Rachel Meacock1, Matt Sutton1, Søren Rud Kristensen1, Mark Harrison2,3. 1. University of Manchester, Manchester, UK (RM, MS, SRK). 2. Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada (MH). 3. Centre for Health Evaluation and Outcome Sciences, Vancouver, BC, Canada (MH).
Abstract
BACKGROUND: Policy evaluations taking a lifetime horizon have converted estimated changes in short-term mortality to expected life year gains using general population life expectancy. However, the life expectancy of the affected patients may differ from the general population. In trials, survival models are commonly used to extrapolate life year gains. The objective was to demonstrate the feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. METHODS: We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population life expectancy and survival models. Patient-level data from Hospital Episode Statistics was utilized for patients admitted to hospitals in England for pneumonia between 1 April 2007 and 31 March 2008 and between 1 April 2009 and 31 March 2010, and linked to death records for the period from 1 April 2007 to 31 March 2011. RESULTS: In our cohort of patients, using parametric survival models rather than general population life expectancy figures reduced the estimated mean life years remaining by 30% (9.19 v. 13.15 years, respectively). However, the estimated mean life year gains associated with the program are larger using survival models (0.380 years) compared to using general population life expectancy (0.154 years). CONCLUSIONS: Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.
BACKGROUND: Policy evaluations taking a lifetime horizon have converted estimated changes in short-term mortality to expected life year gains using general population life expectancy. However, the life expectancy of the affected patients may differ from the general population. In trials, survival models are commonly used to extrapolate life year gains. The objective was to demonstrate the feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. METHODS: We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population life expectancy and survival models. Patient-level data from Hospital Episode Statistics was utilized for patients admitted to hospitals in England for pneumonia between 1 April 2007 and 31 March 2008 and between 1 April 2009 and 31 March 2010, and linked to death records for the period from 1 April 2007 to 31 March 2011. RESULTS: In our cohort of patients, using parametric survival models rather than general population life expectancy figures reduced the estimated mean life years remaining by 30% (9.19 v. 13.15 years, respectively). However, the estimated mean life year gains associated with the program are larger using survival models (0.380 years) compared to using general population life expectancy (0.154 years). CONCLUSIONS: Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.
Entities:
Keywords:
cost-effectiveness analysis; economics (health); pay for performance; survival analysis