Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA)

  1. Morganne Igoe
  2. Praachi Das
  3. Suzanne Lenhart
  4. Alun L. Lloyd
  5. Lan Luong
  6. Dajun Tian
  7. Cristina Lanzas
  8. Agricola Odoi

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Abstract

Background

There is evidence of geographic disparities in COVID-19 hospitalization risks that, if identified, could guide control efforts. Therefore, the objective of this study was to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risks in the St. Louis area.

Methods

Hospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the American Community Survey. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risks, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models.

Results

COVID-19 hospitalization risks were significantly higher in ZCTAs with high diabetes hospitalization risks ( p  < 0.0001), COVID-19 risks ( p  < 0.0001), black population ( p  = 0.0416), and populations with some college education ( p  = 0.0005). The associations between COVID-19 hospitalization risks and the first three predictors varied by geographic location.

Conclusions

There is evidence of geographic disparities in COVID-19 hospitalization risks that are driven by differences in socioeconomic, demographic and health-related factors. The impacts of these factors vary by geographical location implying that a ‘one-size-fits-all’ approach may not be appropriate for management and control. Using both global and local models leads to a better understanding of geographic disparities. These findings are useful for informing health planning to identify geographic areas likely to have high numbers of individuals needing hospitalization as well as guiding vaccination efforts.

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  1. Version published to 10.1186/s12889-022-12716-w
  2. ScreenIT

    SciScore for 10.1101/2021.10.21.21265289: (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
    Descriptive Analyses: All descriptive analyses were performed in R version 4.1.0 [17] using RStudio version 1.4.1103 [18].
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    The COVID-19 ZCTA-level hospitalization risks were directly age-standardized, in STATA version 16 [19], using the 2010 US population as the standard population.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    Strengths and limitations: This study used both global and local models to investigate geographic disparities and identify predictors of COVID-19 hospitalization risks in St. Louis region of Missouri. The use of local models to investigate stationarity of regression coefficients of significant predictors and model non-stationary coefficients is a key strength of the study. This approach is particularly important in guiding local health planning since the importance of different predictors are not constant across the study area implying that different management and control strategies may need to be used in different areas. Therefore, modeling approaches that use both global and local models help to better understand the relationships between the outcome and predictors and may be more useful in guiding control efforts at the local level. However, the study is not without limitations. The hospital data has limitations associated with diagnostic classifications of COVID-19 in situations when the patient has co-morbidities that may have contributed to hospitalization. Additionally, there may be geographic differences in COVID-19 case ascertainment and reporting.

    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.

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  3. Version published to 10.1101/2021.10.21.21265289v1 on medRxiv