Food-insecure households interact more frequently with the food environment: food purchase patterns and dietary adequacy in the DECIDE study, Tanzania

Food-insecure households interact more frequently with the food environment: food purchase patterns and dietary adequacy in the DECIDE study, Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Food-insecure households interact more frequently with the food environment: food purchase patterns and dietary adequacy in the DECIDE study, Tanzania Ramya Ambikapathi, Cristiana K. Verissimo, Victoria Kariathi, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8735511/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This paper examines how food insecurity influences household food purchases and dietary adequacy in the context of a rapidly evolving food environment in Africa. We examine food purchase patterns, nutrient intakes, and food security using data collected in 2019–2020 among people living with HIV in peri-urban Dar es Salaam, Tanzania. Participants reported food purchases and vendor interactions over seven days, evaluated against nutrient intakes from 24-hour diet recalls. Urban food-insecure households interacted more frequently with the food environment than food-secure households, purchasing staples and vegetables more often. Top weekly purchases were tomatoes (62%), sugar (57%), carrots (51%), rice (50%), and maize flour (48%). Staple purchases were associated with lower zinc adequacy (OR: 0.5, P < 0.032), while meat purchase diversity was associated with greater zinc (OR: 1.2, P < 0.039) and vitamin A adequacy (OR: 1.2, P < 0.023). Latent class analysis revealed three distinct weekly purchasing groups: "buy everything," "buy basics," and "purchase sweet and sugary beverages." These groupings were associated with micronutrient adequacy and food security. Informal and semi-formal vendors play a critical role in providing access to nutrient-dense foods across the spectrum of household food security status. Earth and environmental sciences/Environmental social sciences Health sciences/Health care Food environments food security food purchase Tanzania diets Figures Figure 1 Figure 2 Figure 3 Introduction Africa is undergoing rapid urbanization, with large implications for food systems and dietary patterns 1 , 2 . This rapid urbanization is profoundly affecting local food and ecological systems in multiple ways, particularly in peri-urban areas, leading to significant changes in dietary behaviors 3 , 4 . Rapid alterations in food environments, in particular, are part of these broad changes, and are largely driven by urbanization, population growth, and rural-urban migration. These changes pose significant challenges to food and nutrition security in urban areas 4 , 5 . Newly emergent food systems, especially as reflected in food environment transformations, have created a complex landscape that underpins and enables the evolving, so-called “triple burden” of malnutrition, characterized by the simultaneous occurrence of obesity, undernutrition, and micronutrient deficiencies 1 , 2 . The nutrition transition has been associated with changes in dietary patterns globally, with concomitant increases in obesity and non-communicable diseases, now among the leading causes of death. In African countries, non-communicable disease risk is increasing at a faster rate and at a lower economic threshold than seen in high-income countries 2 . Tanzania is an excellent example of how multiple broad demographic and food system trends are transitioning. Tanzania became a low- to middle-income country in 2019 6–8 . Rising income levels typically increase access to a diverse range of foods, often healthy and nutritious. In Tanzania, however, evidence shows that higher income levels have led to consumption of unhealthy foods, primarily in the form of food away from home 6 . Generally, across LMICs, rapidly transitioning peri-urban and urban populations display a pattern of increasing diversity in both healthy and unhealthy food 5 . Among the urban and peri-urban families in low- and middle-income countries, food purchases typically outweigh home production as a source of food 2 , 9 , 10 . Increasingly, rural areas in Africa are also relying on markets for food purchase 10 . This evolving food environment presents a particular challenge for people living with HIV (PLHIV), who face heightened vulnerability to nutritional inadequacy 11 – 14 . Sub-Saharan Africa is disproportionately burdened by the twin epidemics of food insecurity and HIV infection 13 , 15 . Nutrition is a core dimension of comprehensive care for PLHIV, especially in resource-constrained settings where malnutrition and food insecurity are common 12 , 16 . Food insecurity affects approximately 58% of the population in Africa, where 76% of the food-insecure people live in urban and peri-urban areas 5 , 9 , 17 . Food insecurity is associated with PLHIV morbidity and mortality 18 . More recently, anti-retroviral therapies also change dyslipidemia and increase susceptibility to cardiovascular diseases, especially diet-related non-communicable diseases 19 , 20 . Given these intersecting challenges of HIV and food insecurity in rapidly transitioning food environments, understanding the mechanisms through which urban households acquire food becomes essential for targeted interventions and policies. Food purchase patterns reflect not only economic access to food but also household preferences, characteristics of the food environment, and adaptive strategies in response to resource constraints 21 – 23 . Understanding these patterns is particularly important for PLHIV populations, who may have specific nutritional needs and face additional barriers to food access related to stigma, health status, and treatment-related side effects. Despite the critical importance of understanding how urban households access and acquire food, few studies have assessed household food purchase patterns and how these patterns relate to dietary intake and nutrient adequacy, especially in urban East Africa, where both micronutrient inadequacy and obesity exist across the food security spectrum. We posit that food security in urban and peri-urban settings manifests primarily through varied food purchase patterns and intra-household allocation 23 – 27 . Survey tools for measuring food purchase frequency represent a quick, objective, non-intrusive survey method that can serve as an indicator of how diet links to changes in nutritional inadequacy and health 21 , 28 , 29 . To address this gap, this analysis examined household purchase patterns and micronutrient intakes in relation to food security status, using an adapted tool on food purchasing behavior in the last seven days 21 . Food purchasing patterns were compared with nutrient intakes among PLHIV in peri-urban Dar es Salaam, Tanzania. Three key questions with broad public health implications are addressed: (1) How does food security relate to household food purchase patterns (diversity, frequency, patterns)? (2) How are food purchase metrics associated with different dietary components? And (3) what types of shopping and purchase patterns emerge, and how are they associated with nutrient adequacy? Methods This study analyzed quantitative data from the DECIDE study, "Diet, Environment, and Choices of Positive Living (2018–2020)." The DECIDE study (Driver of Food Choice) took place in a peri-urban community in Ukonga, located 13 kilometers from Dar es Salaam city center, Tanzania 30 , 31 . The DECIDE study is nested within the larger demographic surveillance project, the Dar es Salaam Urban Cohort Study (“DUCS”), covering 21,000 families and more than 110,000 individuals 31 . The DECIDE study design and findings on food environments and food choices in Ukonga have been previously described in the literature 30 , 32 . Briefly, the DECIDE study used a mixed-methods approach to characterize family perspectives on food environments by collecting a range of related data, including quantitative 24-hour dietary recall, anthropometric measurements, self-reported morbidity, mental health, water security, and food security. Data were collected at two time points from two adult family members, complemented by a food environment census, qualitative interviews on family food choices, and a systematic qualitative evidence synthesis to develop a family-level dimension of food environments. The DECIDE study design was guided conceptually by Giddens's structural-agency theory and Turner's food environment framework 35 , 36 . Adult people living with HIV were recruited at clinics, provided informed consent to participate in the study, lived in the "DUCS" area, and agreed to reach out to another adult family member, who also provided informed consent separately. Institutional Review Board approval was obtained from Purdue University and the National Institute of Medical Research, Tanzania, and approved all methods and protocols carried out in the study. All methods were carried out in accordance with relevant guidelines and regulations. A total of 326 families were recruited for the DECIDE study, out of which 312 families had complete data. Food security was measured using the Household Food Insecurity Access Scale (HFIAS), a series of nine questions that ask participants about their perceptions of food insecurity over the past four weeks. We used a 49-item food purchase survey to assess the 7-day recall of the frequency and location of food purchases (see supplement document). The design expanded on previous research in Peru by adding nine items on food expenses, credit use, and procurement strategies 21 . Overall, 312 PLHIV participants from the Diet, Environment, and Choices of Positive Living (DECIDE) study in peri-urban Dar es Salaam were surveyed in two rounds: March-June 2019, October 2019, and February 2020 30 . Participants were asked if they purchased any of 49 food items from seven food groups: staples, nuts and seeds, dairy, flesh foods, vegetables, fruit, and snacks/sweets and sugary beverages (S-SSB). They were asked how frequently each item was purchased (1–7 days) and where it was purchased (market, kiosks/shops, umbrella vendors, mobile vendors). Markets and shops were categorized as formal vendors; umbrella vendors as semi-formal; and mobile vendors as informal, based on previous community-based research 30 . Twenty-four-hour dietary recalls were collected in each round and have been previously published on gender differences and meal patterns 34 . Recommended nutrient intakes (RNI) were used to calculate micronutrient adequacy 34 , 38 . We conducted exploratory analyses of household food purchase patterns in relation to food security status, using ANOVA models to assess statistical significance. We then used latent class analyses (LCA) to examine food purchase patterns. LCA is a statistical method, frequently used to analyze shopping, food consumption, or other patterned data, that identifies unobserved latent classes within a population based on patterns of responses to observed variables 31 – 34 . All analyses were run in STATA. Prior to conducting LCA, we conducted exploratory factor analyses on purchase and frequency data for 49 food items to reduce dimensionality and examine correlations among them. Food group-level indicators were created and tested for multicollinearity. We then fit a series of LCA models with different numbers of classes (ranging from 2 to 5) using binary food purchase variables as indicators. Model selection was guided by the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), where lower values indicate a better fit. We also assessed entropy values (range: 0–1) to measure classification certainty, with values > 0.80 indicating good class separation 42 . Based on these criteria, we determined that a 3-class solution using binary food purchase variables provided the optimal balance of model fit, parsimony, and interpretability. Results Table 1 presents baseline sociodemographic, food access, and health characteristics of the 316 people living with HIV enrolled in the DECIDE study. The majority