The Syndemic of Sepsis, Multimorbidity, Aging, and Low Healthcare Access and Quality in the United States

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PROJECT SUMMARY Sepsis is a major global health problem – my previous work has estimated that there were nearly 50 million cases and 11 million associated deaths worldwide in 2017, representing 20% of all deaths that year. While sepsis is a public health challenge in nearly every location in world, there is strong evidence that certain subgroups are at far higher risk than others, with higher incidence among older adults, people with multimorbidity, and those in communities with lower healthcare access and quality. In the United States (US), these risk factors are closely linked and geographically clustered. My overall objective in this proposal is to improve understanding of the factors influencing individual sepsis risk, accounting for community- level features and personal health history over time, to establish critical new insights into sepsis prevention. My central hypothesis is that there is a syndemic of sepsis, multimorbidity, aging, and low healthcare access and quality in the US that is spatially patterned. Syndemics, population-level clustering of social and health problems in which the clustering results in adverse disease interaction, provide a “framework of disease-disease and social condition-disease interactions” that can improve understanding of health conditions within a specific population. To test my hypothesis I will leverage, and spatially link for the first time, multiple unique data sources including individual electronic health records, Census data, community data sources, and statewide health systems data. I will combine these data sources through a community-informed model, building upon the Framework for Reconstructing Epidemiological Dynamics (FRED) agent-based modeling platform. Using the syndemic framework, I will test my central hypothesis via three specific aims: 1) evaluate spatial, social, and health systems-based patterns of incident sepsis hospitalizations among adults in Allegheny County, Pennsylvania over an 8-year period using multilevel models that geographically link electronic health records, Census data, and community data sources at the ZIP code level, 2) apply a syndemic framework within the FRED agent-based modeling platform to test the mechanisms that underlie the relationships between patient-level features of aging and multimorbidity, community factors, and health system access and quality, that impact individual risk for incident sepsis, and 3) use community-engaged model building with sepsis patients, families, and healthcare professionals to refine the sepsis, aging, multimorbidity, and healthcare access and quality syndemic model. The proposed work will greatly impact US public health policy, sepsis prevention efforts, and care for older adults with chronic conditions. Closely mentored by experts in sepsis and quantitative modeling of social contextual features of health, this award will provide essential research training in advanced epidemiologic methods, including multilevel modeling, agent-based modeling, and community-engaged research. This award will support my overall career goal of becoming an independent investigator focused on reducing the global burden of sepsis, with a strong focus on health disparities.