Household Food Purchase Research Articles

Enhancing understanding of food purchasing patterns in the Northeast US using multiple datasets

AbstractDue to correlations between purchasing patterns and diet disparities, differences in food shopping patterns and strategies across income levels and other socio-economic characteristics is a widely-studied research area. Most extant literature uses either primary or secondary data, which are often characterized by, respectively, limited geographical scope and considerable level of detail, or wide geographical reach but low detail. That literature also reveals contrasting results based on methods, data sources and geographic location. In this paper, we use three different datasets to characterize the differences in purchasing patterns across income levels, rural–urban status and other variables of food shoppers in the Northeastern USA and compare these trends with existing research. While many of the findings corroborate previous studies, new findings include less reliance on superstores overall, except for rural respondents, and a greater reliance on limited assortment supermarkets for SNAP and low-income households. Food purchasing differences are described by race and ethnicity, income and education, and children in the household. The analysis presented here includes a portion of the work performed by an interdisciplinary team of researchers engaged in the USDA National Institute of Food and Agriculture's Agriculture and Food Research Initiative project Enhancing Food Security in the Northeast (EFSNE). By using primary data from shoppers' intercept surveys, and secondary data from two large datasets, one of household food purchases and the other of food expenditures, we identify purchasing decisions holding at both the case-study (limited geography) and broader geographic (entire Northeast) levels, which both support previous findings and reveal the need for additional research in this area.

Characterisation of fresh fruit consumption in Spain based on food-related lifestyle

PurposeThe purpose of this paper is to describe fresh fruit consumers in Spain according to their food-related lifestyle (FRL).Design/methodology/approachA random stratified sample of 500 people, representative of the persons responsible for household food purchasing and resident in Spain, was interviewed in 2017 using a revised and adapted version of the FRL instrument (Grunert et al., 1993). Questions about fruit purchasing criteria, consumption habits and demographics were also included. Factor and cluster analysis (Ward method) yielded four segments.FindingsThe segments identified are: “Total indifference (TI)”, small segment with disinterest in extra-domestic and social consumption, nutrition and innovation; “Little time to cook, concerned about nutrition and extra-domestic consumption (LICNE)”, the largest consumers of fresh fruit who show interest in nutrition and health, but no interest in the price of products, convenience foods or liking cooking; “Cooks and preference for natural products (COOKNAT)”, the largest segment, with a medium-high consumption of fresh fruits, who are related to cooking at home, natural products and a concern for the price-quality ratio; and “Unconcerned (UNC)” presents the lowest fresh fruit consumption and shows the lowest interest in natural products and some indifference to the other criteria.Originality/valueThe information obtained in this study gives interesting new insights for the marketing strategies of the fresh fruit suppliers to Spain and the Food and Public Health Administrations.

Sustainable Diets in the UK—Developing a Systematic Framework to Assess the Environmental Impact, Cost and Nutritional Quality of Household Food Purchases

Sustainable diets should not only respect the environment but also be healthy and affordable. However, there has been little work to assess whether real diets can encompass all three aspects. The aim of this study was to develop a framework to quantify actual diet records for health, affordability and environmental sustainability and apply this to UK food purchase survey data. We applied a Life Cycle Assessment (LCA) approach to detailed food composition data where purchased food items were disaggregated into their components with traceable environmental impact data. This novel approach is an improvement to earlier studies in which sustainability assessments were based on a limited number of “food groups”, with a potentially high variation of actual food items within each group. Living Costs and Food Survey data for 2012, 2013 and 2014 were mapped into published figures for greenhouse gas emissions (GHGE, taking into account processing, transport and cooking) and land use, a diet quality index (DQI) based on dietary guidelines and food cost, all standardised per household member. Households were classified as having a ‘more sustainable’ diet based on GHGE, cost and land use being less than the median and DQI being higher than the median. Only 16.6% of households could be described as more sustainable; this rose to 22% for those in the lowest income quintile. Increasing the DQI criteria to >80% resulted in only 100 households being selected, representing 0.8% of the sample. The framework enabled identification of more sustainable households, providing evidence of how we can move toward better diets in terms of the environment, health, and costs.

