Short- and long-term shifts in food purchasing behaviours and nutrition disparities in the USA during and after the COVID-19 pandemic

Abstract

The coronavirus 2019 pandemic disrupted food purchasing behaviours, triggering both short- and long-term nutritional shifts. Here, analysing 9,367,550 weekly shopping baskets from over 30,000 US households (2017–2022), combined with county-specific policies and artificial intelligence-enhanced nutrition profiling, we document distinct changes in household food baskets. During the pandemic and lockdowns, households increased spending on less processed foods, but by the post-pandemic period, spending on ultraprocessed foods rose above pre-pandemic levels. This divergence appeared strongly in processing-based measures (NOVA) but less in nutrient-based profiles (Healthy Eating Index and Food Standards Australia New Zealand), underscoring their complementary perspectives on diet quality. The spending gap between socioeconomic groups widened in 2020 but converged post-pandemic, while a new nutrition gap emerged as higher-income households increased spending on less nutritious foods. Categories such as prepared and frozen foods and carbonated beverages showed persistant effects. These findings highlight the pandemic’s uneven, lasting influence on dietary behaviour and the need for policies promoting healthier, more resilient food systems.

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Subjects

• Nutrition
• Social policy

Data availability

Publicly available data sources used in this study are listed in the References, including COVID-19 case data, county- and state-level policy data, census population data, Consumer Price Index data and public food composition databases. The primary household purchase data used in this study are restricted-use NielsenIQ Consumer Panel data accessed through the NielsenIQ Datasets at the Kilts Center for Marketing Data at the University of Chicago Booth School of Business. These third-party data are not publicly available from the authors and cannot be redistributed because they are subject to NielsenIQ/Kilts Center licensing and data-use restrictions. Qualified researchers may request access directly from the Kilts Center for Marketing Data, subject to its application, approval and data-use requirements. The FLIP-Canada food composition data used to train and validate the nutritional quality and NOVA classification models are third-party data maintained by the University of Toronto’s Food Label Information and Price Program/L’Abbé Laboratory. These data are not publicly available from the authors and cannot be redistributed because they are subject to third-party data-use restrictions. Researchers may request access through the FLIP Program/L’Abbé Laboratory access process, subject to approval and applicable data-use requirements. The minimum dataset necessary to interpret and verify the results consists of the restricted NielsenIQ household purchase data, the FLIP-Canada training data, the linked public datasets described above and the derived analytic variables generated from these sources. Because the NielsenIQ and FLIP data are subject to third-party data-use restrictions, the authors are not permitted to redistribute the underlying data or derived datasets containing restricted information. To support transparency, we provide detailed variable definitions, data-construction procedures and analysis code in the Code availability section. Publicly available input datasets are listed in the References. No additional source data are provided with this paper because the reported results are derived from restricted third-party datasets. List of datasets used and links: (1) NielsenIQ Consumer Panel Datasets, 2017–2022—restricted-use household UPC-level food purchase data available via the Kilts Center for Marketing Data at the University of Chicago Booth School of Business at https://www.chicagobooth.edu/research/kilts/research-data/nielseniq, (2) US Bureau of Labor Statistics/FRED—Consumer Price Index for All Urban Consumers: Food and Beverages in US City Average was used for inflation adjustment and available via FRED at https://fred.stlouisfed.org/series/CPIFABNS, (3) USDA FoodData Central/USDA Global BFPDB—branded food composition, nutrition and ingredient data linked to purchases by UPC is available via FDC at https://fdc.nal.usda.gov/, (4) FLIP-Canada 2017 and FLIP-Canada 2020—University of Toronto Food Label Information and Price Program/L’Abbé Laboratory food composition data used to train and validate NOVA and FSANZ prediction models available via the University of Toronto at https://labbelab.utoronto.ca/projects/food-label-information-price-flip/, (5) The New York Times Coronavirus (COVID-19) Data in the USA—county-level COVID-19 case data available via GitHub at https://github.com/nytimes/covid-19-data, (6) Hikma Health COVID-19 County-Level Policy Data—county-level stay-at-home and reopening policy data available via GitHub at https://github.com/hikmahealth/covid19countymap, (7) COVID-19 State Policies Data available via State Policies at https://statepolicies.com/ and (8) US Census County Population Totals, 2010–2019—county population data used to construct per-capita COVID severity measures available via the United States Census Bureau at https://www.census.gov/programs-surveys/popest.html.

Code availability

Custom code used to construct the analytic datasets, implement the nutrition quality and food-processing classification pipeline, conduct the statistical analyses and generate the tables and figures will be made publicly available via GitHub at https://github.com/guanlanhu/covid-food. The repository will include documentation describing the computational environment, package versions, workflow and instructions for reproducing the analyses. Because the NielsenIQ Consumer Panel data and FLIP-Canada data are restricted third-party datasets, the code repository will not include these underlying data; access to those data is governed by the data-use agreements described in the Data availability statement.

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Acknowledgements

The researcher(s)’ own analyses were calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researcher(s) and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analysing and preparing the results reported herein.

Funding

M.L. and D.S. disclose support for the research of this work from the Canadian Institutes of Health Research (CIHR) (grant nos. PJT-165858 and PJT-152979). G.H. discloses support for the research of this work from the Canadian Institutes of Health Research Implementing Smart Cities Interventions to Build Healthy Cities (SMART) Training Platform (2022–2023). W.L. declares no relevant funding. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Authors and Affiliations

Contributions

G.H., W.L., D.S. and M.R.L. designed research. G.H., W.L. and D.S. performed research and wrote the manuscript. G.H. analysed data. W.L. contributed methodology. All authors reviewed and approved the final manuscript.

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Nature Human Behaviour thanks Tirtha Dhar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–7 and Tables 1–10.

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Hu, G., Lu, W., Soberman, D. et al. Short- and long-term shifts in food purchasing behaviours and nutrition disparities in the USA during and after the COVID-19 pandemic.
Nat Hum Behav (2026). https://doi.org/10.1038/s41562-026-02547-9

• Version of record:14 August 2026

• DOI
  :https://doi.org/10.1038/s41562-026-02547-9