Temporal Analysis of Social Determinants Associated with COVID-19 Mortality

  1. Shayom Debopadhaya
  2. John S. Erickson
  3. Kristin P. Bennett

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Abstract

This study examines how social determinants associated with COVID-19 mortality change over time. Using US county-level data from July 5 and December 28, 2020, the effect of 19 high-risk factors on COVID-19 mortality rate was quantified at each time point with negative binomial mixed models. Then, these high-risk factors were used as controls in two association studies between 40 social determinants and COVID-19 mortality rates using data from the same time points. The results indicate that counties with certain ethnic minorities and age groups, immigrants, prevalence of diseases like pediatric asthma and diabetes and cardiovascular disease, socioeconomic inequalities, and higher social association are associated with increased COVID-19 mortality rates. Meanwhile, more mental health providers, access to exercise, higher income, chronic lung disease in adults, suicide, and excessive drinking are associated with decreased mortality. Our temporal analysis also reveals a possible decreasing impact of socioeconomic disadvantage and air quality, and an increasing effect of factors like age, which suggests that public health policies may have been effective in protecting disadvantaged populations over time or that analysis utilizing earlier data may have exaggerated certain effects. Overall, we continue to recognize that social inequality still places disadvantaged groups at risk, and we identify possible relationships between lung disease, mental health, and COVID-19 that need to be explored on a clinical level.

CCS CONCEPTS

  • Applied computing → Health informatics .

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    SciScore for 10.1101/2021.06.22.21258971: (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
    All analysis was conducted in R [48] using the lme4 package, and visualizations were created using Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: Thank you for sharing your code.

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

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  2. Version published to 10.1101/2021.06.22.21258971v1 on medRxiv