Estimation of Underreporting in Diary Surveys: An Application Using the National Household Food Acquisition and Purchase Survey

Abstract Diary surveys are used to collect data on a variety of topics, including health, time use, nutrition, and expenditures. The US National Household Food Acquisition and Purchase Survey (FoodAPS) is a nationally representative diary survey, providing an important data source for decision-makers to design policies and programs for promoting healthy lifestyles. Unfortunately, a multiday diary survey like the FoodAPS can be subject to various survey errors, especially item nonresponse error occurring at the day level. The FoodAPS public-use data set provides survey weights that adjust only for unit nonresponse. Due to the lack of day-level weights (which could possibly adjust for the item nonresponse that arises from refusals on particular days), the adjustments for unit nonresponse are unlikely to correct any bias in estimates arising from households that initially agree to participate in FoodAPS but then fail to report on particular days. This article develops a general methodology for estimating the extent of underreporting due to this type of item nonresponse error in diary surveys, using FoodAPS as a case study. We describe a methodology combining bootstrap replicate sampling for complex samples and imputation based on a Heckman selection model to predict food expenditures for person-days with missing expenditures. We estimated the item nonresponse error by comparing weighted estimates according to only reported expenditures and both reported expenditures and predictions for missing values. Results indicate that ignoring the missing data would lead to consistent overestimation of the mean expenditures and events per person per day and underestimation of the total expenditures and events. Our study suggests that the household-level weights, which generally account for unit nonresponse, may not be entirely sufficient for addressing the nonresponse occurring at the day level in diary surveys, and proper imputation methods will be important for estimating the size of the underreporting.

Effects of Package Size on Household Food Purchases

AbstractThis article analyzes household food purchase behavior in response to changes in product package size. In particular, we exploit food manufacturer package downsizing strategy and design a difference‐in‐difference analysis to track shifts in household purchase volume in affected and comparison product groups before and after package size changes. We use UPC‐level purchase data from a panel of households over a seven‐year period for analysis. Our empirical approach overcomes two important limitations of the extant literature: it (i) accounts for determinants of purchase behavior and (ii) produces results that elucidate the long‐term effects of package size changes. The main finding is that, on average, smaller package size significantly reduces household purchase volume, which indicates a positive correlation between package size and food‐at‐home consumption. The results also show that downsizing does not have a significant effect on the purchase volume of non‐downsized products and on the number of packages purchased. The implications of the results for policy and potential interventions are discussed.

Processing level and diet quality of the US grocery cart: is there an association?

The majority of groceries purchased by US households are industrially processed, yet it is unclear how processing level influences diet quality. We sought to determine if processing level is associated with diet quality of grocery purchases. We analysed grocery purchasing data from the National Household Food Acquisition and Purchase Survey 2012-2013. Household grocery purchases were categorized by the NOVA framework as minimally processed, processed culinary ingredients, processed foods or ultra-processed foods. The energy share of each processing level (percentage of energy; %E) and Healthy Eating Index-2015 (HEI-2015) component and total scores were calculated for each household's purchases. The association between %E from processed foods and ultra-processed foods, respectively, and HEI-2015 total score was determined by multivariable linear regression. Foods purchased by households with the highest v. lowest ultra-processed food purchases and HEI-2015 total score <40 v. ≥60 were compared using linear regression. USA. Nationally representative sample of 3961 households. Processed foods and ultra-processed foods provided 9·2 (se 0·3) %and 55·8 (se 0·6) % of purchased energy, respectively. Mean HEI-2015 score was 54·7 (se 0·4). Substituting 10 %E from minimally processed foods and processed culinary ingredients for ultra-processed foods decreased total HEI-2015 score by 1·8 points (β = -1·8; 95 % CI -2·0, -1·5). Processed food purchases were not associated with diet quality. Among households with high ultra-processed food purchases, those with HEI-2015 score <40 purchased less minimally processed plant-foods than households with HEI-2015 score ≥60. Increasing purchases of minimally processed foods, decreasing purchases of ultra-processed foods and selecting healthier foods at each processing level may improve diet quality.

Food Access, Program Participation, and Health: Research Using FoodAPS

This piece is an introduction to the symposium on Food Access, Program Participation, and Health: Research using FoodAPS. The symposium includes articles presented at the National Bureau of Economic Research Conference on Food Security on December 7 and 8, 2017. The research herein was supported by the Economic Research Service of the U.S. Department of Agriculture, grant number 59‐5000‐5‐0115. The grant included funding to study the effects of various food assistance programs on outcomes, using data from the National Household Food Acquisition and Purchase Survey (FoodAPS). We describe the programs and the FoodAPS data, and point out highlights from the articles.

The Contribution of the School Environment to the Overall Food Environment Experienced by Children

Despite the important role of schools in the food environment faced by children, most research examining access to healthy food has focused on the retail sector. We create a database of school menus and the nutritional information about each item on the menu from nearly all school districts in the United States to examine the influence of the school‐based food environment on the nutritional choices of children. Using the National Household Food Acquisition and Purchase Survey (FoodAPS), we find that, holding constant the neighborhood retail food environment, exposure to healthier meals at school increases the healthfulness of foods acquired by children throughout the day.

