Illness-Related Work Absence in Mid-April Was Highest on Record | Public Health | JAMA Internal Medicine | JAMA Network
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Figure.  Number of Persons With Jobs Who Were Absent From Work Due to Illness or Injury, January to April, 2019 and 2020
Number of Persons With Jobs Who Were Absent From Work Due to Illness or Injury, January to April, 2019 and 2020
Table.  Persons With Jobs Who Were Absent From Work Due to Illness or Injury, Mid-April 2019, and Mid-April, 2020
Persons With Jobs Who Were Absent From Work Due to Illness or Injury, Mid-April 2019, and Mid-April, 2020
1.
Flood  S, King  M, Rodgers  R, Ruggles  JR. Integrated Public Use Microdata Series, Current Population Survey: Version 7.0 [dataset] [Internet]. Minneapolis, MN: IPUMS; 2020. . Accessed May 16, 2020.
2.
Institute for Health Metrics and Evaluation. COVID-19 projections. https://covid19.healthdata.org/united-states-of-america. Accessed May 18, 2020.
3.
National Partnership for Woman & Families. Paid Sick Days - State and District Statutes: Updated April 2020. Available from: https://www.nationalpartnership.org/our-work/resources/economic-justice/paid-sick-days/paid-sick-days-statutes.pdf.Accessed May 18, 2020.
4.
Davern  M, Jones  A  Jr, Lepkowski  J, Davidson  G, Blewett  LA.  Estimating regression standard errors with data from the Current Population Survey’s public use file.   Inquiry. 2007;44(2):211-224. doi:10.5034/inquiryjrnl_44.2.211 PubMedGoogle ScholarCrossref
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    Research Letter
    July 27, 2020

    Illness-Related Work Absence in Mid-April Was Highest on Record

    Author Affiliations
    • 1Cambridge Health Alliance, Cambridge, Massachusetts
    • 2Harvard Medical School, Boston, Massachusetts
    • 3Hunter College, The City University of New York, New York
    JAMA Intern Med. Published online July 27, 2020. doi:10.1001/jamainternmed.2020.2926

    Data on diagnosed cases and deaths have been used to delineate the course of the coronavirus disease 2019 (COVID-19) pandemic. Information from population employment surveys could shed additional light on the pandemic's effect on the health and behavior of the nation’s workforce.

    Methods

    We analyzed the Current Population Survey (CPS), a monthly survey of approximately 115 000 persons that collects information on employment.1 We classified as “out sick” respondents who reported having a job but being absent the previous week due to their “own illness/injury/medical problems.” We compared trends over the first 4 months of 2020 relative to 2019 using multivariable linear regressions adjusted for age, sex, race/ethnicity, education, year, month, and a month×year interaction term. We assessed the demographic characteristics of out-sick jobholders, and compared out-sick rates in “high–” vs “nonhigh–COVID-19” states,2 and states with and without laws mandating paid sick leave.3 We also assessed the number of persons out sick each month since January 1976.

    We used STATA/SE statistical software (version16.1, STATA Corp), SAS statistical software (version 9.4; SAS Institute, Inc), survey weights to produce national estimates, and Davern's method to calculate approximate standard errors.4 This analysis of publicly available, deidentified data is not considered human subjects research per the Cambridge Health Alliance institutional review board, and is hence exempt from review or informed consent requirements. Analyses were performed May 15 to 18, 2020.

    Results

    Employment was stable at approximately 156 to 158 million from January 2019 to March 2020, but fell to 133.7 million in April 2020. Of jobholders, 1.1 million were out sick in January and February of both 2019 and 2020 (Figure). In 2019, the numbers decreased steadily to 0.92 million (0.58% of jobholders) in April. Trends differed in 2020, rising in March and hitting 2.02 million (1.51% of jobholders) in April, when 1.10 million more workers (an adjusted increase of 0.95% of the workforce) were out sick than in April 2019. More workers were out sick in April 2020 than in any month since January 1976, the earliest month for which such data were available.

    All demographic groups experienced increased rates of sickness-related work absence (Table). However, the increments, relative to 2019, were significantly larger for immigrants (adjusted difference 1.28% vs 0.87% among nonimmigrants), workers aged 55 years or older (1.69% vs 0.71% in younger workers), and workers without college education (1.46% vs 0.41% among those with a bachelor’s degree or higher). Increments were similar in states with and without paid sick-leave laws.

    Discussion

    Work absence due to illness rose to record levels in mid-April 2020 coincident with the peak of COVID-19 hospitalizations and deaths. The excess of persons out sick compared with 2019 was about 5-fold greater than the number of cases of COVID-19 diagnosed that week.4 Although nationwide data on the demographic characteristics of hospitalized patients with COVID-19 are not available, our findings are consonant with data from some locales indicating high illness rates among racial minorities. The inability of low-income workers to telecommute could also have contributed to the apparent disparities we observed.

    We had no data on what illnesses caused absences. Although the confidential survey offered respondents 13 non–illnesses-related options—including child care—as the reason for work absence, some jobholders who stayed home to care for children or others may have attributed their absenteeism to their own illness. Publicity around COVID-19 may have caused workers with non–COVID-19–related symptoms, or anxiety, to stay home. Increasing options for telecommuting and economic duress, however, might have the opposite effect. Similarly, reductions in other viral illnesses due to social distancing, and decreased injuries and air-pollution-related illnesses, might have cut work absences from non–COVID-19 illnesses.

    Our findings shed light on the combined health and economic effects of the COVID-19 pandemic, particularly for immigrant, older, and less-educated workers. Finally, our study suggests that routinely collected CPS data on work absence may provide a rapidly available tool for surveillance of the effect of public health crises on the workforce.

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    Article Information

    Corresponding Author: Adam W. Gaffney, MD, Cambridge Health Alliance, 1493 Cambridge St, Cambridge, MA 02138 (agaffney@challiance.org).

    Accepted for Publication: May 27, 2020.

    Published Online: July 27, 2020. doi:10.1001/jamainternmed.2020.2926

    Author Contributions: Drs Gaffney and Woolhandler had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

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

    Drafting of the manuscript: Gaffney, Woolhandler.

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

    Statistical analysis: Gaffney, Woolhandler.

    Supervision: Himmelstein.

    Conflict of Interest Disclosures: Drs Gaffney, Himmelstein, and Woolhandler serve as leaders of Physicians for a National Health Program (PNHP), a nonprofit organization that favors coverage expansion through a single payer program; however, none of them receive any compensation from that group, although some of Dr Gaffney’s travel on behalf of the organization is reimbursed by it. No other disclosures are reported.

    References
    1.
    Flood  S, King  M, Rodgers  R, Ruggles  JR. Integrated Public Use Microdata Series, Current Population Survey: Version 7.0 [dataset] [Internet]. Minneapolis, MN: IPUMS; 2020. . Accessed May 16, 2020.
    2.
    Institute for Health Metrics and Evaluation. COVID-19 projections. https://covid19.healthdata.org/united-states-of-america. Accessed May 18, 2020.
    3.
    National Partnership for Woman & Families. Paid Sick Days - State and District Statutes: Updated April 2020. Available from: https://www.nationalpartnership.org/our-work/resources/economic-justice/paid-sick-days/paid-sick-days-statutes.pdf.Accessed May 18, 2020.
    4.
    Davern  M, Jones  A  Jr, Lepkowski  J, Davidson  G, Blewett  LA.  Estimating regression standard errors with data from the Current Population Survey’s public use file.   Inquiry. 2007;44(2):211-224. doi:10.5034/inquiryjrnl_44.2.211 PubMedGoogle ScholarCrossref
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