U.C.W. Inequitable coverage of vitamin A supplementation in Nigeria and implications for childhood blindness. BMC Public Health 19, 282 (2019). https://doi.org/10.1186/s12889-019-6413-1 120 Table 59. Vitamin A supplementation among children aged 6-59 months, Nigeria 2021 Received vitamin A capsule in the past six months1 Background characteristics N % [95% CI] P value Age category 6-11 months 475 34.5 [25.7, 44.4] 12-23 months 1082 28.8 [24.3, 33.7] 24-35 months 1129 23.3 [19.0, 28.3] (P = 0.017 *) 36-47 months 1146 21.9 [16.4, 28.5] 48-59 months 849 22.6 [18.1, 28.0] Sex Male 2359 24.8 [20.8, 29.2] (P = 0.593) Female 2322 25.6 [21.7, 30.0] Residence Urban 1902 35.6 [28.6, 43.3] (P <0.001***) Rural 2779 20.2 [16.4, 24.7] Zone North Central 722 41.9 [33.5, 50.8] North East 789 31.7 [20.1, 46.2] North West 899 7.7 [4.6, 12.7] (P <0.001***) South East 697 39.6 [34.5, 45.0] South South 786 26.4 [21.2, 32.3] South West 788 35.9 [31.5, 40.7] Wealth quintile2 Lowest 1006 12.6 [9.5, 16.5] Second 983 17.2 [13.5, 21.7] Middle 922 31.4 [25.3, 38.2] (P <0.001***) Fourth 905 35.9 [29.9, 42.3] Highest 844 43.1 [33.8, 53.0] Educational attainment of caregiver2 None 1254 16.6 [12.6, 21.5] Primary 732 23.4 [19.2, 28.3] (P <0.001***) Secondary 1900 37.0 [32.8, 41.3] Tertiary 488 40.9 [31.0, 51.6] National 46813 25.2 [21.5, 29.3] Data are based on question chs6 of the biomarker questionnaire chs6. Within the last six months, was (name of child) given a vitamin A dose like this (caregiver shown locally sourced Vitamin A capsule)? 1All children are eligible for Vitamin A supplementation Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children 6-59 months who responded nationally: (n= 4916) 2Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 4916) due to “Don’t know” response 121 10% Mother’s recall Vaccination card Health card Other 87% Figure 40. Source of verification among children (6-59 months) who received a Vitamin A dose in the past six months, Nigeria 2021 Question chs7 is linked to question chs6. chs6. Within the last six months, was (name of child) given a vitamin A dose like this (caregiver shown locally sourced Vitamin A capsule)? Chs7. Source of verification Data are weighted to account for survey design and non-response Number of children 6-59 months who responded nationally: (n= 4916) Coverage of nutrition counselling and specific key messages Table 60 presents the percentage of children (aged 6-59 months) whose caregivers received nutrition counselling from a health worker or community volunteer on specific topics. The data are stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. 1. Breastfeeding: Nationally, the prevalence of nutrition counselling from a health worker or community volunteer on breastfeeding among caregivers of children (aged 6-59 months) who received some type of nutrition counselling was 81 percent. There was a statistically significant difference in the percentage of children whose caregivers received information on breastfeeding between the age groups (P < 0.001) and between the zones (P = 0.006). The prevalence was highest within the 6 to 11-month-old age category (95 percent) and lowest in the North Central zone (64 percent). 2. When to start feeding foods other than breastmilk (e.g., after six months): Nationally, the prevalence of nutrition counselling from a health worker or community volunteer on when to start feeding foods other than breastmilk among caregivers of children (aged 6-59 months) was 83 percent. There was a statistically significant difference in the percentage of children whose caregivers received this information among the age groups (P = 0.006). The prevalence was highest in the 6 to 11-month-old age category (93 percent). 3. Giving a variety of types of foods: Nationally, the prevalence of nutrition counselling from a health worker or community volunteer on providing a variety of types of foods among caregivers of children aged 6-59 months was 87 percent. There was no significant variation across the background characteristics. 4. Giving animal source foods specifically, eggs, milk, meats, or fish: Nationally, the prevalence of nutrition counselling from a health worker or community volunteer on giving animal source foods, specifically eggs, milk, meats, or fish, among caregivers of children (aged 6-59 months) was 90 percent. There was a statistically significant difference in the percentage of children whose caregivers received this information between the sex of the child (P = 0.011) and zone (P = 0.040). The percentage of children whose caregivers received this information was higher among female (93 percent) as compared to male (86 percent) children. The percentage of children whose caregivers received this information was highest in the North East zone (96 percent). 122 2% 1% 5. How often to feed the child: Nationally, the prevalence of nutrition counselling from a health worker or community volunteer on how often to feed the child among caregivers of children (aged 6-59 months) was 88 percent. There was no significant variation across the background characteristics. 6. Not feeding sugary drinks (e.g., fizzy drinks): Nationally, the prevalence of nutrition counselling from a health worker or community volunteer on not feeding sugary drinks (e.g. fizzy drinks) among caregivers of children (aged 6-59 months) was 76 percent. There was no significant variation across the background characteristics. 123 124 Table 60. Coverage of nutrition counselling on specific key messages in the past six months among children (aged 6-59 months) whose caregivers1 reported receiving some form of nutrition counselling, Nigeria 2021 Background Breastfeeding When to start feeding other Giving a variety of types of characteristics foods foods Giving animal source foods How often to feed the child Not feeding sugary drinks N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age categories (P < 0.001 ***) (P = 0.006 **) ( P = 0.970 ) ( P = 0.756 ) ( P = 0.589 ) ( P = 0.461 ) 6-11 months 128 94.8 [80.2, 98.8] 128 93.1 [79.4, 97.9] 126 86.5 [73.9, 93.6] 125 90.3 [76.7, 96.3] 127 85.1 [72.7, 92.4] 125 81.8 [70.6, 89.4] 12-23 months 227 91.9 [86.0, 95.4] 227 86.7 [77.8, 92.4] 227 86.2 [79.2, 91.1] 224 88.9 [80.3, 94.0] 223 88.3 [81.4, 92.9] 223 73.7 [63.4, 82.0] 24-35 months 157 78.8 [67.8, 86.7] 157 86.2 [75.2, 92.8] 158 89.2 [80.9, 94.1] 155 90.2 [83.0, 94.5] 159 91.6 [84.1, 95.7] 157 74.0 [58.7, 85.1] 36-47 months 138 56.7 [41.9, 70.3] 137 65.9 [47.9, 80.2] 139 87.3 [77.3, 93.2] 138 86.5 [77.1, 92.4] 139 83.8 [71.4, 91.5] 138 71.2 [59.9, 80.4] 48-59 months 83 75.4 [56.9, 87.7] 83 80.5 [61.9, 91.3] 84 87.5 [72.0, 95.1] 84 94.1 [80.3, 98.4] 82 88.7 [76.0, 95.1] 83 82.8 [70.6, 90.6] Sex ( P = 0.714 ) ( P = 0.428 ) ( P = 0.130 ) ( P = 0.011 *) ( P = 0.076 ) ( P = 0.557 ) Male 364 79.9 [72.6, 85.7] 362 84.4 [77.1, 89.6] 365 84.9 [78.2, 89.8] 361 85.9 [79.5, 90.5] 360 84.6 [77.6, 89.7] 364 74.5 [67.4, 80.5] Female 369 81.4 [71.2, 88.6] 370 81.5 [70.8, 88.9] 369 89.4 [84.1, 93.1] 365 93.1 [88.3, 96.0] 370 90.4 [85.5, 93.8] 362 76.9 [68.6, 83.5] Residence ( P = 0.795 ) ( P = 0.623 ) ( P = 0.052 ) ( P = 0.532 ) ( P = 0.172 ) ( P = 0.112 ) Urban 311 79.5 [62.5, 90.1] 311 80.7 [63.0, 91.2] 311 91.8 [85.7, 95.4] 309 91.1 [82.7, 95.6] 310 90.8 [84.4, 94.7] 308 81.5 [71.8, 88.4] Rural 422 81.5 [74.9, 86.7] 421 84.3 [77.4, 89.4] 423 84.2 [77.5, 89.1] 417 88.5 [83.1, 92.4] 420 85.5 [79.3, 90.1] 418 71.8 [63.4, 78.9] Zone ( P = 0.007 **) ( P = 0.289 ) ( P = 0.561 ) ( P = 0.040 *) ( P = 0.184 ) ( P = 0.002 **) North Central 104 64.4 [54.1, 73.6] 103 82.4 [64.1, 92.5] 311 91.8 [85.7, 95.4] 100 88.2 [80.6, 93.1] 103 86.0 [77.6, 91.6] 101 57.8 [43.1, 71.2] North East 238 74.8 [56.8, 87.1] 237 79.0 [59.3, 90.7] 423 84.2 [77.5, 89.1] 238 95.8 [90.1, 98.3] 237 93.0 [87.4, 96.2] 237 87.5 [78.9, 92.9] North West 67 83.5 [67.3, 92.6] 67 79.5 [65.2, 88.9] 311 91.8 [85.7, 95.4] 66 81.8 [65.6, 91.4] 65 84.1 [64.5, 93.9] 67 81.8 [62.9, 92.2] South East 49 82.5 [68.8, 91.0] 49 89.8 [74.9, 96.3] 423 84.2 [77.5, 89.1] 48 94.1 [78.1, 98.6] 48 92.9 [79.9, 97.7] 48 59.2 [41.7, 74.6] South South 136 89.8 [81.6, 94.6] 136 81.1 [65.8, 90.5] 311 91.8 [85.7, 95.4] 137 88.5 [80.0, 93.7] 138 79.9 [66.2, 88.9] 136 65.7 [51.4, 77.7] South West 139 95.5 [90.0, 98.1] 140 95.6 [89.9, 98.2] 423 84.2 [77.5, 89.1] 137 84.8 [70.9, 92.8] 139 87.0 [75.9, 93.4] 137 69.1 [56.6, 79.4] Wealth quintile2 ( P = 0.477 ) ( P = 0.153 ) ( P = 0.071 ) ( P = 0.752 ) ( P = 0.330 ) ( P = 0.612 ) Lowest 139 77.1 [63.4, 86.7] 138 86.2 [73.6, 93.3] 139 79.3 [65.6, 88.6] 139 89.0 [78.8, 94.6] 137 84.2 [71.6, 91.8] 137 78.7 [67.8, 86.6] Second 153 83.3 [76.0, 88.8] 154 82.0 [74.3, 87.8] 154 85.5 [78.3, 90.6] 150 89.2 [81.8, 93.9] 154 85.0 [77.2, 90.4] 150 71.0 [61.6, 78.9] Middle 152 85.4 [72.8, 92.7] 151 91.1 [77.5, 96.8] 154 90.0 [83.0, 94.3] 152 85.9 [72.8, 93.3] 152 89.2 [80.5, 94.2] 152 72.2 [58.4, 82.7] Fourth 167 76.0 [57.0, 88.3] 167 73.6 [55.0, 86.4] 165 89.9 [80.2, 95.1] 163 91.7 [81.6, 96.5] 166 87.8 [80.1, 92.8] 165 77.7 [67.1, 85.5] Highest 120 84.5 [73.1, 91.6] 120 83.6 [68.5, 92.2] 120 93.4 [84.9, 97.3] 120 92.2 [83.5, 96.5] 119 94.0 [85.7, 97.6] 120 79.4 [64.9, 89.0] Caregiver’s educational attainment2 ( P = 0.419 ) ( P = 0.447 ) ( P = 0.585 ) ( P = 0.877 ) ( P = 0.118 ) ( P = 0.362 ) None 162 76.9 [61.9, 87.2] 162 77.8 [62.2, 88.2] 161 86.1 [76.8, 92.1] 161 89.3 [79.8, 94.6] 161 85.1 [74.7, 91.6] 160 76.3 [64.8, 84.9] Primary 115 86.6 [75.4, 93.2] 113 89.6 [78.3, 95.3] 115 82.6 [68.9, 91.0] 113 87.8 [76.3, 94.2] 114 93.8 [87.4, 97.1] 113 72.5 [63.1, 80.3] Secondary 327 85.0 [78.1, 90.0] 328 84.5 [77.2, 89.8] 328 88.6 [82.9, 92.6] 322 90.6 [85.5, 94.0] 326 85.7 [78.8, 90.7] 323 74.6 [67.3, 80.7] Tertiary 85 76.4 [45.8, 92.5] 85 79.1 [45.7, 94.4] 86 90.2 [78.7, 95.8] 86 91.9 [77.4, 97.4] 85 94.2 [86.5, 97.6] 86 85.5 [71.0, 93.5] National 7333 80.7 [73.2, 86.5] 7324 82.9 [74.8, 88.7] 7345 87.2 [82.5, 90.9] 7266 89.6 [85.2, 92.8] 7307 87.7 [83.1, 91.1] 7268 75.7 [69.4, 81.1] 125 Table 60. Coverage of nutrition counselling on specific key messages in the past six months among children (aged 6-59 months) whose caregivers1 reported receiving some form of nutrition counselling, Nigeria 2021 (continued) The data presented in this table are based on question chs5 of the biomarker questionnaire The data were collected from respondents who answered yes to question chs4 of the biomarker questionnaire chs4. In the last six months, has a health worker or community volunteer spoken with you about how to feed [name of child]? chs5. If yes [to chs4], did the health worker or community volunteer speak with you about any of these topics? 1This data refers to the primary caregiver of the sampled child, regardless of whether it was the child’s mother. Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children 6-59 months who responded nationally whose caregivers receive nutrition counselling: (n= 738) 2Less than (n = 738) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 738) due to missing data 4Less than (n = 738) due to non-response 5Less than (n = 738) due to non-response 6Less than (n = 738) due to non-response 7Less than (n = 738) due to non-response 8Less than (n = 738) due to non-response Use of micronutrient powder or any sprinkles with iron Table 61 presents the use of micronutrient powder or any sprinkles with iron in the past six months among children (aged 6-59) months stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. There was a statistically significant difference in the percentage of children aged 6-59 months who received sprinkles with iron or some form of micronutrient powder in the past six months between zones (P = 0.038) and caregivers’ educational attainment (P = 0.047). The use of sprinkles with iron or any micronutrient powder in the past six months among children (aged 6-59 months) was lowest in the South East zone (2 percent). It was highest among children whose caregivers had attained tertiary education (9 percent). Table 61. Use of micronutrient powder or any sprinkles with iron in the past six months among children (aged 6-59 months), Nigeria 2021 Background Received supply of micronutrient powder or any sprinkles with iron in the past six months characteristics N % [95% CI] P value Age category 6-11 months 487 5.5 [3.6, 8.2] 12-23 months 1117 7.2 [5.0, 10.3] 24-35 months 1160 7.3 [5.3, 9.9] (P = 0.747) 36-47 months 1186 6.6 [4.7, 9.1] 48-59 months 884 7.7 [5.3, 11.1] Sex Male 2430 7.7 [6.0, 9.7] (P = 0.124) Female 2404 6.3 [4.8, 8.2] Residence Urban 1982 8.7 [6.1, 12.2] (P = 0.132) Rural 2852 6.2 [4.6, 8.2] Zone North Central 757 7.8 [5.0, 12.0] North East 794 8.9 [5.6, 14.0] North West 904 4.6 [2.7, 7.7] (P = 0.038*) South East 716 2.0 [0.8, 4.6] South South 810 8.6 [4.6, 15.4] South West 853 10.3 [6.8, 15.4] Wealth quintile1 Lowest 1024 5.7 [3.7, 8.7] Second 1011 6.7 [4.7, 9.5] Middle 962 7.1 [5.1, 9.9] (P = 0.494) Fourth 946 8.9 [6.2, 12.5] Highest 870 7.4 [4.5, 11.8] Caregiver’s educational attainment1 None 1281 5.3 [3.7, 7.4] Primary 753 8.1 [5.8, 11.1] (P = 0.047*) Secondary 1973 8.2 [6.0, 11.0] Tertiary 511 9.4 [5.9, 14.6] National 48342 7.0 [5.6, 8.7] 126 Table 61. Use of micronutrient powder or any sprinkles with iron in the past six months among children (aged 6-59 months), Nigeria 2021 (continued) The data are based on question chs8 of the biomarker questionnaire chs8. In the last six months, did you receive a supply of sprinkles with iron or any micronutrient powder like this (show sprinkles) to give to [name of child]? 35In populations where anaemia is a public health problem, point-of-use fortification of complementary foods with iron-containing micronutrient powders in infants and young children aged 6–59 months is recommended by the WHO to improve iron status and reduce anaemia Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children 6-59 months who responded nationally: (n= 4916) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 4916) due to “Don’t Know” response 35 WHO guideline: Use of multiple micronutrient powders for point-of-use fortification of foods consumed by infants and young children aged 6–23 months and children aged 2–12 years. Geneva: World Health. Organization; 2016. Licence: CC BY-NC-SA 3.0 IGO. 127 Deworming The Nigeria MNDC guidelines36 recommend that deworming be done as per the WHO guidelines. The WHO recommends deworming for all children (12-23 months) and preschool children (1 to 4 years old) to reduce the worm burden of soil-transmitted helminth infection. Where the baseline prevalence for soil-transmitted helminth is more than 50 percent, the WHO recommends37 bi- annual deworming. Where the prevalence is lower, the recommendation is for annual deworming. The guidelines recommend single dose albendazole (400 mg) or mebendazole (500 mg). For bi- annual deworming, a half-dose of albendazole (i.e., 200 mg) is recommended for children younger than 24 months. Table 62 presents the percentage of children (aged 6-59 months) who received deworming treatment in the last six months (including and excluding children 6-11 months old), stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. Although the question was asked across all age categories, only children (12-59 months old) are eligible for deworming. Nationally, the coverage of deworming among children (aged 6-59 months) was 28 percent. Excluding children ineligible for deworming, the national coverage was 29 percent. Reviewing the data for children (aged 6-59 months), there was a statistically significant difference in the percentage of children who received deworming drugs between the age groups (P < 0.001), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), and caregivers’ educational attainment (P < 0.001). The percentage of children who received the deworming drug was highest among children in the 48 to 59-month age category (32 percent). It was higher in children residing in urban (41 percent) compared to rural (21 percent) areas. It was lowest among children in the North West zone (8 percent). The percentage of children (aged 6-59 months) who received the deworming drug was highest among children in the highest wealth quintile (52 percent) and children whose caregivers had attained tertiary education (46 percent). Reviewing the data for children (aged 12-59 months), there was a statistically significant difference in the percentage of children aged 6-59 months who received deworming drugs between the age groups (P < 0.001), residence (P = 0.034), zone (P < 0.001), wealth quintile (P < 0.001), and caregivers’ educational attainment (P < 0.001). The percentage of children who received the deworming drug was highest among children in the 48 to 59-month age category (32 percent). It was higher in children residing in urban (44 percent) compared to rural (22 percent) areas. It was lowest among children in the NW zone (8 percent). The percentage of children aged 6-59 months who received the deworming drug was highest among children in the highest wealth quintile (57 percent) and children whose caregivers had attained tertiary education (49 percent). The information on the 492 children (aged 6 to 11 months) who received (but should not have received) deworming medication is detailed in Table 63. 36 Federal Ministry of Health (FMOH). 2013. National guidelines on micronutrients deficiencies control in Nigeria. Abuja: Federal Ministry of Health. 37 Preventive chemotherapy to control soil-transmitted helminth infections in at-risk population groups. Geneva: World Health Organization; 2017 (http://www.who.int/nutrition/publications/guidelines/deworming/en/). 128 Table 62. Deworming in the past six months among children (6-59 months), Nigeria 2021 Children (6-59 months) given any drug for intestinal Children (12-59 months) Given any drug for intestinal Background worms in the last six months worms in the last six months characteristics N % [95% CI] N % [95% CI] Age category (P < 0.0000***) (P = 0.034*) 6-11 months 494 15.9 [12.2, 20.4] 0 . [.,.] 12-23 months 1138 25.3 [21.9, 29.0] 1138 25.3 [21.9, 29.0] 24-35 months 1181 30.0 [26.2, 34.1] 1181 30.0 [26.2, 34.1] 36-47 months 1201 28.7 [25.3, 32.3] 1201 28.7 [25.3, 32.3] 48-59 months 902 32.3 [27.8, 37.1] 902 32.3 [27.8, 37.1] Sex (P = 0.6419) (P = 0.8690) Male 2471 28.0 [25.0, 31.2] 2245 29.0 [25.8, 32.4] Female 2445 27.2 [24.4, 30.2] 2177 28.7 [25.7, 31.9] Residence (P < 0.0000***) (P < 0.0000***) Urban 2011 41.2 [36.1, 46.5] 1790 44.0 [38.8, 49.3] Rural 2905 21.0 [18.1, 24.2] 2632 21.7 [18.6, 25.1] Zone (P < 0.0000***) (P < 0.0000***) North Central 771 24.5 [18.7, 31.4] 692 25.8 [19.7, 33.1] North East 827 23.9 [18.8, 29.8] 751 25.1 [19.4, 32.0] North West 908 7.8 [4.9, 12.1] 821 8.4 [5.3, 13.1] South East 716 59.4 [53.2, 65.3] 655 61.6 [55.2, 67.7] South South 834 59.8 [52.4, 66.9] 744 60.2 [52.3, 67.7] South West 860 42.5 [37.7, 47.4] 759 46.4 [41.3, 51.7] Wealth quintile1 (P < 0.0000***) (P < 0.0000***) Lowest 1052 12.8 [9.8, 16.5] 965 13.2 [10.1, 17.1] Second 1023 18.0 [14.3, 22.3] 931 18.5 [14.7, 23.0] Middle 975 30.4 [26.7, 34.3] 878 31.3 [27.5, 35.5] Fourth 959 42.3 [37.1, 47.7] 838 45.2 [39.4, 51.1] Highest 886 51.9 [44.2, 59.5] 793 56.7 [50.6, 62.6] Caregiver’s educational attainment1 (P < 0.0000***) (P < 0.0000***) None 1311 15.0 [11.9, 18.8] 1200 15.6 [12.2, 19.7] Primary 764 22.8 [18.9, 27.1] 678 23.8 [19.8, 28.2] Secondary 2000 43.7 [40.3, 47.2] 1786 46.3 [42.9, 49.8] Tertiary 519 46.3 [40.8, 52.0] 464 49.1 [43.6, 54.7] National 4916 27.6 [25.1, 30.2] 44222 28.9 [26.3, 31.7] The data are based on question chs9 of the biomarker questionnaire chs9. Was [name of child] given any drug for intestinal worms in the last six months? The question was asked across all age categories 1Only children 12-59 months are eligible for deworming The data are analyzed for children 6-59 months and children 12-59 months Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children 6-59 months who responded nationally: (n= 4916) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 4916) due to exclusion of children 6 to 11 months 129 Table 63. Background characteristics of children (aged 6 to 11 months) who received some form of drug for intestinal worms in the last six months Background characteristics Children (6-11 months) given any drug for intestinal worms in the last six months1 Sex N % [95% CI] Male 225 17.7 [12.6, 24.2] Female 267 14.5 [10.3, 19.9] Residence Urban 221 19.4 [12.0, 29.9] Rural 271 13.8 [10.0, 18.7] Zone North Central 79 11.8 [5.4, 24.1] North East 76 13.8 [5.6, 30.3] North West 87 1.2 [0.3, 5.4] South East 61 35.0 [22.9, 49.4] South South 89 57.1 [44.6, 68.7] South West 100 11.3 [6.2, 19.8] Wealth quintile Lowest 87 8.3 [4.2, 16.1] Second 92 12.4 [6.8, 21.6] Middle 97 20.9 [12.6, 32.6] Fourth 119 21.1 [14.1, 30.2] Highest 93 19.4 [9.4, 35.6] Caregiver’s educational attainment None 111 8.7 [4.5, 16.1] Primary 85 15.3 [8.8, 25.4] Secondary 213 21.8 [16.0, 29.0] Tertiary 55 26.1 [12.9, 45.8] National 492 15.9 [12.3, 20.4] 1Only children 12-59 months are eligible for deworming Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Use of therapeutic feeds: Use of ready-to-use therapeutic feeds/plumpy’nut Table 64 presents the percentage of children aged 6-59 months who received some form of therapeutic feeds in the past 12 months, and those who received some form of therapeutic feeds the day before the interview stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. There was a statistically significant difference in the percentage of children 6-59 months who received any ready-to-use therapeutic feeds/plumpy’nut in the past 12 months between the zones (P = 0.002). The prevalence was highest among children in the North East zone (6.2 percent). There was a statistically significant difference in the percentage of children aged 6-59 months who received any ready-to-use therapeutic feeds/plumpy’nut a day before the interview between the age groups (P = 0.042). The prevalence was highest among children in the 6 to 11-month age category (0.8 percent). 130 Table 64. Use of therapeutic feeds in the past 12 months and the day before the interviews among children aged 6-59 months, Nigeria 2021 Given any ready to use therapeutic feeds/ Given any ready to use therapeutic feeds/ Background plumpy’nut in the last twelve months plumpy’nut yesterday characteristics N % [95% CI] N % [95% CI] Age category (P = 0.395) (P= 0.042*) 6-11 months 494 1.4 [0.5, 4.1] 494 0.8 [0.3, 2.4] 12-23 months 1138 2.2 [1.3, 3.9] 1138 0.5 [0.2, 1.4] 24-35 months 1181 2.3 [1.3, 3.9] 1181 0.3 [0.1, 0.9] 36-47 months 1201 2.6 [1.5, 4.5] 1201 0.2 [0.0, 1.0] 48-59 months 902 3.5 [1.7, 7.1] 902 0.2 [0.0, 1.5] Sex (P= 0.538) (P= 0.905) Male 2471 2.3 [1.5, 3.6] 2471 0.3 [0.1, 0.7] Female 2445 2.7 [1.5, 4.8] 2445 0.4 [0.2, 1.0] Residence (P= 0.920) (P= 0.317) Urban 2011 2.6 [1.1, 5.7] 2011 0.5 [0.2, 1.1] Rural 2905 2.5 [1.4, 4.2] 2905 0.3 [0.1, 0.8] Zone (P= 0.002**) (P= 0.093) North Central 771 1.5 [0.5, 4.4] 771 0.3 [0.1, 1.4] North East 827 6.2 [2.9, 12.9] 827 0.7 [0.2, 2.3] North West 908 2.3 [1.1, 4.7] 908 0.2 [0.1, 0.6] South East 716 0.5 [0.1, 2.4] 716 0.1 [0.0, 0.5] South South 834 1.7 [0.5, 5.4] 834 0.8 [0.2, 3.8] South West 860 0.5 [0.2, 1.5] 860 0.0 [0.0, 0.3] Wealth quintile1 (P= 0.870) (P= 0.183) Lowest 1052 2.9 [1.6, 5.3] 1052 0.0 [., .] Second 1023 2.0 [0.9, 4.1] 1023 0.5 [0.2, 1.4] Middle 975 2.5 [1.0, 6.2] 975 0.6 [0.2, 2.3] Fourth 959 2.7 [1.5, 5.0] 959 0.5 [0.2, 1.4] Highest 886 2.5 [1.2, 5.1] 886 0.2 [0.1, 0.6] Caregiver’s educational attainment1 (P= 0.429) (P= 0.179) None 1311 3.1 [1.7, 5.5] 1311 0.3 [0.1, 0.8] Primary 764 1.9 [0.9, 4.2] 764 0.0 [., .] Secondary 2000 2.0 [1.1, 3.7] 2000 0.5 [0.2, 1.4] Tertiary 519 2.8 [1.3, 6.0] 519 0.7 [0.2, 2.4] National 4916 2.5 [1.6, 3.9] 4916 0.4 [0.2, 0.7] The data are based on questions chs20 and chs21 of the biomarker questionnaire chs20. In the last 12 months, was [name of child] given any ready to use therapeutic feeds/plumpy’nut like (show locally sourced product) because the child was malnourished? chs21. Did [name of child] consume it yesterday? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001) Number of children 6-59 months who responded nationally: (n= 4916) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires Table 65 presents the percentage of children (aged 6-59 months) who were identified as having wasting, who received any therapeutic feeds in the past 12 months, and those who received some form of therapeutic feeds the day before the interview stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. Nationally, 2.8 percent of children with wasting reported receiving therapeutic feeds/plumpy’nut in the past 12 months. There was no significant variation across the background characteristics. Nationally, 0.6 percent of children with wasting reported receiving therapeutic feeds/plumpy’nut a day before the interview. There was no significant variation across the background characteristics. 131 Table 65. Use of therapeutic feeds in the past 12 months and the day before the interviews among children with wasting (aged 6-59 months), Nigeria 2021 Given any ready to use therapeutic feeds/ Given any ready to use therapeutic feeds/ Background plumpy’nut in the last twelve months plumpy’nut yesterday characteristics N % [95% CI] N % [95% CI] Age category (P = 0.194) (P = 0.840) 6-11 months 106 1.0 [0.1, 6.9] 106 1.0 [0.1, 6.9] 12-23 months 192 2.9 [1.0, 7.6] 192 1.0 [0.1, 6.8] 24-35 months 87 3.5 [0.7, 15.4] 87 0.0 [., .] 36-47 months 59 0.0 [., .] 59 0.0 [., .] 48-59 months 57 9.1 [2.3, 30.3] 57 0.0 [., .] Sex (P = 0.912) (P = 0.606) Male 267 2.9 [1.1, 7.3] 267 0.4 [0.1, 2.8] Female 234 2.7 [0.9, 7.5] 234 0.8 [0.1, 5.4] Residence (P = 0.810) (P = 0.308) Urban 199 2.3 [0.3, 14.1] 199 1.2 [0.2, 7.4] Rural 302 3.0 [1.3, 7.0] 302 0.3 [0.0, 2.3] Zone (P = 0.467) (P = 0.818) North Central 60 3.5 [0.4, 23.9] 60 0.0 [., .] North East 122 5.5 [1.9, 15.3] 122 1.3 [0.2, 8.4] North West 115 2.0 [0.4, 9.0] 115 0.6 [0.1, 4.1] South East 63 0.0 [., .] 63 0.0 [., .] South South 72 0.0 [., .] 72 0.0 [., .] South West 69 0.0 [., .] 69 0.0 [., .] Wealth quintile1 (P = 0.121) (P = 0.398) Lowest 139 6.3 [2.5, 15.0] 139 0.0 [., .] Second 101 3.7 [1.2, 11.0] 101 2.6 [0.6, 9.9] Middle 81 0.0 [., .] 81 0.0 [., .] Fourth 89 0.0 [., .] 89 0.0 [., .] Highest 90 0.0 [., .] 90 0.0 [., .] Caregiver’s educational attainment1 (P = 0.113) (P = 0.595) None 165 6.1 [2.6, 13.5] 165 1.4 [0.3, 5.6] Primary 75 1.9 [0.3, 12.5] 75 0.0 [., .] Secondary 183 0.0 [., .] 183 0.0 [., .] Tertiary 44 0.0 [., .] 44 0.0 [., .] National 501 2.8 [1.2, 6.2] 501 0.6 [0.1, 2.4] The data are based on questions chs20 and chs21 of the biomarker questionnaire chs20. In the last 12 months, was [name of child] given any ready to use therapeutic feeds/plumpy’nut like (show locally sourced product) because the child was malnourished? chs21. Did [name of child] consume it yesterday? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001) Number of children 6-59 months who were identified as having wasting nationally: (n= 501) 132 Self-reported morbidity prevalence and anaemia risk factors among children (aged 6-59 months) A key objective of the survey was to assess morbidity as a critical factor associated with anaemia. Figure 41 presents the prevalence of self-reported morbidity and other anaemia risk factors (reported by caregivers) among children (aged 6-59 months). The prevalence of diarrhoea in the past two weeks among children (6-59 months) was 35 percent. The presence of blood in stool in the past two weeks was reported among 8 percent of children (6-59 months), and those who reported having diarrhoea a day before the interview were 14 percent, nationally. Fever in the past two weeks was reported in 46 percent of children (6-59 months). About 37 percent of children (aged 6-59 months) had cough in the past two weeks, while the prevalence of fast, short, rapid breaths or difficulty breathing at any time in the past two weeks was 13 percent. Pica in the past seven days was reported among 20 percent of children (aged 6-59 months). Figure 41. Prevalence of self-reported morbidity (reported by caregiver), and anaemia risk among children (aged 6-59 months), Nigeria 2021 Data are weighted to account for survey design and non-response Number of children 6-59 months who responded nationally: (n= 4916) 133 Diarrhoea Diarrhoea is defined as having three or more loose or watery stools in past 24 hours. Table 66 presents the prevalence of diarrhoea among children (6-59 months) stratified by age, sex, residence, zone, wealth quintile, caregiver’s educational attainment, source of drinking water, and type of toilet facility in the household. • Had diarrhoea in the past two weeks: There was a statistically significant difference in the percentage of children (6-59 months) who were ill with diarrhoea in the past two weeks before the interview among the age groups (P < 0.001), residence (P = 0.042), zone (P < 0.001), wealth quintile (P = 0.001), and caregivers’ educational attainment (P = 0.001). The prevalence of diarrhoea in the past two weeks among children (6-59 months) was lowest in the 48-59-months old age category (22 percent). It was higher among children residing in rural (37 percent) compared to urban (32 percent) areas. It was highest in the North East zone (45 percent). The prevalence of diarrhoea in the past two weeks among children (aged 6-59 months) was lowest among children in the highest wealth quintile (27 percent). It was lowest among children whose caregivers had attained tertiary education (27 percent). • Had blood in stool in the past two weeks: There was a statistically significant difference in the percentage of children (aged 6-59 months) who had experienced blood in stool in the past two weeks before the interview among residence (P = 0.001), zone (P = 0.015), wealth quintile (P = 0.018) and type of toilet facility (P = 0.027). The prevalence of blood in stool in the past two weeks among children (6-59 months) was higher among children residing in rural (9 percent) against urban (5 percent) areas. It was lowest among children in the South West zone (4 percent) , in children in the highest wealth quintile (3 percent), and whose households use improved toilet facility (5 percent). • Had diarrhoea yesterday: There was a statistically significant difference in the percentage of children (aged 6-59 months) who were ill with diarrhoea a day before the interview among age groups (P < 0.001), sex (P = 0.008), zone (P < 0.001), wealth quintile (P = 0.018), and caregivers’ educational attainment (P = 0.001). The prevalence of diarrhoea the day before the interview among children (aged 6-59 months) was highest in the 6-11 month age category (24 percent). It was higher in the males (15 percent) than in the female (12 percent). It was highest in the North East zone (19 percent). The prevalence was highest among children in the lowest wealth quintile (18 percent). It was highest among children whose caregivers had no education (17 percent). 134 Table 66. Prevalence of diarrhoea1 and blood in stool among children (aged 6-59 months), Nigeria 2021 Had diarrhoea in the past two Had blood in stool in the Background characteristics weeks past two weeks Had diarrhoea yesterday N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.001***) (P = 0.353) (P < 0.001***) 6-11 months 494 45.5 [39.1, 52.1] 487 8.2 [4.9, 13.4] 492 24.0 [18.9, 29.8] 12-23 months 1133 46.0 [40.8, 51.3] 1125 9.3 [6.9, 12.5] 1133 19.4 [16.0, 23.2] 24-35 months 1177 36.6 [32.8, 40.7] 1169 8.0 [5.8, 11.1] 1176 12.1 [9.6, 15.2] 36-47 months 1191 29.8 [25.8, 34.1] 1177 6.2 [4.5, 8.4] 1191 11.0 [8.3, 14.5] 48-59 months 887 22.3 [18.6, 26.6] 881 7.0 [4.6, 10.4] 894 6.1 [4.0, 9.1] Sex (P = 0.206) (P = 0.708) (P = 0.008 **) Male 2452 36.5 [33.5, 39.6] 2427 7.9 [6.2, 10.0] 2452 15.2 [12.8, 18.0] Female 2430 34.2 [30.9, 37.7] 2412 7.5 [5.7, 9.8] 2434 11.9 [9.9, 14.3] Residence (P = 0.042 *) (P = 0.001 **) (P= 0.177) Urban 2002 31.6 [27.7, 35.8] 1975 4.5 [3.2, 6.4] 2000 11.5 [8.4, 15.4] Rural 2880 37.2 [33.8, 40.8] 2864 9.2 [7.2, 11.8] 2886 14.6 [12.2, 17.5] Zone (P < 0.001***) (P = 0.015*) (P < 0.001***) North Central 768 37.6 [31.8, 43.8] 765 9.1 [6.4, 12.7] 768 12.6 [9.2, 16.8] North East 815 44.7 [38.4, 51.1] 810 9.6 [6.7, 13.6] 822 19.4 [14.6, 25.3] North West 905 35.9 [30.1, 42.0] 905 9.4 [5.9, 14.4] 906 17.8 [13.7, 22.9] South East 712 31.2 [26.7, 36.1] 714 4.0 [2.5, 6.5] 714 6.1 [4.3, 8.5] South South 826 31.1 [26.8, 35.7] 789 4.9 [3.1, 7.6] 818 6.6 [4.8, 9.1] South West 856 24.9 [21.5, 28.6] 856 3.5 [2.6, 4.6] 858 5.7 [4.3, 7.7] Wealth quintile2 (P = 0.001 **) (P = 0.018*) (P = 0.001 ***) Lowest 1038 38.7 [33.7, 43.9] 1031 9.9 [7.0, 13.8] 1048 17.9 [14.2, 22.3] Second 1020 40.1 [35.0, 45.5] 1012 9.0 [6.0, 13.1] 1023 16.9 [13.4, 21.0] Middle 968 33.6 [29.6, 37.9] 965 7.2 [5.0, 10.2] 963 9.0 [6.6, 12.1] Fourth 952 31.9 [27.7, 36.4] 948 7.0 [4.1, 11.5] 952 10.2 [7.9, 13.0] Highest 883 27.1 [22.6, 32.3] 862 2.6 [1.5, 4.3] 879 9.4 [5.4, 15.9] Caregiver’s educational attainment2 (P = 0.001 **) (P = 0.133) (P = 0.000 ***) None 1296 39.5 [35.1, 44.0] 1291 9.2 [7.0, 12.0] 1305 17.4 [14.2, 21.1] Primary 761 38.9 [33.2, 44.8] 756 9.2 [5.8, 14.3] 760 14.5 [10.7, 19.3] Secondary 1987 32.8 [30.2, 35.5] 1962 6.9 [5.1, 9.3] 1982 9.2 [7.5, 11.2] Tertiary 518 27.0 [22.0, 32.7] 510 4.2 [1.8, 9.4] 518 10.2 [6.8, 14.9] Source of drinking water2 (P = 0.586) (P = 0.529) (P = 0.794) Improved 3625 35.0 [32.2, 38.0] 3593 7.9 [6.3, 9.8] 3627 13.5 [11.3, 16.0] Unimproved 1237 36.5 [31.9, 41.3] 1226 7.1 [4.8, 10.3] 1239 13.9 [11.3, 17.0] Type of toilet facility2 (P = 0.188) (P = 0.027 *) (P = 0.401) Improved facility 1492 31.2 [27.4, 35.4] 1475 4.6 [3.0, 7.0] 1488 11.0 [7.9, 15.2] Unimproved facility 2110 36.9 [33.1, 40.9] 2091 8.1 [6.1, 10.7] 2114 14.6 [11.6, 18.2] Open defecation 1260 36.4 [30.5, 42.6] 1253 10.1 [6.9, 14.5] 1264 14.0 [10.0, 19.3] National 48823 35.4 [32.7, 38.1] 48394 7.7 [6.2, 9.5] 48865 13.6 [11.6, 15.8] The data are based on questions chs11, chs12, and chs13 of the biomarker questionnaire chs11. Has [name of child] had diarrhoea in the last two weeks? chs12. Was there any blood in the stools? chs13. Did [name of child] have diarrhoea yesterday? 1Diarrhoea is defined as three or more loose or watery stools in a 24-hour period Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children (aged 6-59 months) who responded nationally: (n= 4916) 2Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 4916) due to the response “Don’t Know” 4Less than (n = 4916) due to the response “Don’t Know” 5Less than (n = 4916) due to the response “Don’t Know” 135 Figure 42 presents the reported diarrhoea treatments for children (aged 6-59 months). Table 67 looks at three of the most frequently used diarrhoea treatments for children (that are not homemade) reported by caregivers, pooled at the national level. The data are stratified by age, sex, residence, zone, wealth quintile, mother’s education, source of drinking water, and type of toilet facility. • ORS (Oral Rehydration Salt): About 39 percent of children received ORS treatment for diarrhoea. There was a statistically significant difference in the percentage of children with diarrhoea who received ORS treatment with the type of toilet facility used by the household (P = 0.019). ORS treatment for diarrhoea among children (aged 6-59 months) who had diarrhoea was highest among children in households with improved toilet facility (54 percent). • Pill/Syrup antibiotic: About 28 percent of children received antibiotics treatment for diarrhoea. There was a statistically significant difference in the percentage of children with diarrhoea who received pill/syrup antibiotics among the zone (P = 0.015), wealth quintile (P = 0.025), caregivers’ educational attainment (P = 0.002), and type of toilet facility (P = 0.005). The use of pill/syrup antibiotics among children (aged 6-59 months) with diarrhoea was lowest in the South South zone (14 percent). It was lowest among children in the fourth wealth quintile (18 percent). It was highest among children whose caregivers had no educational attainment (36 percent) and in households with unimproved toilet facility (33 percent). • Antimotility: About 13 percent of children received antimotility treatment for diarrhoea. There was a statistically significant difference in the percentage of children with diarrhoea who received antimotility drugs among sex (P = 0.035), residence (P = 0.001), zone (P < 0.001), and wealth quintile (P = 0.047). The use of pill/syrup antibiotics among children (aged 6-59 months) with diarrhoea was higher in male (16 percent) compared to female (10 percent) children. It was higher among children residing in urban (28 percent) than in rural (7 percent) areas. It was highest in the North East zone (36 percent). It was lowest among children in the lowest wealth quintile (7 percent). Figure 42. Reported treatments for diarrhoea in children (aged 6-59 months), Nigeria 2021 The data are based on questions chs14 and chs15 of the biomarker questionnaire chs14. Was [name of child] given any of the following to drink at any time since he/she started having diarrhoea: A fluid made from a special packet called (local name for ORS packet)? A pre-packaged ORS liquid? (Show locally sourced ORS packet) ORS – Oral Rehydration Salt chs15. What (else) was given to treat diarrhoea? Data are weighted to account for survey design and non-response Number of children (aged 6-59 months) who responded nationally: (n= 4916) 136 Table 67. Most common1 diarrhoea treatment among children (aged 6-59 months), Nigeria 2021 ORS Pill/Syrup Antibiotic Antimotility Background characteristics N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.273 ) (P = 0.478 ) (P = 0.874 ) 6-11 months 95 26.3 [17.4, 37.6] 97 21.4 [12.6, 34.0] 97 13.5 [5.7, 28.9] 12-23 months 171 42.2 [31.6, 53.7] 173 24.9 [17.1, 34.8] 173 11.7 [7.3, 18.2] 24-35 months 117 41.4 [29.8, 54.1] 118 31.2 [22.1, 41.9] 118 11.5 [5.2, 23.5] 36-47 months 90 39.8 [26.4, 55.0] 93 27.0 [16.6, 40.8] 93 14.8 [7.2, 28.0] 48-59 months 44 46.2 [30.5, 62.7] 45 37.8 [18.1, 62.6] 45 16.7 [4.7, 45.1] Sex (P = 0.207 ) (P = 0.371 ) (P = 0.035 *) Male 285 36.4 [28.4, 45.2] 288 25.6 [19.4, 33.0] 288 15.7 [9.2, 25.7] Female 232 42.7 [35.5, 50.2] 238 30.4 [22.1, 40.3] 238 9.5 [5.5, 15.9] Residence (P = 0.148 ) (P = 0.235) (P = 0.001 **) Urban 176 45.5 [37.1, 54.2] 177 22.3 [14.1, 33.4] 177 27.9 [14.6, 46.8] Rural 341 36.6 [28.7, 45.3] 349 29.8 [23.4, 37.1] 349 7.3 [4.4, 12.0] Zone (P = 0.113 ) (P = < 0.0478*) (P < 0.001 ***) North Central 84 32.1 [18.7, 49.3] 86 21.1 [13.0, 32.3] 86 11.4 [4.6, 25.6] North East 150 50.1 [42.2, 57.9] 153 21.7 [13.1, 33.8] 153 36.4 [22.6, 52.8] North West 135 35.4 [24.4, 48.2] 136 35.7 [25.9, 46.9] 136 0.6 [0.1, 4.2] South East 48 22.7 [11.6, 39.6] 48 16.8 [7.7, 32.9] 48 15.9 [8.0, 29.3] South South 51 31.4 [16.2, 52.0] 52 13.9 [5.5, 30.8] 52 11.6 [5.0, 24.8] South West 49 47.3 [31.3, 63.9] 51 28.1 [15.8, 45.0] 51 3.1 [0.4, 18.9] Wealth quintile2 (P = 0.051 ) (P = 0.025 *) (P = 0.047 *) Lowest 142 36.8 [26.4, 48.6] 147 23.4 [13.7, 37.1] 147 7.2 [3.3, 15.1] Second 146 32.5 [22.5, 44.3] 149 38.7 [29.7, 48.6] 149 11.0 [5.2, 21.9] Middle 84 36.0 [24.6, 49.3] 85 21.0 [12.2, 33.5] 85 12.2 [6.4, 21.9] Fourth 89 55.1 [40.9, 68.5] 89 18.3 [10.8, 29.2] 89 20.4 [9.4, 38.8] Highest 53 52.8 [41.5, 63.9] 53 26.0 [14.8, 41.6] 53 31.1 [11.8, 60.5] Caregiver’s educational attainment2 (P = 0.122 ) (P = 0.002 **) (P = 0.056 ) None 183 37.7 [29.1, 47.1] 186 35.5 [26.2, 45.9] 186 9.4 [5.5, 15.6] Primary 85 25.9 [14.3, 42.3] 90 19.5 [11.2, 31.6] 90 11.7 [3.6, 31.7] Secondary 172 47.0 [35.4, 59.0] 173 19.2 [12.4, 28.5] 173 24.1 [12.9, 40.7] Tertiary 37 55.4 [31.0, 77.5] 37 19.8 [9.4, 37.1] 37 27.9 [10.2, 56.9] Source of drinking water2 (P = 0.997 ) (P= 0.733 ) (P = 0.484 ) Improved 384 39.1 [32.3, 46.4] 390 27.3 [21.0, 34.7] 390 12.0 [7.1, 19.6] Unimproved 130 39.1 [29.0, 50.4] 133 28.7 [18.6, 41.6] 133 15.3 [7.5, 28.8] Type of toilet facility2 (P = 0.019 *) (P = 0.005 **) (P = 0.060 ) Improved facility 112 54.4 [44.5, 63.9] 112 20.1 [12.3, 31.3] 112 26.4 [10.9, 51.3] Unimproved facility 268 35.8 [27.0, 45.7] 271 33.3 [25.5, 42.0] 271 10.6 [6.1, 17.9] Open defecation 134 33.8 [25.1, 43.8] 140 20.0 [14.2, 27.5] 140 7.1 [2.4, 19.4] National 5173 39.1 [32.8, 45.8] 5264 27.7 [22.3, 33.9] 5265 13.0 [8.0, 20.5] The data are based on questions chs14 and chs15 of the biomarker questionnaire chs14. Was [name of child] given any of the following to drink at any time since he/she started having diarrhoea: A fluid made from a special packet called (local name for ORS packet)? A pre-packaged ORS liquid? (Show locally sourced ORS packet) chs15. What (else) was given to treat diarrhoea? See Figure 42 linked to chs15 1Most common treatments are the top three (not homemade) as reported by caregivers, pooled at the national level. Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001) Number of children (aged 6-59 months) who had diarrhoea who responded nationally: (n= 526) 2Less than (n = 526) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 526) due to response “Don’t Know” 137 Fever and Cough Table 68 presents the prevalence of fever and cough among children (aged 6-59 months) , stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. • Fever: There was a statistically significant difference in the prevalence of fever among children (aged 6-59 months) in the past two weeks with the zone (P = 0.004). The percentage of children (aged 6-58 months) ill with fever in the past two weeks was lowest in the North West (40 percent) and South West (40 percent) zones. • Cough: There was a statistically significant difference in the prevalence of cough among children (aged 6-59 months) in the past two weeks among age groups (P = 0.019), zone (P < 0.001), wealth quintile (P = 0.001), and caregivers’ educational attainment (P < 0.001). The percentage of children (aged 6-58 months) who were ill with cough in the past two weeks was highest in the 12-23 months age category (41 percent). It was lowest among children in the North West zone (24 percent). It was highest among children in the highest wealth quintile (47 percent). It was lowest among children whose caregivers had no educational attainment (31 percent). • Fast, short, rapid breaths or difficulty breathing: There was a statistically significant difference in the prevalence of fast, short, rapid breaths or difficulty breathing among children (aged 6-59 months) in the past two weeks among zone (P < 0.001), wealth quintile (P < 0.001), and caregivers’ educational attainment (P = 0.035). The percentage of children (aged 6-58 months) who were ill with fast, short, rapid breaths or difficulty breathing in the past two weeks was highest in children in the North East zone (21 percent). It was highest among children in the lowest wealth quintile (19 percent). It was highest in children whose caregivers had no educational attainment (16 percent). 138 Table 68. Prevalence of fever, cough, and difficulty breathing among children (aged 6-59 months), Nigeria 2021 Child ill with a fever at any time Child had a cough in the past Child had fast, short, rapid Background in the past two weeks two weeks breaths or difficulty breathing at characteristics any time in the past two weeks N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.059) (P = 0.019 *) (P = 0.585) 6-11 months 491 46.7 [38.0, 55.6] 494 39.6 [32.5, 47.1] 485 11.8 [8.5, 16.3] 12-23 months 1130 49.8 [45.6, 53.9] 1137 41.4 [37.1, 45.8] 1124 13.3 [10.6, 16.6] 24-35 months 1176 47.5 [42.9, 52.1] 1178 38.2 [34.0, 42.6] 1160 14.2 [11.4, 17.5] 36-47 months 1197 43.8 [38.5, 49.3] 1195 36.0 [32.2, 40.1] 1183 13.2 [10.2, 16.8] 48-59 months 896 39.6 [34.2, 45.1] 900 31.4 [27.3, 35.9] 891 10.8 [8.1, 14.2] Sex (P = 0.302) (P = 0.507) (P = 0.558) Male 2458 46.6 [43.0, 50.3] 2467 36.7 [33.6, 39.8] 2433 13.3 [11.5, 15.3] Female 2432 44.5 [40.8, 48.2] 2437 37.9 [34.4, 41.6] 2410 12.5 [10.4, 14.9] Residence (P = 0.519) (P = 0.097) (P = 0.208) Urban 2002 44.0 [38.3, 49.9] 2004 40.8 [36.3, 45.4] 1977 11.3 [8.7, 14.5] Rural 2888 46.3 [42.6, 50.0] 2900 35.6 [32.0, 39.4] 2866 13.6 [11.9, 15.6] Zone (P = 0.004 **) (P < 0.001 ***) (P < 0.001 ***) North Central 764 47.7 [43.4, 52.0] 770 45.1 [38.5, 51.8] 757 12.2 [9.3, 15.8] North East 824 53.0 [45.4, 60.4] 822 45.1 [37.9, 52.5] 818 21.4 [16.8, 26.9] North West 907 39.8 [32.8, 47.2] 908 23.8 [18.1, 30.6] 908 11.0 [8.6, 14.0] South East 716 52.5 [46.4, 58.5] 716 50.3 [45.1, 55.5] 715 8.6 [6.4, 11.5] South South 820 51.4 [45.4, 57.4] 828 44.9 [40.4, 49.5] 785 13.2 [10.0, 17.3] South West 859 39.6 [36.0, 43.3] 860 39.5 [35.5, 43.6] 860 8.3 [6.0, 11.4] Wealth quintile1 (P = 0.817) (P = 0.001 ***) (P < 0.001 ***) Lowest 1046 45.4 [40.5, 50.4] 1048 34.2 [29.9, 38.9] 1040 18.8 [15.3, 22.9] Second 1019 46.8 [40.6, 53.2] 1023 31.5 [27.0, 36.5] 1012 10.8 [8.2, 13.9] Middle 966 45.9 [41.6, 50.1] 970 38.0 [33.0, 43.3] 950 11.3 [8.5, 14.7] Fourth 953 46.3 [41.5, 51.1] 958 42.2 [38.0, 46.5] 950 10.8 [8.0, 14.6] Highest 885 42.3 [33.7, 51.5] 884 46.9 [39.4, 54.5] 871 10.1 [7.7, 13.2] Caregiver’s educational attainment1 (P = 0.735) (P < 0.001 ***) (P = 0.035 *) None 1304 45.2 [40.4, 50.1] 1308 30.9 [26.7, 35.4] 1299 15.6 [12.3, 19.5] Primary 757 49.0 [43.3, 54.7] 763 38.4 [33.7, 43.4] 753 9.3 [6.9, 12.5] Secondary 1990 46.3 [42.8, 49.9] 1993 46.0 [42.3, 49.7] 1962 12.3 [10.4, 14.6] Tertiary 518 46.6 [38.7, 54.8] 519 44.3 [39.2, 49.6] 509 12.9 [9.6, 17.0] National 48902 45.6 [42.5, 48.7] 49043 37.3 [34.5, 40.1] 48434 12.9 [11.4, 14.5] The data are based on questions chs16, chs17, and chs18 of the biomarker questionnaire chs 16. Has [name of child] been ill with a fever at any time in the last two weeks? chs 17. Has [name of child] had an illness with a cough at any time in the last two weeks? chs 18. Has [name of child] had fast, short, rapid breaths or difficulty breathing at any time in the last two weeks? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children (aged 6-59 months) who responded nationally: (n= 4916) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 4916) due to the response “Don’t Know” 3Less than (n = 4916) due to the response “Don’t Know” 4Less than (n = 4916) due to the response “Don’t Know” 139 Pica The most common cause of eating non-food items (pica) is specific mineral deficiency such as iron. Pica is a symptom of iron deficiency anaemia and is often linked to a craving for ice, clay, and chalk. It is primarily seen in children and pregnant women. Table 69 presents the prevalence of pica in the past seven days among children (aged 6-59 months) stratified by age, sex, residence, zone, wealth quintile, and caregiver’s education status. There was a statistically significant difference in the prevalence of pica in the past seven days among children (aged 6-59 months) between the age groups (P < 0.001) and between the zones (P = 0.005). The percentage of children (aged 6-59 months) with pica was highest among the 6-to- 11-month age category (38 percent). It was lowest in children in the North West zone (14 percent). Table 69. Prevalence of pica among children (aged 6-59 months), Nigeria 2021 Ate earth, clay, mud, or soil from any source (e.g., walls of mud houses, the yard, purchased at the Background market) in the last seven days characteristics N % [95% CI] P value Age category 6-11 months 488 38.3 [30.9, 46.3] 12-23 months 1105 31.0 [26.8, 35.4] 24-35 months 1142 16.0 [12.8, 19.6] (P < 0.001 ***) 36-47 months 1163 12.6 [10.3, 15.4] 48-59 months 870 9.9 [7.5, 12.9] Sex Male 2398 19.5 [16.8, 22.6] (P = 0.793) Female 2370 19.9 [17.4, 22.7] Residence Urban 1953 19.3 [15.6, 23.7] (P = 0.811) Rural 2815 19.9 [17.1, 23.2] Zone North Central 734 23.9 [17.8, 31.3] North East 794 20.6 [15.4, 27.0] North West 901 13.9 [10.0, 19.0] (P = 0.005 **) South East 695 31.4 [26.0, 37.3] South South 793 25.0 [16.8, 35.3] South West 851 19.3 [15.4, 24.0] Wealth quintile1 Lowest 1023 18.8 [15.3, 22.8] Second 1001 19.8 [15.6, 24.7] Middle 939 20.9 [17.7, 24.5] (P = 0.910) Fourth 924 20.3 [16.2, 25.1] Highest 864 18.7 [13.9, 24.7] Caregiver’s educational attainment1 None 1274 17.7 [14.8, 21.2] Primary 753 22.3 [16.9, 28.7] (P = 0.092) Secondary 1925 22.9 [19.1, 27.3] Tertiary 503 18.1 [13.7, 23.5] National 47682 19.7 [17.4, 22.3] The data are based on question chs10 of the biomarker questionnaire chs10. In the last seven days, has [name of child] eaten earth, clay, mud, or soil from any source (e.g., walls of mud houses, the yard, purchased at market)? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children (aged 6-59 months) who responded nationally: (n= 4916) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 4916) due to the response “Don’t Know” 140 Intervention coverage, health status, and anaemia risk factors among adolescent girls (aged 10-14 years) Intervention coverage among adolescent girls (aged 10-14 years) The MNDC guidelines38 describe the following interventions to address micronutrient deficiencies among adolescent girls – deworming, iron, and folate supplementation. Some interventions are reflected in the NFPN39, which prioritizes both the health system and food-based approaches to MNDC. An objective of the survey was to assess the coverage of these interventions among adolescent girls in Nigeria. Figure 43 presents the coverage of nutrition-related interventions (multivitamin, iron or iron/folic acid tablets, deworming) and their use in the last six months among adolescent girls nationally. It was reported that 25 percent of adolescent girls used deworming treatment. The use of iron/folic acid tablets was reported among 11 percent of adolescent girls, while the use of multivitamins was reported among 9 percent of adolescent girls. Figure 43. Coverage of nutrition-specific interventions among adolescent girls, Nigeria 2021 Data are weighted to account for survey design and non-response Number of adolescent girls responding nationally: (n=1002) Table 70 presents the use of multivitamins, iron or iron/folic acid tablets, and deworming treatment among adolescent girls (aged 10-14 years), stratified by age, residence, and wealth quintile. a. Use of multivitamins in the past six months: There was a statistically significant difference in the percentage of adolescent girls who reported use of multivitamins with the wealth quintile (P < 0.001). The use of multivitamins was lowest in respondents in the lowest wealth quintile (0.7 percent). b. Use of multivitamins in the past seven days: Figure 44 presents the frequency of use of multivitamins in the past seven days among adolescent girls. There was no significant variation in the use of multivitamins, at least once in the past seven days, among adolescent girls across the background characteristics. 38 Federal Ministry of Health (FMOH). 2013. National guidelines on micronutrients deficiencies control in Nigeria. Abuja: Federal Ministry of Health. 39 Ministry of Budget & National Planning. 2016. National Policy on Food and Nutrition in Nigeria. Abuja: Ministry of Budget and National Planning. 141 c. Use of iron or iron/folic acid tablets in the past six months: There was a statistically significant difference in the percentage of adolescent girls who reported use of iron or iron/folic acid tablets in the past six months between residence (P = 0.003) and between the wealth quintile (P < 0.001). Their use in the past six months was lower in the rural (8 percent) than in urban (16 percent) areas. Their use was lowest among respondents in the lowest wealth quintile (5 percent). d. Use of iron or iron/folic acid tablets at least once in the past seven days: Figure 45 presents the frequency of use of any iron/folic acid in the past seven days among adolescent girls nationally. There was a statistically significant difference in the prevalence of iron or iron/folic acid tablet use between residence (P = 0.045). Their use in the past seven days was higher in the urban (9 percent) than in the rural (5 percent) areas. e Deworming in the past six months: There was a statistically significant difference in the prevalence of deworming between residence (P = 0.005) and between the wealth quintile (P < 0.001). Deworming was higher among adolescent girls residing in urban (32 percent) than in rural (21 percent) areas. Deworming was lowest among adolescent girls in the second wealth quintile (13 percent). 142 143 Table 70. Use of multivitamin, iron or iron/folic acid tablets, and deworming treatment among adolescent girls (aged 10-14 years), Nigeria 2021 Took any multivitamin tablets in the Took any multivitamin tablets at Took any iron tablets or iron-folic acid Took any iron tablets, or iron-folic Took any drugs for intestinal Background characteristics past six months least once in the past seven days in the past six months acid at least once in the past worms in the past six seven days months N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age (P = 0.138) (P= 0.755) (P= 0.242) (P= 0.391) (P= 0.686) 10 years 265 8.7 [5.2, 14.2] 265 5.7 [2.8, 10.9] 265 11.4 [7.6, 16.6] 265 7.4 [4.5, 11.9] 264 25.6 [19.6, 32.7] 11 years 158 12.0 [5.9, 22.8] 158 5.4 [2.7, 10.7] 158 10.4 [6.5, 16.3] 158 6.2 [3.3, 11.4] 154 29.6 [21.6, 38.9] 12 years 195 6.8 [3.9, 11.8] 195 3.5 [1.6, 7.4] 195 8.7 [5.5, 13.7] 195 5.4 [3.1, 9.2] 194 21.0 [15.2, 28.1] 13 years 195 11.6 [6.7, 19.4] 195 7.2 [3.5, 14.1] 195 15.8 [10.3, 23.3] 195 9.2 [5.0, 16.5] 194 25.0 [17.6, 34.3] 14 years 190 3.7 [1.8, 7.4] 190 1.6 [0.6, 4.4] 190 9.0 [4.9, 16.1] 190 5.5 [2.7, 10.8] 187 25.1 [17.7, 34.3] Residence (P= 0.067) (P= 0.369) (P= 0.003 **) (P= 0.045 *) (P= 0.005 **) Urban 421 11.6 [7.3, 18.1] 421 7.3 [3.7, 13.9] 421 16.1 [11.8, 21.7] 421 9.3 [5.9, 14.3] 416 32.1 [26.6, 38.3] Rural 582 6.6 [4.5, 9.6] 582 3.2 [2.1, 5.0] 582 8.3 [6.0, 11.2] 582 5.4 [3.7, 7.8] 577 20.9 [16.6, 26.1] Wealth quintile1 (P< 0.001 ***) (P= 0.468) (P< 0.001 ***) (P= 0.457) (P< 0.001 ***) Lowest 213 0.7 [0.1, 3.2] 213 0.2 [0.0, 1.2] 213 4.5 [2.3, 8.7] 213 2.0 [0.8, 5.1] 212 15.5 [10.1, 23.1] Second 191 6.3 [3.7, 10.5] 191 4.5 [2.4, 8.3] 191 8.4 [4.7, 14.6] 191 5.1 [2.5, 9.9] 190 12.9 [8.3, 19.5] Middle 206 14.7 [8.4, 24.4] 206 6.4 [2.8, 13.7] 206 16.8 [11.4, 24.2] 206 11.1 [6.9, 17.4] 204 20.7 [14.9, 27.9] Fourth 198 13.1 [8.1, 20.5] 198 8.0 [4.0, 15.5] 198 19.2 [13.2, 27.0] 198 12.1 [7.1, 19.9] 198 45.4 [36.8, 54.3] Highest 191 11.0 [6.1, 19.0] 191 6.6 [3.4, 12.6] 191 9.4 [5.4, 16.0] 191 5.8 [2.8, 11.7] 185 40.4 [30.9, 50.6] National 1003 8.5 [6.3, 11.3] 1003 4.7 [3.1, 7.2] 1003 11.1 [8.9, 13.7] 1003 6.8 [5.1, 9.1] 9932 25.0 [21.6, 28.7] The data are based on questions wtt2, wtt3, wtt4, wtt5, and wrf2 of the biomarker questionnaire wtt2. During the last six months, did you take any multivitamin tablets? wtt3. How many days did you take any of these products in the last seven days? See Figure 44 for more details. wtt4. During the last six months, did you take any iron/folic acid tablets? wtt5. How many days did you take any iron/folic acid tablets in last seven days? See Figure 45 for more details. wrf2. Did you take any drugs for intestinal worms in the past six months? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of adolescent girls who responded nationally: (n=1003) 1Less than (n = 1003) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 1003) due to the response of “Don’t Know” Figure 44 presents the frequency of use of multivitamins in the past seven days among adolescent girls. Among adolescent girls who responded in the affirmative, 37 percent took multivitamins for the entire seven days. 90% 80% 70% 60% 50% 40% 37.2 30% 20.3 20% 10.5 12.6 10% 9.4 6.5 3.5 0% 1 Day 2 Days 3 Days 4 Days 5 Days 6 Days 7 Days Figure 44. Frequency of use of multivitamins in the past seven days among adolescent girls (aged 10-14 years), Nigeria 2021 Based on question wtt3. How many days did you take any of these products [any multivitamin tablets] in the last seven days? Data are weighted to account for survey design and non-response Number of adolescent girls who responded nationally: (n=1003) Figure 45 presents the frequency of use of some form of iron/folic acid tablets in the past seven days among adolescent girls. Among adolescent girls who responded in the affirmative, 16 percent took iron/folic acid tablets for the entire seven days. 90% 80% 70% 60% 50% 40% 30% 25.4 20% 16.9 16.1 13.0 10% 6.6 6.8 6.0 0% 1 Day 2 Days 3 Days 4 Days 5 Days 6 Days 7 Days Figure 45. Frequency of use of any iron/folic acid tablets in the past seven days among adolescent girls (aged 10-14 years), Nigeria 2021 Based on question wtt5. How many days did you take any iron/folic acid tablets in last seven days? Data are weighted to account for survey design and non-response Number of adolescent girls who responded nationally: (n=1003) 144 Percentage Percentage Self-reported morbidity prevalence and anaemia risk factors among adolescent girls (aged 10-14 years) A key objective of the survey was to assess morbidity as a key factor associated with anaemia. Figure 46 presents the overall prevalence of self-reported illness (cough, fever, malaria, and diarrhoea) and hospitalization/clinic visits among adolescent girls in the last two weeks. The prevalence of hospitalization was low (6 percent), while that of cough, fever, malaria, and diarrhoea were 32 percent, 29 percent, 20 percent, and 16 percent nationally, respectively. Figure 46. Overall prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among adolescent girls (aged 10-14 years), Nigeria 2021 Data are weighted to account for survey design and non-response The number of adolescent girls who responded nationally: (n= 1003) Table 71 presents the prevalence of self-reported illness (diarrhoea, cough, fever, and malaria) and hospitalization/clinic visits among adolescent girls in the last two weeks stratified by age, residence, and wealth quintile. a. Diarrhoea in the past two weeks: There was no significant variation in the prevalence of diarrhoea in the past two weeks among adolescent girls across the background characteristics. b. Cough in the past two weeks: There was a statistically significant difference in the prevalence of cough in the past two weeks with the wealth quintiles (P = 0.006). The prevalence was lowest in adolescent girls in the highest wealth quintile (23 percent). c. Difficulty breathing in the past two weeks: There was a statistically significant difference in the prevalence of difficulty breathing with the wealth quintile (P = 0.020). The prevalence was lowest in adolescent girls in the highest wealth quintile (19 percent). d. Fever in the past two weeks: There was no significant variation in the prevalence of fever in the past two weeks among adolescent girls across the background characteristics e. Malaria in the past two weeks: There was no significant variation in the prevalence of malaria in the past two weeks among adolescent girls across the background characteristics. f. Hospitalization in the past two weeks: There was no significant variation in the prevalence of hospitalization in the past two weeks among adolescent girls across the background characteristics. 145 146 Table 71. Prevalence of self-reported illness and hospitalization/clinic visits in the past two weeks among adolescent girls (aged 10-14 years), Nigeria 2021 Had diarrhoea1 in the past two Had a cough in the past two Had difficulty breathing in the Had a fever in the past two Had malaria in the past two Had any hospitalization and /or Background weeks weeks past two weeks weeks weeks clinic visits due to illness in the characteristics last two weeks N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age (P = 0.268) (P = 0.741) (P = 0.843) (P= 0.234) (P = 0.155) (P = 0.074) 10 years 264 19.9 [14.3, 26.9] 264 33.9 [26.0, 42.9] 85 36.7 [24.3, 51.2] 264 36.0 [28.4, 44.5] 258 24.9 [19.2, 31.8] 265 5.6 [3.1, 9.8] 11 years 158 12.1 [7.1, 20.0] 158 30.1 [22.1, 39.5] 56 27.0 [15.9, 41.8] 158 30.9 [21.5, 42.2] 155 20.6 [13.5, 30.2] 158 9.8 [4.2, 21.3] 12 years 195 11.9 [7.4, 18.8] 195 27.8 [19.9, 37.4] 60 34.2 [19.8, 52.4] 195 25.8 [18.8, 34.4] 191 21.5 [15.0, 29.7] 195 9.1 [4.5, 17.6] 13 years 194 18.0 [11.7, 26.6] 194 30.9 [22.6, 40.7] 55 28.9 [15.9, 46.8] 195 26.9 [19.4, 36.0] 191 15.3 [10.0, 22.7] 195 1.9 [0.8, 4.1] 14 years 190 14.7 [9.9, 21.3] 190 35.5 [27.2, 44.9] 71 36.6 [23.8, 51.5] 189 24.0 [17.0, 32.8] 184 14.6 [9.3, 22.3] 190 3.9 [1.7, 8.6] Residence (P = 0.604) (P = 0.971) (P = 0.091) (P = 0.231) (P = 0.362) (P = 0.937) Urban 420 14.7 [10.6, 20.1] 420 31.7 [26.9, 36.9] 124 25.4 [16.1, 37.6] 420 26.3 [21.4, 31.8] 412 17.8 [13.4, 23.3] 421 5.8 [3.4, 9.6] Rural 581 16.4 [12.7, 20.7] 581 31.9 [26.4, 37.9] 203 37.9 [29.6, 47.0] 581 30.8 [25.9, 36.2] 567 21.0 [16.9, 25.7] 582 6.0 [3.7, 9.4] Wealth quintile2 (P = 0.772) (P = 0.006 **) (P = 0.020 *) (P = 0.367) (P = 0.304) (P = 0.496) Lowest 211 13.1 [8.7, 19.3] 213 28.2 [20.7, 37.0] 65 38.0 [23.2, 55.3] 212 28.0 [20.4, 37.2] 205 18.2 [12.4, 25.9] 213 7.6 [3.7, 14.8] Second 191 15.8 [10.6, 23.0] 191 27.0 [20.0, 35.3] 63 51.5 [34.7, 68.0] 191 27.9 [21.6, 35.3] 186 15.5 [10.6, 22.0] 191 3.7 [1.6, 8.5] Middle 206 16.4 [10.8, 23.9] 205 43.9 [34.6, 53.5] 89 33.1 [21.4, 47.4] 205 33.0 [24.9, 42.3] 201 25.6 [18.5, 34.3] 206 8.3 [4.0, 16.6] Fourth 198 19.1 [12.2, 28.7] 197 37.6 [28.9, 47.2] 60 19.1 [10.1, 33.2] 198 33.6 [25.5, 42.8] 194 21.7 [15.5, 29.6] 198 5.0 [2.4, 10.3] Highest 191 14.8 [9.2, 23.1] 191 23.4 [16.5, 32.0] 48 18.5 [9.7, 32.5] 191 22.0 [14.9, 31.3] 189 19.7 [13.0, 28.8] 191 4.5 [1.9, 10.1] National 10013 15.7 [13.0, 19.0] 10014 31.8 [27.9, 36.0] 3274 33.4 [26.8, 40.6] 10015 29.2 [25.5, 33.1] 9796 19.8 [16.8, 23.3] 10037 5.9 [4.1, 8.3] The data are based on questions wah1, wah2, wah3, wah5, wah6, and wah7 of the biomarker questionnaire wah1. Have you been ill with diarrhoea in the past two weeks? wah2. Have you been ill with a cough or breathing problems in the past two weeks? wah3. When you had an illness with a cough, did you breathe faster than usual? wah5. Have you been ill with a fever in the past two weeks? wah6. Have you been ill with malaria in the past two weeks? wah7. Have you had any hospitalization in the last two weeks? 1Diarrhoea is defined as three or more loose or watery stools in 24 hours. Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of adolescent girls who responded nationally: (n=1003) 2Less than (n = 1003) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 1003) due to the response “Don’t Know” 4Less than (n = 1003) due to the response “Don’t Know” 5Less than (n = 1003) due to the response “Don’t Know” 6Less than (n = 1003) due to the response “Don’t Know” 7Less than (n = 1003) due to the response “Don’t Know” Other anaemia risk factors among adolescent girls (aged 10-14 years) Figure 47 presents the overall prevalence of anaemia risk factors (pica and smoking) and diagnosis of anaemia by a healthcare provider in the past six months among adolescent girls. Nationally, the prevalence of self-reported smoking among adolescent girls was low (0.3 percent). The prevalence of pica and clinically diagnosed anaemia among adolescent girls was 9 percent and 4 percent, respectively. Figure 47. Prevalence of anaemia risk factors (pica and smoking) and diagnosis of anaemia in the past six months among adolescent girls (aged 10-14 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of adolescent girls who responded nationally: (n= 1003) Table 72 presents the prevalence of anaemia risk factors (pica and smoking) and diagnosis of anaemia in the past six months among adolescent girls stratified by age, residence, and wealth quintile. a. Pica: There was no significant variation in the prevalence of pica in the past seven days among adolescent girls across the background characteristics. b. Smoking: There was no significant variation in the prevalence of smoking among adolescent girls across the background characteristics. c. Diagnosis of anaemia by a healthcare provider in the past six months: There was no significant variation across the background characteristics. 147 Table 72. Prevalence of pica, smoking, and diagnosis of anaemia in the past six months among adolescent girls (aged 10-14 years), Nigeria 2021 Ate earth, clay, mud, or soil from any source (e.g., walls of mud houses, the Smoked tobacco (excluding Diagnosed with anaemia (by a Background yard, purchased at the market) in the last powder or chew type) healthcare provider) in the past characteristics seven days six months N % [95% CI] N % [95% CI] N % [95% CI] Age (P = 0.893 ) (P = 0.755 ) (P 0.678) 10 years 265 8.7 [4.7, 15.6] 265 0.4 [0.1, 2.6] 262 5.4 [2.8, 10.3] 11 years 158 9.7 [4.1, 20.9] 158 0.0 [ ., .] 155 2.5 [0.8, 7.0] 12 years 195 6.7 [3.2, 13.7] 195 0.5 [0.1, 2.0] 195 3.0 [1.1, 7.5] 13 years 195 10.5 [5.4, 19.4] 195 0.3 [0.0, 1.9] 195 4.5 [1.3, 14.6] 14 years 190 9.4 [5.6, 15.5] 190 0.0 [ ., .] 187 6.0 [2.4, 14.2] Residence (P = 0.582 ) (P = 0.973 ) (P = 0.444) Urban 421 10.1 [6.0, 16.4] 421 0.3 [0.1, 1.0] 417 5.5 [2.3, 12.5] Rural 582 8.3 [5.1, 13.0] 582 0.2 [0.1, 1.0] 577 3.7 [2.2, 6.3] Wealth quintile1 (P = 0.821 ) (P = 0.547 ) (P = 0.086) Lowest 213 9.3 [4.5, 18.5] 213 0.2 [0.0, 1.5] 210 2.1 [1.0, 4.5] Second 191 7.5 [4.0, 13.6] 191 0.7 [0.2, 2.9] 191 6.9 [2.9, 15.4] Middle 206 7.3 [4.1, 12.8] 206 0.0 [ ., .] 204 6.6 [3.3, 12.5] Fourth 198 11.1 [6.1, 19.4] 198 0.2 [0.0, 1.8] 196 4.5 [1.9, 9.9] Highest 191 8.6 [4.4, 16.3] 191 0.0 [ ., .] 189 1.5 [0.4, 6.1] National 10032 8.9 [6.3, 12.5] 10033 0.3 [0.1, 0.7] 9944 4.4 [2.7, 7.1] The data are based on questions wtt1, wah8, and wrf1 of the biomarker questionnaire wtt1. In the last seven days, have you eaten earth/clay/mud/soil from any source? wah8. Do you smoke? (Do not include the powder and chew type) wrf1. Have you been diagnosed with anaemia in the past six months? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of adolescent girls who responded nationally: (n=1003) 1Less than (n = 1003) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 1003) due to the response “Don’t Know” 3Less than (n = 1003) due to the response “Don’t Know” 4Less than (n = 1003) due to the response “Don’t Know” 148 Intervention coverage, health status, and anaemia risk factors among WRA (15-49 years old) The MNDC guidelines40 describe the following interventions to address micronutrient deficiencies among WRA – deworming, iron supplementation, and folate supplementation. Some interventions are reflected in the NFPN41, which prioritizes both the health system and food-based approaches to MNDC. An objective of the survey was to assess the coverage of these interventions among WRA in Nigeria. Intervention coverage among WRA (aged 15-49 years old) Figure 48 presents the overall prevalence of nutrition-related interventions (multivitamin, iron or iron/folic acid tablets, deworming) and their use (six months, seven days) among WRA (aged 15- 49 years) nationally. The use of deworming treatment in the past six months was reported in 19 percent of WRA. Nationally, 13 percent of WRA took a multivitamin in the past six months. The use of iron/folic acid was 15 percent in the past six months. Figure 48. Coverage of nutrition-specific interventions among WRA (aged 15-49 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of WRA who responded nationally: (n= 5238) Table 73 presents the use of multivitamin, iron or iron/folic acid tablets, and deworming treatment among WRA (15-49 months) stratified by age, residence, zone, wealth quintile, and educational attainment. a. Use of multivitamin tablets in the past six months: There was a statistically significant difference in the percentage of WRA reporting use of multivitamins in the past six months among age category (P = 0.013), zone (P < 0.001), and educational attainment (P < 0.001). The use of multivitamin tablets in the past six months was lowest in the 15-to-19-year age category (7 percent). It was lowest among respondents in the North West zone (2 percent) and respondents with no educational attainment (6 percent). b. Use of multivitamin tablets at least once in the past seven days: Figure 49 presents the frequency of use of multivitamin tablets in the past seven days among women of reproductive nationally. It was found that eight percent of WRA took multivitamin tablets at least once in the past seven days before the interview. There was a statistically significant difference in the percentage of WRA reporting the use of multivitamins at least once in the past seven days with the zones (P = 0.003). The use of multivitamin tablets was lowest among respondents in the North West zone (1 percent). 40 Federal Ministry of Health (FMOH). 2013. National guidelines on micronutrients deficiencies control in Nigeria. Abuja: Federal Ministry of Health. 41 Ministry of Budget & National Planning. 2016. National Policy on Food and Nutrition in Nigeria. Abuja: Ministry of Budget and National Planning. 149 c. Use of iron or iron/folic acid tablets in the past six months: There was a statistically significant difference in the percentage of WRA reporting use of iron or iron/folic acid tablets in the past six months among the age groups (P < 0.001), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), and educational attainment (P < 0.001). Use of iron or iron/ folic acid tablets in the past six months in WRA was lowest in the 15 to 19-year age category (8 percent). It was lower in WRA residing in rural (12 percent) than in the urban (19 percent) areas. It was lowest among respondents in the North West zone (2 percent), among WRA in the lowest wealth quintile (7 percent), and among respondents with no educational attainment (7 percent). d. Use of iron or iron/folic acid tablets at least once in the past seven days: Figure 50 presents the frequency of use of any iron/folic acid in the past seven days among women of reproductive nationally. Nine percent of WRA took iron/folic acid tablets at least once in the past seven days before the interview. There was a statistically significant difference in the percentage of WRA reporting use of iron or iron/folic acid tablets at least once in the past seven days between residence (P = 0.045). Their use was higher in the urban (10 percent) than in the rural (8 percent) areas. e. Deworming: There was a statistically significant difference in the percentage of WRA reporting deworming in the past six months among the age groups (P < 0.001), residence (P < 0.001), zone (P < 0.001), wealth quintile (P < 0.001), and educational attainment (P < 0.001). Deworming in the past six months among WRA was highest in the 40 to 49 year age category (24 percent). It was higher among WRA residing in urban (24 percent) than in the rural (15 percent) areas. It was lowest among respondents in the North West zone (7 percent), in the lowest wealth quintile (10 percent), and respondents with no educational attainment (9 percent). 150 151 Table 73. Use of multivitamin, iron or iron/folic acid tablets, and deworming treatment among WRA (aged 15-49 years), Nigeria 2021 Took multivitamin tablets in the past six Took multivitamin tablets at least once in Took iron tablets, iron-folic acid in the Took iron tablets, iron-folic acid at least Took drugs for intestinal worms in the past months the past seven days past six months once in the past seven days six months Background characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.001 ***) (P = 0.073 ) (P < 0.001 ***) (P = 0.391 ) (P < 0.001 ***) 15-19 years 1125 7.1 [5.4, 9.3] 1125 3.1 [2.2, 4.4] 1125 8.2 [6.3, 10.5] 1125 4.3 [3.2, 5.9] 1119 16.4 [13.5, 19.9] 20-29 years 1609 13.1 [11.2, 15.3] 1609 7.3 [5.7, 9.2] 1609 13.7 [11.7, 15.9] 1609 8.2 [6.5, 10.3] 1597 15.9 [13.5, 18.6] 30-39 years 1485 16.5 [14.0, 19.4] 1485 10.0 [8.1, 12.2] 1485 17.7 [15.1, 20.7] 1485 9.8 [7.9, 12.1] 1473 20.9 [18.3, 23.8] 40-49 years 1019 15.3 [12.1, 19.1] 1019 10.3 [7.9, 13.5] 1019 18.4 [15.5, 21.8] 1019 11.7 [9.4, 14.5] 1012 23.6 [19.8, 27.9] Residence (P < 0.016*) (P = 0.176 ) (P < 0.001 ***) (P = 0.045 *) (P < 0.001 ***) Urban 2120 16.1 [13.5, 19.1] 2120 8.4 [6.6, 10.7] 2120 18.7 [15.8, 22.1] 2120 10.0 [7.9, 12.5] 2101 24.4 [20.7, 28.5] Rural 3118 11.2 [9.1, 13.7] 3118 7.3 [5.7, 9.5] 3118 11.7 [9.9, 13.8] 3118 7.6 [6.2, 9.2] 3100 15.2 [13.1, 17.7] Zone (P < 0.001 ***) (P = 0.003 **) (P < 0.001 ***) (P = 0.103 ) (P < 0.001 ***) North Central 858 11.8 [8.4, 16.2] 858 5.5 [3.8, 8.1] 858 16.5 [12.8, 21.0] 858 8.8 [6.3, 12.2] 848 12.6 [10.3, 15.5] North East 833 8.8 [5.3, 14.4] 833 3.4 [2.1, 5.5] 833 9.5 [7.0, 12.7] 833 5.6 [3.9, 8.0] 822 11.0 [8.5, 14.2] North West 913 2.7 [1.6, 4.6] 913 1.1 [0.5, 2.2] 913 2.2 [1.1, 4.4] 913 1.0 [0.4, 2.3] 912 7.2 [4.9, 10.6] South East 870 13.5 [10.9, 16.7] 870 7.1 [5.1, 10.0] 870 20.7 [15.7, 26.9] 870 11.8 [8.2, 16.7] 868 40.5 [34.5, 46.8] South South 867 21.7 [17.7, 26.4] 867 18.0 [13.9, 23.0] 867 17.2 [13.4, 21.8] 867 14.3 [10.6, 18.8] 861 34.0 [27.1, 41.7] South West 897 28.0 [23.3, 33.3] 897 16.5 [12.5, 21.5] 897 32.2 [27.9, 36.8] 897 16.9 [13.7, 20.7] 890 29.5 [25.8, 33.5] Wealth quintile1 (P < 0.001 ***) (P = 0.387 ) (P < 0.001 ***) (P = 0.457 ) (P < 0.001 ***) Lowest 1088 6.0 [4.3, 8.4] 1088 4.0 [2.7, 5.8] 1088 7.2 [5.3, 9.6] 1088 4.7 [3.3, 6.7] 1077 9.6 [7.3, 12.5] Second 1122 9.1 [6.9, 11.7] 1122 5.9 [4.2, 8.4] 1122 11.5 [9.2, 14.3] 1122 6.8 [5.0, 9.1] 1118 12.5 [10.1, 15.4] Middle 1089 14.7 [11.6, 18.5] 1089 7.9 [5.7, 10.9] 1089 18.6 [15.7, 21.9] 1089 11.5 [9.2, 14.1] 1082 17.5 [14.2, 21.3] Fourth 983 18.5 [15.0, 22.6] 983 10.3 [7.9, 13.3] 983 19.3 [16.0, 23.1] 983 10.3 [7.9, 13.2] 975 27.9 [24.1, 31.9] Highest 934 19.4 [16.0, 23.3] 934 11.4 [8.8, 14.8] 934 17.6 [14.5, 21.2] 934 9.9 [7.4, 13.1] 927 30.2 [24.6, 36.4] Educational attainment1 (P < 0.001 ***) (P = 0.411 ) (P < 0.001 ***) ( P = 0.645 ) (P < 0.001 ***) None 1232 5.9 [4.4, 8.1] 1232 3.7 [2.6, 5.1] 1232 7.3 [5.6, 9.5] 1232 4.7 [3.4, 6.3] 1219 9.3 [7.2, 11.9] Primary 838 16.2 [12.5, 20.7] 838 10.7 [7.5, 15.1] 838 20.8 [17.4, 24.7] 838 12.8 [10.1, 16.1] 832 18.0 [14.9, 21.7] Secondary 2380 16.3 [14.3, 18.5] 2380 9.0 [7.5, 10.9] 2380 17.3 [15.2, 19.7] 2380 9.7 [8.1, 11.6] 2366 24.8 [21.8, 28.2] Tertiary 529 20.9 [17.0, 25.5] 529 12.5 [9.3, 16.6] 529 20.5 [16.4, 25.2] 529 11.6 [8.5, 15.5] 526 28.3 [23.4, 33.8] National 52382 13.2 [11.7, 15.0] 52383 7.8 [6.6, 9.2] 52384 14.6 [13.2, 16.3] 52385 8.6 [7.4, 9.8] 5201 19.0 [17.1, 21.1] 152 Table 73. Use of multivitamin, iron or iron/folic acid tablets, and deworming treatment among WRA (aged 15-49 years), Nigeria 2021 (continued) The data are based on questions wtt2, wtt3, wtt4, wtt5, and wrf2 of the biomarker questionnaire wtt2. During the last six months, did you take any multivitamin tablets? wtt3. How many days did you take any of these products in the last week (seven days)? See Figure 49 for more details. wtt4. During the last six months, did you take any iron/folic acid tablets? wtt5. How many days did you take any iron/folic acid tablets in the last seven days? See Figure 50 for more details. wrf2. Did you take any drugs for intestinal worms in the past six months? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of WRA who responded nationally: (n= 5238) 1Less than (n = 5238) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 5238) due to the response “Don’t Know” 3Less than (n = 5238) due to the response “Don’t Know” 4Less than (n = 5238) due to the response “Don’t Know” 5Less than (n = 5238) due to the response “Don’t Know” 6Less than (n = 5238) due to the response “Don’t Know” Figure 49 presents the frequency of use of multivitamins in the past seven days among WRA. Among WRA who responded in the affirmative, 26 percent took multivitamins for the entire seven days. 90% 80% 70% 60% 50% 40% 30% 26.0 19.8 20% 18.1 10.5 10% 9.2 12.1 4.2 0% 1 Day 2 Days 3 Days 4 Days 5 Days 6 Days 7 Days Figure 49. Frequency of use of multivitamins in the past seven days among WRA, Nigeria 2021 Based on question wtt3. How many days did you take any of these products [any multivitamin tablets] in the last seven days? Data are weighted to account for survey design and non-response Number of WRA who responded nationally: (n=5238) Figure 50 presents the frequency of use of any iron/folic acid tablets in the past seven days among WRA. Among WRA who responded in the affirmative, 31 percent took iron/folic acid tablets for the entire seven days. 90% 80% 70% 60% 50% 40% 31.1 30% 20% 17.4 9.7 12.2 10.4 12.2 10% 6.1 0% 1 Day 2 Days 3 Days 4 Days 5 Days 6 Days 7 Days Figure 50. Frequency of use of any iron/folic acid tablets in the past seven days among WRA, Nigeria 2021 Based on question wtt5. How many days did you take any iron/folic acid tablets in the last seven days? Data are weighted to account for survey design and non-response Number of WRA who responded nationally: (n=5238) 153 Percentage Percentage Self-reported morbidity prevalence among WRA (aged 15-49 years) A key objective of the survey was to assess morbidity as an important factor associated with anaemia. Figure 51 presents the overall prevalence of self-reported illness (fever, malaria, cough, and diarrhoea) and hospitalization/clinic visits in the last two weeks among WRA (aged 15-49 years). The prevalence of hospitalization/clinic visits was low (8 percent), while that of fever, malaria, cough, and diarrhoea were 36 percent, 27 percent, 23 percent, and 17 percent nationally, respectively. Figure 51. Overall prevalence of self-reported illness and hospitalization/clinic visits in the past two weeks among WRA (aged 15-49 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of WRA who responded nationally: (n= 5238) Table 74 presents the prevalence of self-reported illness (fever, malaria, cough, and diarrhoea) and hospitalization/clinic visits in the past two weeks among WRA (15-49 years) stratified by age, residence, zone, wealth quintile, and educational attainment. a. Diarrhoea in the past two weeks: There was a statistically significant difference in the prevalence of diarrhoea in the past two weeks among WRA between zones (P = 0.001). The prevalence of diarrhoea was highest among WRA in the North East zone (23 percent). b. Cough in the past two weeks: There was a statistically significant difference in the prevalence of cough in the past two weeks among WRA between zones (P < 0.001). The prevalence of cough was highest among WRA in the North East zone (30 percent). c. Fever in the past two weeks: There was a statistically significant difference in the prevalence of fever in the past two weeks among WRA between the age groups (P = 0.002), zone (P < 0.001), wealth quintile (P = 0.003), and educational attainment (P = 0.002). The prevalence of fever was highest in the 40 to 49-year age group (41 percent). It was highest among WRA in the south zone (49 percent), WRA in the lowest wealth quintile (42 percent), and WRA who attained primary education (42 percent). d. Malaria in the past two weeks: There was a statistically significant difference in the prevalence of malaria in the past two weeks among WRA between the age groups (P< 0.001), zones (P < 154 0.001), and educational attainment (P = 0.001). The prevalence of malaria was highest in the 40 to 49-year age group (32 percent). It was highest among WRA in the South South zone (51 percent) and lowest among WRA who had attained no education (23 percent). e. Hospitalization in the past two weeks: There was a statistically significant difference in the prevalence of hospitalization in the past two weeks among WRA with the zones (P < 0.001). The prevalence of hospitalization was highest among WRA in the North East zone (19 percent). 