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

GBD 2015 Mortality and Causes of Death Collaborators

Abstract

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.

Figures

**Figure 1**
Estimation of all-cause mortality by age and sex and HIV/AIDS incidence, prevalence, and mortality for GBD 2015 Data and analyses are indicated by shape and the flow chart is colour coded by major estimation component. The process depicted is performed twice to bring in updated under-5 population estimates and crude death rates due to HIV/AIDS. The inputs that are updated in the second run of the process are shown by patterned boxes in this flow chart. Because of the very large and changing effects of HIV/AIDS on all-cause mortality in several countries with large HIV epidemics and limited data on all-cause mortality, the estimation of HIV/AIDS and all-cause mortality are closely linked and are presented jointly here. GBD=Global Burden of Disease. 5q0=probability of death from birth to age 5 years. 45q15=probability of death from age 15 to 60 years. ART=antiretroviral therapy. CBH=complete birth histories. CDR=crude death rate. COD=causes of death. DSP=disease surveillance points. ED=educational attainment in years per capita above age 15 years and mother's educational attainment in years per capita for children younger than 5 years. EPP=Estimation and Projection Package. HIV CDR=crude death rate due to HIV/AIDS. LDI=lagged distributed income per capita. SBH=summary birth history. SRS=Sample Registration System. VR=vital registration.

**Figure 3**
Examples of under-5 mortality data availability and estimation (A) Percentage of global under-5 population covered by under-5 mortality data for each year, 1980–2015. The percentage of under-5 population covered was calculated by dividing the population of children aged 0–4 years in locations covered by available under-5 mortality data by the total global under-5 population. Because of lags in reporting of both vital registration data and the release of household survey or census data, the availability of data was much lower for 2014 and 2015 than for previous years. (B) Country-specific example of data and under-5 mortality estimates in Zambia, 1980–2015. The black line shows Gaussian process regression fit with 95% uncertainty interval shown in grey. Black circles denote reference data. Triangles denote complete birth history data. Inverted triangles denote summary birth history data. Transparent symbols are the data post-adjustment for non-sampling error. Hollow shapes represent data identified as outliers. (C) Percentage of global under-5 population covered by under-5 age-specific and sex-specific data for each year, 1980–2015. The percentage of under-5 population covered was calculated by dividing the population of children aged 0–4 years in locations covered by available under-5 age-specific and sex-specific data by the total global under-5 population. Because of lags in reporting both vital registration data and the release of household survey or census data, the availability of data was much lower for 2014 than for previous years, and no data existed for 2015. (D) Country-specific example of probability of female post-neonatal mortality in Bangladesh, 1980–2015. Standard Demographic and Health Surveys generally include large population samples and standard sets of questions. Special Demographic and Health Surveys can survey smaller, more targeted populations, such as women who have given birth. The black line shows probability of death, with 95% uncertainty interval shown in grey. Solid circles represent data sources. Hollow circles represent outliers. The post-neonatal period is 28–364 days.

**Figure 4**
Examples of adult mortality data availability and estimation (A) Percentage of global adult population covered by adult mortality data from vital registration systems, sibling survival surveys, sample registration systems, or censuses, 1980–2015. The percentage of available data was calculated by dividing the population of adults aged 15–59 years in locations covered by available adult mortality data by the total global population aged 15–49 years. Because of lags in reporting both vital registration data and the release of household survey or census data, the availability of data was much lower for 2014 than for previous years, and no data existed for 2015. Country-specific examples of (B) vital registration data and adult male mortality (45q15) estimation for a country with complete vital registration and large population (USA), 1980–2015; (C) vital registration data and adult male mortality (45q15) estimation for a country with complete vital registration and small population (Iceland), 1980–2015; and (D) sibling survival data and adult female mortality (45q15) estimation (Malawi), 1980–2015. Black line shows final estimates of adult mortality among males or females in each country, with 95% uncertainty interval shown in grey. Squares show sibling survival histories. 45q15=probability of death from age 15 years to 60 years.

**Figure 5**
Age-specific mortality estimation with GBD life table method versus observed data excluded from the model Country-specific examples for (A) males and (B) females in France, 2011. The red line shows the GBD 2015 life table system estimates of age-specific mortality rates from birth through age 75 years in log space, compared with observed age-specific mortality (blue line). Year 2011 selected for illustration purposes. GBD=Global Burden of Disease.

**Figure 6**
Comparisons of GBD 2015 estimates and UNAIDS 2014 estimates for Zimbabwe Country-specific example comparing estimates of deaths due to HIV/AIDS (A), new HIV infections (B), and people living with HIV/AIDS (C) in Zimbabwe from GBD 2015 and UNAIDS 2014. Curves show the estimation process of a particular country and highlight the differences in results from the GBD and UNAIDS analysis of the same prevalence data. Numbers are reported in thousands. Uncertainty intervals are shown in red and blue shading. GBD=Global Burden of Disease. UNAIDS=The Joint United Nations Programme on HIV and AIDS.

