A Consumer Food Data System for 2030 and Beyond (2020) / Chapter Skim
Currently Skimming:
2 ERS's Current Consumer Food and Nutrition Data Infrastructure
2 ERS's Current Consumer Food and Nutrition Data Infrastructure
Pages 37-84
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From page 37... ...
The Next Generation Data Platform was initiated by ERS in collaboration with FNS and the ensus Bureau to add state-level administrative data C from SNAP and WIC to the Census Bureau's Data Linkage Infrastructure, available to researchers only in a Federal Statistical Research Data Center (FSRDC) , a secure data center (see Box 2.1)
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From page 38... ...
38 A CONSUMER FOOD DATA SYSTEM FIGURE 2.1 Overview of the Consumer Food and Nutrition Data System. NOTES: ARS = Agricultural Research Service; ATUS = American Time Use Survey; ERS = Economic Research Service; FARA = Food Access Research Atlas; FEA = Food Environment Atlas; FoodAPS = National Household Food Acquisition and Purchase Survey; FSRDC = Federal Statistical Research Data Center; IRI = IRI Worldwide, a vendor of proprietary data; NHANES = National Health and Nutrition Examination Survey; Nielsen = a vendor of proprietary data; NORC = National Opinion Research Center at the University of Chicago, home of the NORC Data Enclave; NPD = NPD Group, a vendor of proprietary data; SNAP = Supplemental Nutrition Assistance Program; WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.
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From page 39... ...
The Agency for Healthcare Research and Quality, the Bureau of Economic Analysis, the Bureau of Labor Statistics, the Census Bureau, and the National Center for Health Statistics are all partners in the FSRDCs and contribute data directly to them. Each agency has its own review and approval process.
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From page 40... ...
. As noted above, the most important project undertaken by ERS in this category is its work in support of the Next Generation Data Platform, which makes state-level SNAP and WIC administrative data available for research at an FSRDC.
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From page 41... ...
program and Medicare. Thanks to the Next Generation Data Platform, SNAP and WIC administrative data can be linked to Census Bureau survey data for approved projects.
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From page 42... ...
Included in that category are the Food Security Module, added to 11 surveys, the Flexible Consumer Behavior Survey, which was added to the National Health and Nutrition Examination Survey (NHANES) , and the Eating and Health Module, added to the BLS American Time Use Survey.
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From page 43... ...
Nevertheless, survey data can provide insights into household- and personlevel variables about outcomes, information that is frequently missing in administrative data. Table 2.1 lists and summarizes the national probability sample surveys that the panel thinks have been most important to the analysis of consumer food and nutrition conditions over the past decade or more.
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From page 44... ...
Data on food at home, food Includes What We Eat in away from home, total amount America Module, Food of Food Stamps. Food Security Security Module, and Flexible Module included in some Consumer Behavior Survey.
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Since expenditures with 3-month includes the Food Security 2011 includes adult food recall (rent and utilities) ; Module and other topical security module.
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From page 46... ...
There have also been a large number of one-time surveys connected with particular studies, such as the large and ambitious National Food Stamp Program Survey conducted by FNS in 1996,6 the Healthy Incentives Pilot7 conducted by FNS in 2011–2012, and the Summer Electronic enefit Transfer for Children study conducted by FNS in 2011–2014.8 B These surveys have yielded important insights about food security, nutrition outcomes, and poverty at national, subnational and household levels that have a wide range of policy research applications. Some of these oneoff surveys have even been part of randomized controlled trials, thereby extending causal understanding of the way policy changes affect outcomes in peoples' lives.
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From page 47... ...
ERS has been planning a second version of the survey, FoodAPS-2, which is described later in this subsection. FoodAPS was conducted with interviews spread over a few months, making it difficult to leverage changes in policy or the food environment to understand in a causal fashion how such changes impact food choices, food security, nutrition, or nutrition-related health outcomes FoodAPS used a nationally representative probability sample of 4,826 households.
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From page 48... ...
Delivery Sequence File (DSF) , a list of addresses of SNAP participants from 22 of the 27 state SNAP agencies in which primary sampling units were selected, or traditional field listing.
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, and the Food Security Module (described in the next section)
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. FoodAPS combines administrative data with survey data to generate more reliable -- although not perfect -- estimates of program participation.
