@article {1500, title = {Predicting future years of healthy life for older adults.}, journal = {J Clin Epidemiol}, volume = {51}, year = {1998}, month = {1998 Apr}, pages = {343-53}, abstract = {

Cost-effectiveness studies often need to compare the cost of a program to the lifetime benefits of the program, but estimates of lifetime benefits are not routinely available, especially for older adults. We used data from two large longitudinal studies of older adults (ages 65-100) to estimate transition probabilities from one health state to another, and used those probabilities to estimate the mean additional years of healthy life that an older adult of specified age, sex, and health status would experience. We found, for example, that 65-year-old women in excellent health can expect 16.8 years of healthy life in the future, compared to only 8.5 years for women in poor health. We also provide estimates of discounted years of healthy life and future life expectancy. These estimates may be used to extend the effective length of the study period in cost-effectiveness studies, to examine the impact of chronic diseases or risk factors on years of healthy life, or to investigate the relationship of years of life to years of healthy life. Several applications are described.

}, keywords = {Aged, Aged, 80 and over, Cost-Benefit Analysis, Female, Forecasting, Health Promotion, Health Status, Humans, Life Expectancy, Life Tables, Male, Probability, Quality-Adjusted Life Years, Sex Factors}, issn = {0895-4356}, author = {Diehr, P and Patrick, D L and Bild, D E and Burke, G L and Williamson, J D} } @article {953, title = {The effect of different public health interventions on longevity, morbidity, and years of healthy life.}, journal = {BMC Public Health}, volume = {7}, year = {2007}, month = {2007 Apr 05}, pages = {52}, abstract = {

BACKGROUND: Choosing cost-effective strategies for improving the health of the public is difficult because the relative effects of different types of interventions are not well understood. The benefits of one-shot interventions may be different from the benefits of interventions that permanently change the probability of getting sick, recovering, or dying. Here, we compare the benefits of such types of public health interventions.

METHODS: We used multi-state life table methods to estimate the impact of five types of interventions on mortality, morbidity (years of life in fair or poor health), and years of healthy life (years in excellent, very good, or good health).

RESULTS: A one-shot intervention that makes all the sick persons healthy at baseline would increase life expectancy by 3 months and increase years of healthy life by 6 months, in a cohort beginning at age 65. An equivalent amount of improvement can be obtained from an intervention that either decreases the probability of getting sick each year by 12\%, increases the probability of a sick person recovering by 16\%, decreases the probability that a sick person dies by 15\%, or decreases the probability that a healthy person dies by 14\%. Interventions aimed at keeping persons healthy increased longevity and years of healthy life, while decreasing morbidity and medical expenditures. Interventions focused on preventing mortality had a greater effect on longevity, but had higher future morbidity and medical expenditures. Results differed for older and younger cohorts and depended on the value to society of an additional year of sick life.

CONCLUSION: Interventions that promote health and prevent disease performed well, but other types of intervention were sometimes better. The value to society of interventions that increase longevity but also increase morbidity needs further research. More comprehensive screening and treatment of new Medicare enrollees might improve their health and longevity without increasing future medical expenditures.

}, keywords = {Actuarial Analysis, Cost-Benefit Analysis, Disease Management, Health Priorities, Health Promotion, Health Services, Health Status, Humans, Life Expectancy, Medicare, Models, Biological, Morbidity, United States, United States Public Health Service}, issn = {1471-2458}, doi = {10.1186/1471-2458-7-52}, author = {Diehr, Paula and Derleth, Ann and Cai, Liming and Newman, Anne B} } @article {1281, title = {Comparing costs associated with risk stratification rules for t-year survival.}, journal = {Biostatistics}, volume = {12}, year = {2011}, month = {2011 Oct}, pages = {597-609}, abstract = {

Accurate risk prediction is an important step in developing optimal strategies for disease prevention and treatment. Based on the predicted risks, patients can be stratified to different risk categories where each category corresponds to a particular clinical intervention. Incorrect or suboptimal interventions are likely to result in unnecessary financial and medical consequences. It is thus essential to account for the costs associated with the clinical interventions when developing and evaluating risk stratification (RS) rules for clinical use. In this article, we propose to quantify the value of an RS rule based on the total expected cost attributed to incorrect assignment of risk groups due to the rule. We have established the relationship between cost parameters and optimal threshold values used in the stratification rule that minimizes the total expected cost over the entire population of interest. Statistical inference procedures are developed for evaluating and comparing given RS rules and examined through simulation studies. The proposed procedures are illustrated with an example from the Cardiovascular Health Study.

}, keywords = {Biostatistics, Coronary Disease, Cost-Benefit Analysis, Costs and Cost Analysis, Humans, Models, Statistical, Risk Assessment, Risk Factors, Time Factors}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxr001}, author = {Cai, Tianxi and Tian, Lu and Lloyd-Jones, Donald M} }