Social Disadvantage, Politics, and Severe Acute Respiratory Syndrome Coronavirus 2 Trends: A County-level Analysis of United States Data

  1. Ahmad Mourad
  2. Nicholas A Turner
  3. Arthur W Baker
  4. Nwora Lance Okeke
  5. Shanti Narayanasamy
  6. Robert Rolfe
  7. John J Engemann
  8. Gary M Cox
  9. Jason E Stout

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Abstract

Background

Understanding the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for public health control efforts. Social, demographic, and political characteristics at the United States (US) county level might be associated with changes in SARS-CoV-2 case incidence.

Methods

We conducted a retrospective analysis of the relationship between the change in reported SARS-CoV-2 case counts at the US county level during 1 June–30 June 2020 and social, demographic, and political characteristics of the county.

Results

Of 3142 US counties, 1023 were included in the analysis: 678 (66.3%) had increasing and 345 (33.7%) nonincreasing SARS-CoV-2 case counts between 1 June and 30 June 2020. In bivariate analysis, counties with increasing case counts had a significantly higher Social Deprivation Index (median, 48 [interquartile range {IQR}, 24–72]) than counties with nonincreasing case counts (median, 40 [IQR, 19–66]; P = .009). Counties with increasing case counts were significantly more likely to be metropolitan areas of 250 000–1 million population (P < .001), to have a higher percentage of black residents (9% vs 6%; P = .013), and to have voted for the Republican presidential candidate in 2016 by a ≥10-point margin (P = .044). In the multivariable model, metropolitan areas of 250 000–1 million population, higher percentage of black residents, and a ≥10-point Republican victory were independently associated with increasing case counts.

Conclusions

Increasing case counts of SARS-CoV-2 in the US during June 2020 were associated with a combination of sociodemographic and political factors. Addressing social disadvantage and differential belief systems that may correspond with political alignment will play a critical role in pandemic control.

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  1. Version published to 10.1093/cid/ciaa1374
  2. ScreenIT

    SciScore for 10.1101/2020.07.11.20151647: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: As all data were publicly available and no individual identifiers were used, this study did not require institutional review board review.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    R version 4.0.0 (R Core Team, Vienna, Austria) using the RStudio interface was used for data analysis.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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.

    About SciScore

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  3. Version published to 10.1101/2020.07.11.20151647v1 on medRxiv