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Association between urban shrinkage and excess mortality during the COVID-19 pandemic

Nature Cities volume 2, pages 708–719 (2025)Cite this article

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Abstract

As the COVID-19 pandemic exposed and amplified structural vulnerabilities in cities, understanding how urban shrinkage exacerbates public health risks has become increasingly urgent. This study examines the relationship between urban shrinkage and excess mortality in 1,142 US counties during the COVID-19 pandemic (March 2020–February 2023). Using Kruskal–Wallis tests and mixed-effects models, we analyzed how different patterns and degrees of urban shrinkage influenced monthly excess deaths per 100,000 population and the frequency of mortality peaks. The results indicate that shrinking counties showed 165% higher excess deaths and 142% more mortality peaks than growing counties. Notably, counties experiencing simultaneous declines in both population and gross regional domestic product demonstrated worse outcomes than those with only one dimension of shrinkage. Furthermore, excess deaths increased proportionally with the severity of demographic and economic contraction. Socioeconomic disadvantages prevalent in shrinking counties—including lower income levels, higher proportions of older adults, lower educational attainment and higher unemployment rates—were also associated with elevated excess deaths. These findings underscore the need for place-based public health strategies tailored to address the structural vulnerabilities faced by shrinking and socioeconomically disadvantaged communities.

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Fig. 1: Temporal trend of monthly observed, expected and excess deaths per 100,000 population in all counties.
Fig. 2: Spatial map of the number of monthly excess deaths per 100,000 population and number of peaks.
Fig. 3: Temporal trends of monthly excess deaths per 100,000 population and cumulative monthly excess death per 100,000 population by urban types.
Fig. 4: Plots of monthly excess deaths per 100,000 population and number of peaks in all counties.

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Data availability

Raw monthly mortality data for each county from January 2015 to March 2023 that are used for the calculation of excess death are available from the Centers for Disease Control and Prevention’s WONDER database (https://wonder.cdc.gov/). Raw GRDP data for each county from 2010 to 2020 that are used for the severity of urban shrinkage are available from the Bureau of Economic Analysis (https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas). Raw population data for each county (including age-specific population) from 2010 to 2020 are available from the US Census Bureau (https://www.census.gov/data/datasets/time-series/demo/popest/2020s-counties-total.html). Raw median household income, education and unemployment data for each county are available from the US Department of Agriculture (https://www.ers.usda.gov/data-products/county-level-data-sets/). Raw population ratio by race for each county is available from the US Census Bureau (https://data.census.gov/table/ACSDT5Y2020.B02001?q=United%20States&t=White&g=010XX00US,$0500000&y=2020). Raw area data for each county, required for calculating population density, are available from the US Census Bureau (https://data.census.gov/table?g=010XX00US$0500000&d=GEO+Geography+Information). Overall, the organized data, including each county’s monthly observed, expected and excess deaths, and the CAGR of GRDP, CAGR of population, median income, education, unemployment, ratio of older people and population by race, are available via GitHub at https://github.com/GukhwaJang/journal_shrinking_covid. Additional data and information that support the findings of this study are available from the corresponding author upon request.

Code availability

The R code for calculating excess deaths for each county and the Python code for calculating the number of peaks in monthly excess deaths and performing statistical analysis are available via GitHub at https://github.com/GukhwaJang/journal_shrinking_covid.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant number RS-2024-00359695, S.K.).

Author information

Authors and Affiliations

  1. Integrated Major in Smart City Global Convergence, Seoul National University, Seoul, Republic of Korea

    Gukhwa Jang, Saehoon Kim & Jae Seung Lee

  2. Department of Environmental Design, Urban Design Concentration, Graduate School of Environmental Studies and Environmental Planning Institute, Seoul National University, Seoul, Republic of Korea

    Saehoon Kim & Jae Seung Lee

Authors
  1. Gukhwa Jang
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  2. Saehoon Kim
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Contributions

G.J.: data curation, formal analysis, visualization, writing—original draft and writing—review and editing. S.K.: conceptualization, methodology, supervision and writing—review and editing. J.S.L.: formal analysis, validation and writing—review and editing.

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Correspondence to Saehoon Kim.

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The authors declare no competing interests.

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Nature Cities thanks ChengHe Guan, Maxwell Harttand the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Spatial distribution of four urban types, marked with counties showing the highest variation in population and GRDP.

This graph shows a total of 691 growing counties (colored blue), 243 shrinking counties (colored red), 169 pop-growth, eco-decline counties (colored yellow), and 39 pop-decline, econ-growth counties (colored green). Additionally, two counties with the highest variation in population and GRDP for each type are marked. The county boundaries were based on public domain vector data from the United States Census Bureau (https://www.census.gov/en.html) and visualized using QGIS software.

Extended Data Fig. 2 Box plot and post-hoc test results of a) average monthly excess deaths per 100,000 population by urban type and b) average number of peaks by urban type.

Graphs a and b are box plots representing the average monthly excess deaths per 100,000 people (a) and the average number of peaks (b) by urban type, respectively, for counties within each urban type. Both graphs display the p-values from the Bonferroni test as a post hoc test of the Kruskal-Wallis test for each urban type. Only significant differences between urban types with a p-value less than 0.05 are indicated. An asterisk (*) denotes p < 0.05, double asterisks (**) denote p < 0.01, and triple asterisks (***) denote p < 0.001.

Supplementary information

Supplementary Information

Supplementary Tables 1–12 and Fig. 1.

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Jang, G., Kim, S. & Lee, J.S. Association between urban shrinkage and excess mortality during the COVID-19 pandemic. Nat Cities 2, 708–719 (2025). https://doi.org/10.1038/s44284-025-00278-y

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