本文由小咖机器人翻译整理

期刊来源：Lancet

原文链接：https://doi.org/10.1016/S0140-6736(24)00367-2

摘要内容如下：

背景

按根本死因定期详细报告人口健康状况是公共卫生决策的基础。针对具体原因的死亡率估计数及其对全球预期寿命的影响是衡量降低死亡率进展情况的重要指标。在大规模死亡率激增（如COVID-19大流行）之后，这些估计尤其重要。对死亡率和预期寿命进行系统分析，可以比较全球和不同时期死亡原因的后果，从而细致入微地了解这些原因对全球人口的影响。

方法

全球疾病负担、伤害和风险因素研究（GBD）2021死因分析从1990年到2021年，每年在204个国家和地区以及811个次国家地点，按年龄、性别、地点和年份对288个死亡原因的死亡率和寿命损失年数（YLLs）进行了估计。该分析使用了56604个数据来源，包括来自生命登记和口头尸检以及调查、人口普查、监测系统和癌症登记等的数据。与以前的GBD轮次一样，使用死因集合模型（一种为GBD开发的建模工具，用于评估不同统计模型和协变量排列的样本外预测有效性，并将这些结果结合起来以产生特定原因死亡率估计值）估计大多数原因的特定原因死亡率，并采用适用于数据不足、研究期间报告发生重大变化或异常流行病学的模型原因的替代策略。YLLs计算为每个死因-年龄-性别-地点-年份的死亡人数与每个年龄段的标准预期寿命的乘积。作为建模过程的一部分，不确定性区间（UI）是使用每个指标的1000-draw分布中的2.5和97.5百分位数生成的。我们按死因、地点和年份对预期寿命进行了分解，以显示1990年至2021年死因对预期寿命的影响。我们还使用了变异系数和受90%死亡影响的人口比例来突出死亡率的集中。调查结果以计数和年龄标准化率的形式报告。GBD 2021中死亡原因估计的方法改进包括扩大5岁以下年龄组以包括四个新的年龄组，改进方法以说明稀疏数据的随机变化，以及纳入COVID-19和其他与大流行相关的死亡率，其中包括与大流行相关的超额死亡率，不包括COVID-19、下呼吸道感染、麻疹、疟疾和百日咳。在这一分析中，199个新的国家-年的死因登记数据、5个国家-年的监测数据、21个国家-年的死因推断数据和94个国家-年的其他数据类型被添加到前几轮GBD中使用的数据中。

调查结果

2019年全球年龄标准化死亡的主要原因与1990年相同；按降序排列，这些疾病是缺血性心脏病、中风、慢性阻塞性肺病和下呼吸道感染。然而，在2021年，COVID-19取代中风成为第二大年龄标准化死亡原因，每10万人中有94.0人死亡（95%UI 89.2-100.0）。COVID-19大流行改变了前五大病因的排名，将中风降至第三位，慢性阻塞性肺病降至第四位。2021年，COVID-19导致的最高年龄标准化死亡率出现在撒哈拉以南非洲（每10万人271.0例死亡[250.1-290.7]）和拉丁美洲和加勒比地区（每10万人195.4例死亡[182.1-211.4]）。COVID-19的最低年龄标准化死亡率出现在高收入超级地区（每10万人48.1例死亡[47.4-48.8]）和东南亚、东亚和大洋洲（每10万人23.2例死亡[16.3-37.2]）。在全球范围内，1990年至2019年期间，22个调查原因中有18个的预期寿命稳步提高。对全球和区域预期寿命的分解显示了积极的影响，即肠道感染、下呼吸道感染、中风和新生儿死亡等死亡的减少有助于提高研究期间的存活率。然而，在2019年至2021年期间，全球预期寿命净减少1.6岁，主要是由于COVID-19和其他流行病相关死亡率的增加。在研究期间，超级地区之间的预期寿命差异很大，东南亚、东亚和大洋洲总体上增加了8.3岁（6.7-9.9），而COVID-19导致的预期寿命减少最小（0.4岁）。COVID-19导致的最大预期寿命减少发生在拉丁美洲和加勒比地区（3.6岁）。此外，截至2021年，288个死亡原因中有53个高度集中在不到全球人口50%的地区，而且自1990年以来，这些死亡原因越来越集中，当时只有44个原因显示出这种模式。针对肠道和下呼吸道感染、疟疾、HIV/AIDS、新生儿疾病、结核病和麻疹，对集中现象进行了启发式讨论。

解释

COVID-19大流行破坏了预期寿命的长期增长和许多主要死亡原因的减少，其不利影响在人口中分布不均。尽管发生了大流行，但在与几个显著的死亡原因作斗争方面继续取得进展，从而在研究期间提高了全球预期寿命。从1990年到2021年，七个GBD超级区域中的每一个都显示出总体改善，掩盖了大流行年份的负面影响。此外，我们关于导致预期寿命增加的死亡原因的地区差异的研究结果具有明确的政策效用。对不断变化的死亡率趋势的分析表明，曾经在全球范围内广泛存在的几个原因现在越来越集中在地理上。死亡率集中的这些变化，以及对不断变化的风险、干预措施和相关政策的进一步调查，为加深我们对降低死亡率战略的理解提供了一个重要机会。检查死亡率集中的模式可能会揭示实施了成功的公共卫生干预措施的地区。将这些成功经验应用于某些死亡原因仍然根深蒂固的地方，可以为致力于提高世界各地人民预期寿命的政策提供信息。

英文原文如下：

Abstracts

BACKGROUND  Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.

METHODS  The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.

FINDINGS  The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.

INTERPRETATION  Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.

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