Does School Lunch Fill the “SNAP Gap” at the End of the Month?

This article investigates the relationship between the timing of Supplemental Nutrition Assistance Program (SNAP) benefit payments and participation in school lunch and breakfast using the National Household Food Acquisition and Purchase Survey. An event study approach examines participation over the five‐day window before and after the SNAP payment. We find that school lunch participation decreases by 17–23 percentage points immediately after the SNAP payment among 11–18 year olds while breakfast drops by 19–36 percentage points. The decline begins the day prior to payment. We find no effects for 5–10 year olds. Models examining participation over the full SNAP month using individual fixed effects yield similar findings. Among teenagers, participation in school lunch and breakfast decline in the first two weeks of the SNAP month, increasing afterward. Non‐school meals show the opposite pattern. Overall, results indicate SNAP households rely more on school lunch and breakfast toward the end of the SNAP month.

Investigating Treatment Effects of Participating Jointly in SNAP and WIC when the Treatment Is Validated Only for SNAP

The U.S. Department of Agriculture operates several food assistance programs aimed at alleviating food insecurity. We study whether participation in both participation in both SNAP and WIC alleviates food insecurity compared with participation in SNAP alone. We bound underlying causal effects by applying nonparametric treatment effect methods that allow for endogenous selection and underreported program participation when validation data are available for one program (treatment) but not the other. We estimate average treatment effects using data from the National Household Food Acquisition and Purchase Survey (FoodAPS). FoodAPS includes administrative data to validate SNAP participation. Information on local food prices allows us to construct a food expenditure‐based monotone instrumental variable that does not require a typical instrumental variable exclusion restriction. Under relatively weak monotonicity assumptions, we identify that the impact of participating in both programs relative to SNAP alone is strictly positive, suggesting that the programs are nonredundant. This evidence can support improved design and targeting of food programs.

WIC Participation and Relative Quality of Household Food Purchases: Evidence from FoodAPS

We examine the effect of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) on the quality of household food purchases using the National Household Food Acquisition and Purchase Survey (FoodAPS) and propensity score matching. A healthy purchasing index (HPI) is used to measure nutritional quality of household food purchases. WIC foods explain the improvement in quality of food purchases, not self‐selection of more nutrition‐conscious households into the program. The improvement in purchase quality was driven entirely by WIC participating households who redeemed WIC foods during the interview week. There was no significant difference between WIC participants who did not redeem WIC foods and eligible nonparticipants. In this sample, there is no evidence that lack of access to clinics has adverse effects on participation nor is there evidence that HPI depends on supermarket access. A supervised machine learning process supports our main conclusion on the importance of WIC foods.

SNAP and Paycheck Cycles

It is well documented that individuals do not spend the Supplemental Nutrition Assistance Program (SNAP) benefits smoothly over the month after receipt. Rather, recipients spend a disproportionate share of benefits at the beginning of the benefit month. This has costs for recipients and stores. There is also evidence that other income streams, such as Social Security and paychecks, are not spent smoothly. The presence of these other income streams may bias estimates of the effects of this SNAP cycle on consumption for working SNAP beneficiaries and those who receive other government benefits. We use data from United States Department of Agriculture's National Household Food Acquisition and Purchase Survey to explore how the SNAP cycle is affected by accounting for these other income streams. We find suggestive evidence that the cycle is more pronounced for workers who are paid on a weekly or monthly basis, but little evidence that cycles in other income streams mitigate or exacerbate the SNAP cycle.

The Effect of SNAP and School Food Programs on Food Security, Diet Quality, and Food Spending: Sensitivity to Program Reporting Error

There is an extensive research literature on the effects of the Supplemental Nutrition Assistance Program (SNAP) on food‐related outcomes which has shown somewhat mixed results but generally favorable effects. However, most of the research has used data sets whose information on SNAP participation is gathered from responses on household surveys, and such responses are subject to reporting error. This study uses the National Household Food Acquisition and Purchase Survey data set to examine the effect of reporting error on food‐related outcomes, for that data set contains information on SNAP participation gathered from government administrative records. Our analysis shows that the degree of reporting error is small and has little effect on the estimated impact of participation in the SNAP program on food security, diet quality, and food spending. A supplemental analysis of the effect of school food programs likewise shows no difference in using survey or administrative data in the analysis.

Delay discounting and household food purchasing decisions: The SHoPPER study.

