Incorporating scale dependence in disease burden estimates: The case of human African trypanosomiasis in Uganda
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Hackett, F., Berrang Ford, L., Fèvre, E.M. and Simarro, P. 2014. Incorporating scale dependence in disease burden estimates: The case of human African trypanosomiasis in Uganda. PLoS Neglected Tropical Diseases 8(2): e2704.
Permanent link to cite or share this item: https://hdl.handle.net/10568/56869
Background The WHO has established the disability-adjusted life year (DALY) as a metric for measuring the burden of human disease and injury globally. However, most DALY estimates have been calculated as national totals. We mapped spatial variation in the burden of human African trypanosomiasis (HAT) in Uganda for the years 2000–2009. This represents the first geographically delimited estimation of HAT disease burden at the sub-country scale. Methodology/Principal Findings Disability-adjusted life-year (DALY) totals for HAT were estimated based on modelled age and mortality distributions, mapped using Geographic Information Systems (GIS) software, and summarised by parish and district. While the national total burden of HAT is low relative to other conditions, high-impact districts in Uganda had DALY rates comparable to the national burden rates for major infectious diseases. The calculated average national DALY rate for 2000–2009 was 486.3 DALYs/100 000 persons/year, whereas three districts afflicted by rhodesiense HAT in southeastern Uganda had burden rates above 5000 DALYs/100 000 persons/year, comparable to national GBD 2004 average burden rates for malaria and HIV/AIDS. Conclusions/Significance These results provide updated and improved estimates of HAT burden across Uganda, taking into account sensitivity to under-reporting. Our results highlight the critical importance of spatial scale in disease burden analyses. National aggregations of disease burden have resulted in an implied bias against highly focal diseases for which geographically targeted interventions may be feasible and cost-effective. This has significant implications for the use of DALY estimates to prioritize disease interventions and inform cost-benefit analyses.