**Figure 7**
Availability and quality of cause of death data in the GBD 2015 database (A) Total deaths with a WHO-standard death certificate available in the GBD 2015 cause of death database classified by the variant of the International Classification of Diseases used for reporting. Cause of death data have been reported in national variants of ICD-8, ICD-9, and ICD-10 during the interval 1980–2015. Because of lags in reporting of both vital registration data and the release of household survey or census data, the availability of data was much lower for 2014 than for previous years and no data existed for 2015. (B) Percentage of global population covered by cause-specific data in the cause of death database for GBD 2015, 1980–2015; the percentage of available data was calculated by dividing the population of locations covered by available cause-specific data by the total global population. This figure is computed using vital registration, verbal autopsy, maternal, cancer, and injury sources. (C) Overall classification of each GBD subnational level 1 geography by availability and quality of cause of death data for the period 1980 to 2015. Countries have been assigned on the basis of the available time series of data into one of six categories. The figure uses GBD subnational level 1 geographies because subnational level 2 cannot be easily seen on a map. Extensive complete representative vital registration was defined as 25 total years or more of vital registration data with an estimated 95% completeness or above. All geographies that do not meet the threshold for extensive complete representative vital registration are classified as one of the following: limited years of complete representative vital registration, defined as 5 years or more of vital registration data with an estimated 95% completeness or above; incomplete representative vital registration, defined as at least 1 year of vital registration data with an estimated 70% completeness or above; more than 200 cause-years VA or non-representative VR, defined as more than 200 cause-years of verbal autopsy or at least 1 year of vital registration with an estimated 50% completeness or above; less than 200 cause-years of VA; or no data. Cause-years are defined as the number of years for each cause for which data are available. GBD=Global Burden of Disease. ICD=International Classification of Diseases. BTL=basic tabulation list. VA=verbal autopsy. VR=vital registration. ATG=Antigua and Barbuda. VCT=Saint Vincent and the Grenadines. LCA=Saint Lucia. TTO=Trinidad and Tobago. TLS=Timor-Leste. FSM=Federated States of Micronesia.

**Figure 8**
Distribution of out-of-sample model performance for CODEm models used for GBD 2015 Model performance was assessed by use of the root mean square error of the ensemble model predictions of the log of the age-specific death rates for a cause assessed with 15% of the data held out from the statistical model building. The figure shows the distribution of root mean square error across the set all models for all causes. Model performance varies substantially across causes. GBD=Global Burden of Disease. CODEm=cause of death ensemble modelling.

**Figure 9**
SDI quintiles by GBD subnational level 1 geography, 2015 SDI is calculated for each geography as a function of lag-dependent income per capita, average educational attainment in the population older than age 15 years, and the total fertility rate. SDI units are interpretable; a zero represents the lowest level of income per capita and educational attainment and highest total fertility rate observed during 1980–2015, whereas a one represents the highest income per capita and educational attainment and lowest total fertility rate observed in the same period. Cutoffs on the SDI scale for the quintiles have been selected on the basis of examination of the entire distribution of geographies 1980–2015. GBD=Global Burden of Disease. SDI=Socio-demographic Index. ATG=Antigua and Barbuda. VCT=Saint Vincent and the Grenadines. LCA=Saint Lucia. TTO=Trinidad and Tobago. TLS=Timor-Leste. FSM=Federated States of Micronesia.

**Figure 10**
Co-evolution of life expectancy and probabilities of death with SDI globally and for GBD super-regions, 1980 to 2015 (A) Life expectancy at birth and SDI; (B) under-5 death rate (5q0) and SDI; (C) probability of death between 15 and 50 years of age (35q15) and SDI; and (D) probability of death between 50 and 70 years of age (20q50) and SDI. Coloured lines show global and super-region values. Each point in a line represents 1 year, starting at 1980 and ending at 2015. In all super-regions, SDI has increased year on year so progress in SDI is associated with later years for a given super-region. Black lines show trajectories expected for each geography on the basis of SDI alone. GBD=Global Burden of Disease. SDI=Socio-demographic Index. 5q0=probability of death from birth to age 5 years. 35q15=probability of death from age 15 years to 50 years. 20q50=probability of death from age 50 years to 70 years.

**Figure 11**
Global deaths due to fatal discontinuities by cause group for each year, 1980–2015 Numbers shown are total deaths. Fatal discontinuities are events that lead to abrupt changes in deaths in a geography. The causes for these fatal discontinuities include wars, natural disasters, industrial accidents, large transport accidents, epidemics, famines, or other injuries.

**Figure 12**
Global age-standardised death rates for males versus females, by GBD cause Level 3, 1990 and 2015 The y-axis and x-axis are shown on a log scale to enable comparisons between males and females spanning a wide range of values. Black lines show where death rates are identical for males and females. Causes that only affect one sex, including maternal disorders, chlamydia, cervical, uterine, ovarian, prostate, testicular cancers, and gynaecological diseases are not shown. GBD=Global Burden of Disease. COPD=chronic obstructive pulmonary disease. C=cancer.

**Figure 13**
Global decomposition of changes in leading 30 causes of death, 2005 to 2015 Changes due to population growth, population ageing, and changes in age-specific mortality rates are shown. Causes are reported in descending order by total number of deaths for all ages and both sexes combined in 2015. The black circle shows the overall median percentage change in global deaths from 2005 to 2015. Causes with increases in overall death rates have a circle to the right of the zero, whereas a circle to the left of the zero denotes causes with decreases in overall death rates. The contributions of population growth, ageing, and change in age-specific death rates sum to the total change in numbers of deaths.

