Inequality in health outcomes in relation to Americans' socioeconomic status (SES) is rising: a recent study by the Brookings Institution found that life expectancy for men and women in the top 10% of career earnings was over 10 years greater than those in the bottom 10%. Cardiovascular disease ? still leading cause of death for Americans ? merits study with respect to these findings. More research on how SES affects atherosclerotic risk is needed. The goal of our project is to develop advanced forecasting algorithms for atherosclerotic cardiovascular disease (ASCVD)-related events ? both at baseline and longitudinally ? using systems-based modeling methodologies which incorporate probabilistic representations of patients' socioeconomic and environmental characteristics. This represents a paradigm shift beyond existing models used in guiding primary and secondary prevention of atherosclerotic disease; in particular, risk models developed by the American College of Cardiology Foundation and the American Heart Association (ACCF/AHA) are based solely on physiological risk factors. We believe that the prediction performance of ASCVD risk models can be significantly improved by incorporating socioeconomic and environmental risks, especially in an era where improved primary and secondary prevention and increased socioeconomic inequality have resulted in complex phenomena among elderly Americans with respect to ASCVD risk. Our preliminary work indicates a significant degree of neighborhood-level variability in major ASCVD events (myocardial infarction, stroke or cardiovascular death), with low-SES neighborhoods associated with event rates over three times that of high-SES neighborhoods. Moreover, neighborhood SES explained four times the amount of neighborhood-level variation in ASCVD event rates than that explained by the ACCF/AHA Pooled Cohort Equations Risk Model. Our proposed project will therefore provide an essential risk modeling platform to health care systems focused on optimizing the health of populations that are highly heterogeneous with respect to socioeconomic and environmental characteristics. These models will be developed in a team-based environment, including translational scientists from general internal medicine, cardiology, social work, spatial epidemiology, urban poverty, community development, immunology, and data and population health sciences. Informing the models will be a newly-established, cutting-edge regional research registry, based on electronic health data from Northeast Ohio's two largest health systems, Cleveland Clinic and MetroHealth. Ultimately, this research is anticipated to yield new mechanistic insights and hypotheses, more accurate prediction models for cardiovascular outcomes, and a basis for informing decisions at multiple strategic and programmatic levels.
Poorer Americans are now expected to live 10 fewer years than wealthier Americans. Doctors and other providers need tools to identify people who are at greatest risk of poor heart disease outcomes. Our research will improve on current approaches by understanding the complexity of neighborhood, economic and clinical factors and how they determine heart disease risk.
| Dalton, Jarrod E; Perzynski, Adam T; Zidar, David A et al. (2017) Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study. Ann Intern Med 167:456-464 |
