Health Forecasting Page Master.
Although health forecasting is an emerging field, many important articles have been published in the research literature.
- Numerous researchers have published extensively regarding methods of population forecasting. The International Journal of Forecasting dedicated an entire issue to population forecasting theory and practice. (Vol. 8, No. 3, November 1992)
- R. D. Lee and L. R. Carter (1992), "Modeling and Forecasting U.S. Mortality," Journal of the American Statistical Association, 87 (419), 659-671.A recent evaluation of the performance of this forecasting model by one of the authors is available on the web.
- Significant research has been done to assess the accuracy of official population forecasts and the underlying forecasting techniques in the United States and internationally. Several interesting articles are listed below.
- J. M. Alho and B. D. Spencer (1985) , "Uncertain Population Forecasting," Journal of American Statistical Association, 80 (390), 306-314.
- J. M. Alho and B. D. Spencer (1990), " Errors Models for Official Mortality Forecasts. Journal of American Statistical Association, 85 (411), 609-616.
- H. Booth, J. Maindonald, and L. Smith (2002), "Applying Lee-Carter under conditions of variable mortality decline," Population Studies, 56, 325-336.
- A 2001 report published by the American Academy of Microbiology summarizes the state-of-the-field with regard to modeling of the relationship between climate and human health through changes in vector-born and infectious diseases.
- The National Center for Health Workforce Analysis July 2002 report, Projected Supply, Demand and Shortages of Registered Nurses: 2000-2020 is an excellent example of health resource forecasting.
- L. Greenberg and J. M. Cultice (1997), "Forecasting the Need for Physicians in the United States: The Health Resources and Services Administration's Physician Requirements Model," Health Services Research, 31 (6), 723-737.
- The Center for Disease Control and Preventionâ€™s Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC) software is a computational program used to estimate the number of annual deaths, years of potential life lost (YPLL), excess medical expenditures, and lost future productivity caused by smoking for the nation and states.
- The Transdisciplinary Tobacco Use Research Center (TTURC) at the University of California, Irvineis developing a microsimulation model to estimate the impact of policy changes on smoking patterns.
- Researchers at Statistics Canada have developed a continuous-time model, POHEM to assess the impact of different policy interventions or technologies on the health of the Canadian population.
- M. Wolfson (1994), "POHEM â€“ A framework for understanding and modeling the health of human populations," World Health Statistics Quarterly, 47, 157-175.
- B. P. Will et al. (2001), " First do no harm: Extending the debate on the provision of preventive tamoxifen," British Journal of Cancer, 85 (9), 1280-1288.
- Milton Weinstein at Harvard developed the Coronary Heart Disease Policy Model to model the impact of policy and technological advances on the incidence, prevalence, mortality from coronary heart disease, and changes in the cost of health care.
- M. C. Weinstein et al. (1987), "Forecasting Coronary Heart Disease Incidence, Mortality, and Cost: The Coronary Heart Disease Policy Model," American Journal of Public Health, 77, 1417-1426 .
- L. Goldman et al. (2001), "The Effect of Risk Factor Reductions Between 1981 and 1990 on Coronary Heart Disease Incidence, Prevalence, Mortality and Cost," Journal of theAmerican College of Cardiology, 38 (4),1012-1017.
- D. Ruwaard et al. (1993), "Forecasting the Number of Diabetic Patients in the Netherlands in 2005," American Journal of Public Health, 83, 989-995.
- Kenneth Manton at Duke University â€™s Center for Demographic Studies has developed two health forecasting models to:
- analyze discrete state health changes using population and vital statistics data
- describe both discrete and continuous changes using data from longitudinally-followed community populations.
Both models can be modified based on expert judgment to deal with simulations of a multiplicity of possible interventions.
- There are numerous examples of forecasting methods for health care expenditures in the United States and internationally. The first two articles critiques existing methods used by government actuaries to project future health expenditures and presents a probabilistic or stochastic models to assess uncertainty.
- R. Lee and T. Miller (2002), "An Approach to Forecasting Health Expenditures, with Applications to the US Medicare System," Health Services Research 37 (5), 1365-1386.
- R. Lee and S. Tuljapurkar (1997), "Death and Taxes: Longer Life, Consumption, and Social Security," Demography: 67-81.
- T. E. Getzen and J. P. Poullier (1992), "International Health Spending Forecasts: Concepts and Evaluation," Social Science and Medicine 34 (9), 1057-1068. In projecting international health expenditures, the authors found that a combined method using econometric methods, exponential smoothing, ARIMA, and moving averages was more accurate than any single method according to mean absolute error (MAE).