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1000 Titel
  • 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
1000 Autor/in
  1. Wang, Haidong |
  2. Naghavi, Mohsen |
  3. Zeeb, Hajo |
1000 Mitwirkende/r
  1. GBD 2015 Mortality and Causes of Death Collaborators |
1000 Erscheinungsjahr 2016
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2016-10-08
1000 Erschienen in
1000 Quellenangabe
  • 388(10053): 1459–1544
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2016
1000 Lizenz
1000 Verlagsversion
  • http://dx.doi.org/10.1016/S0140-6736(16)31012-1 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388903/ |
1000 Ergänzendes Material
  • http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)31012-1/supplemental |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/V2FuZywgSGFpZG9uZw==|https://frl.publisso.de/adhoc/creator/TmFnaGF2aSwgTW9oc2Vu|http://orcid.org/0000-0001-7509-242X|https://frl.publisso.de/adhoc/contributor/R0JEIDIwMTUgTW9ydGFsaXR5IGFuZCBDYXVzZXMgb2YgRGVhdGggQ29sbGFib3JhdG9ycw==
1000 Label
1000 Förderer
  1. Wellcome Trust |
  2. Bill & Melinda Gates Foundation |
  3. Wellcome Trust-DBT India Alliance (2015–20) |
  4. Edward and Alma Saraydar Neurosciences Fund |
  5. London Health Sciences Foundation |
  6. Ministry of Health |
  7. Universidad de Ciencias Aplicadas y Ambientales, UDCA, Bogotá, Colombia |
  8. German National Cohort BMBF |
  9. National Cancer Institute of the National Institutes of Health |
  10. NIH/NINDS |
  11. Australian National Health and Medical Research Council |
  12. Brazilian Ministry of Health |
  13. Australian Government Department of Health |
  14. Royal Melbourne Hospital |
  15. CeRIMP, Regional Centre for Occupational Diseases and Injuries, Tuscany Region, Florence, Italy |
  16. The Academy of Finland |
1000 Fördernummer
  1. 095066
  2. OPP1119467; OPP1093011; OPP1106023; OPP1132415
  3. -
  4. -
  5. -
  6. 25000192049/2014-14
  7. -
  8. OIER 1301/22
  9. K99CA201542
  10. R-01 30678
  11. -
  12. 25000192049/2014-14
  13. -
  14. -
  15. -
  16. 287488
1000 Förderprogramm
  1. Senior Research Fellowship
  2. -
  3. Clinical and Public Health Intermediate Fellowship
  4. -
  5. -
  6. -
  7. -
  8. -
  9. -
  10. -
  11. Principal Research fellowship
  12. -
  13. Research Funding Program
  14. Research Funding Program
  15. -
  16. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Wellcome Trust |
    1000 Förderprogramm Senior Research Fellowship
    1000 Fördernummer 095066
  2. 1000 joinedFunding-child
    1000 Förderer Bill & Melinda Gates Foundation |
    1000 Förderprogramm -
    1000 Fördernummer OPP1119467; OPP1093011; OPP1106023; OPP1132415
  3. 1000 joinedFunding-child
    1000 Förderer Wellcome Trust-DBT India Alliance (2015–20) |
    1000 Förderprogramm Clinical and Public Health Intermediate Fellowship
    1000 Fördernummer -
  4. 1000 joinedFunding-child
    1000 Förderer Edward and Alma Saraydar Neurosciences Fund |
    1000 Förderprogramm -
    1000 Fördernummer -
  5. 1000 joinedFunding-child
    1000 Förderer London Health Sciences Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
  6. 1000 joinedFunding-child
    1000 Förderer Ministry of Health |
    1000 Förderprogramm -
    1000 Fördernummer 25000192049/2014-14
  7. 1000 joinedFunding-child
    1000 Förderer Universidad de Ciencias Aplicadas y Ambientales, UDCA, Bogotá, Colombia |
    1000 Förderprogramm -
    1000 Fördernummer -
  8. 1000 joinedFunding-child
    1000 Förderer German National Cohort BMBF |
    1000 Förderprogramm -
    1000 Fördernummer OIER 1301/22
  9. 1000 joinedFunding-child
    1000 Förderer National Cancer Institute of the National Institutes of Health |
    1000 Förderprogramm -
    1000 Fördernummer K99CA201542
  10. 1000 joinedFunding-child
    1000 Förderer NIH/NINDS |
    1000 Förderprogramm -
    1000 Fördernummer R-01 30678
  11. 1000 joinedFunding-child
    1000 Förderer Australian National Health and Medical Research Council |
    1000 Förderprogramm Principal Research fellowship
    1000 Fördernummer -
  12. 1000 joinedFunding-child
    1000 Förderer Brazilian Ministry of Health |
    1000 Förderprogramm -
    1000 Fördernummer 25000192049/2014-14
  13. 1000 joinedFunding-child
    1000 Förderer Australian Government Department of Health |
    1000 Förderprogramm Research Funding Program
    1000 Fördernummer -
  14. 1000 joinedFunding-child
    1000 Förderer Royal Melbourne Hospital |
    1000 Förderprogramm Research Funding Program
    1000 Fördernummer -
  15. 1000 joinedFunding-child
    1000 Förderer CeRIMP, Regional Centre for Occupational Diseases and Injuries, Tuscany Region, Florence, Italy |
    1000 Förderprogramm -
    1000 Fördernummer -
  16. 1000 joinedFunding-child
    1000 Förderer The Academy of Finland |
    1000 Förderprogramm -
    1000 Fördernummer 287488
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6406408.rdf
1000 Erstellt am 2018-01-22T14:37:16.894+0100
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1000 Vgl. frl:6406408
1000 Oai Id
  1. oai:frl.publisso.de:frl:6406408 |
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