Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis
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Abstract
The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown.
Objective
We examined the temporal association of county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the USA in the year starting in March 2020.
Methods
Counties (n=3091) with ≥50 COVID-19 cases by 6 March 2021 were included in the study. Associations between SVI (and its subcomponents) and county-level racial composition with incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time-varying associations between weekly number of cases/deaths and SVI or racial composition. Data were adjusted for percentage of population aged ≥65 years, state-level testing rate, comorbidities using the average Hierarchical Condition Category score, and environmental factors including average fine particulate matter of diameter ≥2.5 μm, temperature and precipitation.
Results
Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio per 10 percentile increase: 1.02, 95% CI 1.02 to 1.03, p<0.001) and death per capita (1.04, 95% CI 1.04 to 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of white residents were disproportionately represented (‘third wave’). By spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of black residents also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis.
Conclusion
Except for the winter ‘third wave’, when majority of the white communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations experienced worse COVID-19 outcomes.
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SciScore for 10.1101/2021.06.04.21258355: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization The time-varying associations between SVI (and its subcomponents) of a county with the weekly outcome variables were assessed by fitting a negative-binomial mixed-effects model with weekly total confirmed case numbers or weekly total death numbers as the outcome and county-specific random intercepts to account for overdispersion, correlation in the outcome within counties, and heterogeneity across counties. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The data was collected by the U.S. Census Bureau as self-reported race/ethnicity between 2015-2019. 4 Confounders: … SciScore for 10.1101/2021.06.04.21258355: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization The time-varying associations between SVI (and its subcomponents) of a county with the weekly outcome variables were assessed by fitting a negative-binomial mixed-effects model with weekly total confirmed case numbers or weekly total death numbers as the outcome and county-specific random intercepts to account for overdispersion, correlation in the outcome within counties, and heterogeneity across counties. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The data was collected by the U.S. Census Bureau as self-reported race/ethnicity between 2015-2019. 4 Confounders: Covariates included in all models were proportion of county population aged ≥65 years4, state-level COVID-19 testing rate obtained from the COVID Tracking Project database,5 2018 Hierarchical Condition Category (HCC) risk score acquired from the Centers for Medicare and Medicaid Services (CMS) database as a proxy for county-level medical comorbidity, and environmental factors. COVID Tracking Projectsuggested: NoneResults 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: 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 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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