|Titre :||Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories (2018)|
|Auteurs :||K. J. FOREMAN ; N. MARQUEZ ; A. DOLGERT ; K. FUKUTAKI ; N. FULLMAN ; M. MCGAUGHEY ; M. A. PLETCHER ; A. E. SMITH ; K. TANG ; C. W. YUAN ; J. C. BROWN ; J. FRIEDMAN ; J. HE ; K. R. HEUTON ; M. HOLMBERG ; D. J. PATEL ; P. REIDY ; A. CARTER ; K. CERCY ; A. CHAPIN ; D. DOUWES-SCHULTZ ; T. FRANK ; F. GOETTSCH ; P. Y. LIU ; V. NANDAKUMAR ; M. B. REITSMA ; V. REUTER ; N. SADAT ; R. J. D. SORENSEN ; V. SRINIVASAN ; R. L. UPDIKE ; H. YORK ; A. D. LOPEZ ; R. LOZANO ; S. S. LIM ; A. H. MOKDAD ; S. E. VOLLSET ; C. J. L. MURRAY|
|Type de document :||Article : Périodique|
|Dans :||Lancet (The) (Vol.392, n°10159, November 10, 2018)|
|Article en page(s) :||2052-2090|
|Discipline :||EPI (Epidémiologie / Epidemiology)|
Thésaurus TOXIBASEEVOLUTION ; SANTE ; CAUSE DE DECES ; TABAC ; MODELE STATISTIQUE ; FACTEUR DE RISQUE ; PATHOLOGIE ORGANIQUE ; MORTALITE ; INFECTION ; CANCER ; ALCOOL ; PRODUIT ILLICITE ; GEOGRAPHIE ; EPIDEMIOLOGIE
Background: Understanding potential trajectories in health and drivers of health is crucial to guiding long-term investments and policy implementation. Past work on forecasting has provided an incomplete landscape of future health scenarios, highlighting a need for a more robust modelling platform from which policy options and potential health trajectories can be assessed. This study provides a novel approach to modelling life expectancy, all-cause mortality and cause of death forecasts - and alternative future scenarios - for 250 causes of death from 2016 to 2040 in 195 countries and territories.
Methods: We modelled 250 causes and cause groups organised by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) hierarchical cause structure, using GBD 2016 estimates from 1990-2016, to generate predictions for 2017-40. Our modelling framework used data from the GBD 2016 study to systematically account for the relationships between risk factors and health outcomes for 79 independent drivers of health. We developed a three-component model of cause-specific mortality: a component due to changes in risk factors and select interventions; the underlying mortality rate for each cause that is a function of income per capita, educational attainment, and total fertility rate under 25 years and time; and an autoregressive integrated moving average model for unexplained changes correlated with time. We assessed the performance by fitting models with data from 1990-2006 and using these to forecast for 2007-16. Our final model used for generating forecasts and alternative scenarios was fitted to data from 1990-2016. We used this model for 195 countries and territories to generate a reference scenario or forecast through 2040 for each measure by location. Additionally, we generated better health and worse health scenarios based on the 85th and 15th percentiles, respectively, of annualised rates of change across location-years for all the GBD risk factors, income per person, educational attainment, select intervention coverage, and total fertility rate under 25 years in the past. We used the model to generate all-cause age-sex specific mortality, life expectancy, and years of life lost (YLLs) for 250 causes. Scenarios for fertility were also generated and used in a cohort component model to generate population scenarios. For each reference forecast, better health, and worse health scenarios, we generated estimates of mortality and YLLs attributable to each risk factor in the future.
Findings: Globally, most independent drivers of health were forecast to improve by 2040, but 36 were forecast to worsen. As shown by the better health scenarios, greater progress might be possible, yet for some drivers such as high body-mass index (BMI), their toll will rise in the absence of intervention. We forecasted global life expectancy to increase by 4.4 years (95% UI 2.2 to 6.4) for men and 4.4 years (2.1 to 6.4) for women by 2040, but based on better and worse health scenarios, trajectories could range from a gain of 7.8 years (5.9 to 9.8) to a non-significant loss of 0.4 years (-2.8 to 2.2) for men, and an increase of 7.2 years (5.3 to 9.1) to essentially no change (0.1 years [-2.7 to 2.5]) for women. In 2040, Japan, Singapore, Spain, and Switzerland had a forecasted life expectancy exceeding 85 years for both sexes, and 59 countries including China were projected to surpass a life expectancy of 80 years by 2040. At the same time, Central African Republic, Lesotho, Somalia, and Zimbabwe had projected life expectancies below 65 years in 2040, indicating global disparities in survival are likely to persist if current trends hold. Forecasted YLLs showed a rising toll from several non-communicable diseases (NCDs), partly driven by population growth and ageing. Differences between the reference forecast and alternative scenarios were most striking for HIV/AIDS, for which a potential increase of 120.2% (95% UI 67.2-190.3) in YLLs (nearly 118 million) was projected globally from 2016-40 under the worse health scenario. Compared with 2016, NCDs were forecast to account for a greater proportion of YLLs in all GBD regions by 2040 (67.3% of YLLs [95% UI 61.9-72.3] globally); nonetheless, in many lower-income countries, communicable, maternal, neonatal, and nutritional (CMNN) diseases still accounted for a large share of YLLs in 2040 (eg, 53.5% of YLLs [95% UI 48.3-58.5] in Sub-Saharan Africa). There were large gaps for many health risks between the reference forecast and better health scenario for attributable YLLs. In most countries, metabolic risks amenable to health care (eg, high blood pressure and high plasma fasting glucose) and risks best targeted by population-level or intersectoral interventions (eg, tobacco, high BMI, and ambient particulate matter pollution) had some of the largest differences between reference and better health scenarios. The main exception was sub-Saharan Africa, where many risks associated with poverty and lower levels of development (eg, unsafe water and sanitation, household air pollution, and child malnutrition) were projected to still account for substantive disparities between reference and better health scenarios in 2040.
Interpretation: With the present study, we provide a robust, flexible forecasting platform from which reference forecasts and alternative health scenarios can be explored in relation to a wide range of independent drivers of health. Our reference forecast points to overall improvements through 2040 in most countries, yet the range found across better and worse health scenarios renders a precarious vision of the future - a world with accelerating progress from technical innovation but with the potential for worsening health outcomes in the absence of deliberate policy action. For some causes of YLLs, large differences between the reference forecast and alternative scenarios reflect the opportunity to accelerate gains if countries move their trajectories toward better health scenarios - or alarming challenges if countries fall behind their reference forecasts. Generally, decision makers should plan for the likely continued shift toward NCDs and target resources toward the modifiable risks that drive substantial premature mortality. If such modifiable risks are prioritised today, there is opportunity to reduce avoidable mortality in the future. However, CMNN causes and related risks will remain the predominant health priority among lower-income countries. Based on our 2040 worse health scenario, there is a real risk of HIV mortality rebounding if countries lose momentum against the HIV epidemic, jeopardising decades of progress against the disease. Continued technical innovation and increased health spending, including development assistance for health targeted to the world's poorest people, are likely to remain vital components to charting a future where all populations can live full, healthy lives.
Funding: Bill & Melinda Gates Foundation.
|Domaine :||Alcool / Alcohol ; Drogues illicites / Illicit drugs ; Tabac / Tobacco|
|Refs biblio. :||54|
|Affiliation :||Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA|