Allocating COVID Vaccines Based on Health and Socioeconomic Factors Could Cut Save Lives

Spatial autocorrelation in COVID-19 mortality and related health and socioeconomic indicators. Counties with significant Local Moran’s I are shown; color indicates measures of autocorrelation. Credit: Kandula S and Shaman J, 2021, PLOS Medicine, CC-BY 4.0

Study suggests spatial relationship between COVID-19 mortality and population-level health factors.

An estimated 43 percent of the variability in U.S. COVID-19 deaths is related to county-level socioeconomic indicators and health vulnerabilities, with the strongest association seen in the proportion of people with chronic kidney disease and in nursing homes. The study by researchers at the Columbia University Mailman School of Public Health suggests that assigning vaccines based on these factors may help minimize serious consequences, especially deaths. The results are published in the open access journal PLOS Medicine.

“It is well known that deaths from COVID-19 are concentrated in communities with underlying health and socioeconomic vulnerabilities. Our study estimates an increase in risk of some of the most important health and socioeconomic characteristics in the US,” says Sasikiran Kandula, MS, the study’s lead author and senior associate in the Department of Environmental Health Sciences at Columbia Mailman School. of Public Health.

“This information can guide the distribution of vaccines, particularly in parts of the world where vaccine supplies are limited, to bring them to communities where they are most needed,” adds senior author Jeffrey Shaman, PhD, professor of environmental health sciences at Columbia Mailman School of Public Health.

Currently, vaccination strategies for COVID-19 in the United States are determined by individual characteristics such as age and occupation. The effectiveness of population-level health and socioeconomic indicators to determine the risk of death from COVID-19 has not been sufficiently studied.

To test their hypothesis that health and socioeconomic indicators can accurately model the risk of COVID-19 death, Shaman and Kandula extracted county-level estimates of 14 indicators related to COVID-19 death from publicly available data sources. They then modeled the county-level proportion of COVID-19 deaths explained by identified health and socioeconomic indicators, and assessed the estimated effect of each predictor.

They found that 43 percent of the variability in U.S. COVID-19 deaths can be traced to 9 county-level socioeconomic indicators and health vulnerabilities, after adjusting for associations in death rates between adjacent counties.

Among the health indicators, mortality is estimated to increase by 43 per thousand inhabitants for every 1 percent increase in the prevalence of chronic kidney disease, and by 10 for chronic heart disease, 7 for diabetes, 4 for COPD, 4 for high cholesterol, 3 for high blood pressure and 3 for obesity prevalence, respectively. Among socioeconomic indicators, mortality is estimated to increase by 39 deaths per thousand for every 1 percent increase in the percentage living in nursing homes, and by 3 and 2 for every 1 percent increase in the percentage of the population that is older (65+ years) and uninsured 18-64 year olds, respectively. The death rate is estimated to decrease by 2 for every thousand dollars increase in per capita income.

While the study suggests a link between health and socioeconomic indicators and COVID-19 mortality, the study was limited by delays in reporting COVID-19 cases and deaths, and therefore may have been underestimated.

Reference: July 13, 2021, PLOS Medicine.
DOI: 10.1371/journal.pmed.1003693

The study is funded in part by a grant from the National Science Foundation (DMS-2027369) and a gift from the Morris-Singer Foundation to JS. The authors report the following conflicts of interest: JS and Columbia University disclose ownership of SK Analytics and JS discloses personal fees from BNI (Business Network International). SK consulted for SK Analytics.