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

• Published in: PLoS medicine Vol. 18; no. 7; p. e1003693
• Main Authors: Kandula, Sasikiran, Shaman, Jeffrey
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 13.07.2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Chronic obstructive pulmonary disease; Computer and Information Sciences; Coronaviruses; COVID-19; COVID-19 vaccines; Diabetes; Disease transmission; Earth Sciences; Epidemics; Ethnicity; Hypertension; Kidney diseases; Kidneys; Medicine and Health Sciences; Mortality; Nursing; Obesity; Pandemics; Population; Socio-economic aspects; Statistical analysis; Vaccination; Vaccines; Variables; United States; New York; Italy; United Kingdom > UK; United States > US; China
• ISSN: 1549-1676; 1549-1277; 1549-1676
• DOI: 10.1371/journal.pmed.1003693

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. 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.sup.2 ); and (ii) effect estimates of each predictor. 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.