Elevated COVID-19 Case Rates of Government Employees, District of Columbia, 2020–2022

  1. Xinyi Hua
  2. Jingjing Yin
  3. Isaac C H Fung

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Objectives

To estimate the rate ratio (RR) of reported Coronavirus Disease 2019 (COVID-19) cases among governmental employees from seven District of Columbia (D.C.) departments from March 2020 to February 2022.

Methods

Poisson regression models were used to estimate the RR by department, using D.C. residents as the reference and the person-day as the offset. The COVID-19 surveillance data and the full-time equivalent hours for each department were obtained from the D.C. governmental websites.

Results

Five of the seven departments had statistically significant higher COVID-19 case rates than D.C. residents. Stratified by four pandemic stages, RR of Fire and Emergency Medical Services (FEMS), Office of Unified Communication (OUC), and Metropolitan Police Department (MPD) were consistently >1: FEMS: 3.34 (95% confidence interval, CI [2.94, 3.77]), 2.39 (95% CI [2.06, 2.75]), 2.48 (95% CI [2.06, 2.95]), and 3.90 (95% CI [3.56, 4.26]), respectively; OUC: 1.47 (95% CI [0.92, 2.18]), 2.72 (95% CI [1.93, 3.69]), 1.85 (95% CI [1.09, 2.92]), and 2.18 (95% CI [1.62, 2.85]), respectively; and MPD: 2.33 (95% CI [2.11, 2.58]), 1.96 (95% CI [1.75, 2.18]), 1.52 (95% CI [1.29, 1.77]), and 1.76 (95% CI [1.60, 1.92]), respectively.

Conclusions

The results suggested higher case rates for emergency responders and frontline personnel than for general population in D.C.

Article activity feed

  1. Version published to 10.1093/annweh/wxac049
  2. ScreenIT

    SciScore for 10.1101/2022.01.18.22269410: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • No protocol registration statement was detected.

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

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2022.01.18.22269410v1 on medRxiv