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Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

Monday, 31st of October 2016

“Since 1990, overall health has improved in most countries, with particularly large gains occurring in the past 10 years. Although improved health means longer lifespans, it also translates to more years of functional health lost. The fraction of overall life expectancy spent in poor health is generally constant or has slightly declined in some countries, a result driven by declines in DALYs due to communicable, maternal, nutritional, and neonatal causes and increases in others, mainly non-communicable diseases.”

Excerpt below; full text is at http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)31460-X/fulltext

 

The Lancet, Volume 388, No. 10053, p1603–1658, 8 October 2016

Articles

Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

DOI: http://dx.doi.org/10.1016/S0140-6736(16)31460-X

Summary

Background

Healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) provide summary measures of health across geographies and time that can inform assessments of epidemiological patterns and health system performance, help to prioritise investments in research and development, and monitor progress toward the Sustainable Development Goals (SDGs). We aimed to provide updated HALE and DALYs for geographies worldwide and evaluate how disease burden changes with development.

Methods

We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015. We calculated DALYs by summing years of life lost (YLLs) and years of life lived with disability (YLDs) for each geography, age group, sex, and year. We estimated HALE using the Sullivan method, which draws from age-specific death rates and YLDs per capita. We then assessed how observed levels of DALYs and HALE differed from expected trends calculated with the Socio-demographic Index (SDI), a composite indicator constructed from measures of income per capita, average years of schooling, and total fertility rate.

Findings

Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional (Group 1) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). Much of this epidemiological transition was caused by changes in population growth and ageing, but it was accelerated by widespread improvements in SDI that also correlated strongly with the increasing importance of NCDs. Both total DALYs and age-standardised DALY rates due to most Group 1 causes significantly decreased by 2015, and although total burden climbed for the majority of NCDs, age-standardised DALY rates due to NCDs declined. Nonetheless, age-standardised DALY rates due to several high-burden NCDs (including osteoarthritis, drug use disorders, depression, diabetes, congenital birth defects, and skin, oral, and sense organ diseases) either increased or remained unchanged, leading to increases in their relative ranking in many geographies. From 2005 to 2015, HALE at birth increased by an average of 2·9 years (95% uncertainty interval 2·9–3·0) for men and 3·5 years (3·4–3·7) for women, while HALE at age 65 years improved by 0·85 years (0·78–0·92) and 1·2 years (1·1–1·3), respectively. Rising SDI was associated with consistently higher HALE and a somewhat smaller proportion of life spent with functional health loss; however, rising SDI was related to increases in total disability. Many countries and territories in central America and eastern sub-Saharan Africa had increasingly lower rates of disease burden than expected given their SDI. At the same time, a subset of geographies recorded a growing gap between observed and expected levels of DALYs, a trend driven mainly by rising burden due to war, interpersonal violence, and various NCDs.

Interpretation

Health is improving globally, but this means more populations are spending more time with functional health loss, an absolute expansion of morbidity. The proportion of life spent in ill health decreases somewhat with increasing SDI, a relative compression of morbidity, which supports continued efforts to elevate personal income, improve education, and limit fertility. Our analysis of DALYs and HALE and their relationship to SDI represents a robust framework on which to benchmark geography-specific health performance and SDG progress. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform financial and research investments, prevention efforts, health policies, and health system improvement initiatives for all countries along the development continuum.

Funding

Bill & Melinda Gates Foundation.

Introduction

Summary measures of population health are crucial inputs to guide health system investments and set priorities at global, regional, national, and subnational levels. The Millennium Development Goals (MDGs), which sought to reduce extreme poverty and improve health, expired in 2015, and were replaced by the 2030 Agenda for Sustainable Development, or Sustainable Development Goals (SDGs).¹ The shift from the MDGs to the SDGs reflects a broadening of the global development agenda,^2, 3 expanding to include targets for non-communicable diseases (NCDs) and indicators that consider the interplay of environmental, societal, and economic factors on health.⁴ Within this context, summary population health measures are advantageous because they can easily be used to show progress toward SDG 3—to “ensure healthy lives and promote well-being for all at all ages”—and provide a metric by which comparative progress on other SDGs can be monitored.⁵ Summary measures also provide insights into whether, as societies live longer, they spend more or less of their time with functional health loss, known as the expansion or compression of morbidity, respectively, which has profound implications for societies and the financing of health systems.

