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Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors

Nature Medicine (2024)Cite this article

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Abstract

There are large differences in premature mortality in the USA by race/ethnicity, education, rurality and social vulnerability index groups. Using existing concentration–response functions, published particulate matter (PM2.5) air pollution estimates, population estimates at the census tract level and county-level mortality data from the US National Vital Statistics System, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in PM2.5-attributable mortality were consistently more pronounced by race/ethnicity than by education, rurality or social vulnerability index, with the Black American population having the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Our model estimates that over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic white population was attributable to PM2.5 in the years 2000 to 2011. This difference decreased only marginally between 2000 and 2015, from 53.4% (95% confidence interval 51.2–55.9%) to 49.9% (95% confidence interval 47.8–52.2%), respectively. Our findings underscore the need for targeted air quality interventions to address environmental health disparities.

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Fig. 1: Age-adjusted PM2.5-attributable mortality rate by race/ethnicity, education level, rurality level and the social vulnerability index.
Fig. 2: Extent to which the difference in the age-adjusted mortality rate between each race/ethnicity and Black American people can be attributed to PM2.5.
Fig. 3: Age-adjusted PM2.5-attributable mortality rate and all-cause mortality rate for each race/ethnicity stratified by educational attainment.
Fig. 4: All-cause mortality attributed to PM2.5.
Fig. 5: Differences in the age-adjusted PM2.5-attributable mortality rate between race/ethnicities at the county level for the period 2000 to 2016.
Fig. 6: County-level scatter-plot of the age-adjusted PM2.5-attributable mortality per 100,000 for selected racial/ethnic groups and education levels.

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Data availability

This study uses multiple publicly accessible datasets. Population counts are sourced from the US Census Bureau (https://data.census.gov), IPUMS (https://data2.nhgis.org/main) and the CDC’s NCHS (https://www.cdc.gov/nchs/nvss/bridged_race.htm). Census tract geometries are sourced from US Census Bureau (https://www2.census.gov/geo/tiger). The Longitudinal Tract Database is sourced from Brown University’s Spatial Structures in the Social Sciences (https://s4.ad.brown.edu/Projects/Diversity/Researcher/Bridging.htm). Air pollution estimates are sourced from Zenodo (https://doi.org/10.5281/zenodo.2616769)48 and the Environmental Protection Agency (https://www.epa.gov/outdoor-air-quality-data). Rural urban classification was obtained from the CDC’s National Center for Health Statistics (https://www.cdc.gov/nchs/data_access/urban_rural.htm). Social vulnerability-related classification was from the CDC’s Agency for Toxic Substances and Disease Registry (https://www.atsdr.cdc.gov/placeandhealth/svi/index.html). Restricted-use mortality counts were obtained from the CDC’s NCHS (https://www.cdc.gov/nchs/nvss/nvss-restricted-data.htm), which mandates that all cells with fewer than ten deaths and at the subnational level must be suppressed. Data derived from death certificates are, thus, only shared at the national level. The national-level final estimates have been deposited in Zenodo (https://doi.org/10.5281/zenodo.11243236)49.

Code availability

The data analysis in this study was conducted using R (v.4.1.2). The code is accessible on GitHub (https://github.com/FridljDa/pm25_inequality).

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Acknowledgements

We thank G. Carrasco-Escobar (University of California, San Diego) for his help with the spatial analyses. We thank anonymous reviewers for helpful comments. P.G. is a Chan Zuckerberg Biohub–San Francisco investigator. D.F. is supported by the Gerhard C. Starck Foundation. E.B. is supported by NIH grants R01AI127250 and R01HD104835. S.H.N. is supported by the Robert Woods Johnson Foundation. T.B. is supported by NIH grant R01CA228147 and by the California Environmental Protection Agency’s Office of Environmental Health Hazard Assessment (no. 21-E0018).

Author information

Author notes

  1. These authors contributed equally: Pascal Geldsetzer, Daniel Fridljand.

Authors and Affiliations

  1. Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA

    Pascal Geldsetzer, Daniel Fridljand & Eran Bendavid

  2. Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA

    Pascal Geldsetzer & Mathew V. Kiang

  3. Chan Zuckerberg Biohub – San Francisco, San Francisco, CA, USA

    Pascal Geldsetzer

  4. Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany

    Daniel Fridljand

  5. Department of Mathematics, Yale University, New Haven, CT, USA

    Daniel Fridljand

  6. Department of Health Policy, Stanford University, Stanford, CA, USA

    Eran Bendavid

  7. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA

    Sam Heft-Neal & Marshall Burke

  8. Doerr School of Sustainability, Stanford University, Stanford, CA, USA

    Marshall Burke

  9. Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, USA

    Alexander H. Thieme

  10. Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany

    Alexander H. Thieme

  11. Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany

    Alexander H. Thieme

  12. Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

    Tarik Benmarhnia

  13. Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santéenvironnement et travail) - UMR_S 1085, Rennes, France

