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

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.

Paradata analyses to inform population-based survey capture of pregnancy outcomes: EN-INDEPTH study

BACKGROUND

Paradata are (timestamped) records tracking the process of (electronic) data collection. We analysed paradata from a large household survey of questions capturing pregnancy outcomes to assess performance (timing and correction processes). We examined how paradata can be used to inform and improve questionnaire design and survey implementation in nationally representative household surveys, the major source for maternal and newborn health data worldwide.

METHODS

The EN-INDEPTH cross-sectional population-based survey of women of reproductive age in five Health and Demographic Surveillance System sites (in Bangladesh, Guinea-Bissau, Ethiopia, Ghana, and Uganda) randomly compared two modules to capture pregnancy outcomes: full pregnancy history (FPH) and the standard DHS-7 full birth history (FBH+). We used paradata related to answers recorded on tablets using the Survey Solutions platform. We evaluated the difference in paradata entries between the two reproductive modules and assessed which question characteristics (type, nature, structure) affect answer correction rates, using regression analyses. We also proposed and tested a new classification of answer correction types.

RESULTS

We analysed 3.6 million timestamped entries from 65,768 interviews. 83.7% of all interviews had at least one corrected answer to a question. Of 3.3 million analysed questions, 7.5% had at least one correction. Among corrected questions, the median number of corrections was one, regardless of question characteristics. We classified answer corrections into eight types (no correction, impulsive, flat (simple), zigzag, flat zigzag, missing after correction, missing after flat (zigzag) correction, missing/incomplete). 84.6% of all corrections were judged not to be problematic with a flat (simple) mistake correction. Question characteristics were important predictors of probability to make answer corrections, even after adjusting for respondent's characteristics and location, with interviewer clustering accounted as a fixed effect. Answer correction patterns and types were similar between FPH and FBH+, as well as the overall response duration. Avoiding corrections has the potential to reduce interview duration and reproductive module completion by 0.4 min.

CONCLUSIONS

The use of questionnaire paradata has the potential to improve measurement and the resultant quality of electronic data. Identifying sections or specific questions with multiple corrections sheds light on typically hidden challenges in the survey's content, process, and administration, allowing for earlier real-time intervention (e.g.,, questionnaire content revision or additional staff training). Given the size and complexity of paradata, additional time, data management, and programming skills are required to realise its potential.

Sociodemographic Differences in the Dietary Quality of Food-at-Home Acquisitions and Purchases among Participants in the U.S. 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.

Delay discounting and household food purchasing decisions: The SHoPPER study

OBJECTIVE

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.

METHOD

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.

RESULTS

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.

CONCLUSIONS

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).

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.