Restaurant outlet density and the healthfulness of food purchases: evidence from FoodAPS

Introduction

The average American household's diet and food purchasing patterns are out of sync with federal recommendations. Researchers have connected this with the large and growing rates of obesity, diabetes, and other diet-related ailments in the U.S. Restaurant food has been discussed a potential contributor to unhealthful diets, as it is often calorically dense. We investigate the association between household access to restaurants and diet quality using USDA FoodAPS data and NPD ReCount data.

Methods

We define radii around households to measure restaurant outlet counts and apply a regression analysis incorporating household characteristics.

Results

We find that neither restaurant counts nor openings share many statistically or economically significant associations with average dietary quality. Household characteristics and demographics are far more powerful in explaining variation in diet quality.

Discussion

Our findings align with the large and growing body of empirical research that suggests that personal preferences and other household characteristics are more important than the food environment in explaining food choices and diet quality. Given the extant research on the importance of access to large supermarkets, our results suggest that access to food retailers is more important in explaining diet quality than access to restaurants.

Impact of Supplemental Nutrition Assistance Program Benefit Reduction or Loss on Food-at-Home Acquisitions and Community Food Program Use

Since Supplemental Nutrition Assistance Program (SNAP) benefits are vital for food-at-home (FAH) acquisitions among participating families, changes in participation or benefit amounts may impact FAH purchase and use of community-based food programs (CFP). The association of the loss of or a reduction in SNAP benefits with FAH acquisitions and CFP use was assessed using 2012–2013 National Household Food Acquisition and Purchase Survey data. Households with incomes equal to or below 130% of the Federal Poverty Level were categorized as (1) current SNAP households, (2) households with benefit loss in the preceding year, or (3) households with benefit loss for more than a year. Current SNAP households were classified as receiving (1) lesser-than-usual benefits or (2) usual benefits. Regression analyses examined associations of the loss of or a reduction in benefits with the Healthy Eating Index-2015 (HEI-2015) scores of FAH purchases and CFP use. Benefit loss in the preceding year was related to a lower total HEI-2015 score for FAH acquisitions, whereas benefit reduction was associated with lower green/bean and added sugar scores and increased CFP use. This study suggests that the loss of or a reduction in SNAP benefits may adversely impact the quality of FAH purchases. The findings also suggest that efforts enhancing the nutrition environment of community food sources could support healthy food acquisition by families experiencing benefit reduction.

Validation of a Home Food Environment Instrument Assessing Household Food Patterning and Quality

The home food environment (HFE) is associated with dietary intake; yet measuring HFE quality often requires burdensome collection of detailed inventories. This project evaluated the capacity of the Home Inventory to Describe Eating and Activity, version 2 (Home-IDEA2) to capture HFE quality by measuring the presence or absence of household foods. Validity was tested using a modified application of the Healthy Eating Index-2010 (HEI). Comparative data were drawn from the National Food Acquisition and Purchase Survey (FoodAPS) Food-at-Home Public Use File. HEI scores were calculated for 4202 households in FoodAPS using Home-IDEA2 inventories and full reported inventories. Paired t-tests compared: (1) estimated vs. total edible grams (EEG; TEG); (2) limited vs. all reported foods; and (3) EEG + limited foods vs. TEG + all reported foods. Sensitivity and range of scores were compared. Mean HEI scores for Home-IDEA2 were higher (p < 0.003) than FoodAPS: (1) 51.6 ± 16.1 vs. 49.6 ± 18.1 (food amounts); (2) 53.5 ± 15.8 vs. 49.8 ± 15.4 (food items); (3) 55.5 ± 15.7 vs. 49.8 ± 15.4 (full instrument); differences were small. Scores demonstrated comparable sensitivity and range. The study found that the Home-IDEA2 can capture HFE quality adequately with low data collection burden.

Heterogeneities in Consumer Diet Quality and Health Outcomes of Consumers by Store Choice and Income

Obesity and other diet-related health conditions have received much attention in the public health literature over the past two decades. This study investigates the relationship between household food budget shares at different food outlets with diet quality and weight-related health outcomes in the United States. Our analysis used event-level food purchase data from the national household food acquisition and purchases survey (FoodAPS). We find that, after controlling for observables, food purchase location is significantly associated with diet quality and body mass index (BMI). Our findings indicate that larger food budget shares at convenience stores and restaurants are linked with poor diet quality based on the healthy eating index-2015 (HEI-2015) scores and higher BMI. We further explored potential heterogeneity on outcomes of interest across income groups. Results suggest heterogeneous effects may exist across income groups: low-income households, who spent a larger share of their food budget at convenience stores and fast-food restaurants are related to poor diet quality and more likely to be obese. Our findings will help improve understanding of the causes of diet-related health problems and may illuminate potential avenues of intervention to address obesity.