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. Data users noted several strengths, compared to other data sources, such as inclusion of administrative data on participation in SNAP in participating states, oversampling of SNAP participants, completeness of food acquisition sources including both food consumed at home and food consumed away from home, and linkage of purchase events to specific retailer locations.
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From page 52... ...
State administrative data files were used in FoodAPS at several stages, including for the initial sampling frame for the SNAP par ticipant sample and, later, for checking SNAP participation status. For 20 of the 27 states that participated in FoodAPS, state SNAP Quality Control agencies provided administrative files that could be used to corroborate participation status (Page et al., 2019)
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From page 53... ...
In addition, an initial screener had a simpler set of income questions that was designed to allow triage for purposes of selecting sampled households to meet targets for SNAP participants and low-income nonparticipants. This initial screener appears to have generated underestimates of household income, leading to the misclassification of households at the time of recruitment, which complicated efforts to achieve sample-size goals even when the income variables could later be corrected based on the longer full battery of income questions (Page et al., 2019)
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From page 54... ...
(Note that ERS presented the proposed enhancements to FoodAPS-2 to the panel in 2018.17 These enhancements are summarized in Box 2.3.) In addition to information about food acquisition, a great deal has been learned from FoodAPS about the use of SNAP (cross-checked with administrative data)
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• Food security -- through use of an 18-question food security module. • Subjective food needs, food sensitivities, health conditions (e.g., diabetes, high blood pressure, high cholesterol)
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From page 56... ...
ERS sponsors three modules on surveys conducted by agencies other than USDA. First, the Food Security Module, fielded each year since 1995 on the Current Population Survey, tracks household food security over time.
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ponsored by the National Center for Health Statistics. Since 1999, NHANES S has included the 18-item household food security module with a 12-month reference period.
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In 1998–2002, SPD included the 18-item S household food security module with a 12-month reference period.
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annual history of the food security module with the CPS makes it most useful for looking at the effects of policies. The NHANES data are most useful for cross-checking how food security is related to food intake, program participation, and objective measures of health.
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From page 60... ...
Arteaga and Heflin (2014) used variation in state kindergarten eligibility dates to explore the protective effects of national school lunch program participation on household food security among households with a kindergarten-age child in the Early Childhood Longitudinal Study -- Birth cohort (ECLS-B)
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From page 61... ...
A key innovation has been to link food economics, food consumption, and health outcome variables in NHANES, making it possible to conduct research to determine how food security is associated with objective health measures. One of the primary objectives of adding FCBS to NHANES is to provide national data on both health variables and consumer use of food policy initiatives (such as packaged food product or restaurant menu labeling)
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2.2. ADMINISTRATIVE DATA SOURCES Administrative data are collected by government agencies (state, federal, or local)
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From page 63... ...
They do offer the potential to study program effects when they are linked to population data in order to study the eligible population for programs. But not all participants can be identified from administrative data with the PIKs, which allow them to be linked to Census Bureau survey and administrative data.
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From page 64... ...
For expensive programs such as SNAP, WIC, and school meals programs, it is critical to have reliable evidence quantifying the gains in improving food security as well as minimizing health issues such as obesity. The completeness and accuracy of information on the program participant population are known strengths of administrative data, although recent evidence from FoodAPS suggests that self-reports and administrative data on food program participation are similarly correlated with expenditure and disbursal data.26 The administrative data available for SNAP vary by state but can include state-level participant information, including components of income necessary for determining eligibility for SNAP, data on benefits by month, program participants' mailing and physical addresses, and likely locations where their benefits were used.
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From page 65... ...
were used as part of the sampling frame; current SNAP population lists were linked to respondent data to verify self-reports of SNAP participation for those selected into the sample from a non-SNAP stratum; and linkage with STARS was used to estimate the distance from each respondent's home to an authorized SNAP retailer. The Next Generation Data Platform Much of the progress in the use of administrative data in the federal statistical system is accomplished through the Census Bureau's authority to collect and use administrative records.
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From page 66... ...
In 2012, ERS and FNS formed a strategic partnership with the Census Bureau called the Next Generation Data Platform. This joint project is a long-term effort to acquire state-level administrative data for USDA nutrition assistance programs, especially SNAP and WIC, and to make those data available for linkage to administrative files from other agencies and to surveys conducted by the Census Bureau, already available in the Census Bureau's Data Linkage Infrastructure.
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From page 67... ...
