Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023

疾病负担 环境卫生 疾病负担 医学 疾病 全球卫生 双重负担 死因 社会经济学 经济增长 公共卫生 梅德林 地理 发展经济学 质量调整寿命年
作者
Mohsen Naghavi,Hmwe Hmwe Kyu,A Bhoomadevi,Mohammad Amin Aalipour,Hasan Aalruz,Hazim Ababneh,Bedru Jemal Abafita,Ukachukwu Okoroafor Abaraogu,Cristiana Abbafati,Madineh Abbasi,Faezeh Abbaspour,Hedayat Abbastabar,Abdallah H A Abd Al Magied,Samar Abd ElHafeez,Ashraf N. Abdalla,Mohammed Abdalla,Emad M. Abdallah,Barkhad Aden Abdeeq,Nadin M I Abdel Razeq,Ahmed A. Abdelgalil
出处
期刊:The Lancet [Elsevier BV]
卷期号:406 (10513): 1811-1872 被引量:212
标识
DOI:10.1016/s0140-6736(25)01917-8
摘要

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ZZZ完成签到 ,获得积分10
刚刚
song发布了新的文献求助10
1秒前
陈丽发布了新的文献求助10
1秒前
wanci应助poem采纳,获得10
2秒前
3秒前
狂野忆文完成签到,获得积分10
4秒前
笑点低的谷槐完成签到,获得积分10
4秒前
丘比特应助小车采纳,获得10
6秒前
6秒前
6秒前
molihuakai应助青岛彭于晏采纳,获得10
6秒前
筋筋子发布了新的文献求助10
7秒前
8秒前
8秒前
科研通AI6.4应助asd采纳,获得10
8秒前
沉眠猫猫虫完成签到,获得积分10
9秒前
9秒前
科研通AI6.2应助song采纳,获得10
9秒前
10秒前
pililili发布了新的文献求助10
10秒前
李健的小迷弟应助Vincent采纳,获得30
10秒前
蔡1发布了新的文献求助10
11秒前
隐形曼青应助dog采纳,获得10
11秒前
deng发布了新的文献求助30
11秒前
12秒前
13秒前
次我发布了新的文献求助10
14秒前
xyq发布了新的文献求助10
15秒前
Sun完成签到,获得积分20
15秒前
15秒前
15秒前
16秒前
汉堡包应助Amber采纳,获得10
17秒前
南乔星完成签到 ,获得积分10
18秒前
科研通AI6.3应助景琦采纳,获得10
18秒前
19秒前
SciGPT应助zzs采纳,获得10
20秒前
丘比特应助Suntx采纳,获得10
20秒前
快乐山柏发布了新的文献求助10
20秒前
22秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Erwählung und Berufung bei Paulus: Bedeutung, Entwicklung und Funktion einer Vorstellung in ihrem frühjüdischen und griechisch-römischen Kontext 850
Matrix Methods in Data Mining and Pattern Recognition 510
Structural Geology: A Quantitative Introduction 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7216778
求助须知:如何正确求助?哪些是违规求助? 8848301
关于积分的说明 18672636
捐赠科研通 6873135
什么是DOI,文献DOI怎么找? 3185148
关于科研通互助平台的介绍 2347060
邀请新用户注册赠送积分活动 2159429