of participants were female (75.3%) with a median age of 41 years (IQR: 33, 48). Most participants (81.0%) had completed Standard 7 or higher education. Nearly half (46.5%) were currently married or living with a partner, while 16.7% were widowed and 16.3% had never married. Approximately one-third of participants rented their homes (34.9%), had a home garden (34.5%), and used a refrigerator (32.3%). The median time since HIV diagnosis was 4 years (IQR: 2, 8). Most participants reported good (60.9%) or very good (30.8%) adherence to antiretroviral therapy over the previous month. Nearly half (47.4%) did not share toilet facilities with other households, while 29.8% shared facilities with 2 to 5 households. Two-thirds of the sampled participants experience severe or moderate food insecurity. The characteristics of the included sample population are similar to those of families enrolled in the DUCS surveillance system, irrespective of HIV status 7 . Table 1 Participant demographics of the DECIDE study in peri-urban Tanzania. N = 312 Gender Male 24.7 (77) Female 75.3 (235) Participant age 42 (34,49) Standard 7 or above 80.8 (252) What is your current marital status? Never married 16.3 (51) Currently married or living with partner 46.5 (145) Separated/Deserted 15.4 (48) Divorced 5.1 (16) Widowed 16.7 (52) Rent this house? Yes 34.9 (109) Home garden 33.6 (105) Uses fridge 31.4 (98) Years since HIV diagnosis 4 (2,8) Rate your adherence to ARVs over the last month? Poor 2.2 (7) Fair 6.1 (19) Good 60.9 (190) Very good 30.8 (96) Share toilet facilities with your household? None 47.4 (148) One 15.1 (47) Two to five 29.8 (93) More than five 7.7 (24) Household Food Insecurity Access Food secure 28.2 (88) Mildly FIA 8.7 (27) Moderate FIA 29.2 (91) Severe FIA 34.0 (106) What are the household food purchase trends and patterns by vendor type? We summarize three measures of household food purchase patterns: purchase (yes/no), frequency, and diversity at the food group level. Overall, 85% of the participants bought food in the last 7 days. Analysis across two survey rounds (N = 312 and N = 288) reveals consistent purchasing behavior: 81–85% report food purchases in the previous week, and the median is 7–9 items purchased from a list of 49 foods. Vegetables were the most commonly purchased food group (69–70%, specifically tomatoes and carrots), followed by staples (66–68%), fruit (54–60%), and flesh foods (57–59%). Nearly half of the sample (47–48%) purchased sweets, snacks, and sugary beverages (S-SSB), whereas dairy products were purchased by a small minority (8–10%). The frequency of purchases showed that vegetables were purchased most often (median 3 times per week), while other food groups were purchased 1–2 times per week. Purchase diversity within food groups remained relatively low across both rounds, with participants typically purchasing only 1–3 different items in each category. Vegetables showed the greatest variety (median of 3 types), followed by staples and flesh foods (2 items each), while fruit, S-SSB, and dairy showed minimal diversity (0–1 items). The stability of these patterns across the two survey rounds suggests habitual food purchasing behavior, with vegetables dominating both purchase frequency and variety. Dairy products appear to be absent from food purchases. Over 50% of participants report purchasing food yesterday, with an average spend of 5,000 Tanzanian shillings (~ 2 USD in 2019) to feed about four people. Of the participants who bought food the previous day, 10% reported buying on credit. Table 2 Food Purchase Patterns in the last 7 days, DECIDE Cohort, Peri-urban Dar es Salaam, Tanzania. Purchase patterns Round 1 Round 2 N = 312 N = 274 Number of foods purchased by individual 9 (3,15) 8 (3,13) Individual purchased food in the last week 85.6 (267) 88.7 (243) Purchase of Food Groups (% Yes) Purchase staples? 65.4 (204) 67.5 (185) Purchase dairy? 8.3 (26) 9.9 (27) Purchase flesh foods? 58.3 (182) 56.9 (156) Purchase vegetables? 69.6 (217) 69.0 (189) Purchase fruit? 59.6 (186) 53.3 (146) Purchase S-SSB? 47.4 (148) 47.1 (129) Variety of Purchase Within Food Groups (# of items; median (IQR)) Diversity of staples purchase 2 (1,3) 2 (1,3) Diversity dairy purchase 0 (0,0) 0 (0,0) Diversity of flesh foods purchase 1 (0,3) 1 (0,3) Diversity of vegetables purchase 3 (1,5) 3 (1,4) Diversity of fruit purchase 1 (0,3) 1 (0,2) Diversity of S-SSB purchase 1 (0,1) 1 (0,1) Frequency of Purchase of a Food Group (# of times; median (IQR)) Frequency of staples purchase 5 (3,8) 6 (3,8) Frequency of dairy purchase 2 (1,3) 1 (1,2) Frequency of flesh foods purchase 4 (2,6) 4 (2,5) Frequency of vegetables purchase 8 (2,17) 7 (1,12) Frequency of fruit purchase 4 (2,8) 3 (2,6) Frequency of S-SSB purchase 2 (1,4) 2 (1,3) Food expenses and management strategies Did you spend money on food, including purchases on credit, to feed your family? 59.9 (187) 51.5 (141) Yesterday, about how much money did you spend in food to feed your family? (in Tanzanian Shillings) 5000 (4000,10000) 5000 (4000,8000) How many people were fed with the food that you bought yesterday? 4 (2,6) 4 (3,5) In the past week, did you receive credit in order to buy food for your household? 9.1 (17) 9.9 (14) Figure 1 illustrates household food sourcing patterns across three major food categories (Staples, Meat, Fruits and Vegetables) and the sources of food from formal vendors (market/kiosk), semi-formal vendors (umbrella/pallet-based vendors), and informal vendors (mobile). For staples such as rice and bread, formal market/kiosk vendors dominate, whereas semi-formal umbrella vendors play a more significant role in meat purchases, particularly for chicken and processed meats. Fresh fruits and vegetables were predominantly sourced from semi-formal and informal vendors. Overall, the data reveal that formal vendors remain the predominant source of staple foods, while informal vendors are a significant source of nutrient-dense foods. Do food-insecure households interact differently in the food environment? Food-insecure households purchase food differently than do food-secure households, and they also exhibit distinct patterns of food acquisition within the food environment (Table 3 ). First, a higher percentage of households experiencing moderate or severe food insecurity report purchasing food in the past 7 days than food-secure or mildly food-insecure households (90% vs. 78%; p-value of 0.005). When examining specific food categories, a higher proportion of moderately or severely food-insecure households purchased staples (72.6% vs. 54.3%; p < 0.001) and vegetables (75.1% vs. 61.2%; p = 0.009) than food-secure or mildly food-insecure households. Dairy purchasing showed the opposite pattern, with fewer moderately or severely food-insecure households purchasing dairy (4.0% vs. 15.5%; p < 0.001). No significant differences were observed in the proportion of purchasing flesh foods, fruit, or sugar-sweetened beverages. Second, households with moderate or severe food insecurity had much higher food purchasing frequency for each food item than food-secure households. For example, the mean purchase frequency for food-secure participants was lower at 2.3 times per week than for participants who were moderately or severely food-insecure (2.6 times per week per food item, p-value of 0.015). Most notably, there was a greater frequency of purchase of staples with 6.7 times per week (i.e., almost daily) among moderate and severe food-insecure households, compared to 4.9 times among food-secure or mildly food-insecure households (p < 0.008). Similarly, dairy was purchased more frequently by food-insecure households (4.0 times per week) than by food-secure or mildly food-insecure households (1.9 times per week; p < 0.025). No significant differences were found in purchase frequency for flesh foods, vegetables, fruit, or sugar-sweetened beverages. Despite a high frequency of food purchases, food-insecure households showed lower diversity of purchases within food groups than other groups. The diversity of dairy products purchased was lower among households with moderate or severe food insecurity (p < 0.001). Similarly, households with moderate or severe food insecurity purchased fewer types of flesh foods (1.7 vs. 2.2, p = 0.040). Data from the second round (not shown) exhibited similar trends. Discretizing the food insecurity category into “any food insecurity” vs. “food secure” yielded the same inference. Table 3 Food purchase patterns by food security status in the DECIDE cohort, Round 1. Questions ask about the food purchased in the last 7 days Food secure/mild insecure Moderate/Severe FIA Total p-value N = 115 N = 197 N = 312 Individual purchased food in the last week 78.3 (90) 89.8 (177) 85.6 (267) 0.005 Mean frequency of food purchased 2.3 [± 0.9] 2.6 [± 1.1] 2.5 [± 1.0] 0.015 Number of foods purchased by individual 9.4 [± 8.3] 9.8 [± 7.1] 9.7 [± 7.6] 0.59 % Purchased individual food groups? Purchase staples? 53.9 (62) 72.1 (142) 65.4 (204) 0.001 Purchase dairy? 15.7 (18) 4.1 (8) 8.3 (26) < 0.001 Purchase flesh foods? 56.5 (65) 59.4 (117) 58.3 (182) 0.62 Purchase vegetables? 60.9 (70) 74.6 (147) 69.6 (217) 0.011 Purchase fruit? 59.1 (68) 59.9 (118) 59.6 (186) 0.89 Purchase S-SSB? 51.3 (59) 45.2 (89) 47.4 (148) 0.30 Food purchase diversity within food groups Diversity of staples purchase 1.6 [± 1.5] 1.9 [± 1.3] 1.8 [± 1.4] 0.15 Diversity dairy purchase 0.2 [± 0.4] 0.1 [± 0.3] 0.1 [± 0.3] < 0.001 Diversity of flesh foods purchase 2.2 [± 2.0] 1.7 [± 1.9] 1.9 [± 2.0] 0.040 Diversity of vegetable purchase 3.0 [± 2.3] 2.9 [± 2.0] 3.0 [± 2.1] 0.71 Diversity of fruit purchase 2.0 [± 1.6] 1.6 [± 1.6] 1.7 [± 1.6] 0.050 Diversity of S-SSB purchase 0.8 [± 0.7] 0.6 [± 0.7] 0.7 [± 0.7] 0.033 Food purchase frequency within food groups Frequency of staples purchase 4.9 [± 4.1] 6.7 [± 4.6] 6.2 [± 4.5] 0.008 Frequency of dairy purchase 1.9 [± 1.3] 4.0 [± 3.1] 2.6 [± 2.2] 0.025 Frequency of flesh foods purchase 5.0 [± 3.0] 4.4 [± 3.3] 4.6 [± 3.2] 0.23 Frequency of vegetable purchase 9.8 [± 8.6] 10.2 [± 8.8] 10.1 [± 8.7] 0.77 Frequency of fruit purchase 6.6 [± 5.1] 6.1 [± 6.6] 6.3 [± 6.1] 0.60 Frequency of S-SSB purchase 3.2 [± 2.7] 2.8 [± 2.0] 2.9 [± 2.3] 0.33 How are food purchase metrics related to micronutrient adequacy? Next, we developed and validated food purchase metrics with micronutrient adequacy. First, based on ANOVA results for various purchase metrics and food security, we identified eight candidate purchase metrics: purchase of staples, vegetables, and dairy; diversity of dairy foods; purchases of snacks and sweet and sugary beverages; and, lastly, frequency of staples and dairy purchases. To validate these purchase patterns, we first examined micronutrient adequacy across the study population (Fig. 2 ). Overall, the proportion of participants achieving minimum micronutrient adequacy was high for zinc (78%) but low for iron (48%), vitamin A (35%), and calcium (27%). Micronutrient adequacy varied significantly by food security status (Fig. 2 ). Overall, the mean micronutrient adequacy indicated that approximately 72% of food-secure households achieved adequacy, compared with 60% of mildly food-insecure and 55% of moderately food-insecure households (p < 0.001). For calcium, a higher percentage of food-secure households achieved adequacy compared to those with mild or moderate food insecurity (approximately 40% vs. 30% and 28%, respectively; p < 0.05). For zinc adequacy, 90% of both food-secure and mildly food-insecure households met adequacy criteria, compared with approximately 80% of moderately food-insecure households (p < 0.001). Few food purchase metrics were significantly associated with micronutrient adequacy (Table 4 ). Among the eight candidate purchase metrics examined, the diversity of flesh-food purchases was most strongly associated with micronutrient adequacy, showing significant associations with zinc, iron, and vitamin A (p < 0.01 for vitamin A). Single associations were observed between purchasing staples and zinc adequacy, purchasing dairy and vitamin A adequacy, and purchasing