Bioactuarial models of national mortality time series data

The incidence and prevalence of chronic degenerative disease in America's elderly population are important determinants of the need for long-term care health services. Though a wide range of data on disease incidence and prevalence is available from a variety of different health studies, a Congressional Budget Office study (1977) concluded that data limitations are a major factor in the lack of precise national long-term care cost estimates. In this paper, we present a modeling strategy to make better use of existing data by using biomedically motivated actuarial models to integrate multiple data sources into a comprehensive model of population health dynamics. The development of a specific model for application to a disease of interest involves three distinct phases. First, biomedical evidence and data are used to specify a cohort model of chronic disease morbidity and mortality. Second, the model is fitted to cohort mortality data with estimates of its parameters being derived by maximum likelihood procedures. Third, the morbidity distribution in the national population is generated from the parameter estimates. The model is used to examine lung cancer morbidity and mortality patterns for U. S. white and non-white males in 1977. A review of these patterns suggests that, based on current concepts of lung cancer incidence and natural history, over 2 percent of white males in the United States have lung cancer at some stage of development, though most of this prevalence is pre-clinical.