A Comprehensive Life Cycle Assessment of Greenhouse Gas Emissions from U.S. Household Food Choices

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.

Taxing the heat out of the U.S. food system

Examining the influence of perceived and objective time constraints on the quality of household food purchases

Evidence‐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.

The U.S. Department of Agriculture’s (USDA) National Household Food Acquisition and Purchase Survey (FoodAPS) is the first nationally representative household survey to collect data on foods purchased or acquired during a survey week, producing results that are both nationally representative and representative of Supplemental Nutrition Assistance Program (SNAP) participants as well as of low-income non-SNAP households. In order to assess the quality of FoodAPS data, this report compares estimates from FoodAPS to estimates from other national-level food-related surveys, examining: (1) general demographic and socio-economic characteristics; (2) food expenditures; (3) food security; (4) SNAP participation and income; and (5) diet behavior and health. FoodAPS estimates of total, food-at-home (FAH) spending are greater than estimates from the Consumer Expenditure Survey (CE) but less than those from the National Health and Nutrition Examination Survey (NHANES). Compared to other national-level surveys, FoodAPS estimates a greater share of households with low or very low food security.

USDA’s Supplemental Nutrition Assistance Program (SNAP) is designed to increase the food purchasing power of low-income households. A recent USDA survey—the National Household Food Acquisition and Purchase Survey (FoodAPS)—provides a unique opportunity to gain a comprehensive understanding of the food spending of SNAP households. This study finds that, when adjusted for household size and composition, average food spending in SNAP households is lower than in other U.S. households, even those that are eligible for SNAP but choose not to participate. Food-at-home spending accounts for a greater share of the total food expenditures of SNAP households than of other households. SNAP benefits account for over 60 percent of the average food-at-home expenditures of SNAP households. They also play a strong role in the food budgets of households with children and those in poverty, especially those in deep poverty. Among both SNAP households and eligible nonparticipant households, those that are food secure spend more on food than those that are food insecure. Finally, this study finds clear evidence of a cyclical pattern in the food spending of SNAP households across the benefit month.

Housing costs and food purchasing characteristics: The role of SNAP participation and SNAP purchasing power.

Population exposure to food and nutrients can be estimated from household food purchases, but store surveys of foods and their composition are more available, less costly and might provide similar information. Our aim was to compare estimates of nutrient exposure from a store survey of packaged food with those from household panel food purchases. A cross-sectional store survey of all packaged foods for sale in two major supermarkets was undertaken in Auckland, New Zealand, between February and May 2012. Longitudinal household food purchase data (November 2011 to October 2012) were obtained from the nationally representative, population-weighted New Zealand Nielsen HomeScan® panel. Data on 8440 packaged food and non-alcoholic beverage products were collected in the store survey. Food purchase data were available for 1229 households and 16 812 products. Store survey data alone produced higher estimates of exposure to Na and sugar compared with estimates from household panel food purchases. The estimated mean difference in exposure to Na was 94 (95 % CI 72, 115) mg/100 g (20 % relative difference; P<0·01), to sugar 1·6 (95 % CI 0·8, 2·5) g/100 g (11 %; P<0·01), to SFA -0·3 (95 % CI -0·8, 0·3) g/100 g (6 %; P=0·3) and to energy -18 (-71, 35) kJ/100 g (2 %; P=0·51). Compared with household panel food purchases, store survey data provided a reasonable estimate of average population exposure to key nutrients from packaged foods. However, caution should be exercised in using such data to estimate population exposure to Na and sugar and in generalising these findings to other countries, as well as over time.

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.

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 non-participants. 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.

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.

Nonresponse and Underreporting Errors Increase over the Data Collection Week Based on Paradata from the National Household Food Acquisition and Purchase Survey

Multiple-frame sampling has been regarded as a device for increasing efficiency in identifying small subpopulations. However, there has been a lack of empirical evidence in supporting the efficiency of the multiple-frame approach and in guiding best practices. The current study focuses on a special scenario in which two frames were used to recruit sample members. Using paradata from the U.S. National Household Food Acquisition and Purchase Survey (FoodAPS), the current analysis focuses on recruiting households that received Supplementary Nutrition Assistance Program (SNAP) as a sub-goal of the survey sampling. SNAP households account for around one-fifth of the general U.S. population, compared to a survey goal of 30 percent of responding households. Our findings were consistent with theoretical expectations. Having and using additional SNAP list frames improved the efficiency of identifying SNAP households as opposed to screening a general address-based sample frame. This efficiency remained even as the SNAP list frames aged.

Where households across income levels shop for food is of central concern within a growing body of research focused on where people live relative to where they shop, what they purchase and eat, and how those choices influence the risk of obesity and chronic disease. We analyzed data from the National Household Food Acquisition and Purchase Survey (FoodAPS) using a conditional logit model to determine where participants shop for food to be prepared and eaten at home and how individual and household characteristics of food shoppers interact with store characteristics and distance from home in determining store choice. Store size, whether or not it was a full-service supermarket, and the driving distance from home to the store constituted the three significant main effects on store choice. Overall, participants were more likely to choose larger stores, conventional supermarkets rather than super-centers and other types of stores, and stores closer to home. Interaction effects show that participants receiving Supplemental Nutrition Assistance Program (SNAP) were even more likely to choose larger stores. Hispanic participants were more likely than non-Hispanics to choose full-service supermarkets while White participants were more likely to travel further than non-Whites. This study demonstrates the value of explicitly spatial discrete choice models and provides evidence of national trends consistent with previous smaller, local studies.

Nonresponse and Underreporting Errors Increase over the Data Collection Week Based on Paradata from the National Household Food Acquisition and Purchase Survey