vegetables and iron adequacy. Purchase frequency metrics and diversity of dairy or sugar-sweetened beverages showed no significant associations with any micronutrient adequacy. Table 4: Adjusted models examining associations between purchase metrics, calcium, zinc, iron, and vitamin A adequacy of the diets Purchase metrics Calcium adequacy Zinc adequacy Iron adequacy Vitamin A adequacy Purchase staples? NS * NS NS Purchase dairy? NS NS NS * Purchase veg? NS NS * NS Diversity dairy purchase NS NS NS NS Diversity of flesh foods purchase NS * * ** Diversity of SSB purchase NS NS NS NS Frequency of staples purchase NS NS NS NS Frequency of dairy purchase NS NS NS NS *** p<0.001 ** p<0.01 * p<0.05; All models adjusted for gender, age, assets, education, type of dwelling unit (rent or own), and duration of ARV treatment What classes of food purchase patterns emerge, and how are they associated with micronutrient adequacy? Using latent class models, we identified three classes of purchase patterns that best fit the data structure. The most common pattern, "Buy Everything" (50% of participants), was characterized by weekly purchases of staples, flesh foods, vegetables, fruits, and sugar-sweetened beverages (SSB). The second pattern, "Buy SSB Weekly" (29% of participants), purchased very little each week, aside from sweet and sugary beverages. The third pattern, "Buy Basics" (21% of participants), primarily purchased staples and vegetables weekly. In Fig. 3 , the left panel shows bar graphs of food group purchases over the past 7 days, with the y-axis showing the percentage of participants in each shopper class who purchased each food group and the x-axis showing the food groups. The right panel displays three plots showing the odds of nutrient adequacy by class membership. The "Buy Sugar-Sweetened Beverages Weekly" class purchased very little other than sugar-sweetened beverages each week. Class membership was associated with increased odds of zinc and iron adequacy, even after adjusting for demographics and household characteristics. The "Buy Basics" class was characterized by high proportions of participants purchasing staples and vegetables weekly. Class membership was associated with decreased odds of zinc and iron adequacy, reflecting greater food insecurity among those purchasing only basic staples weekly. The "Buy Everything" class was characterized by participants who purchased staples, flesh foods, vegetables, fruits, and sugar-sweetened beverages weekly. Class membership in this group was not associated with micronutrient adequacy. Discussion This study examined household food purchase patterns and micronutrient intakes among people living with HIV (PLHIV) in peri-urban Dar es Salaam, Tanzania. We observed frequent household interaction with the food environment, with particularly high purchasing frequency for vegetables and staple foods. Food-insecure households purchased food more frequently than food-secure households, likely reflecting limited purchasing power that necessitates smaller, more frequent shopping trips rather than bulk purchases. Our findings reveal that formal vendors serve as the primary source of staple foods, while semi-formal and informal vendors provide most nutrient-dense foods such as meat, fish, and vegetables, on par with other studies in East Africa 43 , 44 . Few food purchase metrics were significantly associated with micronutrient adequacy, except for meat purchase diversity. Our findings highlight important patterns in the food environment regarding how households access different food types. Staples and sweet or sugary beverages were typically purchased from formal food environments (cement stores and shops), while meats, fish, and vegetables were predominantly sourced from umbrella vendors and mobile sellers operating in semi-formal and informal settings. This pattern has significant policy implications, particularly in light of ongoing debates about food environment regulation and modernization 43 – 46 . Several emerging countries have pursued strategies to increase regulation and the "supermarketization" of the food environment 44 , 47 , 48 . However, our data demonstrate that nutrient-dense products, including meats, fish, and vegetables, are most commonly purchased from informal/semi-formal retail outlets. Thus, policies emphasizing the formalization and regulation of food environments must be carefully designed to avoid disrupting access to these nutrient sources, particularly for low-income households that rely heavily on these informal channels. Importantly, food security status did not significantly affect where households purchased produce and fruits, with both food-secure and food-insecure households predominantly sourcing these items from semi-formal and informal food environments. This suggests that informal vendors play a universal role in providing access to fresh produce across socioeconomic strata in peri-urban settings. However, food security did influence purchasing patterns in other ways. Urban food-secure households purchased more diverse food groups (including sweets and sugary beverages) and a greater variety of items within those groups but shopped less frequently in the preceding week than food-insecure households. It's likely that these food-secure households can afford to buy discretionary items like sweet beverages, have greater overall purchasing power, and therefore buy a more diverse range of foods, including animal-source foods that provide zinc and iron. In contrast, households buying only basic staples are probably doing so out of necessity, resulting in monotonous diets with inadequate micronutrient density. This aligns with our finding that meat purchase diversity was associated with better micronutrient adequacy, which both serve as markers of household purchasing power rather than direct contributors to nutrient intake. These results align with research from Bangladesh, where households experiencing food insecurity buy rice more frequently, in smaller quantities 49 . Very few studies have examined other specific food purchase frequencies and food security, which is a novel contribution of this analysis. We identified three distinct shopping profiles among peri-urban households in Dar es Salaam: (1) households that purchase sweet and sugary beverages weekly; (2) those that purchase only basic foods weekly; and (3) those that purchase everything, including a wide variety of food items, weekly. Interestingly, shopping profiles that included weekly purchases of sweet and sugary beverages were associated with greater zinc and iron adequacy, whereas households that purchased only basic foods weekly exhibited lower zinc and iron adequacy. This study has several important limitations. The food purchase tool did not collect data on food quantity, limiting our ability to assess household-level portion sizes and total food availability. Another limitation is the imperfect match of the 24-hour dietary intake recall period and the assessment of food purchase patterns (7-day recall) over two distinct time periods. Finally, the generalizability of our findings may be limited by the characteristics of our study population. Participants included people living with HIV and their families, who may have higher nutrition knowledge due to targeted nutrition education programs for this population. Additional research is needed to validate these findings in more diverse populations. Our findings build upon a growing body of evidence documenting the centrality of food purchasing in African food systems, irrespective of rurality. Previous research has established that purchasing is the predominant mode of food procurement across the continent 50 , with bartering, crediting, gifting, and home gardening persisting but accounting for only a small percentage of food acquisition. This pattern holds even in rural areas, where purchasing has become increasingly prevalent 10 . Urbanization has further intensified reliance on purchased prepared foods, with several studies documenting increased consumption of food away from home, i.e. prepared foods, that are often energy-dense 6 , 8 . This shift coincides with what Reardon and colleagues have termed the "processed food revolution" in developing countries, characterized by the rapid expansion in the availability and consumption of processed and ultra-processed foods, and even ‘prepared ultra-processed foods’ 2 . Overall reliance of the food-insecure urban population on informal vendors has been noted in other East African settings 43 , 44 . The current study extends this work by examining how households in peri-urban areas interact with food environments through their specific food purchase patterns and procurement sources, and how these patterns relate to dietary micronutrient adequacy. Food security shapes how households interact with the food environment - not just what they buy, but where they shop and how often. Informal and semi-formal vendors play a critical role in providing access to nutrient-dense foods in peri-urban settings across the spectrum of household food security status. Policies aimed at modernizing food retail need to account for this, or they risk disrupting access to essential nutrients, especially for food-insecure households. Declarations Funding sources: Diet, Environment, and Choices of positive living (DECIDE study): Evaluating personal and external food environment influences on diets among PLHIV and families in Dar es Salaam, Tanzania, study is funded through the Drivers of Food Choice Grants Program by Bill and Melinda Gates Foundation (ID: OPP1110043) and UK AID. RA, JW, and GK are supported by CGIAR Better Diets and Nutrition Science Program. Author Contribution R.A. designed the tools, led the analysis, and wrote the manuscript with input from all co-authors; RA, D.M., GL, led the implementation of the study that provided data for these analyses; D.M., A.M, led the fieldwork and data collection; RA, NSG, CKV, SFM, VK, GK, JW, and GS provided input on the analyses. All authors read and approved the final manuscript. Acknowledgement The Diet, Environment, and Choices of Positive Living (DECIDE) study is a collaborative project led by Purdue University, the University of Chicago, Muhimbili University, and the Africa Academy of Public Health. The authors acknowledge and are grateful for the collaboration and support of the families participating in the DECIDE study, as well as the dedication of the regional and field staff. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Ambikapathi, R. et al. Global food systems transitions have enabled affordable diets but had less favourable outcomes for nutrition, environmental health, inclusion and equity. Nat. Food . 3 , 764–779 (2022). Reardon, T. et al. The processed food revolution in African food systems and the double burden of malnutrition. Glob Food Secur. 28 , 100466 (2021). Hemerijckx, L. M. et al. Mapping the consumer foodshed of the Kampala city region shows the importance of urban agriculture. Npj Urban Sustain. 3 , 11 (2023). Osei-Kwasi, H. et al. Factors influencing dietary behaviours in urban food environments in Africa: a systematic mapping review. Public. Health Nutr. 23 , 2584–2601 (2020). HLPE. Strengthening Urban and Peri-Urban Food Systems to Achieve Food Security and Nutrition, in the Context of Urbanization and Rural Transformation . (2024). https://sfcs.fao.org/docs/devhlpelibraries/report-19/hlpe-19---main-report_en_cd1459en.pdf Ignowski, L., Belton, B., Tran, N. & Ameye, H. Dietary inadequacy in Tanzania is linked to the rising cost of nutritious foods and consumption of food-away-from-home. Glob Food Secur. 37 , 100679 (2023). Unwin, N. et al. Rural to urban migration and changes in cardiovascular risk factors in Tanzania: a prospective cohort study. BMC Public. Health . 10 , 272 (2010). Ameye, H. Dietary quality in rural areas, secondary towns, and cities: Insights from Tanzania. Food Secur. 15 , 1563–1584 (2023). FAO. The State of Food Security and Nutrition in the World 2023: Urbanization, Agrifood Systems Transformation and Healthy Diets across the Rural–Urban Continuum. (FAO, IFAD, UNICEF, WFP, WHO & Rome Italy, (2023). 