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

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.

SUBMITTER: Heijink R

PROVIDER: S-EPMC3146826 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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

Heijink Richard R van Baal Pieter P Oppe Mark M Koolman Xander X Westert Gert G

Population health metrics 20110623 1

<h4>Background</h4>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.<h4>Methods</h4>We calculated ...[more]

PMID: 21699675

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Project description:IntroductionNo information is available in Belgium on life expectancy adjusted for health-related quality of life (HRQoL). Quality-adjusted life expectancy (QALE) captures the multidimensionality of health by accounting for losses in mortality and HRQoL linked to physical, mental, and social impairments. The objective of this study is to estimate for Belgium QALE, the changes in QALE between 2013 and 2018 and the contribution of mortality, HRQoL and its dimensions to this trend.MethodsThe Belgian Health Interview Survey (BHIS), a representative sample of the general population, included the EQ-5D-5L instrument in 2013 and 2018. The tool assesses HRQoL comprising five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) using a 5-level severity scoring to define a large variety of health states. The Sullivan method was used to compute at different ages QALE by gender using mortality data from the Belgian statistical office and average EQ-5D scores from the BHIS. QALE was calculated for 2013 and 2018, and changes in QALE over time were decomposed into mortality and ill-health effect.ResultsIn 2018, QALE at age 15 years (QALE15) was 56.3 years for women and 55.8 years for men, a decrease from 2013 by 0.7 year for women and a stagnation for men. In men, the decrease in mortality counterbalanced the decline in HRQoL. The decline in QALE in women is driven by a decrease in mortality rates that is too small to compensate for the substantial decline in HRQoL before the age of 50 years. In women at older ages, improvements in HRQoL are observed. In women, QALE15 is decreasing due to an increase in pain/discomfort, anxiety/depression and problems in usual activities. In men at age 15, the pain/discomfort and anxiety/depression domains contributed to the stagnation. QALE65 increased somewhat, due to an improvement in self-care and mobility for both genders, and usual activities and anxiety/depression in men only.ConclusionThe strength of QALE as member of the family of composite indicators, the health expectancies, is the multidimensional structure of the underlying health component, including both ill-health with different health domains as levels of severity. The ability to decompose differences in the health expectancy not only into a mortality and health component but also into the different health dimensions allows to better inform on general population health trends. Next, compared to other health expectancy indicators, QALE is more sensitive to changes at younger ages.

Project description:IntroductionSmoking contributes substantially to mortality levels and trends. Its role in country differences in mortality has, however, hardly been quantified. The current study formally assesses the-so far unknown-changing contribution of smoking to country differences in life expectancy at birth (e0) across Europe.MethodsUsing all-cause mortality data and indirectly estimated smoking-attributable mortality rates by age and sex for 30 European countries from 1985 to 2014, the differences in e0 between each individual European country and the weighted average were decomposed into a smoking- and a nonsmoking-related part.ResultsIn 2014, e0 ranged from 70.8 years in Russia to 83.1 years in Switzerland. Men exhibited larger country differences than women (variance of 21.9 and 7.0 years, respectively). Country differences in e0 increased up to 2005 and declined thereafter. Among men, the average contribution of smoking to the country differences in e0 was highest around 1990 (47%) and declined to 35% in 2014. Among women, the average relative contribution of smoking declined from 1991 to 2011, and smoking resulted in smaller differences with the average e0 level in the majority of European countries. For both sexes combined, the contribution of smoking to country differences in e0 was higher than 20% throughout the period.ConclusionsSmoking contributed substantially to the country differences in e0 in Europe, their increases up to 1991, and their decreases since 2005, especially among men. Policies that discourage smoking can help to reduce inequalities in mortality levels across Europe in the long run.ImplicationsSmoking contributes substantially to country differences in life expectancy at birth (e0) in Europe, particularly among men, for whom the contribution was highest around 1990 (47%) and declined to 35% in 2014. In line with the anticipated progression of the smoking epidemic, the differences between European countries in e0 due to smoking are expected to further decline among men, but to increase among women. The role of smoking in mortality convergence since 2005 illustrates that smoking policies can help to reduce inequalities in life expectancy levels across Europe, particularly when they target smoking in countries with low e0.

Project description:ObjectivesTo use real-world data to develop a flexible generic decision model to predict cost, life expectancy, and quality-adjusted life-years (QALYs) for follicular lymphoma (FL) in the general patient population.MethodsAll patients newly diagnosed with FL in the UK's population-based Haematological Malignancy Research Network (www.hmrn.org) between 2004 and 2011 were followed until 2015 (N = 740). Treatment pathways, QALYs, and costs were incorporated into a discrete event simulation to reflect patient heterogeneity, including age and disease management. Two scenario analyses, based on the latest National Institute for Health and Clinical Excellence (NICE) guidelines (rituximab induction therapy for newly diagnosed asymptomatic patients and rituximab maintenance therapy for patients between treatments), were conducted and their economic impacts were compared to current practice.ResultsIncidence-based analysis revealed expected average lifetime costs ranging from £6,165 [US$7,709] to £63,864 [US$79,862] per patient, and average life expectancy from 75 days to 17.56 years. Prevalence-based analysis estimated average annual treatment costs of £60-65 million [US$75-80 million], accounting for approximately 10% of the United Kingdom's annual National Health Service budget for hematological cancers as a whole. Assuming that treatment effects reported in trials are applicable to all patient groups, scenario analyses for two recent NICE guidelines demonstrated potential annual cost savings for the United Kingdom that ranged with uptake frequency from £0.6 million to £11 million [US$0.75-2.75 million].ConclusionsCosts, survival, and QALYs associated with FL vary markedly with patient characteristics and disease management. Allowing the production of more realistic outcomes across the patient population as a whole, our model addresses this heterogeneity and is a useful tool with which to evaluate new technologies/treatments to support healthcare decision makers.

Project description:BackgroundWhile many studies have examined differences between body mass index (BMI) categories in terms of mortality risk and health-related quality of life (HRQL), little is known about the effect of body weight on health expectancy. We examined life expectancy (LE), health-adjusted life expectancy (HALE), and proportion of LE spent in nonoptimal (or poor) health by BMI category for the Canadian adult population (age ? 20).MethodsRespondents to the National Population Health Survey (NPHS) were followed for mortality outcomes from 1994 to 2009. Our study population at baseline (n=12,478) was 20 to 100 years old with an average age of 47. LE was produced by building abridged life tables by sex and BMI category using data from the NPHS and the Canadian Chronic Disease Surveillance System. HALE was estimated using the Health Utilities Index from the Canadian Community Health Survey as a measure of HRQL. The contribution of HRQL to loss of healthy life years for each BMI category was also assessed using two methods: by calculating differences between LE and HALE proportional to LE and by using a decomposition technique to separate out mortality and HRQL contributions to loss of HALE.ResultsAt age 20, for both sexes, LE is significantly lower in the underweight and obesity class 2+ categories, but significantly higher in the overweight category when compared to normal weight (obesity class 1 was nonsignificant). HALE at age 20 follows these same associations and is significantly lower for class 1 obesity in women. Proportion of life spent in nonoptimal health and decomposition of HALE demonstrate progressively higher losses of healthy life associated with lowered HRQL for BMI categories in excess of normal weight.ConclusionsAlthough being in the overweight category for adults may be associated with a gain in life expectancy as compared to normal weight adults, overweight individuals also experience a higher proportion of these years of life in poorer health. Due to the descriptive nature of this study, further research is needed to explore the causal mechanisms which explain these results, including the important differences we observed between sexes and within obesity subcategories.

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

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

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