A Multiple Linear Regression Analysis of Rural-Urban COVID-19 Risk Disparities in Texas

  1. Amber K. Luo
  2. Sophia Zhong
  3. Charles Sun
  4. Jasmine Wang
  5. Alexander White

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Abstract

As the number of COVID-19 cases in the U.S. rises, the differential impact of the pandemic in urban and rural regions becomes more pronounced, and the major factors relating to this difference remain unclear. Using the 254 counties of Texas as units of analysis, we utilized multiple linear regression to investigate the influence of 83 county-level predictor variables including race demographics, age demographics, healthcare and financial status, and prevalence of and mortality rate from COVID-19 risk factors on the incidence rate and case fatality rate from COVID-19 in Texas on September 15, 2020. Here, we report that urban counties experience, on average, 41.1% higher incidence rates from COVID-19 than rural counties and 34.7% lower case fatality rates. Through comparisons between our models, we found that this difference was largely attributable to four major predictor variables: namely, the proportion of elderly residents, African American residents, and Hispanic residents, and the presence of large nursing homes. According to our models, counties with high incidence rates of COVID-19 are predicted to have high proportions of African American residents and Hispanic residents coupled with low proportions of elderly residents. Furthermore, we found that counties with the highest case fatality rates are predicted to have high proportions of elderly residents, obese residents, and Hispanic residents, coupled with low proportions of residents ages 20-39 and residents who report smoking cigarettes. In our study, major variables and their effects on COVID-19 risk are quantified, highlighting the most vulnerable populations and regions of Texas.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.05.20248921: (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

    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 exist a number of limitations in our study and opportunities for further work. Several counties with a value of 0 for the response variable were removed from our models to allow for a log transformation on the response variable. Excluding counties with no cases or deaths causes our study to underestimate the impact of variables that may be particularly significant in those counties, potentially leading to the omission of important variables in determining incidence rate or case fatality rate from COVID-19 and lowering the accuracy of prediction in counties with no deaths and/or confirmed cases. As the pandemic progresses, this limitation may fade. Another limitation was the designation of urban/rural and variables for super spreading contexts as discrete variables. In reality, the urban/rural designation is not black or white. Many counties cannot be classified as definitively urban or rural, with some sections being urban and others rural. A simple discrete variable does not capture this variability, and a more proper designation may be large metropolitan/midsize metro/small metro/micropolitan/semirural/rural. Regarding super spreading contexts, a discrete variable does not adequately convey the extent to which the specific super spreading context impacts the county, and a variable that captures the proportion of people in the population who are employed or reside in a super spreading context may be a more robust predictor. Future research can extend the premise of our...

    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.

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  2. Version published to 10.1101/2021.01.05.20248921v1 on medRxiv