10.4060/cc3017en Dzanku, F. M., Liverpool-Tasie, L. S. O. & Reardon, T. The importance and determinants of purchases in rural food consumption in Africa: Implications for food security strategies. Glob Food Secur. 40 , 100739 (2024). Dabalo, D., Beyene, T., Abebe, M. & Ayana, M. Magnitude of food insecurity and its associated factors among adult HIV patients on antiretroviral therapy at public health facilities in Ambo town, West Shewa, Ethiopia. Food Humanity . 5 , 100729 (2025). Ivers, L. C. et al. HIV/AIDS, Undernutrition, and Food Insecurity. Clin. Infect. Dis. 49 , 1096–1102 (2009). Anema, A., Vogenthaler, N., Frongillo, E. A., Kadiyala, S. & Weiser, S. D. Food Insecurity and HIV/AIDS: Current Knowledge, Gaps, and Research Priorities. Curr. HIV/AIDS Rep. 6 , 224–231 (2009). UNAIDS data 2020 | UNAIDS. https://www.unaids.org/en/resources/documents/2020/unaids-data Anema, A. et al. Food security in the context of HIV: Towards harmonized definitions and indicators. AIDS Behav. 18 , 476–489 (2014). Byron, E., Gillespie, S. & Nangami, M. Integrating Nutrition Security with Treatment of People Living with HIV: Lessons from Kenya. Food Nutr. Bull. 29 , 87–97 (2008). FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2024 (FAO; IFAD ; UNICEF ; WFP ; WHO ;, 2024). Anema, A. et al. Relationship between food insecurity and mortality among HIV-positive injection drug users receiving antiretroviral therapy in British Columbia, Canada. PLoS ONE 8 , (2013). Dominick, L. et al. HIV-related cardiovascular diseases: the search for a unifying hypothesis. Am. J. Physiol. -Heart Circ. Physiol. 318 , H731–H746 (2020). Okello, S. et al. Prevention of cardiovascular disease among people living with HIV in sub-Saharan Africa. Prog Cardiovasc. Dis. 63 , 149–159 (2020). Ambikapathi, R. et al. Food purchase patterns indicative of household food access insecurity, children’s dietary diversity and intake, and nutritional status using a newly developed and validated tool in the Peruvian Amazon. Food Secur. 10 , 999–1011 (2018). Farah, I. et al. Food and beverage purchases at formal and informal outlets in Mexico. Public. Health Nutr. 26 , 1034–1043 (2023). Aryeetey, R., Oltmans, S. & Owusu, F. Food retail assessment and family food purchase behavior in Ashongman Estates, Ghana. Afr. J. Food Agric. Nutr. Dev. 16 , 11386–11403 (2016). Odunitan-Wayas, F. et al. Food purchasing characteristics and perceptions of neighborhood food environment of South Africans living in low-, middle- and high-socioeconomic neighborhoods. Sustain Switz 10 , (2018). Akparibo, R. et al. Food security in ghanaian urban cities: A scoping review of the literature. Nutrients 13 , (2021). Rischke, R., Kimenju, S. C., Klasen, S. & Qaim, M. Supermarkets and food consumption patterns: The case of small towns in Kenya. Food Policy . 52 , 9–21 (2015). Galié, A., Farnworth, C. R., Njiru, N. & Alonso, S. Intra-household handling and consumption dynamics of milk in peri-urban informal markets in tanzania and kenya: A gender lens. Sustain Switz 13 , (2021). Flax, V. L., Thakwalakwa, C., Schnefke, C. H., Phuka, J. C. & Jaacks, L. M. Food purchasing decisions of Malawian mothers with young children in households experiencing the nutrition transition. Appetite 156 , 104855 (2021). Lyonnais, M. J., Rafferty, A. P., Jilcott Pitts, S., Blanchard, R. J. & Kaur, A. P. Examining Shopping Patterns, Use of Food-Related Resources, and Proposed Solutions to Improve Healthy Food Access Among Food Insecure and Food Secure Eastern North Carolina Residents. Int J. Env Res. Public. Health 17 , (2020). Ambikapathi, R. et al. 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. Glob Food Secur 28 , (2021). Leyna, G. H. et al. Profile: The Dar Es Salaam Health and Demographic Surveillance System (Dar es Salaam HDSS). Int. J. Epidemiol. 46 , 801–808 (2017). Boncyk, M. What people like depends on what is available: Food Choices of PLHIV in Peri-Urban Tanzania. (2020). Ambikapathi, R. Expanding the food environment framework to include family in the context of living with HIV: A qualitative evidence synthesis. (2021). Ambikapathi, R. et al. Gender and Age Differences in Meal Structures, Food Away from Home, Chrono-Nutrition, and Nutrition Intakes among Adults and Children in Tanzania Using a Newly Developed Tablet-Based 24-Hour Recall Tool. Curr. Dev. Nutr. 6 , nzac015 (2022). Giddens, A. The Constitution of Society: Outline of the Theory of Structuration (University of California Press, 1984). Turner, C. et al. Food Environment Research in Low- and Middle-Income Countries: A Systematic Scoping Review. Adv. Nutr. nmz031 (2019). 10.1093/advances/nmz031 Coates, J., Swindale, A. & Bilinsky, P. Household Food Insecurity Access Scale (HFIAS) for measurement of food access: indicator guide: version 3. (2007). WHO, F. and, Joint, F. A. O., WHO Expert Consultation on Human Vitamin and & Requirements, M. (1998). https://www.fao.org/3/y2809e/y2809e.pdf VanKim, N. A., Erickson, D. J. & Laska, M. N. Food Shopping Profiles and Their Association with Dietary Patterns: A Latent Class Analysis. J. Acad. Nutr. Diet. 115 , 1109–1116 (2015). Berger, N., Cummins, S., Allen, A., Smith, R. D. & Cornelsen, L. Patterns of beverage purchases amongst British households: A latent class analysis. PLoS Med. 17 , e1003245 (2020). Multiple-Group Latent Class Analysis. in. Latent Class and Latent Transition Analysis 111–148 (Wiley, 2009). 10.1002/9780470567333.ch5 Dietary Diversity in Nepal. A Latent Class Approach - PubMed. https://pubmed.ncbi.nlm.nih.gov/33685257/ Berger, M. & van Helvoirt, B. Ensuring food secure cities – Retail modernization and policy implications in Nairobi, Kenya. Food Policy . 79 , 12–22 (2018). Wanyama, R., Gödecke, T., Chege, C. G. K. & Qaim, M. How important are supermarkets for the diets of the urban poor in Africa? Food Secur. 11 , 1339–1353 (2019). Even, B. et al. Unpacking food environment policy landscapes for healthier diets in emerging countries: the case of Viet Nam. Front. Public. Health . 13 , 1548956 (2025). Roy, A. S., Mazaniello-Chézol, M., Rueda-Martinez, M., Shafique, S. & Adams, A. M. Food systems determinants of nutritional health and wellbeing in urban informal settlements: A scoping review in LMICs. Soc. Sci. Med. 322 , 115804 (2023). Wertheim-Heck, S. C. O. We have to eat, right? Food safety concerns and shopping for daily vegetables in modernizing Vietnam. in We have to eat, right? Food safety concerns and shopping for daily vegetables in modernizing Vietnam vii + 241 pp. (2015). Wertheim-Heck, S. C. O. & Raneri, J. E. Food policy and the unruliness of consumption: An intergenerational social practice approach to uncover transforming food consumption in modernizing Hanoi, Vietnam. Global Food Security 26 (2020). Na, M., Gross, A. L. & West, K. P. Validation of the food access survey tool to assess household food insecurity in rural Bangladesh. BMC Public. Health . 15 , 863 (2015). Frayne, B. et al. The state of urban food insecurity in southern Africa. Urban Food Secur. Ser. 2 , 1–54 (2010). Additional Declarations No competing interests reported. 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Gunaratna","email":"","orcid":"","institution":"Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Nilupa","middleName":"S.","lastName":"Gunaratna","suffix":""},{"id":609848229,"identity":"7c26ac67-5cb2-4942-a94f-df6fc2f92291","order_by":10,"name":"Gina Kennedy","email":"","orcid":"","institution":"Alliance of Bioversity International and CIAT","correspondingAuthor":false,"prefix":"","firstName":"Gina","middleName":"","lastName":"Kennedy","suffix":""},{"id":609848230,"identity":"8aa1dba5-46e4-40a9-9598-40fe25be91b5","order_by":11,"name":"Germana Leyna","email":"","orcid":"","institution":"Tanzania Food and Nutrition Centre","correspondingAuthor":false,"prefix":"","firstName":"Germana","middleName":"","lastName":"Leyna","suffix":""}],"badges":[],"createdAt":"2026-01-29 22:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8735511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8735511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105296901,"identity":"905482ea-6d73-4744-af07-3404a29bfbec","added_by":"auto","created_at":"2026-03-24 13:14:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69559,"visible":true,"origin":"","legend":"\u003cp\u003eHousehold food sourcing patterns for staple meats and vegetables\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8735511/v1/bcf729fb22017f255cee8b52.png"},{"id":105296902,"identity":"cf1a53b0-6d1d-430f-92cd-559560504de6","added_by":"auto","created_at":"2026-03-24 13:14:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15708,"visible":true,"origin":"","legend":"\u003cp\u003eMicronutrient adequacy of PLHIV adults by food security status.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8735511/v1/c7360c56fd078a96628ae8aa.png"},{"id":105296900,"identity":"59275653-46a5-4a0c-af04-7fcc51700479","added_by":"auto","created_at":"2026-03-24 13:14:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57156,"visible":true,"origin":"","legend":"\u003cp\u003eThree types of latent class food purchase patterns and associated nutrient adequacy of diets among PLHIV, Tanzania.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8735511/v1/bdcd7ae5e2a1c90035b9e93b.png"},{"id":105728098,"identity":"133f4e1d-cac1-46e9-8067-0486b7dd14e0","added_by":"auto","created_at":"2026-03-30 11:09:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1187342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8735511/v1/81cfa765-a9d2-4947-87a6-01fc8af51881.pdf"},{"id":105296903,"identity":"45f66f97-a925-4563-bf21-8993ff5b8e37","added_by":"auto","created_at":"2026-03-24 13:14:47","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":546304,"visible":true,"origin":"","legend":"","description":"","filename":"PurchasepatternsTZ.doc","url":"https://assets-eu.researchsquare.com/files/rs-8735511/v1/b064ed698f60531ad1b47ebe.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Food-insecure households interact more frequently with the food environment: food purchase patterns and dietary adequacy in the DECIDE study, Tanzania","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAfrica is undergoing rapid urbanization, with large implications for food systems and dietary patterns \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This rapid urbanization is profoundly affecting local food and ecological systems in multiple ways, particularly in peri-urban areas, leading to significant changes in dietary behaviors \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Rapid alterations in food environments, in particular, are part of these broad changes, and are largely driven by urbanization, population growth, and rural-urban migration. These changes pose significant challenges to food and nutrition security in urban areas\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Newly emergent food systems, especially as reflected in food environment transformations, have created a complex landscape that underpins and enables the evolving, so-called \u0026ldquo;triple burden\u0026rdquo; of malnutrition, characterized by the simultaneous occurrence of obesity, undernutrition, and micronutrient deficiencies \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe nutrition transition has been associated with changes in dietary patterns globally, with concomitant increases in obesity and non-communicable diseases, now among the leading causes of death. In African countries, non-communicable disease risk is increasing at a faster rate and at a lower economic threshold than seen in high-income countries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Tanzania is an excellent example of how multiple broad demographic and food system trends are transitioning. Tanzania became a low- to middle-income country in 2019\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. Rising income levels typically increase access to a diverse range of foods, often healthy and nutritious. In Tanzania, however, evidence shows that higher