155 156 Table 74. Prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among WRA (aged 15-49 years), Nigeria 2021 Had diarrhoea1 in the past two Had any hospitalization and /or clinic Background weeks Had a cough in the past two weeks Had a fever in the past two weeks Had malaria in the past two weeks visits due to illness in the last two characteristics weeks N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.559) (P = 0.190) (P = 0.002 **) (P < 0.001 ***) (P = 0.085) 15-19 years 1124 16.0 [13.3, 19.0] 1124 25.7 [22.0, 29.7] 1117 30.7 [26.4, 35.3] 1101 19.9 [16.4, 23.9] 1124 6.0 [3.8, 9.2] 20-29 years 1602 18.2 [15.9, 20.7] 1605 23.4 [21.0, 26.0] 1601 36.1 [32.3, 40.0] 1564 26.0 [23.2, 28.9] 1608 8.2 [6.7, 10.1] 30-39 years 1482 17.0 [14.4, 19.9] 1481 20.9 [18.3, 23.8] 1476 37.3 [33.8, 41.0] 1444 27.8 [24.8, 31.1] 1485 8.4 [6.5, 10.8] 40-49 years 1019 15.9 [13.0, 19.3] 1016 23.1 [19.4, 27.3] 1017 40.8 [36.4, 45.3] 993 32.3 [28.3, 36.6] 1019 10.4 [7.9, 13.5] Residence (P = 0.771) (P = 0.055) (P = 0.050) (P = 0.672) (P = 0.138) Urban 2115 17.2 [14.7, 20.0] 2112 20.7 [17.5, 24.3] 2111 32.9 [29.2, 36.7] 2078 25.9 [22.7, 29.4] 2120 9.6 [7.1, 12.8] Rural 3112 16.7 [14.8, 18.8] 3114 24.8 [22.7, 27.1] 3100 38.7 [34.5, 43.1] 3024 27.0 [23.9, 30.3] 3116 7.3 [6.0, 8.8] Zone (P = 0.001 **) (P < 0.001 ***) (P < 0.001 ***) (P < 0.001 ***) (P < 0.001 ***) North Central 858 17.4 [14.5, 20.7] 858 25.3 [21.6, 29.5] 852 42.4 [35.8, 49.3] 815 27.5 [23.1, 32.3] 857 8.8 [6.6, 11.7] North East 829 23.0 [18.5, 28.2] 831 30.3 [25.3, 35.9] 828 39.5 [34.3, 44.9] 798 21.6 [16.6, 27.6] 832 19.3 [14.5, 25.2] North West 912 13.2 [10.3, 16.7] 913 18.3 [14.6, 22.8] 911 27.2 [20.3, 35.5] 897 13.7 [10.4, 17.8] 913 4.2 [2.6, 6.6] South East 869 21.2 [17.7, 25.3] 870 29.0 [25.6, 32.7] 870 42.5 [36.9, 48.3] 867 42.4 [37.6, 47.3] 870 7.2 [5.2, 9.9] South South 863 16.8 [13.1, 21.2] 859 25.8 [21.0, 31.3] 854 48.9 [43.0, 54.8] 848 51.0 [46.1, 55.9] 867 5.1 [3.4, 7.6] South West 896 14.8 [11.6, 18.5] 895 17.5 [14.7, 20.6] 896 30.2 [26.1, 34.7] 877 25.0 [21.1, 29.3] 897 6.5 [4.8, 8.8] Wealth quintile2 (P = 0.740) (P = 0.562) (P = 0.003 **) (P = 0.399) (P = 0.490) Lowest 1083 18.2 [14.7, 22.2] 1085 25.4 [22.4, 28.8] 1081 42.2 [36.2, 48.5] 1048 23.4 [19.3, 28.0] 1088 8.5 [6.6, 10.8] Second 1119 17.5 [14.4, 21.2] 1120 22.6 [19.2, 26.3] 1118 38.7 [34.0, 43.7] 1084 28.7 [24.0, 34.0] 1120 9.1 [6.4, 12.8] Middle 1087 17.1 [14.1, 20.7] 1088 23.9 [20.1, 28.2] 1078 36.8 [32.2, 41.7] 1064 27.3 [23.3, 31.6] 1089 6.7 [5.1, 8.9] Fourth 982 16.3 [13.4, 19.6] 980 22.0 [18.6, 25.9] 981 33.6 [29.1, 38.5] 968 27.9 [23.7, 32.6] 983 7.7 [5.3, 11.0] Highest 934 15.0 [11.9, 18.8] 931 21.5 [17.5, 26.1] 931 28.8 [24.6, 33.4] 916 24.8 [20.5, 29.7] 934 9.4 [7.0, 12.6] Educational attainment2 (P = 0.962) (P = 0.074) (P = 0.002 **) (P = 0.001 ***) (P = 0.642) None 1227 17.3 [14.2, 21.0] 1230 24.1 [20.4, 28.2] 1223 39.3 [34.1, 44.8] 1185 22.6 [19.4, 26.2] 1231 8.1 [6.0, 11.0] Primary 838 16.4 [13.4, 20.0] 835 22.4 [18.6, 26.8] 830 41.7 [37.3, 46.2] 819 33.6 [29.1, 38.5] 838 7.8 [5.9, 10.2] Secondary 2374 16.7 [14.5, 19.1] 2377 24.8 [22.3, 27.4] 2371 34.5 [31.6, 37.5] 2330 27.2 [24.4, 30.2] 2379 8.2 [6.3, 10.5] Tertiary 529 17.3 [13.8, 21.5] 525 17.2 [13.3, 21.9] 528 27.5 [22.6, 33.2] 515 25.9 [21.5, 30.9] 529 10.2 [7.4, 13.9] National 52273 16.9 [15.4, 18.5] 52264 23.1 [21.3, 25.1] 52115 36.3 [33.4, 39.2] 51026 26.5 [24.4, 28.8] 5236 8.3 [7.0, 9.8] 157 Table 74. Prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among WRA (aged 15-49 years), Nigeria 2021 (continued) The data are based on questions wah1, wah2, wah3, wah5, wah6, and wah7 of the biomarker questionnaire wah1. Have you been ill with diarrhoea in the past two weeks? wah2. Have you been ill with a cough or breathing problems in the past two weeks? wah3. When you had an illness with a cough, did you breathe faster than usual? wah5. Have you been ill with a fever in the past two weeks? wah6. Have you been ill with malaria in the past two weeks? wah7. Have you had any hospitalization in the last two weeks? 1Diarrhoea is defined as three or more loose or watery stools in 24 hours. Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Sample size for WRA nationally: (n= 5238) 2Less than (n = 5238) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 5238) due to the response “Don’t Know” 4Less than (n = 5238) due to the response “Don’t Know” 5Less than (n = 5238) due to the response “Don’t Know” 6Less than (n = 5238) due to the response “Don’t Know” 7Less than (n= 5339) due to the response “Don’t Know” Other anaemia risk factor among WRA (aged 15-49 years) Figure 52 presents the overall prevalence of anaemia risk (pica and smoking) and diagnosis of anaemia by a healthcare provider in the past six months among WRA (aged 15-49 years). Nationally, the prevalence of smoking among WRA was low (0.5 percent). The prevalence of pica and clinically diagnosed anaemia among WRA was 5 and 6 percent, respectively. Figure 52. Prevalence of anaemia risk (pica and smoking) and diagnosis of anaemia in the past six months among WRA (aged 15-49 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of women of reproductive who responded nationally: (n= 5238) Table 75 presents the prevalence of anaemia risk (pica and smoking) and diagnosis of anaemia in the past six months among WRA (aged 15-49 years) stratified by age, residence, zone, wealth quintile, and educational attainment. a. Pica in the past seven days: There was a statistically significant difference in the prevalence of pica in the past seven days among WRA between the age groups (P = 0.013) and residence (P < 0.001). The prevalence of pica was highest in the 15 to 19-year age group (7 percent). It was highest in WRA living in the South East zone (14 percent). b. Smoking: There was no significant variation in the prevalence of smoking among WRA across the background characteristics. c. Diagnosis of anaemia by a healthcare provider in the last six months: There was a statistically significant difference in the prevalence of diagnosis of anaemia by a healthcare provider in the past six months between residence (P = 0.036). The percentage of WRA diagnosed with anaemia by a healthcare provider in the past six months was higher among respondents residing in rural (7 percent) than in urban (5 percent) areas. 158 Table 75. Prevalence of pica, smoking, and diagnosis of anaemia in the past six months among WRA (15-49 years), Nigeria 2021 Ate earth, clay, mud, or soil from any source (e.g., walls of mud Smoked tobacco (excluding Diagnosed with anaemia by a Background houses, the yard, purchased at the powder or chew type) healthcare provider in the past six characteristics market) in the past 7 days months N % [95% CI] N % [95% CI] N % [95% CI] Age (P = 0.013 *) (P = 0.611 ) (P = 0.166 ) 15-19 years 1125 6.9 [5.1, 9.2] 1125 0.7 [0.3, 2.1] 1118 4.8 [3.3, 6.8] 20-29 years 1609 5.4 [3.8, 7.6] 1609 0.5 [0.2, 1.1] 1592 5.3 [4.1, 6.9] 30-39 years 1485 4.2 [3.2, 5.6] 1485 0.4 [0.2, 0.9] 1471 7.0 [5.5, 8.9] 40-49 years 1019 3.0 [2.1, 4.4] 1019 0.5 [0.2, 1.3] 1012 6.9 [4.9, 9.5] Residence (P = 0.926 ) (P = 0.453 ) (P = 0.036 *) Urban 2120 5.0 [3.8, 6.5] 2120 0.4 [0.1, 1.0] 2112 4.8 [3.7, 6.1] Rural 3118 4.9 [3.8, 6.1] 3118 0.6 [0.3, 1.2] 3081 6.8 [5.5, 8.5] Zone (P < 0.001 ***) (P = 0.474 ) (P = 0.117 ) North Central 858 4.2 [2.7, 6.4] 858 1.0 [0.4, 2.6] 852 6.1 [4.5, 8.1] North East 833 6.7 [5.1, 8.8] 833 0.3 [0.1, 1.1] 810 8.1 [5.2, 12.4] North West 913 5.7 [3.9, 8.3] 913 0.7 [0.2, 2.3] 907 5.1 [3.3, 7.8] South East 870 14.3 [10.9, 18.6] 870 0.2 [0.0, 0.9] 870 4.7 [3.2, 6.9] South South 867 1.6 [0.9, 3.0] 867 0.4 [0.2, 1.2] 859 7.8 [5.6, 10.7] South West 897 1.2 [0.6, 2.5] 897 0.4 [0.1, 1.1] 895 4.5 [3.1, 6.4] Wealth quintile1 (P = 0.490 ) (P = 0.323 ) (P = 0.149 ) Lowest 1088 5.1 [3.6, 7.4] 1088 0.8 [0.3, 2.2] 1066 7.0 [5.3, 9.3] Second 1122 4.7 [3.0, 7.2] 1122 0.6 [0.2, 1.4] 1110 7.2 [5.1, 10.0] Middle 1089 5.9 [4.2, 8.3] 1089 0.4 [0.1, 0.9] 1082 5.7 [4.1, 7.7] Fourth 983 3.6 [2.5, 5.3] 983 0.1 [0.0, 0.7] 981 4.3 [3.0, 6.2] Highest 934 5.2 [3.7, 7.1] 934 0.8 [0.3, 2.3] 932 5.3 [3.9, 7.2] Educational attainment1 (P = 0.996 ) (P = 0.577 ) (P = 0.059 ) None 1232 4.9 [3.4, 7.0] 1232 0.6 [0.2, 2.0] 1205 8.2 [6.0, 11.3] Primary 838 4.8 [3.4, 6.8] 838 0.6 [0.2, 1.5] 834 5.5 [3.8, 8.0] Secondary 2380 5.0 [3.9, 6.3] 2380 0.6 [0.3, 1.3] 2372 5.2 [4.2, 6.5] Tertiary 529 4.7 [2.9, 7.7] 529 0.1 [0.0, 0.5] 528 5.7 [3.6, 8.8] National 5238 4.9 [4.1, 5.8] 5238 0.5 [0.3, 0.9] 51933 6.0 [5.1, 7.1] The data are based on questions wtt1, wah8, and wrf1 of the biomarker questionnaire wtt1. In the last seven days, have you eaten earth/clay/mud/soil from any source? wah8. Do you smoke? (do not include the powder and chew type) wrf1. Have you been diagnosed with anaemia in the past six months? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of WRA who responded nationally: (n= 5238) 2Less than (n = 5238) due to relatively fewer respondents for the household and dietary intake questionnaires 5Less than (n = 5238) due to the response “Don’t Know” 159 Intervention coverage, health status, and anaemia risk factors among pregnant women (15-49 years old) The MNDC guidelines42 describe the following interventions and structures to address micronutrient deficiencies among pregnant women – deworming, antenatal care, and iron/folate supplementation. Some interventions, including nutrition education, are also reflected in the NFPN43, which prioritizes both the health system and food-based approaches to MNDC. An objective of the survey was to assess the coverage of these interventions among pregnant women in Nigeria. Intervention coverage among pregnant women (15-49 years old) Figure 53 presents the overall prevalence of antenatal care, iron/folic acid use, and nutrition counselling among pregnant women (aged 15-49 years). Nationally, 44 percent of pregnant women reported receiving at least one antenatal care visit. Sixty-six (66) percent of pregnant women took iron/folic acid tablets the day before the interview, while 87 percent reported taking iron/folic acid tablets at least once in the past seven days before the interview. Thirty-four percent of pregnant women reported speaking to a health worker or community volunteer about what foods to eat during pregnancy, while 32 percent of women reported talking to a health worker or community volunteer about breastfeeding their newborn. Figure 53. Overall prevalence of any nutrition-related interventions – antenatal care, supplementation, and nutrition counselling - among pregnant women (aged 15-49 years), Nigeria 2021 Data are weighted to account for survey design and non-response Number of pregnant women who responded nationally: (n= 863) Antenatal care Antenatal care (ANC) entails periodic visits by pregnant women to designated health centres staffed and equipped for maternity services. The WHO recommends44 a minimum of eight ANC contacts: five contacts in the third trimester, one contact in the first trimester, and two contacts in the second trimester (see Table 76). These give pregnant women the opportunity for appropriate counselling, micronutrient supplementation (folic acid and iron), medical screening, vaccination, and preventive treatment for malaria, all aimed at ensuring safe pregnancy outcomes. Conditions such as hepatitis (A, B, and C), HIV pregnancy-induced hypertension, and gestational diabetes are usually screened for during ANC visits. 42 Federal Ministry of Health (FMOH). 2013. National guidelines on micronutrients deficiencies control in Nigeria. Abuja: Federal Ministry of Health. 43 Ministry of Budget & National Planning. 2016. National Policy on Food and Nutrition in Nigeria. Abuja: Ministry of Budget and National Planning. 44 World Health Organization (WHO). WHO Recommendations on Antenatal Care for a Positive Pregnancy Experience: Summary. Geneva, Switzerland: WHO; 2018. Licence: CC BY-NC-SA 3.0 IGO. 160 In addition, ANC visits can result in the early detection of high-risk pregnancies as women with risk factors suggestive of possible obstetric complication(s) are identified through careful review of their medical history and appropriate medical screening. ANC visits allow pregnant women to receive specialized and individualized pregnancy management plan(s) as needed. Table 76. WHO recommendations on antenatal care for a positive pregnancy experience WHO ANC recommends a minimum of eight contacts: five contacts in the third trimester, one contact in the first trimester, and two contacts in the second trimester, as detailed below First trimester Contact 1: up to 12 weeks Second trimester Contact 2: 20 weeks Contact 3: 26 weeks Third trimester Contact 4: 30 weeks Contact 5: 34 weeks Contact 6: 36 weeks Contact 7: 38 weeks Contact 8: 40 weeks Return for delivery at 41 weeks if has not given birth. Note: Intermittent preventive treatment of malaria in pregnancy should be started at ≥ 13 weeks. Table 77 presents the percentage of pregnant women receiving antenatal care stratified by age, residence, wealth quintile, and educational attainment. There was a statistically significant difference in the prevalence of antenatal care among pregnant women between residence (P = 0.004) and the zones (P < 0.001). The percentage of pregnant women seeking antenatal care was higher in urban (53 percent) than in rural (40 percent) areas. It was highest among pregnant women in the highest wealth quintile (59 percent). 161 Table 77. Prevalence of at least one antenatal care visit among pregnant women, Nigeria 2021 Had seen a health worker for antenatal care during this pregnancy so far Background characteristics N % [95% CI] P value Age 15-19 years 76 32.0 [19.9, 47.0] 20-29 years 451 41.5 [35.5, 47.8] 30-39 years 293 49.8 [43.0, 56.7] (P = 0.155) 40-49 years 43 48.4 [26.7, 70.8] Residence Urban 352 53.1 [46.4, 59.8] Rural 511 39.5 [33.6, 45.6] (P = 0.004 **) Wealth quintile1 Lowest 184 34.3 [26.9, 42.6] Second 179 32.1 [23.8, 41.8] Middle 167 50.9 [38.6, 63.0] (P < 0.001 ***) Fourth 178 56.5 [46.2, 66.3] Highest 152 58.9 [49.2, 68.0] Educational attainment1 None 203 36.7 [28.1, 46.2] Primary 132 43.6 [31.4, 56.6] Secondary 388 49.3 [41.8, 56.7] (P = 0.052) Tertiary 94 57.2 [46.4, 67.3] National 863 43.8 [39.1, 48.6] The data are based on question wpw1 of the biomarker questionnaire wpw1. Have you seen any health worker for antenatal care during this pregnancy? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of pregnant women who responded nationally: (n= 863) 1Less than (n = 863) due to relatively fewer respondents for the household and dietary intake questionnaires 162 Figure 54 presents the reported timing of the first antenatal care visit by month of pregnancy among pregnant women nationally. The WHO recommends the first ANC contact within three months (12 weeks) of conception. Only 38 percent of the respondents met this recommendation, with eight of pregnant women receiving their first ANC contact in their third trimester. 90% 80% 70% 60% 50% 40% 30% 25.0 21.5 20% 17.4 12.8 11.5 10% 3.5 4.8 2.9 0.6 0% Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Figure 54. Timing of the first antenatal care visit by month of pregnancy among pregnant women, Nigeria 2021 Based on question wpw2. How many months pregnant were you when first received antenatal care? Data are weighted to account for survey design and non-response Number of pregnant women who responded nationally: (n=863) Figure 55 presents the adequacy of the number of antenatal care visits among pregnant women by the length of pregnancy nationally. The WHO recommends at least one ANC contact in the first trimester, at least two contacts in the second trimester, and at least five contacts in the third trimester of pregnancy. Relatively fewer pregnant women received adequate ANC contacts across the 40 weeks of pregnancy. Figure 55. Adequacy of number of antenatal care visits by the length of pregnancy among pregnant women, Nigeria 2021 Based on question wpw3. How many times have you received antenatal care so far? Data are weighted to account for survey design and non-response. Number of pregnant women who responded nationally: (n=595) One woman in the 10th month of pregnancy not depicted (she had adequate visits). 163 Percentage Use of iron and/or folic acid tablets Table 78 presents the percentage of pregnant women (aged 15 to 49 years) who consumed a tablet or syrup containing iron in the past seven days and those who consumed a tablet or syrup containing iron and/or folic acid the day before the interview. The data are stratified by age, residence, wealth quintile, and educational attainment. • Consumed a tablet or syrup containing iron in the past seven days: There was no significant variation across the background characteristics. • Consumed a tablet or syrup containing iron and/or folic acid yesterday: There was a statistically significant difference in the use of tablet/syrup containing iron and/or folic acid among pregnant women the day before the interview with educational attainment (P = 0.006). The prevalence was highest among pregnant women with no educational attainment (87 percent). Table 78. Percentage of pregnant women (aged 15 to 49 years) who consumed a tablet or syrup containing iron at least once in the last seven days and those who consumed a tablet or syrup containing iron and/or folic acid the day before the interview, Nigeria 2021 Consumed a tablet or syrup containing iron at Consumed a tablet or syrup containing Iron and/or Background least once in the past seven days? folic acid yesterday? characteristics N % [95% CI] N % [95% CI] Age category (P = 0.847) (P = 0.577 ) 15-19 years 10 89.9[50.0, 98.8] 10 79.5 [44.3, 95.0] 20-29 years 55 85.0 [70.3, 93.1] 64 68.2 [52.2, 80.9] 30-39 years 41 87.1 [71.8, 94.7] 46 59.0 [41.2, 74.7] 40-49 years 4 100.0 [ ., .] 4 81.1 [27.3, 98.0] Residence (P = 0.714) (P = 0.818 ) Urban 52 88.6 [74.4, 95.4] 60 64.8 [50.4, 77.0] Rural 58 86.0 [73.8, 93.1] 64 67.2 [51.2, 80.0] Wealth quintile1 (P = 0.969) (P = 0.329 ) Lowest 17 89.0 [68.4, 96.8] 21 59.3 [29.5, 83.4] Second 20 83.9 [61.1, 94.6] 21 78.4 [56.6, 91.0] Middle 20 84.4 [57.2, 95.6] 21 77.4 [53.6, 91.0] Fourth 29 87.0 [61.7, 96.5] 33 51.2 [33.5, 68.6] Highest 24 90.6 [70.3, 97.5] 27 72.1 [47.5, 88.1] Educational attainment1 (P = 0.133) (P = 0.006 **) None 20 97.2 [82.4, 99.6] 22 87.4 [71.9, 94.9] Primary 15 71.0 [39.7, 90.1] 16 46.0 [22.0, 72.1] Secondary 61 81.9 [67.5, 90.7] 69 56.2 [41.9, 69.5] Tertiary 10 85.9 [40.0, 98.2] 12 80.0 [46.1, 94.9] National 1102 87.0 [78.5, 92.4] 124 66.2 [55.3, 75.7] The data are based on question wpw7 of the biomarker questionnaire wpw7. How many days in the last seven days (one week) did you consume a tablet or syrup containing Iron? wpw8. Did you consume a tablet or syrup containing Iron and/or folic acid yesterday? Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of pregnant women who responded nationally: (n= 124) 1Less than (n = 124) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 124) due to the response “Don’t Know” 164 Figure 56 presents the frequency of use of a tablet or syrup containing iron in the past seven days. Fifty (50) percent of the pregnant women who responded in the affirmative took iron tablets or syrup for the past seven days. Figure 56. Frequency of use of iron tablet or syrup in the past seven days among pregnant women, Nigeria 2021 Based on question wpw7. How many days in the last seven days (one week) did you consume a tablet or syrup containing iron? Data are weighted to account for survey design and non-response. Number of pregnant women who responded nationally: (n=863) Nutrition counselling Table 79 presents the percentage of pregnant women (aged 15-49 years) who had spoken to a health worker or community volunteer about what foods to eat during pregnancy and breastfeeding their newborn. The data are stratified by age, residence, wealth quintile, and educational attainment. • Spoke with a health worker or community volunteer about what foods to eat during pregnancy: There was a statistically significant difference in the prevalence of pregnant women who had spoken to a health worker or community volunteer about what foods to eat during pregnancy among the age groups (P = 0.029), residence (P < 0.001), wealth quintile (P < 0.001), and educational attainment (P < 0.001). The prevalence was highest in the 40-49 -years old age category (45 percent). It was higher among pregnant women residing in the urban (46 percent) than in the rural (28 percent) areas. It was highest among pregnant women in the highest wealth quintile (55 percent) and among pregnant women who had attained tertiary education (55 percent). • Spoke with a health worker or community volunteer about breastfeeding your newborn: There was a statistically significant difference in the prevalence of pregnant women who had spoken to a health worker or community volunteer about breastfeeding their newborn among residence (P < 0.001), wealth quintile (P < 0.001), and educational attainment (P < 0.001). The prevalence was lower among pregnant women residing in rural (26 percent) compared to urban (44 percent) areas. It was highest among pregnant women in the highest wealth quintile (49 percent) and among pregnant women who had attained tertiary education (57 percent). 165 Table 79. Percentage of pregnant women (aged 15-49 years) who had spoken to a health worker or community volunteer about what foods to eat during pregnancy and about breastfeeding their newborn Background Health worker or community volunteer spoke with Health worker or community volunteer spoke with you about breastfeeding your characteristics you about what foods to eat during pregnancy newborn N % [95% CI] N % [95% CI] Age category (P = 0.029 *) (P = 0.152) 15-19 years 76 21.7 [12.1, 35.9] 76 17.8 [9.3, 31.2] 20-29 years 451 29.9 [24.3, 36.2] 451 29.6 [23.1, 36.9] 30-39 years 293 41.7 [35.2, 48.6] 293 37.3 [30.9, 44.3] 40-49 years 43 44.9 [23.5, 68.4] 43 36.6 [16.4, 63.0] Residence (P < 0.001 ***) (P = 0.000 ***) Urban 352 46.4 [39.4, 53.5] 352 43.9 [36.3, 51.8] Rural 511 28.2 [23.3, 33.6] 511 25.7 [21.3, 30.7] Wealth quintile1 (P < 0.001 ***) (P = 0.000 ***) Lowest 184 24.9 [18.6, 32.5] 184 24.6 [18.4, 32.0] Second 179 21.5 [14.5, 30.8] 179 18.2 [12.0, 26.8] Middle 167 37.7 [29.6, 46.5] 167 32.6 [24.5, 41.8] Fourth 178 47.5 [39.4, 55.7] 178 47.5 [38.9, 56.2] Highest 152 54.5 [44.5, 64.1] 152 49.4 [40.1, 58.7] Educational attainment1 (P < 0.001 ***) (P = 0.000 ***) None 203 24.2 [17.7, 32.2] 203 22.8 [16.6, 30.4] Primary 132 31.9 [23.4, 41.8] 132 25.2 [17.8, 34.4] Secondary 388 41.1 [34.2, 48.3] 388 37.8 [31.6, 44.5] Tertiary 94 54.7 [44.4, 64.5] 94 57.2 [47.4, 66.6] National 863 34.0 [29.7, 38.5] 863 31.5 [27.4, 35.9] The data are based on questions wpw9 and wpw10 of the biomarker questionnaire wpw9. So far, during this pregnancy, has a health worker or community volunteer spoken with you about what foods to eat during pregnancy? wpw10. So far, during this pregnancy, has a health worker or community volunteer spoken with you about breastfeeding your newborn? Data are weighted to account for survey design and non-response. N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Sample size for pregnant women nationally: (n= 863) 1Less than (n = 863) due to relatively fewer respondents for the household and dietary intake questionnaires 166 Self-reported morbidity prevalence A key objective of the survey was to assess morbidity as a critical factor associated with anaemia. Figure 57 presents the overall prevalence of self-reported illness (fever, malaria, diarrhoea, and cough) and hospitalization/clinic visits in the past two weeks among pregnant women (aged 15-49 years). Nationally, the prevalence of fever, malaria, diarrhoea, and cough among pregnant women was 40, 30, 21, and 20 percent, respectively. The prevalence of hospitalization among pregnant women was 19 percent. Figure 57. Overall prevalence of self-reported illness (fever, malaria, diarrhoea, and cough) and hospitalization/clinic visits in the last two weeks among pregnant women, Nigeria 2021 Data are weighted to account for survey design and non-response Number of pregnant women who responded nationally: (n= 863) Table 80 presents the prevalence of self-reported illness (fever, malaria, diarrhoea, and cough) and hospitalization/clinic visits in the past two weeks among pregnant women (aged 15-49 years) stratified by age, residence, wealth quintile, and educational attainment. a. Diarrhoea in the past two weeks: There was no significant variation in the prevalence of diarrhoea in the past two weeks among pregnant women across the background characteristics. b. Cough in the past two weeks: There was no significant variation in the prevalence of cough in the past two weeks among pregnant women across the background characteristics. c. Fever in the past two weeks: There was no significant variation in the prevalence of fever in the past two weeks among pregnant women across the background characteristics. d. Malaria in the past two weeks: There was no significant variation in the prevalence of malaria in the past two weeks among pregnant women across the background characteristics. e. Hospitalization in the past two weeks: There was no significant variation in the prevalence of hospitalization in the past two weeks among pregnant women across the background characteristics. 167 168 Table 80. Prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among pregnant women (aged 15-49 years), Nigeria 2021 Had diarrhoea1 in the past two Had cough in the past two Had any hospitalization and /or clinic Background weeks weeks Had fever in the past two weeks Had malaria in the past two weeks visits due to illness in the past two characteristics weeks N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age (P = 0.883) (P = 0.669) (P = 0.799) (P = 0.763) (P = 0.620) 15-19 years 76 23.7 [12.8, 39.6] 76 23.0 [12.7, 37.9] 76 34.3 [20.8, 51.0] 75 26.5 [14.6, 43.2] 76 19.1 [9.5, 34.8] 20-29 years 450 20.3 [15.7, 25.9] 450 21.8 [17.3, 27.2] 449 39.3 [32.4, 46.6] 436 28.9 [22.6, 36.0] 450 20.2 [15.8, 25.5] 30-39 years 292 20.6 [14.9, 27.7] 293 17.4 [12.4, 23.8] 291 42.4 [35.2, 49.9] 283 33.6 [25.9, 42.3] 293 17.0 [12.1, 23.2] 40-49 years 43 27.2 [9.0, 58.3] 43 22.1 [10.2, 41.4] 43 44.5 [22.8, 68.6] 42 28.0 [11.7, 53.1] 43 10.5 [3.0, 31.0] Residence (P = 0.523) (P = 0.211) (P = 0.392) (P = 0.623) (P = 0.070) Urban 351 19.6 [15.4, 24.5] 351 23.5 [18.7, 29.2] 349 37.3 [31.0, 44.1] 341 28.5 [22.5, 35.5] 352 23.0 [17.5, 29.7] Rural 510 21.8 [17.3, 27.1] 511 19.0 [14.8, 24.1] 510 41.4 [35.0, 48.2] 495 30.9 [24.5, 38.3] 510 16.4 [12.8, 20.9] Wealth quintile2 (P = 0.154) (P = 0.639) (P = 0.092) (P = 0.654) (P = 0.713) Lowest 182 24.4 [16.2, 35.0] 184 19.1 [12.9, 27.3] 183 39.0 [30.8, 48.0] 179 35.7 [25.6, 47.3] 183 17.0 [11.7, 24.1] Second 179 20.1 [14.1, 27.8] 178 20.0 [13.9, 27.9] 179 41.2 [30.8, 52.6] 173 28.6 [19.9, 39.1] 179 15.4 [9.7, 23.5] Middle 167 27.3 [19.1, 37.2] 167 25.6 [17.3, 36.0] 165 48.2 [39.9, 56.7] 161 29.7 [21.0, 40.1] 167 20.4 [13.2, 30.0] Fourth 178 17.9 [11.7, 26.5] 178 21.0 [13.5, 31.2] 177 41.0 [31.9, 50.9] 170 27.3 [17.9, 39.3] 178 21.0 [13.1, 32.0] Highest 152 11.8 [7.4, 18.2] 152 16.3 [10.8, 23.8] 152 26.5 [19.7, 34.7] 150 27.0 [19.6, 35.9] 152 22.5 [13.8, 34.4] Educational attainment2 (P = 0.409) (P = 0.580) (P = 0.260) (P = 0.914) (P = 0.220) None 202 22.5 [16.5, 29.8] 203 22.2 [15.5, 30.7] 202 40.9 [31.2, 51.3] 196 30.8 [22.8, 40.2] 202 21.5 [15.3, 29.4] Primary 132 28.3 [20.2, 38.0] 131 27.0 [19.5, 36.0] 131 40.4 [31.0, 50.7] 127 28.5 [20.6, 38.1] 132 13.9 [7.9, 23.2] Secondary 387 21.1 [16.0, 27.2] 388 20.7 [16.1, 26.1] 387 40.9 [33.4, 49.0] 378 32.5 [25.6, 40.4] 388 18.1 [13.7, 23.4] Tertiary 94 16.0 [8.3, 28.5] 94 17.9 [10.6, 28.7] 93 24.1 [15.1, 36.2] 90 30.7 [21.3, 42.0] 94 27.5 [18.3, 39.2] National 8613 21.1 [17.7, 24.9] 8624 20.4 [17.0, 24.3] 8595 40.1 [35.3, 45.2] 8366 30.2 [25.3, 35.5] 8627 18.5 [15.3, 22.2] 169 Table 80. Prevalence of self-reported illness and hospitalization/clinic visits in the last two weeks among pregnant women (aged 15-49 years), Nigeria 2021 (continued) The data are based on questions wah1, wah2, wah5, wah6, and wah7 of the biomarker questionnaire wah1. Have you been ill with diarrhoea in the past two weeks? wah2. Have you been ill with a cough or breathing problems in the past two weeks? wah5. Have you been ill with a fever in the past two weeks? wah6. Have you been ill with malaria in the past two weeks? wah7. Have you had any hospitalization in the last two weeks? 1Diarrhoea is defined as three or more loose or watery stools in a 24-hour period Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of pregnant women who responded nationally: (n= 863) 2Less than (n 863) due to relatively fewer respondents for the household and dietary intake questionnaires 3Less than (n = 863) due to the response “Don’t Know” 4Less than (n = 863) due to the response “Don’t Know” 5Less than (n = 863) due to the response “Don’t Know” 6Less than (n = 863) due to the response “Don’t Know” 7Less than (n = 863) due to the response “Don’t Know” Other anaemia risk factors among pregnant WRA (aged 15-49 years) Nationally, smoking prevalence among pregnant women (aged 15-49 years) was 0.4 percent. Table 81 presents the prevalence of smoking among pregnant women (aged 15-49 years) stratified by age, residence, wealth quintile, and educational attainment. There was a statistically significant difference in the prevalence of smoking among pregnant women with residence (P = 0.039). The prevalence was higher among pregnant women residing in urban (1 percent) than in rural (0.1 percent) areas. Table 81. Prevalence of smoking among pregnant women, Nigeria 2021 Background characteristics Smoked tobacco (excluding powder or chew type) N % [95% CI] P value Age category 15-19 years 76 0.0 [ ., .] 20-29 years 451 0.3 [0.1, 1.4] 30-39 years 293 0.4 [0.1, 1.8] (P = 0.266) 40-49 years 43 2.0 [0.3, 13.6] Residence Urban 352 1.0 [0.4, 2.8] Rural 511 0.1 [0.0, 1.0] (P = 0.039 *) Wealth quintile1 Lowest 184 0.8 [0.2, 3.1] Second 179 0.4 [0.1, 2.9] Middle 167 0.4 [0.1, 2.8] (P = 0.770) Fourth 178 0.2 [0.0, 1.6] Highest 152 0.0 [ ., .] Educational attainment1 None 203 0.3 [0.1, 1.4] Primary 132 0.8 [0.1, 5.4] Secondary 388 0.3 [0.0, 2.1] (P = 0.777) Tertiary 94 0.0 [ ., .] National 863 0.4 [0.2, 1.0] The data are based on question wah8 of the biomarker questionnaire wah8. Do you smoke? (Do not include the powder and chew type) Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of pregnant women who responded nationally: (n= 863) 1Less than (n 863) due to relatively fewer respondents for the household and dietary intake questionnaires 170 Malaria, plasma glucose, H. pylori, helminths, Hba1c, haemoglobin genotype This chapter presents the prevalence of malaria, plasma glucose, H. pylori, helminths, HbA1c, and haemoglobin genotype as assessed in the target population (see Table 82). Malaria, plasma glucose, H. pylori, helminths, Hba1c As earlier indicated, self-reported morbidity was assessed from a questionnaire administered to all target groups. In addition, malaria, plasma glucose, Helicobacter pylori (H. pylori), helminth, and glycated haemoglobin (HbA1c) were assessed from blood and stool samples for specific target groups, as detailed in the field (see Table 81). All sample collection started early in the morning and was completed before midday. Malaria, H. pylori, and helminth: The field tests for malaria, H. pylori, and helminth provided dichotomous results (positive or negative/ sighted or not sighted for microscopy). Plasmodium falciparum malaria parasitemia in the venous blood sample was detected using a rapid diagnostic test kit (RDT). The presence of IgG antibodies specific to Helicobacter pylori (H. pylori) in the blood sample was detected using a rapid qualitative immune assay test RDT. For soil-transmitted helminths, the presence of helminth eggs in stool samples was detected using microscopy. Plasma glucose: Plasma glucose measures the amount of glucose (sugar) currently in the blood system. Whole blood glucose concentration was measured using a HemoCue (Hb-301) instrument, and the results were converted to equivalent plasma values using a constant factor of 1.11. Random plasma glucose tests were done between early morning and midday. As reported in the results, elevated plasma glucose is defined as plasma glucose > 200 mmol/L or mg/dL. 45Plasma equivalent glucose (mmol/L or mg/dL) = Whole blood glucose (mmol/L or mg/dL) x 1.11. HbA1c: Glycated haemoglobin is a form of haemoglobin chemically linked to sugar. Most monosaccharides, including glucose, spontaneously bond with haemoglobin when present in humans’ bloodstream. The test is often called A1c or HbA1c. HbA1c reflects the average blood glucose (sugar) level for the last two to three months. Haemoglobin A1c was tested in blood samples using a Bio-Rad D10 auto-analyzer in a laboratory setting. Elevated HbA1c, as reported in the results, is defined as the amount of glucose attached to haemoglobin > 5.6 percent. Table 82. Blood analysis done in the field and laboratory for respective respondents Respondent Malaria Plasma glucose H. pylori Helminths HbA1c Haemoglobin genotype Children (aged 6-59 months) Yes No Yes Yes No Yes Adolescents (aged 10-14 years) Yes No Yes No No No WRA (aged 15-49 years old) Yes Yes Yes Yes Yes Yes Pregnant women (aged 15-49 years) Yes No Yes Yes No No 45 Kuwa K, Nakayama T, Hoshino T, Tominaga M. Relationships of glucose concentrations in capillary whole blood, venous whole blood and venous plasma. Clin Chim Acta. 2001 May;307(1-2):187-92. doi: 10.1016/s0009- 8981(01)00426-0. PMID: 11369356. 171 Figure 58 presents the prevalence at the national level of malaria, H. pylori, helminths, elevated plasma glucose, and elevated HbA1c as assessed in the target population. a. Malaria: The national prevalence of malaria among children (aged 6-59 months), adolescent girls, WRA, and pregnant women was 24, 33, 13, and 14 percent, respectively. b. H. pylori: The national prevalence of H. pylori among children (aged 6-59 months), adolescent girls, WRA, and pregnant women was 36, 55, 64, and 59 percent, respectively. c. Helminths: The national prevalence of helminth among children (6-59 months), WRA, and pregnant women was 11, 6, and 4 percent, respectively. d. Elevated plasma glucose (plasma glucose > 200 mmol/L or mg/dL): The national prevalence of elevated plasma glucose among WRA was 0.2 percent. e. Elevated HbA1c (glycated haemoglobin > 5.6%): The national prevalence of elevated HbA1c among WRA was 16 percent. Prevalence of malaria, H. pylori, and helminths among children (aged 6-59 months) Table 83 shows the prevalence of malaria, H. pylori, and helminths among children (aged 6-59 months). Malaria: The prevalence of malaria among children 6-59 months stratified by age, sex, residence, zone, and wealth quintile are shown in Table 83. There was a statistically significant difference in the prevalence of malaria among children among age category (P < 0.001), residence (P < 0.001), wealth quintile (P < 0.001), and educational attainment (P < 0.001). The prevalence of malaria was lowest in the 6 to 11-months age category (15 percent). It was higher in children residing in rural (30 percent) compared to urban (12 percent) areas. It was lowest in children in the highest wealth quintile (7 percent) and in children whose caregivers had attained tertiary education (8 percent). H. pylori: The prevalence of H. pylori among children (aged 6-59 months) stratified by age, sex, residence, zone, and wealth quintile is shown in Table 83. There was a statistically significant difference in the prevalence of H. pylori among children between age groups (P = 0.012) and the zones (P = 0.006). The prevalence of H. pylori was highest in children in the 48 to 59-months age category (42 percent) and children in the South East zone (52 percent). Helminth: The prevalence of helminth among children (aged 6-59 months) stratified by age, sex, residence, zone, and wealth quintile is shown in Table 83. There was a statistically significant difference in the prevalence of helminth among children between residence (P = 0.004), the zones (P < 0.001), wealth quintiles (P = 0.018), and educational attainment of the caregiver (P = 0.002). The prevalence of helminth was higher in children residing in rural (13 percent) compared to urban (7 percent) areas. It was lowest in the South East zone (0.3 percent). It was highest in children in the lowest wealth quintile (15 percent) and in children whose caregivers had no education (14 percent). 