Delay discounting is a neurocognitive trait that has been linked to poor nutritional health and obesity, but its role in specific dietary choices is unclear. This study tested whether individual differences in delay discounting are related to the healthfulness of household food purchases and reliance on nonstore food sources such as restaurants. The food purchases of 202 primary household food shoppers were objectively documented for 14 days through a food receipt collection and analysis protocol. The nutrient content of household food purchases was derived for each participant, and the overall diet quality (Healthy Eating Index-2015) and energy density (kcal/g) of foods and beverages were calculated. The proportion of energy from nonstore food sources was also derived. Delay discounting was assessed with a choice task featuring hypothetical monetary rewards. Data were available for 12,624 foods and beverages purchased across 2,340 shopping episodes. Approximately 13% of energy was purchased from restaurants and other nonstore food sources. Steeper discounting rates were associated with lower overall Healthy Eating Index-2015 scores and a higher energy density (kcal/g) of purchased foods. Associations were attenuated but remained statistically significant when accounting for body mass index and sociodemographic variables. Discounting rates were unrelated to reliance on nonstore food sources or the energy density of purchased beverages. Delay discounting is related to the healthfulness of food purchases among primary household shoppers. As food purchasing is a key antecedent of dietary intake, delay discounting may be a viable target in dietary and weight management interventions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Comparing demographic and health characteristics of new and existing SNAP recipients: application of a machine learning algorithm

The Supplemental Nutrition Assistance Program (SNAP) expanded significantly after the Great Recession of 2008-2009, but no studies have characterized this new group of recipients. Few data sets provide details on whether an individual is a new or established recipient of SNAP. We sought to identify new and existing SNAP recipients, and to examine differences in sociodemographic characteristics, health, nutritional status, and food purchasing behavior between new and existing recipients of SNAP after the recession. We created a probabilistic algorithm to identify new and existing SNAP recipients using the 1999-2013 waves of the Panel Study of Income Dynamics. We applied this algorithm to the National Household Food Acquisition and Purchase Survey (FoodAPS), fielded during 2012-2013, to predict which individuals were likely to be new SNAP recipients. We then compared health and nutrition characteristics between new, existing, and never recipients of SNAP in FoodAPS. New adult SNAP recipients had higher socioeconomic status, better self-reported health, and greater food security relative to existing recipients, and were more likely to smoke relative to never recipients. New child SNAP recipients were less likely to eat all meals and had lower BMI relative to existing recipients. New SNAP households exhibited differences in food access and expenditures, although dietary quality was similar to that of existing SNAP households. We developed a novel algorithm for predicting new and existing SNAP recipiency that can be applied to other data sets,and subsequently demonstrated differences in health characteristics between new and existing recipients. The expansion of SNAP since the Great Recession enrolled a population that differed from the existing SNAP population and that may benefit from different types of nutritional and health services than those traditionally offered.

Greenhouse gas emissions, total food spending and diet quality by share of household food spending on red meat: results from a nationally representative sample of US households.

To determine if US household food purchases with lower levels of red meat spending generate lower life-cycle greenhouse gas emissions (GHGE), greater nutritional quality and improved alignment with the Dietary Guidelines for Americans. Affordability of purchasing patterns by red meat spending levels was also assessed. Household food purchase and acquisition data were linked to an environmentally extended input-output life-cycle assessment model to calculate food GHGE. Households (n 4706) were assigned to quintiles by the share of weekly food spending on red meat. Average weekly kilojoule-adjusted GHGE, total food spending, nutrients purchased and 2010 Healthy Eating Index (HEI-2010) were evaluated using ANOVA and linear regression. USA.ParticipantsHouseholds participating in the 2012-2013 National Household Food Acquisition and Purchase Survey. There was substantial variation in the share of the household food budget spent on red meat and total spending on red meat. The association between red meat spending share and total food spending was mixed. Lower red meat spending share was mostly advantageous from a nutritional perspective. Average GHGE were significantly lower and HEI-2010 scores were significantly higher for households spending the least on red meat as a share of total food spending. Only very low levels of red meat spending as a share of total food spending had advantages for food affordability, lower GHGE, nutrients purchased and diet quality. Further studies assessing changes in GHGE and other environmental burdens, using more sophisticated analytical techniques and accounting for substitution towards non-red meat animal proteins, are needed.

