全球智库信息集成服务系统

Key Findings

• Researchers created a unique data set that links individual-level vital statistics, electronic health records, community-based social service records, and other socio-environmental data describing ten years of births in Allegheny County, Pennsylvania.
• The research team designed and implemented machine learning algorithms and causal inference models to predict which women and their children were at highest risk of infant mortality, the interventions that women were most likely to use, and which interventions would most effectively reduce the risks for each woman and child.
• The interventions found to be most effective—broad preconception care, frequent prenatal care, doula support, and home visiting—aim to improve the health of the mother and result in lower mortality risk for the infant, especially when initiated before or early in pregnancy.
• Providers of health care and community-based social services can use the models with their patients or clients at high risk of infant mortality to tailor intervention options to their needs. Health care and social services can be better coordinated through better provider awareness of services across sectors and utilization of new tools.
• The models, methods, and tools developed are flexible and can be used by other localities and for other health conditions.