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

作者：Kyle J Foreman, Neal Marquez, Andrew J. Dolgert, Kai Fukutaki, Nancy Fullman, Madeline McGaughey, Martin A Pletcher, Amanda Smith, Kendrick Tang, Chun-Wei Yuan, Jonathan C. Brown, Joseph Friedman, Jiawei He, Kyle R Heuton, Mollie Holmberg, Disha J. Patel, Patrick Reidy, Austin Carter, Kelly Cercy, Abigail Chapin, Dirk Douwes‐Schultz, Tahvi Frank, Falko Goettsch, Patrick Y Liu, Vishnu Nandakumar, Marissa B Reitsma, Vincent Reuter, Nafis Sadat, Reed J D Sorensen, Vinay Srinivasan, Rachel L Updike, Hunter York, Alan D López, Rafael Lozano, Stephen S Lim, Ali H. Mokdad, Stein Emil Vollset, Christopher J L Murray · 发表于：The Lancet · 年份：2018 · DOI：10.1016/s0140-6736(18)31694-5 · 被引用次数：3053 · 研究领域：Insurance, Mortality, Demography, Risk Management、Global Health Care Issues、Employment and Welfare Studies

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 usi...