    Tarik Benmarhnia

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Contributions

Conceptualization was the responsibility of P.G. The methodology was the responsibility of P.G., D.F., M.V.K., E.B., S.H.N., M.B., A.T. and T.B. Investigation was the responsibility of D.F. Software/formal analysis was the responsibility of D.F. Visualization was the responsibility of P.G., D.F., M.V.K., A.T. and T.B. Funding acquisition was the responsibility of P.G. Project administration was the responsibility of P.G. and T.B. Supervision was the responsibility of P.G. and T.B. Writing of the original draft was the responsibility of P.G., D.F. and T.B. Writing, review and editing was the responsibility of P.G., D.F., M.V.K., E.B., S.H.N., M.B., A.T. and T.B.

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Correspondence to Pascal Geldsetzer.

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Nature Medicine thanks MyDzung Chu, Yaguang Wei and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Age-adjusted mortality per 100,000 attributable to PM2.5 exposure by subpopulation for the age group 25+ years.

Abbreviations: SES = Socioeconomic Status, HC = Household Characteristics, MS = Minority Status, HTT = Housing Type & Transportation, SVI = Social Vulnerability Index, NH=Non-Hispanic.

Extended Data Fig. 2 Age-adjusted PM2.5- attributable mortality rate and all-cause mortality rate for each racial/ethnic group stratified by the social vulnerability index.

The solid lines depict mean estimates. The dashed lines depict 95% confidence intervals. Abbreviations: NH=Non-Hispanic.

Extended Data Fig. 3 Age-adjusted PM2.5- attributable mortality rate and all-cause mortality rate for each racial/ethnic group stratified by socioeconomic status.

The solid lines depict mean estimates. The dashed lines depict 95% confidence intervals. Abbreviations: NH=Non-Hispanic, SES = Socioeconomic Status.

Extended Data Fig. 4 Age-adjusted PM2.5- attributable mortality rate and all-cause mortality rate for each racial/ethnic group stratified by household characteristics.

The solid lines depict mean estimates. The dashed lines depict 95% confidence intervals. Abbreviations: NH=Non-Hispanic, HC = Household Characteristics.

Extended Data Fig. 5 Age-adjusted PM2.5- attributable mortality rate and all-cause mortality rate for each racial/ethnic group stratified by housing type and transportation.

The solid lines depict mean estimates. The dashed lines depict 95% confidence intervals. Abbreviations: NH=Non-Hispanic, HTT = Housing Type & Transportation.

Extended Data Fig. 6 Age-adjusted PM2.5- attributable mortality rate and all-cause mortality rate for each racial/ethnic group stratified by rurality.

The solid lines depict mean estimates. The dashed lines depict 95% confidence intervals.

Extended Data Fig. 7 Coefficient of Variation (CoV) of age- adjusted mortality attributable to PM2.5 by racial/ethnic group.

The line for ‘High HC’ corresponds to the CoV between the racial/ethnic group with the high household characteristics group. The CoV is the standard deviation of the age-adjusted PM2.5- attributable mortality rate divided by the mean age-adjusted PM2.5- attributable mortality rate.

Extended Data Fig. 8 Coefficient of Variation (CoV) of age- adjusted mortality attributable to PM2.5 by educational attainment for different sociodemographic groups for the age group 25+ years.

The line for ‘High HC’ corresponds to the CoV between the educational attainment levels with the High Household Characteristics group. The CoV is the standard deviation of the age- adjusted PM2.5-attributable mortality rate divided by the mean age- adjusted PM2.5-attributable mortality rate.

Extended Data Fig. 9 Coefficient of Variation (CoV) of age- adjusted all-cause mortality attributable to PM2.5 when stratifying by different sociodemographic groups for the age group 25+ years.

The line for ‘Ethnicity*rural_urban_class’ corresponds to the CoV between the 15 racial/ethnic and urbanicity group combinations available. The CoV is the standard deviation of the age-adjusted PM2.5-attributable mortality rate divided by the mean age-adjusted PM2.5-attributable mortality rate. Abbreviations: SES = Socioeconomic Status, HC = Household Characteristics, MS = Minority Status, HTT = Housing Type & Transportation, SVI = Social Vulnerability Index.

Extended Data Fig. 10 Diagram summarizing the analysis steps to estimate age-adjusted PM2.5-attributable mortality rates.

All ‘Process’ steps in the diagram were conducted separately for each year and subgroup in our study period. For example, we assigned the mean PM2.5 exposure level for an entire year to a given census tract.

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Supplementary Figs. 1–34 and Supplementary Tables 1–3.

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Geldsetzer, P., Fridljand, D., Kiang, M.V. et al. Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors. Nat Med (2024). https://doi.org/10.1038/s41591-024-03117-0

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