Standardized incidence ratio of the COVID-19 pandemic: a case study in a Midwestern state

  1. Emma Spors
  2. Semhar Michael

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Abstract

Motivation

The Coronavirus disease 2019 (COVID-19) has made a dramatic impact around the world, with some communities facing harsher outcomes than others. We sought to understand how counties in the state of South Dakota (SD) fared compared to expected based on a reference population and what factors contributed to negative outcomes from the pandemic in SD.

Methods

The Standardized Incidence Ratios (SIR) of all counties, using age-adjusted and crude adjusted hospitalization and death rates were computed using the SD age-adjusted rate as a reference population. In addition, a penalized generalized linear regression model was used to identify factors that are associated with COVID-19 hospitalization and death rates. This model was then used to compute a new SIR after controlling for other socio-demographic and -economic factors.

Results

We identified counties that had more or less severe outcomes than what would be expected based on the rate of SD after age adjustment. Additionally, race, education, and testing rate were some of the significant factors associated with the outcome. The SIR values after controlling for these additional factors showed change in magnitude from the range of 4 times more severe to 1.5 times more severe out-come than what is expected. Interestingly the lower end of this interval did not have a major change.

Conclusion

The age adjusted SIR model used in this study allowed for the identification of counties with more or less severe than what is expected based on the state rate. These counties tended to be those with high nonwhite percentage, which mostly included counties with American Indian reservations. Although several predictors are associated with hospitalization and deaths, the penalized model confirmed what is already reported in literature that race and education level have a very high association with the outcome variables. As can be expected the further adjusted SIR mostly changed in those counties with higher than expected outcomes. We believe that these results may provide useful information to improve the implementation of mitigation strategies to curb the damage of this or future pandemics by providing a way for data-driven resource allocation.

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  1. Version published to 10.1101/2021.09.28.21263671v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.09.28.21263671: (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 data preparation and statistical analysis were performed using R and RStudio [18, 19].
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    The package tidyverse was used for general data manipulation and all graphs were created with the ggplot2 package [26, 27].
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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:
    Some limitations to this study comes from the data itself. Many of the socioeconomic data sets were based on self-reported information which may confer bias to those numbers. In addition, South Dakota has a rural state with many counties being sparsely populated as the median county population was 5,430. This lead to some disproportionate rates. Take, for example, the cumulative age-adjusted death rates in Jones and Buffalo Counties. Jones had a population near 900 and reported no deaths as of May 1st. Buffalo had a population near 1,962 and reported 13 deaths. Jones County had the lowest age-adjusted death rate while Buffalo County had the highest death rate. Due to their small size, for some counties, a small difference in numbers can lead to a big change in the adjusted rates.

    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.

    About SciScore

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  3. Version published to 10.1101/2021.09.28.21263671v1 on medRxiv