Demographic Data Analytics Informs Public Policies During Pandemic

Researchers from UCLA Fielding School of Public Health have identified high-risk population groups in California during the COVID-19 pandemic using demographic data analytics.

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The study, published in the journal Epidemics, could provide valuable information for public policies relating to the pandemic.

The team used daily COVID-19 data for California from the California Department of Public Health (CDPH) and the 2017-2018 wave of the California Health Interview Survey (CHIS), a statewide research project from the Fielding School’s UCLA Center for Health Policy Research (CHPR).

Researchers developed a method that combines publicly available COVID-19 case and fatality data with population demographic survey data.

This can help overcome the limitations of aggregated summary statistics and yields estimates of COVID-19 test-based infection and case fatality rates across different subgroup population demographics – critical measurements for guiding public policy related to the control and prevention of COVID-19.

“In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy, and identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic,” said Christina Ramirez, UCLA Fielding School of Public Health professor of biostatistics.

“This has been challenging during the pandemic 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.”

The results of the study showed that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups. The gender gap widened as age increased. The findings also demonstrated that while elderly people with COVID-19 are at higher risk of mortality, test-based infection rates don’t always increase with age in a linear way.

“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,” Ramirez said. “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. This finding unfortunately aligns with frequently-seen trends throughout the pandemic. Research has shown that the virus is more prevalent among black and Hispanic populations, leading to wider disparities in healthcare.

“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 Dr. Marc Suchard, UCLA Fielding School of Public Health professor of biostatistics.

“What is shows is that as tragic as the pandemic has been for Californians generally, it has hit certain groups even harder.”

The UCLA team noted that going forward, they could further refine their model by adding in predictive analytics capabilities.

“Another promising avenue for future work is combining this method with a COVID-19 prediction model to provide detailed demographic projections of COVID-19 cases and mortalities,” researchers concluded.

“This would be a substantial improvement over most COVID-19 prediction models, as they tend to be quite limited in their ability to forecast the demographic characteristics of the infected.”