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Health Disparities

Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021

Educational Objective
To identify the key insights or developments described in this article

1 Credit CME

JAMA

Published Online: July 7, 2022

Original Investigation

Article Information and Disclosures

Hannes Schwandt, PhD^1,2,3;

Janet Currie, PhD^3,4;

Till von Wachter, PhD^3,5,6;

Jonathan Kowarski, MA^5,6;

Derek Chapman, PhD⁷;

Steven H. Woolf, MD, MPH⁷

Author Affiliations

¹School of Education and Social Policy, Northwestern University, Evanston, Illinois
²Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
³National Bureau of Economic Research (NBER), Cambridge, Massachusetts
⁴Department of Economics, Princeton University, Princeton, New Jersey
⁵Department of Economics, University of California, Los Angeles
⁶California Policy Lab, University of California, Los Angeles
⁷Center on Society and Health, Virginia Commonwealth University School of Medicine, Richmond

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Key Points

Question How did the first 2 years of the COVID-19 pandemic affect life expectancy in California and the relationship between census tract income and life expectancy relative to prepandemic years?

Findings In this retrospective analysis of 1 988 606 deaths in California during 2015 to 2021, life expectancy declined from 81.40 years in 2019 to 79.20 years in 2020 and 78.37 years in 2021. Life expectancy differences between the census tracts in the highest and lowest income percentiles increased from 11.52 years in 2019 to 14.67 years in 2020 and 15.51 years in 2021.

Meaning This ecological study of deaths in the state of California demonstrated that life expectancy declines in 2020 increased in 2021 and that the life expectancy gap by income level increased during the first 2 years of the COVID-19 pandemic relative to the prepandemic period.

Abstract

Importance The COVID-19 pandemic caused a large decrease in US life expectancy in 2020, but whether a similar decrease occurred in 2021 and whether the relationship between income and life expectancy intensified during the pandemic are unclear.

Objective To measure changes in life expectancy in 2020 and 2021 and the relationship between income and life expectancy by race and ethnicity.

Design, Setting, and Participants Retrospective ecological analysis of deaths in California in 2015 to 2021 to calculate state- and census tract–level life expectancy. Tracts were grouped by median household income (MHI), obtained from the American Community Survey, and the slope of the life expectancy-income gradient was compared by year and by racial and ethnic composition.

Exposures California in 2015 to 2019 (before the COVID-19 pandemic) and 2020 to 2021 (during the COVID-19 pandemic).

Main Outcomes and Measures Life expectancy at birth.

Results California experienced 1 988 606 deaths during 2015 to 2021, including 654 887 in 2020 to 2021. State life expectancy declined from 81.40 years in 2019 to 79.20 years in 2020 and 78.37 years in 2021. MHI data were available for 7962 of 8057 census tracts (98.8%; n = 1 899 065 deaths). Mean MHI ranged from $21 279 to $232 261 between the lowest and highest percentiles. The slope of the relationship between life expectancy and MHI increased significantly, from 0.075 (95% CI, 0.07-0.08) years per percentile in 2019 to 0.103 (95% CI, 0.098-0.108; P < .001) years per percentile in 2020 and 0.107 (95% CI, 0.102-0.112; P < .001) years per percentile in 2021. The gap in life expectancy between the richest and poorest percentiles increased from 11.52 years in 2019 to 14.67 years in 2020 and 15.51 years in 2021. Among Hispanic and non-Hispanic Asian, Black, and White populations, life expectancy declined 5.74 years among the Hispanic population, 3.04 years among the non-Hispanic Asian population, 3.84 years among the non-Hispanic Black population, and 1.90 years among the non-Hispanic White population between 2019 and 2021. The income–life expectancy gradient in these groups increased significantly between 2019 and 2020 (0.038 [95% CI, 0.030-0.045; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI: 0.005-0.044; P = .02] years per percentile among Asian individuals; 0.015 [95% CI, 0.010-0.020; P < .001] years per percentile among Black individuals; and 0.011 [95% CI, 0.007-0.015; P < .001] years per percentile among White individuals) and between 2019 and 2021 (0.033 [95% CI, 0.026-0.040; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI, 0.010-0.038; P = .002] years among Asian individuals; 0.024 [95% CI, 0.011-0.037; P = .003] years per percentile among Black individuals; and 0.013 [95% CI, 0.008-0.018; P < .001] years per percentile among White individuals). The increase in the gradient was significantly greater among Hispanic vs White populations in 2020 and 2021 (P < .001 in both years) and among Black vs White populations in 2021 (P = .04).

Conclusions and Relevance This retrospective analysis of census tract–level income and mortality data in California from 2015 to 2021 demonstrated a decrease in life expectancy in both 2020 and 2021 and an increase in the life expectancy gap by income level relative to the prepandemic period that disproportionately affected some racial and ethnic minority populations. Inferences at the individual level are limited by the ecological nature of the study, and the generalizability of the findings outside of California are unknown.

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Article Information

Corresponding Author: Hannes Schwandt, PhD, Northwestern University, 2120 Campus Dr, Evanston, IL 60208 (schwandt@northwestern.edu).

Accepted for Publication: June 9, 2022.

Published Online: July 7, 2022. doi:10.1001/jama.2022.10952

Author Contributions: Dr Schwandt had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Schwandt, Currie, von Wachter, Kowarski.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Schwandt, Currie.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Schwandt, von Wachter, Kowarski, Chapman.

Obtained funding: von Wachter.

Administrative, technical, or material support: Schwandt.

Supervision: Currie, von Wachter, Woolf.

Conflict of Interest Disclosures: Dr Schwandt reported receiving grants from the National Institutes of Health during the conduct of the study. Dr von Wachter reported receiving grants from the National Institute of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: This research was supported by the US National Institute on Aging through grant #P01AG005842 to the National Bureau of Economic Research. Dr Woolf received partial funding from grant UL1TR002649 from the National Center for Advancing Translational Sciences.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: This research was facilitated by the California Policy Lab, University of California Los Angeles, which helped access the data, hosted the research on its secure infrastructure, and provided financial support. We thank David Cutler, PhD (Department of Economics, Harvard University, Cambridge, Massachusetts), Angus Deaton, PhD (Department of Economics, Princeton University), and Jonathan Skinner, PhD (Department of Economics, Dartmouth College, Hanover New Hampshire), who provided helpful comments. None of these individuals received compensation for their contributions.

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Credit Designation Statement: The American Medical Association designates this Journal-based CME activity activity for a maximum of 1.00  AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:

1.00 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
1.00 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
1.00 MOC points in the American Board of Pediatrics’ (ABP) Maintenance of Certification (MOC) program;
1.00 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
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