income levels have led to consumption of unhealthy foods, primarily in the form of food away from home \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Generally, across LMICs, rapidly transitioning peri-urban and urban populations display a pattern of increasing diversity in both healthy and unhealthy food\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Among the urban and peri-urban families in low- and middle-income countries, food purchases typically outweigh home production as a source of food\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Increasingly, rural areas in Africa are also relying on markets for food purchase\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis evolving food environment presents a particular challenge for people living with HIV (PLHIV), who face heightened vulnerability to nutritional inadequacy\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Sub-Saharan Africa is disproportionately burdened by the twin epidemics of food insecurity and HIV infection\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Nutrition is a core dimension of comprehensive care for PLHIV, especially in resource-constrained settings where malnutrition and food insecurity are common\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Food insecurity affects approximately 58% of the population in Africa, where 76% of the food-insecure people live in urban and peri-urban areas\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Food insecurity is associated with PLHIV morbidity and mortality\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. More recently, anti-retroviral therapies also change dyslipidemia and increase susceptibility to cardiovascular diseases, especially diet-related non-communicable diseases\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven these intersecting challenges of HIV and food insecurity in rapidly transitioning food environments, understanding the mechanisms through which urban households acquire food becomes essential for targeted interventions and policies. Food purchase patterns reflect not only economic access to food but also household preferences, characteristics of the food environment, and adaptive strategies in response to resource constraints\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Understanding these patterns is particularly important for PLHIV populations, who may have specific nutritional needs and face additional barriers to food access related to stigma, health status, and treatment-related side effects.\u003c/p\u003e \u003cp\u003eDespite the critical importance of understanding how urban households access and acquire food, few studies have assessed household food purchase patterns and how these patterns relate to dietary intake and nutrient adequacy, especially in urban East Africa, where both micronutrient inadequacy and obesity exist across the food security spectrum. We posit that food security in urban and peri-urban settings manifests primarily through varied food purchase patterns and intra-household allocation\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Survey tools for measuring food purchase frequency represent a quick, objective, non-intrusive survey method that can serve as an indicator of how diet links to changes in nutritional inadequacy and health\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address this gap, this analysis examined household purchase patterns and micronutrient intakes in relation to food security status, using an adapted tool on food purchasing behavior in the last seven days\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Food purchasing patterns were compared with nutrient intakes among PLHIV in peri-urban Dar es Salaam, Tanzania. Three key questions with broad public health implications are addressed: (1) How does food security relate to household food purchase patterns (diversity, frequency, patterns)? (2) How are food purchase metrics associated with different dietary components? And (3) what types of shopping and purchase patterns emerge, and how are they associated with nutrient adequacy?\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study analyzed quantitative data from the DECIDE study, \"Diet, Environment, and Choices of Positive Living (2018\u0026ndash;2020).\" The DECIDE study (Driver of Food Choice) took place in a peri-urban community in Ukonga, located 13 kilometers from Dar es Salaam city center, Tanzania \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The DECIDE study is nested within the larger demographic surveillance project, the Dar es Salaam Urban Cohort Study (\u0026ldquo;DUCS\u0026rdquo;), covering 21,000 families and more than 110,000 individuals \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe DECIDE study design and findings on food environments and food choices in Ukonga have been previously described in the literature\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Briefly, the DECIDE study used a mixed-methods approach to characterize family perspectives on food environments by collecting a range of related data, including quantitative 24-hour dietary recall, anthropometric measurements, self-reported morbidity, mental health, water security, and food security. Data were collected at two time points from two adult family members, complemented by a food environment census, qualitative interviews on family food choices, and a systematic qualitative evidence synthesis to develop a family-level dimension of food environments. The DECIDE study design was guided conceptually by Giddens's structural-agency theory and Turner's food environment framework\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Adult people living with HIV were recruited at clinics, provided informed consent to participate in the study, lived in the \"DUCS\" area, and agreed to reach out to another adult family member, who also provided informed consent separately. Institutional Review Board approval was obtained from Purdue University and the National Institute of Medical Research, Tanzania, and approved all methods and protocols carried out in the study. All methods were carried out in accordance with relevant guidelines and regulations. A total of 326 families were recruited for the DECIDE study, out of which 312 families had complete data.\u003c/p\u003e \u003cp\u003eFood security was measured using the Household Food Insecurity Access Scale (HFIAS), a series of nine questions that ask participants about their perceptions of food insecurity over the past four weeks. We used a 49-item food purchase survey to assess the 7-day recall of the frequency and location of food purchases (see supplement document). The design expanded on previous research in Peru by adding nine items on food expenses, credit use, and procurement strategies \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Overall, 312 PLHIV participants from the Diet, Environment, and Choices of Positive Living (DECIDE) study in peri-urban Dar es Salaam were surveyed in two rounds: March-June 2019, October 2019, and February 2020\u003csup\u003e30\u003c/sup\u003e. Participants were asked if they purchased any of 49 food items from seven food groups: staples, nuts and seeds, dairy, flesh foods, vegetables, fruit, and snacks/sweets and sugary beverages (S-SSB). They were asked how frequently each item was purchased (1\u0026ndash;7 days) and where it was purchased (market, kiosks/shops, umbrella vendors, mobile vendors). Markets and shops were categorized as formal vendors; umbrella vendors as semi-formal; and mobile vendors as informal, based on previous community-based research\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Twenty-four-hour dietary recalls were collected in each round and have been previously published on gender differences and meal patterns\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Recommended nutrient intakes (RNI) were used to calculate micronutrient adequacy\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe conducted exploratory analyses of household food purchase patterns in relation to food security status, using ANOVA models to assess statistical significance. We then used latent class analyses (LCA) to examine food purchase patterns. LCA is a statistical method, frequently used to analyze shopping, food consumption, or other patterned data, that identifies unobserved latent classes within a population based on patterns of responses to observed variables\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. All analyses were run in STATA.\u003c/p\u003e \u003cp\u003ePrior to conducting LCA, we conducted exploratory factor analyses on purchase and frequency data for 49 food items to reduce dimensionality and examine correlations among them. Food group-level indicators were created and tested for multicollinearity. We then fit a series of LCA models with different numbers of classes (ranging from 2 to 5) using binary food purchase variables as indicators. Model selection was guided by the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), where lower values indicate a better fit. We also assessed entropy values (range: 0\u0026ndash;1) to measure classification certainty, with values\u0026thinsp;\u0026gt;\u0026thinsp;0.80 indicating good class separation\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Based on these criteria, we determined that a 3-class solution using binary food purchase variables provided the optimal balance of model fit, parsimony, and interpretability.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents baseline sociodemographic, food access, and health characteristics of the 316 people living with HIV enrolled in the DECIDE study. The majority of participants were female (75.3%) with a median age of 41 years (IQR: 33, 48). Most participants (81.0%) had completed Standard 7 or higher education. Nearly half (46.5%) were currently married or living with a partner, while 16.7% were widowed and 16.3% had never married. Approximately one-third of participants rented their homes (34.9%), had a home garden (34.5%), and used a refrigerator (32.3%). The median time since HIV diagnosis was 4 years (IQR: 2, 8). Most participants reported good (60.9%) or very good (30.8%) adherence to antiretroviral therapy over the previous month. Nearly half (47.4%) did not share toilet facilities with other households, while 29.8% shared facilities with 2 to 5 households. Two-thirds of the sampled participants experience severe or moderate food insecurity. The characteristics of the included sample population are similar to those of families enrolled in the DUCS surveillance system, irrespective of HIV status\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant demographics of the DECIDE study in peri-urban Tanzania.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;312\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7 (77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.3 (235)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (34,49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard 7 or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.8 (252)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhat is your current marital status?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3 (51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married or living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5 (145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Deserted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.4 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7 (52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRent this house? Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.9 (109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome garden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.6 (105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUses fridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.4 (98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears since HIV diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate your adherence to ARVs over the last month?