**Figure 14**
Leading 30 Level 3 causes of global YLLs for both sexes combined for 1990, 2005, and 2015, with percent change in number of YLLs, and all-age and age-standardised rates Causes are connected by lines between time periods. For the time periods 1990 to 2005 and 2005 to 2015, three measures of change are shown: percent change in the number of YLLs, percent change in the all-age YLL rate, and percent change in the age-standardised YLL rate. Statistically significant changes are shown in bold. YLLs=years of life lost. COPD=chronic obstructive pulmonary disease. STDs=sexually transmitted diseases excluding HIV. An interactive version of this figure is available online at http://vizhub.healthdata.org/gbd-compare.

**Figure 15**
Expected relationship between age-standardised YLL rates per 100 000 people for the 21 GBD Level 2 causes and SDI (A), the expected relationship between population and SDI (B), and the expected relationship between all-age YLL rates per 100 000 people for the 21 GBD Level 2 causes and SDI (C), by sex The stacked curves in A and C represent the average relationship between SDI and each cause of YLLs observed across all geographies over the time period 1980 to 2015. In each figure, the y-axis spans from the lowest SDI up to the highest SDI. To the left of the midline are male rates, and the female rates are to the right; higher rates are further from the midline. GBD=Global Burden of Disease. SDI=Socio-demographic Index. YLL=years of life lost.

**Figure 16**
Attribution of changes in life expectancy at birth to changes in major groups of causes of death, 2005 to 2015 Changes are shown for countries and territories (and subnational units in the UK) for females (A) and males (B). Locations are ordered by decreasing life expectancy at birth in 2015. Blue lines show life expectancy at birth in 2005 and black lines show life expectancy at birth in 2015. Causes to the left of the 2005 life expectancy values reflect causes that contributed to reductions in life expectancy from 2005 to 2015. Causes to the right of the 2005 life expectancy values contributed to increases in life expectancy from 2005 to 2015.

**Figure 17**
Leading ten causes of YLLs with the ratio of observed YLLs to YLLs expected on the basis of SDI in 2015, by location The ratio of observed YLLs to YLLs expected based on SDI is provided in parentheses for each cause, and cells are colour coded by ratio ranges (calculated to place a roughly equal number of cells into each bin). Shades of blue represent much lower observed YLLs than expected levels based on SDI, whereas red shows that observed YLLs exceed expected levels. SDI=Socio-demographic Index. YLL=years of life lost. IHD=ischaemic heart disease. LRI=lower respiratory infection. NN enceph=neonatal encephalitis. COPD=chronic obstructive pulmonary disease. Congenital=congenital disorders. C=cancer. Alzheimer=Alzheimer's disease and other dementias. HTN HD=hypertensive heart disease. Cirr hepB=cirrhosis due to hepatitis B. NN preterm=neonatal preterm birth complications. CKD=chronic kidney disease. TB=tuberculosis. Intestinal infect=intestinal infectious disease. NN sepsis=neonatal sepsis. Endocrine=endocrine, metabolic, blood, and immune disorders. Other cardio=other cardiovascular diseases. CMP=cardiomyopathies. Haemog=haemoglobinopathies and haemolytic anaemias. Cirr alcohol=cirrhosis due to alcohol use. Violence=interpersonal violence. Alcohol=alcohol use disorders. Other cirr=cirrhosis due to other causes. Cirr hepC=cirrhosis due to hepatitis C. Drugs=drug use disorders. F Body=pulmonary aspiration and foreign body in airway. PEM=protein-energy malnutrition. Mech=exposure to mechanical forces. Other transport=other transport injuries. Med treat=adverse effects of medical treatment. Leish=leishmaniasis. Other NN=other neonatal disorders. Iron=iron-deficiency anaemia. Whooping=whooping cough.

