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. 2021 Mar:28:100474.
doi: 10.1016/j.gfs.2020.100474.

Informal food environment is associated with household vegetable purchase patterns and dietary intake in the DECIDE study: Empirical evidence from food vendor mapping in peri-urban Dar es Salaam, Tanzania

Ramya Ambikapathi  1 , Gerald Shively  2 , Germana Leyna  3 , Dominic Mosha  4 , Ally Mangara  5 , Crystal L Patil  6 , Morgan Boncyk  1 , Savannah L Froese  1   7 , Cristiana K Verissimo  7 , Patrick Kazonda  5 , Mary Mwanyika-Sando  4 , Japhet Killewo  5 , Nilupa S Gunaratna  1
Affiliations

Informal food environment is associated with household vegetable purchase patterns and dietary intake in the DECIDE study: Empirical evidence from food vendor mapping in peri-urban Dar es Salaam, Tanzania

Ramya Ambikapathi et al. Glob Food Sec. 2021 Mar.

Abstract

We study the relationship between the food environment (FE) and the food purchase patterns, dietary intakes, and nutritional status of individuals in peri-urban Tanzania. In Africa, the prevailing high density of informal vendors creates challenges to characterizing the FE. We present a protocol and tool developed as part of the Diet, Environment, and Choices of positive living (DECIDE) study to measure characteristics of the FE. We mapped 6627 food vendors in a peri-urban settlement of Dar es Salaam, of which over 60% were semi-formal and informal (mobile) vendors. We compute and compare four FE metrics inspired by landscape ecology-density, dispersion, diversity, and dominance-to better understand how the informal food environment relates to food purchase patterns, diets, and nutritional status among households with persons living with human immunodeficiency virus (PLHIV).

Keywords: Adults; Diets; Food environment; Food purchase patterns; Informal economy; PLHIV; Tanzania.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Illustration of food vendors grouped by formal, semi-formal, and informal categories in peri-urban Dar es Salaam, Tanzania. Criteria for grouping and type of information collected by vendor categories is summarized in table below. Illustration adapted from “Vendors Galore and More” (Boustedt and Mair, 2013).
Fig. 2
Fig. 2
Map of food vendor types in the DECIDE study area.
Fig. 3
Fig. 3
Food groups sold by type of establishment and gender of the food vendor n the DECIDE study area1. 1Gender of the food vendors had 11% data missing, with the majority missing were from semi-formal vendors 74% (n = 547 food vendors), followed by formal vendors with 15% (n = 110 food vendors), 11% (n = 86) missing informal vendors. Important to note that we recorded the gender of the main food vendor, who could have been the owner or the employee.
Fig. 4
Fig. 4
Household food sourcing survey: purchase frequency of vegetables in the last 7 days by formal, informal, and semi-formal food vendors.
Fig. 5
Fig. 5
Results from multivariate regression models on 6 key outcomes: panels shows odds ratio of vegetable purchase in the last 7 days, linear regression results on vegetable purchase variety, energy intake (kilocalories), energy adjusted for bodyweight, waist-to-hip ratios and body mass index (kcal). All models adjusted for head of household status, asset quartiles, gender, age, house ownership, years since HIV diagnosis, education, presence of home garden and fridge.
Fig. 6
Fig. 6
Results from multivariate regression models on 6 key outcomes: panels shows odds ratio of vegetable purchase in the last 7 days, linear regression results on vegetable purchase variety, energy intake (kilocalories), energy adjusted for bodyweight, waist-to-hip ratios and body mass index (kcal). All models adjusted for head of household status, assert quartiles, gender, age, house ownership, years since HIV diagnosis, education, presence of home garden and fridge.
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