BACKGROUND: Although the United States is among the countries with the highest mortalities of COVID-19, inadequate geospatial studies have analyzed the disease mortalities across the nation. METHODS: In this county-level study, we investigated age-adjusted co-mortalities of 20 diseases, including cardiovascular, cancer, drug and alcohol disorder, respiratory and infectious diseases with COVID-19 over the first ten months of epidemic. One-way analysis of variance was applied to the Local Moran's I classes (High-High and Low-Low clusters, and non-significant counties of COVID-19) to examine whether the mean mortality measures of covariates that fall into the classes are significantly different. Moreover, a mixed-effects multinomial logistic regression model was employed to estimate the effects of mortalities on COVID-19 classes. RESULTS: Results showed that the distribution of COVID-19 case fatality ratio (CFR) and mortality rate co-occurrence of High-High clusters were mainly concentrated in Louisiana, Connecticut, and New Jersey. Also, positive associations were observed between High-High cluster of COVID-19 CFR and Asthma (OR = 4.584, 95 % Confidence Interval (CI): 2.583–8.137), Hepatitis (OR = 5.602, CI: 1.265–24.814) and Leukemia (OR = 2.172, CI: 1.518–3.106) mortality rates compared to the non-significant counties, respectively. CONCLUSIONS: Our results indicated that counties with higher mortality of some cancers and respiratory diseases are more vulnerable to fall into clusters of HH COVID-19 CFR. Future vaccine allocation and more medical professionals and treatment equipment should be a priority to those High-High clusters.
Spatial statistical analysis of pre-existing mortalities of 20 diseases with COVID-19 mortalities in the continental United States