RISE looks at recent headlines concerning social determinants of health (SDoH).
Children’s Hospital Association calls on Congress to prioritize pediatric health care
Children’s Hospital Association (CHA) CEO Mark Wietecha has sent a letter to Congress on behalf of 220 children’s hospitals requesting critical support for pediatric health care.
“At a time when our children’s hospitals are flooded with kids dealing with respiratory illnesses, mental health crises, and other health care needs, congressional action is urgently needed before the end of the year to ensure children’s hospitals have the resources and capacity they need to best take care of our nation’s children. Our children cannot wait for much needed federal support,” said Wietecha, in a statement.
The letter outlines several ways in which Congress can address the “immediate needs” of children across the country:
- Invest in addressing the children’s mental health crisis through federal Medicaid investment, support for mental health workforces and community-based organizations, and dedicated funding for the pediatric mental health infrastructure
- Strengthen support systems for the pediatric workforce, particularly the Children’s Hospitals Graduate Medical Education program to provide the necessary training of pediatric specialists
- Protect children’s health coverage by extending 12-month continuous eligibility for children in Medicaid and the Children’s Health Insurance Program
Cardiac arrest death rates drop everywhere except Black, rural communities
New research from the American Heart Association (AHA) found cardiac arrest deaths have dropped significantly in all racial groups in the United States except Black communities.
According to researchers, who analyzed cardiac arrest trends using data from the Centers for Disease Control and Prevention, there has been a consistent overall drop in cardiac arrest deaths (more than 40 percent) from 1999 to 2020. However, while the average annual decline for all racial groups was 2.4 percent, after looking further into four racial groups, including Black, white, American Indian or Alaska Native, and Asian or Pacific Islander, they found Black people only experienced an annual average decrease of 1.8 percent, the lowest among all racial groups.
Looking even further into where people lived, researchers also found the cardiac arrest death rate was more than twice as high in rural areas than in big cities.
"On a society-wide level, we need to find ways for better training and awareness so we can get rid of these embedded disparities," said Muchi Ditah Chobufo, M.D., the study's lead researcher and cardiology fellow at West Virginia University School of Medicine in Morgantown, in a statement.
AI, SDoH can help identify individuals with highest future medical spending
Risk prediction models using artificial intelligence (AI) to combine claims data with nontraditional data, such as SDoH data, were found to identify members with the highest future medical spending more effectively than traditional models that rely solely on claims and demographic data, according to a recent study from The American Journal of Managed Care (AJMC).
For the study, researchers analyzed the data of a Medicaid population between May 2018 and April 2019 to compare the performance of an AI risk prediction score that uses both traditional data elements available to managed care organizations as well as nontraditional data elements such as SDoH, with the Chronic Illness and Disability Payment System risk score, which predicts future spending using only traditional data elements.
The findings suggest that AI can improve risk stratification programs by integrating multiple data sources into one consolidated model as well as enable the allocation of care management resources to patients with the greatest need.
“Many payers and delivery systems engage in care management efforts to reduce medical spending by identifying patients likely to incur high costs, then intervening to reduce preventable spending,” wrote study co-authors. “Unfortunately, despite many available predictive models, identification of members with the highest future spending remains challenging. Our results suggest that this is due in part to the heavy reliance of these models on demographic and claims data and their inability to incorporate other sources of data.”
Care management programs could better identify high-cost members and therefore reduce future spending if they leverage other data beyond claims data for risk stratification, noted researchers.