Counties with Lower Insurance Coverage and Housing Problems Are Associated with Both Slower Vaccine Rollout and Higher COVID-19 Incidence

  1. Gregory Donadio
  2. Mayank Choudhary
  3. Emily Lindemer
  4. Colin Pawlowski
  5. Venky Soundararajan

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Abstract

Equitable vaccination distribution is a priority for outcompeting the transmission of COVID-19. Here, the impact of demographic, socioeconomic, and environmental factors on county-level vaccination rates and COVID-19 incidence changes is assessed. In particular, using data from 3142 US counties with over 328 million individuals, correlations were computed between cumulative vaccination rate and change in COVID-19 incidence from 1 December 2020 to 6 June 2021, with 44 different demographic, environmental, and socioeconomic factors. This correlation analysis was also performed using multivariate linear regression to adjust for age as a potential confounding variable. These correlation analyses demonstrated that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: −0.460, p-value: <0.001). In addition, severe housing problems and high housing costs were strongly correlated with increased COVID-19 incidence (Spearman correlations: 0.335, 0.314, p-values: <0.001, <0.001). This study shows that socioeconomic factors are strongly correlated to both COVID-19 vaccination rates and incidence rates, underscoring the need to improve COVID-19 vaccination campaigns in marginalized communities.

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  1. Version published to 10.3390/vaccines9090973
  2. ScreenIT

    SciScore for 10.1101/2021.03.24.21254270: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Spearman rank correlations and corresponding p-values were computed using the SciPy package9 in Python.
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    As stated in our Methods section, only 68 of the original 131 variables were able to be utilized due to limitations with data availability. We note that many variables that were lacking in complete data were those at the intersection of racial minority status and other socioeconomic factors, such as homicide rates within specific racial segments. Specifically, 52 of the 63 incomplete variables were specific to racial minority groups, and all data variables with less than 35% completeness were specific to racial minority groups. Had this been available, we may have been able to parse out more specific relationships of COVID-19, vaccination coverage, and racial minorities. We also note that one of the challenges in assessing both vaccine coverage as well as new incidence rates is in the diversity of state roll-out plans, in terms of timeline and eligibility criteria. A future retrospective analysis comparing individual states is an important next step to be taken when more data has been collected across the nation. It is well-established that factors pertaining to race, wealth, housing and education status are tightly intertwined when it comes to healthcare16,17. To this end, it is not surprising that we see similar trends with lower education, poorer housing status, income inequality, and racial minorities that move in the same direction in our analyses. All of these factors show some relationship with poorer vaccine coverage, and higher recent incidence rates. We highlight in...

    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.

    About SciScore

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  3. Version published to 10.1101/2021.03.24.21254270v1 on medRxiv