References

1. Roulson J, Benbow EW, Hasleton PS. Discrepancies between clinical and autopsy diagnosis and the value of post mortem histology; a meta-analysis and review. Histopathology. 2005;47:551–559.
1. Murray CJ, Lozano R, Flaxman AD. Using verbal autopsy to measure causes of death: the comparative performance of existing methods. BMC Med. 2014;12:5.
1. Serina P, Riley I, Stewart A. A shortened verbal autopsy instrument for use in routine mortality surveillance systems. BMC Med. 2015;13:302.
1. Naghavi M, Makela S, Foreman K, O'Brien J, Pourmalek F, Lozano R. Algorithms for enhancing public health utility of national causes-of-death data. Popul Health Metr. 2010;8:9.
1. Foreman KJ, Lozano R, Lopez AD, Murray CJ. Modeling causes of death: an integrated approach using CODEm. Popul Health Metr. 2012;10:1.
1. Sonderegger-Iseli K, Burger S, Muntwyler J, Salomon F. Diagnostic errors in three medical eras: a necropsy study. Lancet. 2000;355:2027–2031.
1. GBD 2013 Mortality and Causes of Death Collaborators Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385:117–171.
1. Wang H, Liddell CA, Coates MM. Global, regional, and national levels of neonatal, infant, and under-5 mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:957–979.
1. Kassebaum NJ, Bertozzi-Villa A, Coggeshall MS. Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:980–1004.
1. Murray CJL, Lopez AD, the Harvard School of Public Health. World Health Organization. World Bank . The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Published by the Harvard School of Public Health on behalf of the World Health Organization and the World Bank: Distributed by Harvard University Press; Cambridge, MA: 1996.
1. Murray CJL, Ortblad KF, Guinovart C. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:1005–1070.
1. Wang H, Dwyer-Lindgren L, Lofgren KT. Age-specific and sex-specific mortality in 187 countries, 1970–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2071–2094.
1. Newton JN, Briggs ADM, Murray CJL. Changes in health in England, with analysis by English regions and areas of deprivation, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386:2257–2274.
1. Zhou M, Wang H, Zhu J. Cause-specific mortality for 240 causes in China during 1990–2013: a systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet. 2016;387:251–272.
1. Gómez-Dantés H, Fullman N, Lamadrid-Figueroa H. Dissonant health transition in the states of Mexico, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2016 published online Oct 5.
1. From evidence into action: opportunities to protect and improve the nation's health. Public Health England; 2014.
1. Bhalla K, Harrison JE. GBD-2010 overestimates deaths from road injuries in OECD countries: new methods perform poorly. Int J Epidemiol. 2015;44:1648–1656.
1. Rudan I, Chan KY. Global health metrics needs collaboration and competition. Lancet. 2015;385:92–94.
1. Rajaratnam JK, Marcus JR, Levin-Rector A. Worldwide mortality in men and women aged 15–59 years from 1970 to 2010: a systematic analysis. Lancet. 2010;375:1704–1720.
1. The GATHER Working Group Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet. 2016 published online June 28.
1. Murray CJ, Ezzati M, Flaxman AD. GBD 2010: design, definitions, and metrics. Lancet. 2012;380:2063–2066.
1. Murray CJ, Lopez AD. Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet. 1997;349:1269–1276.
1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747–1757.
1. Lozano R, Naghavi M, Foreman K. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2095–2128.
1. Obermeyer Z, Rajaratnam JK, Park CH. Measuring adult mortality using sibling survival: a new analytical method and new results for 44 countries, 1974–2006. PLoS Med. 2010;7:e1000260.
1. Murray CJL, Rajaratnam JK, Marcus J, Laakso T, Lopez AD. What can we conclude from death registration? Improved methods for evaluating completeness. PLoS Med. 2010;7:e1000262.
1. Hill K, You D, Choi Y. Death distribution methods for estimating adult mortality: Sensitivity analysis with simulated data errors. Demogr Res. 2009;21:235–254.
1. United Nations Population Division | Department of Economic and Social Affairs. (accessed March 2, 2016).
1. Introduction to UNICEF's work on statistics and monitoring UNICEF. (accessed March 2, 2016).
1. Home - Wittgenstein Centre. (accessed March 2, 2016).
1. United Nations. Department of International Economic and Social Affairs . Model life tables for developing countries. United Nations; New York: 1982.
1. Coale AJ, Demeny PG, the Princeton University. Office of Population Research . Regional model life tables and stable populations. Princeton University Press; Princeton, NJ: 1966.
1. Murray CJL, Ferguson BD, Lopez AD, Guillot M, Salomon JA, Ahmad O. Modified logit life table system: principles, empirical validation, and application. Popul Stud (Camb) 2003;57:165–182.
1. Eaton J. Read-epp-spectrum. GitHub. (accessed March 7, 2016).
1. UNAIDS AIDSinfo Online Database. 2014. (accessed April 13, 2016).
1. 2014 Progress reports submitted by countries. (accessed April 5, 2016).