172 173 Figure 58. Overall prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated HbA1c among children 6-59 months, adolescent girls, WRA, and pregnant women, respectively, Nigeria 2021. Malaria: the presence of Plasmodium falciparum malaria parasitemia in blood sample detected using RDT H. pylori: the presence of IgG antibodies specific to Helicobacter pylori (H. pylori) in blood sample detected using a rapid qualitative immune assay test RDT Helminth: the presence of helminth eggs in stool samples detected using microscopy Plasma glucose: Random plasma glucose test taken in the AM. Elevated plasma glucose defined as > 200 mg/dl HbA1c: Haemoglobin A1c was tested in a blood sample using a Bio-Rad D10 auto-analyzer. Elevated HbA1c defined as > 5.6% Data are weighted to account for survey design and non-response Number of children aged 6-59 months (C6-59m) who responded nationally: Malaria (n = 5641), H. pylori (n= 4672), Helminth (n= 4240) Number of adolescent girls (ADOL) who responded nationally: Malaria (n = 996), H. pylori (n= 984) Number of WRA (WRA) who responded nationally: Malaria (n = 5159), H. pylori (n= 5161), Helminth (n= 4669), HbA1c (n= 5309), Plasma glucose (n = 5109) Number of pregnant women (PW) who responded nationally: Malaria (n = 959), H. pylori (n= 959), Helminth (n= 846) Table 83. Prevalence of malaria, H. pylori, and helminths among children (aged 6-59 months), Nigeria 2021 Background Malaria H. pylori Helminths characteristics N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.001***) (P = 0.012*) (P = 0.485) 6-11 months 448 14.7 [10.7, 19.8] 446 31.2 [24.6, 38.6] 402 7.0 [4.2, 11.5] 12-23 months 1060 17.7 [14.4, 21.6] 1056 32.2 [28.1, 36.7] 968 11.1 [8.0, 15.2] 24-35 months 1121 25.9 [20.8, 31.8] 1121 34.2 [30.0, 38.8] 1043 10.7 [8.4, 13.5] 36-47 months 1173 28.1 [22.8, 34.2] 1172 38.0 [32.6, 43.7] 1053 11.6 [8.9, 15.0] 48-59 months 876 29.0 [24.5, 33.9] 876 41.8 [36.2, 47.6] 774 11.0 [8.1, 14.7] Sex (P = 0.198) (P = 0.086) (P = 0.819) Male 2341 23.0 [19.3, 27.3] 2341 37.3 [33.1, 41.7] 2130 10.9 [8.7, 13.6] Female 2337 25.0 [21.2, 29.2] 2330 34.3 [30.8, 38.0] 2110 10.5 [8.6, 12.9] Residence (P < 0.001***) (P = 0.494) (P = 0.004**) Urban 1909 12.2 [9.1, 16.2] 1907 34.0 [27.6, 40.9] 1681 7.0 [4.8, 10.0] Rural 2769 29.9 [25.3, 34.9] 2764 36.7 [32.7, 41.0] 2559 12.7 [10.5, 15.2] Zone (P = 0.072) (P = 0.006**) (P < 0.001***) North Central 727 19.1 [12.7, 27.7] 726 43.7 [35.6, 52.2] 677 19.9 [15.2, 25.7] North East 797 16.6 [11.0, 24.3] 799 33.6 [24.4, 44.2] 792 16.4 [11.5, 22.9] North West 843 29.0 [21.4, 38.1] 837 28.4 [22.5, 35.3] 745 9.9 [7.3, 13.5] South East 692 22.2 [14.7, 32.0] 692 51.9 [43.1, 60.6] 673 0.3 [0.1, 1.2] South South 811 28.2 [21.3, 36.3] 811 41.1 [34.5, 48.1] 760 3.4 [1.5, 7.6] South West 808 24.8 [19.4, 31.2] 806 36.4 [29.4, 43.9] 593 4.7 [3.1, 7.0] Wealth quintile1 (P < 0.001***) (P = 0.345) (P = 0.018*) Lowest 1000 32.9 [27.7, 38.6] 1000 37.7 [32.7, 43.1] 936 14.5 [11.2, 18.6] Second 966 32.7 [26.1, 40.2] 963 37.6 [32.0, 43.5] 894 11.9 [9.1, 15.4] Middle 936 21.7 [17.9, 26.2] 933 36.9 [31.4, 42.8] 855 8.9 [5.7, 13.7] Fourth 920 13.9 [10.9, 17.8] 921 33.4 [28.8, 38.4] 821 7.2 [4.8, 10.6] Highest 835 7.2 [4.5, 11.3] 833 30.6 [22.9, 39.5] 720 8.6 [5.9, 12.4] Caregivers’ educational attainment1 (P < 0.001***) (P = 0.186) (P = 0.002**) None 1239 29.6 [24.4, 35.4] 1236 33.4 [29.4, 37.8] 1118 14.3 [11.4, 17.8] Primary 738 25.7 [20.6, 31.6] 736 39.3 [32.9, 46.0] 674 11.1 [7.5, 16.1] Secondary 1919 19.3 [15.8, 23.4] 1918 37.8 [32.8, 43.2] 1756 7.9 [6.1, 10.3] Tertiary 487 7.6 [4.0, 13.9] 486 32.2 [25.3, 40.1] 432 7.9 [4.7, 13.2] National 4678 24.0 [20.5, 28.0] 46712 35.8 [32.3, 39.5] 4240 10.7 [9.1, 12.6] Malaria: the presence of Plasmodium falciparum malaria parasitemia in blood sample detected using RDT H. pylori: the presence of IgG antibodies specific to Helicobacter pylori (H. pylori) in blood sample detected using a rapid qualitative immune assay test RDT Helminth: the presence of helminth eggs in stool samples detected using microscopy Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children aged (6-59 months) who responded nationally: Malaria (n = 4678), H. pylori (n= 4672), Helminth (n= 4240) 1Less than [Malaria (n = 4678), H. pylori (n= 4672), Helminth (n= 4240)] due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 4671) due to invalid results 174 Prevalence of malaria and H. pylori among adolescent girls (aged 10-14 years) Table 84 shows the prevalence of malaria and H. pylori among adolescent girls (aged 10-14 years). Malaria: Table 84 shows the prevalence of malaria in adolescent girls stratified by age, residence, and wealth quintile. There was a statistically significant difference in the prevalence of malaria among adolescent girls between residence (P < 0.001) and wealth quintile (P < 0.001). The prevalence of malaria was higher in adolescent girls residing in the rural (43 percent) than in the urban (17 percent) areas. The prevalence was lowest among adolescent girls in the highest wealth quintile (6 percent). H. pylori: Table 84 shows the prevalence of H. pylori in adolescent girls stratified by age, residence, and wealth quintile. There was a statistically significant difference in the prevalence of H. pylori among adolescent girls between the ages (P = 0.007). The prevalence was highest in 12-year-olds (65 percent). Table 84. Prevalence of malaria and H. pylori among adolescent girls (aged 10-14 years), Nigeria 2021 Background Malaria H. pylori characteristics N % [95% CI] N % [95% CI] Age (P = 0.839) (P = 0.007**) 10 years 263 34.8 [26.9, 43.5] 258 56.3 [47.8, 64.4] 11 years 158 33.0 [22.8, 45.1] 158 42.4 [32.1, 53.5] 12 years 191 34.4 [24.8, 45.5] 186 64.8 [55.5, 73.2] 13 years 196 28.7 [20.9, 38.0] 193 47.2 [37.7, 57.0] 14 years 189 35.6 [25.5, 47.3] 189 61.0 [50.6, 70.5] Residence (P < 0.001***) (P = 0.127) Urban 420 17.2 [12.1, 23.8] 418 49.8 [42.1, 57.4] Rural 577 42.7 [35.6, 50.1] 566 57.9 [50.8, 64.6] Wealth quintile1 (P < 0.001***) (P = 0.809) Lowest 213 49.1 [39.8, 58.5] 210 57.8 [48.0, 67.0] Second 188 46.3 [36.6, 56.4] 187 57.6 [47.1, 67.3] Middle 206 31.3 [22.6, 41.5] 202 51.6 [41.0, 62.2] Fourth 195 21.6 [15.3, 29.5] 191 53.2 [43.8, 62.4] Highest 193 6.1 [3.0, 12.4] 192 52.3 [42.3, 62.1] National 997 33.4 [28.2, 39.1] 984 54.9 [49.6, 60.1] Malaria: the presence of Plasmodium falciparum malaria parasitemia in blood sample detected using RDT H. pylori: the presence of IgG antibodies specific to Helicobacter pylori (H. pylori) in blood sample detected using a rapid qualitative immune assay test RDT Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of adolescent girls who responded nationally: Malaria (n = 997), H. pylori (n= 984) 1Less than [ Malaria (n = 997), H. pylori (n= 984)] due to relatively fewer respondents for the household and dietary intake questionnaires 175 Prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated glycated haemoglobin (HbA1c) among WRA (aged 15-49 years) Table 85 shows the prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated glycated haemoglobin (HbA1c) among WRA. Malaria: Table 85 shows the prevalence of malaria in WRA stratified by age, residence, zone, and wealth quintile. There was a statistically significant difference in the prevalence of malaria among WRA between the age groups (P < 0.001), residence (P < 0.001), wealth quintile (P < 0.001), and educational attainment (P < 0.001). The prevalence of malaria was highest in the 15 to19-years age category (21 percent). It was higher among women residing in rural (15 percent) compared to urban (8 percent) areas. It was lowest in WRA in the highest wealth quintile (4 percent) and women who had attained tertiary education (5 percent). H. pylori: Table 85 shows the prevalence of H. pylori in WRA stratified by age, residence, zone, and wealth quintile. There was a statistically significant difference in the prevalence of H. pylori among WRA between the age groups (P = 0.043) and zones (P < 0.001). The lowest prevalence of H. pylori was in the 15 to 19-years (61 percent) and 20 to 29-years (61 percent) age categories. The prevalence was lowest in the North West zone (53 percent). Helminth: Table 85 shows the prevalence of helminth in WRA stratified by age, residence, zone, and wealth quintile. There was a statistically significant difference in the prevalence of helminth among WRA between residence (P = 0.022), zones (P < 0.001), wealth quintile (P = 0.012), and educational attainment (P = 0.016). The prevalence of helminth was higher in WRA residing in rural (7 percent) compared to urban (4 percent) areas. It was highest in WRA in the North East zone (12 percent). It was highest in WRA in the lowest wealth quintile (9 percent) and lowest in women who had attained tertiary education (4 percent). Elevated plasma glucose (plasma glucose > 200 mg/dl): Table 85 shows the prevalence of elevated plasma glucose in WRA stratified by age, residence, zone, and wealth quintile. There was no significant variation in the prevalence of elevated plasma glucose in WRA across the background characteristics. Elevated glycated haemoglobin (HbA1c > 5.6%): Table 85 shows the prevalence of elevated HbA1c in WRA stratified by age, residence, zone, and wealth quintile. There was a statistically significant difference in the prevalence of elevated HbA1c among WRA between the age groups (P = 0.001), residence (P < 0.001), and wealth quintile (P = 0.003). The prevalence of elevated HbA1c was highest in the 40 to 49-years age category (22 percent). It was higher in WRA residing in urban (21 percent) than in rural (13 percent) areas. It was lowest among WRA in the lowest wealth quintile (11 percent).. 176 177 Table 85. Prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated HbA1c among WRA (aged 15-49 years), Nigeria 2021 Background Malaria H. pylori Helminth Elevated plasma glucose Elevated HbA1c characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age (P < 0.001***) (P = 0.043*) (P = 0.907) (P = 0.005) (P = 0.001***) 15-19 years 1124 20.6 [16.8, 25.1] 1124 61.0 [56.0, 65.7] 1000 6.1 [4.3, 8.6] 1098 0.0 [ ., .] 1093 14.4 [11.1, 18.6] 20-29 years 1637 11.2 [9.4, 13.3] 1638 61.4 [57.2, 65.5] 1460 5.8 [4.3, 7.7] 1572 0.0 [ ., .] 1559 13.7 [11.1, 16.8] 30-39 years 1495 10.1 [7.9, 12.9] 1497 66.3 [62.1, 70.3] 1373 5.6 [4.2, 7.4] 1444 0.5 [0.2, 1.4] 1430 17.5 [14.2, 21.4] 40-49 years 1012 8.6 [5.8, 12.6] 1011 66.4 [62.0, 70.5] 935 5.2 [3.6, 7.3] 995 0.2 [0.1, 0.7] 1000 21.8 [18.5, 25.6] Residence (P < 0.001***) (P = 0.169) (P = 0.022*) (P = 0.325) (P < 0.001***) Urban 2138 8.2 [6.2, 10.8] 2138 60.7 [54.7, 66.3] 1865 4.0 [2.8, 5.9] 2078 0.1 [0.0, 0.3] 2013 20.9 [17.0, 25.4] Rural 3130 15.3 [13.3, 17.7] 3132 65.5 [61.9, 68.9] 2903 6.8 [5.4, 8.5] 3031 0.2 [0.1, 0.6] 3069 12.7 [10.7, 15.1] Zone (P = 0.411) (P < 0.001***) (P < 0.001***) (P = 0.636) (P = 0.218) North Central 863 14.8 [11.1, 19.4] 862 71.7 [65.4, 77.3] 785 8.9 [6.2, 12.7] 826 0.0 [ ., .] 832 16.8 [13.0, 21.6] North East 855 9.8 [6.8, 13.9] 857 58.2 [47.1, 68.5] 836 11.7 [8.3, 16.3] 818 0.0 [ ., .] 824 20.4 [14.6, 27.7] North West 901 14.0 [10.6, 18.2] 901 52.9 [47.6, 58.0] 792 4.1 [2.7, 6.3] 869 0.4 [0.1, 1.3] 880 15.0 [10.0, 21.8] South East 880 10.5 [7.0, 15.5] 880 75.9 [70.6, 80.5] 846 1.0 [0.2, 6.1] 861 0.8 [0.3, 2.1] 845 21.4 [17.3, 26.3] South South 881 13.0 [9.1, 18.2] 883 76.2 [70.6, 81.1] 827 2.9 [1.5, 5.6] 858 0.0 [ ., .] 837 15.6 [12.3, 19.6] South West 888 11.5 [8.5, 15.4] 887 63.0 [57.6, 68.1] 682 2.5 [1.1, 5.3] 877 0.0 [ ., .] 864 12.8 [9.5, 17.0] Wealth quintile1 (P < 0.001***) (P = 0.373) (P = 0.012*) (P = 0.352) (P = 0.003**) Lowest 1095 17.9 [15.0, 21.3] 1095 61.7 [56.8, 66.4] 1013 8.9 [6.7, 11.7] 1050 0.2 [0.0, 1.2] 1057 10.5 [7.9, 13.9] Second 1122 18.7 [15.5, 22.5] 1124 64.8 [60.1, 69.3] 1046 6.2 [4.2, 8.9] 1081 0.1 [0.0, 0.7] 1083 14.6 [11.3, 18.5] Middle 1104 10.2 [8.1, 12.7] 1105 67.1 [62.1, 71.9] 975 5.2 [3.4, 8.1] 1073 0.4 [0.1, 1.7] 1066 18.5 [15.0, 22.5] Fourth 988 9.5 [7.3, 12.2] 987 62.4 [56.5, 68.0] 877 4.2 [2.6, 6.5] 963 0.0 [ ., .] 961 19.4 [15.0, 24.6] Highest 937 4.0 [2.6, 6.3] 937 61.1 [54.3, 67.4] 839 3.5 [1.9, 6.3] 920 0.2 [0.0, 0.5] 901 20.3 [15.3, 26.4] Educational attainment1 (P < 0.001***) (P = 0.069) (P = 0.016*) (P = 0.552) (P = 0.065) None 1235 15.2 [12.5, 18.2] 1237 60.8 [56.4, 65.0] 1109 7.5 [5.8, 9.7] 1170 0.0 [ ., .] 987 13.3 [10.6, 16.5] Primary 850 13.8 [11.1, 17.0] 849 68.0 [62.3, 73.1] 795 6.5 [4.4, 9.7] 828 0.1 [0.0, 0.4] 819 16.9 [13.7, 20.7] Secondary 2397 11.6 [9.6, 13.9] 2399 63.2 [58.4, 67.7] 2162 4.5 [3.4, 6.1] 2342 0.1 [0.0, 0.3] 2394 18.0 [14.9, 21.6] Tertiary 533 4.5 [2.6, 7.5] 531 69.2 [62.8, 75.0] 474 3.6 [2.0, 6.5] 523 0.1 [0.0, 0.7] 705 14.5 [11.1, 18.7] National 52682 12.5 [10.9, 14.3] 52703 63.6 [60.4, 66.6] 4768 5.7 [4.7, 6.9] 5109 0.2 [0.1, 0.4] 50824 16.4 [14.2, 18.9] 178 Table 85. Prevalence of malaria, H. pylori, helminths, elevated plasma glucose, and elevated HbA1c among WRA (aged 15-49 years), Nigeria 2021 (continued) Malaria: the presence of Plasmodium falciparum malaria parasitemia in blood sample detected using RDT H. pylori: the presence of IgG antibodies specific to Helicobacter pylori (H. pylori) in blood sample detected using a rapid qualitative immune assay test RDT Helminth: the presence of helminth eggs in stool samples detected using microscopy Plasma glucose: random plasma glucose test taken in the AM. Elevated plasma glucose defined as > 200 mg/dl HbA1c: Haemoglobin A1c was tested in a blood sample using a Bio-Rad D10 auto-analyzer. Elevated HbA1c defined as > 5.6% Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of WRA who responded nationally: Malaria (n = 5269), H. pylori (n= 5272), Helminth (n= 4768), Plasma glucose (n = 5109), HbA1c (n= 5089) 1Less than [ Malaria (n = 5269), H. pylori (n= 5272), Helminth (n= 4768), Plasma glucose (n = 5109), HbA1c (n= 5089)] due to relatively fewer respondents for the HH and dietary intake questionnaires 2Less than (n = 5269) due to invalid results 3Less than (n = 5272) due to invalid results 4Less than (n = 5089) due to invalid results Prevalence of malaria, H. pylori, and helminths among pregnant women (aged 15-49 years) Table 86 shows the prevalence of malaria, H. pylori, and helminths among pregnant women stratified by age, residence, wealth quintile, and educational attainment. Malaria: There was a statistically significant difference in the prevalence of malaria among pregnant women between the age groups (P = 0.002) and residence (P = 0.003). The prevalence was lowest in the 30-39-years age category (8 percent). It was higher among pregnant women residing in rural (17 percent) than in urban (8 areas) areas. H. pylori: There was no significant variation in the prevalence of H. pylori among pregnant women across the background characteristics. Helminth: There was no significant variation in the prevalence of helminth among pregnant women across the background characteristics. Table 86. Prevalence of malaria, H. pylori, and helminths among pregnant women (aged 15-49 years) Nigeria 2021 Background Malaria H. pylori Helminth characteristics N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.0017**) (P = 0.115) (P = 0.889) 15-19 years 73 34.8 [20.7, 52.2] 73 63.5 [49.3, 75.7] 63 5.3 [2.1, 12.7] 20-29 years 446 14.4 [10.6, 19.1] 445 58.5 [51.2, 65.5] 394 4.3 [2.3, 7.7] 30-39 years 286 7.7 [4.0, 14.5] 287 54.4 [46.0, 62.6] 252 4.6 [2.5, 8.3] 40-49 years 43 10.9 [2.7, 34.9] 43 79.2 [62.8, 89.6] 38 2.2 [0.3, 14.9] Residence (P = 0.0026**) (P = 0.376) (P = 0.255) Urban 347 8.3 [5.7, 12.0] 348 55.6 [47.5, 63.5] 291 3.0 [1.4, 6.5] Rural 501 16.5 [12.6, 21.4] 500 60.2 [54.1, 66.0] 456 5.1 [3.2, 7.8] Wealth quintile1 (P = 0.1426 (P = 0.178) (P = 0.539) Lowest 181 16.9 [10.9, 25.2] 181 56.0 [47.2, 64.5] 165 5.9 [3.2, 10.8] Second 174 16.2 [10.4, 24.3] 174 62.7 [52.2, 72.1] 158 5.0 [2.5, 9.9] Middle 165 17.0 [9.9, 27.6] 165 68.5 [56.9, 78.1] 151 5.2 [1.9, 13.3] Fourth 176 10.2 [4.3, 22.7] 176 51.7 [38.7, 64.5] 145 2.4 [0.9, 6.2] Highest 149 3.7 [1.4, 8.9] 149 51.6 [41.0, 62.1] 126 1.8 [0.3, 11.3] Educational attainment1 (P = 0.119) (P = 0.078) (P = 0.579) None 198 16.7 [11.3, 24.0] 198 56.0 [45.7, 65.7] 174 3.7 [1.9, 7.2] Primary 131 12.2 [7.5, 19.4] 131 53.9 [41.2, 66.1] 111 3.6 [1.3, 9.7] Secondary 383 13.4 [9.1, 19.2] 384 66.3 [59.1, 72.9] 343 5.8 [3.1, 10.6] Tertiary 93 3.7 [1.2, 11.0] 92 47.5 [36.7, 58.5] 78 2.8 [0.9, 8.9] National 848 13.8 [10.9, 17.3] 848 58.7 [53.8, 63.4] 747 4.4 [3.0, 6.4] Malaria: the presence of Plasmodium falciparum malaria parasitemia in blood sample detected using RDT H. pylori: the presence of IgG antibodies specific to Helicobacter pylori (H. pylori) in blood sample detected using a rapid qualitative immune assay test RDT Helminth: the presence of helminth eggs in stool samples detected using microscopy Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of pregnant women who responded nationally: Malaria (n = 848), H. pylori (n= 848), Helminth (n= 747) 1Less than [ Malaria (n = 848), H. pylori (n= 848), Helminth (n= 747)] due to relatively fewer respondents for the household and dietary intake questionnaires 179 Haemoglobin genotype (Blood disorders) Inherited blood disorders are common among children in many parts of Africa46. The different blood disorders include α-thalassemia, β-thalassemia, sickle cell, haemoglobin E, and glucose- 6-phosphate dehydrogenase deficiency (G6PD). These are broadly classified into structural or qualitative disorders.47 Sickle Cell Disease (SCD) is a structural disorder of the blood characterized by a mutation in the beta-globin gene located on chromosome 11. The nucleotide adenine at position six is substituted with thymine causing a change in amino acid sequence from glutamic acid to valine. On the other hand, thalassaemias are a group of qualitative disorders characterized by a reduced production rate or absence of the globulin units of haemoglobin. Haemoglobin genotype of children (aged 6-59 months) and WRA was assessed using electrophoresis in a laboratory. The tests were done using HPLC. Capillary electrophoresis was used to confirm rare variants identified on HPLC. A key objective of the survey was to assess blood disorders as an important factor associated with anaemia. Figure 59 presents the national prevalence of haemoglobin genotype and inherited blood disorders among children (aged 6-59 months) and WRA (aged 15-49 years). The percentage of children (aged 6-59 months) with normal haemoglobin (HbAA) was 78 percent. The prevalence of sickle cell trait (HbAS) among children was 19 percent. The prevalence of SCD is 0.7 percent. The percentage of WRA with normal haemoglobin (HbAA) was 75 percent. The prevalence of sickle cell trait (HbAS) among children was 23 percent and prevalence of SCD is 0.2 percent. Table 87 presents the prevalence of haemoglobin genotype and inherited blood disorders among children (aged 6-59 months) stratified by age, residence, zone, and wealth quintile. There was no significant variation in the prevalence of inherited blood disorders in children across the background characteristics. Table 88 presents the prevalence of haemoglobin genotype and inherited blood disorders among WRA (aged 15-49 years) stratified by residence, zone, and wealth quintile. There was a statistically significant difference in the prevalence of sickle cell among WRA between residence (P = 0.002). The prevalence of SCD (HbSS) was higher in women residing in rural (0.3 percent) than in urban (0.0 percent) areas. 46 Suchdev PS, Ruth LJ, Earley M, Macharia A, Williams TN. The burden and consequences of inherited blood disorders among young children in western Kenya. Matern Child Nutr. 2014 Jan;10(1):135-44. doi: 10.1111/j.1740-8709.2012.00454.x. Epub 2012 Sep 13. PMID: 22973867; PMCID: PMC3963444. 47 Modell B, Darlison M. Global epidemiology of haemoglobin disorders and derived service indicators. Bull World Health Organ. 2008 Jun;86(6):480-7. doi: 10.2471/blt.06.036673. PMID: 18568278; PMCID: PMC2647473. 180 181 Figure 59. Prevalence of haemoglobin genotype and prevalence of inherited blood disorders by target group at national level (linked to Tables 88 and 89), Nigeria 2021 Haemoglobin genotype (blood disorders) was assessed using HPLC in a laboratory setting Capillary electrophoresis was used to confirm rare variants identified on HPLC Data are weighted to account for survey design and non-response Number of children (aged 6-59 months) who responded nationally: (n= 4548) Number of children presenting with Hb: AA (n=3,469), AC (n=69); AD (n=7); AS (n= 877); CC (n=1); SS (n= 33) Number of WRA who responded nationally:(n= 5137) Number of women presenting with Hb: AA (n=3,924), AC (n=58); AD (n= 5); AS (n=166); CC (n=0); SS (n= 11) 182 Table 87. Prevalence of haemoglobin genotype (HbAA, HbAS) and prevalence of inherited blood disorders (HbSS) among children (aged 6-59 months), Nigeria 2021 Haemoglobin Genotype Background characteristics AA AS SS N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.383) (P = 0.180) (P = 0.568) 6-11 months 448 79.1 [74.1, 83.3] 448 17.1 [13.2, 21.8] 448 1.1 [0.3, 4.8] 12-23 months 1037 78.4 [74.9, 81.5] 1037 18.6 [15.7, 21.9] 1037 0.8 [0.3, 2.0] 24-35 months 1130 78.2 [74.3, 81.6] 1130 19.6 [16.3, 23.3] 1130 0.6 [0.2, 1.4] 36-47 months 1127 79.9 [76.5, 83.0] 1127 17.0 [14.1, 20.3] 1127 1.0 [0.4, 2.2] 48-59 months 795 75.0 [69.9, 79.4] 795 22.4 [18.5, 26.9] 795 0.3 [0.1, 0.8] Sex (P = 0.990) (P = 0.803) (P = 0.913) Male 2272 78.2 [74.9, 81.2] 2272 18.7 [16.0, 21.6] 2272 0.8 [0.3, 1.7] Female 2265 78.2 [75.4, 80.8] 2265 19.1 [16.7, 21.7] 2265 0.7 [0.4, 1.4] Residence 0.2987 0.0672) 0.3398 Urban 1828 79.7 [76.0, 83.0] 1828 16.5 [13.7, 19.8] 1828 1.0 [0.5, 2.0] Rural 2709 77.3 [74.4, 80.0] 2709 20.3 [17.8, 23.0] 2709 0.6 [0.3, 1.3] Zone (P = 0.140) (P = 0.425) (P = 0.661) North Central 695 82.6 [78.2, 86.2] 695 15.9 [12.5, 20.1] 695 0.4 [0.1, 1.2] North East 783 78.9 [73.0, 83.8] 783 19.1 [14.5, 24.8] 783 1.1 [0.3, 3.8] North West 828 78.9 [73.7, 83.3] 828 17.9 [13.9, 22.8] 828 0.8 [0.4, 2.0] South East 678 78.2 [74.5, 81.5] 678 20.8 [17.4, 24.6] 678 0.2 [0.1, 0.8] South South 783 76.7 [73.1, 80.0] 783 22.4 [19.1, 26.1] 783 0.5 [0.2, 1.5] South West 770 72.7 [67.9, 77.0] 770 20.0 [16.7, 23.8] 770 0.8 [0.3, 2.1] Wealth quintile1 (P = 0.288) (P = 0.278) (P = 0.708) Lowest 971 79.8 [76.5, 82.8] 971 17.9 [14.9, 21.4] 971 0.7 [0.3, 1.9] Second 946 75.1 [69.2, 80.1] 946 22.1 [17.7, 27.1] 946 0.8 [0.3, 2.2] Middle 911 78.0 [73.8, 81.6] 911 18.8 [15.6, 22.5] 911 0.6 [0.2, 1.7] Fourth 891 79.1 [75.3, 82.5] 891 17.6 [14.6, 21.0] 891 1.1 [0.5, 2.3] Highest 811 80.8 [75.7, 85.0] 811 16.3 [11.7, 22.3] 811 0.4 [0.1, 1.0] National 45372 78.2 [75.9, 80.3] 45373 18.9 [16.9, 21.0] 45374 0.7 [0.4, 1.3] Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children (aged 6-59 months) who responded nationally: (n= 4548) 1Less than (n = 4548) due to relatively fewer respondents for the household and dietary intake questionnaires 2Less than (n = 4548) due to invalid results 3Less than (n = 4548) due to invalid results 4Less than (n = 4548) due to invalid results 183 Table 88. Prevalence of haemoglobin genotype (HbAA, HbAS) and prevalence of inherited blood disorders (HbSS) among WRA (aged 15-49 years), Nigeria 2021 Haemoglobin genotype Background AA AS SS characteristics N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.772) (P = 0.796) (P = 0.103) 15-19 years 1110 76.0 [72.9, 78.9] 1110 22.8 [20.0, 26.0] 1110 0.3 [0.1, 0.6] 20-29 years 1576 75.7 [73.0, 78.2] 1576 22.4 [20.0, 25.1] 1576 0.4 [0.1, 1.1] 30-39 years 1437 74.1 [71.1, 76.8] 1437 24.3 [21.6, 27.2] 1437 0.0 [0.0, 0.3] 40-49 years 1006 74.5 [69.7, 78.7] 1006 23.8 [19.6, 28.6] 1006 0.0 [ ., .] Residence (P = 0.695) (P = 0.417) (P = 0.002**) Urban 2028 75.5 [72.8, 78.0] 2028 22.5 [20.2, 25.1] 2028 0.0 [0.0, 0.2] Rural 3101 74.8 [72.7, 76.8] 3101 23.9 [21.8, 26.1] 3101 0.3 [0.1, 0.7] Zone (P = 0.183) (P = 0.066) (P = 0.881) North Central 839 76.4 [73.3, 79.3] 839 23.0 [20.2, 26.1] 839 0.2 [0.0, 0.6] North East 830 76.9 [72.5, 80.8] 830 21.9 [18.3, 25.9] 830 0.1 [0.0, 0.9] North West 886 72.1 [68.0, 75.9] 886 26.5 [22.6, 30.8] 886 0.3 [0.1, 1.3] South East 859 78.0 [75.0, 80.8] 859 21.2 [18.4, 24.1] 859 0.1 [0.0, 0.9] South South 840 75.6 [72.3, 78.6] 840 24.1 [21.1, 27.4] 840 0.2 [0.1, 0.6] South West 875 75.3 [71.9, 78.4] 875 20.1 [17.2, 23.3] 875 0.1 [0.0, 0.6] Wealth quintile1 (P = 0.286) (P = 0.396) (P = 0.115) Lowest 1065 74.8 [70.8, 78.4] 1065 24.1 [20.4, 28.3] 1065 0.1 [0.0, 0.5] Second 1100 75.5 [72.0, 78.7] 1100 22.9 [19.7, 26.4] 1100 0.3 [0.1, 0.8] Middle 1070 72.3 [68.7, 75.5] 1070 25.7 [22.5, 29.1] 1070 0.5 [0.1, 1.6] Fourth 969 75.3 [71.9, 78.4] 969 22.8 [19.7, 26.3] 969 0.0 [ ., .] Highest 911 78.0 [74.0, 81.6] 911 20.6 [17.0, 24.7] 911 0.1 [0.0, 0.5] National 51292 75.1 [73.4, 76.7] 51293 23.3 [21.7, 24.9] 51294 0.2 [0.1, 0.4] Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of WRA who responded nationally: (n= 5137) 1Less than (n = 5137) due to relatively fewer respondents for the HH and dietary intake questionnaires 2Less than (n = 5137) due to invalid results 3Less than (n = 5137) due to invalid results 4Less than (n = 5137) due to invalid results Anaemia This chapter presents results on anaemia in the target population assessed from whole blood samples analyzed in the field. Anaemia is the most evident consequence of iron deficiency. It is associated with significant morbidity and has been the focus for evaluating iron status.48 Anaemia is characterized by low levels of haemoglobin (the protein in RBC responsible for carrying oxygen) in the blood. Iron is an essential component of haemoglobin, and iron deficiency is estimated to contribute to approximately one-half of anaemia cases worldwide49. Self-reported anaemia risk and use of multivitamin/ iron supplements were assessed from the questionnaire for all target groups. Other micronutrient deficiencies (i.e., vitamin B12, folate, and vitamin A) and non-nutritional causes (i.e., blood disorders, malaria, hookworm, and other helminths) can also cause anaemia. Anaemia impairs children’s physical and cognitive development, increases susceptibility to infections, and results in fatigue and reduced work capacity among adults. Anaemia also increases the risk of child and maternal mortality.1 Anaemia, for all target groups, was assessed by measuring haemoglobin levels (grams per liter) in whole venous blood using a HemoCue (Hb-201) instrument. The cut-offs for the respective target groups for diagnosis of anaemia based on haemoglobin levels (grams per liter)50 are as follows (see Table 89): Table 89. Anaemia cut-offs for the respective target groups Anaemia (low haemoglobin) Target group Non-anaemia Mild Moderate Severe Children (aged 6-59 months) Hb≥ 100 g/L 100-109 g/L 70-99 g/L < 70 g/L Adolescent girls (10-11 years) Hb≥ 115 g/L 110-114 g/L 80-109 g/L < 80 g/L Adolescent girls (12-14 years) WRA (aged 15-49 years) Hb≥ 120 g/L 110-119 g/L 80-109 g/L < 80 g/L Pregnant women (aged 15-49 years) Hb≥ 110 g/L 100-109 g/L 70-99 g/L < 70 g/L Individual haemoglobin values (g/dl) presented in the results were adjusted51 to account for: • Pregnancy: first trimester (+1.0), second (+1.5), third (+1.0), trimester unknown (+1.0). • Altitude: Hb adjustment = -0.032 x (altitude x 0.0032808) + 0.022 x (altitude x 0.0032808)2; • Ethnicity: African extraction (+1.0); and • Cigarette smoking: smoker, amount unknown (- 0.3). 48 Lynch, S., Pfeiffer, C. M., Georgieff, M. K., Brittenham, G., Fairweather-Tait, S., Hurrell, R. F., McArdle, H. J., & Raiten, D. J. (2018). Biomarkers of Nutrition for Development (BOND)-Iron Review. The Journal of nutrition, 148(suppl_1), 1001S–1067S. https://doi.org/10.1093/jn/nxx036 49 Kassebaum NJ; GBD 2013 Anaemia Collaborators. The Global Burden of Anaemia. Hematol Oncol Clin North Am. 2016 Apr;30(2):247-308. doi: 10.1016/j.hoc.2015.11.002. PMID: 27040955. 50 WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, 2011 (WHO/NMH/NHD/MNM/11.1) (http://www.who.int/vmnis/ indicators/haemoglobin. pdf, accessed [±30 December 2021]). 51 Sullivan KM, Mei Z, Grummer-Strawn L, Parvanta I. Haemoglobin adjustments to define anaemia. Trop Med Int Health. 2008 Oct;13(10):1267-71. doi: 10.1111/j.1365-3156.2008.02143.x. Epub 2008 Aug 20. PMID: 18721184. 184 Figure 60 presents the national prevalence of anaemia by the target group. • Children (6-59 months old): Anaemia was present in 62 percent of children (6-59 months old). The prevalence of mild, moderate, and severe anaemia was 31, 29, and 2 percent, respectively. • Adolescent girls (10-14 years old): Anaemia was present in 41 percent of adolescent girls. The prevalence of mild, moderate, and severe anaemia was 16, 24, and 1 percent, respectively. • WRA (15-49 years old): Anaemia was present in 55 percent of WRA. The prevalence of mild, moderate, and severe anaemia was 31, 22, and 1 percent, respectively. • Pregnant women (15-49 years old): Anaemia was present in 86 percent of pregnant women. The prevalence of mild, moderate, and severe anaemia was 20, 62, and 4 percent, respectively. Prevalence of anaemia among children (aged 6-59 months) The prevalence of anaemia among children (aged 6-59 months) stratified by age, sex, residence, zone, and wealth quintile is shown in Table 90. a. Any anaemia: There was a statistically significant difference in the prevalence of any anaemia among children (aged 6-59 months) between the age groups (P < 0.001), residence (P < 0.001), zones (P < 0.001), wealth quintiles (P < 0.001), and caregivers’ educational attainment (P < 0.001). The prevalence of any anaemia was highest in the 6 to 11-months age category (74 percent). It was higher in children residing in rural (67 percent) than in the urban (51 percent) areas. It was highest in children in the North West zone (73 percent). The prevalence of any anaemia was lowest in children in the highest wealth quintile (47 percent) and in children whose caregivers had attained tertiary education (46 percent). b. Mild anaemia: There was no significant variation in the prevalence of mild anaemia among children (aged 6-59 months) across the background characteristics. c. Moderate anaemia: There was a statistically significant difference in the prevalence of moderate anaemia among children (aged 6-59 months) between the age groups (P < 0.001), residence (P < 0.001), zones (P < 0.001), wealth quintiles (P < 0.001), and caregivers’ educational attainment (P < 0.001). The prevalence of moderate anaemia was lowest in the 48 to 59-months age category (18 percent). It was higher in children residing in rural (33 percent) than in urban (21 percent) areas. It was highest in children in the North West zone (40 percent). The prevalence of moderate anaemia was lowest in children in the highest wealth quintile (17 percent) and highest in children whose caregivers had no education (35 percent). d. Severe anaemia: There was a statistically significant difference in the prevalence of severe anaemia among children (aged 6-59 months) between residence (P = 0.001) and wealth quintiles (P < 0.0001). It was higher in children residing in rural (2 percent) than in urban (0.8 percent) areas. It was highest in children in the lowest wealth quintile (17 percent). The relationship between anaemia, infection, haemoglobin genotype (blood disorders), and the use of micronutrient powder among children (aged 6-59 months) is shown in Table 91. About 74 percent of children with severe anaemia had malaria (P < 0.001), and 78 percent of children with any anaemia had normal haemoglobin genotype (P < 0.001). Severity of anaemia was also associated with normal haemoglobin genotype (P < 0.001). Children with moderate anaemia (52 percent) had fever in the past two weeks, while 61 percent of children with severe anaemia had fever in the past two weeks (P = 0.006). 185 186 Figure 60. Overall prevalence of any, mild, moderate, and severe anaemia by target group, Nigeria 2021 Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Haemoglobin measurements were adjusted to account for pregnancy, altitude, and cigarette smoking as needed Data are weighted to account for survey design and non-response Number of children (aged 6-59 months) who responded nationally: (n= 4674) Number of adolescent girls who responded nationally: (n= 999) Number of WRA who responded nationally:(n= 5272) Number of pregnant women who responded nationally: (n= 847) 187 Table 90. Prevalence of anaemia among children (aged 6-59 months), Nigeria 2021 Background Any anaemia Mild anaemia Moderate anaemia Severe anaemia characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P < 0.001***) (P = 0.697) (P < 0.001***) (P = 0.383) 6-11 months 447 74.0 [68.5, 78.9] 447 34.0 [28.0, 40.5] 447 38.2 [32.5, 44.2] 447 1.9 [0.8, 4.2] 12-23 months 1057 70.8 [66.0, 75.1] 1057 30.5 [27.0, 34.3] 1057 37.8 [33.2, 42.6] 1057 2.5 [1.3, 4.8] 24-35 months 1121 63.5 [59.4, 67.5] 1121 31.9 [27.7, 36.4] 1121 29.2 [24.4, 34.5] 1121 2.5 [1.3, 4.8] 36-47 months 1173 56.5 [51.7, 61.2] 1173 29.5 [26.2, 33.0] 1173 25.8 [21.9, 30.0] 1173 1.2 [0.6, 2.4] 48-59 months 876 48.9 [43.5, 54.4] 876 29.6 [25.0, 34.5] 876 18.1 [14.1, 22.9] 876 1.3 [0.7, 2.6] Sex (P = 0.709) (P = 0.865) (P = 0.902) (P = 0.217) Male 2340 62.1 [58.6, 65.5] 2340 30.9 [28.3, 33.6] 2340 29.0 [25.2, 33.1] 2340 2.2 [1.5, 3.4] Female 2334 61.4 [58.2, 64.5] 2334 30.6 [28.2, 33.1] 2334 29.3 [25.9, 32.9] 2334 1.6 [1.0, 2.5] Residence (P < 0.001***) (P = 0.345) (P < 0.001***) (P = 0.001***) Urban 1906 51.4 [47.9, 54.8] 1906 29.6 [26.9, 32.3] 1906 21.0 [18.4, 23.8] 1906 0.8 [0.5, 1.4] Rural 2768 66.9 [63.5, 70.1] 2768 31.3 [29.0, 33.7] 2768 33.1 [29.3, 37.2] 2768 2.4 [1.7, 3.5] Zone (P < 0.001***) (P = 0.127) (P < 0.001***) (P = 0.069) North Central 725 55.5 [50.5, 60.5] 725 33.4 [30.0, 37.0] 725 21.1 [16.8, 26.2] 725 1.0 [0.4, 2.5] North East 799 54.4 [48.4, 60.2] 799 27.7 [23.9, 31.8] 799 24.9 [20.9, 29.4] 799 1.8 [0.8, 3.8] North West 839 72.7 [67.2, 77.7] 839 29.7 [26.2, 33.5] 839 40.1 [33.8, 46.9] 839 2.9 [1.6, 5.0] South East 691 58.9 [52.3, 65.2] 691 32.4 [29.2, 35.8] 691 25.2 [19.9, 31.3] 691 1.3 [0.7, 2.7] South South 812 61.4 [56.1, 66.5] 812 35.1 [30.0, 40.5] 812 24.2 [20.8, 27.8] 812 2.2 [1.3, 3.4] South West 808 54.2 [48.8, 59.6] 808 30.2 [26.4, 34.3] 808 23.3 [19.2, 28.0] 808 0.7 [0.3, 1.9] Wealth quintile1 (P < 0.001***) (P = 0.243) (P < 0.001***) (P = 0.002***) Lowest 999 68.2 [63.8, 72.2] 999 28.0 [24.9, 31.3] 999 36.0 [31.5, 40.8] 999 4.1 [2.5, 6.7] Second 964 70.1 [65.3, 74.4] 964 33.1 [28.2, 38.3] 964 35.4 [29.8, 41.4] 964 1.6 [0.8, 3.3] Middle 936 59.0 [54.8, 63.1] 936 32.8 [28.9, 36.9] 936 25.5 [21.5, 30.0] 936 0.7 [0.4, 1.4] Fourth 920 54.8 [50.4, 59.2] 920 31.0 [27.0, 35.4] 920 22.6 [18.7, 27.0] 920 1.2 [0.6, 2.6] Highest 834 46.5 [41.5, 51.6] 834 28.4 [24.9, 32.1] 834 17.4 [13.3, 22.4] 834 0.8 [0.2, 2.4] Caregiver’s educational attainment1 (P < 0.001***) (P = 0.463) (P < 0.001***) (P = 0.079) None 1236 67.7 [63.9, 71.3] 1236 30.5 [26.7, 34.6] 1236 34.8 [30.1, 39.8] 1236 2.4 [1.5, 3.8] Primary 737 62.3 [57.3, 67.1] 737 33.2 [28.5, 38.4] 737 26.3 [21.9, 31.1] 737 2.8 [1.3, 6.2] Secondary 1918 56.5 [52.7, 60.2] 1918 31.8 [29.0, 34.7] 1918 23.5 [20.6, 26.8] 1918 1.2 [0.8, 2.0] Tertiary 488 45.9 [39.6, 52.3] 488 27.1 [22.5, 32.3] 488 18.2 [11.9, 26.8] 488 0.6 [0.2, 1.4] National 4674 61.8 [58.9, 64.5] 4674 30.7 [28.9, 32.6] 4674 29.1 [26.3, 32.2] 4674 1.9 [1.4, 2.6] 188 Table 90. Prevalence of anaemia among children (aged 6-59 months), Nigeria 2021 (continued) Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-301) instrument Non-anaemia in children (aged 6-59 months) is defined as Hb≥ 100 g/L Anaemia in children (aged 6-59 months) is defined as mild (100-109 g/L), moderate (70-99 g/L), or severe (< 70 g/L) Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of children (aged 6-59 months) who responded nationally: (n= 4674) 1Less than (n = 4674) due to relatively fewer respondents for the household and dietary intake questionnaires 189 Table 91. Anaemia among children (aged 6-59 months) by infection-related characteristics, haemoglobin genotype, and supplement use, Nigeria 2021 Non-anaemia Any anaemia Mild Anaemia Moderate Anaemia Severe Anaemia Characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Malaria status (P < 0.001***)1 (P < 0.001***)2 Yes 11.2 [9.0, 13.8] 32.1 [27.6, 36.8] 23.4 [20.1, 26.9] 38.6 [31.9, 45.7] 73.5 [60.1, 83.7] 1976 2694 1472 1148 74 No 88.8 [86.2, 91.0] 67.9 [63.2, 72.4] 76.7 [73.1, 79.9] 61.4 [54.3, 68.1] 26.5 [16.3, 39.9] Helminth status (P = 0.089) (P < 0.001***) Yes 9.5 [7.5, 12.0] 11.8 [9.8, 14.2] 9.0 [7.2, 11.3] 15.3 [12.1, 19.2] 4.5 [1.5, 12.2] 1751 2370 1306 1001 63 No 90.5 [88.0, 92.5] 88.2 [85.8, 90.2] 91.0 [88.7, 92.8] 84.7 [80.8, 87.9] 95.5 [87.8, 98.5] H. pylori status (P = 0.555) (P = 0.760) Yes 36.6 [32.3, 41.1] 35.4 [31.6, 39.3] 35.6 [31.4, 40.1] 34.8 [30.0, 40.0] 39.8 [27.5, 53.5] 1976 2689 1469 1146 74 No 63.4 [58.9, 67.7] 64.6 [60.7, 68.4] 64.4 [59.9, 68.6] 65.2 [60.0, 70.0] 60.2 [46.5, 72.5] Haemoglobin genotype (P < 0.001***) (P < 0.001***) AA 82.5 [79.5, 85.1] 78.1 [75.2, 80.8] 77.4 [73.8, 80.6] 79.2 [74.9, 82.9] 74.0 [54.5, 87.1] AS 1887 17.5 [14.9, 20.5] 2521 20.7 [18.3, 23.4] 1393 22.6 [19.4, 26.1] 1062 19.2 [15.9, 23.1] 66 12.4 [3.4, 36.3] SS 0.1 [0.0, 0.2] 1.2 [0.7, 2.0] 0.1 [0.0, 0.4] 1.6 [0.9, 3.0] 13.6 [6.3, 26.9] Child had diarrhoea in the past two weeks (P < 0.001***) (P = 0.007**) Yes 30.3 [27.4, 33.4] 38.6 [35.0, 42.3] 34.7 [31.1, 38.5] 42.8 [37.9, 48.0] 36.4 [23.5, 51.6] 1950 2668 1461 1133 74 No 69.7 [66.6, 72.6] 61.4 [57.7, 65.0] 65.3 [61.5, 68.9] 57.2 [52.0, 62.1] 63.6 [48.4, 76.5] Child had fever in the past two weeks (P < 0.001***) (P = 0.006**) Yes 41.7 [37.9, 45.7] 48.3 [44.6, 52.0] 44.3 [40.1, 48.5] 51.7 [46.8, 56.5] 61.4 [45.4, 75.3] 1962 2663 1458 1131 74 No 58.3 [54.3, 62.1] 51.7 [48.0, 55.4] 55.8 [51.5, 59.9] 48.3 [43.5, 53.2] 38.6 [24.7, 54.6] Child had cough in the past two weeks (P = 0.610) (P = 0.859) Yes 38.3 [34.5, 42.2] 37.3 [34.2, 40.5] 36.5 [32.9, 40.3] 38.0 [33.4, 43.0] 38.6 [24.4, 55.1] 1962 2677 1464 1139 74 No 61.7 [57.8, 65.4] 62.7 [59.5, 65.8] 63.5 [59.7, 67.1] 62.0 [57.0, 66.6] 61.4 [44.9, 75.6] Use of iron and micronutrient powder (P = 0.037*) (P = 0.729) in the past six months Yes 8.0 [6.2, 10.4] 6.0 [4.6, 7.8] 5.6 [4.2, 7.5] 6.3 [4.5, 8.8] 7.3 [2.4, 20.1] 1979 2695 1472 1149 74 No 92.0 [89.6, 93.8] 94.0 [92.2, 95.4] 94.4 [92.5, 95.8] 93.7 [91.2, 95.5] 92.7 [79.9, 97.6] 190 Table 91. Anaemia among children (aged 6-59 months) by infection-related characteristics, haemoglobin genotype, and supplement use, Nigeria 2021 (continued) Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in children (aged 6-59 months) is defined as Hb≥ 100 g/L Anaemia in children (aged 6-59 months) is defined as mild (100-109 g/L), moderate (70-99 g/L), or severe (< 70 g/L) Data are weighted to account for survey design and non-response N, (unweighted) number of respondents who answered yes or no/ had an infection (yes) or didn’t (no) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). 1Chi-square test on 2x2 table of anaemia status (yes/no) verses condition status (yes/no) 2For those with any anaemia, Chi-square test of 3x2 table of anaemia severity (mild, moderate, severe) verses condition status (yes/no) Prevalence of anaemia among adolescent girls (aged 10-14 years) The prevalence of anaemia among adolescent girls stratified by age, residence, wealth quintile, and use of supplements is shown in Table 92. a. Any anaemia: There was a statistically significant difference in the prevalence of any anaemia among adolescent girls between the ages (P = 0.024). The prevalence was lowest in 14-year- olds (29 percent). b. Mild anaemia: There was a statistically significant difference in the prevalence of mild anaemia among adolescent girls between the residence (P = 0.031). The prevalence was higher in adolescent girls residing in urban (20 percent) versus rural (14 percent) areas. c. Moderate anaemia: There was no significant variation in the prevalence of moderate anaemia among adolescent girls across the background characteristics. d. Severe anaemia: There was a statistically significant difference in the prevalence of severe anaemia among adolescent girls between the residence (P = 0.033). The prevalence was higher in adolescent girls residing in rural (1.7 percent) versus urban (0.4 areas) areas. The relationship between anaemia, infection, and the use of supplements in adolescent girls (aged 10-14 years) is shown in Table 93. About 84 percent of adolescent girls with severe anaemia were suffering from malaria (P = 0.016). Table 92. Prevalence of anaemia among adolescent girls (aged 10-14 years), Nigeria 2021 Background Any anaemia Mild anaemia Moderate anaemia Severe anaemia characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.024*) (P = 0.653) (P = 0.101) (P = 0.418) 10 264 47.3 [40.0, 54.7] 264 19.0 [13.6, 25.9] 264 25.8 [19.9, 32.8] 264 2.4 [0.6, 8.8] 11 159 35.8 [26.6, 46.1] 159 14.1 [9.1, 21.2] 159 20.9 [13.3, 31.5] 159 0.7 [0.1, 5.1] 12 191 46.4 [37.1, 56.1] 191 17.2 [11.2, 25.4] 191 28.0 [20.6, 36.7] 191 1.3 [0.2, 6.5] 13 196 42.6 [33.2, 52.6] 196 14.0 [9.3, 20.6] 196 28.2 [19.8, 38.3] 196 0.5 [0.1, 1.9] 14 189 29.4 [21.0, 39.4] 189 13.9 [8.8, 21.1] 189 14.9 [9.6, 22.2] 189 0.7 [0.1, 3.6] Residence (P = 0.387) (P = 0.031*) (P = 0.911) (P = 0.033*) Urban 421 43.9 [36.5, 51.6] 421 19.9 [15.7, 24.8] 421 23.7 [17.6, 31.0] 421 0.4 [0.1, 1.3] Rural 578 39.6 [33.5, 46.0] 578 13.7 [10.7, 17.5] 578 24.1 [19.8, 29.1] 578 1.7 [0.7, 4.2] Wealth quintile1 P = 0.2411 P = 0.4867 P = 0.0702 P = 0.5752 Lowest 213 46.6 [36.4, 57.2] 213 13.8 [8.9, 20.8] 213 30.9 [22.9, 40.2] 213 2.0 [0.4, 8.8] Second 188 33.5 [25.0, 43.3] 188 13.2 [8.3, 20.6] 188 18.4 [12.9, 25.7] 188 1.9 [0.5, 7.1] Middle 207 41.1 [31.6, 51.4] 207 20.4 [13.8, 29.2] 207 20.4 [14.0, 28.8] 207 0.3 [0.0, 2.4] Fourth 195 45.8 [36.9, 55.0] 195 16.0 [11.3, 22.3] 195 29.0 [20.6, 39.2] 195 0.7 [0.1, 3.7] Highest 194 38.4 [30.0, 47.6] 194 17.9 [12.2, 25.5] 194 19.7 [12.7, 29.2] 194 0.8 [0.2, 4.4] National 999 41.2 [36.4, 46.2] 999 16.0 [13.4, 18.9] 999 24.0 [20.4, 28.0] 999 1.2 [0.5, 2.8] Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in adolescent girls (aged 10-11 years) is defined as Hb≥ 115 g/L Anaemia in adolescent girls (aged 10-11 years) is defined as mild (110-114 g/L), moderate (80-109 g/L), or severe (< 80 g/L) Non-anaemia in adolescent girls (aged 12-14 years) is defined as Hb≥ 120 g/L Anaemia in adolescent girls (aged 12-14 years) is defined as mild (110-119 g/L), moderate (80-109 g/L) or severe < 80 g/L) Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of adolescent girls who responded nationally: (n=999) 1Less than (n = 999) due to relatively fewer respondents for the HH and dietary intake questionnaires 191 192 Table 93. Anaemia among adolescent girls (aged 10-14 years) by infection-related characteristics and supplement use, Nigeria 2021 No anaemia Any anaemia Mild Anaemia Moderate Anaemia Severe Anaemia Characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Malaria status (P = 0.005**)1 (P = 0.016*)2 Yes 27.7 [21.2, 35.2] 41.6 [34.3, 49.3] 36.8 [27.9, 46.8] 42.6 [33.7, 52.0] 83.9 [51.1, 96.3] 560 437 180 246 11 No 72.3 [64.8, 78.8] 58.4 [50.7, 65.7] 63.2 [53.3, 72.1] 57.4 [48.0, 66.3] 16.1 [3.7, 48.9] H. pylori status (P = 0.307) (P = 0.686) Yes 56.8 [50.4, 63.0] 52.2 [44.7, 59.6] 49.1 [39.5, 58.7] 53.7 [44.4, 62.7] 62.7 [20.5, 91.6] 554 430 175 244 11 No 43.2 [37.0, 49.6] 47.8 [40.4, 55.3] 50.9 [41.3, 60.5] 46.3 [37.3, 55.6] 37.3 [8.4, 79.5] Respondent had malaria in the past two weeks (P = 0.163) (P = 0.274) Yes 18.2 [14.6, 22.5] 22.1 [17.9, 27.1] 27.6 [19.7, 37.2] 18.4 [12.9, 25.5] 26.6 [6.2, 66.6] 545 422 171 240 11 No 81.8 [77.5, 85.3] 77.8 [72.9, 82.1] 72.4 [62.8, 80.3] 81.6 [74.5, 87.1] 73.4 [33.5, 93.8] Respondent had fever in the past two weeks (P = 0.165) (P = 0.249) Yes 27.2 [22.6, 32.4] 32.2 [26.9, 38.0] 38.4 [29.4, 48.4] 28.6 [22.2, 36.1] 22.1 [4.2, 64.7] 555 434 178 245 11 No 72.8 [67.6, 77.4] 67.8 [62.0, 73.2] 61.6 [51.6, 70.6] 71.4 [63.9, 77.8] 77.9 [35.3, 95.8] Respondent had cough in the past two weeks (P = 0.046*) (P = 0.495) Yes 28.5 [23.6, 33.9] 39.8 [30.5, 49.8] 35.7 [28.9, 43.0] 20.7 [3.6, 64.6] 557 432 177 244 11 No 71.6 [66.1, 76.4] 63.2 [56.5, 69.4] 60.2 [50.2, 69.4] 64.3 [57.0, 71.1] 79.3 [35.4, 96.4] Respondent had diarrhoea in the past two weeks (P = 0.686) (P = 0.646) Yes 16.4 [12.6, 21.0] 15.2 [11.3, 20.1] 15.4 [9.8, 23.5] 15.6 [11.0, 21.5] 5.0 [1.0, 22.0] 555 434 178 245 11 No 83.6 [79.0, 87.4] 84.8 [79.9, 88.7] 84.6 [76.5, 90.2] 84.4 [78.4, 89.0] 95.0 [78.0, 99.0] Use of multivitamin tablets in the past six months (P = 0.143) (P = 0.111) Yes 6.8 [4.6, 10.2] 10.6 [6.8, 16.1] 11.2 [6.0, 19.8] 9.8 [6.1, 15.3] 19.3 [3.0, 64.5] 561 438 181 246 11 No 93.2 [89.8, 95.4] 89.4 [83.9, 93.2] 88.8 [80.2, 94.0] 90.2 [84.7, 93.9] 80.7 [35.5, 96.9] Use of iron tablets or iron-folic acid in the past six months (P = 0.248) (P = 0.984) Yes 10.0 [7.3, 13.5] 12.6 [9.5, 16.6] 12.8 [8.1, 19.7] 12.6 [8.6, 18.2] 10.8 [1.8, 45.1] 561 438 181 246 11 No 90.0 [86.5, 92.7] 87.4 [83.4, 90.5] 87.2 [80.3, 91.9] 87.4 [81.8, 91.4] 89.2 [54.9, 98.2] Use of multivitamin tablets in the past seven days (P = 0.408) (P = 0.408) Yes 49.0 [28.7, 69.6] 61.9 [40.2, 79.7] 47.3 [19.5, 76.8] 70.0 [47.0, 86.0] 91.2 [38.0, 99.4] 42 49 19 28 2 No 51.0 [30.4, 71.3] 38.1 [20.3, 59.8] 52.8 [23.2, 80.5] 30.0 [14.0, 53.0] 8.9 [0.6, 62.0] 193 Table 93. Anaemia among adolescent girls (aged 10-14 years) by infection-related characteristics and supplement use, Nigeria 2021 (continued) Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in adolescent girls (aged 10-11 years) is defined as Hb≥ 115 g/L Anaemia in adolescent girls (aged 10-11 years) is defined as mild (110-114 g/L), moderate (80-109 g/L), or severe (< 80 g/L) Non-anaemia in adolescent girls (aged 12-14 years) is defined as Hb≥ 120 g/L Anaemia in adolescent girls (aged 12-14 years) is defined as mild (110-119 g/L), moderate (80-109 g/L) or severe < 80 g/L) Data are weighted to account for survey design and non-response N, (unweighted) number of respondents who answered yes or no/ had an infection (yes) or didn’t (no) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). 