Two types of population health summary measures exist: health expectancies and health gaps.⁶Healthy life expectancy (HALE), which originates from Sullivan,⁷ provides a single summary measure of population health by weighting years lived with a measure of functional health loss experienced before death. Many health expectancy measures have been proposed, but HALE is the only one that captures a full range of functional health loss.^8, 9, 10 Health gap measures capture differences between a population and some normative standard such as a maximum lifespan in full health. Disability-adjusted life-years (DALY) are a widely used gap measure,^{6, 9, 10, 11} representing the sum of years of life lost (YLLs) due to premature mortality and years lived with disability (YLDs). YLLs quantify the gap between observed mortality and a normative life expectancy,¹² and YLDs capture the prevalence of conditions that lead to non-fatal health loss while accounting for the severity of those conditions. Health gap measures can be easily disaggregated to examine contributions of relative morbidity and mortality, individual diseases, injuries, and attributable risk factors.

The Global Burden of Diseases, Injuries, and Risk Factors (GBD) study is the most comprehensive source of comparable summary population health measures because of its inclusion of country-level results, uncertainty quantification, and its effort to maximise comparability across geography, time, and across different health conditions. Alternative summary health assessments are not as standardised or comprehensive, with studies reporting only incomplete time-series, no uncertainty measures, or only a subset of countries and causes.^13, 14, 15 WHO published DALY estimates for 2 years (2000 and 2012), with 132 causes and 174 countries and without uncertainty intervals. These estimates were derived primarily from GBD 2010 results, but were modified in 60 countries and for 12 cause groups separately estimated by WHO and UN agencies.^13, 16, 17 WHO applied the same approach for GBD 2013 results and used their own life tables to produce HALE estimates for 2015.¹⁴The European Commission (EC) and the Organisation for Economic Co-operation and Development (OECD) also reported healthy life expectancy estimates for European countries from 2004 through 2014, but these were based on self-reported health status.^18, 19

Research in context

Evidence before this study

Disability-adjusted life-years (DALYs), a summary measure of population health based on estimates of premature mortality and non-fatal health loss, originated from the initial Global Burden of Disease (GBD) study in 1993. DALYs, in combination with other summary measures such as healthy life expectancy (HALE), offer relatively simple yet powerful metrics against which progress and challenges in improving disease burden and extending healthy lifespans can be effectively monitored over time. Published in 2012, GBD 2010 provided updated estimates of DALYs due to 291 causes and HALE in 187 countries from 1990 to 2010. GBD 2013 extended this time series to 2013, with 188 countries, and 306 causes. Novel analyses for quantifying epidemiological transitions were introduced as part of GBD 2013, enabling a comparison of shifts in years of life lost (YLLs) and years lived with disability (YLDs) with increasing levels of development. WHO has produced estimates of DALYs and HALE largely based on GBD 2010 and GBD 2013; however, modifications were implemented for a subset of causes, disability weights, and countries, and a normative life table of 91·9 years at birth was used for calculating YLLs.

Added value of this study

For GBD 2015, we generated estimates of HALE and DALYs for 315 causes by geography, sex, and age group from 1990 to 2015 for 195 countries and territories. We constructed a summary metric referred to as the Socio-demographic Index (SDI) based on measures of income per capita, average years of schooling, and total fertility rate. We estimated SDI for each geography-year, and characterised the average relationship for each age, sex, and cause for DALYs and HALE with SDI. Using these relationships, we calculated expected levels of DALYs, life expectancy, and HALE for each geography over time. We compared observed patterns of both DALYs and HALE with those expected on the basis of SDI, allowing us to explore where health gains exceeded—or lagged behind—corresponding changes in development.

Implications

Since 1990, overall health has improved in most countries, with particularly large gains occurring in the past 10 years. Although improved health means longer lifespans, it also translates to more years of functional health lost. The fraction of overall life expectancy spent in poor health is generally constant or has slightly declined in some countries, a result driven by declines in DALYs due to communicable, maternal, nutritional, and neonatal causes and increases in others, mainly non-communicable diseases. Country-specific drivers of disease burden, particularly when observed DALYs are higher than expected on the basis of SDI, should inform country-specific inquiry and action.

Here we present GBD 2015 findings for DALYs and HALE, building upon updated estimates of mortality, causes of death, and non-fatal health loss.^12, 20 Overall analytic approaches are similar to previous GBD studies,^9, 10 but include new mortality and morbidity data, refined methods, and expanded geographies.^12, 20 This report supersedes all previous GBD studies on DALYs and HALE through the estimation of a complete time-series for 1990 to 2015. To facilitate a more in-depth examination of the drivers of DALY and HALE trends, we assess how HALE, along with overall and cause-specific DALYs, change as geographies move through the development continuum. We use this analysis to benchmark overall progress and decompose observed disease burden compared with levels expected for specific causes on the basis of development alone, to highlight potential areas for policy investment or further research.