1. Guha-Sapir D, Below R, Hoyois P. EM-DAT: The CRED/OFDA International Disaster Database. Université Catholique de Louvain. (accessed April 5, 2016).
1. Department of Peace and Conflict Research Uppsala Conflict Data Program. Uppsala University, Uppsala, Sweden. (accessed April 5, 2016).
1. The International Institute for Strategic Studies Armed Conflict Database. London. (accessed April 5, 2016).
1. The Robert S Strauss Center For International Security and Law. The University of Texas at Austin. (accessed April 5, 2016).
1. Birnbaum JK, Murray CJ, Lozano R. Exposing misclassified HIV/AIDS deaths in South Africa. Bull World Health Organ. 2011;89:278–285.
1. Peterson HM, Flaxman AD. Meta-regression with DisMod-MR: how robust is the model? Lancet. 2013;381:S110.
1. Flaxman AD, Vos T, Murray CJL. An integrative metaregression framework for descriptive epidemiology. University of Washington Press; Seattle: 2015.
1. National Center for Health Statistics (US) 1993 National Mortality Followback Survey. US Dept of Health & Human Services, Public Health Service, Centers for Disease Control; Hyattsville, MD: 1991.
1. Duncan ME, Pitcher A, Goldacre MJ. Atrial fibrillation as a cause of death increased steeply in England between 1995 and 2010. Europace. 2014;16:797–802.
1. Kotloff KL, Nataro JP, Blackwelder WC. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. Lancet. 2013;382:209–222.
1. Liu J, Platts-Mills JA, Juma J. Use of quantitative molecular diagnostic methods to identify causes of diarrhoea in children: a reanalysis of the GEMS case-control study. Lancet. 2016;388:1291–1301.
1. Shi T, McLean K, Campbell H, Nair H. Aetiological role of common respiratory viruses in acute lower respiratory infections in children under five years: A systematic review and meta-analysis. J Glob Health. 2015;5:010408.
1. Bonten MJM, Huijts SM, Bolkenbaas M. Polysaccharide conjugate vaccine against pneumococcal pneumonia in adults. N Engl J Med. 2015;372:1114–1125.
1. Feikin DR, Scott JAG, Gessner BD. Use of vaccines as probes to define disease burden. Lancet. 2014;383:1762–1770.
1. Murray CJL, Barber RM, Foreman KJ, the GBD 2013 DALYs and HALE Collaborators Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet. 2015;386:2145–2191.
1. United Nations Development Programme . Human development report 2015. United Nations; Geneva: 2016.
1. A hand up: Global progress towards universal education. Issuu. (accessed April 1, 2016).
1. Das Gupta P. Standardization and decomposition of rates: a user's manual. US Bureau of the Census; Washington, DC: 1993.
1. Beltrán-Sánchez H, Preston SH, Canudas-Romo V. An integrated approach to cause-of-death analysis: cause-deleted life tables and decompositions of life expectancy. Demogr Res. 2008;19:1323–1350.
1. Omran AR. The epidemiologic transition. A theory of the epidemiology of population change. Milbank Mem Fund Q. 1971;49:509–538.
1. Bloom DE, Canning D. Policy forum: public health. The health and wealth of nations. Science. 2000;287:1207–1209.
1. Preston SH. The changing relation between mortality and level of economic development. Popul Stud (Camb) 1975;29:231–248.
1. Deaton A. Health, Inequality, and Economic Development. J Econ Lit. 2003;41:113–158.
1. Caldwell JC. Routes to Low Mortality in Poor Countries. Popul Dev Rev. 1986;12:171–220.
1. Cutler D, Deaton A, Lleras-Muney A. The Determinants of Mortality. J Econ Perspect. 2006;20:97–120.
1. Howitt P, Darzi A, Yang G-Z. Technologies for global health. Lancet. 2012;380:507–535.
1. McMichael AJ, Woodruff RE, Hales S. Climate change and human health: present and future risks. Lancet. 2006;367:859–869.
1. Ramsey R, Giskes K, Turrell G, Gallegos D. Food insecurity among adults residing in disadvantaged urban areas: potential health and dietary consequences. Public Health Nutr. 2012;15:227–237.
1. Farmer PE. Shattuck Lecture. Chronic infectious disease and the future of health care delivery. N Engl J Med. 2013;369:2424–2436.
1. Spellberg B, Bartlett JG, Gilbert DN. The future of antibiotics and resistance. N Engl J Med. 2013;368:299–302.
1. Manolio TA, Chisholm RL, Ozenberger B. Implementing genomic medicine in the clinic: the future is here. Genet Med. 2013;15:258–267.
1. Sahoo SK. Nanotechnology in Health Care. CRC Press; 2012.
1. Lundgren JD, Babiker AG, Gordin F, the INSIGHT START Study Group Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015;373:795–807.
1. Fast-Track strategy to end the AIDS epidemic by 2030. (accessed March 7, 2016).
1. FIRS calls for continued international support to end AIDS epidemic by 2030. Dec 7, 2015. (accessed March 7, 2016).
1. Lo S, Horton R. AIDS and global health: the path to sustainable development. Lancet. 2015;386:106–108.
1. Secretary-General Calls for End to AIDS by 2030, in Message to General Assembly | Meetings Coverage and Press Releases. (accessed March 7, 2016).
1. Dieleman JL, Graves C, Johnson E. Sources and focus of health development assistance, 1990–2014. JAMA. 2015;313:2359–2368.