The National Household Food Acquisition and Purchase Survey: Innovations and Research Insights

AbstractEvidence‐based policies that effectively address adverse public health trends, including the increasing burden of diet‐related disease and food insecurity, require quality and comprehensive data. For food and nutrition policy, that means data on household and individual food choices and the many factors influencing food demand, including income, food assistance program participation, food security status, and the local food environment. To meet this data need, the USDA sponsored the National Household Food Acquisition and Purchase Survey (FoodAPS), an innovative survey that collected nationally‐representative data on household food purchases and acquisitions, including from low‐income households and households participating in the Supplemental Nutrition Assistance Program (SNAP). To further enable and enrich analysis, the household survey data were linked to SNAP administrative records, USDA nutrient data, and geographic information related to the local food environment. This article provides a thorough overview of FoodAPS, including the rationale for the survey, recent research findings and insights on American diet quality, food assistance programs, and food environment, as well as the challenges encountered from developing, collecting, and processing the data.

Spatial correlation between excess weight, purchase of ultra-processed foods, and human development in Brazil.

The aim of this study was to analyze the spatial distribution of excess weight in Brazil and its correlation with household food insecurity, purchase of foods by type of processing, and Human Development Index (HDI). An ecological study was undertaken using data from three surveys conducted by the Brazilian Institute of Geography and Statistics. Spatial analysis techniques were used to perform univariate and bivariate analysis. The prevalence of excess weight was 34.2% (CI 95% 33.8-34.6%). Excess weight showed a moderate and significant spatial autocorrelation (0.581; p = 0.01), with higher prevalence in states in the South, Southeast and Center-West regions. A positive moderate spatial correlation was shown between the prevalence of excess weight and HDI (0.605; p < 0.05) and purchase of ultra-processed foods (0.559; p < 0.05), while a negative moderate spatial correlation was observed between prevalence of excess weight and household food insecurity (-0.561; p < 0.05). It can be concluded that there is an unequal distribution of excess weight across Brazil. The highest prevalence rates were found in states in the Southeast, South, and Center-West regions, associated with higher HDI values and higher ultra-processed food purchases as a proportion of overall household food purchases.

Nutrition quality of food purchases varies by household income: the SHoPPER study

BackgroundLower household income has been consistently associated with poorer diet quality. Household food purchases may be an important intervention target to improve diet quality among low income populations. Associations between household income and the diet quality of household food purchases were examined.MethodsFood purchase receipt data were collected for 14 days from 202 urban households participating in a study about food shopping. Purchase data were analyzed using NDS-R software and scored using the Healthy Eating Index 2010 (HEI 2010). HEI total and subscores, and proportion of grocery dollars spent on food categories (e.g. fruits, vegetables, sugar sweetened beverages) were examined by household income-to-poverty ratio.ResultsCompared to lower income households, after adjusting for education, marital status and race, higher income households had significantly higher HEI total scores (mean [sd] = 68.2 [13.3] versus 51.6 [13.9], respectively, adjusted p = 0.05), higher total vegetable scores (mean [sd] = 3.6 [1.4] versus 2.3 [1.6], respectively, adjusted p < .01), higher dairy scores (mean [sd] = 5.6 [3.0] versus 5.0 [3.3], p = .05) and lower proportion of grocery dollars spent on frozen desserts (1% [.02] versus 3% [.07], respectively, p = .02).ConclusionsLower income households purchase less healthful foods compared with higher income households. Food purchasing patterns may mediate income differences in dietary intake quality.Trial registrationClinicalTrials.gov identifier: NCT02073643.

Healthy and sustainable diets that meet greenhouse gas emission reduction targets and are affordable for different income groups in the UK.

To model dietary changes required to shift the UK population to diets that meet dietary recommendations for health, have lower greenhouse gas emissions (GHGE) and are affordable for different income groups. Linear programming was used to create diets that meet dietary requirements for health and reduced GHGE (57 and 80 % targets) by income quintile, taking account of food budgets and foods currently purchased, thereby keeping dietary change to a minimum.Setting/ParticipantsNutrient composition, GHGE and price data were mapped to 101 food groups in household food purchase data (UK Living Cost and Food Survey (2013), 5144 households). Current diets of all income quintiles had similar total GHGE, but the source of GHGE differed by types of meat and amount of fruit and vegetables. It was possible to create diets with a 57 % reduction in GHGE that met dietary and cost restraints in all income groups. In the optimised diets, the food sources of GHGE differed by income group due to the cost and keeping the level of deviation from current diets to a minimum. Broadly, the changes needed were similar across all groups; reducing animal-based products and increasing plant-based foods but varied by specific foods. Healthy and lower-GHGE diets could be created in all income quintiles but tailoring changes to income groups to minimise deviation may make dietary changes more achievable. Specific attention must be given to make interventions and policies appropriate for all income groups.