1Chi-square test on 2x2 table of anaemia status (yes/no) verses condition status (yes/no) 2For those with any anaemia, Chi-square test of 3x2 table of anaemia severity (mild, moderate, severe) verses condition status (yes/no) Prevalence of anaemia among WRA (aged 15-49 years) The prevalence of anaemia among WRA stratified by age, residence, zone, wealth quintile, educational attainment, and use of supplements is shown in Table 94. a. Any anaemia: There was a statistically significant difference in the prevalence of any anaemia among WRA between residence (P < 0.001), zones (P < 0.001), and wealth quintiles (P = 0.013). The prevalence of any anaemia in WRA was higher in women residing in rural (58 percent) than in urban (50 percent) areas. It was lowest in women in the North East zone (46 percent) and highest among WRA in the lowest wealth quintile (60 percent). b. Mild anaemia: There was a statistically significant difference in the prevalence of mild anaemia among WRA between the zones (P < 0.001). The prevalence was highest in WRA in South West zone (38 percent). c. Moderate anaemia: There was a statistically significant difference in the prevalence of moderate anaemia among WRA between residence (P < 0.001), wealth quintiles (P < 0.001), and educational attainment (P < 0.001). The prevalence of moderate anaemia was higher in WRA residing in rural (25 percent) than in urban (16 percent) areas. It was lowest in the South West zone (16 percent). It was highest among WRA in the lowest wealth quintile (29 percent), and lowest among WRA who had attained tertiary education (18 percent). d. Severe anaemia: There was a statistically significant difference in the prevalence of severe anaemia among WRA between residence (P = 0.003). The prevalence was higher in WRA residing in rural (1.6 percent) than in urban (0.6 percent) areas. The relationship between anaemia, infection, haemoglobin genotype (blood disorders), and use of supplements among WRA (aged 15-49 years) is shown in Table 95. WRA with mild (77 percent), moderate (76 percent), and severe (66 percent) anaemia had normal haemoglobin. 194 195 Table 94. Prevalence of anaemia among WRA (aged 15-49 years), Nigeria 2021 Any anaemia Mild anaemia Moderate anaemia Severe anaemia Background characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.073) (P = 0.136) (P = 0.064) (P = 0.252) 15-19 years 1126 52.2 [48.0, 56.4] 1126 31.4 [28.2, 34.8] 1126 19.8 [16.9, 23.1] 1126 1.0 [0.6, 1.7] 20-29 years 1637 52.9 [49.2, 56.6] 1637 29.0 [26.0, 32.3] 1637 22.3 [19.6, 25.2] 1637 1.6 [0.9, 2.8] 30-39 years 1497 55.8 [52.6, 58.9] 1497 34.2 [31.3, 37.2] 1497 20.8 [18.1, 23.8] 1497 0.8 [0.4, 1.6] 40-49 years 1012 58.7 [54.2, 63.1] 1012 31.6 [27.6, 35.7] 1012 25.8 [22.4, 29.5] 1012 1.4 [0.9, 2.3] Residence (P < 0.001***) (P = 0.381) (P < 0.001***) (P = 0.003**) Urban 2140 49.8 [46.6, 53.0] 2140 32.4 [29.7, 35.3] 2140 16.7 [14.7, 18.9] 2140 0.6 [0.4, 1.1] Rural 3132 57.8 [54.8, 60.7] 3132 30.8 [28.6, 33.1] 3132 25.4 [23.2, 27.7] 3132 1.6 [1.1, 2.3] Zone (P < 0.001***) (P < 0.001***) (P = 0.005**) (P = 0.346) North Central 862 52.9 [48.4, 57.4] 862 30.1 [26.0, 34.6] 862 21.6 [18.1, 25.5] 862 1.2 [0.6, 2.3] North East 857 45.7 [40.9, 50.7] 857 23.9 [20.8, 27.5] 857 21.1 [17.7, 25.0] 857 0.7 [0.3, 1.4] North West 900 55.4 [49.7, 60.9] 900 28.7 [25.4, 32.3] 900 25.0 [21.2, 29.1] 900 1.7 [0.9, 3.2] South East 881 62.9 [58.2, 67.4] 881 36.9 [33.9, 40.1] 881 24.4 [20.9, 28.3] 881 1.5 [0.8, 3.0] South South 883 61.2 [56.0, 66.1] 883 36.1 [32.3, 40.0] 883 23.8 [19.8, 28.3] 883 1.3 [0.7, 2.3] South West 889 55.0 [50.8, 59.1] 889 38.3 [34.7, 42.2] 889 15.8 [13.0, 19.1] 889 0.9 [0.4, 1.7] Wealth quintile1 (P = 0.013*) (P = 0.456) (P < 0.001***) (P = 0.066) Lowest 1094 59.9 [55.5, 64.1] 1094 29.3 [26.3, 32.5] 1094 28.5 [25.5, 31.6] 1094 2.1 [1.2, 3.6] Second 1123 55.9 [51.5, 60.1] 1123 30.2 [26.9, 33.7] 1123 24.2 [21.2, 27.6] 1123 1.4 [0.7, 2.5] Middle 1105 54.3 [50.3, 58.2] 1105 33.1 [29.6, 36.8] 1105 20.3 [17.6, 23.3] 1105 0.9 [0.5, 1.7] Fourth 988 50.3 [46.2, 54.4] 988 32.2 [29.0, 35.6] 988 17.6 [14.8, 20.8] 988 0.6 [0.2, 1.3] Highest 940 51.9 [47.8, 55.9] 940 32.5 [28.8, 36.5] 940 18.2 [15.3, 21.6] 940 1.1 [0.6, 1.9] Educational attainment1 (P = 0.124) (P = 0.756) (P < 0.001***) (P = 0.457) None 1234 57.6 [53.3, 61.7] 1234 31.1 [28.2, 34.1] 1234 25.0 [22.3, 27.9] 1234 1.5 [0.9, 2.5] Primary 850 56.3 [51.4, 61.0] 850 30.6 [26.6, 34.8] 850 24.8 [20.8, 29.3] 850 0.9 [0.4, 1.8] Secondary 2401 52.6 [49.6, 55.7] 2401 32.7 [30.0, 35.4] 2401 19.0 [17.0, 21.1] 2401 1.0 [0.6, 1.6] Tertiary 533 51.1 [45.1, 57.2] 533 32.8 [27.8, 38.2] 533 17.6 [13.8, 22.0] 533 0.8 [0.3, 2.0] National 5272 54.6 [52.3, 56.9] 5272 31.4 [29.7, 33.2] 5272 22.0 [20.3, 23.7] 5272 1.2 [0.9, 1.7] Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in WRA (aged 15-49 years) is defined as Hb≥ 120 g/L Anaemia in WRA (aged 15-49 years) is defined as mild (110-119 g/L), moderate (80-109 g/L), or severe (< 80 g/L) Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of WRA who responded nationally: (n= 5272) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 196 Table 95. Anaemia among WRA (aged 15-49 years) by infection-related characteristics, haemoglobin genotype, and supplement use, Nigeria 2021 No anaemia Any anaemia Mild Anaemia Moderate Anaemia Severe Anaemia Characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Malaria status (P < 0.001***)1 (P = 0.308)2 Yes 9.0 [7.3, 11.0] 15.5 [13.5, 17.8] 15.0 [12.4, 18.1] 15.7 [13.1, 18.8] 25.0 [12.7, 43.3] 2315 2950 1706 1167 77 No 91.0 [89.0, 92.7] 84.5 [82.2, 86.5] 85.0 [81.9, 87.6] 84.3 [81.2, 86.9] 75.0 [56.7, 87.3] Helminth status (P = 0.814) (P = 0.260) Yes 5.5 [4.3, 7.0] 5.7 [4.5, 7.3] 5.2 [3.7, 7.2] 6.2 [4.3, 8.8] 11.0 [5.6, 20.5] 2094 2635 1533 1031 71 No 94.5 [93.0, 95.7] 94.3 [92.7, 95.5] 94.8 [92.8, 96.3] 93.8 [91.2, 95.8] 89.0 [79.5, 94.4] H. pylori status (P = 0.567) (P = 0.261) Yes 63.1 [58.8, 67.1] 64.2 [61.0, 67.2] 65.7 [62.1, 69.2] 62.3 [57.9, 66.6] 57.6 [41.5, 72.2] 2313 2952 1708 1167 77 No 36.9 [32.9, 41.2] 35.8 [32.8, 39.0] 34.3 [30.9, 37.9] 37.7 [33.4, 42.1] 42.4 [27.8, 58.5] Haemoglobin genotype (P = 0.095) (P < 0.001***) AA 77.0 [74.7, 79.2] 76.0 [73.6, 78.3] 76.5 [74.0, 78.8] 75.8 [71.5, 79.7] 66.3 [53.1, 77.4] AS 2228 23.0 [20.8, 25.3] 2766 23.6 [21.4, 26.1] 1627 23.5 [21.2, 26.0] 1068 23.7 [19.8, 28.0] 71 27.1 [17.0, 40.3] SS 0.0 [., .] 0.4 [0.2, 0.8] 0.0 [., .] 0.5 [0.2, 1.6] 6.5 [2.7, 14.8] Respondent had malaria in the past two weeks (P = 0.168) (P = 0.388) Yes 25.4 [22.6, 28.3] 27.6 [25.2, 30.2] 26.6 [23.6, 29.7] 29.0 [25.6, 32.6] 31.7 [20.3, 45.8] 2227 2775 1631 1071 73 No 74.6 [71.7, 77.4] 72.4 [69.8, 74.8] 73.4 [70.3, 76.3] 71.0 [67.4, 74.3] 68.3 [54.2, 79.7] Respondent had fever in the past two weeks (P = 0.109) (P = 0.931) Yes 34.7 [31.5, 38.1] 37.9 [34.4, 41.5] 37.6 [34.2, 41.1] 38.2 [33.5, 43.1] 39.7 [25.1, 56.3] 2276 2831 1670 1086 75 No 65.3 [61.9, 68.5] 62.1 [58.5, 65.6] 62.4 [58.9, 65.8] 61.8 [56.9, 66.4] 60.3 [43.7, 74.9] Respondent had cough in the past two weeks (P = 0.011*) (P = 0.461) Yes 21.1 [18.8, 23.6] 25.0 [22.7, 27.5] 24.4 [21.7, 27.3] 25.5 [22.2, 29.1] 32.8 [19.2, 50.0] 2286 2836 1667 1095 74 No 78.9 [76.4, 81.2] 75.0 [72.5, 77.3] 75.6 [72.7, 78.3] 74.5 [70.9, 77.8] 67.2 [50.0, 80.8] Respondent had diarrhoea in the past two weeks (P = 0.098) (P = 0.272) Yes 16.0 [14.1, 18.0] 18.0 [16.1, 20.1] 16.7 [14.6, 19.1] 20.1 [17.4, 23.1] 16.9 [6.1, 39.0] 2289 2834 1668 1092 74 No 84.0 [81.9, 85.9] 82.0 [79.9, 83.8] 83.3 [80.9, 85.4] 79.9 [76.9, 82.6] 83.1 [61.0, 93.9] Use of multivitamin tablets in the past six months (P = 0.264) (P = 0.120) Yes 11.9 [10.2, 13.7] 13.1 [11.2, 15.2] 14.3 [12.0, 16.9] 11.3 [9.0, 14.1] 14.2 [7.4, 25.5] 2317 2955 1710 1168 77 No 88.2 [86.3, 89.8] 86.9 [84.8, 88.8] 85.7 [83.1, 88.0] 88.7 [85.9, 91.0] 85.8 [74.4, 92.6] Use of iron tablets or iron-folic acid in the past six months (P = 0.196) (P = 0.064) Yes 12.9 [11.1, 14.9] 14.6 [12.7, 16.8] 16.3 [13.9, 19.0] 12.6 [9.9, 16.0] 9.2 [4.2, 18.9] 2317 2955 1710 1168 77 No 87.1 [85.1, 88.9] 85.4 [83.2, 87.3] 83.7 [81.1, 86.1] 87.4 [84.0, 90.1] 90.8 [81.1, 95.8] 197 Table 95. Anaemia among WRA (aged 15-49 years) by infection-related characteristics, haemoglobin genotype, and supplement use, Nigeria 2021 (continued) No anaemia Any anaemia Mild Anaemia Moderate Anaemia Severe Anaemia Characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Use of multivitamin tablets in the past seven days (P = 0.150) (P = 0.583) Yes 54.2 [45.6, 62.6] 61.6 [52.9, 69.6] 63.6 [54.9, 71.4] 58.4 [45.9, 70.0] 55.2 [24.6, 82.3] 307 429 268 150 11 No 45.8 [37.4, 54.4] 38.4 [30.4, 47.1] 36.4 [28.5, 45.1] 41.6 [30.0, 54.1] 44.8 [17.7, 75.4] Use of iron tablets or iron-folic acid in the past seven days (P = 0.999) (P = 0.252) Yes 58.1 [50.3, 65.4] 58.1 [50.4, 65.4] 61.3 [54.3, 67.9] 51.8 [38.7, 64.8] 64.6 [26.4, 90.3] 354 490 316 166 8 No 41.9 [34.5, 49.7] 41.9 [34.6, 49.6] 38.7 [32.1, 45.7] 48.2 [35.2, 61.3] 35.4 [9.7, 73.6] Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in WRA (15-49 years) is defined as Hb≥ 120 g/L Anaemia in WRA (aged 15-49 years) is defined as mild (110-119 g/L), moderate (80-109 g/L), or severe (< 80 g/L) Data are weighted to account for survey design and non-response N, (unweighted) number of respondents who answered yes or no/ had an infection (yes) or didn’t (no) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). 1Chi-square test on 2x2 table of anaemia status (yes/no) verses condition status (yes/no) 2For those with any anaemia, Chi-square test of 3x2 table of anaemia severity (mild, moderate, severe) verses condition status (yes/no) Prevalence of anaemia among pregnant women (aged 15-49 years) The prevalence of anaemia among pregnant women stratified by age, residence, wealth quintile, educational attainment, and use of supplements is shown in Table 96. a. Any anaemia: There was a statistically significant difference in the prevalence of any anaemia among pregnant women between the age groups (P = 0.016) and educational attainment (P = 0.002). The prevalence was lowest in pregnant in the 40 to 49-years age category (64 percent). It was highest in pregnant women who had attained tertiary education (74 percent). b. Mild anaemia: There was a statistically significant difference in the prevalence of mild anaemia among pregnant women between residence (P = 0.049) and the wealth quintiles (P = 0.038). The prevalence was higher in pregnant women residing in urban (25 percent) than in rural (18 percent) areas. It was highest in pregnant women in the highest wealth quintile (31 percent). c. Moderate anaemia: There was no significant variation in the prevalence of moderate anaemia among pregnant women across the background characteristics. d. Severe anaemia: There was no significant variation in the prevalence of severe anaemia among pregnant women across the background characteristics. The relationship between anaemia, infection, and the use of supplements in pregnant women (aged 15-49 years) is shown in Table 97. There was no significant relationship across the characteristics. 198 199 Table 96. Prevalence of anaemia among pregnant women (aged 15-49 years), Nigeria 2021 Any anaemia Mild anaemia Moderate anaemia Severe anaemia Background characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Age category (P = 0.016*) (P = 0.139) (P = 0.291) (P = 0.153) 15-19 years 73 85.6 [68.0, 94.4] 73 9.9 [4.7, 19.9] 73 66.9 [50.6, 79.9] 73 8.8 [3.3, 21.1] 20-29 years 445 88.6 [84.5, 91.7] 445 23.0 [18.0, 28.9] 445 61.8 [55.0, 68.0] 445 3.8 [2.4, 6.1] 30-39 years 286 86.6 [81.2, 90.6] 286 17.5 [12.4, 24.0] 286 64.9 [57.4, 71.7] 286 4.2 [2.3, 7.6] 40-49 years 43 64.1 [42.6, 81.2] 43 18.8 [8.0, 38.1] 43 44.5 [23.9, 67.3] 43 0.8 [0.1, 5.7] Residence (P = 0.639) (P = 0.049*) (P = 0.181) (P = 0.103) Urban 348 85.4 [80.3, 89.4] 348 24.5 [19.8, 29.8] 348 58.3 [52.3, 64.1] 348 2.6 [1.3, 5.2] Rural 499 86.8 [82.5, 90.2] 499 17.5 [13.3, 22.7] 499 64.3 [57.7, 70.4] 499 5.0 [3.3, 7.6] Wealth quintile1 (P = 0.858) (P = 0.038*) (P = 0.144) (P = 0.517) Lowest 181 86.5 [78.8, 91.6] 181 19.2 [13.1, 27.3] 181 61.3 [53.8, 68.4] 181 5.9 [3.1, 11.1] Second 173 86.6 [79.4, 91.6] 173 21.9 [14.2, 32.2] 173 61.4 [51.1, 70.7] 173 3.3 [1.5, 7.1] Middle 165 88.9 [82.0, 93.3] 165 11.1 [6.9, 17.6] 165 72.7 [62.1, 81.2] 165 5.1 [2.4, 10.2] Fourth 176 83.9 [75.6, 89.8] 176 19.0 [13.1, 26.6] 176 61.2 [49.5, 71.7] 176 3.8 [1.6, 8.5] Highest 149 85.8 [78.7, 90.8] 149 31.0 [21.8, 41.9] 149 52.7 [42.6, 62.7] 149 2.1 [0.6, 6.6] Caregivers educational attainment1 (P = 0.002**) (P = 0.333) (P = 0.092) (P = 0.453) None 197 92.6 [87.8, 95.7] 197 22.7 [15.3, 32.5] 197 65.7 [56.3, 74.1] 197 4.2 [2.1, 8.2] Primary 131 84.9 [76.2, 90.8] 131 16.1 [9.6, 25.6] 131 61.9 [50.3, 72.3] 131 6.9 [3.5, 13.3] Secondary 384 82.8 [76.4, 87.7] 384 18.9 [14.3, 24.5] 384 59.9 [53.0, 66.5] 384 4.0 [2.4, 6.6] Tertiary 92 74.1 [62.0, 83.4] 92 28.6 [19.9, 39.3] 92 43.6 [32.2, 55.8] 92 1.8 [0.3, 11.9] National 847 86.3 [83.1, 89.1] 847 19.8 [16.4, 23.6] 847 62.3 [57.5, 67.0] 847 4.2 [2.9, 6.1] Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in pregnant women is defined as Hb≥ 110 g/L Anaemia in pregnant women is defined as mild (100-109 g/L), moderate (70-99 g/L), or severe (< 70 g/L) Data are weighted to account for survey design and non-response N, number of respondents in the sub-group (unweighted) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). Number of pregnant women who responded nationally: (n= 847) 1Less than (n = 4916) due to relatively fewer respondents for the household and dietary intake questionnaires 200 Table 97. Anaemia among pregnant women (aged 15-49 years) by infection-related characteristics and supplement use Nigeria, 2021 No anaemia Any anaemia Mild Anaemia Moderate Anaemia Severe Anaemia Characteristics N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] N % [95% CI] Malaria status (P = 0.163)1 (P = 0.359)2 Yes 6.8 [2.0, 20.0] 15.0 [11.8, 18.9] 11.4 [7.1, 17.8] 15.8 [11.9, 20.6] 20.0 [8.9, 39.3] 129 717 178 492 47 No 93.2 [80.0, 97.9] 85.0 [81.1, 88.2] 88.6 [82.2, 92.9] 84.2 [79.4, 88.1] 80.0 [60.8, 91.1] Helminth status (P = 0.755) (P = 0.070) Yes 3.9 [1.7, 8.6] 4.5 [2.9, 6.8] 2.0 [0.9, 4.8] 5.4 [3.4, 8.6] 1.9 [0.3, 12.4] 118 622 152 428 42 No 96.1 [91.4, 98.3] 95.5 [93.2, 97.1] 97.9 [95.2, 99.1] 94.6 [91.4, 96.6] 98.1 [87.6, 99.8] H. pylori status (P = 0.424) (P = 0.778) Yes 63.0 [51.7, 73.0] 58.0 [52.6, 63.2] 57.8 [46.6, 68.3] 57.6 [51.9, 63.1] 64.9 [45.9, 80.1] 129 718 178 493 47 No 37.0 [27.0, 48.3] 42.0 [36.8, 47.4] 42.2 [31.8, 53.4] 42.4 [36.9, 48.1] 35.1 [19.9, 54.1] Respondent had malaria in the past two weeks (P = 0.819) (P = 0.800) Yes 63.0 [51.7, 73.0] 58.0 [52.6, 63.2] 57.8 [46.6, 68.3] 57.6 [51.9, 63.1] 64.9 [45.9, 80.1] 129 718 178 493 47 No 37.0 [27.0, 48.3] 42.0 [36.8, 47.4] 42.2 [31.8, 53.4] 42.4 [36.9, 48.1] 35.1 [19.9, 54.1] Respondent had fever in the past two weeks (P = 0.651) (P = 0.268) Yes 38.1 [27.3, 50.3] 40.9 [35.7, 46.3] 34.4 [25.8, 44.2] 42.9 [36.7, 49.3] 41.2 [26.2, 58.1] 129 705 174 485 46 No 61.9 [49.7, 72.7] 59.1 [53.7, 64.3] 65.6 [55.8, 74.2] 57.1 [50.7, 63.3] 58.8 [41.9, 73.8] Respondent had cough in the past two weeks (P = 0.858) (P = 0.462) Yes 20.4 [13.6, 29.4] 21.3 [17.3, 25.8] 17.3 [11.5, 25.1] 22.4 [17.5, 28.2] 22.7 [11.4, 40.1] 129 708 175 486 47 No 79.6 [70.6, 86.4] 78.7 [74.2, 82.7] 82.7 [74.9, 88.5] 77.6 [71.8, 82.5] 77.3 [59.9, 88.6] Respondent had diarrhoea in the past two weeks (P = 0.853) (P = 0.066) Yes 20.3 [13.1, 30.0] 21.2 [17.4, 25.6] 22.5 [15.3, 31.9] 19.6 [15.1, 24.9] 39.5 [24.4, 56.9] 129 707 175 485 47 No 79.7 [70.0, 86.9] 78.8 [74.4, 82.6] 77.5 [68.1, 84.7] 80.4 [75.1, 84.9] 60.5 [43.1, 75.6] Use of tablet or syrup containing iron in the past (P = 0.363) (P = 0.0361*) seven days Yes 85.8 [56.6, 96.5] 74.3 [61.3, 84.1] 87.1 [66.6, 95.8] 70.9 [56.6, 81.9] 25.3 [3.3, 76.9] 14 106 28 74 4 No 14.2 [3.5, 43.4] 25.7 [15.9, 38.7] 12.9 [4.2, 33.4] 29.1 [18.1, 43.4] 74.7 [23.1, 96.7] 201 Table 97. Anaemia among pregnant women (aged 15-49 years) by infection-related characteristics and supplement use Nigeria, 2021 (continued) Use of tablet or syrup containing iron and/folic (P = 0.169) (P = 0.867) acid yesterday Yes 49.7 [26.2, 73.3] 68.2 [56.1, 78.3] 72.2 [47.8, 88.1] 66.6 [53.4, 77.6] 65.7 [15.8, 95.1] 14 106 28 74 4 No 50.3 [26.8, 73.8] 31.8 [21.7, 43.9] 27.8 [11.9, 52.3] 33.4 [22.4, 46.6] 34.3 [4.9, 84.2] Anaemia was measured in the field from a venous blood sample using a HemoCue (Hb-201) instrument Non-anaemia in adolescent girls (aged 10-11 years) is defined as Hb≥ 115 g/L Anaemia in adolescent girls (aged 10-11 years) is defined as mild (110-114 g/L), moderate (80-109 g/L), or severe (< 80 g/L) Non-anaemia in adolescent girls (aged 12-14 years) is defined as Hb≥ 120 g/L Anaemia in adolescent girls (aged 12-14 years) is defined as mild (110-119 g/L), moderate (80-109 g/L) or severe < 80 g/L) Data are weighted to account for survey design and non-response N, (unweighted) number of respondents who answered yes or no/ had an infection (yes) or didn’t (no) CI, Confidence Interval Differences between groups were compared using Chi-square test (* signifies P <0.05, ** signifies P <0.01, *** signifies P <0.001). 1Chi-square test on 2x2 table of anaemia status (yes/no) verses condition status (yes/no) 2For those with any anaemia, Chi-square test of 3x2 table of anaemia severity (mild, moderate, severe) verses condition status (yes/no) Conclusions The NFCMS 2021 collected information on four distinct components: (1) socioeconomic and demographic information of sample HHs; (2) dietary intake - types and amounts of foods consumed in the last 24 hours; (3) anthropometry - height/length, weight, age; and (4) micronutrient status through a series of biomarkers such as haemoglobin genotype, HbA1c, status of iron and inflammation, VA, folate, zinc, iodine, vitamin B1, vitamin B2, malaria, H. pylori, haemoglobin, plasma glucose, and helminths from biological samples, precisely blood, urine, stool, and haemoglobin. This preliminary report presents a first look at selected findings from the NFCMS 2021 and covers respondent’s household’s socioeconomic and demographic characteristics including information collected from household listing, diet questionnaire, food sample analysis, anthropometry, biomarker questionnaire, and indices analyzed in-country. The report does not include findings from the 24-hr dietary recall and biomarker indices being analyzed outside the country; thus, drawing conclusion may be premature. In addition, linking of components to allow for further statistical analysis of the data to determine relationships among indices is yet to be conducted. Nonetheless, from the preliminary results available, the following conclusions can be made: (1) two in every three households drank water from an improved water source located on premises and that the most common main source of drinking water was borehole; (2) there is high level of food insecurity and that the proportion of food insecurity reduced with higher education; (3) consumption of biofortified crops is low ; (4) stunting and anaemia are public health problems, and that there are zonal differences ; and (5) coverage of some national interventions is low. Therefore, the results present opportunities for the formulation of evidence-based policies and programmes and a baseline from which to monitor changes over time. 202 Citations Maziya-Dixon, B. et al. (2004). Nigeria Food Consumption and Nutrition Survey (NFCNS) 2001 – 2003, Ibadan, Nigeria: IITA. https://hdl.handle.net/10568/100010 Gibson, R. S. and Ferguson, E. L. (2018). An interactive 24-hour recall for assessing the adequacy of iron and zinc intakes in developing countries, Amsterdam: International Food Policy Research Institute (IFPRI) and International Center for Tropical Agriculture (CIAT). Coates, Jennifer C., Colaiezzi, B. A., Winnie Bell, U., Charrondiere, R., and Leclercq., C. (2017). Overcoming Dietary Assessment Challenges in Low-Income Countries: Technological Solutions Proposed by the International Dietary Data Expansion (INDDEX) Project. Nutrients 9, 289; doi:10.3390/nu9030289. Neoliberalism, Globalization, and Inequalities: Consequences for Health and Quality of Life. Amityville NY 11701, United States: Baywood. Publishing Company; 2007:1– Publishing Company; 2007:1–6. Friesen, V.M., Jungjohann, S., Mbuya, M.N.N., Harb, J., Visram, A., Hug, J., Garrett, G.S., and Neufeld, L.M. Fortification Assessment Coverage Toolkit (FACT) Manual. Global Alliance for Improved Nutrition (Geneva) and Oxford Policy Management (Oxford), 2019. HarvestPlus (2019): Frequently Asked Questions (2019). https://www.harvestplus.org/about/ faqs#Question1 Indicators for assessing infant and young child feeding practices: definitions and measurement methods. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2021. Licence: CC BYNC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo. Meenakshi, J.V., Johnson, N., Manyong, V., et al. (2010) How cost-effective is biofortification in combating micronutrient malnutrition? An ex-ante assessment. World Dev 38: 64-75. Mejia E.G., Aguilera-Gutiérrez Y., Martin-Cabrejas M.A. and Mejia L.A. 2015. Industrial processing of condiments and seasonings and its implications for micronutrient fortification. Annals of the New York Academy of Sciences, 1357(1): 8-28. Mustapha, A. L., and Suleiman. A. (2006). Profitability of cottage level manual groundnut oil extraction among women in Kano state. Centre for Dryland Agriculture, Bayero University, Kano. Standards Organization of Nigeria. Nigerian Industrial Standard: standard for ground nut oil. NIS 388:2000, ICS 67.200.10. Standards Organization of Nigeria. 2000a. Standards Organization of Nigeria. Nigerian Industrial Standard: standards for soya bean oil. NIS 392:2000, ICS 67.200.10. Standards Organization of Nigeria. 2000b. Standards Organization of Nigeria. Nigerian Industrial Standard: refined white sugar. NIS 90:2000, ICS 67.180.10. Standards Organization of Nigeria. 2000c. Standards Organization of Nigeria. Nigerian Industrial Standard: standard for (table or cooking) salt. NIS 723:2010, ICS 67.220.20. Standards Organization of Nigeria. 2005. Standards Organization of Nigeria. Nigerian Industrial Standard: standard for maize flour. NIS 723:2010, ICS 67.220.20. Standards Organization of Nigeria. 2010. Standards Organization of Nigeria. Nigerian Industrial Standard: standard for wheat flour. NIS 121:2015, ICS 67.220.20. Standards Organization of Nigeria. 2015a. Standards Organization of Nigeria. Nigerian Industrial Standard: standard for wheat semolina. NIS 396:2015, ICS 67.220.20. Standards Organization of Nigeria. 2015b. 203 Annexes Annexes AAnnneex x1 .1 C. oCvoevraegrea ganed a rnedsp roensspeo rnastees rcaatlecsu lactaeldc ulsaintegd u unwsienigh utendw deaitgahted data Table 98. Cluster (Sampled EAs for the Survey) Coverage Rate by Zone and Sector (Rural- Table 98. Cluster (Sampled EAs for the Survey) CoverUagrbe aRna)te by Zone and Sector (Rural-Urban) Number of Clusters Sampled Number of Clusters Covered Coverage Rate (%) Zone Total Rural Urban Total Rural Urban Total Rural Urban Covered Covered Covered NC 65 47 18 59 41 18 90.8 87.2 100.0 NE 65 48 17 55 40 15 84.6 83.3 88.2 NW 65 49 16 61 45 16 93.8 91.8 100.0 SE 65 34 31 64 34 30 98.5 100.0 96.8 SS 65 40 25 62 38 24 95.4 95.0 96.0 SW 65 16 49 63 15 48 96.9 93.8 98.0 National 390 234 156 364 213 151 93.3 91.0 96.8 223 204 205 Table 99. National Response Rates of TargeTta Gbrloeu 9ps9/.S Necatotiro bnya Ml Rodeuslepsonse Rates of Target Groups/Sector by Modules Response to Target Total Sampled/ at least 1 Response to Specific Modules Group Final Respondent Module OVERALL1 Haemoglobin Biomarker Plasma Haemoglobin Diet HbA1c genotype Anthropometry questionnaire glucose (Anaemia) Helminth H. pylori Malaria Respondents 5171 4968 0 4548 4912 4916 0 4674 4240 4672 4678 CU5 Sampled 5555 5555 0 5555 5555 5555 0 5555 5555 5555 5555 Response Rate 93.1 89.4 81.9 88.4 88.5 84.1 76.3 84.1 84.2 Respondents 1010 0 0 0 1006 1002 0 998 0 984 996 Adolescent Sampled 1202 0 0 0 1202 1202 0 1202 0 1202 1202 Response Rate 84.0 83.7 83.4 83.0 81.9 82.9 Respondents 5471 5281 5089 5137 5239 5239 5109 5162 4669 5161 5159 WRA Sampled 6071 6071 6071 6071 6071 6071 6071 6071 6071 6071 6071 Response Rate 90.1 87.0 83.8 84.6 86.3 86.3 84.2 85.0 76.9 85.0 85.0 Pregnant Respondents 1031 1006 0 0 976 863 0 958 846 959 959 Women Sampled 1134 1134 0 0 1134 1134 0 1134 1134 1134 1134 Response Rate 90.9 88.7 86.1 76.1 84.5 74.6 84.6 84.6 Respondents 7573 6674 3075 5826 7281 7203 3080 7081 6079 7075 7082 Rural Sampled 8183 7478 3563 6810 8183 8183 3563 8183 7478 8183 8183 Response Rate 92.5 89.2 86.3 85.6 89.0 88.0 86.4 86.5 81.3 86.5 86.5 Respondents 5110 4581 2014 3859 4852 4817 2029 4711 3676 4701 4710 Urban Sampled 5779 5282 2508 4816 5779 5779 2508 5779 5282 5779 5779 Response Rate 88.4 86.7 80.3 80.1 84.0 83.4 80.9 81.5 69.6 81.3 81.5 Respondents 12683 11255 5089 9685 12133 12020 5109 11792 9755 11776 11792 National Sampled 13962 12760 6071 11626 13962 13962 6071 13962 12760 13962 13962 Response Rate 90.8 88.2 83.8 83.3 86.9 86.1 84.2 84.5 76.4 84.3 84.5 1 Overall Sampled is the total number selected for the survey; Overall Respondents is total number that responded to at least one of the modules; and Overall Response Rate is the percentage of the sampled respondents that answered to at least one module (that did not refuse to participate at all levels). 224 CU5 Adolescent WRA Women Pregnant All Rural CU5 Adolescent WRA Sector Rural Urban 206 Table 100. Response Rate by Sector (RuralT-Uarbblaen )1, 0Ta0r.g Rete Gsrpoounpss ea nRda Stpee bciyfi cS Mecodtourl e(sRural-Urban), Target Groups and Specific Modules Response to Target Total Sampled/ at least 1 Response to Specific Modules Group Final Respondent Module 1 OVERALL Haemoglobin Biomarker Plasma Haemoglobin Diet HbA1c genotype Anthropometry questionnaire glucose (Anaemia) Helminth H. pylori Malaria Respondents 3057 2909 0 2718 2925 2926 0 2793 2622 2793 2796 Sampled 3247 3247 0 3247 3247 3247 0 3247 3247 3247 3247 Response Rate 94.1 89.6 83.7 90.1 90.1 86.0 80.8 86.0 86.1 Respondents 600 0 0 0 599 595 0 590 0 582 590 Sampled 705 0 0 0 705 705 0 705 0 705 705 Response Rate 85.1 85.0 84.4 83.7 82.6 83.7 Respondents 3295 3162 3075 3108 3168 3171 3080 3119 2924 3120 3116 Sampled 3563 3563 3563 3563 3563 3563 3563 3563 3563 3563 3563 Response Rate 92.5 88.7 86.3 87.2 88.9 89.0 86.4 87.5 82.1 87.6 87.5 Respondents 621 603 0 0 589 511 0 579 533 580 580 Sampled 668 668 0 0 668 668 0 668 668 668 668 Response Rate 93.0 90.3 88.2 76.5 86.7 79.8 86.8 86.8 Respondents 7573 6674 3075 5826 7281 7203 3080 7081 6079 7075 7082 Sampled 8183 7478 3563 6810 8183 8183 3563 8183 7478 8183 8183 Response Rate 92.5 89.2 86.3 85.6 89.0 88.0 86.4 86.5 81.3 86.5 86.5 Respondents 2114 2059 0 1830 1987 1990 0 1881 1618 1879 1882 Sampled 2308 2308 0 2308 2308 2308 0 2308 2308 2308 2308 Response Rate 91.6 89.2 79.3 86.1 86.2 81.5 70.1 81.4 81.5 Respondents 410 0 0 0 407 407 0 408 0 402 406 Sampled 497 0 0 0 497 497 0 497 0 497 497 Response Rate 82.5 81.9 81.9 82.1 80.9 81.7 Respondents 2176 2119 2014 2029 2071 2068 2029 2043 1745 2041 2043 Sampled 2508 2508 2508 2508 2508 2508 2508 2508 2508 2508 2508 Response Rate 86.8 84.5 80.3 80.9 82.6 82.5 80.9 81.5 69.6 81.4 81.5 PREG Respondents 410 403 0 0 387 352 0 379 313 379 379 225 All Urban Total Sector National 207 Table 100. R esponse Rate by Sector (Rural-Urban), Target Groups and Specific Modules (continued) Response to Target Total Sampled/ at least 1 Response to Specific Modules Group Final Respondent Module 1 OVERALL Haemoglobin Biomarker Plasma Haemoglobin Diet HbA1c genotype Anthropometry questionnaire glucose (Anaemia) Helminth H. pylori Malaria Sampled 466 466 0 0 466 466 0 466 466 466 466 Response Rate 88.0 86.5 83.0 75.5 81.3 67.2 81.3 81.3 Respondents 5110 4581 2014 3859 4852 4817 2029 4711 3676 4701 4710 Sampled 5779 5282 2508 4816 5779 5779 2508 5779 5282 5779 5779 Response Rate 88.4 86.7 80.3 80.1 84.0 83.4 80.9 81.5 69.6 81.3 81.5 Respondents 12683 11255 5089 9685 12133 12020 5109 11792 9755 11776 11792 Sampled 13962 12760 6071 11626 13962 13962 6071 13962 12760 13962 13962 Response Rate 90.8 88.2 83.8 83.3 86.9 86.1 84.2 84.5 76.4 84.3 84.5 Overall Sampled is the total number selected for the survey; Overall Respondents is total number that responded to at least one of the modules; and Overall Response Rate is the percentage of the sampled respondents that answered to at least one module (that did not refuse to participate at all levels). 226 208 Table 101. Zonal Response Rates of Target Groups by MTaobdulele 1s01. Zonal Response Rates of Target Groups by Modules Zone Target Groups Total Sampled/ Response Final to at least Respondent 1 Module Response to Specific Modules 1 Haemoglobin Biomarker OVERALL Diet HbA1c genotype Anthropometry questionnaire Plasma glucose Haemoglobin (Anaemia) Helminth H. pylori Malaria Respondents 787 756 0 699 771 771 0 725 677 726 727 Sampled 865 865 0 865 865 865 0 865 865 865 865 CU5 Response Rate 91.0 87.4 80.8 89.1 89.1 83.8 78.3 83.9 84.0 Respondents 152 0 0 0 151 151 0 150 0 149 150 Sampled 181 0 0 0 181 181 0 181 0 181 181 Adolescent Response Rate 84.0 83.4 83.4 82.9 82.3 82.9 Respondents 882 857 835 842 861 861 826 845 768 844 846 Sampled 974 974 974 974 974 974 974 974 974 974 974 WRA Response Rate 90.6 88.0 85.7 86.4 88.4 88.4 84.8 86.8 78.9 86.7 86.9 Respondents 162 160 0 0 155 135 0 151 134 151 151 Sampled 179 179 0 0 179 179 0 179 179 179 179 Pregnant Response Rate 90.5 89.4 86.6 75.4 84.4 74.9 84.4 84.4 Respondents 1983 1773 835 1541 1938 1918 826 1871 1579 1870 1874 Sampled 2199 2018 974 1839 2199 2199 974 2199 2018 2199 2199 NC Total Response Rate 90.2 87.9 85.7 83.8 88.1 87.2 84.8 85.1 78.2 85.0 85.2 Respondents 871 827 0 783 833 827 0 799 792 799 797 Sampled 922 922 0 922 922 922 0 922 922 922 922 CU5 Response Rate 94.5 89.7 84.9 90.3 89.7 86.7 85.9 86.7 86.4 Respondents 167 0 0 0 167 166 0 164 0 162 164 Sampled 187 0 0 0 187 187 0 187 0 187 187 Adolescent Response Rate 89.3 89.3 88.8 87.7 86.6 87.7 Respondents 869 830 824 830 839 832 818 831 811 832 829 Sampled 914 914 914 914 914 914 914 914 914 914 914 WRA Response Rate 95.1 90.8 90.2 90.8 91.8 91.0 89.5 90.9 88.7 91.0 90.7 Respondents 187 182 0 0 180 152 0 177 171 178 178 Sampled 197 197 0 0 197 197 0 197 197 197 197 Pregnant Response Rate 94.9 92.4 91.4 77.2 89.8 86.8 90.4 90.4 Respondents 2094 1839 824 1613 2019 1977 818 1971 1774 1971 1968 Sampled 2220 2033 914 1836 2220 2220 914 2220 2033 2220 2220 NE Total Response Rate 94.3 90.5 90.2 87.9 90.9 89.1 89.5 88.8 87.3 88.8 88.6 Respondents 985 916 0 829 905 908 0 839 745 838 843 Sampled 1022 1022 0 1022 1022 1022 0 1022 1022 1022 1022 CU5 Response Rate 96.4 89.6 81.1 88.6 88.8 82.1 72.9 82.0 82.5 Respondents 162 0 0 0 160 158 0 160 0 158 159 NW Adolescent Sampled 209 0 0 0 209 209 0 209 0 209 209 227 209 Table 101. Zonal Response Rates of Target Groups by Modules (continued) Zone Target Groups Total Sampled/ Response Final to at least Respondent 1 Module Response to Specific Modules 1 Haemoglobin Biomarker OVERALL Diet HbA1c genotype Anthropometry questionnaire Plasma glucose Haemoglobin (Anaemia) Helminth H. pylori Malaria Response Rate 77.5 76.6 75.6 76.6 75.6 76.1 Respondents 979 944 880 887 908 911 869 876 773 878 877 Sampled 1024 1024 1024 1024 1024 1024 1024 1024 1024 1024 1024 WRA Response Rate 95.6 92.2 85.9 86.6 88.7 89.0 84.9 85.5 75.5 85.7 85.6 Respondents 201 192 0 0 177 154 0 169 151 169 170 Sampled 217 217 0 0 217 217 0 217 217 217 217 Pregnant Response Rate 92.6 88.5 81.6 71.0 77.9 69.6 77.9 78.3 Respondents 2327 2052 880 1716 2150 2131 869 2044 1669 2043 2049 Sampled 2472 2263 1024 2046 2472 2472 1024 2472 2263 2472 2472 Total Response Rate 94.1 90.7 85.9 83.9 87.0 86.2 84.9 82.7 73.8 82.6 82.9 Respondents 751 731 0 678 716 716 0 691 673 692 692 Sampled 809 809 0 809 809 809 0 809 809 809 809 CU5 Response Rate 92.8 90.4 83.8 88.5 88.5 85.4 83.2 85.5 85.5 Respondents 173 0 0 0 173 171 0 171 0 170 171 Sampled 210 0 0 0 210 210 0 210 0 210 210 Adolescent Response Rate 82.4 82.4 81.4 81.4 81.0 81.4 Respondents 914 855 845 859 871 871 861 871 837 870 870 Sampled 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 WRA Response Rate 87.5 81.8 80.9 82.2 83.3 83.3 82.4 83.3 80.1 83.3 83.3 Respondents 144 140 0 0 138 128 0 138 132 138 138 Sampled 154 154 0 0 154 154 0 154 154 154 154 Pregnant Response Rate 93.5 90.9 89.6 83.1 89.6 85.7 89.6 89.6 Respondents 1982 1726 845 1537 1898 1886 861 1871 1642 1870 1871 Sampled 2218 2008 1045 1854 2218 2218 1045 2218 2008 2218 2218 SE Total Response Rate 89.4 86.0 80.9 82.9 85.6 85.0 82.4 84.4 81.8 84.3 84.4 Respondents 876 854 0 789 833 834 0 812 760 811 811 Sampled 954 954 0 954 954 954 0 954 954 954 954 CU5 Response Rate 91.8 89.5 82.7 87.3 87.4 85.1 79.7 85.0 85.0 Respondents 180 0 0 0 180 180 0 179 0 174 179 Sampled 208 0 0 0 208 208 0 208 0 208 208 Adolescent Response Rate 86.5 86.5 86.5 86.1 83.7 86.1 Respondents 907 888 841 844 861 866 858 860 808 860 858 Sampled 1046 1046 1046 1046 1046 1046 1046 1046 1046 1046 1046 WRA Response Rate 86.7 84.9 80.4 80.7 82.3 82.8 82.0 82.2 77.2 82.2 82.0 Respondents 169 167 0 0 161 142 0 161 149 161 160 SS Pregnant Sampled 194 194 0 0 194 194 0 194 194 194 194 228 210 Table 101. Zo nal Response Rates of Target Groups by Modules (continued) Zone Target Groups Total Sampled/ Response Final to at least Respondent 1 Module Response to Specific Modules 1 Haemoglobin Biomarker OVERALL Diet HbA1c genotype Anthropometry questionnaire Plasma glucose Haemoglobin (Anaemia) Helminth H. pylori Malaria Response Rate 87.1 86.1 83.0 73.2 83.0 76.8 83.0 82.5 Respondents 2132 1909 841 1633 2035 2022 858 2012 1717 2006 2008 Sampled 2402 2194 1046 2000 2402 2402 1046 2402 2194 2402 2402 Total Response Rate 88.8 87.0 80.4 81.7 84.7 84.2 82.0 83.8 78.3 83.5 83.6 Respondents 901 884 0 770 854 860 0 808 593 806 808 Sampled 983 983 0 983 983 983 0 983 983 983 983 CU5 Response Rate 91.7 89.9 78.3 86.9 87.5 82.2 60.3 82.0 82.2 Respondents 176 0 0 0 175 176 0 174 0 171 173 Sampled 207 0 0 0 207 207 0 207 0 207 207 Adolescent Response Rate 85.0 84.5 85.0 84.1 82.6 83.6 Respondents 920 907 864 875 899 898 877 879 672 877 879 Sampled 1068 1068 1068 1068 1068 1068 1068 1068 1068 1068 1068 WRA Response Rate 86.1 84.9 80.9 81.9 84.2 84.1 82.1 82.3 62.9 82.1 82.3 Respondents 168 165 0 0 165 152 0 162 109 162 162 Sampled 193 193 0 0 193 193 0 193 193 193 193 Pregnant Response Rate 87.0 85.5 85.5 78.8 83.9 56.5 83.9 83.9 Respondents 2165 1956 864 1645 2093 2086 877 2023 1374 2016 2022 Sampled 2451 2244 1068 2051 2451 2451 1068 2451 2244 2451 2451 SW Total Response Rate 88.3 87.2 80.9 80.2 85.4 85.1 82.1 82.5 61.2 82.3 82.5 1 Overall Sampled is the total number selected for the survey; Overall Respondents is total number that responded to at least one of the modules; and Overall Response Rate is the percentage of the sampled respondents that answered to at least one module (that did not refuse to participate at all levels). Table 102. Response Rates by Age Group and Zone Geo-political Zone Age group NC NE NW SE SS SW All Zones 6 to 11 months 90.3 93.3 95.6 81.6 95.2 89.7 91.4 12 to 23 months 91.4 96.0 96.7 94.6 89.8 90.1 93.1 24 to 59 months 90.9 94.1 96.4 93.8 91.9 92.6 93.3 10 to 14 years 84.0 89.3 77.5 82.4 86.5 85.0 84.0 15 to 24 years 90.5 95.1 96.0 84.7 84.5 83.7 89.4 25 to 34 years 90.7 94.3 96.3 89.0 86.8 87.5 90.7 35 to 49 years 90.4 95.8 92.3 91.1 89.2 87.4 90.7 All Ages 90.2 94.3 94.1 89.4 88.8 88.3 90.8 229 211 Zone Target Groups Total Sampled/ Response Final to at least Respondent 1 Module Response to Specific Modules 1 Haemoglobin Biomarker OVERALL Diet HbA1c genotype Anthropometry questionnaire Plasma glucose Haemoglobin (Anaemia) Helminth H. pylori Malaria Response Rate 87.1 86.1 83.0 73.2 83.0 76.8 83.0 82.5 Respondents 2132 1909 841 1633 2035 2022 858 2012 1717 2006 2008 Sampled 2402 2194 1046 2000 2402 2402 1046 2402 2194 2402 2402 Total Response Rate 88.8 87.0 80.4 81.7 84.7 84.2 82.0 83.8 78.3 83.5 83.6 Respondents 901 884 0 770 854 860 0 808 593 806 808 Sampled 983 983 0 983 983 983 0 983 983 983 983 CU5 Response Rate 91.7 89.9 78.3 86.9 87.5 82.2 60.3 82.0 82.2 Respondents 176 0 0 0 175 176 0 174 0 171 173 Sampled 207 0 0 0 207 207 0 207 0 207 207 Adolescent Response Rate 85.0 84.5 85.0 84.1 82.6 83.6 Respondents 920 907 864 875 899 898 877 879 672 877 879 Sampled 1068 1068 1068 1068 1068 1068 1068 1068 1068 1068 1068 WRA Response Rate 86.1 84.9 80.9 81.9 84.2 84.1 82.1 82.3 62.9 82.1 82.3 Respondents 168 165 0 0 165 152 0 162 109 162 162 Sampled 193 193 0 0 193 193 0 193 193 193 193 Pregnant Response Rate 87.0 85.5 85.5 78.8 83.9 56.5 83.9 83.9 Respondents 2165 1956 864 1645 2093 2086 877 2023 1374 2016 2022 Sampled 2451 2244 1068 2051 2451 2451 1068 2451 2244 2451 2451 SW Total Response Rate 88.3 87.2 80.9 80.2 85.4 85.1 82.1 82.5 61.2 82.3 82.5 1 Overall Sampled is the total number selected for the survey; Overall Respondents is total number that responded to at least one of the modules; and Overall Response Rate is the percentage of the sampled respondents that answered to at least one module (that did not refuse to participate at all levels). Table 102. Response Rates by Age Group and Zone T able 102. Response Rates by Age Group and Zone Geo-political Zone Age group NC NE NW SE SS SW All Zones 6 to 11 months 90.3 93.3 95.6 81.6 95.2 89.7 91.4 12 to 23 months 91.4 96.0 96.7 94.6 89.8 90.1 93.1 24 to 59 months 90.9 94.1 96.4 93.8 91.9 92.6 93.3 10 to 14 years 84.0 89.3 77.5 82.4 86.5 85.0 84.0 15 to 24 years 90.5 95.1 96.0 84.7 84.5 83.7 89.4 25 to 34 years 90.7 94.3 96.3 89.0 86.8 87.5 90.7 35 to 49 years 90.4 95.8 92.3 91.1 89.2 87.4 90.7 All Ages 90.2 94.3 94.1 89.4 88.8 88.3 90.8 229 212 AAnnnneexx 22. .A Antnhtrhorpoopmoemtrye atrnyd aBniodm Barikoemr caorkmepro cneonmt –poSncoepnet o–f Sthceo ppreel iomfi ntahrey prerpeolirmt inary report Anthropometry and Biomarker component – Scope of the preliminary report Biomarker component: Information presented in the preliminary report is based on anthropometric measurements, the biomarker questionnaire (interventions, health status, and anaemia risk), and the measurement of biological samples by target groups. 