Health and long-term care planning for an aging population is an important and necessary function for both the public and private sector. Unfortunately, efforts at planning have often been limited by difficulties in making estimates and forecasts of chronic disease prevalence in the population. These difficulties are a direct result of the natural history of chronic diseases that normally have long presymptomatic stages. Because of this characteristic, predictions of the magnitude of chronic disease prevalence in the United States often fail to reflect an important dimension of the health state of the population: that there is a sizeable proportion of chronic disease prevalence and risk existing in the population in a preclinical phase. The authors present a strategy for obtaining more complete estimates of chronic disease prevalence. This approach entails the creation of an illness–death model representing the natural history of individual chronic diseases and the application of the model to infer morbidity incidence and prevalence patterns from national mortality statistics. They illustrate the approach with an example of lung cancer and discuss the applications of the outputs of the modeling strategy for health care resource planning.