Decomposing cross-country differences in quality adjusted life expectancy: The impact of value sets
Population Health Metrics, ISSN: 1478-7954, Vol: 9, Issue: 1, Page: 17
2011
- 50Citations
- 43Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations50
- Citation Indexes45
- 45
- CrossRef19
- Policy Citations5
- 5
- Captures43
- Readers43
- 43
Article Description
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. © 2011 Heijink et al; licensee BioMed Central Ltd.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=79959406120&origin=inward; http://dx.doi.org/10.1186/1478-7954-9-17; http://www.ncbi.nlm.nih.gov/pubmed/21699675; https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-9-17; https://dx.doi.org/10.1186/1478-7954-9-17; http://www.pophealthmetrics.com/content/9/1/17; https://pophealthmetrics.biomedcentral.com/counter/pdf/10.1186/1478-7954-9-17; http://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-9-17; http://link.springer.com/article/10.1186/1478-7954-9-17/fulltext.html; https://link.springer.com/track/pdf/10.1186/1478-7954-9-17; https://link.springer.com/articles/10.1186/1478-7954-9-17; https://link.springer.com/article/10.1186/1478-7954-9-17
Springer Nature
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know