Consequences of COVID-19 vaccine allocation inequity in Chicago

  1. Sharon Zeng
  2. Kenley M. Pelzer
  3. Robert D. Gibbons
  4. Monica E. Peek
  5. William F. Parker

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Abstract

During Chicago’s initial COVID-19 vaccine rollout, the city disproportionately allocated vaccines to zip codes with high incomes and predominantly White populations. However, the impact of this inequitable distribution on COVID-19 outcomes is unknown. This observational study determined the association between zip-code level vaccination rate and COVID-19 mortality in residents of 52 Chicago zip codes. After controlling for age distribution and recovery from infection, a 10% higher vaccination rate by March 28, 2021, was associated with a 39% lower relative risk of death during the peak of the spring wave of COVID-19. Using a difference-in-difference analysis, Chicago could have prevented an estimated 72% of deaths in the least vaccinated quartile of the city (vaccination rates of 17.8 – 26.9%) if it had had the same vaccination rate as the most vaccinated quartile (39.9 – 49.3%). Inequitable vaccine allocation in Chicago likely exacerbated existing racial disparities in COVID-19 mortality.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.22.21263984: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Study Design and Population: This study was a secondary analysis of publicly available, de-identified data and was granted exemption status by the University of Chicago Biological Sciences Division/University of Chicago Medical Center Institutional Review Board.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Our study has several limitations. First, we had zip code level (not patient-level) data which limited the study design and our ability to control for observed confounders. Second, unmeasured time-varying zip code level confounders could bias our difference-in-difference estimate. However, the parallel trends between high and low zip codes during the pre-spring wave period make confounding by time-invariant unobserved zip code variables unlikely. Finally, the data’s integrity depends on the accurate recording of the zip code of residence for both vaccinations and deaths in the city of Chicago database. Unhoused persons or people without a permanent address are unlikely to be recorded accurately in the data.

    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.

  2. Version published to 10.1101/2021.09.22.21263984v1 on medRxiv