Nationally Representative Food Acquisition and Purchase Survey (FoodAPS)

Insufficient research has explored whether sociodemographic differences in self-reported, individual-level diet quality are similarly reflected by grocery purchase quality. This cross-sectional analysis of n = 3961 U.S. households from the nationally representative Food Acquisition and Purchase Survey (FoodAPS) compared Healthy Eating Index (HEI)-2015 scores from 1 week of food-at-home acquisitions across self-reported demographic factors (race/ethnicity, Supplemental Nutrition Assistance Program (SNAP) participation, food security, and household-level obesity status). Multivariable-adjusted, survey-weighted regression models compared household HEI-2015 scores across sociodemographic groups. Respondents were primarily White and female, with a mean age of 50.6 years, 14.0% were food insecure, and 12.7% were SNAP-participating. Mean HEI-2015 scores were 54.7; scores differed across all sociodemographic exposures (p < 0.05). Interactions (p < 0.1) were detected between SNAP participation and (1) food insecurity and (2) household-level obesity, and race/ethnicity and (1) household-level obesity. HEI-2015 scores were higher among food secure, non-SNAP households than among food insecure, SNAP-participating households (53.9 ± 0.5 vs. 50.3 ± 0.7, p = 0.007); non-SNAP households without obesity had significantly higher HEI-2015 scores than other households. Household-level obesity was associated with lower HEI-2015 scores in White (50.8 ± 0.5 vs. 52.5 ± 0.7, p = 0.046) and Black (48.8 ± 1.5 vs. 53.1 ± 1.4, p = 0.018) but not Hispanic households (54.4 ± 1.0 vs. 52.2 ± 1.2, p = 0.21). Sociodemographic disparities in household HEI-2015 scores were consistent with previous research on individual-level diet quality.

Daily cost of consumer food wasted, inedible, and consumed in the United States, 2001-2016

Background

Consumer food waste in the United States represents substantial amounts of wasted nutrients, as well as needless environmental impact from wasted agricultural inputs, energy use, and greenhouse gas emissions. Efforts to reduce food waste at the consumer level are urgently needed to address the most prominent nutrition and environmental sustainability issues we now face. Importantly, individuals report that saving money is a salient motivator for reducing food waste, yet contemporary evidence on the consumer cost of wasted food is lacking. The objectives of this study are to 1) estimate the daily per capita cost of food wasted, inedible, and consumed 2) at home and away from home, and 3) by food group.

Methods

This study utilizes cross-sectional, nationally-representative data on food intake from the National Health and Nutrition Examination Survey (2001–2016), linked with nationally representative data on food waste from published literature, as well as data on food prices and food price inflation from multiple publicly-available sources. Survey-weighted procedures estimated daily per capita expenditure on food waste for 39,758 adults aged ≥20 y.

Results

Total daily per capita food expenditure was $13.27, representing 27% wasted, 14% inedible, and 59% consumed. The greatest daily food waste expenditures were observed for meat and seafood purchased for consumption outside of the home ($0.94, 95% CI: $0.90–0.99), and fruits and vegetables purchased for consumption in the home ($0.68, $0.63–0.73).

Conclusions

The most cost-effective ways to reduce food waste at the consumer level are to focus waste reduction efforts on meat and seafood purchased for consumption outside of the home and fruits and vegetables purchased for consumption in the home. A number of strategies are available to help consumers reduce their food waste, which can increase their financial flexibility to purchase more healthy foods while simultaneously reducing environmental impact.

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

OBJECTIVE

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.

DESIGN

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.

SETTING

USA.

PARTICIPANTS

Nationally representative sample of 3961 households.

RESULTS

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.

CONCLUSIONS

Increasing purchases of minimally processed foods, decreasing purchases of ultra-processed foods and selecting healthier foods at each processing level may improve diet quality.

Checklist to Capture Food, Physical Activity, and Sedentary Devices in the Home Environment: The Home Inventory Describing Eating and Activity (Home-IDEA2)

OBJECTIVE

To modify and test a self-report checklist for the home food and activity environment, Home-Inventory Describing Eating and Activity (IDEA2), psychometrically with families of young children.

DESIGN

A mixed-methods approach: (1) cognitive interviews informed instrument design (content validity), (2) parent and trained independent raters concurrently and independently completed the Home-IDEA2 during home visits (criterion validity); (3) the Home-IDEA2 was compared with a national sample via the Food Acquisition and Purchase Survey (FoodAPS) database (construct validity).

SETTING

Rural communities in Colorado.

PARTICIPANTS

Families of preschoolers: cognitive interviews (n = 20) and home visits (n = 26).

MAIN OUTCOME MEASURES AND ANALYSIS

Content validity: responses to each question were analyzed using a general inductive approach. Criterion validity: Cohen's kappa (κ) coefficient was calculated for each Home-IDEA2 item. Construct validity: frequencies were calculated to determine the proportion of Home-IDEA2 food items found in FoodAPS households.

RESULTS

Cognitive interviews identified the need for more food descriptions and the reduction of total items from 154 to 134. Testing resulted in acceptable agreement (κ = 0.6-1.0) for 82 items (58 food, 16 physical activity, and 8 electronics), moderate agreement for 36 (35 food and 1 physical activity; κ = 0.4-0.5), and poor agreement for 16 (15 food and 1 electronics; κ ≤ 0.3). Overall reliability improved from 55% to 65% for Home-IDEA2 items. Comparison of Home-IDEA2 items with FoodAPS demonstrated that 60% of foods found in US homes were covered by the Home-IDEA2.

