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Assessment and Adjustment of Body Weight Measures in Scanner Data

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  • Young, Sabrina K.
  • Page, Elina T.
  • Okrent, Abigail
  • Sweitzer, Megan

Abstract

Household scanner data are a rich resource for understanding food purchasing habits in the United States. The IRI Consumer Network provides a detailed account of the retail food purchases for a large, nationally representative sample. These data further include self-reported height and weight for a subset of households that complete the MedProfiler survey. Together, the Consumer Network and MedProfiler surveys provide a unique opportunity to study the relationship between diet and obesity. This report includes an assessment of the MedProfiler height and weight data in determining body mass index (BMI) for children and adults, using MedProfiler data from 2012 to 2018 and National Health and Nutrition Examination Survey data from 2011–2012 to 2017–2018. In addition, because self-reported height and weight may often be misreported in survey data, the report explores adjustment methods to account for any self-reporting measurement bias. Finally, since food-purchase data are collected at the household level, the report includes a comparison of methods for defining the obesity status of a household.

Suggested Citation

  • Young, Sabrina K. & Page, Elina T. & Okrent, Abigail & Sweitzer, Megan, 2023. "Assessment and Adjustment of Body Weight Measures in Scanner Data," Technical Bulletins 338949, United States Department of Agriculture, Economic Research Service.
    • Handle: RePEc:ags:uerstb:338949
    DOI: 10.22004/ag.econ.338949
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    References listed on IDEAS

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    1. Mancino, Lisa & Guthrie, Joanne & Ver Ploeg, Michele & Lin, Biing-Hwan, 2018. "Nutritional Quality of Foods Acquired by Americans: Findings From USDA's National Household Food Acquisition and Purchase Survey," Economic Information Bulletin 276248, United States Department of Agriculture, Economic Research Service.
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    3. Matthias Staudigel, 2012. "How do obese people afford to be obese? Consumption strategies of Russian households," Agricultural Economics, International Association of Agricultural Economists, vol. 43(6), pages 701-714, November.
    4. Chen Zhen & Eric A. Finkelstein & James M. Nonnemaker & Shawn A. Karns & Jessica E. Todd, 2014. "Predicting the Effects of Sugar-Sweetened Beverage Taxes on Food and Beverage Demand in a Large Demand System," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 1-25.
    5. Carlson, Andrea C. & Page, Elina Tselepidakis & Zimmerman, Thea Palmer & Tornow, Carina E. & Hermansen, Sigurd, 2019. "Linking USDA Nutrition Databases to IRI Household-Based and Store-Based Scanner Data," Technical Bulletins 291970, United States Department of Agriculture, Economic Research Service.
    6. Sweitzer, Megan & Brown, Derick & Karns, Shawn & Muth, Mary K. & Siegel, Peter & Zhen, Chen, 2017. "Food-at-Home Expenditures: Comparing Commercial Household Scanner Data From IRI and Government Survey Data," Technical Bulletins 291969, United States Department of Agriculture, Economic Research Service.
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