Using system effects modelling to evaluate food safety impact and barriers in low-income-countries: An example from urban Cambodia
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Roesel, K., Craven, L., Ty, C., Hung Nguyen-Viet and Grace, D. 2018. Using system effects modelling to evaluate food safety impact and barriers in low-income-countries: an example from urban Cambodia. Poster prepared for the 15th International Symposium of Veterinary Epidemiology and Economics, Chiang Mai, Thailand, 15 November 2018. Nairobi, Kenya: ILRI.
Permanent link to cite or share this item: https://hdl.handle.net/10568/98397
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Objective(s): The study tested the applicability to low-and-middle income settings of a System Effects model developed for high-income countries. The objective is to better understand the damage caused by foodborne diseases, and barriers for consumers in accessing safer food. Materials and methods: In January 2018, ten group sessions with 66 participants were held in Phnom Penh, Cambodia. Five were organized in low and five in middle income areas of the city. The participants, half of them women, were purposively recruited, of similar background but not knowing each other. Each group discussion consisted of two exercises that was completed by each participant individually. The first exercise mapped impacts to visually depict the complexity of peoples’ experience of unsafe food including damage caused, flows of effects, and interconnections between them. In the second exercise, barriers to avoiding unsafe food were illustrated. Circumstances, incidents, pre-existing conditions that make it harder to get safe food were described. Results: More than 600 consequence items of eating unsafe food were listed by all participants, with little variation between low and middle income groups as well as between men and women. While most concerned health and economic impact, women in the middle income group listed several social consequences. More than 250 items described barriers to accessing safe food, most dealing with lack of money, lack of accessibility as well as limitations to tell safe from unsafe food. The items were coded and grouped, adjacency matrices generated, impacts and barriers aggregated and the density of connections made between different impacts and barriers evaluated. Conclusions: The findings can help to understand impact and components of resilience that could help inform food safety intervention design.
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