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1 (19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.9 (190)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.8 (96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare toilet facilities with your household?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.4 (148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1 (47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo to five\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.8 (93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than five\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7 (24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Food Insecurity Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood secure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2 (88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMildly FIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7 (27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate FIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2 (91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere FIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.0 (106)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eWhat are the household food purchase trends and patterns by vendor type?\u003c/h3\u003e\n\u003cp\u003eWe summarize three measures of household food purchase patterns: purchase (yes/no), frequency, and diversity at the food group level. Overall, 85% of the participants bought food\u003c/p\u003e \u003cp\u003ein the last 7 days. Analysis across two survey rounds (N\u0026thinsp;=\u0026thinsp;312 and N\u0026thinsp;=\u0026thinsp;288) reveals consistent purchasing behavior: 81\u0026ndash;85% report food purchases in the previous week, and the median is 7\u0026ndash;9 items purchased from a list of 49 foods. Vegetables were the most commonly purchased food group (69\u0026ndash;70%, specifically tomatoes and carrots), followed by staples (66\u0026ndash;68%), fruit (54\u0026ndash;60%), and flesh foods (57\u0026ndash;59%). Nearly half of the sample (47\u0026ndash;48%) purchased sweets, snacks, and sugary beverages (S-SSB), whereas dairy products were purchased by a small minority (8\u0026ndash;10%). The frequency of purchases showed that vegetables were purchased most often (median 3 times per week), while other food groups were purchased 1\u0026ndash;2 times per week.\u003c/p\u003e \u003cp\u003ePurchase diversity within food groups remained relatively low across both rounds, with participants typically purchasing only 1\u0026ndash;3 different items in each category. Vegetables showed the greatest variety (median of 3 types), followed by staples and flesh foods (2 items each), while fruit, S-SSB, and dairy showed minimal diversity (0\u0026ndash;1 items). The stability of these patterns across the two survey rounds suggests habitual food purchasing behavior, with vegetables dominating both purchase frequency and variety. Dairy products appear to be absent from food purchases.\u003c/p\u003e \u003cp\u003eOver 50% of participants report purchasing food yesterday, with an average spend of 5,000 Tanzanian shillings (~\u0026thinsp;2 USD in 2019) to feed about four people. Of the participants who bought food the previous day, 10% reported buying on credit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFood Purchase Patterns in the last 7 days, DECIDE Cohort, Peri-urban Dar es Salaam, Tanzania.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase patterns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRound 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;312\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;274\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of foods purchased by individual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (3,15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3,13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual purchased food in the last week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.6 (267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.7 (243)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase of Food Groups\u003c/p\u003e \u003cp\u003e(% Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase staples?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.4 (204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5 (185)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase dairy?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9 (27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase flesh foods?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3 (182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.9 (156)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase vegetables?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.6 (217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0 (189)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase fruit?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.6 (186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3 (146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase S-SSB?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.4 (148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.1 (129)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety of Purchase Within Food Groups \u003c/p\u003e \u003cp\u003e(# of items; median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of staples purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity dairy purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of flesh foods purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of vegetables purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of fruit purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of S-SSB purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of Purchase of a Food Group\u003c/p\u003e \u003cp\u003e(# of times; median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of staples purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of dairy purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of flesh foods purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of vegetables purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (1,12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of fruit purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2,6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of S-SSB purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood expenses and management strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDid you spend money on food, including purchases on credit, to feed your family?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.9 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.5 (141)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYesterday, about how much money did you spend in food to feed your family? (in Tanzanian Shillings)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5000 (4000,10000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5000 (4000,8000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow many people were fed with the food that you bought yesterday?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn the past week, did you receive credit in order to buy food for your household?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.1 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates household food sourcing patterns across three major food categories (Staples, Meat, Fruits and Vegetables) and the sources of food from formal vendors (market/kiosk), semi-formal vendors (umbrella/pallet-based vendors), and informal vendors (mobile). For staples such as rice and bread, formal market/kiosk vendors dominate, whereas semi-formal umbrella vendors play a more significant role in meat purchases, particularly for chicken and processed meats. Fresh fruits and vegetables were predominantly sourced from semi-formal and informal vendors. Overall, the data reveal that formal vendors remain the predominant source of staple foods, while informal vendors are a significant source of nutrient-dense foods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDo food-insecure households interact differently in the food environment?\u003c/h3\u003e\n\u003cp\u003eFood-insecure households purchase food differently than do food-secure households, and they also exhibit distinct patterns of food acquisition within the food environment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). First, a higher percentage of households experiencing moderate or severe food insecurity report purchasing food in the past 7 days than food-secure or mildly food-insecure households (90% vs. 78%; p-value of 0.005). When examining specific food categories, a higher proportion of moderately or severely food-insecure households purchased staples (72.6% vs. 54.3%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and vegetables (75.1% vs. 61.2%; p\u0026thinsp;=\u0026thinsp;0.009) than food-secure or mildly food-insecure households. Dairy purchasing showed the opposite pattern, with fewer moderately or severely food-insecure households purchasing dairy (4.0% vs. 15.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed in the proportion of purchasing flesh foods, fruit, or sugar-sweetened beverages.\u003c/p\u003e \u003cp\u003eSecond, households with moderate or severe food insecurity had much higher food purchasing frequency for each food item than food-secure households. For example, the mean purchase frequency for food-secure participants was lower at 2.3 times per week than for participants who were moderately or severely food-insecure (2.6 times per week per food item, p-value of 0.015). Most notably, there was a greater frequency of purchase of staples with 6.7 times per week (i.e., almost daily) among moderate and severe food-insecure households, compared to 4.9 times among food-secure or mildly food-insecure households (p\u0026thinsp;\u0026lt;\u0026thinsp;0.008). Similarly, dairy was purchased more frequently by food-insecure households (4.0 times per week) than by food-secure or mildly food-insecure households (1.9 times per week; p\u0026thinsp;\u0026lt;\u0026thinsp;0.025). No significant differences were found in purchase frequency for flesh foods, vegetables, fruit, or sugar-sweetened beverages.\u003c/p\u003e \u003cp\u003eDespite a high frequency of food purchases, food-insecure households showed lower diversity of purchases within food groups than other groups. The diversity of dairy products purchased was lower among households with moderate or severe food insecurity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, households with moderate or severe food insecurity purchased fewer types of flesh foods (1.7 vs. 2.2, p\u0026thinsp;=\u0026thinsp;0.040). Data from the second round (not shown) exhibited similar trends. Discretizing the food insecurity category into \u0026ldquo;any food insecurity\u0026rdquo; vs. \u0026ldquo;food secure\u0026rdquo; yielded the same inference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFood purchase patterns by food security status in the DECIDE cohort, Round 1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuestions ask about the food purchased in the last 7 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood secure/mild insecure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate/Severe FIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;115\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;197\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;312\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual purchased food in the last week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.3 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.8 (177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.6 (267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean frequency of food purchased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 [\u0026plusmn;\u0026thinsp;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 [\u0026plusmn;\u0026thinsp;1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 [\u0026plusmn;\u0026thinsp;1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of foods purchased by individual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 [\u0026plusmn;\u0026thinsp;8.