COVID-19 Vaccine Coverage Index: Identifying barriers to COVID-19 vaccine uptake across U.S. counties

  1. Anubhuti Mishra
  2. Staci Sutermaster
  3. Peter Smittenaar
  4. Nicholas Stewart
  5. Sema K. Sgaier

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Abstract

Importance

The United States is in a race against time to vaccinate its population to contain the COVID-19 pandemic. With limited resources, a proactive, targeted effort is needed to reach widespread community immunity.

Objective

Identify county-level barriers to achieving rapid COVID-19 vaccine coverage and validate the index against vaccine rollout data.

Design

Ecological study

Setting

Population-based

Participants

Longitudinal COVID-19 vaccination coverage data for 50 states and the District of Columbia and 3118 counties from January 12 through May 25, 2021.

Exposure(s)

The COVID-19 Vaccine Coverage index (CVAC) ranks states and counties on barriers to coverage through 28 indicators across 5 themes: historic undervaccination, sociodemographic barriers, resource-constrained health system, healthcare accessibility barriers, and irregular care-seeking behaviors. A score of 0 indicates the lowest level of concern, whereas a score of 1 indicates the highest level of concern.

Main Outcome(s) and Measure(s)

State-level vaccine administrations from January 12 through May 25, 2021, provided by the Centers for Disease Control and Prevention (CDC) and Our World In Data. County-level vaccine coverage as of May 25, 2021, provided by the CDC.

Results

As of May 25, 2021, the CVAC strongly correlated with the percentage of population fully vaccinated against COVID-19 by county (r = -0.39, p=2.2×10 −16 ) and state (r=-0.77, p=4.9×10 −11 ). Low-concern states and counties have fully vaccinated 26.5% [t=6.8, p=1.7×10 −7 ] and 26% (t=22.0, p=2.2×10 −16 ) more people, respectively, compared to their high-concern counterparts. This vaccination gap is at its highest point since the start of vaccination and continues to grow. Higher concern on each of the five themes predicts a lower rate of vaccination at the county level (all p<.001). We identify five types of counties with distinct barrier profiles.

Conclusions and Relevance

The CVAC measures underlying barriers to vaccination and is strongly associated with the speed of rollout. As the coverage gap between high- and low-concern regions continues to grow, the CVAC can inform a precision public health response targeted to underlying barriers.

Key Points

Question

Which U.S. counties face barriers to COVID-19 vaccine rollout, and are these communities vaccinating fewer individuals?

Findings

The COVID-19 Vaccine Coverage Index (CVAC) comprises five themes reflecting county-level concern for low coverage. We report rural, regional, and racial divides in exposure to these vaccination barriers. The top third of states and counties of highest concern have vaccinated 19% and 20% fewer people, respectively, compared to regions of least concern.

Meaning

The CVAC can help contextualize progress to widespread COVID-19 vaccine coverage, identifying underlying community-level factors that could be driving suboptimal rollout to inform precision solutions.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.06.17.21259116: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Index design: The index was designed using the COIN framework.
    COIN
    suggested: (CoIN, RRID:SCR_005332)
    Statistical Analysis: Vaccination rates were calculated by CVAC tertile and plotted as a time series at state and county levels using ggplot2 in R.18 Spearman rank correlation coefficients were calculated using the Tidyverse package in R19 to assess the relationship between CVAC and vaccine rollout.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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

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  2. Version published to 10.1101/2021.06.17.21259116v1 on medRxiv