230 AAnnnnexe 3x. A3n. tAhrnotphormoeptroy mDaetat rQyu Daliatyt aR eQpourat lity Report Data quality assessment report template with results from WHO Anthro Survey Analyzer Analysis date: 17 March 2022 09:56:35 Link: https://worldhealthorg.shinyapps.io/anthro/ This report is a template that includes key data quality checks that can help to identify issues with the data and considerations when interpreting results. Other outputs relevant to your analyses can be saved directly from the tool interactive dashboards and added to the report. For guidance on interpreting the results, the user should refer to the document "Recommendations for improving the quality of anthropometric data and its analysis and reporting" by the Working Group on Anthropometric Data Quality for the WHO-UNICEF Technical Expert Advisory Group on Nutrition Monitoring (TEAM). The document is available at www.who.int/nutrition/team, under "Technical reports and papers." Missing data Percentage (number of cases) of children missing information on variables used in the analysis The total number of children: 4912. Data Distribution Distribution by standard age grouping and sex 231 213 Distribution by age in years and sex The number of cases and proportions of mismatches between length/height measurement position and recommended position, by age group. Expected Observed Age group position Total mismatch* % mismatch* 0 to 11 months lying 511 6 1.2% 0 to 8 months lying 208 0 0.0% 12 to 23 months lying 1152 176 15.3% 24 to 35 months standing 1225 119 9.7% 36 to 47 months standing 1188 15 1.3% 48 to 59 months standing 829 7 0.8% Total 4905 323 6.6% Number of children with missing information on measurement position: 7 A mismatch means children under 24 months were measured standing (height) or children 24 months or older were measured lying down (recumbent length). Digit preference charts Decimal digit preference for weight and length/height 232 214 Whole number digit preference for weight Whole number digit preference for length/height 233 215 Z-score distribution of indicators Z-score distribution by index Z-score distribution by index and sex 234 216 Z-score distribution by index and age group Percentage of flagged z-scores based on WHO flagging system by index 235 217 218 Z-score summary table Z-score distribution of unweighted summary statistics by index Group Unweighted N Mean (zlen) Standard deviation (zlen) Skewness (zlen) Kurtosis (zlen) Mean (zwei) Standard deviation (zwei) Skewness (zwei) Kurtosis (zwei) All 4912 -0.98 1.85 0.33 3.89 -0.95 1.39 0.08 4.11 Age group: 6 to 11 months 518 -0.24 1.81 0.27 4.18 -0.83 1.49 0.36 4.16 Age group: 12 to 23 months 1152 -0.95 1.89 0.55 4.12 -1.03 1.52 0.26 4.09 Age group: 24 to 35 months 1225 -1.10 1.90 0.43 3.83 -0.90 1.44 -0.04 3.85 Age group: 36 to 47 months 1188 -1.16 1.79 0.23 3.73 -0.91 1.25 -0.01 3.75 Age group: 48 to 59 months 829 -1.01 1.72 -0.06 3.74 -1.03 1.24 -0.27 4.25 Sex: Male 2466 -1.03 1.84 0.39 4.01 -0.97 1.42 0.13 4.27 Sex: Female 2446 -0.93 1.86 0.26 3.78 -0.92 1.37 0.03 3.93 Z-score distribution of unweighted summary statistics by index (continued) Group Unweighted N Mean (zbmi) Standard deviation (zbmi) Skewness (zbmi) Kurtosis (zbmi) Mean (zwfl) Standard deviation (zwfl) Skewness (zwfl) Kurtosis (zwfl) All 4912 -0.46 1.20 0.02 4.11 -0.58 1.16 0.04 4.26 Age group: 6 to11 months 518 -1.06 1.33 0.47 4.00 -0.94 1.37 0.57 4.46 Age group: 12 to 23 months 1152 -0.67 1.27 0.04 3.69 -0.80 1.25 0.13 3.89 Age group: 24 to 35 months 1225 -0.27 1.16 -0.21 4.77 -0.42 1.13 -0.16 4.60 Age group: 36 to 47 months 1188 -0.22 1.11 0.16 4.22 -0.36 1.07 0.00 4.41 Age group: 48 to 59 months 829 -0.45 1.00 0.53 5.01 -0.57 0.98 0.35 4.49 Sex: Male 2466 -0.45 1.22 0.00 4.01 -0.58 1.19 -0.01 4.04 Sex: Female 2446 -0.48 1.18 0.05 4.22 -0.57 1.14 0.10 4.50 236 Annex 3 (con’t) Summary of recommended data quality checks The Working Group (WG) on Anthropometry Data Quality recommendation is that data quality is assessed and reported based on assessment on the following seven parameters: (i) completeness; (ii) sex ratio; (iii) age distribution; (iv) digit preference of heights and weights; (v) implausible z score values; (vi) standard deviation of z scores; and (vii) normality of z scores. The WG recommends that (i) data quality checks should not be considered in isolation; (ii) formal tests or scoring should not be conducted; and (iii) the checks should be used to help users identify issues with the data quality to improve interpretation of the malnutrition estimates from the survey. A summary of details on the various checks is provided below to help their use. Full details and more comprehensive guidance, including how to calculate, can be found in the full report on the WG's recommendations52. (i) Completeness: although not all statistics are included in the WHO Anthro Survey Analyzer, report on the structural integrity of the aspects listed below should be included in the final report. • PSUs: Percent of selected PSUs that were visited • Households: Percent of selected HHs in the PSUs interviewed or recorded as not interviewed (specifying why) • HH members: Percent of HH rosters that were completed • Children: Percent of all eligible children are interviewed and measured, or recorded as not interviewed or measured (specifying why), with no duplicate cases • Dates of birth: Percent of dates of birth for all eligible children that were complete (ii) Sex ratio • What: Ratio of girls to boys in the survey and compare to expected for the country. The observed ratios should be compared to the expected patterns based on reliable sources. • Why: To identify potential selection biases (iii) Age distribution • What: Age distributions by age in completed years (six bars weighted), months (72 bars), and calendar month of birth (12 bars) as histograms • Why: To identify potential selection biases or misreporting (iv) Height and weight digit preference • What: Terminal digits, as well as whole number integer distributions through histograms • Why: Digit preference may be a tell-tale sign of data fabrication or inadequate care and attention during data collection and recording. It should be presented by a team or other relevant disaggregation categories when possible. (v) Implausible z score values • What: The percent of cases outside WHO flags53 for each HAZ, WHZ, and WAZ • Why: A percent above one percent can indicate potential data quality issues in measurements or age determination. It should be presented by a team or other relevant disaggregation categories. (vi) Standard deviations • What: SD for each HAZ, WHZ, and WAZ 52 Working Group on Anthropometric Data Quality, for the WHO-UNICEF Technical Expert Advisory Group on Nutrition Monitoring (TEAM). Recommendations for improving the quality of anthropometric data and its analysis and reporting. Available at www.who.int/nutrition/team (under “Technical reports and papers”). 53 WHO Anthro Software for personal computers - Manual (2011). Available at www.who.int/childgrowth/software/anthro_pc_manual_v322.pdf?ua=1. 237 219 • Why: Large SDs may signify data quality problems and/or population heterogeneity. It is unclear what causes SD's size, and more research is needed to determine the appropriate interpretation. It should be noted that SDs are typically wider for HAZ than WHZ or WAZ, and that HAZ SD is typically widest in youngest (0-5 months old) and increases as children age through to five years. No substantial difference should be observed between boys and girls. It should be presented by a team or other relevant disaggregation categories. (vii) Checks of normality • What: Measures of asymmetry (skew) and tailedness (kurtosis) of HAZ, WHZ, and WAZ, as well as density plots • Why: A general assumption that three indices are normally distributed but unclear if applicable to populations with varying patterns of malnutrition. One can use the rule of thumb ranges of <-0.5 or >+0.5 for skewness to indicate asymmetry and <2 or >4 for kurtosis to indicate heavy or light tails. Further research is needed to understand patterns in different contexts. Anyhow, the comparisons among the distribution by disaggregation categories might help interpret the results. 238 220 221 AAnnnnexe x4. 4In.f aInnft aanndt aYnoudn Yg oCuhnildg FCeehdilindg F Pereacdtiicnegs Practices TTaabblele 1 1030.3 I.n Ifnanfat natn adn ydo uynogu ncgh ilcdh filede dfeinegd ipnrga cptricaecstices CChhiillddrreenn ((66--1111 mmoonntthhss)) CChhiillddrreenn ((1122--1177 mmoonntthhss)) CChhiillddrreenn ((1188--2233 mmoonntthhss)) CChhiillddrreenn ((66--2233 mmoonntthhss)) CChhiillddrreenn ((2244--5599 mmoonntthhss)) N1 % 95%CI2 N1 % 95%CI2 N1 % 95%CI2 N1 % 95%CI2 N1 % 95%CI2 EEvveerr bbrreeaassttffeedd Residence (P = 0.364) (P = 0.902) (P = 0.301) (P = 0.350) (P = 0.761) Urban 240 9999..66 99.0 100.0 232 9977..99 95.9 99.8 230 8899..44 81.5 97.4 702 9955..77 93.3 98.0 1381 9911..22 88.6 93.7 Rural 269 9999..00 97.9 100.0 372 9977..77 95.9 99.6 311 9944..22 89.9 98.6 952 9977..00 95.5 98.5 1911 9900..66 87.7 93.4 Sex (P = 0.765) (P = 0.200) (P = 0.141) (P = 0.091) (P = 0.957) Male 235 9999..44 98.4 100.0 286 9966..88 94.5 99.1 257 8888..77 82.0 95.5 778 9955..11 92.8 97.4 1687 9900..77 88.2 93.3 Female 274 9999..22 98.1 100.0 318 9988..77 97.0 100.0 284 9955..33 90.2 100.0 876 9977..88 96.0 99.5 1605 9900..88 88.3 93.4 National 509 9999..33 98.6 100.0 604 9977..88 96.4 99.2 541 9922..33 88.2 96.4 1654 9966..55 95.2 97.8 3292 9900..88 88.8 92.8 CCuurrrreennttllyy bbrreeaassttffeedd CCoonnttiinnuueedd bbrreeaassttffeeeeddiinngg CCuurrrreennttllyy bbrreeaassttffeedd Residence (P = 0.425) (P = 0.026)* (P = 0.007)** (P = 0.015)* (P = 0.090) Urban 240 9944..88 91.7 97.8 232 7711..88 61.9 81.6 230 2233..33 15.2 31.3 702 63.6 57.5 69.7 1381 44..22 2.9 5.4 Rural 269 9922..66 88.0 97.1 372 8833..55 79.1 87.8 311 3388..11 30.9 45.4 952 72.3 68.7 76.0 1911 66..00 4.3 7.7 Sex (P = 0.520) (P = 0.641) (P = 0.128) (P = 0.215) (P = 0.777) Male 235 9922..55 87.6 97.3 286 7788..66 72.8 84.4 257 2277..33 19.2 35.3 778 6677..00 62.3 71.7 1687 55..55 3.4 7.6 Female 274 9944..44 90.9 97.8 318 8800..22 75.3 85.2 284 3366..33 28.4 44.1 876 7700..77 66.9 74.4 1605 55..11 3.7 6.6 National 509 9933..55 90.5 96.5 604 7799..55 75.3 83.6 541 3322..11 26.6 37.6 1654 6699..00 65.9 72.1 3292 55..33 4.1 6.5 BBoottttllee ffeeeeddiinngg 00––2233 mmoonntthhss oolldd ((cchhiilldd ddrraannkk aannyytthhiinngg ffrroomm aa bboottttllee wwiitthh aa nniippppllee yyeesstteerrddaayy)) Residence (P = 0.271) (P = 0.816) (P = 0.203) (P = 0.949) (P = 0.451) Urban 240 3311..44 24.2 38.6 232 1177..00 9.9 24.0 230 1100..88 5.6 16.0 702 1199..88 14.7 24.9 1381 33..99 0.5 7.3 Rural 268 2266..00 19.6 32.4 369 1188..11 12.0 24.2 311 1155..66 10.3 20.8 948 1199..66 16.1 23.1 1909 22..55 1.6 3.4 Sex (P = 0.387) (P = 0.656) (P = 0.964) (P = 0.746) (P = 0.228) Male 234 2255..55 18.3 32.8 286 1188..66 12.4 24.8 257 1133..77 7.9 19.6 777 1199..22 15.3 23.0 1685 22..44 1.3 3.5 Female 274 3300..77 22.8 38.6 315 1166..99 11.4 22.5 284 1133..55 8.0 19.1 873 2200..11 15.9 24.4 1605 33..77 1.4 5.9 239 222 Annex 4. Infant and Young Child Feeding Practices Table 103. Infant and young child feeding practices CChhiillddrreenn ((66--1111 mmoonntthhss)) CChhiillddrreenn ((1122--1177 mmoonntthhss)) CChhiillddrreenn ((1188--2233 mmoonntthhss)) CChhiillddrreenn ((66--2233 mmoonntthhss)) CChhiillddrreenn ((2244--5599 mmoonntthhss)) N1 % 95%CI2 N1 % 95%CI2 N1 % 95%CI2 N1 % 95%CI2 N1 % 95%CI2 EEvveerr bbrreeaassttffeedd Residence (P = 0.364) (P = 0.902) (P = 0.301) (P = 0.350) (P = 0.761) Urban 240 9999..66 99.0 100.0 232 9977..99 95.9 99.8 230 8899..44 81.5 97.4 702 9955..77 93.3 98.0 1381 9911..22 88.6 93.7 Rural 269 9999..00 97.9 100.0 372 9977..77 95.9 99.6 311 9944..22 89.9 98.6 952 9977..00 95.5 98.5 1911 9900..66 87.7 93.4 Sex (P = 0.765) (P = 0.200) (P = 0.141) (P = 0.091) (P = 0.957) Male 235 9999..44 98.4 100.0 286 9966..88 94.5 99.1 257 8888..77 82.0 95.5 778 9955..11 92.8 97.4 1687 9900..77 88.2 93.3 Female 274 9999..22 98.1 100.0 318 9988..77 97.0 100.0 284 9955..33 90.2 100.0 876 9977..88 96.0 99.5 1605 9900..88 88.3 93.4 National 509 9999..33 98.6 100.0 604 9977..88 96.4 99.2 541 9922..33 88.2 96.4 1654 9966..55 95.2 97.8 3292 9900..88 88.8 92.8 CCuurrrreennttllyy bbrreeaassttffeedd CCoonnttiinnuueedd bbrreeaassttffeeeeddiinngg CCuurrrreennttllyy bbrreeaassttffeedd Residence (P = 0.425) (P = 0.026)* (P = 0.007)** (P = 0.015)* (P = 0.090) Urban 240 9944..88 91.7 97.8 232 7711..88 61.9 81.6 230 2233..33 15.2 31.3 702 63.6 57.5 69.7 1381 44..22 2.9 5.4 Rural 269 9922..66 88.0 97.1 372 8833..55 79.1 87.8 311 3388..11 30.9 45.4 952 72.3 68.7 76.0 1911 66..00 4.3 7.7 Sex (P = 0.520) (P = 0.641) (P = 0.128) (P = 0.215) (P = 0.777) Male 235 9922..55 87.6 97.3 286 7788..66 72.8 84.4 257 2277..33 19.2 35.3 778 6677..00 62.3 71.7 1687 55..55 3.4 7.6 Female 274 9944..44 90.9 97.8 318 8800..22 75.3 85.2 284 3366..33 28.4 44.1 876 7700..77 66.9 74.4 1605 55..11 3.7 6.6 Ta Nbalteio 1n0a3l . Infant an5d0 y9o ung99 33c..h55i ld f9e0e.d5i ng p9r6a.5c tices6 0(4c onti77n99u..e55d ) 75.3 83.6 541 3322..11 26.6 37.6 1654 6699..00 65.9 72.1 3292 55..33 4.1 6.5 BBoottttllee ffeeeeddiinngg 00––2233 mmoonntthhss oolldd ((cchhiilldd ddrraannkk aannyytthhiinngg ffrroomm aa bboottttllee wwiitthh aa nniippppllee yyeesstteerrddaayy)) Residence (P = 0.271) (P = 0.816) (P = 0.203) (P = 0.949) (P = 0.451) Urban 240 3311..44 24.2 38.6 232 1177..00 9.9 24.0 230 1100..88 5.6 16.0 702 1199..88 14.7 24.9 1381 33..99 0.5 7.3 Rural 268 2266..00 19.6 32.4 369 1188..11 12.0 24.2 311 1155..66 10.3 20.8 948 1199..66 16.1 23.1 1909 22..55 1.6 3.4 Sex (P = 0.387) (P = 0.656) (P = 0.964) (P = 0.746) (P = 0.228) Male 234 2255..55 18.3 32.8 286 1188..66 12.4 24.8 257 1133..77 7.9 19.6 777 1199..22 15.3 23.0 1685 22..44 1.3 3.5 Female 274 3300..77 22.8 38.6 315 1166..99 11.4 22.5 284 1133..55 8.0 19.1 873 2200..11 15.9 24.4 1605 33..77 1.4 5.9 National 508 2288..33 23.4 33.2 601 1177..77 13.1 22.4 5412 39 1133..66 9.8 17.5 1650 1199..77 16.8 22.6 3290 3.0 1.6 4.4 1 Unweighted sample size 2 Data are weighted to account for survey design and non-response. Differences between groups were compared using Chi-square test (* signifies P<0.05, ** signifies P<0.01, *** signifies P<0.001). 240 Annex 5. Biofortification Coverage Annex 5. Biofortification Coverage TTaabblele 1 10044. .C Conosnusmumede dy eylelollwow c acsassasvaav oar o arn ayn fyo ofodo pdr opdroudcutsc tms amdaed fero fmro mit iint itnh eth pea psta 3s0t 3d0a ydsays Consumed yellow cassava in the past 30 days2 N1 % 95%CI P value National Non-pregnant women (aged15-49 years) 5273 3.2 2.4 4.1 NP non-lactating women 4565 3.2 2.4 4.1 NP lactating women 708 3.3 1.5 5.0 Pregnant women (aged15-49 years) 1004 3.7 2.2 5.2 NA3 Children (aged 6-59 months) 4938 3.4 2.5 4.3 Children (aged 6-23 months) 1650 2.9 1.8 4.0 Children (aged 24-59 months) 3288 3.7 2.7 4.7 Residence, Non-pregnant women (aged15-49 years) Urban 2152 2.8 1.6 4.0 (P = 0.322) Rural 3121 3.6 2.5 4.8 Residence, NP non-lactating women Urban 1916 2.7 1.5 4.0 (P = 0.289) Rural 2649 3.7 2.4 5.0 Residence, NP lactating women Urban 236 3.2 0.0 6.4 (P = 0.958) Rural 472 3.3 1.3 5.3 Residence, Pregnant women Urban 411 3.9 1.4 6.4 (P = 0.887) Rural 593 3.6 1.7 5.6 Residence, Children (aged 6-59 months) Urban 2081 2.4 1.0 3.8 (P = 0.109 Rural 2857 4.0 2.7 5.3 Sex, Children (aged 6-59 months) Male 2462 3.4 2.3 4.5 (P = 0.942) Female 2476 3.4 2.3 4.6 Regional, Non-pregnant women (aged 15-49 years) North Central 857 3.7 1.3 6.1 North East 829 7.6 4.1 11.0 North West 942 1.0 0.0 2.0 (P = 0.0003***) South East 853 4.2 2.2 6.2 South South 888 3.5 1.5 5.4 South West 904 1.7 0.7 2.8 Wealth quintile, Non-pregnant women (15-49 years old) 241 223 Annex 5. Biofortification Coverage Table 104. Consumed yellow cassava or any food products made from it in the past 30 days Consumed yellow cassava in the past 30 days2 N1 % 95%CI P value National Non-pregnant women (aged15-49 years) 5273 3.2 2.4 4.1 NP non-lactating women 4565 3.2 2.4 4.1 NP lactating women 708 3.3 1.5 5.0 Pregnant women (aged15-49 years) 1004 3.7 2.2 5.2 NA3 Children (aged 6-59 months) 4938 3.4 2.5 4.3 Children (aged 6-23 months) 1650 2.9 1.8 4.0 Children (aged 24-59 months) 3288 3.7 2.7 4.7 Residence, Non-pregnant women (aged15-49 years) Urban 2152 2.8 1.6 4.0 (P = 0.322) Rural 3121 3.6 2.5 4.8 Residence, NP non-lactating women Urban 1916 2.7 1.5 4.0 (P = 0.289) Rural 2649 3.7 2.4 5.0 Residence, NP lactating women Urban 236 3.2 0.0 6.4 (P = 0.958) Rural 472 3.3 1.3 5.3 Residence, Pregnant women Urban 411 3.9 1.4 6.4 (P = 0.887) Rural 593 3.6 1.7 5.6 Residence, Children (aged 6-59 months) Urban 2081 2.4 1.0 3.8 (P = 0.109 Rural 2857 4.0 2.7 5.3 Sex, Children (aged 6-59 months) Male 2462 3.4 2.3 4.5 (P = 0.942) Female 2476 3.4 2.3 4.6 Regional, Non-pregnant women (aged 15-49 years) North Central 857 3.7 1.3 6.1 North East 829 7.6 4.1 11.0 North West 942 1.0 0.0 2.0 (P = 0.0003***) South East 853 4.2 2.2 6.2 South South 888 3.5 1.5 5.4 TableS 1ou0t4h. WCeosnt s umed yellow cassava or any fo9o0d4 products1 .m7 ade from it in the past 30 days (continued) 0.7 2.8 Wealth quintile, Non-pregnant women (15-49 years old) Lowest 1079 4.1 2.0 6.1 Second 121410 3.5 2.0 5.1 Middle 1099 2.7 1.4 3.9 (P = 0.516) Fourth 996 2.4 1.3 3.6 Highest 967 3.5 1.9 5.0 1 Unweighted sample size. This analysis excludes respondents who could not report whether they consumed the food. 2 Data are weighted to account for survey design and non-response. 3 Differences across groups were not tested statistically. Differences between groups were compared using Chi-square test (* signifies P<0.05, ** signifies P<0.01, *** signifies P<0.001). 242 224 225 Table 105T.a Fbreleq u1e0n5cy. Fofr ecqonuseunmcpyt ioofn coof nyselulomwp ctaiosnsa ovfa yoer lalonwy f ocoads sparovdau cotrs amnayd efo forodm p irt oind uthcet sp amsta 3d0e d faryosm a mito inng t hcoen psuamste r3s0 days among consumers NN11 FFrreeqquueennccyy ooff ccoonnssuummppttiioonn22 11--99 ddaayyss 1100--1199 ddaayyss 2200--2299 ddaayyss DDaaiillyy %% 9955%%CCII %% 9955%%CCII %% 9955%%CCII %% 9955%%CCII NNaattiioonnaall Non-pregnant women (15-49 188 years old) 7777..44 70.6 84.2 1133..33 8.4 18.2 77..77 2.7 12.7 11..66 0.0 3.3 NP non-lactating women 170 7755..99 68.3 83.4 1155..00 9.2 20.8 77..33 2.4 12.2 11..88 0.0 3.8 NP lactating women 18 8877..77 67.8 100.0 11..77 0.0 5.2 1100..66 0.0 30.2 -- - - Pregnant women (15-49 years 39 old) 6655..33 48.2 82.4 1166..44 2.0 30.8 1100..44 1.3 19.5 77..88 0 16.9 Children (6-59 months old) 165 7733..55 64.5 82.6 1111..99 5.5 18.3 88..22 1.2 15.1 66..44 0.8 12.1 Children (6-23 months old) 47 8877..22 77.8 96.5 77..00 0.0 14.3 33..66 0.0 8.9 22..22 0.0 5.2 Children (24-59 months 118 old) 6688..33 55.7 80.8 1133..88 6.2 21.3 99..99 0.4 19.5 88..00 0.7 15.4 1 Unweighted sample size for respondents who consumed yellow cassava (or any food products made from it) the previous 30 days. This analysis excludes respondents who could not report whether they consumed the food or could not report frequency of consumption. 2 Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. 243 Table 106. Consumed OFSP or any food products made from it in the past 30 days Table 106. Consumed OFSP or any food products made from it in the past 30 days Consumed sweet potato in past 30 days2 N1 % 95%CI P value National3 Non-pregnant women (15-49 years old) 5275 4.6 3.3 5.9 NP non-lactating women 4567 4.7 3.3 6.1 NP lactating women 708 4.2 1.9 6.4 Pregnant women )15-49 years old) 1004 4.7 2.7 6.8 NA3 Children (6-59 months old) 4943 4.8 3.3 6.4 Children (6-23 months old) 1652 3.2 1.9 4.5 Children (24-59 months old) 3291 5.6 3.7 7.5 Residence, Non-pregnant women (15-49 years old) Urban 2156 5.6 3.1 8.0 (P = 0.211) Rural 3119 3.7 2.5 5.0 Residence, NP non-lactating women Urban 1920 5.8 3.1 8.5 (P = 0.166) Rural 2647 3.6 2.4 4.9 Residence, NP lactating women Urban 236 4.0 0.0 8.3 (P = 0.885) Rural 472 4.3 2.0 6.7 Residence, Pregnant women Urban 412 5.4 1.9 8.8 (P = 0.661) Rural 592 4.4 1.8 7.0 Residence, Children (6-59 months old) Urban 2084 4.2 1.2 7.2 (P = 0.583) Rural 2859 5.2 3.3 7.1 Sex, Children (6-59 months old) Male 2464 4.5 3.0 6.0 (P = 0.453) Female 2479 5.1 3.2 6.9 Regional, Non-pregnant women (15-49 years old) North Central 855 2.2* 1.0 3.4 North East 829 16.3* 10.5 22.1 North West 942 1.9* 0.8 3.1 (P < South East 855 2.1* 0.8 3.5 0.0001***) South South 888 2.5* 0.6 4.5 South West 906 2.4* 1.4 3.4 Wealth quintile, Non-pregnant women (15-49 years old) Lowest 1077 5.4 3.4 7.3 (P = 0.698) Second 1110 4.5 2.5 6.4 244 226 Table 106. Consumed OFSP or any food products made from it in the past 30 days Consumed sweet potato in past 30 days2 N1 % 95%CI P value National3 Non-pregnant women (15-49 years old) 5275 4.6 3.3 5.9 NP non-lactating women 4567 4.7 3.3 6.1 NP lactating women 708 4.2 1.9 6.4 Pregnant women )15-49 years old) 1004 4.7 2.7 6.8 NA3 Children (6-59 months old) 4943 4.8 3.3 6.4 Children (6-23 months old) 1652 3.2 1.9 4.5 Children (24-59 months old) 3291 5.6 3.7 7.5 Residence, Non-pregnant women (15-49 years old) Urban 2156 5.6 3.1 8.0 (P = 0.211) Rural 3119 3.7 2.5 5.0 Residence, NP non-lactating women Urban 1920 5.8 3.1 8.5 (P = 0.166) Rural 2647 3.6 2.4 4.9 Residence, NP lactating women Urban 236 4.0 0.0 8.3 (P = 0.885) Rural 472 4.3 2.0 6.7 Residence, Pregnant women Urban 412 5.4 1.9 8.8 (P = 0.661) Rural 592 4.4 1.8 7.0 Residence, Children (6-59 months old) Urban 2084 4.2 1.2 7.2 (P = 0.583) Rural 2859 5.2 3.3 7.1 Sex, Children (6-59 months old) Male 2464 4.5 3.0 6.0 (P = 0.453) Female 2479 5.1 3.2 6.9 Regional, Non-pregnant women (15-49 years old) North Central 855 2.2* 1.0 3.4 North East 829 16.3* 10.5 22.1 North West 942 1.9* 0.8 3.1 (P < South East 855 2.1* 0.8 3.5 0.0001***) South South 888 2.5* 0.6 4.5 TableS 1o0u6th. WCoesnts umed OFSP or any food produ9c0ts6 made from2. 4it* in the past 13.04 days (cont3i.n4u ed) Wealth quintile, Non-pregnant women (15-49 years old) Lowest 1077 5.4 3.4 7.3 (P = 0.698) Second 1110 4.5 2.5 6.4 Middle 1099 3.8 2.3 5.4 Fourth 294947 3.8 1.5 6.1 Highest 970 5.6 1.0 10.2 1 Unweighted sample size. This analysis excludes respondents who could not report whether they consumed the food. 2 Data are weighted to account for survey design and non-response. 3 Differences across groups were not tested statistically. Differences between groups were compared using Chi-square test (* signifies P<0.05, ** signifies P<0.01, *** signifies P<0.001). 227 245 228 Table 107. FreqTuaebnlcey 1o0f 7co. nFsruemqputeionnc oyf oOfF cSoPn osr uamnyp ftoioodn porfo dOuFcStsP m oadr ea fnroym fo ito idn tphreo pdausct t3s0 mdaayds ea mfroonmg cito inns uthmee rpsast 30 days among consumers NN11 FFrreeqquueennccyy ooff ccoonnssuummppttiioonn22 11--99 ddaayyss 1100--1199 ddaayyss 2200--2299 ddaayyss DDaaiillyy %% 9955%%CCII %% 9955%%CCII %% 9955%%CCII %% 9955%%CCII NNaattiioonnaall Non-pregnant women (15-49 222 -- - - years old) 8833..66 76.9 90.3 1111..88 6.2 17.5 44..66 1.0 8.1 NP non-lactating women 192 8822..44 75.1 89.6 1122..44 6.1 18.8 55..22 1.3 9.1 -- - - NP lactating women 30 9922..77 82.4 100.0 77..33 0.0 17.6 -- - - -- - - Pregnant women (15-49 years 45 old) 8833..33 72.1 94.5 22..55 0.0 6.0 1100..66 0.1 21.1 33..77 0.0 9.8 Children (6-59 months old) 199 9911..44 84.3 98.5 77..00 0.2 13.8 11..00 0.0 2.4 00..66 0.0 1.8 Children (6-23 months old) 49 110000..00 100.0 100.0 -- - - -- - - -- - - Children( 24-59 months 150 old) 8888..99 79.6 98.3 99..00 0.1 17.9 11..33 0.0 3.0 00..88 0.0 2.3 1 Unweighted sample size for respondents who consumed yellow cassava (or any food products made from it) the previous 30 days. This analysis excludes respondents who could not report whether they consumed the food or could not report frequency of consumption. 2 Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. 246 TTaabblele 1 0180.8 C. oCnosnusmuemde odr aonragneg me amizaei zoer aonr ya nfoyo fdo opdro pdruocdtusc mtsa mdea fdreo mfro itm in i tt hine tphaes pt a3s0t d3a0y sdays Consumed orange maize in past 30 days2 N1 % 95%CI P value National3 Non-pregnant women (15-49 years old) 5264 13.4 10.9 15.9 NP non-lactating women 4556 13.2 10.6 15.8 NP lactating women 708 14.8 9.6 20.0 Pregnant women (15-49 years old) 1005 14.9 11.5 18.3 NA3 Children (6-59 months old) 4934 13.2 10.7 15.8 Children (6-23 months old) 1647 12.2 9.3 15.2 Children (24-59 months old) 3287 13.8 11.0 16.5 Residence, Non-pregnant women (15-49 years old) Urban 2148 15.3 10.4 20.1 (P = 0.226) Rural 3116 11.8 9.3 14.2 Residence, NP non-lactating women Urban 1912 15.2 10.1 20.3 (P = 0.199) Rural 2644 11.3 9.0 13.7 Residence, NP lactating women Urban 236 15.9 6.0 25.8 (P = 0.747) Rural 472 14.0 8.6 19.5 Residence, Pregnant women Urban 412 17.0 11.2 22.7 (P = 0.374) Rural 593 13.6 9.2 18.0 Residence, Children (6-59 months old) Urban 2077 15.3 10.1 20.6 (P = 0.299) Rural 2857 12.0 9.1 14.9 Sex, Children (6-59 months old) Male 2461 12.7 10.2 15.1 (P = 0.479) Female 2473 13.8 10.3 17.4 Regional, Non-pregnant women (15-49 years old) North Central 852 10.8* 7.1 14.6 North East 830 38.3* 29.3 47.4 North West 942 6.0* 2.6 9.5 (P < South East 849 13.6* 10.3 16.9 0.0001***) South South 888 3.6* 1.5 5.6 South West 903 11.4* 8.5 14.4 Wealth quintile, Non-pregnant women (15-49 years old) Lowest 1078 13.2 9.3 17.1 (P = 0.749) Second 1109 15.1 10.8 19.4 247 229 Table 108. Consumed orange maize or any food products made from it in the past 30 days Consumed orange maize in past 30 days2 N1 % 95%CI P value National3 Non-pregnant women (15-49 years old) 5264 13.4 10.9 15.9 NP non-lactating women 4556 13.2 10.6 15.8 NP lactating women 708 14.8 9.6 20.0 Pregnant women (15-49 years old) 1005 14.9 11.5 18.3 NA3 Children (6-59 months old) 4934 13.2 10.7 15.8 Children (6-23 months old) 1647 12.2 9.3 15.2 Children (24-59 months old) 3287 13.8 11.0 16.5 Residence, Non-pregnant women (15-49 years old) Urban 2148 15.3 10.4 20.1 (P = 0.226) Rural 3116 11.8 9.3 14.2 Residence, NP non-lactating women Urban 1912 15.2 10.1 20.3 (P = 0.199) Rural 2644 11.3 9.0 13.7 Residence, NP lactating women Urban 236 15.9 6.0 25.8 (P = 0.747) Rural 472 14.0 8.6 19.5 Residence, Pregnant women Urban 412 17.0 11.2 22.7 (P = 0.374) Rural 593 13.6 9.2 18.0 Residence, Children (6-59 months old) Urban 2077 15.3 10.1 20.6 (P = 0.299) Rural 2857 12.0 9.1 14.9 Sex, Children (6-59 months old) Male 2461 12.7 10.2 15.1 (P = 0.479) Female 2473 13.8 10.3 17.4 Regional, Non-pregnant women (15-49 years old) North Central 852 10.8* 7.1 14.6 North East 830 38.3* 29.3 47.4 North West 942 6.0* 2.6 9.5 (P < South East 849 13.6* 10.3 16.9 0.0001***) South South 888 3.6* 1.5 5.6 TableS 1o0ut8h. WCoesnts umed orange maize or any foo9d0 p3r oducts m1a1d.4e* from it in t8h.e5 past 30 da1y4s.4 ( continued) Wealth quintile, Non-pregnant women (15-49 years old) Lowest 1078 13.2 9.3 17.1 (P = 0.749) Second 1109 15.1 10.8 19.4 Middle 1096 12.1 8.7 15.4 Fourth 294973 14.1 9.7 18.4 Highest 966 12.5 7.1 18.0 1 Unweighted sample size. This analysis excludes respondents who could not report whether they consumed the food. 2 Data are weighted to account for survey design and non-response. 3 Differences across groups were not tested statistically. Differences between groups were compared using Chi-square test (* signifies P<0.05, ** signifies P<0.01, *** signifies P<0.001). 230 248 231 Table 109.T Farbeqleu e1n0c9y .o Ff creonqsuuemnpctyio no fo cf oornasnugme mpatiiozen oorf a onrya fnoogde pmroadiuzcet so mr aadney f rfoomo dit pinr othdeu pcatsst m30a ddaey sf raommon igt icno nthseum pearsst 30 days among consumers NN11 FFrreeqquueennccyy ooff ccoonnssuummppttiioonn22 11--99 ddaayyss 1100--1199 ddaayyss 2200--2299 ddaayyss DDaaiillyy %% 9955%%CCII %% 9955%%CCII %% 9955%%CCII %% 9955%%CCII NNaattiioonnaall Non-pregnant women (15-49 663 years old) 5555..77 48.0 63.5 1133..00 9.4 16.5 1144..99 10.9 19.0 1166..44 9.7 23.0 NP non-lactating women 569 5555..22 47.9 62.4 1133..99 10.0 17.8 1155..77 11.2 20.2 1155..33 9.2 21.4 NP lactating women 94 5588..88 40.4 77.2 77..99 1.3 14.5 1100..99 1.5 20.4 2222..44 10.1 34.7 Pregnant women (15-49 years 141 old) 5555..33 41.5 69.1 1133..00 5.5 20.5 1100..11 3.1 17.1 2211..66 11.5 31.8 Children (6-59 months old) 586 5544..22 45.5 63.0 1111..99 8.7 15.2 1133..77 10.0 17.4 2200..11 12.1 28.2 Children (6-23 months old) 186 5511..00 41.1 60.9 1111..11 5.6 16.7 1155..33 8.9 21.7 2222..55 11.6 33.5 Children (24-59 months old) 400 5555..77 45.8 65.5 1122..33 8.5 16.1 1133..00 9.2 16.7 1199..11 10.3 27.8 1 Unweighted sample size for respondents who consumed yellow cassava (or any food products made from it) the previous 30 days. This analysis excludes respondents who could not report whether they consumed the food or could not report frequency of consumption. 2 Data are weighted to account for survey design and non-response. Differences across groups were not tested statistically. 249 232 Annex 6. FortiAficnanteioxn 6 .C Foovretirfiacgaetion Coverage Table 110. Type, sourcTe,a abnlde b 1ra1n0d .o fT vyepgeeta, bsleo ouirl coebt,a iannedd f obrr tahen dho ousf evheolgd,e btya tbalrege ot gilr ooubpt1ained for the household, by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI %% 95%CI TThhee hhoouusseehhoolldd uusseess vveeggeettaabbllee ooiill N=52812 N=10062 N=49472 Yes 9900..33 88.5 92.1 9900..00 87.0 93.0 8888..00 85.8 90.3 No 99..77 7.9 11.5 1100..00 7.0 13.0 1122..00 9.7 14.2 MMaaiinn ttyyppee ooff vveeggeettaabbllee ooiill uusseedd iinn N= 474933 N=91133 N=441133 hhoouusseehhoolldd Groundnut oil 5500..88 47.5 54.2 5500..00 45.3 54.6 5511..66 48.1 55.1 Palm olein/palm oil 4433..88 40.0 47.5 4466..99 42.2 51.5 4422..99 39.1 46.7 Soybean oil 11..66 1.0 2.2 00..88 0.0 1.8 11..11 0.6 1.6 Oil blend 11..33 0.6 2.0 11..55 0.2 2.7 11..88 0.3 3.3 Sunflower oil 00..44 0.1 0.7 -- - - 00..55 0.1 0.9 Other 00..88 0.4 1.3 00..44 0.0 0.9 00..88 0.3 1.3 Unknown44 11..33 0.8 1.9 00..44 0.0 0.9 11..33 0.8 1.8 HHooww hhoouusseehhoolldd oobbttaaiinneedd vveeggeettaabbllee ooiill N=464655 N=90255 N=432055 tthhee llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 9911..66 90.1 93.2 9911..22 88.5 94.0 9900..66 88.7 92.5 Homemade 77..99 6.3 9.5 88..22 5.5 10.9 88..99 7.0 10.9 Received from relative/friend/food 00..44 0.3 0.6 00..66 0.1 1.1 00..55 0.2 0.7 BBrraanndd ooff vveeggeettaabbllee ooiill oobbttaaiinneedd tthhee N=43206 N=8476 N=40486 llaasstt ttiimmee Unbranded 3300..44 27.3 33.4 3322..77 28.0 37.4 33.8 30.5 37.2 King's 100% vegetable oil 2222..00 19.3 24.7 1155..55 12.3 18.8 17.7 15.1 20.1 Power oil - Pure vegetable oil 1122..77 11.1 14.3 1122..33 9.5 15.0 11.8 10.2 13.4 Golden Penny-pure soya oil 00..99 0.5 1.2 00..99 0.1 1.7 0.8 0.4 1.2 Turkey 00..99 0.3 1.4 -- - - 0.7 0.3 1.2 Oki 00..66 0.2 1.0 -- - - 0.5 0.1 0.9 Laziz - Pure vegetable oil 00..55 0.2 0.8 00..44 0.0 0.7 0.4 0.2 0.6 250 233 Table 110. Type, source, and brand of vegetable oil obtained for the household, by target group1 (continued) Sunola - Soybean oil 00..55 0.1 0.9 00..22 0.0 0.5 0.3 0.0 0.6 Mamador 00..33 0.1 0.5 -- - - 0.1 0.1 0.2 Gino 00..33 0.0 0.6 -- - - Controller 00..22 0.0 0.4 -- - - 0.7 0.0 1.4 Solive 00..11 0.0 0.2 -- - - 0.1 0.0 0.2 Other 11..55 1.0 1.9 22..00 0.9 3.0 1.1 0.7 1.5 Unknown44 2299..22 26.0 32.4 3366..11 30.5 41.8 33.8 30.5 37.2 1 Data are weighted to account for survey design and non-response. 2 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could not report the type, source or brand of food vehicle used in the household. 5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 251 234 Table 111. Type, sourceT, aanbdle b r1an1d1 o. f Twyhpeaet ,fl osuoru orbctaein, eadn fodr tbhrea hnodus oehf owldh, beya tta rfgloetu grr ooubp1tained for the household, by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI %% 95%CI TThhee hhoouusseehhoolldd uusseess wwhheeaatt fflloouurr N=52812 N=10062 N=42 Yes 2288..22 24.3 32.2 2266..77 21.7 31.6 2255..99 22.0 29.8 No 7711..88 67.8 75.7 7733..33 68.4 78.3 7744..11 70.2 78.0 MMaaiinn ttyyppee ooff wwhheeaatt fflloouurr uusseedd iinn N=122633 N=23433 hhoouusseehhoolldd All-purpose flour 5599..00 53.0 65.0 5588..66 51.5 65.7 6622..77 57.1 68.3 Refined wheat flour 1166..33 12.1 20.6 1166..44 11.0 21.7 1166..00 11.3 20.6 Whole wheat 1144..66 11.3 17.9 99..55 5.1 13.8 1100..22 7.5 12.9 Bread flour 22..88 1.4 4.3 66..11 2.3 9.8 22..99 1.3 4.5 Cake flour 00..77 0.2 1.3 00..33 0.0 0.7 00..44 0.1 0.8 Self-rising flour 00..44 0.0 1.1 -- - - 00..11 0.0 0.2 Other -- - - 00..22 0.0 0.6 -- - - Unknown4 66..11 3.6 8.6 99..00 3.4 14.7 77..88 3.9 11.7 HHooww hhoouusseehhoolldd oobbttaaiinneedd wwhheeaatt fflloouurr N=114055 N=21155 N=101455 tthhee llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 9966..00 94.1 98.0 9999..44 98.7 100.0 9988..33 97.4 99.1 Homemade 33..66 1.8 5.5 00..66 0.0 1.3 11..44 0.7 2.0 Received from 00..33 0.0 0.7 -- - - 00..44 0.0 0.8 relative/friend/food BBrraanndd ooff wwhheeaatt fflloouurr oobbttaaiinneedd tthhee llaasstt N=10956 N=2106 N=9936 ttiimmee Dangote 2222..00 16.5 27.6 1199..44 11.7 27.1 21.7 17.6 25.8 Unbranded 1166..77 13.8 19.6 1155..88 9.6 22.0 16.2 13.4 18.9 Bua flour 1155..11 9.1 21.1 1177..33 9.6 25.0 12.8 8.4 17.1 Golden Penny 77..11 4.9 9.3 66..00 2.3 9.7 6.1 4.2 8.0 Honeywell 33..88 2.4 5.2 33..11 1.2 5.1 3.3 2.0 4.6 Other 22..66 1.1 4.1 11..11 0.0 2.3 1.8 0.4 3.2 Unknown44 3322..88 28.3 37.3 3377..33 28.1 46.5 38.1 34.0 42.2 1 Data are weighted to account for survey design and non-response. 2 252 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could not report the type, source or brand of food vehicle used in the household.5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 253 235 Table 112. Type, sourceT, aanbdl ebr a1n1d2 o.f Tmyaipzee fl, osuor uobrctaein,e ad nfodr tbher ahnouds eohfo mld abyiz taer gfelot gurro uop1 btained for the household by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI %% 95%CI TThhee hhoouusseehhoolldd uusseess mmaaiizzee fflloouurr N=52812 N=10062 N=49472 Yes 5577..44 53.8 61.0 6622..00 56.9 67.2 6622..00 58.3 65.6 No 4422..66 39.0 46.2 3388..00 32.8 43.1 3388..00 34.4 41.7 MMaaiinn ttyyppee ooff mmaaiizzee fflloouurr uusseedd iinn N=257333 N=51533 N=247633 hhoouusseehhoolldd White maize flour 9911..77 89.7 93.6 9900..55 87.1 93.9 9922..33 90.6 94.0 Yellow maize flour 77..66 5.7 9.5 88..00 4.9 11.1 66..44 5.0 7.7 Other 00..22 0.0 0.4 00..33 0.0 0.9 00..88 0.2 1.5 Unknown44 00..66 0.2 1.0 11..22 0.1 2.3 00..55 0.1 1.0 HHooww hhoouusseehhoolldd oobbttaaiinneedd mmaaiizzee fflloouurr N=254255 N=50655 N=244755 tthhee llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 5511..66 47.5 55.8 5533..77 47.4 60.1 5522..77 48.6 56.7 Homemade 4477..88 43.6 52.0 4466..00 39.7 52.3 4477..00 43.0 51.0 Received from relative/friend/food 00..55 0.2 0.9 00..33 0.0 0.7 00..11 0.0 0.2 BBrraanndd ooff mmaaiizzee fflloouurr oobbttaaiinneedd tthhee llaasstt N=12316 N=2496 N=12396 ttiimmee Unbranded 5544..00 48.7 59.4 4499..88 41.3 58.4 49.6 44.9 54.4 Ammani Foods, Maize Flour 11..22 0.0 3.0 00..66 0.0 1.7 0.9 0.1 1.7 Ultimate, Maize flour 00..88 0.1 1.4 00..66 0.0 1.4 -- - - Other 00..44 0.0 0.8 00..11 0.0 0.3 0.6 0.1 1.1 Unknown44 4433..66 38.3 48.8 4499..00 40.4 57.5 49.6 44.9 54.4 1 Data are weighted to account for survey design and non-response. 2 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could not report the type, source or brand of food vehicle used in the household. 5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 254 236 Table 113. Type, source, and brand of semolina flour obtained for the household by target group1 Table 113. Type, source, and brand of semolina flour obtained for the household by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI %% 95%CI TThhee hhoouusseehhoolldd uusseess sseemmoolliinnaa fflloouurr N=52812 N=10062 N=49472 Yes 2288..77 25.4 32.1 2222..55 18.1 27.0 2255..11 21.5 28.7 No 7711..33 67.9 74.6 7777..55 73.0 81.9 7744..99 71.3 78.5 MMaaiinn ttyyppee ooff sseemmoolliinnaa fflloouurr uusseedd iinn N=157833 N=28233 N=148133 hhoouusseehhoolldd Wheat based 6655..11 60.8 69.5 7733..77 67.5 79.9 6677..22 62.0 72.3 Wheat-Maize 2266..99 23.0 30.8 1199..88 13.1 26.5 2255..33 20.6 30.0 Other -- - - 00..55 0.0 1.4 00..66 0.0 1.5 Unknown44 77..99 5.3 10.5 66..00 2.2 9.9 66..99 4.4 9.4 HHooww hhoouusseehhoolldd oobbttaaiinneedd sseemmoolliinnaa N=145855 N=26655 N=137655 fflloouurr tthhee llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 9999..44 98.9 99.8 9999..66 99.1 100.0 9999..44 98.9 99.9 Homemade 00..11 0.0 0.2 -- -- 00..11 0.0 0.3 Received from relative/friend/food 00..66 0.1 1.0 00..44 0.0 0.9 00..22 0.1 1.0 BBrraanndd ooff sseemmoolliinnaa fflloouurr oobbttaaiinneedd tthhee N=14606 N=2676 N=13766 llaasstt ttiimmee Golden Penny Semovita 5544..99 50.9 58.8 5522..33 44.5 60.1 48.9 43.2 54.5 Dangote Semolina 1133..00 10.2 15.9 1122..55 5.8 19.2 13.7 10.5 16.9 Honeywell Semolina 1122..88 9.9 15.8 1111..55 7.5 15.5 13.0 10.7 15.4 Mamagold 33..22 1.8 4.7 55..44 1.6 9.1 2.9 1.4 4.3 Unbranded 11..77 0.8 2.6 22..77 0.3 5.2 3.5 1.6 5.5 Supreme Semolina 11..44 0.7 2.0 11..11 0.1 2.0 1.1 0.6 1.7 Pure Prima 00..44 0.1 0.7 -- -- 0.4 0.1 0.7 Other 00..55 0.0 1.0 00..55 0.0 1.0 0.3 0.0 0.5 Unknown44 1122..11 9.2 14.9 1144..11 9.2 19.1 16.2 11.7 20.8 1 Data are weighted to account for survey design and non-response. 2 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could n2o5t5 r eport the type, source or brand of food vehicle used in the household. 5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 256 237 Table 114. Type, source, and brand of sugar obtained for the household by target group1 Table 114. Type, source, and brand of sugar obtained for the household by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI %% 95%CI TThhee hhoouusseehhoolldd uusseess ssuuggaarr N=52812 N=10062 N=49472 Yes 8888..22 86.3 90.2 8855..11 81.5 88.6 8877..44 84.8 90.0 No 1111..88 9.8 13.7 1144..99 11.4 18.5 1122..66 10.0 15.2 MMaaiinn ttyyppee ooff vv uusseedd iinn hhoouusseehhoolldd N=471533 N=88133 N=444533 White granulated 8866..66 84.4 88.8 8877..77 84.4 91.0 8888..11 86.1 90.0 White cube 1111..00 9.0 13.1 99..22 6.3 12.1 88..99 7.1 10.7 Brown granulated 11..33 0.5 2.1 22..00 0.0 4.1 11..00 0.4 1.5 Brown cube 00..77 0.3 1.0 00..55 0.0 1.1 11..33 0.5 2.2 Unknown44 00..44 0.1 0.8 00..66 0.0 1.2 00..55 0.2 0.9 HHooww hhoouusseehhoolldd oobbttaaiinneedd ssuuggaarr N=469255 N=87155 N=440155 tthhee llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 9999..88 99.5 100.0 9999..88 99.4 100.0 9999..88 99.7 100.0 Homemade 00..22 0.0 0.5 00..00 0.0 0.1 00..11 0.0 0.2 Received from -- - - 00..22 0.0 0.6 00..00 0.0 0.1 relative/friend/food BBrraanndd ooff ssuuggaarr oobbttaaiinneedd tthhee llaasstt N=46966 N=8746 N=44216 ttiimmee Unbranded 4422..44 39.3 45.6 4433..22 38.2 48.2 41.4 37.9 44.8 Dangote - Refined 1199..66 17.2 21.9 1166..66 13.0 20.1 18.5 16.3 20.8 Granulated White Sugar Golden Penny - Premium 11..99 1.4 2.4 11..55 0.2 2.8 1.1 0.7 1.5 quality white granulated sugar Bua - Premium Refined Sugar 11..88 1.1 2.5 00..66 0.1 1.1 1.6 1.0 2.1 Family - Refined granulated 11..55 0.9 2.0 11..55 0.3 2.8 0.9 0.6 1.2 Sugar St Loius 11..00 0.6 1.4 00..88 0.3 1.4 0.9 0.6 1.2 Dogan 00..88 0.5 1.1 00..88 0.3 1.3 0.6 0.4 0.9 Other 00..11 0.0 0.2 00..22 0.0 0.5 0.1 0.0 0.2 Unknown44 3300..99 28.3 33.5 257 3344..88 30.7 38.9 34.9 31.6 38.2 1 Data are weighted to account for survey design and non-response. 