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8 [\u0026plusmn;\u0026thinsp;7.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7 [\u0026plusmn;\u0026thinsp;7.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e% Purchased individual food groups?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase staples?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.9 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.1 (142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.4 (204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase dairy?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.7 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase flesh foods?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.4 (117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.3 (182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase vegetables?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.9 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.6 (147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.6 (217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase fruit?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.1 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.9 (118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.6 (186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase S-SSB?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.3 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.2 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.4 (148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFood purchase diversity within food groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of staples purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 [\u0026plusmn;\u0026thinsp;1.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 [\u0026plusmn;\u0026thinsp;1.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 [\u0026plusmn;\u0026thinsp;1.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity dairy purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2 [\u0026plusmn;\u0026thinsp;0.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 [\u0026plusmn;\u0026thinsp;0.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 [\u0026plusmn;\u0026thinsp;0.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of flesh foods purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 [\u0026plusmn;\u0026thinsp;2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 [\u0026plusmn;\u0026thinsp;1.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 [\u0026plusmn;\u0026thinsp;2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of vegetable purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 [\u0026plusmn;\u0026thinsp;2.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 [\u0026plusmn;\u0026thinsp;2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 [\u0026plusmn;\u0026thinsp;2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of fruit purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 [\u0026plusmn;\u0026thinsp;1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 [\u0026plusmn;\u0026thinsp;1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 [\u0026plusmn;\u0026thinsp;1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity of S-SSB purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 [\u0026plusmn;\u0026thinsp;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 [\u0026plusmn;\u0026thinsp;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 [\u0026plusmn;\u0026thinsp;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood purchase frequency within food groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of staples purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9 [\u0026plusmn;\u0026thinsp;4.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7 [\u0026plusmn;\u0026thinsp;4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2 [\u0026plusmn;\u0026thinsp;4.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of dairy purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 [\u0026plusmn;\u0026thinsp;1.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 [\u0026plusmn;\u0026thinsp;3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6 [\u0026plusmn;\u0026thinsp;2.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of flesh foods purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 [\u0026plusmn;\u0026thinsp;3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 [\u0026plusmn;\u0026thinsp;3.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6 [\u0026plusmn;\u0026thinsp;3.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of vegetable purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8 [\u0026plusmn;\u0026thinsp;8.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2 [\u0026plusmn;\u0026thinsp;8.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1 [\u0026plusmn;\u0026thinsp;8.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of fruit purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6 [\u0026plusmn;\u0026thinsp;5.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1 [\u0026plusmn;\u0026thinsp;6.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3 [\u0026plusmn;\u0026thinsp;6.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of S-SSB purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 [\u0026plusmn;\u0026thinsp;2.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8 [\u0026plusmn;\u0026thinsp;2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9 [\u0026plusmn;\u0026thinsp;2.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eHow are food purchase metrics related to micronutrient adequacy?\u003c/h3\u003e\n\u003cp\u003eNext, we developed and validated food purchase metrics with micronutrient adequacy. First, based on ANOVA results for various purchase metrics and food security, we identified eight candidate purchase metrics: purchase of staples, vegetables, and dairy; diversity of dairy foods; purchases of snacks and sweet and sugary beverages; and, lastly, frequency of staples and dairy purchases.\u003c/p\u003e \u003cp\u003eTo validate these purchase patterns, we first examined micronutrient adequacy across the study population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, the proportion of participants achieving minimum micronutrient adequacy was high for zinc (78%) but low for iron (48%), vitamin A (35%), and calcium (27%). Micronutrient adequacy varied significantly by food security status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, the mean micronutrient adequacy indicated that approximately 72% of food-secure households achieved adequacy, compared with 60% of mildly food-insecure and 55% of moderately food-insecure households (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For calcium, a higher percentage of food-secure households achieved adequacy compared to those with mild or moderate food insecurity (approximately 40% vs. 30% and 28%, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For zinc adequacy, 90% of both food-secure and mildly food-insecure households met adequacy criteria, compared with approximately 80% of moderately food-insecure households (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFew food purchase metrics were significantly associated with micronutrient adequacy (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the eight candidate purchase metrics examined, the diversity of flesh-food purchases was most strongly associated with micronutrient adequacy, showing significant associations with zinc, iron, and vitamin A (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for vitamin A). Single associations were observed between purchasing staples and zinc adequacy, purchasing dairy and vitamin A adequacy, and purchasing vegetables and iron adequacy. Purchase frequency metrics and diversity of dairy or sugar-sweetened beverages showed no significant associations with any micronutrient adequacy.\u003c/p\u003e \u003cp\u003eTable 4: Adjusted models examining associations between purchase metrics, calcium, zinc, iron, and vitamin A adequacy of the diets\u003c/p\u003e\n\u003ctable class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePurchase metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCalcium adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eZinc adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIron adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVitamin A adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePurchase staples?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n 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dairy purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiversity of flesh foods purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\n \u003cv:shape id=\"_x0000_i1027\" type=\"#_x0000_t75\" alt=\"Arrow Up with solid fill\" 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src=\"file:///C%3A/Users/smt8250/AppData/Local/Temp/msohtmlclip1/01/clip_image002.png\" o:title=\"\" cropbottom=\"-163f\" cropleft=\"-41184f\" cropright=\"-37960f\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\u0026nbsp;\u003c/v:shape\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img177427914174.png\" alt=\"image\"\u003e*\n \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img177427914174.png\" alt=\"image\"\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiversity of SSB purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency of staples purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency of dairy purchase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.001 ** p\u0026lt;0.01 * p\u0026lt;0.05; All models adjusted for gender, age, assets, education, type of dwelling unit (rent or own), and duration of ARV treatment\u003c/p\u003e\n\u003ch3\u003eWhat classes of food purchase patterns emerge, and how are they associated with micronutrient adequacy?\u003c/h3\u003e\n\u003cp\u003eUsing latent class models, we identified three classes of purchase patterns that best fit the data structure. The most common pattern, \"Buy Everything\" (50% of participants), was characterized by weekly purchases of staples, flesh foods, vegetables, fruits, and sugar-sweetened beverages (SSB). The second pattern, \"Buy SSB Weekly\" (29% of participants), purchased very little each week, aside from sweet and sugary beverages. The third pattern, \"Buy Basics\" (21% of participants), primarily purchased staples and vegetables weekly. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the left panel shows bar graphs of food group purchases over the past 7 days, with the y-axis showing the percentage of participants in each shopper class who purchased each food group and the x-axis showing the food groups. The right panel displays three plots showing the odds of nutrient adequacy by class membership.\u003c/p\u003e \u003cp\u003eThe \"Buy Sugar-Sweetened Beverages Weekly\" class purchased very little other than sugar-sweetened beverages each week. Class membership was associated with increased odds of zinc and iron adequacy, even after adjusting for demographics and household characteristics. The \"Buy Basics\" class was characterized by high proportions of participants purchasing staples and vegetables weekly. Class membership was associated with decreased odds of zinc and iron adequacy, reflecting greater food insecurity among those purchasing only basic staples weekly. The \"Buy Everything\" class was characterized by participants who purchased staples, flesh foods, vegetables, fruits, and sugar-sweetened beverages weekly. Class membership in this group was not associated with micronutrient adequacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined household food purchase patterns and micronutrient intakes among people living with HIV (PLHIV) in peri-urban Dar es Salaam, Tanzania. We observed frequent household interaction with the food environment, with particularly high purchasing frequency for vegetables and staple foods. Food-insecure households purchased food more frequently than food-secure households, likely reflecting limited purchasing power that necessitates smaller, more frequent shopping trips rather than bulk purchases. Our findings reveal that formal vendors serve as the primary source of staple foods, while semi-formal and informal vendors provide most nutrient-dense foods such as meat, fish, and vegetables, on par with other studies in East Africa \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Few food purchase metrics were significantly associated with micronutrient adequacy, except for meat purchase diversity.