The combination of threats from COVID-19 and extreme heat in many socially vulnerable communities across the United States is a real concern for public health officials, highlighting the need to better understand how compounding factors can increase COVID-19 risks. A new study by IUPUI researchers examined the relationships between COVID-19 cases and deaths, social vulnerability and environmental measurements in 3,400 counties across the country's 48 contiguous states. Their findings show that from March 1 to Dec. 31, 2020, COVID-19 affected different counties across the U.S. at different points in time, and the most socially vulnerable communities were impacted more significantly during the summer months. They also identified social and environmental risk factors that may put communities at higher risk. Researcher Daniel Johnson says the work shows links between social vulnerability and environmental determinants of COVID-19 and will aid in modeling new outbreaks and decreasing the impact of COVID-19 in our most vulnerable communities. While COVID-19 may not be as impactful in the future as it was at first, Johnson says it will likely be an annual concern for health authorities. His hope is that they can use the models researchers’ have built to pinpoint the communities most at risk. Johnson’s team used CDC data and environmental data to visualize the monthly spread of COVID-19 on a county-by-county basis. Their findings show that being a person of color or lacking a high school diploma were the most significant contributors to increased risk of both COVID-19 infection and death. For people 65 and older, age contributed significantly to deaths but not to cases. When examining solely environmental measures, their models showed that temperature had the strongest effect on a community's risk for infection and death. Increases in temperature lowered the risk of infection and death in most areas; however, this effect was not as pronounced in areas of higher social vulnerability. In examining COVID-19-related deaths, the researchers found that mortality rates followed the same trend as cases but peaked about four to six weeks later -- something they observed across the entire U.S. Johnson and his team plan to continue this work, combining both social determinants of health and environmental factors to create a better system to predict patterns of community risk.
In other news, in the United States, young children with developmental delays or disabilities are eligible to receive services through state-based programs funded by the federal government and by individual states. But while the programs provide a wide range of therapies for young children, researchers at IU want to know if they are equitable. A new study by IU researcher Katie Herron is exploring that question with respect to First Steps, the state of Indiana's early intervention system that serves over 20,000 families each year. Differences in outcomes for white and Black families in Indiana’s First Steps program have been recorded in required federal reports, Herron says, but until now, there has not been any systematic examination of what’s behind those differences. In the study’s initial phase, Herron and colleagues analyzed existing quantitative First Steps data regarding the relationship between race and multiple factors. Their early findings revealed marked differences between experiences of Black and white families in the First Steps system including the fact that Black families are more likely to be referred by social service agencies, while white families are more likely to be referred by pediatricians and that white families enter First Steps earlier than Black families. Additionally, the research has shown that progress is significantly less for Black families -- they experience fewer successful outcomes across all categories -- and that Black families are more likely to discontinue First Steps participation by passively withdrawing from the program. The study is now focused on determining the factors behind these differences. Herron hopes this qualitative data will reveal where biases exist in the system and lead to change.