2 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could not report the type, source or brand of food vehicle used in the household. 5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 258 238 Table 115. Type, sourcTe,a anbdl ebr a1n1d 5of. sTalyt opbeta,i nseod uforrc thee, h aounsdeh bolrda bny dta rogeft sgraolutp o1 btained for the household by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI %% 95%CI TThhee hhoouusseehhoolldd uusseess ssaalltt N=52812 N=10062 N=49472 Yes 9999..33 99.0 99.6 9999..11 98.5 99.8 9999..22 98.8 99.6 No 00..77 0.4 1.0 00..99 0.2 1.5 00..88 0.4 1.2 MMaaiinn ttyyppee ooff ssaalltt uusseedd iinn N=471533 N=88033 N=491033 hhoouusseehhoolldd Table salt-fine 6655..88 62.3 69.3 6688..44 63.6 73.2 6633..99 60.5 66.6 Edible/cooking salt-Coarse 2299..33 26.2 32.4 2266..66 22.5 30.7 3300..22 27.6 32.9 Edible salt for industrial use 11..11 0.6 1.7 00..99 0.1 1.7 00..99 0.5 1.3 Sea salt-fine 00..66 0.2 1.0 11..00 0.0 2.2 00..66 0.1 1.1 Salt-low sodium 00..44 0.0 0.7 00..22 0.0 0.5 00..66 0.2 0.9 Sea salt-coarse 00..33 0.0 0.6 00..11 0.0 0.4 00..22 0.0 0.5 Unknown44 22..55 1.8 3.3 22..88 0.6 4.9 33..77 2.5 4.9 HHooww hhoouusseehhoolldd oobbttaaiinneedd ssaalltt tthhee N=458655 N=86255 N=474855 llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 9999..88 99.7 99.9 9999..99 99.8 100.0 9999..88 99.6 99.9 Homemade 00..11 0.0 0.1 -- -- - 00..22 0.0 0.3 Received from 00..11 0.0 0.2 00..11 0.0 0.2 00..11 0.0 0.1 relative/friend/food BBrraanndd ooff ssaalltt oobbttaaiinneedd tthhee llaasstt N=46206 N=8646 N=47816 ttiimmee Dangote - refined and iodized 2299..99 27.0 32.8 2233..33 19.2 27.4 salt 26.1 22.4 29.7 Unbranded 2200..22 17.7 22.7 2233..88 19.5 28.1 22.6 19.5 25.7 Mr. Chef - pure refined and 1199..44 16.8 22.1 1177..00 13.6 20.4 iodized salt 15.0 12.8 17.2 Uncle palm - iodized salt 44..33 3.1 5.5 33..99 2.3 5.6 3.7 2.7 4.6 Annapurna 00..55 0.1 0.8 00..33 0.0 0.6 0.3 0.1 0.6 Royal salt - edible iodized salt 00..33 0.1 0.5 00..55 0.0 1.1 -- - - Other 00..22 0.0 0.4 00..11 0.0 0.2 0.2 0.0 0.3 Unknown44 2255..22 22.5 27.9 259 3311..11 26.6 35.6 32.5 28.4 36.5 1 Data are weighted to account for survey design and non-response. 2 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could not report the type, source or brand of food vehicle used in the household. 5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 260 239 Table 116. Type, source, and brand of bouillon obtained for the household by target group1 Table 116. Type, source, and brand of bouillon obtained for the household by target group1 Non-pregnant women Pregnant women Children (6-59 months old) (15-49 years old) (15-49 years old) %% 95%CI %% 95%CI % 95%CI TThhee hhoouusseehhoolldd uusseess bboouuiilllloonn N=52492 N=10062 N=49472 ccuubbee Yes 9988..99 98.5 99.3 9988..66 97.7 99.5 9988..88 98.3 99.2 No 11..11 0.7 1.5 11..44 0.5 2.3 11..22 0.8 1.7 MMaaiinn ttyyppee ooff bboouuiilllloonn uusseedd iinn N=517833 N=98433 N=487033 hhoouusseehhoolldd Cube 9911..33 89.3 93.2 8888..11 83.5 92.6 8888..11 85.5 90.8 Granule 66..55 4.9 8.2 99..99 5.4 14.4 88..55 6.2 10.9 Powder 11..55 0.9 2.1 11..77 0.5 2.9 22..55 1.5 3.5 Other 00..11 0.0 0.1 -- - - 00..66 0.2 1.1 Unknown44 00..66 0.3 1.0 00..33 0.0 0.7 00..22 0.0 0.4 HHooww hhoouusseehhoolldd oobbttaaiinneedd bboouuiilllloonn N=514155 N=97355 N=486555 tthhee llaasstt ttiimmee iitt wwaass oobbttaaiinneedd55 Purchased 9999..88 99.6 99.9 9999..99 99.7 100.0 9999..88 99.6 99.9 Homemade 00..11 0.0 0.3 00..11 0.0 0.3 00..11 0.0 0.2 Received from 00..11 0.0 0.1 -- - - 00..00 0.0 0.1 relative/friend/food BBrraanndd ooff bboouuiilllloonn oobbttaaiinneedd tthhee N=51356 N=9746 N=48656 llaasstt ttiimmee Maggi 5544..88 51.2 58.4 5555..88 50.2 61.4 55.0 50.4 59.5 Ajinomoto 1100..00 7.7 12.3 1155..11 10.1 20.1 13.0 9.9 16.2 Onga 99..99 7.5 12.2 88..44 5.6 11.3 8.7 6.6 10.7 Knorr 88..00 6.6 9.4 55..55 3.8 7.3 5.7 4.6 6.8 Tasty 77..66 6.1 9.0 55..33 3.6 7.1 6.1 4.7 7.6 Mr Cheff 22..77 1.9 3.4 11..22 0.3 2.1 2.5 1.4 3.5 Terra seasoning cubes 22..44 1.5 3.2 22..66 1.1 4.2 2.3 1.3 3.3 Royco 11..77 0.9 2.6 33..88 0.1 7.5 1.9 0.7 3.2 Gino max seasoning cube 11..55 1.0 1.9 00..88 0.3 1.3 1.0 0.6 1.3 Suppy seasoning cubes 00..22 0.1 0.4 -- - - 0.1 0.0 0.2 Ami seasoning cube 00..22 0.1 0.3 261 -- - - -- - - Super seasoning Vedan 00..11 0.0 0.2 -- - - 0.4 0.0 0.9 Other 00..33 0.0 0.6 11..22 0.3 2.1 0.5 0.2 0.8 Unknown44 00..77 0.5 1.0 00..22 0.0 0.5 1.1 0.5 1.7 1 Data are weighted to account for survey design and non-response. 2 Unweighted sample size for all respondents. 3 Unweighted sample size for respondents who used the food vehicle in the household (excluding respondents with a missing value). 4 The response was classified as “unknown” when the respondent could not report the type, source or brand of food vehicle used in the household. 5 Unweighted sample size for respondents who used the food vehicle in the household and the main type of food vehicle was not “other” or “unknown” (excluding respondents with a missing value). 6 Unweighted sample size for respondents who used the food vehicle in the household, the main type of food vehicle was not “other” or “unknown” and the food vehicle was not “home-made”. (excluding respondents with a missing value). Differences across groups were not tested statistically. 262 Annex 7. Summary of Food Samples Collected and Analyzed Annex 7. Summary of Food Sample s Collected and Analyzed Table 117. Summary of the food samples collected, processed and distributed for laboratory Table 117. Summary of the food samples colleacntaeldy,s perso cessed and distributed for laboratory analyses Food vehicles Total collected Salt 1153 Vegetable oil 338 Sugar 400 Semolina flour 89 Wheat flour 51 Total 2031 Sample Distribution by Laboratory Table 118. MicroChem Lab., TSaobulteh 1A1fr8ic. aMicroChem Lab., South Africa Food samples Total Analyses to run Comments Wheat flour 37 VA, Fe & Zn All samples are at Semolina 78 VA, Fe & Zn least 20 g weight. Vegetable Oil 232 VA Sugar 274 VA Total 621 Table 119. Intertek Lab., GermanTyable 119. Intertek Lab., Germany Food samples Total Analyses to run Comments Salt 73 Iodine All samples are at Wheat flour 11 VA, Fe & Zn least 30g weight. Semolina 17 VA, Fe & Zn Sugar 32 VA Total 133 Table 120. BATO Lab., Lagos Table 120. BATO Lab., Lagos Food samples Total Analyses to run Comments Salt 30 Iodine All samples are at Semolina 8 VA, Fe & Zn least 30g weight. Sugar 14 VA Vegetable Oil 22 VA Total 74 Table 121. FIIRO, Oshodi Lagos Table 121. FIIRO, Oshodi Lagos Food samples Total Analyses to run Comments Salt 14 Iodine All samples are at least 20g weight. Total 14 263 240 An nex 8. Household listing form Annex 8. Household listing form Form ID No: (Leave Blank) Nigeria National Food Consumption and Micronutrient Survey (NFCMS) Household Listing Form IDENTIFICATION NAME CODE ID-1 ZONE ID-2 STATE ID-3 LGA ID-4 Locality ID-5 EA ID-6 EA Serial No ID-7 Sector (Urban = 1; Rural = 2) ID-8 Building Number LATITUDE (N) LONGITUDE (E) ID-9.GPS Coordinates? ID-10. Address of Building:……..………..………………………………………..………………………………..….…………… …………………………………………………………………………………………………………. ID-11. Use of Household Unit Residential only = 1 Institutional = 6 Commercial only = 2 Hotel/Restaurant = 7 Religious only = 3 Vacant = 8 Residential/commercial = 4 Uncompleted = 9 Residential/Religious = 5 Others = 10 ID-12S/No of Residential HU:______ ID-13S/No of households: ______ ID-14. Name of Head of Household: .…………………………………………………………. ID-15. Phone number(s): ….………..………….……...…… …..……………………………… 264 241 Purpose(s) of survey: We are conducting a national survey to assess the micronutrient status and dietary intake of women(15-49 years old), including pregnant and lactating women, and children( 6‒59 months old), as well as the micronutrient status of non- pregnant adolescent girls (aged 10‒14 years) and to identify key factors associated with poor nutrition in these populations. The information generated will provide a foundation for the formulation of evidence informed policies and programs. In the short to medium term, the information will provide a baseline from which to monitor changes over time. We would very much appreciate your participation in this survey. This information will help the government to plan health and nutrition services. The results from this survey will be kept strictly confidential and will not be shared with anyone other than members of our survey team 1 = Yes → Give objective answer any question that is relevant to the survey Do you have any questions for me? 0 = No 1 = Yes May I begin the interview now? 0 = No → Thank the respondent and record reason for no consent Interviewer’s Visits No of Visit 1 2 3 Final Visit Date Day: ____________ Interviewer’s name Month ____________ Year: ___________ Result* Next Date ____/____/20_____ ____/____/20____ Visit: _ ____/____/20_____ Total No of visits to HH Time _____:_____ _____:_____ _____:_____ ______ Time Interview Started _____:_____ _____:_____ _____:_____ _____:_____ Time Interview Ended *Result codes 1. Completed 2. No household member at home or no competent respondent at home at time of visit 3. Entire household absent for extended period of time 4. Postponed 5. Refused 6. Dwelling vacant or address not a dwelling 7. Dwelling destroyed 8. Dwelling not found 9. Other (specify) 265 242 Data Collection, Editing, and Entry Record Data collection Field editing Office editing Data entry ________________ _________________ _________________ _________________ Name _ ________________ Date _________________ _________________ _________________ _ 266 243 244 Respondent: Head of household or any knowledgeable adult member of the household HHRR--00 HHRR--11 HHRR--22 HHRR--33 HHRR--44 HHRR--55 HHRR--66 HHRR--77 HHRR--88 HHRR--99 HHRR--1100 USUAL RESIDENTS SIDENCE WOMEN OF AND VIS GE CHILDREN UNDER 5 YEARS REPRODUCTIVE ITORS SEX RE A AGE Please give me the names Is [NAME} Does [NAME] How old is If under 5 Date of Birth Certificate of Birth Source of Birth If female of the persons who usually male or usually live here? [NAME]? years Certificate 14-49 years live in the HH and guests of female? Enter What is the Ask if [NAME] has a or any written the HH who stayed here (at least 6 completed date of birth of birth certificate documentation Ask if [name] is last night, months of year) years at last (name)? or any written currently pregnant birthday documentation Start with the head of e < than 1 Enter Age in Enter code of the household M F Yes No Ag year = 0 Months DD/MM/YYYY Yes No the source Yes No Dk 01 01 1 2 1 2 1 2 1 2 9 02 1 2 1 2 1 2 1 2 9 03 1 2 1 2 1 2 1 2 9 04 1 2 1 2 1 2 1 2 9 05 1 2 1 2 1 2 1 2 9 06 1 2 1 2 1 2 1 2 9 07 1 2 1 2 1 2 1 2 9 08 1 2 1 2 1 2 1 2 9 HR.2: HR9: Relationship to Head Source of Birth Certificate Head ................... 01 Parent-in-law................. 07 Spouse .................. 02 Brother/Sister ............... 08 Birth certificate........................ 1 Own child............. 03 Others relatives............. 09 Child health card.................... 2 Child-in-law........... 04 Adopted/foster/stepchild 10 Holy Card............................... 3 Grand Child........... 05 Not related.. ................. 11 Local event calendar............... 4 Recall ............................ 5 Index method .................. 6 (compare with other child who has 267 Line Number Relationship to Head of Household HH SEC Questionnaire Form ID No Zone State EA HH Indiv AAnnexx 9 9. H. Houosuehsoelhd oQlude Qstiuoensnatiorennaire Nigeria National Food Consumption and Micronutrient Survey(NFCMS) Household Socio-Economic Questionnaire (To be administered by Field Enumerators) IDENTIFICATION NAME CODE ID-1 ZONE ID-2 STATE ID-3 LGA ID-4 Locality ID-5 EA ID-6 EA Serial No ID-7 Sector(Urban = 1; Rural = 2) ID-8 Building Number LATITUDE (N) LONGITUDE (E) ID-9.GPS Coordinates?   ID-10. Address of Building:………………………...……………………..………..……………… ……………………………..………………………………..….… ID-11. Use of Household Unit Residential only = 1 Institutional = 6 Commercial only = 2 Hotel/Restaurant = 7 Religious only = 3 Vacant = 8 Residential/commercial = 4 Uncompleted = 9 Residential/Religious = 5 Others = 10 ID-12. S/No of Residential HU:_______ ID-13. S/No of HH: ________ ID-14. Name of Head of Household: __________________________________________ ID-15. Phone number(s): ________________________ __________________________ 245 250 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Consent Statement: I have read this form and/or someone has read it to me. I was encouraged to ask questions and given time to ask questions. Any questions that I had, have been answered satisfactorily. I agree to take part in the household interview. I know that after choosing to be in the interview, I may withdraw at any time. My taking part is voluntary. I have been offered a copy of this consent form. Do you agree to do the household interview? 1 = Yes→ ‘YES’ means that you agree to do the interview. 0 = No→ ‘NO’ means that you will NOT do the interview. Head of household signature or mark_________________________ Date: ____/____/20____ Name of head of household [PRINTED] _______________________________________________ Household ID number: ______________________ [FOR ILLITERATE PARTICIPANTS] Signature of witness________________________________________ Date: ____/____/20____ Printed name of witness_____________________________________ Signature of person obtaining consent__________________________ Date: ____/____/20____ Name of person obtaining consent: [PRINTED] _______________________ Survey staff ID number _________________________________ If the witness is from the survey staff, state his/her role in the survey, his/her staff ID number and describe the reason why an impartial witness could not be identified: _________________________________________________________________________ _________________________________________________________________________ 246 251 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Do you have any questions for me? 1 = Y e s → G i v e o b j e c t i v e a n s w e r a n y question that is relevant to the survey 0 = No May I begin the interview now? 1 = Yes 0 = No→ Thank the respondent and record reason for no consent Interviewer’s Visits No of Visit 1 2 3 Date(dd/mm./yy) _____/_____/20_____ _____/_____/20_____ _____/_____/20_____ Interviewer’s name RESULT(codes 1-8) Date ____/____/20_____ ____/____/20_____ ____/____/20_____ Next Visit: Time _____:_____ _____:_____ _____:_____ Time Interview Started _____:_____ _____:_____ _____:_____ Time Interview Ended _____:_____ _____:_____ _____:_____ Result codes 10. Completed 11. Incomplete 12. Refused 13. Incapacitated 14. Not found 15. Ineligible 16. Away for a long period 17. Other (specify) 247 252 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Data Collection, Editing, and Entry Record Data collection Field editing Office editing Data entry __________ _________________ _________________ _________________ Name _______ Date _________________ _________________ _________________ _________________ 248 253 HH SEC Questionnaire Form ID No Zone State EA HH Indiv RESPONDENT IDENTIFICATION CONFIRMATION Q/N QUESTION RESPONSE CODE INSTRUCTION RIC-1 Confirm respondent Yes ……………………….……. 1 What is your name? Update/correct in No ………………….………….. 2 ---→ the Roster [NAME LINKED TO LINE LISTING?] RIC-2 Confirm respondent Yes ……………………….……. 1 Update/correct in [GENDER LINKED TO LINE LISTING?] No ………………….………….. 2 ---→ the Roster RIC-3 Confirm respondent Yes ……………………….……. 1 How old are you? Update/correct in No ………………….………….. 2 ---→ the Roster [AGE LINKED TO LINE LISTING?] RIC-4 Confirm completion of household questionnaire: Yes ……………………….……. 1 Did anyone in your household answers questions about your household during a No ………………….………….. 2 ---→ Identify initial previous visit? respondent RIC-5 Confirm completion of household Myself...……………..……… 1 questionnaire: If yes, was that you or someone else? Someone else………..…… 2 RIC-6 Confirm respondent Yes ……………………….……. 1 [SIGN CONSENT FORM?] No ………………….………….. 2 ---→ Ensure respondent sign to continue RIC-7 Line number of the respondent in the HH Roster 249 ccliv HH SEC Questionnaire Form ID No Zone State EA HH Indiv GENERAL HOUSEHOLD INFORMATION Q/N QUESTION RESPONSE CODE INSTRUCTION GHI-1 What is the ethnic group of Hausa…...……......………...…… 01 [NAME of household head]? Yoruba….....…...………...…… 02 Igbo…..………..………...…...… 03 Ijaw…..………………...…...… 04 Kanuri ………..……...…...…… 05 Fulani ……...………...…...…… 06 Ibibio……...………...…....…… 07 Tiv…..…...………...….……… 08 Others(Specify) __________ 98 GHI-2 What is the religion of [NAME Christian.………. ………...…..…………… 1 of household head]? Muslim.…. …………...…...….……….....… 2 Traditional.…… …………...…….…..……… 3 No Religion ……………...………...….…… 4 Others(Specify) ____________________ 8 Don’t know .......…………...………...…........ 9 GHI-3 What is the highest level of None………...………………...…………… 1 school [NAME of household Primary ………...………………………… 2 head] has completed? Secondary ………...………...…………… 3 Technical / vocational certificate.............. 4 Higher / university/ college........................ 5 Others(Specify) ____________________ 8 Don’t know ……….......………….................. 9 GHI-4 What kind of work does Not working and didn't work in last 12 months 01 [NAME of household head] Professional, Technical and Related Workers 02 mainly do for income? Administrative and Managerial Workers 03 Office and Administrative Support Workers 04 (SELECT ONE ANSWER ONLY) Sales and Related Workers…………………… 05 Service Workers………………………………... 06 Installations, Maintenance and Repair Workers 07 Agricultural, Animal Husbandry and Forestry Workers, Fishermen and Hunters………… 08 Production, Construction and Extractions Workers………………………………………… 09 Transportation and Material Moving Workers 10 Others(Specify) ____________________ 96 Don’t know ……….......………….................... 99 255 250 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Q/N QUESTION RESPONSE CODE INSTRUCTION GHI-5 What is the main source of Piped water drinking water for members of Piped water into dwelling.................................. 01 your household? Piped water into compound, yard or plot.......... 02 Piped to neighbor............................................ 03 DO NOT READ LIST Public tap or standpipe..................................... 04 PROBE FOR ONE Borehole or tube well........................................ 05 RESPONSE Dug well Protected well................................................... 06 Unprotected well.............................................. 07 Water from Spring Protected spring............................................. 08 Unprotected spring.......................................... 09 Rainwater Rainwater collection........................................ 10 Delivered or kiosk water Truck-tanker..................................................... 11 Cart with small tank/drum................................ 12 Water kiosk...................................................... 13 Packaged water Bottled water.................................................... 14 Sachet water.................................................... 15 Surface water River/ stream, pond/ lake/ dam/canal/irrigation 16 channel) Other, (specify) _______________________ 98 GHI-6 Piped water Ask if GHI-5 = 01-05 , 14, 15 Piped water into dwelling.................................. 01 Piped water into compound, yard or plot.......... 02 What is the main source of Piped to neighbor............................................ 03 water used by your household Public tap or standpipe..................................... 04 for other purposes such as Borehole or tube well........................................ 05 cooking and hand-washing? Dug well Protected well................................................... 06 DO NOT READ LIST Unprotected well.............................................. 07 PROBE FOR ONE Water from Spring RESPONSE Protected spring............................................. 08 Unprotected spring.......................................... 09 Rainwater Rainwater collection........................................ 10 Delivered or kiosk water Truck-tanker..................................................... 11 Cart with small tank/drum................................ 12 Water kiosk...................................................... 13 Packaged water 256 251 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Bottled water.................................................... 14 Sachet water.................................................... 15 Surface water River/ stream, pond/ lake/ dam/canal/irrigation 16 channel) Other, (specify) _______________________ 98 GHI-7 Ask if GHI-6 = 01 or 02 In own dwelling…………….…………..………. 1 Where is the water source In own yard/plot…………………….…………… 2 located Elsewhere………………………..…………….. 3 GHI-8 Ask if GHI-7 = 3 [99 = don’t know] How long does it take to go to your main drinking water _______ minutes source water source, get water, and come back? Q/N QUESTION RESPONSE CODE INSTRUCTION GHI-9 In the last month, has there been any time when your household Yes, at least once............................................ 1 did not have sufficient quantities No, always sufficient....................................... 2 of drinking water when needed? 9 Don’t know....................................................... GHI-10 In the past month, for how many days was water from this source 00 = no interruption unavailable when needed 99 = don’t know [ _______] days GHI-11 Do you or any other member of Yes …………..…………………………….…….. 1 this household do anything to No …………………………………………....….. 2 → Skip to GHI-13 the water to make it safer to Don’t Know........................................................ 9 → Skip to GHI-13 drink? GHI-12 What do you usually do to make the BOIL …………………………………………….. A water safer to drink? ADD BLEACH / CHLORINE ………………….. B STRAIN IT THROUGH A CLOTH ……………. PROBE: USE WATER FILTER (CERAMIC, SAND, C Anything else? COMPOSITE, ETC.) ………………………… SOLAR DISINFECTION D RECORD ALL METHODS LET IT STAND AND SETTLE ………………… E MENTIONED. OTHER (specify) ______________________ F DON’T KNOW: ………………………………….. X Z 257 252 HH SEC Questionnaire Form ID No Zone State EA HH Indiv GHI-13 What kind of toilet facility do Flush/pour flush toilet members of your household Flush toilet connected to piped sewer system 01 usually use? Flush toilet connected to septic tank 02 Flush toilet connected to pit latrine 03 (DO NOT READ LIST. PROBE Flush toilet connected to somewhere else 04 FOR ONE RESPONSE) Flush toilet connected to don’t know where 05 Pit Latrine Ventilated improved pit latrine........................ 06 Pit latrine with slab.......................................... 07 Pit latrine without slab/open pit......................... 08 Composting Toilet............................................ 09 Bucket toilet...................................................... 10 Hanging toilet/hanging latrine.......................... 11 No facility/use bush or field.............................. 12 Other, (Specify) _______________________ 98 GHI-14 Do you share your toilet facility Yes …………..…………………………….…….. 1 with other households? No ……………………………………………….. 2 GHI-15 Where is the toilet facility In own dwelling…………….…………..………. 1 located? In own yard/plot…………………….…………… 2 Elsewhere………………………..…………….. 3 GHI-16 Has your (pit latrine or septic tank) ever been emptied? Yes emptied…………………………………….. 1 No, never emptied……………………...………. 2 → Skip to GHI-19 Ask if GHI-11 Don’t know........................................................ 9 → Skip to GHI-19 = 02, 03, 06, 07, 08 or 09 GHI-17 The last time (pit latrine or septic Yes, by a service provider …………………… 1 tank) was emptied, was it No, never emptied……….……………..………. 2 → Skip to GHI-19 emptied by a service provider? Don’t know....................................................... 9 → Skip to GHI-19 Ask if GHI-14 = 1 GHI-18 Where were the contents of the To a treatment plant......................................... 1 (pit latrine or septic tank) Buried in a covered.......................................... 2 emptied to? Uncovered pit/bush/field/open ground........... 3 Ask if GHI-14 = 1 Surface water (river/dam/lake/pond/ stream/canal/irrigation channel)....................... 4 Others, (specify) ______________________ 8 Don’t know....................................................... 9 GHI-19 We would like to learn about the OBSERVED places where members of this Fixed facility observed (sink / tap) household wash their hands. in dwelling ...................................................... in yard /plot .................................................... 1 Can you please show me where Mobile object observed 2 members of your household most (bucket / jug / kettle) ...................................... often wash their hands? 3 NOT OBSERVED Record result and observation. No handwashing place in dwelling / yard / plot ....................................................... Skip to GHI-23 No permission to see ........................................ Skip to GHI-22 258 253 HH SEC Questionnaire Form ID No Zone State EA HH Indiv 4→ Other reason (specify) ...................................... 5→ Skip to GHI-23 6→ GHI-20 Observe presence of water at the WATER IS AVAILABLE 1 place for handwashing. WATER IS NOT AVAILABLE 2 Verify by checking the tap/pump, or basin, bucket, water container or similar objects for presence of water. GHI-21 Observe presence of soap or YES, SOAP/DETERGENT AVAILABLE ........... 1→ Skip to GHI-25 detergent at the place for NO, SOAP/DETERGENT NOT AVAILABLE .... 2→ Skip to GHI-23 handwashing? GHI-22 Where do you or other members of Fixed facility (Sink / Tap) your household most often wash In dwelling 1 your hands? In yard / plot 2 Mobile object (Bucket / Jug / Kettle) 3 No handwashing place in dwelling / yard / plot 4 Other (specify) 6 GHI-23 Do you have any soap or detergent Yes …………..…………………………….…….. 1 in your house for washing hands? No ……………………………………………….. 2→ Skip to GHI-26 GHI-24 Can you please show it to me? Yes, shown …………………………………. 1 No, not shown ……………………………… 2→ Skip to GHI-26 GHI-25 RECORD YOUR OBSERVATION. Bar or Liquid soap …………. ………………….. A Record all that apply. Detergent (Powder / Liquid / Paste) ………….. B GHI-26 What is the main way in which Burning............................................................. 1 this household disposes refuse? Refuse heap..................................................... 2 Bush................................................................. 3 (Select one answer only) Pay someone to dispose.................................. 4 Government disposal services......................... 5 Others, (specify) ______________________ 8 Don’t know....................................................... 9 259 254 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Q/N QUESTION RESPONSE CODE INSTRUCTION GHI-27 What type of fuel does your Electricity.......................................................... 01 household mainly use for Gas.................................................................... 02 cooking? Kerosene/ paraffin............................................ 03 Solar.................................................................. 04 (Select one answer only) Coal/lignite......................................................... 05 Read options? Charcoal............................................................ 06 Wood................................................................. 07 Animal dung cakes............................................. 08 Grass/shrubs/ straw............................................ 09 Do not cook........................................................ 10 Others, (specify) _______________________ 98 GHI-28 Observe the main material of Natural floor (earth/sand/mud, dung)................. 1 the floor of the dwelling Rudimentary floor (wood planks, Record observation palm/bamboo)................................................... 2 Finished floor (polished wood, vinyl, ceramic tiles, cement/concrete, carpet, rug..................... 3 Others, (specify) ________________________ 8 GHI-29 Observe the main material of Natural floor (earth/sand/mud, dung)............ 1 the roof of the dwelling Rudimentary floor (wood planks, Record observation palm/bamboo).................................................... 2 Finished floor (polished wood, vinyl, ceramic tiles, cement/concrete, carpet, rug.................... 3 Others, (specify) _______________________ 8 GHI-30 Observe the main material of Natural walls (no walls, cane/palm/trunks, dirt)..... 1 the exterior walls of the Rudimentary walls (bamboo with mud, stone with dwelling mud, uncovered adobe, plywood, cardboard, Record observation reused wood).................................................. 2 Finished walls (cement, stone with lime/cement, bricks, cement blocks, covered adobe, wood planks/shingles).................................................. Others, (specify) _______________________ 3 8 GHI-31 How many rooms in this household are used for _______ Rooms sleeping? GHI-32 Does this household have Yes …………..…………………………….…….. 1 electricity? No …………………………………………....….. 2 Don’t Know........................................................ 9 GHI-33 How many of the following Animal Number animals do this household own? owned Chickens or other poultry? IF NONE, RECORD '00'. IF 95 OR MORE, RECORD '95'. IF Goats? UNKNOWN, RECORD '99'. Sheep? Milk cows or bulls 260 255 HH SEC Questionnaire Form ID No Zone State EA HH Indiv Pigs? Donkeys/ Mules? Horses? Camels? GHI-34 Does your household mostly consume, mostly sell, or both Mostly consume................................................ 1 sell and consume these Mostly sell.......................................................... 2 animals?THIS WILL LOOP Both consume and sell...................................... 3 FOR ALL ANIMALS>0 IN GHI- Don’t Know....................................................... 9 24 ABOVE Q/N QUESTION RESPONSE CODE INSTRUCTION GHI-35 Does this household own any Yes …………..……………………….…….. 1 livestock, herds, other farm No ……………………………………....….. 2 animals, or poultry (even if these Don’t Know................................................ 9 animals are not here right now)? GHI-36 Does your household currently Yes …………..……………………….…….. 1 raise any of these animals (rabbit, No ……………………………………....….. 2 guinea pigs, grass cutters, snails Don’t Know................................................ 9 or other small animals) for your household’s own consumption? GHI-37 Does anyone in this household Yes …………..……………………….…….. 1 currently raise fish for your No ……………………………………....….. 2 household’s own consumption? Don’t Know................................................ 9 GHI-38 Does anyone in this household Yes …………..……………………….…….. 1 currently catch / harvest fish from No ……………………………………....….. 2 the wild for your household’s own Don’t Know................................................ 9 consumption? GHI-39 Does your household currently Yes …………..……………………….…….. 1 have a garden where you grow No ……………………………………....….. 2 vegetables? Don’t Know................................................ 9 GHI-40 (If yes to vegetable garden) What Mostly consume........................................ 1 does your household do with what Mostly sell.................................................. 2 you produce? Both consume and sell............................... 3 Don’t Know................................................. 9 GHI-41 (If no to vegetable garden) do you Yes …………..……………………….…….. 1 have access to any land where No ……………………………………....….. 2 you could grow vegetables? Don’t Know................................................ 9 GHI-42 Does your household currently Yes …………..……………………….…….. 1 have any trees or bushes that No ……………………………………....….. 2 produce fruits? Don’t Know................................................ 9 GHI-43 (If yes to fruits) what does your Mostly consume........................................ 1 household do with what you Mostly sell.................................................. 2 produce? Both consume and sell............................... 3 Don’t Know................................................. 9 261 256 HH SEC Questionnaire Form ID No Zone State EA HH Indiv GHI-44 Does any member of this Yes …………..……………………….…….. 1 household have an account in a No ……………………………………....….. 2 bank or other financial institution? Don’t Know................................................ 9 GHI-45 Yes No Radio 1 2 Television 1 2 Cable TV 1 2 Refrigerator 1 2 Generator 1 2 Air conditional 1 2 Does your household have a Computer 1 2 functional: CD/DVD Player 1 2 Electric iron 1 2 Electric Fan 1 2 Washing Machine 1 2 Bed with Mattress 1 2 Chair 1 2 Table 1 2 Cupboard/Cabinet 1 2 Q/N QUESTION RESPONSE CODE INSTRUCTION GHI-46 Yes No Watch 1 2 Mobile Phone 1 2 Bicycle 1 2 Does any member of this household have a functional: Motorcycle 1 2 Tricycle or Keke 1 2 Animal Drawn Cart 1 2 Car, Bus or Truck 1 2 Boat with Motor 1 2 Canoe or Boat without motor 1 2 GHI-47 How long does it take in minutes to Households in walk from your home to the nearest _______ minutes urban locations bus stop? only GHI-48 How long does it take in minutes to Households in walk from your home to the nearest rural locations _______ minutes road that is motorable at all times of only the year and in all weather conditions? GHI-49 How long does it take in minutes to go from your home to the nearest _______ minutes healthcare facility, which could be a 262 257 HH SEC Questionnaire Form ID No Zone State EA HH Indiv hospital, a health clinic, a medical doctor, or a health post? GHI-50 Motorized Car/truck………………………………………. 01 Public bus……………………………………… 02 Motorcycle…………………………………….. How do you travel to this healthcare 03 Tricycle/Keke NAPEP……………………….. facility from your home? 04 Boat with motor……………………………….. 05 IF MORE THAN ONE WAY OF TRAVEL Not motorized IS MENTIONED, CIRCLE THE ONE Animal drawn cart…………………………….. HIGHEST ON THE LIST. Bicycle…………………………………………. 06 Boat without motor………………………….. 07 Walking………………………………………… 08 Others, (specify) _____________________ 09 98 GHI-51 How long does it take in minutes to go from your home to the nearest _______ minutes food market? GHI-52 Motorized Car/truck………………………………………. 01 Public bus……………………………………… 02 How do you travel to this food Motorcycle…………………………………….. 03 market from your home? Tricycle/Keke NAPEP……………………….. 04 Boat with motor……………………………….. IF MORE THAN ONE WAY OF 05 TRAVEL IS MENTIONED, CIRCLE THE ONE HIGHEST ON THE Not motorized LIST. Animal drawn cart…………………………….. Bicycle…………………………………………. 06 Boat without motor………………………….. 07 Walking………………………………………… 08 Others, (specify) _____________________ 09 98 GHI-53 Daily…………………………..………………. 1 2-5 days per week………….……………….. 2 How often is this food market open? 1 day per week……………….……………… 3 Others, (specify) _____________________ 8 263 258 HH SEC Questionnaire Form ID No Zone State EA HH Indiv HOUSEHOLD FOOD INSECURITY EXPERIENCE SCALE Q/N QUESTION RESPONSE CODE INSTRUCTION HFI-1 During the last 12 months, was there a time when you or others in your household worried Yes …………………….…….. 1 about not having enough food to eat because No ………………..……....….. 2 of a lack of money or other resources? Don’t Know............................ 9 HFI-2 Still thinking about the last 12 months, was there a time when you or others in your Yes …………………….…….. 1 household were unable to eat healthy and No ………………..……....….. 2 nutritious food because of a lack of money or Don’t Know............................ 9 other resources? HFI-3 Was there a time when you or others in your Yes …………………….…….. 1 household ate only a few kinds of food because No ………………..……....….. 2 of a lack of money or other resources? Don’t Know............................ 9 HFI-4 Was there a time when you or others in your household had to skip a meal because there Yes …………………….…….. 1 was not enough money or other resources to No ………………..……....….. 2 get food? Don’t Know............................ 9 HFI-5 Still thinking about the last 12 months, was there a time when you or others in your Yes …………………….…….. 1 household ate less than you thought you No ………………..……....….. 2 should because of a lack of money or other Don’t Know............................ 9 resources? HFI-6 Was there a time when your household ran out Yes …………………….…….. 1 of food because of a lack of money or other No ………………..……....….. 2 resources? Don’t Know............................ 9 HFI-7 Was there a time when you or others in your household were hungry but did not eat Yes …………………….…….. 1 because there was not enough money or other No ………………..……....….. 2 resources for food? Don’t Know............................ 9 HFI-8 Was there a time when you or others in your household went without eating for a whole day Yes …………………….…….. 1 because of a lack of money or other No ………………..……....….. 2 resources? Don’t Know............................ 9 264 259 HH SEC Questionnaire Form ID No Zone State EA HH Indiv HOUSEHOLD COPING STRATEGIES Q/N QUESTION RESPONSE CODE INSTRUCTION HCS-1 In the past seven days were there Yes ……………………………..….…….. 1 times when your household did not No ……………….................…..…....….. 2 have enough food or money to buy Don’t Know............................................ 9 food? HCS-2 Rely on less preferred and less expensive foods…………………………. 1 If Yes, how many days in the past seven days did your household use Borrow food, or rely on help from a friend the following coping strategies when or relative…………………………….….. you did not have enough food or 2 money to buy food? Limit portion size at mealtimes? 3 Number of days out of the past seven. Restrict consumption by adults in order [Write number. for small children to eat ………………... If not used, write ‘00’] 4 Reduce number of meals eaten in a day? 5 END of HOUSEHOLD/RESPONDENT QUESTIONNAIRE NB: 1. Response code in figures [e.g. 1, 2, 9, 01, 02 ... 