I-6 USING PROJECTIONS OF LIFE EXPECTANCY IN ECONOMIC DECISION MAKING

Tuesday, October 22, 2013: 11:45 AM
Key Ballroom 7,9,10 (Hilton Baltimore)
Quantitative Methods and Theoretical Developments (MET)

Petros Pechlivanoglou, PhD1, Mike Paulden, MA., MSc.1, William W. L. Wong, Ph.D.1, Sarah Bermingham, MSc2 and Ba' Pham, MSc, PhD, (c)1, (1)University of Toronto, Toronto, ON, Canada, (2)Health Quality Ontario, Toronto, ON, Canada
Purpose: Current best practice guidelines for decision analytic modeling suggest the use of life tables for the derivation of all-cause mortality probabilities. These probabilities are typically assumed as fixed with no uncertainty over the model time horizon. The aim of this study is to investigate the impact of mortality projections using historical life tables on the health outcomes and the costs estimated through a decision analytic model. 

Method: A previously published model on the cost-effectiveness of screening a cohort of male immigrants (average 35 years of age) for Chronic Hepatitis B in Canada has been updated using projected mortality probabilities. The Lee-Carter principal component and the random walk with drift methods were applied on historical life tables (1977-2002, Statistics Canada) to derive cohort-specific and time- specific projections of the mortality probabilities. Prediction uncertainty around the mortality probabilities was captured and was incorporated in the probabilistic sensitivity analysis of the decision analytic model.  The impact of mortality projections on the model outcomes at different discount rates was assessed.

Result: When fixed mortality probabilities and a discount rate of 5% were assumed, screening of male immigrants was associated with an improvement in quality-adjusted life expectancy (0.024 QALYs), additional costs ($1,665) and an ICER of $69,209/QALY gained.  When projected mortality probabilities, using the Lee-Carter method, and a discount rate of 5% were assumed, screening was associated with larger gains in QALYs (0.027 per person) while costs remained approximately the same ($1,666 per person) resulting in an ICER of $ 59,967/QALY. For a discount rate of 3.5% the impact of the assumption on the mortality probabilities was more profound on the ICER of screening vs no screening (fixed mortality: $50,507/QALY, projected mortality: $42,364/QALY). The results were similar for the random walk with drift method.

Conclusion: This study illustrates the importance of accounting for future gains in life expectancy within decision analytic models, especially when the costs of an intervention are incurred earlier in life and benefits are accrued across lifetime. In cases when the ICER estimates are compared to a threshold representing either an opportunity cost or a willingness to pay value, accounting for future gains in life expectancy could alter the interpretation of the cost-effectiveness results.