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Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

Haidong Wang2301 5th Avenue, Suite 600, Seattle, WA 98121, USAMohsen Naghavi2301 5th Avenue, Suite 600, Seattle, WA 98121, USAChristine A. Allen2301 5th Avenue, Suite 600, Seattle, WA 98121, USARyan M BarberChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaZulfiqar A Bhutta2301 5th Avenue, Suite 600, Seattle, WA 98121, USAAustin Carter2301 5th Avenue, Suite 600, Seattle, WA 98121, USADaniel CaseyChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaFiona CharlsonINTER VA Indonesia VAAlan Zian ChenINTER VA Indonesia VAMatthew M CoatesInstitute for Health Metrics and Evaluation (Megan CoggeshallINTER VA Indonesia VALalit DandonaINTER VA Indonesia VADaniel DickerChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaHolly E ErskineChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaAlize J FerrariChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaChristina Fitzmaurice2301 5th Avenue, Suite 600, Seattle, WA 98121, USAKyle J ForemanINTER VA Indonesia VAMohammad H. ForouzanfarChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaMaya FraserChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaNancy FullmanInstitute for Health Metrics and Evaluation (Peter W. GethingINTER VA Indonesia VAEllen M Goldberg2301 5th Avenue, Suite 600, Seattle, WA 98121, USANicholas GraetzInstitute for Health Metrics and Evaluation (Juanita A. Haagsma2301 5th Avenue, Suite 600, Seattle, WA 98121, USASimon I HayInstitute for Health Metrics and Evaluation (Chantal HuynhINTER VA Indonesia VACatherine O. Johnson2301 5th Avenue, Suite 600, Seattle, WA 98121, USANicholas J KassebaumChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaYohannes KinfuInstitute for Health Metrics and Evaluation (Xie Rachel KulikoffINTER VA Indonesia VAMichael KutzInstitute for Health Metrics and Evaluation (Hmwe Hmwe Kyu2301 5th Avenue, Suite 600, Seattle, WA 98121, USAHeidi J. LarsonInstitute for Health Metrics and Evaluation (Janni LeungChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaXiaofeng Liang2301 5th Avenue, Suite 600, Seattle, WA 98121, USAStephen S LimINTER VA Indonesia VAMargaret L. Lind2301 5th Avenue, Suite 600, Seattle, WA 98121, USARafael Lozano2301 5th Avenue, Suite 600, Seattle, WA 98121, USANeal Marquez2301 5th Avenue, Suite 600, Seattle, WA 98121, USAGeorge A. Mensah2301 5th Avenue, Suite 600, Seattle, WA 98121, USAJoe MikesellINTER VA Indonesia VAAli H. MokdadChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaMeghan MooneyInstitute for Health Metrics and Evaluation (Grant Nguyen2301 5th Avenue, Suite 600, Seattle, WA 98121, USAElaine O. NsoesieChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaDavid M. PigottChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaChristine Pinho2301 5th Avenue, Suite 600, Seattle, WA 98121, USAGregory A. RothInstitute for Health Metrics and Evaluation (Joshua A. SalomonInstitute for Health Metrics and Evaluation (Logan SandarInstitute for Health Metrics and Evaluation (Naris SilpakitChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaAmber SligarInstitute for Health Metrics and Evaluation (Reed J D SorensenInstitute for Health Metrics and Evaluation (Jeffrey D Stanaway2301 5th Avenue, Suite 600, Seattle, WA 98121, USACaitlyn SteinerINTER VA Indonesia VAStephanie TeepleInstitute for Health Metrics and Evaluation (Bernadette A ThomasInstitute for Health Metrics and Evaluation (Christopher TroegerINTER VA Indonesia VAAmelia VanderZandenInstitute for Health Metrics and Evaluation (Stein Emil Vollset2301 5th Avenue, Suite 600, Seattle, WA 98121, USAValentine Wanga2301 5th Avenue, Suite 600, Seattle, WA 98121, USAHarvey WhitefordINTER VA Indonesia VATimothy M Wolock2301 5th Avenue, Suite 600, Seattle, WA 98121, USALeo ZoecklerINTER VA Indonesia VAKalkidan Hassen AbateCristiana Abbafati2301 5th Avenue, Suite 600, Seattle, WA 98121, USAKaja AbbasINTER VA Indonesia VAFoad Abd-Allah2301 5th Avenue, Suite 600, Seattle, WA 98121, USASemaw Ferede AberaINTER VA Indonesia VADaisy Maria Xavier de Abreu2301 5th Avenue, Suite 600, Seattle, WA 98121, USALaith J. Abu‐RaddadChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaGebre Yitayih AbyuTom AchokiINTER VA Indonesia VAAdemola AdelekanInstitute for Health Metrics and Evaluation (Zanfina AdemiArsène Kouablan Adou2301 5th Avenue, Suite 600, Seattle, WA 98121, USAJosé Carmelo AdsuarINTER VA Indonesia VAKossivi Agbélénko AfanviAshkan AfshinINTER VA Indonesia VAEmilie AgardhINTER VA Indonesia VAArnav AgarwalChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaAnurag AgrawalChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaAli KiadaliriInstitute for Health Metrics and Evaluation (Oluremi N AjalaInstitute for Health Metrics and Evaluation (A. S. Akanda2301 5th Avenue, Suite 600, Seattle, WA 98121, USARufus AkinyemiInstitute for Health Metrics and Evaluation (Tomi Akinyemiju2301 5th Avenue, Suite 600, Seattle, WA 98121, USANadia Akseer2301 5th Avenue, Suite 600, Seattle, WA 98121, USAFaris Lami2301 5th Avenue, Suite 600, Seattle, WA 98121, USASamer Alabed2301 5th Avenue, Suite 600, Seattle, WA 98121, USAZiyad Al‐AlyInstitute for Health Metrics and Evaluation (Khurshid AlamInstitute for Health Metrics and Evaluation (Noore AlamInstitute for Health Metrics and Evaluation (Deena Alasfoor2301 5th Avenue, Suite 600, Seattle, WA 98121, USASaleh Fahed AldhahriInstitute for Health Metrics and Evaluation (Robert W AldridgeINTER VA Indonesia VAMiguel Angel AlegrettiChina DSP ICD9 China DSP ICD10 Russia ICD9-TAB Russia ICD10-TAB ICD9-USSR-TAB India MCCD ICD9 India MCCD ICD10 India CRS India SCD IndiaAlicia V AlemanInstitute for Health Metrics and Evaluation (Zewdie Aderaw AlemuInstitute for Health Metrics and Evaluation (Lily AlexanderINTER VA Indonesia VA
2016en
ABI

Аннотация

BACKGROUND: Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. METHODS: We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). FINDINGS: Globally, life expectancy from birth increased from 61·7 years (95% uncertainty interval 61·4-61·9) in 1980 to 71·8 years (71·5-72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7-17·4), to 62·6 years (56·5-70·2). Total deaths increased by 4·1% (2·6-5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0% (15·8-18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1% (12·6-16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1% (11·9-14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1%, 39·1-44·6), malaria (43·1%, 34·7-51·8), neonatal preterm birth complications (29·8%, 24·8-34·9), and maternal disorders (29·1%, 19·3-37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. INTERPRETATION: At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. FUNDING: Bill & Melinda Gates Foundation.

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