LOS ANGELES – A team of UCLA researchers has developed a method to identify those most at risk of the coronavirus pandemic in an attempt to guide public policy related to the control and prevention of COVID-19.
The team from UCLA’s Fielding School of Public Health used demographic data to estimate test-based infection and test-based case fatality rates across California.
Their analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not always increase with age in a linear fashion.
“Our approach combines aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate test-based infection and case fatality rates for population subgroups across combinations of demographic characteristics,” said co-author Marc Suchard, professor of biostatistics. “What it shows is that as tragic as the pandemic has been for Californians generally, it has hit certain groups even harder.”
Christina Ramirez, also a professor of biostatistics, said the workforce population with ages “from 18-59 have a higher infection rate comparing with children, adolescents, and other senior citizens, except for people in their 80 and above. We also found that the elevated infection and mortality risk for males and greater mortality risk for all races increase with age.”
The subgroups with the highest five test-based case fatality rates are all-male groups with race as African American, Asian, multi-race, Latino, and white, followed by African American females, indicating that African Americans are an especially vulnerable California subpopulation.
The research, published in the December edition of the peer-reviewed journal Epidemics, introduces a method that combines aggregated publicly available COVID-19 case and fatality data with population demographic survey data. This helps to overcome the limitations of aggregated summary statistics “because governmental agencies typically release aggregate COVID-19 data as summaries; these may identify broad disparities in outcomes, but typically do not provide granular data that would include combinations of demographic characteristics such as age, race and gender,” Ramirez said.
The study can be viewed at www.sciencedirect.com/science/article/pii/S1755436520300396.