Social Class, Race/Ethnicity, and COVID-19 Mortality Among Working Age Adults in the United States

  1. Elizabeth B. Pathak
  2. Janelle Menard
  3. Rebecca B. Garcia
  4. Jason L. Salemi

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Abstract

Importance

Substantial racial/ethnic disparities in COVID-19 mortality have been documented. Social class is a likely explanation of mortality disparities across and within racial/ethnic groups. This is the first U.S. study of social class and COVID-19 mortality in working age adults.

Objectives

To determine the joint effects of social class, race/ethnicity, and gender on the burden of COVID-19 mortality. A secondary objective was to determine whether differences in opportunities for remote work were correlated with COVID-19 death rates for sociodemographic groups.

Design

Annual mortality study which used a special government tabulation of 2020 COVID-19 related deaths stratified by decedents’ social class (educational attainment) and race/ethnicity.

Setting

United States in 2020.

Participants

COVID-19 decedents aged 25 to 64 years old (n=69,001).

Exposures

Social class (working class, some college, college graduate), race/ethnicity (Hispanic, Black, Asian, Indigenous, multiracial, and non-Hispanic white), and gender (women, men). Detailed census data on occupations held by adults in 2020 in each of the 36 sociodemographic groups studied were used to quantify the possibility of remote work for each group.

Main Outcomes and Measures

Age-adjusted COVID-19 death rates for 36 sociodemographic groups defined by social class, race/ethnicity, and gender. Disparities were quantified by relative risks and 95% confidence intervals. College graduates were the (low risk) referent group for all relative risk calculations.

Results

A higher proportion of Hispanics, Blacks, and Indigenous people were working class in 2020. COVID-19 mortality was five times higher in the working class vs. college graduates (72.2 vs. 14.6 deaths per 100,000, RR=4.94, 95% CI 4.82-5.05). The joint detriments of lower socioeconomic position, Hispanic ethnicity, and male gender resulted in a COVID-19 death rate which was over 27 times higher (178.0 vs. 6.5 deaths/100,000, RR=27.4, 95%CI 25.9-28.9) for working class Hispanic men vs. college graduate white women. In regression modeling, percent employed in never remote jobs explained 72% of the variance in COVID-19 death rates.

Conclusions and Relevance

SARS-CoV-2 infection control efforts should prioritize the working class (i.e. those with no college education), particularly those employed in “never remote” jobs with inflexible and unsafe working conditions (i.e. blue collar, service, and retail sales workers).

KEY POINTS

Question

Did COVID-19 mortality rates among non-elderly adults vary significantly by social class, race/ethnicity, and gender in 2020?

Findings

Among 69,001 COVID-19 decedents, age-adjusted COVID-19 deaths rates were 5 times higher in working class vs. college graduate adults 25-64 years old. Working class Hispanic, Black, and Indigenous men suffered the highest burden of COVID-19 mortality, while college graduate white women experienced the lowest death rate.

Meaning

COVID-19 mitigation efforts should prioritize the working class (i.e. those with no college education), particularly blue collar, service, and retail sales workers.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.11.23.21266759: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableWe analyzed provisional death counts for 2020 stratifed by four sociodemographic variables: 1) educational attainment (no college, some college, college graduate); 2) race and ethnicity (white non-Hispanic, Hispanic, Black non-Hispanic, Asian non-Hispanic, American Indian/Alaska Native non-Hispanic, Native Hawaiian and other Pacific Islander non-Hispanic, more than one race non-Hispanic, unknown); 3) gender (male, female, unknown); and 4) age group (25-39 years, 40-54 years, 55-64 years).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Study Limitations and Public Health Data Gaps: It is likely that COVID-19 deaths in the U.S. have been undercounted (i.e., cause of death has been misclassified), and this misclassification is likely to be differential by social class, resulting in a bias toward the null in our estimates of social class disparities. Misclassification occurs when there is insufficient medical information available at the time of death. Lack of access to medical care and out-of-hospital mortality can result in the use of non-specific cause of death coding on death certificates. We have previously shown that the percent of all non-injury deaths coded to “symptoms, signs, and ill-defined conditions” increased from 2019 to 2020 among working age adults.50 A simple step toward improving COVID-19 surveillance data, which could be implemented immediately across a wide range of data systems, is to add one yes/no question to all individual adult patient encounter medical records: “Has this person completed one or more years of college?” A “no” response on this single data item would identify the working class. A follow-up question for those who replied “yes” (“Does this person have a 4-year college degree?”) would easily identify the three social classes analyzed in this study.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.11.23.21266759v1 on medRxiv