This research study focuses on improving the quality of elderly health forecasting in the U.S. The study constructs a set of new health predicting models with different levels of complexity; evaluates the quality of their predictions; and utilizes the verified models to predict future prevalence of cancer, coronary heart disease (CHD), stroke, diabetes, and Alzheimer's disease (AD) under different scenarios. The models use information about factors affecting health and survival available from five datasets: the Framingham Heart Study (FHS), Health and Retirement study merged with Medicare files (HRS-M), National Long Term Care Survey linked to Medicare records (NLTCS-M), the Surveillance, the Epidemiology and End Results data merged with Medicare records (SEER-M) and the 5% Medicare (5%-M) file. Results from this study clarify roles of genetic mechanisms in forming health and longevity traits, reduce uncertainty in health forecasting and contribute to ongoing improvements in the health care system and elderly population health in the U.S.
Project Website
Primary Funding Agency
NIA