\u003c/p\u003e \u003cp\u003eOur findings highlight important patterns in the food environment regarding how households access different food types. Staples and sweet or sugary beverages were typically purchased from formal food environments (cement stores and shops), while meats, fish, and vegetables were predominantly sourced from umbrella vendors and mobile sellers operating in semi-formal and informal settings. This pattern has significant policy implications, particularly in light of ongoing debates about food environment regulation and modernization\u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Several emerging countries have pursued strategies to increase regulation and the \"supermarketization\" of the food environment\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, our data demonstrate that nutrient-dense products, including meats, fish, and vegetables, are most commonly purchased from informal/semi-formal retail outlets. Thus, policies emphasizing the formalization and regulation of food environments must be carefully designed to avoid disrupting access to these nutrient sources, particularly for low-income households that rely heavily on these informal channels.\u003c/p\u003e \u003cp\u003eImportantly, food security status did not significantly affect where households purchased produce and fruits, with both food-secure and food-insecure households predominantly sourcing these items from semi-formal and informal food environments. This suggests that informal vendors play a universal role in providing access to fresh produce across socioeconomic strata in peri-urban settings. However, food security did influence purchasing patterns in other ways.\u003c/p\u003e \u003cp\u003eUrban food-secure households purchased more diverse food groups (including sweets and sugary beverages) and a greater variety of items within those groups but shopped less frequently in the preceding week than food-insecure households. It's likely that these food-secure households can afford to buy discretionary items like sweet beverages, have greater overall purchasing power, and therefore buy a more diverse range of foods, including animal-source foods that provide zinc and iron. In contrast, households buying only basic staples are probably doing so out of necessity, resulting in monotonous diets with inadequate micronutrient density. This aligns with our finding that meat purchase diversity was associated with better micronutrient adequacy, which both serve as markers of household purchasing power rather than direct contributors to nutrient intake. These results align with research from Bangladesh, where households experiencing food insecurity buy rice more frequently, in smaller quantities\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Very few studies have examined other specific food purchase frequencies and food security, which is a novel contribution of this analysis.\u003c/p\u003e \u003cp\u003eWe identified three distinct shopping profiles among peri-urban households in Dar es Salaam: (1) households that purchase sweet and sugary beverages weekly; (2) those that purchase only basic foods weekly; and (3) those that purchase everything, including a wide variety of food items, weekly. Interestingly, shopping profiles that included weekly purchases of sweet and sugary beverages were associated with greater zinc and iron adequacy, whereas households that purchased only basic foods weekly exhibited lower zinc and iron adequacy.\u003c/p\u003e \u003cp\u003eThis study has several important limitations. The food purchase tool did not collect data on food quantity, limiting our ability to assess household-level portion sizes and total food availability.\u003c/p\u003e \u003cp\u003eAnother limitation is the imperfect match of the 24-hour dietary intake recall period and the assessment of food purchase patterns (7-day recall) over two distinct time periods. Finally, the generalizability of our findings may be limited by the characteristics of our study population. Participants included people living with HIV and their families, who may have higher nutrition knowledge due to targeted nutrition education programs for this population. Additional research is needed to validate these findings in more diverse populations.\u003c/p\u003e \u003cp\u003eOur findings build upon a growing body of evidence documenting the centrality of food purchasing in African food systems, irrespective of rurality. Previous research has established that purchasing is the predominant mode of food procurement across the continent\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, with bartering, crediting, gifting, and home gardening persisting but accounting for only a small percentage of food acquisition. This pattern holds even in rural areas, where purchasing has become increasingly prevalent\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Urbanization has further intensified reliance on purchased prepared foods, with several studies documenting increased consumption of food away from home, i.e. prepared foods, that are often energy-dense\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This shift coincides with what Reardon and colleagues have termed the \"processed food revolution\" in developing countries, characterized by the rapid expansion in the availability and consumption of processed and ultra-processed foods, and even \u0026lsquo;prepared ultra-processed foods\u0026rsquo;\u003csup\u003e2\u003c/sup\u003e. Overall reliance of the food-insecure urban population on informal vendors has been noted in other East African settings\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe current study extends this work by examining how households in peri-urban areas interact with food environments through their specific food purchase patterns and procurement sources, and how these patterns relate to dietary micronutrient adequacy. Food security shapes how households interact with the food environment - not just what they buy, but where they shop and how often. Informal and semi-formal vendors play a critical role in providing access to nutrient-dense foods in peri-urban settings across the spectrum of household food security status. Policies aimed at modernizing food retail need to account for this, or they risk disrupting access to essential nutrients, especially for food-insecure households.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding sources:\u003c/h2\u003e \u003cp\u003eDiet, Environment, and Choices of positive living (DECIDE study): Evaluating personal and external food environment influences on diets among PLHIV and families in Dar es Salaam, Tanzania, study is funded through the Drivers of Food Choice Grants Program by Bill and Melinda Gates Foundation (ID: OPP1110043) and UK AID. RA, JW, and GK are supported by CGIAR Better Diets and Nutrition Science Program.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.A. designed the tools, led the analysis, and wrote the manuscript with input from all co-authors; RA, D.M., GL, led the implementation of the study that provided data for these analyses; D.M., A.M, led the fieldwork and data collection; RA, NSG, CKV, SFM, VK, GK, JW, and GS provided input on the analyses. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe Diet, Environment, and Choices of Positive Living (DECIDE) study is a collaborative project led by Purdue University, the University of Chicago, Muhimbili University, and the Africa Academy of Public Health. The authors acknowledge and are grateful for the collaboration and support of the families participating in the DECIDE study, as well as the dedication of the regional and field staff.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmbikapathi, R. et al. Global food systems transitions have enabled affordable diets but had less favourable outcomes for nutrition, environmental health, inclusion and equity. \u003cem\u003eNat. Food\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 764\u0026ndash;779 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReardon, T. et al. 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The state of urban food insecurity in southern Africa. \u003cem\u003eUrban Food Secur. Ser.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 1\u0026ndash;54 (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Food environments, food security, food purchase, Tanzania, diets","lastPublishedDoi":"10.21203/rs.3.rs-8735511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8735511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines how food insecurity influences household food purchases and dietary adequacy in the context of a rapidly evolving food environment in Africa. We examine food purchase patterns, nutrient intakes, and food security using data collected in 2019\u0026ndash;2020 among people living with HIV in peri-urban Dar es Salaam, Tanzania. Participants reported food purchases and vendor interactions over seven days, evaluated against nutrient intakes from 24-hour diet recalls. Urban food-insecure households interacted more frequently with the food environment than food-secure households, purchasing staples and vegetables more often. Top weekly purchases were tomatoes (62%), sugar (57%), carrots (51%), rice (50%), and maize flour (48%). Staple purchases were associated with lower zinc adequacy (OR: 0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.032), while meat purchase diversity was associated with greater zinc (OR: 1.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.039) and vitamin A adequacy (OR: 1.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.023). Latent class analysis revealed three distinct weekly purchasing groups: \"buy everything,\" \"buy basics,\" and \"purchase sweet and sugary beverages.\" These groupings were associated with micronutrient adequacy and food security. Informal and semi-formal vendors play a critical role in providing access to nutrient-dense foods across the spectrum of household food security status.\u003c/p\u003e","manuscriptTitle":"Food-insecure households interact more frequently with the food environment: food purchase patterns and dietary adequacy in the DECIDE study, Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 13:14:42","doi":"10.21203/rs.3.rs-8735511/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-30T00:55:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T11:19:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165159151363818011026097537100062004915","date":"2026-03-21T09:53:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26378316454819339528398639113275294233","date":"2026-03-21T04:51:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T05:02:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T20:12:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-12T06:11:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T14:35:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-10T13:31:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52731a7d-82c6-4ecb-bb3d-c60f345e93a6","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64897504,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":64897505,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-03-24T13:14:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 13:14:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8735511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8735511","identity":"rs-8735511","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}