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Potential impacts of policies to reduce purchasing of ultra-processed foods in Mexico at different stages of the social transition: an agent-based modelling approach
- Publication Year :
- 2022
- Publisher :
- UK : Cambridge University Press, 2022.
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Abstract
- Objectives:To develop a simulation framework for assessing how combinations of taxes, nutrition warning labels and advertising levels could affect purchasing of ultra-processed foods (UPF) in Latin American countries and to understand whether policies reinforce or reduce pre-existing social disparities in UPF consumption.Design:We developed an agent-based simulation model using international evidence regarding the effect of price, nutrition warning labels and advertising on UPF purchasing.Setting:We estimated policy effects in scenarios representing two stages of the ‘social transition’ in UPF purchasing: (1) a pre-transition scenario, where UPF purchasing is higher among high-income households, similar to patterns in Mexico; and (2) a post-transition scenario where UPF purchasing is highest among low-income households, similar to patterns in Chile.Participants:A population of 1000 individual agents with levels of age, income, educational attainment and UPF purchasing similar to adult women in Mexico.Results:A 20 % tax would decrease purchasing by 24 % relative to baseline in both the pre- and post-transition scenarios, an effect that is similar in magnitude to that of a nutrition warning label policy. A 50 % advertising increase or decrease had a comparatively small effect. Nutrition warning labels were most effective among those with higher levels of educational attainment. Labelling reduced inequities in the pre-transition scenario (i.e. highest UPF purchasing among the highest socio-economic group) but widened inequities in the post-transition scenario.Conclusions:Effective policy levers are available to reduce UPF purchasing, but policymakers should anticipate that equity impacts will differ depending on existing social patterns in UPF purchasing.
Details
- Language :
- English
- ISSN :
- 13689800
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....29468b1b038ab7fb7e599fecb1b35e06