Dataset Information

US Household Food Shopping Patterns: Dynamic Shifts Since 2000 And Socioeconomic Predictors.

ABSTRACT: Under the assumption that differential food access might underlie nutritional disparities, programs and policies have focused on the need to build supermarkets in underserved areas, in an effort to improve dietary quality. However, there is limited evidence about which types of stores are used by households of different income levels and differing races/ethnicities. We used cross-sectional cluster analysis to derive shopping patterns from US households' volume food purchases by store from 2000 to 2012. Multinomial logistic regression identified household socioeconomic characteristics that were associated with shopping patterns in 2012. We found three food shopping patterns or clusters: households that primarily shopped at grocery stores, households that primarily shopped at mass merchandisers, and a combination cluster in which households split their purchases among multiple store types. In 2012 we found no income or race/ethnicity differences for the cluster of households that primarily shopped at grocery stores. However, low-income non-Hispanic blacks (versus non-Hispanic whites) had a significantly lower probability of belonging to the mass merchandise cluster. These varied shopping patterns must be considered in future policy initiatives. Furthermore, it is important to continue studying the complex rationales for people's food shopping patterns.

SUBMITTER: Stern D

PROVIDER: S-EPMC4734755 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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US Household Food Shopping Patterns: Dynamic Shifts Since 2000 And Socioeconomic Predictors.

Stern Dalia D Robinson Whitney R WR Ng Shu Wen SW Gordon-Larsen Penny P Popkin Barry M BM

Health affairs (Project Hope) 20151101 11

Under the assumption that differential food access might underlie nutritional disparities, programs and policies have focused on the need to build supermarkets in underserved areas, in an effort to improve dietary quality. However, there is limited evidence about which types of stores are used by households of different income levels and differing races/ethnicities. We used cross-sectional cluster analysis to derive shopping patterns from US households' volume food purchases by store from 2000 t ...[more]

PMID: 26526241

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Project description:BACKGROUND:Modifying a household's food environment by targeting a single retailer type, like supermarkets, has a limited impact on dietary outcomes. This may be because the food environment has a limited impact on shopping behaviors, or because households are not as reliant on supermarkets as we assume. However, our understanding of how households shop for food, especially when considering the use of both food at home (FAH) retailers, such as supermarkets, and away from home retailers (FAFH), such as restaurants, is limited. Thus, understanding how households shop for food is a necessary first step when developing programs to modify food purchasing behavior. METHODS:K-means cluster analysis was used to identify weekly food shopping trip patterns based on the percentage of trips to FAH and FAFH retailers in the 2013 Food Acquisition and Purchase Survey (FoodAPS) dataset (n?=?4665 households). Multinomial logistic regression was used to examine the relationship between shopping trip patterns, household and food environment characteristics. RESULTS:Three patterns emerged: primarily supermarket, primarily supercenter, or mix (i.e. no dominant retailer type, but high FAFH use). Households with incomes below 185% of the federal poverty line were evenly divided between patterns that rely primarily on FAH retailers, and the mix pattern. While nearly 70% of households with incomes above 185% of the federal poverty line are in the mix cluster. Supermarket and superstore availability significantly influenced the likelihood of belonging to those clusters respectively, while having a child, higher income, and attitudes towards healthy meal preparation time or taste significantly influenced the likelihood of belonging to the mix cluster. CONCLUSION:Although lower-income households are more likely to rely primarily on FAH retailers, household's, regardless of income, that primarily utilize FAH retailers show a strong preference for either superstores or supermarkets suggesting a need for interventions to reach both retailer types. However, altering the food environment alone may not be sufficient to discourage use of FAFH retailers as households relying on FAFH retailers are significantly influenced by meal preparation time and healthy food taste.

Project description:BackgroundHousehold food insecurity is a potent social determinant of health and health care costs in Canada, but understanding of the social and economic conditions that underlie households' vulnerability to food insecurity is limited.MethodsData from the 2011-12 Canadian Community Health Survey were used to determine predictors of household food insecurity among a nationally-representative sample of 120,909 households. Household food insecurity over the past 12 months was assessed using the 18-item Household Food Security Survey Module. Households were classified as food secure or marginally, moderately, or severely food insecure based on the number of affirmative responses. Multivariable binary and multinomial logistic regression analyses were used to determine geographic and socio-demographic predictors of presence and severity of household food insecurity.ResultsThe prevalence of household food insecurity ranged from 11.8% in Ontario to 41.0% in Nunavut. After adjusting for socio-demographic factors, households' odds of food insecurity were lower in Quebec and higher in the Maritimes, territories, and Alberta, compared to Ontario. The adjusted odds of food insecurity were also higher among households reliant on social assistance, Employment Insurance or workers' compensation, those without a university degree, those with children under 18, unattached individuals, renters, and those with an Aboriginal respondent. Higher income, immigration, and reliance on seniors' income sources were protective against food insecurity. Living in Nunavut and relying on social assistance were the strongest predictors of severe food insecurity, but severity was also associated with income, education, household composition, Aboriginal status, immigration status, and place of residence. The relation between income and food insecurity status was graded, with every $1000 increase in income associated with 2% lower odds of marginal food insecurity, 4% lower odds of moderate food insecurity, and 5% lower odds of severe food insecurity.ConclusionsThe probability of household food insecurity in Canada and the severity of the experience depends on a household's province or territory of residence, income, main source of income, housing tenure, education, Aboriginal status, and household structure. Our findings highlight the intersection of household food insecurity with public policy decisions in Canada and the disproportionate burden of food insecurity among Indigenous peoples.

Project description:To target food security interventions for smallholder households, decision makers need large-scale information, such as maps on poverty, food security and key livelihood activities. Such information is often based on expert knowledge or aggregated data, despite the fact that food security and poverty are driven largely by processes at the household level. At present, it is unclear if and how household level information can contribute to the spatial prediction of such welfare indicators or to what extent local variability is ignored by current mapping efforts. A combination of geo-referenced household level information with spatially continuous information is an underused approach to quantify local and large-scale variation, while it can provide a direct estimate of the variability of welfare indicators at the most relevant scale. We applied a stepwise regression kriging procedure to translate point information to spatially explicit patterns and create country-wide predictions with associated uncertainty estimates for indicators on food availability and related livelihood activities using household survey data from Uganda. With few exceptions, predictions of the indicators were weak, highlighting the difficulty in capturing variability at larger scale. Household explanatory variables identified little additional variation compared to environmental explanatory variables alone. Spatial predictability was strongest for indicators whose distribution was determined by environmental gradients. In contrast, indicators of crops that were more ubiquitously present across agroecological zones showed large local variation, which often overruled large-scale patterns. Our procedure adds to existing approaches that often only show large-scale patterns by revealing that local variation in welfare is large. Interventions that aim to target the poor must recognise that diversity in livelihood activities for income generation within any given area often overrides the variability of livelihood activities between distant regions in the country.

Dataset Information

US Household Food Shopping Patterns: Dynamic Shifts Since 2000 And Socioeconomic Predictors.

Publications

US Household Food Shopping Patterns: Dynamic Shifts Since 2000 And Socioeconomic Predictors.

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