Open Access Research

Decomposing cross-country differences in quality adjusted life expectancy: the impact of value sets

Richard Heijink12*, Pieter van Baal34, Mark Oppe3, Xander Koolman5 and Gert Westert6

Author Affiliations

1 Scientific centre for care and welfare (Tranzo), Tilburg University, Warandelaan 2, 5037 AB Tilburg, The Netherlands

2 Centre for Prevention and Health Services Research, National Institute for Public Health and the Environment, PO Box 1, 3720 BA Bilthoven, The Netherlands

3 Institute of Health Policy & Management and Institute for Medical Technology Assessment Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands

4 Expertise Centre for Methodology and Information Services, National Institute for Public Health and the Environment, PO Box 1, 3720 BA Bilthoven, The Netherlands

5 Faculty Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands

6 Scientific Institute for Quality of Healthcare (IQ healthcare), Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands

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Population Health Metrics 2011, 9:17  doi:10.1186/1478-7954-9-17

Published: 23 June 2011

Abstract

Background

The validity, reliability and cross-country comparability of summary measures of population health (SMPH) have been persistently debated. In this debate, the measurement and valuation of nonfatal health outcomes have been defined as key issues. Our goal was to quantify and decompose international differences in health expectancy based on health-related quality of life (HRQoL). We focused on the impact of value set choice on cross-country variation.

Methods

We calculated Quality Adjusted Life Expectancy (QALE) at age 20 for 15 countries in which EQ-5D population surveys had been conducted. We applied the Sullivan approach to combine the EQ-5D based HRQoL data with life tables from the Human Mortality Database. Mean HRQoL by country-gender-age was estimated using a parametric model. We used nonparametric bootstrap techniques to compute confidence intervals. QALE was then compared across the six country-specific time trade-off value sets that were available. Finally, three counterfactual estimates were generated in order to assess the contribution of mortality, health states and health-state values to cross-country differences in QALE.

Results

QALE at age 20 ranged from 33 years in Armenia to almost 61 years in Japan, using the UK value set. The value sets of the other five countries generated different estimates, up to seven years higher. The relative impact of choosing a different value set differed across country-gender strata between 2% and 20%. In 50% of the country-gender strata the ranking changed by two or more positions across value sets. The decomposition demonstrated a varying impact of health states, health-state values, and mortality on QALE differences across countries.

Conclusions

The choice of the value set in SMPH may seriously affect cross-country comparisons of health expectancy, even across populations of similar levels of wealth and education. In our opinion, it is essential to get more insight into the drivers of differences in health-state values across populations. This will enhance the usefulness of health-expectancy measures.