1. Schneider MT, Birger M, Haakendstad A. Tracking development assistance for HIV/AIDS: The international response to a global epidemic. AIDS. 2016 published online March 4.
1. Bhatt S, Weiss DJ, Cameron E. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526:207–211.
1. Liu J, Gratz J, Amour C. A laboratory-developed TaqMan Array Card for simultaneous detection of 19 enteropathogens. J Clin Microbiol. 2013;51:472–480.
1. Liu J, Kabir F, Manneh J. Development and assessment of molecular diagnostic tests for 15 enteropathogens causing childhood diarrhoea: a multicentre study. Lancet Infect Dis. 2014;14:716–724.
1. Stanaway JD, Shepard DS, Undurraga EA. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis. 2016;16:712–723.
1. Musso D, Cao-Lormeau VM, Gubler DJ. Zika virus: following the path of dengue and chikungunya? Lancet. 2015;386:243–244.
1. WHO | Dengue vaccine research. WHO. (accessed March 2, 2016).
1. Jonker FAM, Calis JCJ, Phiri K. Real-time PCR demonstrates Ancylostoma duodenale is a key factor in the etiology of severe anemia and iron deficiency in Malawian pre-school children. PLoS Negl Trop Dis. 2012;6:e1555.
1. Qian M-B, Chen Y-D, Liang S, Yang G-J, Zhou X-N. The global epidemiology of clonorchiasis and its relation with cholangiocarcinoma. Infect Dis Poverty. 2012;1:4.
1. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. International Agency for Research on Cancer A review of human carcinogens. Lyon, France; Geneva, Switzerland: International Agency for Research on Cancer; Distributed by WHO Press. 2012. (accessed April 13, 2016).
1. Haagsma JA, Graetz N, Bolliger I. The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. Inj Prev. 2016;22:3–18.
1. Agua-Agum J, Ariyarajah A, Aylward B, the WHO Ebola Response Team West African Ebola epidemic after one year-slowing but not yet under control. N Engl J Med. 2015;372:584–587.
1. Walker PGT, White MT, Griffin JT, Reynolds A, Ferguson NM, Ghani AC. Malaria morbidity and mortality in Ebola-affected countries caused by decreased health-care capacity, and the potential effect of mitigation strategies: a modelling analysis. Lancet Infect Dis. 2015;15:825–832.
1. Takahashi S, Metcalf CJE, Ferrari MJ. Reduced vaccination and the risk of measles and other childhood infections post-Ebola. Science. 2015;347:1240–1242.
1. Moon S, Sridhar D, Pate MA. Will Ebola change the game? Ten essential reforms before the next pandemic. The report of the Harvard-LSHTM Independent Panel on the Global Response to Ebola. Lancet. 2015;386:2204–2221.
1. Murray CJ, Lopez AD, Chin B, Feehan D, Hill KH. Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918–20 pandemic: a quantitative analysis. Lancet. 2006;368:2211–2218.
1. Gates B. The next epidemic-lessons from Ebola. N Engl J Med. 2015;372:1381–1384.
1. WHO | WHO statement on the first meeting of the International Health Regulations (2005) (IHR 2005) Emergency Committee on Zika virus and observed increase in neurological disorders and neonatal malformations. WHO. (accessed March 9, 2016).
1. World Health Organization . Antimicrobial resistance: global report on surveillance, 2014. World Health Organization; Geneva, Switzerland: 2014.
1. World Health Organization . Global tuberculosis report 2015. World Health Organization; Geneva, Switzerland: 2015.
1. Shallcross LJ, Howard SJ, Fowler T, Davies SC. Tackling the threat of antimicrobial resistance: from policy to sustainable action. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140082.
1. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature. 2005;438:310–317.
1. Whitmee S, Haines A, Beyrer C. Safeguarding human health in the Anthropocene epoch: report of The Rockefeller Foundation-Lancet Commission on planetary health. Lancet. 2015;386:1973–2028.
1. Rockström J, Steffen W, Noone K. A safe operating space for humanity. Nature. 2009;461:472–475.
1. Malhotra J, Malvezzi M, Negri E, Vecchia CL, Boffetta P. Risk factors for lung cancer worldwide. Eur Respir J. 2016;48:889–902.
1. Bidell MR, McLaughlin M, Faragon J, Morse C, Patel N. Desirable characteristics of hepatitis C treatment regimens: a review of what we have and what we need. Infect Dis Ther. 2016 published online July 6.
1. World Health Organization (WHO) WHO Mortality Database Version February 2014. World Health Organization (WHO); Geneva, Switzerland: 2014.
1. Olshansky SJ, Ault AB. The fourth stage of the epidemiologic transition: the age of delayed degenerative diseases. Milbank Q. 1986;64:355–391.
1. Mohd Hanafiah K, Groeger J, Flaxman AD, Wiersma ST. Global epidemiology of hepatitis C virus infection: new estimates of age-specific antibody to HCV seroprevalence. Hepatology. 2013;57:1333–1342.
1. Lawitz E, Mangia A, Wyles D. Sofosbuvir for previously untreated chronic hepatitis C infection. N Engl J Med. 2013;368:1878–1887.
1. Scheltens P, Blennow K, Breteler MMB. Alzheimer's disease. Lancet. 2016;388:505–517.
1. Matthews FE, Arthur A, Barnes LE, the Medical Research Council Cognitive Function and Ageing Collaboration A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet. 2013;382:1405–1412.
