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.
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.
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DOI：
10.1016/s0140-6736(18)31694-5
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发表时间：
2018-11-10
期刊：
影响因子：
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通讯作者：
Murray CJL
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作者：
Foreman KJ;Marquez N;Dolgert A;Fukutaki K;Fullman N;McGaughey M;Pletcher MA;Smith AE;Tang K;Yuan CW;Brown JC;Friedman J;He J;Heuton KR;Holmberg M;Patel DJ;Reidy P;Carter A;Cercy K;Chapin A;Douwes-Schultz D;Frank T;Goettsch F;Liu PY;Nandakumar V;Reitsma MB;Reuter V;Sadat N;Sorensen RJD;Srinivasan V;Updike RL;York H;Lopez AD;Lozano R;Lim SS;Mokdad AH;Vollset SE;Murray CJL
了解健康的潜在轨迹和健康驱动因素对于指导长期投资和政策实施至关重要。过去的预测工作提供了未来卫生情景的不完整图景，突出表明需要一个更强大的建模平台，可以据此评估政策选择和潜在的卫生轨迹。这项研究为195个国家和地区2016年至2040年的250种死因的预期寿命、全因死亡率和死因预测以及可替代的未来情景建模提供了一种新方法。我们对全球疾病、伤害和风险因素负担研究（GBD）分层原因结构组织的250个原因和原因组进行了建模，使用GBD 2016年1990-2016年的估计，以生成2017-40年的预测。我们的建模框架使用了GBD 2016研究的数据，系统地解释了79个独立健康驱动因素的风险因素与健康结果之间的关系。我们开发了一个三成分的死因特异性死亡率模型：一个成分是由于风险因素和选择干预措施的变化；每个原因的潜在死亡率是人均收入、受教育程度和25岁以下总生育率和时间的函数；和一个自回归的综合移动平均模型与时间相关的无法解释的变化。我们通过拟合1990-2006年的数据模型来评估业绩，并利用这些数据预测2007 - 2016年的业绩。我们用于生成预测和替代情景的最终模型拟合了1990-2016年的数据。我们对195个国家和地区使用了这个模型，为每个地点的每个措施生成了到2040年的参考情景或预测。此外，我们根据所有GBD风险因素、人均收入、受教育程度、选择干预覆盖率和过去25岁以下总生育率的跨地区年化变化率的第85和第15个百分位数，分别生成了较好的健康和较差的健康情景。我们使用该模型生成250种原因的全因年龄-性别特异性死亡率、预期寿命和寿命损失年数（YLLs）。还生成了生育情景，并在队列组成模型中用于生成人口情景。对于每个参考预测，健康状况较好和健康状况较差的情况，我们生成了未来归因于每种风险因素的死亡率和yll的估计。在全球范围内，预计到2040年，大多数健康的独立驱动因素将得到改善，但有36个将恶化。正如更好的健康状况所显示的那样，更大的进步可能是可能的，但对于一些驱动因素，如高身体质量指数（BMI），在缺乏干预的情况下，他们的死亡率将会上升。我们预测全球平均寿命增加4·4年(95% UI 2 * 2到6 * 4)男人和4·4年(2 * 1到6 * 4)在2040年为女性,但基于更好和更糟糕的健康情况,轨迹可以从获得7·8年(5·9到9·8)与亏损0·4年(2·8到2·2)对于男人,和增加7·2年(5·3 - 9·1)基本上没有变化(0·1年(2 * 7 - 2 * 5))。2040年，日本、新加坡、西班牙、瑞士的男女预期寿命都将超过85岁，到2040年，包括中国在内的59个国家的预期寿命将超过80岁。与此同时，中非共和国、莱索托、索马里和津巴布韦的预期寿命到2040年将低于65岁，这表明如果目前的趋势持续下去，全球生存差距可能会持续下去。预测的死亡人数显示，几种非传染性疾病造成的死亡人数不断上升，部分原因是人口增长和老龄化。参考预测与替代情景之间的差异在艾滋病毒/艾滋病方面最为显著，在健康状况较差的情景下，2016 - 2040年全球艾滋病毒/艾滋病死亡人数（近1.18亿人）预计可能增加12.2% （95% UI为67.2 - 19.3）。与2016年相比，预计到2040年，非传染性疾病在所有GBD区域的YLLs中所占比例将更高（全球占全球YLLs的67.3% [95% UI 61·9-72·3]）；尽管如此，在许多低收入国家，传染性疾病、孕产妇、新生儿和营养性疾病（CMNN）仍占2040年死亡总人数的很大一部分（例如，在撒哈拉以南非洲，占死亡总人数的53.5% [95% UI 48.3 - 58.5]）。对于可归因的yl，参考预测与较好的健康情景之间存在许多健康风险的巨大差距。在大多数国家，可通过卫生保健解决的代谢风险（如高血压和空腹血糖高）和人群层面或部门间干预措施最能针对的风险（如烟草、高BMI和环境颗粒物污染）在参考情景和较好健康情景之间存在一些最大差异。主要的例外是撒哈拉以南非洲，在那里，与贫穷和较低发展水平有关的许多风险（例如，不安全的水和卫生设施、家庭空气污染和儿童营养不良）预计在2040年仍将造成参考情景和较好健康情景之间的实质性差异。通过目前的研究，我们提供了一个强大的、灵活的预测平台，从中可以探索与广泛的健康独立驱动因素相关的参考预测和替代健康情景。我们的参考预测指出，到2040年，大多数国家的健康状况将得到总体改善，但健康状况较好和较差的情况之间的差异使人们对未来的前景感到不确定——技术创新的进步在加速，但如果不采取审慎的政策行动，健康结果可能会恶化。对于造成长期死亡率的一些原因，参考预测和备选情景之间的巨大差异反映出，如果国家朝着更好的健康情景发展，就有机会加速收益，如果国家落后于其参考预测，就有令人震惊的挑战。一般而言，决策者应针对可能继续转向非传染性疾病的情况进行规划，并将资源用于导致大量过早死亡的可改变风险。如果今天优先考虑这些可改变的风险，将来就有机会减少可避免的死亡率。然而，CMNN病因和相关风险仍将是低收入国家的主要卫生重点。根据我们对2040年健康状况恶化的设想，如果各国失去防治艾滋病毒流行的势头，那么艾滋病毒死亡率就有反弹的实际风险，危及数十年来防治该疾病的进展。持续的技术创新和增加卫生支出，包括针对世界上最贫穷人口的卫生发展援助，可能仍然是规划所有人口都能过上充实、健康生活的未来的重要组成部分。比尔和梅林达·盖茨基金会。
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. 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. 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. 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. Bill & Melinda Gates Foundation.