title: Time to include time to death? The future of health care expenditure predictions.
abstract: Government projections of future health care expenditures--a great concern given the aging baby-boom generation--are based on econometric regressions that control explicitly for age but do not control for end-of-life expenditures. Because expenditures increase dramatically on average at the end of life, predictions of future cost distributions based on regressions that omit time to death as an explanatory variable will be biased upward (or, more explicitly, the coefficients on age will be biased upward) if technology or other social factors continue to prolong life. Although health care expenditure predictions for a current sample will not be biased, predictions for future cohorts with greater longevity will be biased upwards, and the magnitude of the bias will increase as the expected longevity increases. We explore the empirical implications of incorporating time to death in longitudinal models of health expenditures for the purpose of predicting future expenditures. Predictions from a simple model that excludes time to death and uses current life tables are 9% higher than from an expanded model controlling for time to death. The bias increases to 15% when using projected life tables for 2020. The predicted differences between the models are sufficient to justify reassessment of the value of inclusion of time to death in models for predicting health care expenditures.
authors.affiliation: Department of Health Policy and Administration, University of North Carolina at Chapel Hill, 27599-7411, USA. sally_stearns@unc.edu
authors.forename: Sally C
authors.initials: SC
authors.lastname: Stearns
authors.forename: Edward C
authors.initials: EC
authors.lastname: Norton
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003643:Death; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data / Q000639:trends*; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D006278:Medicare / Q000191:economics; D017059:Models, Econometric; D014481:United States