1. Satizabal CL, Beiser AS, Chouraki V, Chêne G, Dufouil C, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med. 2016;374:523–532.
1. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer's disease: an analysis of population-based data. Lancet Neurol. 2014;13:788–794.
1. Ösby U, Westman J, Hällgren J, Gissler M. Mortality trends in cardiovascular causes in schizophrenia, bipolar and unipolar mood disorder in Sweden 1987–2010. Eur J Public Health. 2016 published online Jan 8.
1. Fekadu A, Medhin G, Kebede D. Excess mortality in severe mental illness: 10-year population-based cohort study in rural Ethiopia. Br J Psychiatry. 2015;206:289–296.
1. Olfson M, Gerhard T, Huang C, Crystal S, Stroup TS. Premature Mortality Among Adults With Schizophrenia in the United States. JAMA Psychiatry. 2015;72:1172–1181.
1. Ferrari AJ, Norman RE, Freedman G. The burden attributable to mental and substance use disorders as risk factors for suicide: findings from the Global Burden of Disease Study 2010. PLoS One. 2014;9:e91936.
1. Forouzanfar MH, Alexander L, Anderson HR, the GBD 2013 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386:2287–2323.
1. Lönnroth K, Roglic G, Harries AD. Improving tuberculosis prevention and care through addressing the global diabetes epidemic: from evidence to policy and practice. Lancet Diabetes Endocrinol. 2014;2:730–739.
1. Lim SS, Vos T, Flaxman AD. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2224–2260.
1. GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global burden of Disease Study 2015. Lancet. 2016;388:1659–1724.
1. Gregg EW, Li Y, Wang J. Changes in diabetes-related complications in the United States, 1990–2010. N Engl J Med. 2014;370:1514–1523.
1. Dinesh Shah A, Langenberg C, Rapsomaniki E. Type 2 diabetes and incidence of a wide range of cardiovascular diseases: a cohort study in 1·9 million people. Lancet. 2015;385(suppl 1):S86.
1. Vos T, Barber RM, Bell B, the Global Burden of Disease Study 2013 Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386:743–800.
1. Garcia-Garcia G, Monteon-Ramos JF, Garcia-Bejarano H. Renal replacement therapy among disadvantaged populations in Mexico: a report from the Jalisco Dialysis and Transplant Registry (REDTJAL) Kidney Int Suppl. 2005;68:S58–S61.
1. Lunyera J, Mohottige D, Isenburg MV, Jeuland M, Patel UD, Stanifer JW. CKD of Uncertain Etiology: A Systematic Review. Clin J Am Soc Nephrol. 2016;11:379–385.
1. Thomas B, Wulf S, Bikbov B. Maintenance Dialysis throughout the World in Years 1990 and 2010. J Am Soc Nephrol. 2015;26:2621–2633.
1. Xie X, Liu Y, Perkovic V. Renin-angiotensin system inhibitors and kidney and cardiovascular outcomes in patients with CKD: a bayesian network meta-analysis of randomized clinical trials. Am J Kidney Dis. 2016;67:728–741.
1. Özyilmaz A, de Jong PE, Gansevoort RT. Screening for chronic kidney disease can be of help to prevent atherosclerotic end-organ damage. Nephrol Dial Transplant. 2012;27:4046–4052.
1. Komenda P, Ferguson TW, Macdonald K. Cost-effectiveness of primary screening for CKD: a systematic review. Am J Kidney Dis. 2014;63:789–797.
1. Cummings P, Rivara FP, Olson CM, Smith KM. Changes in traffic crash mortality rates attributed to use of alcohol, or lack of a seat belt, air bag, motorcycle helmet, or bicycle helmet, United States, 1982–2001. Inj Prev. 2006;12:148–154.
1. Bunn F, Collier T, Frost C, Ker K, Roberts I, Wentz R. Traffic calming for the prevention of road traffic injuries: systematic review and meta-analysis. Inj Prev. 2003;9:200–204.
1. Abu-Zidan FM, Abbas AK, Hefny AF, Eid HO, Grivna M. Effects of seat belt usage on injury pattern and outcome of vehicle occupants after road traffic collisions: prospective study. World J Surg. 2012;36:255–259.
1. Staton C, Vissoci J, Gong E. Road traffic injury prevention initiatives: a systematic review and metasummary of effectiveness in low and middle income countries. PLoS One. 2016;11:e0144971.
1. Pérez K, Marí-Dell'Olmo M, Tobias A, Borrell C. Reducing road traffic injuries: effectiveness of speed cameras in an urban setting. Am J Public Health. 2007;97:1632–1637.
1. Traffic general directorate Spanish road safety strategy 2011–2020. Executive summary. (accessed April 13, 2016).
1. ENSR National road safety strategy. 2008–2015. (accessed April 13, 2016).
1. Luoma J, Sivak M, the University of Michigan. Sustainable Worldwide Transportation. University of Michigan. Transportation Research Institute . Road-safety management in Brazil, Russia, India, and China. University of Michigan Transportation Research Institute; Ann Arbor, MI: 2012.
1. Hyder AA, Vecino-Ortiz AI. BRICS: opportunities to improve road safety. Bull World Health Organ. 2014;92:423–428.
1. Bishai D, Quresh A, James P, Ghaffar A. National road casualties and economic development. Health Econ. 2006;15:65–81.
1. United Nations Office on Drugs and Crime . Global study on homicide 2013: trends, contexts, data. UNODC; Vienna, Austria: 2013.
1. Butchart A, Mikton C, World Health Organization. United Nations Office on Drugs and Crime. United Nations Development Programme . Global status report on violence prevention, 2014. World Health Organization; Geneva: 2014.
