Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

  1. Sasikiran Kandula
  2. Jeffrey Shaman

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Abstract

With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.

Methods and findings

County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R 2 ); and (ii) effect estimates of each predictor.

Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R 2 . Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R 2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.

Conclusions

Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

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  1. Version published to 10.1371/journal.pmed.1003693
  2. ScreenIT

    SciScore for 10.1101/2021.01.25.21250468: (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:
    This study has a few limitations. Case and death counts were retrieved a week after the end of the study period. Given the lags in data reporting, particularly with deaths, events occurring at the end of the study period may not have been recorded and the rates used are underestimates. Similarly, the outcomes may not yet be known for cases recorded near the end of the study period. The adjacency based spatial weight matrix that was used in this study does not sufficiently capture the spread of COVID-19. Cases that occur in a county are not only correlated with those in counties geographically adjacent to it but also with counties with which it has strong population mixing; for example, counties with metropolitan centers into which commuters travel from the suburbs, or counties with major airports. Spatial weight matrices that capture mobility patterns may be more appropriate and lead to better spatial models. Similarly, methods that can explicitly account for spatial autocorrelation in predictors remain to be explored. Finally, the model structure presented may not be parsimonious in the number of predictors. Although we dropped a third of the predictors initially considered (to correct observed collinearity), model forms with a smaller subset of independent variables may yield near identical R2 and need to be explored. This is also belied by the lack of significance of some of the predictors included in the spatial models. One approach could start with a minimal set of predi...

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 17. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.01.25.21250468v1 on medRxiv