Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

  1. Brian Neelon
  2. Fedelis Mutiso
  3. Noel T. Mueller
  4. John L. Pearce
  5. Sara E. Benjamin-Neelon

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Abstract

Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time.

Methods

We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM 2.5 ), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19.

Results

At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties.

Conclusions

The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

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  1. Version published to 10.1371/journal.pone.0248702
  2. ScreenIT

    SciScore for 10.1101/2020.09.09.20191643: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Institutional Review Boards at the Medical University of South Carolina and Johns Hopkins Bloomberg School of Public Health deemed this research exempt from review.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot 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: We detected the following sentences addressing limitations in the study:
    There are also limitations to this analysis. First, our analysis is largely descriptive with the goal of generating hypotheses to inform policy and guide future research. For example, future studies might review the policy actions that gave rise to the crossover effects we observed early and late in the pandemic for several of the SVI themes. Second, we used county-level SVI data from 2018. It is possible that social vulnerability factors may have changed between 2018 and 2020, but we used the most recent SVI data available from CDC. Third, we downloaded several of the adjustment variables from the Robert Wood Johnson Foundation’s 2019 County Health Rankings & Roadmaps database, which may not be the most current source for variables such as PM2.5. Fourth, it was challenging to model deaths because most counties reported no deaths on any given day. Future studies could employ zero-inflated models to better account for this aspect of the data [43–45]. Future work could also examine temporal trends in locations of correctional facilities, long-term care facilities, nursing homes, Indian reservations and Tribal lands, and other places with high rates of infection [46–49]. Finally, we examined trends in the US only; future work might replicate our study in developing countries or those with emerging outbreaks. Examining the impact of COVID-19 on vulnerable communities in the US is of growing importance [15, 50]. Mounting evidence suggests that social determinants of health and com...

    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 10.1101/2020.09.09.20191643v1 on medRxiv