My preprint, "Spatial Patterns of COVID-19 Mortality: Socioeconomic Determinants in U.S. Counties," is now on medRxiv.
This research examines how factors like insurance, race, and income relate to COVID-19 mortality trends, highlighting key health disparities.
Read here: https://www.medrxiv.org/content/10.1101/2024.10.30.24316443v1
Feedback welcome as it enters peer review! #COVID19Research #Epidemiology #PublicHealth
Spatial Patterns of COVID-19 Mortality: Examining Socioeconomic Determinants in U.S. Counties Using Cluster Analysis
Abstract Aim: This study aims to investigate the spatial patterns of COVID-19 mortality across U.S. counties and identify the socioeconomic determinants that influence these mortality trends, using spatial epidemiological methods. Subject and Methods: We conducted a spatial analysis of COVID-19 mortality data from over 3,000 U.S. counties, applying cluster detection techniques, including SatScan, to identify areas with significant mortality trends. Spatial regression models, including spatial lag and spatial error models, were employed to examine the impact of socioeconomic variables, such as race, income inequality, and insurance rates, on COVID-19 mortality. The analysis controlled for multicollinearity and spatial autocorrelation in the data. Results: Counties with higher proportions of Black populations and higher uninsured rates exhibited significantly lower COVID-19 trends over the study period. Spatial clustering revealed regions in the northwestern and eastern/northeastern United States with a mix of positive and negative mortality rate trends. The spatial lag model showed the strongest fit, confirming the importance of spatial dependency in explaining mortality patterns. Conclusion: This study highlights the significant spatial disparities in COVID-19 mortality across U.S. counties. The findings emphasize the need for targeted public health interventions in vulnerable regions to address these disparities. Keywords: COVID-19 mortality, socioeconomic status (SES), spatial regression, health disparities, spatial clusters ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://www.rwardrup.com/download/data-r-code-for-covid-19-spatial-clusters-ses/