99] ➔ only one response option is allowed 2. Response code in alphabets [e.g. A, B, C, D ..... X] ➔ multiple response options are allowed 3. Panel Headings are in BLUE Colour 4. Instructions to enumerators are in BLUE Fonts 5. Code for “Others, (specify)” a. = 8 if it is a single-digit code b. = 88 if it is a double-digit code 6. Code for “Don’t know” a. = 9 if it is a single-digit code b. = 99 if it is a double-digit code 265 260 Annex 10. Diet Questionnaire (for first visit) Annex 10. Diet Questionnaire (for first visit) Nigeria National Food Consumption and Micronutrient Survey (NFCMS) DIET INTAKE Questionnaire FOR WOMEN FIRST HOME VISIT – “Dietary Intake Survey Form” Preliminary Session I would like to start by asking you some questions to confirm that I am speaking to the intended person Q/N QUESTION RESPONSE CODE SKIP PATTERN RIC-8 a What is your name? Yes 1 If yes, go to RIC-2. Select ‘Yes’ if the name given is the same No 2 or similar to [NAME]. Select ‘No’ if the name given is different. i Is [NAME] available for an interview now? Yes 1 If yes, go to RIC-2. No 2 ii Is it possible to reschedule and interview Yes 1 If yes, go to iv. with [NAME] No 2 iii Why is it not possible to interview [NAME]? Text_____________ End interview iv Are you able to get a date for the Yes 1 rescheduled interview? No 2 v When would [NAME] be available for an __ __ __ __ __ __ __ __ interview? D D – M M - Y Y Y Y vi Select the time of the day Morning 1 Afternoon 2 Evening 3 RIC-9 How old are you? If the age is correct, go to Check if the reported age is close to the __________ RIC-3. age provided during the line-listing. [NAME] was reported to be [AGE] years old The age is different by more than 2 years, probe further to establish is this is the correct respondent. Age Verification RIC-10 Can I see an identification card such as Yes If yes, go to RIC-4 (National ID, Voter’s card, Driver’s No If no, go to RIC-5 License, Birth certificate, or International passport)? This is asked to confirm the date of birth 266 261 RIC-11 Record date of birth as documented __ __ __ __ __ __ __ __ Skip to question RIC-8 D D – M M - Y Y Y Y RIC-12 Do you know your FULL date of birth? Yes If no, go to b No a What is your date of birth? __ __ __ __ __ __ __ __ D D – M M - Y Y Y Y b Do you know the year you were born? Yes If no, go to RIC-7 No c What year you were born? _ _ _ _ d Do you know the month you were born? Yes No e What month you were born? January 1 February 2 March 3 April 4 May 5 June 6 July 7 August 8 September 9 October 10 November 11 December 12 RIC-13 Based on your date of birth, you are [AGE] Yes 1 If no, go back to RIC-5 years old. Is it correct? No 2 You mentioned earlier in this interview that you were [AGE] years old RIC-14 Can you recall an event that happened Enter short text. when you were born? Confirm previous visit RIC-15 Did anyone in your household answer Yes 1 Please can you arrange a questions about your household during a No 2 household visit as soon previous visit? as possible. Inform your supervisor. Consent If 18 years old or older Give the respondent the dietary survey information sheet and the consent form. Read out the information provided then ask the respondent if she has questions. Answer any questions asked. Ask the respondent or a witness to fill in and sign the consent form. Please confirm that you, or a witness, signed the informed consent form for the dietary survey. Yes Go to next question 267 262 No The current respondent does not agree to be interviewed, therefore the interview must be ended. If 15-17 years old, establish if emancipated Please note that [NAME] is[AGE] years old, therefore establish if she is emancipated. Do you live with your parents? Yes 1 If yes, not emancipated No 2 If no, ask if married Are you married? Yes 1 If yes, emancipated No 2 If no, as if HH head Are you the head of your household? Yes 1 If yes, emancipated No 2 If no, not emancipated If emancipated Give the respondent the dietary survey information sheet and the consent form. Read out the information provided then ask the respondent if she has questions. Answer any questions asked. Ask the respondent or a witness to fill in and sign the consent form. Please confirm that you, or a witness, signed the informed consent form for the dietary survey. Yes Go to next question No The current respondent does not agree to be interviewed, therefore the interview must be ended. If not emancipated: Give the guardian and girl the dietary survey information sheet. Give and the guardian the consent form and the girl the assent form. Read out the information provided then ask them if they have questions. Answer any questions asked. Ask the guardian or a witness to fill in and sign the consent form. Ask the girl or a witness to fill in and sign the assent form. Please confirm that you, or a witness, signed the assent form and a guardian, or a witness, signed the consent form for the dietary survey. Yes Go to next question No The current respondent does not agree to be interviewed, therefore the interview must be ended. May I begin the interview now? Yes 1 If yes, go to next section No 2 Why do you prefer not to continue the 1 interview? ___________________ 2 Is it possible to schedule an interview with Yes 1 If yes, schedule interview [NAME]? No 2 If no, end interview When would [NAME] be available for an __ __ __ __ __ __ __ __ interview? D D – M M - Y Y Y Y Select the time of the day Morning 1 Afternoon 2 Evening 3 268 263 Respondent Socio-Demographics Let me ask you a few general questions about you. Q/N QUESTION RESPONSE CODE INSTRUCTION RSD-1 What is your ethnic group? Hausa 1 Do NOT read the responses Yoruba 2 out loud Igbo 3 Ijaw 4 Kanuri 5 Fulani 6 Ibibio 7 Tiv 8 Etc 88 Etc Etc Other (Specify) RSD-2 What is your religion? Christian 1 Do NOT read the responses Muslim 2 out loud Traditional 3 No Religion 4 88 Other (Specify) 99 RSD-3 What is the highest level of None 1 school you completed? Primary 2 Junior secondary 3 Senior secondary 4 Technical / vocational certificate 5 Higher / university/ college 6 Other (Specify) 88 99 RSD-4 Have you done any paid or Yes 1 unpaid work outside your home No 2 in the last seven days? No response 77 Skip to next Don’t Know 99 Section RSD-5 In the last seven days, what Artisan (such as hair dressor, tailor, soap kind of work did you do? maker) Farmer Business/trader Civil servant Education/teacher Security personnel (such as police, army) Health worker Other (Specify) RSD-6 Was this paid or unpaid? Paid 1 Unpaid 2 Both 3 No response 77 Don’t Know 99 269 264 Pregnancy and Lactation We need to interview a few pregnant women so we will ask a few questions about pregnancy, this information will remain confidential. Q/N QUESTION RESPONSE CODE INSTRUCTION PAL-1 Are you pregnant? Yes 1 No 2 Skip to PLP5 No response 77 Don’t know 99 PAL-2 How many months pregnant _________ are you? No response 77 Fill in the number of months as Don’t know/Can’t remember 99 a value between 0 and 10. Or fill in 77 if no response. Or 99 if don’t know/cant remember. PAL-3 Do you know the expected Yes 1 delivery month? No 2 Skip to PLP5 No response 77 Don’t know PAL-4 What is the expected delivery January 1 month? February 2 March 3 April 4 May 5 June 6 July 7 August 8 September 9 October 10 November 11 December 12 PAL-5 Are you currently Yes 1 breastfeeding? No 2 Skip to next section No Response 9 PAL-6 If currently breastfeeding, did Yes 1 you breastfeed a child No 2 yesterday during the day or No Response 99 night? PAL-7 How old is the youngest child __________ years you are breastfeeding? Enter a value between 0 and 4. Indicate age in years and __________ months months. Enter 77 if no Enter a value beweten 0 and 11. response or 99 if don’t know. 270 265 Biofortified Food Consumption Now I would like to ask about how often you consumes specific foods Q/N QUESTION RESPONSE CODE INSTRUCTION BFW-1 In the last 30 days, did you eat Yes 1 yellow cassava or any food No 2 Skip to BFW-3 products made from it? Don’t Know 99 BFW-2 In the last 30 days, how many DAYS [ __ __ ] days did you eat yellow Don’t Know 99 cassava or any food products made from it? Fill in the number of days reported as value between 1 and 30, or fill in 99 if unknown BFW-3 In the last 30 days, did you eat Yes 1 orange-fleshed sweet potato No 2 Skip to BFW-5 or any food products made Don’t Know 99 from it? BFW-4 In the last 30 days, how many DAYS [ __ __ ] days did you eat orange- Don’t Know 99 fleshed sweet potato or any food products made from it? Fill in the number of days reported as value between 1 and 30, or fill in 99 if unknown BFW-5 In the last 30 days, did you eat Yes 1 orange maize or any food No 2 Skip to Next Section products made from it? Don’t Know 99 BFW-6 In the last 30 days, how many DAYS [ __ __ ] days did you eat orange maize Don’t Know 99 or any food products made from it? Fill in the number of days reported as value between 1 and 30, or fill in 99 if unknown 271 266 Fortification Coverage Now I’m going to ask you few questions about some food items (vegetable oil, wheat flour, semolina, sugar,salt and boullion). If you have any of these at home, could you please bring them out here so I can see them? Q/N QUESTION RESPONSE CODE INSTRUCTION FCW-1 Does your household use any of Vegetable oil Yes 1 If yes, go to the the following to prepare foods at No 2 relevant option home? Wheat flour Yes No Maize flour Yes No Semolina flour Yes No Sugar Yes No Salt Yes No Bouillon Yes No Q/N QUESTION RESPONSE CODE INSTRUCTION FCW-2 What is the main type of vegetable Groundnut oil 1 oil that your household uses? Oil blend 2 Select ONE response Palm olein/palm oil 3 Soybean oil 4 Sunflower oil 5 Other (Specify) 88 Don’t know/Can’t remember FCW-3 The last time your household got Purchased 1 vegetable oil, how did you get it? Home made 2 Skip to FCW-4 Select ONE response. Received from relative/friend/food aid 3 Other (Specify) ____________ 88 Don’t know/ Can’t remember 99 272 267 FCW-4 The last time your household got King's 100% vegetable oil 1 vegetable oil, what was the brand? Laziz - Pure vegetable oil 2 Select ONE response Power oil - Pure vegetable oil 3 Sunola - Soybean oil 4 Winner-100% pure soya oil 5 Golden Penny-pure soya oil 6 Bulk/open source with no brand name 7 Other (Specify) 88 Don’t know/ Can’t remember 99 What is the main type of wheat flour All-purpose flour 1 that your household uses on most Bread flour 2 days? Cake flour 3 Select ONE response Refined wheat flour 4 Self-rising flour 5 Whole wheat 6 Other (Specify) 88 Don’t know/Can’t remember FCW-5 The last time your household got Purchased 1 wheat flour, how did you get it? Home made 2 Skip to FCW-7 Select ONE response Received from relative/friend/food aid 3 Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-6 The last time your household got Golden Penny 1 wheat flour, what was the brand? Dangote 2 Select ONE response Bakewell 3 Bua flour 4 Honeywell 8 Eagle flour 9 Open bulk source with no brand name 88 Other (Specify) 99 Don’t know/ Can’t remember What is the main type of maize flour White maize flour 1 that your household uses? Yellow maize flour 2 Select ONE response Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-7 The last time your household got Purchased 1 maize flour, how did you get it? Home made 2 Skip to FCW-10 Select ONE response Received from relative/friend/food aid 3 Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-8 The last time your household got Not branded 1 maize flour, what was the brand? 273 268 Select ONE response Ultimate - Maize flour 2 Jifatu - Maize flour meal 3 Ammani Foods - Maize Flour 4 Munro - Corn Flour 5 Other (Specify) 6 Don’t know/ Can’t remember 88 99 What is the main type of semolina Wheat based 1 that your household uses? Wheat-Maize 2 Select ONE response Other (Specify) 3 Don’t know/Can’t remember 88 FCW-9 The last time your household got Purchased 1 semolina, how did you get it? Home made 2 Skip to FCW-14 Select ONE response Received from relative/friend/food aid 3 Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-10 The last time your household got Not branded 1 semolina, what was the brand? Golden Penny Semovita 2 Select ONE response Dangote semolina 3 Honeywell Semolina 4 Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-11 What is the main type of sugar that White granulated 1 your household uses? White cube 2 Select ONE response Brown granulated 3 Brown cube 4 Don’t know/ Can’t remember 88 Other (Specify) FCW-12 The last time your household got Purchased 1 sugar, how did you get it? Home made 2 Skip to FCW-17 Select ONE response Received from relative/friend/food aid 3 Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-13 The last time your household got Family - Refined granulated Sugar 1 sugar, what was the brand? Dangote - Refined Granulated White Sugar 2 Select ONE response Bua - Premium Refined Sugar 3 Golden Penny - Premium quality white 4 granulated sugar 5 Family - Sugar Cubes Open bulk source with no brand name 6 Other (Specify) 88 274 269 Don’t know/ Can’t remember 99 FCW-14 What is the main type of salt that Table salt-fine 1 your household uses? Sea salt-fine 2 Select ONE response Salt-low sodium 3 Sea salt-coarse 4 Edible/cooking salt-Coarse 88 Edible salt for industrial use Other (Specify) Don’t know/ Can’t remember FCW-15 The last time your household got Purchased 1 salt, how did you get it? Home made 2 Skip to Next Select ONE response Received from relative/friend/food aid 3 Section Other (Specify) 88 Don’t know/ Can’t remember 99 FCW-16 The last time your household got Dangote - Refined and iodized salt 1 salt, what was the brand? Royal Salt - Edible iodized Salt 2 Select ONE response Mr. Chef - Pure refined and iodized salt 3 Dangote - Fine edible salt iodized 4 Uncle Palm - Iodized salt 5 Open bulk source with no brand name 6 Other (Specify) 88 Don’t know/ Can’t remember 99 What is the main type of boullion Cube that your household uses? Granule Select ONE response Powder Liquid Other (Specify) Don’t know/ Can’t remember The last time your household got Purchased 1 bouillon, how did you get it? Home made 2 Select ONE response Received from relative/friend/food aid 3 Other (Specify) 88 Don’t know/ Can’t remember 99 The last time your household got Maggi boullion, what was the brand? Knorr Select ONE response Royco Onga Mr Cheff Ajinomoto Other (Specify) Don’t know/ Can’t remember 275 270 276 Nigeria National Food Consumption and Micronutrient Survey (NFCMS) Annexx 1 1.1Q. uQeusteiosntinoaniren afoir eC hfoildrr Cenhildren FIRST HOME VISIT – “Dietary Intake Survey Form” Respondent Identification Confirmation I would like to start by asking you some questions to confirm we are speaking about the correct child. Q/N QUESTION RESPONSE CODE INSTRUCTION RIC-1 What is the child’s name? Yes 1 Select ‘Yes’ if the name given is the same No 2 or similar to [CHILD NAME]. Select ‘No’ if the name given is different i Is [CHILD NAME]’s primary caregiver Yes 1 If yes, go to RIC-2. available for an interview now? No 2 ii Is it possible to reschedule and interview Yes 1 If yes, go to iv. with [CHILD NAME] No 2 iii Why is it not possible to interview [CHILD Text_____________ End interview NAME]’s caregiver? iv Are you able to get a date for the Yes 1 rescheduled interview? No 2 v When would [CHILD NAME] be available __ __ __ __ __ __ __ __ for an interview? D D – M M - Y Y Y Y vi Select the time of the day Morning 1 Afternoon 2 Evening 3 RIC-2 Is [CHILD NAME] a boy or girl? Boy 1 If Girl 2 [CHILD NAME] was recorded as being [female/male] during the line listing. If gender is different than what was previously recorded, probe further to establish if this is the correct respondent. RIC-3 How old is [CHILD NAME] __________ years Enter age in years and months. Enter a value between 0 and 4. __________ months Enter a value beweten 0 and 11. Check if the reported age is close to the age provided during the line-listing. [CHILD NAME] was reported to be [AGE] months old. The calculated age is [AGE] months old. 277 271 The age is different by more than 2 months, probe further to establish is this is the correct respondent. Age Verification RIC-4 Do you have a vaccination card or a birth Yes If not available, go to RIC- certifiate for [CHILD NAME]? No 9 This is asked to confirm the date of birth. Ask to see document. RIC-5 Record date of birth as documented __ __ __ __ __ __ __ __ Go to next section D D – M M - Y Y Y Y RIC-6 Do you have a vaccination card for Yes If not available, go to RIC- [CHILD NAME]? No 9 This is asked to confirm the date of birth. Ask to see document. RIC-7 x May I see where [CHILD NAME] Yes vaccinations are written down? No RIC-8 Record date of birth as documented __ __ __ __ __ __ __ __ Go to next section D D – M M - Y Y Y Y RIC-9 Do you see any records of Vitamin A Yes Only if they have administration? No vaccination card RIC-10 Record date of last vitamin A dose given __ __ __ __ __ __ __ __ Only if they have D D – M M - Y Y Y Y vaccination card None recorded RIC-11 Do you have a birth certificate for [CHILD Yes If not available, go to RIC- NAME]? No 11 This is asked to confirm the date of birth RIC-12 Record date of birth as documented __ __ __ __ __ __ __ __ D D – M M - Y Y Y Y RIC-16 Do you know the FULL date of birth of Yes If no, go to b [CHILD NAME] ? No a What is [CHILD NAME]’s date of birth? __ __ __ __ __ __ __ __ Go to next session D D – M M - Y Y Y Y b Do you know the year [CHILD NAME] was Yes If no, go to RIC-7 born? No c What year you was [CHILD NAME] born? _ _ _ _ d Do you know the month [CHILD NAME] Yes were born? No e What month was [CHILD NAME] born? January 1 February 2 March 3 278 272 April 4 May 5 June 6 July 7 August 8 September 9 October 10 November 11 December 12 RIC-17 Based on your date of birth, you [CHILD Yes 1 If no, go back to RIC-5 NAME] is [AGE] years old. Is it correct? No 2 You mentioned earlier in this interview that [CHILD NAME] was [YEARS] years and [MONTHS] months old RIC-13 Can you recall an event that happened when [CHILD NAME] was born? Enter short text. RIC-14 Did anyone in [CHILD NAME]’s household answers questions about his/her Yes 1 household during a previous visit? No 2 Informed Consent Give the guardian the dietary survey information sheet. Read out the information provided then ask them if they have questions. Answer any questions asked. Ask the guardian or a witness to fill in and sign the consent form. Please confirm that you, or a witness, signed the informed consent form for the dietary survey. Yes Go to next question No The current respondent does not agree to be interviewed, therefore the interview must be ended. May I begin the interview now? Yes 1 If yes, go to next section No 2 Why do you prefer not to continue the 1 interview? ___________________ 2 Is it possible to schedule an interview with Yes 1 If yes, schedule interview [CHILD NAME]’s primary caregiver? No 2 If no, end interview When would [CHILD NAME]’s primary __ __ __ __ __ __ __ __ caregiver be available for an interview? D D – M M - Y Y Y Y Select the time of the day Morning 1 Afternoon 2 Evening 3 279 273 Child Caregiver Characteristics Now I would like to ask a few questions about you because you are a caregiver, not the child. Q/N QUESTION RESPONSE CO INSTRUCTION DE CCC-1 Are you the person mostly Yes 1 responsible for feeding [CHILD No 2 NAME]? CCC-2 Were you with [CHILD NAME] most Yes 1 If yes, skip to CCC-4 of the day yesterday? No 2 CCC-3 Is there another person available Yes 1 now who can help tell us what No 2 [CHILD NAME] ate yesterday? CCC-4 What is your relationship to [NAME Mother 1 OF CHILD]? Father 2 Other family member 3 Other (Specify) 8 CCC-5 Note the sex of the respondent Male 1 Female 2 CCC-6 How old are you? If the age is <16 y, Enter age in years, enter 99 if age show message “You is unknown. need to get someone 99 who is 16 years or older to proceed”. RSD-7 What is your name? _______________________ RSD-8 What is your ethnic group? Hausa 1 Yoruba 2 Do NOT read the responses out Igbo 3 loud Ijaw 4 Kanuri 5 Fulani 6 Ibibio 7 Tiv 8 Etc. 88 Other (Specify) RSD-9 What is your religion? Christian 1 Do NOT read the responses out Muslim 2 loud Traditional 3 No Religion 4 Other (Specify) 88 No response 99 RSD-10 What is the highest level of school None 1 you completed? Primary 2 Select ONE response Junior Secondary 3 Senior Secondary 4 280 274 Technical / vocational certificate 5 Higher / university/ college 88 Other (Specify) 99 Infant and Young Child Feeding Now I would like to ask you a few questions about breastfeeding and bottle feeding of [CHILD NAME]. Q/N QUESTION RESPONSE CODE INSTRUCTION IYC-1 Has [CHILD NAME] ever been Yes 1 breastfed? No 2 Skip to IYC3 Don’t Know 99 IYC-2 Was [CHILD NAME] breastfed Yes 1 yesterday during the day or at No 2 night? 99 IYC-3 Did [CHILD NAME] drink Yes 1 Skip to next section anything from a bottle with a No 2 nipple yesterday during the day Don’t Know 99 or night? IYC-4 What was fed to [CHILD Breast Milk 0,1 NAME] from a bottle with a Formula milk/other milks 0,1 nipple yesterday during the day Water with sugar 0,1 or night? Juice (Herbal/fruits) 0,1 Select MORE THAN ONE Pap 0,1 response if relevant Other (Specify) 0,1 Other text text Biofortified Food Consumption Now I would like to ask you a few questions about food that [CHILD NAME] may eat. Q/N QUESTION RESPONSE CODE INSTRUCTION BFC-1 In the last 30 days, did [CHILD NAME] eat Yes 1 yellow cassava or any food products No 2 Skip to BFC-3 made from it? Don’t Know 8 BFC-2 In the last 30 days, on how many days did DAYS [ __ __ ] [CHILD NAME] eat yellow cassava or any Don’t Know 99 food products made from it? BFC-3 In the last 30 days, did [CHILD NAME] eat Yes 1 orange-fleshed sweet potato or any No 2 Skip to BFC-5 food products made from it? Don’t Know 99 BFC-4 In the last 30 days, on how many days did DAYS [ __ __ ] [CHILD NAME] eat orange-fleshed sweet Don’t Know 99 potato or any food products made from it? BFC-5 In the last 30 days, did [CHILD NAME] eat Yes 1 orange maize or any food products No 2 Skip to Next Section made from it? Don’t Know 8 BFC-6 In the last 30 days, on how many days did DAYS [ __ __ ] [CHILD NAME] eat orange maize or any Don’t Know food products made from it? 99 281 275 Fortification Coverage Now I’m going to ask you few questions about some foods (vegetable oil, wheat flour, semolina flour, sugar salt and bullion). If there are any of these foods in [CHILD NAME]’s household, could you please bring them out here so I can see them? Q/N QUESTION RESPONSE CODE INSTRUCTION FCW-17 Does [CHILD NAME]’s household Vegetable oil Yes 1 If yes, go to the use any of the following to prepare No 2 relevant option foods at home? Wheat flour Yes No Maize flour Yes No Semolina flour Yes No Sugar Yes No Salt Yes No Bouillon Yes No Vegetable oil FCW-18 What is the main type of vegetable Groundnut oil oil that [CHILD NAME]’s Oil blend household uses? Palm olein/palm oil Select ONE response Soybean oil Sunflower oil Other (Specify) Don’t know/Can’t remember FCW-19 The last time the [CHILD NAME] Purchased household got vegetable oil, how Home made Skip to FCW-4 did they get it? Received from relative/friend/food aid Select ONE response Other (Specify) Don’t know/Can’t remember FCW-20 The last time the household of King's 100% vegetable oil [CHILD NAME] got vegetable oil, Laziz - Pure vegetable oil what was the brand? Power oil - Pure vegetable oil Select only one answer Sunola - Soybean oil Winner-100% pure soya oil Golden Penny-pure soya oil Bulk/open source with no brand name 282 276 Other (Specify) Don’t know/ Can’t remember FCW-21 What is the main type of wheat flour that [CHILD NAME]’s household uses? Select only one answer FCW-22 The last time the [CHILD NAME] Purchased household got wheat flour, how did Home made Skip to FCW-7 they get it? Received from relative/friend/food aid Select only one answer Other (Specify) Don’t know/ Can’t remember FCW-23 The last time the household of [CHILD NAME] got wheat flour, what was the brand? Select only one answer maize flour FCW-24 What is the main type of maize flour that [CHILD NAME]’s household uses? Select only one answer FCW-25 The last time the [CHILD NAME] Purchased household got maize flour, how did Home made Skip to FCW-10 they get it? Received from relative/friend/food aid Select only one answer Other (Specify) Don’t know/ Can’t remember FCW-26 The last time the household of [CHILD NAME] got maize flour, what was the brand? Select only one answer semolina 283 277 FCW-27 What is the main type of semolina flour that [CHILD NAME]’s household uses on most days? Select only one answer FCW-28 The last time the [CHILD NAME] Purchased household got semolina flour, how Home made Skip to FCW-14 did they get it? Received from relative/friend/food aid Select only one answer Other (Specify) Don’t know/ Can’t remember FCW-29 The last time the household of [CHILD NAME] got semolina flour, what was the brand? Select only one answer sugar FCW-30 What is the main type of sugar that [CHILD NAME]’s household uses on most days? Select only one answer FCW-31 The last time the [CHILD NAME] Purchased household got sugar, how did they Home made Skip to FCW-17 get it? Received from relative/friend/food aid Select only one answer Other (Specify) Don’t know/ Can’t remember FCW-32 The last time the household of [CHILD NAME] got sugar, what was the brand? Select only one answer salt FCW-33 What is the main type of salt that [CHILD NAME]’s household uses on most days? Select only one answer FCW-34 The last time the [CHILD NAME] Purchased household got salt, how did they Home made Skip to Next get it? Received from relative/friend/food aid Section Select only one answer Other (Specify) Don’t know/ Can’t remember 284 278 FCW-35 The last time the household of [CHILD NAME] got salt, what was the brand? Select only one answer bullion What is the main type of bullion that [CHILD NAME]’s household uses? Select ONE response The last time [CHILD NAME]’s Purchased household got bullion, how did they Home made get it? Received from relative/friend/food aid Other (Specify) Don’t know/ Can’t remember The last time the household of [CHILD NAME) got bullion, what was the brand? Select ONE response 285 279 AAnnnnexe 1x2 1. B2i.o Bmiaorkmera Qrkueesrt iQonuneasirtei o(Qn)n54 aire (Q)54 Q1. Children (6-59 months old) RESPONDENT IDENTIFICATION CONFIRMATION For infants and young children RIC-18 Confirm respondent. What is the child's name? Yes ……………………….……. 1 [NAME LINKED TO LINE LISTING] No ………………….………….. 2 ---→ Update/correct in the Roster RIC-19 Confirm respondent. Is (CHILD NAME) a boy or girl? Yes ……………………….……. 1 [GENDER LINKED TO LINE LISTING] No ………………….………….. 2 ---→ Update/correct in the Roster RIC-20 Confirm respondent How old is (CHILD NAME) Yes ……………………….……. 1 [AGE LINKED TO LINE LISTING] No ………………….………….. 2 ---→ Update/correct in the Roster RIC-21 Confirm completion of household questionnaire: Yes ……………………….……. 1 Did anyone in your household answers No ………………….………….. 2 ---→ questions about your household during a previous visit? RIC-22 Confirm completion of household Myself...……………..…… 1 questionnaire: If yes, was that you or someone else? Someone else………..…… 2 RIC-23 Confirm consent is signed Yes ……………………….……. 1 [SIGN PHYSICAL CONSENT FORM] No ………………….………….. 2 ---→ Ensure respondent signs Request for Assent to continue Confirm assent RIC-24 Line number of the respondent in the HH Roster 54 Paper version before digitization on CommCare App 286 280 QUESTIONS RELATED INTERVENTION COVERAGE & HEALTH STATUS (CHILDREN 6-59 MONTHS OLD) (If grp = 4) chs1. Do you have a card or other document where 01= Yes (NAME)'s vaccinations are written down? 00= No chs2. May I see the card or other document where 01= Yes if chs1 = 1 (NAME)'s vaccinations are written down? 00= No chs3. Document down most recent date of vitamin A given Day/month/year chs4. In the last six months, has a health worker or 01= Yes community volunteer spoken with you about how to 00= No feed [NAME CHILD]? 98= Don't Know chs5. If yes, the health worker or community volunteer No= Yes= Dk= Ask if chs4 = 1 speak with about any of these topics? 0 1 98 (READ EACH ITEM AND RECORD RESPONSE) Breastfeeding When to start feeding foods other than breastmilk (e.g., after 6 months) Giving a variety of types of foods Giving animal source foods specifically (eggs, milk, meats or fish) How often to feed the child Not feeding sugary drinks (e.g., fizzy drinks) chs6. Within the last six months, was (NAME) given a 01= Yes vitamin A dose like (this/any of these)? 00= No SHOW COMMON CAPSULES 98= Don't Know chs7. Source of verification Mother's recall 01 Ask if chs6 = 1 Health card 02 Vaccination card 03 Other (Specify)____98 chs8. In the last six months, did you receive a supply of 01= Yes sprinkles with iron or any micronutrient powder like 00= No (SHOW IMAGE WITH PACKAGING) to give to 98= Don’t Know [NAME]? chs9. Was (NAME) given any drug for intestinal worms in 01= Yes the last six months? 00= No 98= Don't Know chs10. In the la st 7 days, has (NAME) eaten earth, clay, mud, 01= Yes or soil from any source (e.g., walls of mud houses, 00= No the yard, purchased at the market)? 98= Don’t Know chs11. Has (N AME) had diarrhea in the last two weeks? 01= Yes 00= No 98= Don’t Know chs12. Was th ere any blood in the stools? 01= Yes 00= No 98= Don’t Know chs13. Did (NA ME) have diarrhea yesterday? 01= Yes 00= No 98= Don’t Know chs14. Was he /she given any of the following to drink at any 01= Yes Ask if chs13 = time since he/she started having the diarrhea: A fluid 00= No 1 98= Don’t Know 287 281 made from a special packet called [LOCAL NAME FOR ORS PACKET]? A pre-packaged ORS liquid? A government-recommended homemade fluid? (Show image) chs15. What ( else) was given to treat the diarrhea? Pill or Syrup Antibiotic ……………………………….1 Ask if chs13 = Antimotility……………………………………………….. 1 2 Zinc………………………………………………………….3 Other (Not antibiotic, antimotility, or zinc)...4 Unknown Pill or Syrup…………………..5 Injection Antibiotic ……………………………6 Non-Antibiotic……………………………………..7 Unknown Injection ………………………………8 Intravenous…………………………………………...9 Home Remedy/Herbal Medicine ……..10 Others Specify …………………………………….98 chs16. Has (N AME) been ill with a fever at any time in the 01= Yes last 2 weeks? 00= No 98= Don't Know chs17. Has (N AME) had an illness with a cough at any time 01= Yes in the last 2 weeks? 00= No 98= Don't Know chs18. Has (N AME) had fast, short, rapid breaths or 01= Yes difficulty breathing at any time in the last 2 weeks? 00= No 98= Don't Know chs19. Was th e fast or difficult breathing due to a problem 01= Yes Ask if chs18= 1 in the chest or to a blocked or runny nose? 00= No 98= Don't Know chs20. In the l ast 12 months, was (NAME) given any ready- 01= Yes to-use therapeutic feeds/plumpy'nut like (SHOW 00= No COMMON PACKAGING) because the child was 98= Don’t Know malnourished? chs21. Did [CH ILD] consume it yesterday? 1= Yes Ask if chs20 = 0= No 1 98 = Don’t Know 288 282 Anthropometry Questionnaire Question Number Questions Options Skip name_respondent Please confirm that CHILD is [ ] years old 01= This respondent age and and is [ ] Gender. gender is the same 02= The respondent age and gender is different 03= ONLY the respondent's AGE is different 04= ONLY the respondent's GENDER is different month_label_notification Confirm correct age in month 01= Yes 02= No new_age Enter respondent's age If name_respondent !=1 new_age Enter respondent's age in month If name_respondent !=1 preg_notification Please note that the respondent was 01= Yes reported to be pregnant during the listing. 02= No Is she currently pregnant? confirm_stand Please kindly confirm that respondent is 01= The CHILD can stand If respondent is able to stand during the measurement 02= The CHILD is disabled and is <=24months during height measurement. unable to stand 03= The CHILD is ill and therefore cannot stand. Enter the Accurate weight #1 of the fw1 respondent (in kg). Enter the Accurate weight #1 of cg1 CAREGIVER ONLY (in kg). Enter the Accurate weight #1 of the cgcu51 CAREGIVER and CHILD (in kg). Enter the Accurate weight #1 of CHILD cu51 Please confirm that you are able to remove 01= There is no problem with or push aside any barrettes, braids, or barrettes, braids, or hairstyles. hairstyles that might interfere with the 02= I am able to remove or adjust measurement of respondent_name barrettes, braids, or hairstyles 03= I am NOT able to remove or height_note adjust barrettes, braids, or hairstyles height_note 01= Yes Is respondent overdressed 02= No Enter the accurate height/length 1 of the h1 respondent in cm fw2 Enter the accurate weight #2 of respondent Enter the accurate weight #2 of the cg2 CAREGIVER only Enter the accurate weight #2 of the cgcu52 CAREGIVER and CHILD cu52 Enter the accurate weight #2 of CHILD Enter the accurate height/length 2 of the h2 respondent in cm fw3 Enter the accurate weight #3 of respondent Enter the accurate weight #3 of the cg3 CAREGIVER only Enter the accurate weight #3 of the cgcu53 CAREGIVER and CHILD cu53 Enter the accurate weight #3 of CHILD 289 283 Enter the accurate height/length 3 of the h3 respondent in cm 01= I can confirm that my height Scale ID is still the same. 02= I have another Height equipment height_scale_id_confirm Confirm height Scale ID If weight_scale_id_con new_scale_height_id New height Scale ID firm = 2 01= I can confirm that my weight Scale ID is still the same. 02= I have another weight equipment weight_scale_id_confirm Confirm weight Scale ID If weight_scale_id_con new_scale_weight_id New weight Scale ID firm = 2 290 284 AnneAxn 1n3e.x A13d. oAldeoslecsecnent tg giirrllss ((1100-1-41 4ye yaresaorlsd )o alndd) WanRAd (W15R-4A9 y(e1a5rs-4o9ld y) ears old) RESPONDENT IDENTIFICATION CONFIRMATION Q/N QUESTION RESPONSE CODE INSTRUCTION RIC-25 Confirm respondent Yes ……………………….……. 1 What is your name? Update/correct in No ………………….………….. 2 ---→ the Roster [NAME LINKED TO LINE LISTING?] RIC-26 Confirm respondent Yes ……………………….……. 1 Update/correct in [GENDER LINKED TO LINE LISTING?] No ………………….………….. 2 ---→ the Roster RIC-27 Confirm respondent Yes ……………………….……. 1 How old are you? Update/correct in No ………………….………….. 2 ---→ the Roster [AGE LINKED TO LINE LISTING?] RIC-28 Confirm completion of household questionnaire: Yes ……………………….……. 1 Did anyone in your household answers No ………………….………….. 2 ---→ Identify initial questions about your household during a respondent previous visit? RIC-29 Confirm completion of household Myself...……………..……… 1 questionnaire: If yes, was that you or someone else? Someone else………..…… 2 RIC-30 Confirm respondent Yes ……………………….……. 1 [SIGN CONSENT FORM?] No ………………….………….. 2 ---→ Ensure respondent SIGN ASSENT FORM/ GIVE ASSENT sign to continue RIC-31 Line number of the respondent in the HH Roster 291 285 ANAEMIA RISK (WRA AND ADOLESCENT GIRL) Now I would like to ask you some questions about your health. We will first ask about the last six months. wrf1. Have you been diagnosed with anaemia in 01= Yes the past six months? 00= No 98= Don't Know wrf2. Did you take any drugs for intestinal 01= Yes worms in the past six months? 00= No 98 = Don't Know Now I would like to ask you about your health in the last 2 weeks. wah1. Ha ve you been ill with diarrhoea in the 01= Yes past 2 weeks? 00= No DEFINED AS THREE OR MORE LOOSE OR 98 = Don't Know WATERY STOOLS IN A 24-HOUR PERIOD wah2. Ha ve you been ill with a cough or 01= Yes breathing problems in the past 2 weeks? 00= No 98 = Don't Know wah3. Wh en you had an illness with a cough, did 01= Yes you breathe faster than usual with short, 00= No rapid breaths or have difficulty breathing? 98 = Don’t Know wah4. Wa s the fast or difficult breathing due to Chest only …..............................................01 a problem in the chest or to a blocked or Blocked or runny nose only. …….............02 runny nose? Both ………....................................………..03 Other (specify)__________________77 Don't know ………………..……….98 wah5. Ha ve you been ill with a fever in the past 01= Yes two weeks? 00= No 98 = Don't Know wah6. Ha ve you been ill with malaria in the past 01= Yes two weeks? 00= No 98 = Don't Know wah7. Ha ve you had any hospitalization and /or 01= Yes clinic visits due to illness in the last two 00= No weeks? 98 = Don't Know wah8. Do you smoke? (do not include the 00= No powder and chew type) 01= Yes Now we would like to ask you some questions about other topics wtt1. In the last seven days, have you eaten 00= No earth, clay, mud or soil from any source 01= Yes (e.g, walls of mud houses, the yard, purchased at the market)? wtt2. Du ring the last six months, did you take 01= Yes any multivitamin tablets for yourself? 00= No (SHOW TABLETS) 98 = Don't Know ASK TO SEE THE TABLETS wtt3. Ho w many days did you take any of these Number of days……… products in the last week (7 days) (IF NONE, ENTER 00) (IF DON'T KNOW, ENTER 98) wtt4. Du ring the last six months, did you take 01= Yes any iron tablets, iron-folic acid tablets for 00= No yourself? 98 = Don't Know (SHOW TABLETS) ASK TO SEE THE TABLETS wtt5. Ho w many days did you take any iron Number of days……… tablets, iron-folic acid tablets in the last (IF NONE, ENTER 00) week (7 days) (IF DON'T KNOW, ENTER 98) 292 286 AAnnnneex x1 41. 4P.r ePgrneagnnt awnotm weno m(15e-n49 ( 1ye5a-r4s9o ylde)ars old) RESPONDENT IDENTIFICATION CONFIRMATION Q/N QUESTION RESPONSE CODE INSTRUCTION RIC-32 Confirm respondent Yes ……………………….……. 1 What is your name? Update/correct in No ………………….………….. 2 ---→ the Roster [NAME LINKED TO LINE LISTING?] RIC-33 Confirm respondent Yes ……………………….……. 1 Update/correct in [GENDER LINKED TO LINE LISTING?] No ………………….………….. 2 ---→ the Roster RIC-34 Confirm respondent Yes ……………………….……. 1 How old are you? Update/correct in No ………………….………….. 2 ---→ the Roster [AGE LINKED TO LINE LISTING?] RIC-35 Confirm completion of household questionnaire: Yes ……………………….……. 1 Did anyone in your household answers No ………………….………….. 2 ---→ Identify initial questions about your household during a respondent previous visit? RIC-36 Confirm completion of household Myself...……………..……… 1 questionnaire: If yes, was that you or someone else? Someone else………..…… 2 RIC-37 Confirm respondent Yes ……………………….……. 1 [SIGN CONSENT FORM?] No ………………….………….. 2 ---→ Ensure respondent sign to continue RIC-38 Line number of the respondent in the HH Roster 293 287 ANAE MIA RISK Now I would like to ask you about your health in the last two weeks. wah9. Ha ve you been ill with diarrhoea in the past two weeks? 0 1 = Y e s DEFINED AS THREE OR MORE LOOSE OR WATERY 00= No STOOLS IN A 24-HOUR PERIOD 98 = Don't Know wah10. Ha ve you been ill with a cough or breathing problems in 01= Yes the past two weeks? 00= No 98 = Don't Know wah11. Wh en you had an illness with a cough, did you breathe 01= Yes faster than usual with short, rapid breaths or have 00= No difficulty breathing? 98 = Don’t Know wah12. Wa s the fast or difficult breathing due to a problem in Chest only ….......................01 the chest or to a blocked or runny nose? Blocked or runny nose only..........02 Both ………......................………..03 Other (specify)_______________77 Don't know ………………..……….98 wah13. Ha ve you been ill with a fever in the past two weeks? 01= Yes 00= No 98 = Don't Know wah14. Ha ve you been ill with malaria in the past two weeks? 01= Yes 00= No 98 = Don't Know wah15. Ha ve you had any hospitalization and /or clinic visits due 01= Yes to illness in the last two weeks? 00= No 98 = Don't Know wah16. Do you smoke? (do not include the powder and chew 00= No type) 01= Yes INTERVENTION COVERAGE FOR PREGNANT WOMEN wpw1. Have you seen any health worker for antenatal care 01 = Yes during this pregnancy so far? 00 = No wpw2. How many months pregnant were you when you [ ____ ] Months first received antenatal care for this pregnancy? Don’t know wpw3. How many times have you received antenatal care [ ____ ] times so far? Don’t know wpw4. During this pregnancy, have you received or 1 = Yes purchased any tablets, syrups, or tonics containing 0 = No iron? 98 Don't Know SHOW COMMON VARIETIES – IFA wpw5. Did you receive for free or purchase these tablets or 1 = Receive for free syrup? 2 = Purchase Don't know wpw6. How many iron-folic acid IFA tablets did you receive [ ___ ] Tablets [Enter 0-180] during your pregnancy Don't know wpw7. How many days in the last 7 days (one week) did you [ ___ ] Days [Enter 0-7] consume a tablet or syrup containing iron? Don’t know wpw8. Did you consume a tablet or syrup containing iron 1 = Yes and/folic acid yesterday? 0 = No 98= Don't know wpw9. So far, during this pregnancy, has a health worker or 1 = Yes community volunteer spoken with you about what 0 = No foods to eat during pregnancy? 98= Don’t know wpw10. So far, during this pregnancy, has a health worker or 1 = Yes community volunteer spoken with you about 0 = No breastfeeding your newborn? 98 = Don’t know 294 288