CONCLUSIONS AND IMPLICATIONS

This study resulted in a valid, reliable, and low-burden self-report measure for food, activity, and electronic home environments by low-income, minority parents of preschoolers.

Where do U.S. households purchase healthy foods? An analysis of food-at-home purchases across different types of retailers in a nationally representative dataset

Food shopping decisions are pathways between food environment, diet and health outcomes, including chronic diseases such as diabetes and obesity. The choices of where to shop and what to buy are interrelated, though a better understanding of this dynamic is needed. The U.S. Department of Agriculture's nationally representative Food Acquisitions and Purchase Survey food-at-home dataset was joined with other databases of retailer characteristics and Healthy Eating Index-2010 (HEI) of purchases. We used linear regression models with general estimating equations to assess relationships between trip, store, and shopper characteristics with trip HEI scores. We examined HEI component scores for conventional supermarkets and discount/limited assortment retailers with descriptive statistics. Overall, 4962 shoppers made 11,472 shopping trips over one-week periods, 2012-2013. Trips to conventional supermarkets were the most common (53.6%), followed by supercenters (18.6%). Compared to conventional supermarkets, purchases at natural/gourmet stores had significantly higher HEI scores (β = 6.48, 95% CI = [4.45, 8.51], while those from "other" retailers (including corner and convenience stores) were significantly lower (-3.89, [-5.87, -1.92]). Older participants (versus younger) and women (versus men) made significantly healthier purchases (1.19, [0.29, 2.10]). Shoppers with less than some college education made significantly less-healthy purchases, versus shoppers with more education, as did households participating in SNAP, versus those with incomes above 185% of the Federal Poverty Level. Individual, trip, and store characteristics influenced the healthfulness of foods purchased. Interventions to encourage healthy purchasing should reflect these dynamics in terms of how, where, and for whom they are implemented.

Interviewer effects in food acquisition surveys

OBJECTIVE

To understand the effects of interviewers on the responses they collect for measures of food security, income and selected survey quality measures (i.e. discrepancy between reported Supplemental Nutrition Assistance Program (SNAP) status and administrative data, length of time between initial and final interview, and missing income data) in the US Department of Agriculture's National Household Food Acquisition and Purchase Survey (FoodAPS).

DESIGN

Using data from FoodAPS, multilevel models with random interviewer effects were fitted to estimate the variance in each outcome measure arising from effects of the interviewers. Covariates describing each household's socio-economic status, demographics and experience in taking the survey, and interviewer-level experience were included as fixed effects. The variance components in the outcomes due to interviewers were estimated. Outlier interviewers were profiled.

SETTING

Non-institutionalized households in the continental USA (April 2012-January 2013).

SUBJECTS

Individuals (n 14 317) in 4826 households who responded to FoodAPS.

RESULTS

There was a substantial amount of variability in the distributions of the outcomes examined (i.e. time between initial and final interview, reported values for food security, individual income, missing income) among the FoodAPS interviewers, even after accounting for the fixed effects of the household- and interviewer-level covariates and removing extreme outlier interviewers.

CONCLUSIONS

Interviewers may introduce error in food acquisition survey data when they are asked to interact with the respondents. Managers of future surveys with similarly complex data collection procedures could consider using multilevel models to adaptively identify and retrain interviewers who have extreme effects on data collection outcomes.

Discrete Choice Model of Food Store Trips Using National Household Food Acquisition and Purchase Survey (FoodAPS)

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

Background: Food acquisition diary surveys are important for studying food expenditures, factors affecting food acquisition decisions, and relations between these decisions with selected measures of health (e.g., body mass index, self-reported health). However, to our knowledge, no studies have evaluated the errors associated with these diary surveys, which can bias survey estimates and research findings. The use of paradata, which has been largely ignored in previous literature on diary surveys, could be useful for studying errors in these surveys.Objective: We used paradata to assess survey errors in the National Household Food Acquisition and Purchase Survey (FoodAPS).Methods: To evaluate the patterns of nonresponse over the diary period, we fit a multinomial logistic regression model to data from this 1-wk diary survey. We also assessed factors influencing respondents' probability of reporting food acquisition events during the diary process by using logistic regression models. Finally, with the use of an ordinal regression model, we studied factors influencing respondents' perceived ease of participation in the survey.Results: As the diary period progressed, nonresponse increased, especially for those starting the survey on Friday (where the odds of a refusal increased by 12% with each fielding day). The odds of reporting food acquisition events also decreased by 6% with each additional fielding day. Similarly, the odds of reporting ≥1 food-away-from-home event (i.e., meals, snacks, and drinks obtained outside the home) decreased significantly over the fielding period. Male respondents, larger households, households that eat together less often, and households with frequent guests reported a significantly more difficult time getting household members to participate, as did non-English-speaking households and households currently experiencing difficult financial conditions.Conclusions: Nonresponse and underreporting of food acquisition events tended to increase in the FoodAPS as data collection proceeded. This analysis of paradata available in the FoodAPS revealed these errors and suggests methodologic improvements for future food acquisition surveys.