Funding awards have been issued for two projects in response to the Institute for Public Health spring 2018 Call for Proposals on precision public health. Stay tuned for future funding calls on this topic here.
Precision Targets to Reduce the Burden of Chronic Kidney Disease
The burden of chronic kidney disease in the United States is significant and rising dramatically; in Missouri, death rates due to the disease have increased by 62% since 1990, and chronic kidney disease is now the eighth leading cause of death in St. Louis County. Very little is known about the key contextual factors at the community and population level that may be driving the burden of chronic kidney disease. This study links Veterans Administration databases with the County Health Ranking datasets to examine the relationship between risk of development of chronic kidney disease and county-level characteristics in six domains including 1) demographics, 2) physical environment, 3) social and economic factors, 4) health behaviors, 5) clinical care, and 6) health outcomes. The study team will then quantify the burden of disease attributable to each risk factor at the county level. This approach will allow rank ordering of risk factors according to their contribution to chronic kidney disease burden and will inform the identification of precision targets to reduce the burden of chronic kidney disease at the county level.
Designing Data-Driven Homelessness Prevention: A Precision Public Health Approach
Patrick Fowler, PhD Associate Professor Washington University Brown School
Peter Hovmand, PhD, Professor, Washington University Brown School
Sanmay Das, PhD, Associate Professor, Washington University School of Engineering and Applied Sciences
Jennifer Heggeman, MSW, Legal Services of Eastern Missouri and St. Louis County Homeless Continuum of Care
Lack of safe and affordable housing perpetuates disparities that threaten the well-being the most disadvantaged. Existing social services strain under the demand for low-income housing assistance, and subsequently, the homeless service system – intended to support those during housing crises – remains continuously overburdened and leaves missed opportunities for homelessness prevention. A need exists for processes and procedures that accurately screen and target homelessness prevention. This project uses machine-learning applications with population-level administrative data to empirically identify families who need and respond most to homelessness prevention. Participatory methods investigate decision-making supports that improve the efficiency of prevention services that keep families housed. The project carefully applies data-driven approaches to ensure new insights sustainably address health inequities without further entrenching social biases. Decision-making supports and systems that improve efficiency of screening and customized housing assistance offer potential for scalable approaches.