One of the promises of the Next Generation Data Platform is the potential for analysis of linkages of survey data to a number of different administrative datasets. For example, with such a linked dataset, researchers could also learn about interaction effects associated with multiple program participation, such as by comparing those participating in SNAP alone, those in SNAP plus Medicaid, those in SNAP plus TANF, and those in SNAP plus unemployment insurance.
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From page 68... ...
To date, ERS has acquired and used commercial/proprietary data that fall into one of three categories: retail scanner data, household panel and scanner data, and food store and restaurant data.31 Specific companies providing these data to ERS are listed in Box 2.5. One of the challenges with the use of any outside data source is understanding its quality and coverage, key to understanding how the data can best be used.
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From page 69... ...
: Names and geospatial locations of food stores in the United States with sales greater than $1 million, used in Food Access Research Atlas and Food Environment Atlas. • eCount, NPD Group (1998–2017)
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Unlike retail scanner data collected at check-out, household scanner data are collected using hand-held scanning devices provided to participating households or using a mobile cellphone app. In this way, purchases can be captured for the panel of households.
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From page 71... ...
, and IRI's InfoScan, summarized above under retail scanner data. The InfoScan retail data also include store information, including store name and corporate parent, address, and retail outlet type (i.e., grocery, convenience, dollar, drug, liquor, mass merchandiser, and club stores)
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From page 72... ...
For example, retail scanner data have the advantages of providing granular food prices subject to the caveats above, geographic distribution across individual stores or markets where there is coverage, and product-level details such as brand, size/weight, type of package, health and nutrition claims (e.g., gluten-free, type of sugar added, and "good for reducing risk" of heart disease or diabetes)
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From page 73... ...
Of course, identifying WIC recipients through self-reporting is less reliable than identifying them from administrative data. Projects are in place to estimate SNAP food package weights and the retail value of the average food distribution program on Indian reservations (FDPIR)
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From page 74... ...
Food Environment Atlas (FEA) : Includes more than 200 indicators of a com munity's ability to access healthy food and its success in doing so, covering characteristics such as store and restaurant availability, food assistance use, food prices and taxes, local foods initiatives, and health and physical activity.
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From page 75... ...
Because no other comparable data source provides the same level of granularity, detail, and frequency, which is needed for many types of food policy analyses, scanner data will continue to be important in a range of research policy areas. Specifically, scanner data can be used • to analyze the effects of federal regulations on the healthiness of food acquired (e.g., new Nutrition Facts Labels, revised serving sizes, and the banning of partially hydrogenated oils [trans fatty acids]
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. Among the easiest types of data to access are nonfederal administrative data from open data sources; commoditized data; and certain types of social media data (in particular, Twitter)
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From page 77... ...
, who propose corrections for researchers using Nielsen Homescan data. Perpetual Dynamic Algorithm While nonsurvey/nonadministrative data can often provide useful analyses, it is important to remember that many of these data are derived 49 See summary of Muth's presentation to the panel in Appendix C
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From page 78... ...
. There is a similar issue when proprietary data products change or offer different versions over time.50 There can be a loss of -- or significant change in -- a data source or a production system, which then leads to different conclusions being drawn.51 One practical example involves the difficulty in making cross-time comparisons when manufacturers assign a new barcode to an existing product (which may be done when a product undergoes a substantial change of some sort)
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From page 79... ...
The need to understand the "why" behind attitudes and behaviors is still quite germane, but often it is not knowable from organic data alone. For example, scanner data tend not to include whole classes of goods, such as non-UPC products like fresh fruit and vegetables (Jensen, 2018)
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From page 80... ...
to integrate ARS food composition databases with IRI scanner data to support creation of prices for foods consumed (as collected in NHANES) (Carlson 53 Link to FoodData Central: see https://fdc.nal.usda.gov/index.html.
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From page 81... ...
ERS plans to expand scanner data applications. For example, EFPGs could be included in future iterations of FoodAPS for purposes of food environment studies.
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From page 82... ...
FADS measures 200 food commodity supplies through the supply chain from the farmer to domestic consumption. The project described by Lin combined food consumption data from NHANES for 2007–2010 with the Food Intakes Converted to Retail Commodities Database and Food Patterns Equivalent Database to estimate food consumption by food groups as specified in the 2010 Dietary Guidelines for Americans.
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From page 83... ...
Crosswalk databases are still needed for food processing and for the community food environment. For example, packaged brownie mix and macaroni and cheese in a box need to be translated into food-pattern equivalents along with nutrient data.
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