1. Darke S. The toxicology of homicide offenders and victims: a review. Drug Alcohol Rev. 2010;29:202–215.
1. Kuhns JB, Exum ML, Clodfelter TA, Bottia MC. The prevalence of alcohol-involved homicide offending: a meta-analytic review. Homicide Stud. 2013 published online July 3.
1. Cusimano MD, Sameem M. The effectiveness of middle and high school-based suicide prevention programmes for adolescents: a systematic review. Inj Prev. 2011;17:43–49.
1. Hahn RA, Bilukha O, Crosby A, the Task Force on Community Preventive Services Firearms laws and the reduction of violence: a systematic review. Am J Prev Med. 2005;28(suppl 1):40–71.
1. Motohashi Y, Kaneko Y, Sasaki H, Yamaji M. A decrease in suicide rates in Japanese rural towns after community-based intervention by the health promotion approach. Suicide Life Threat Behav. 2007;37:593–599.
1. Mann JJ, Apter A, Bertolote J. Suicide prevention strategies: a systematic review. JAMA. 2005;294:2064–2074.
1. WHO . Global report on drowning: preventing a leading killer. World Health Organization; Geneva: 2014.
1. Wallis BA, Watt K, Franklin RC, Taylor M, Nixon JW, Kimble RM. Interventions associated with drowning prevention in children and adolescents: systematic literature review. Inj Prev. 2015;21:195–204.
1. Leavy JE, Crawford G, Portsmouth L. Recreational drowning prevention interventions for adults, 1990–2012: a review. J Community Health. 2015;40:725–735.
1. Burnham G, Lafta R, Doocy S, Roberts L. Mortality after the 2003 invasion of Iraq: a cross-sectional cluster sample survey. Lancet. 2006;368:1421–1428.
1. Burnham G, Roberts L. A debate over Iraqi death estimates. Science. 2006;314:1241.
1. Tapp C, Burkle FM, Jr, Wilson K. Iraq War mortality estimates: a systematic review. Confl Health. 2008;2:1.
1. Burnham G, Doocy S, Dzeng E, Lafta R, Roberts L. The Human Cost of the War in Iraq. A Mortality Study, 2002–2006. Bloomberg School of Public Health, Johns Hopkins University; School of Medicine, Al Mustansiriya University; Center for International Studies, Massachusetts Institute of Technology; Baltimore, MD; Baghdad, Iraq; Cambridge, MA: 2006.
1. Syrian Arab Republic. OCHA (accessed April 18, 2016).
1. United Nations High Commissioner for Refugees UNHCR: Total number of Syrian refugees exceeds four million for first time. UNHCR. (accessed April 18, 2016).
1. Streatfield PK, Khan WA, Bhuiya A. Cause-specific mortality in Africa and Asia: evidence from INDEPTH health and demographic surveillance system sites. Glob Health Action. 2014 published online Oct 7.
1. Flaxman AD, Serina PT, Hernandez B, Murray CJL, Riley I, Lopez AD. Measuring causes of death in populations: a new metric that corrects cause-specific mortality fractions for chance. Popul Health Metr. 2015;13:28.
1. Lozano R, Freeman MK, James SL, the Population Health Metrics Research Consortium (PHMRC) Performance of InterVA for assigning causes of death to verbal autopsies: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:50.
1. Bloomberg Philanthropies Launches $100 Million Data for Health Program in Developing Countries Bloomberg Philanthropies. (accessed April 21, 2016).
1. AbouZahr C, de Savigny D, Mikkelsen L, Setel PW, Lozano R, Lopez AD. Towards universal civil registration and vital statistics systems: the time is now. Lancet. 2015;386:1407–1418.
1. Lopez AD, Setel PW. Better health intelligence: a new era for civil registration and vital statistics? BMC Med. 2015;13:73.
1. AbouZahr C, de Savigny D, Mikkelsen L. Civil registration and vital statistics: progress in the data revolution for counting and accountability. Lancet. 2015;386:1373–1385.
1. ihmeuw/dismod_mr. GitHub. (accessed April 6, 2016).
1. IHME/CODEm-2010. GitHub. (accessed April 6, 2016).
1. Helleringer S, Pison G, Kanté AM, Duthé G, Andro A. Reporting errors in siblings' survival histories and their impact on adult mortality estimates: results from a record linkage study in Senegal. Demography. 2014;51:387–411.
1. Stover J, Brown T, Marston M. Updates to the Spectrum/Estimation and Projection Package (EPP) model to estimate HIV trends for adults and children. Sex Transm Infect. 2012;88(suppl 2):i11–i16.
1. WHO | Estimates for 2000–2015. WHO. (accessed March 9, 2016).
1. World Health Organization Number of deaths (thousands)-Data by WHO region. Global Health Observatory data repository. (accessed April 21, 2016).
1. JH Bloomberg School of Public Health Maternal Child Epidemiology Estimation. Johns Hopkins Bloomberg School of Public Health. (accessed April 21, 2016).
1. Lanata CF, Fischer-Walker CL, Olascoaga AC, Torres CX, Aryee MJ, Black RE, the Child Health Epidemiology Reference Group of the World Health Organization and UNICEF Global causes of diarrheal disease mortality in children <5 years of age: a systematic review. PLoS One. 2013;8:e72788.
1. Ferlay J, Soerjomataram I, Dikshit R. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–E386.
1. UNAIDS Fact sheet 2015. (accessed April 6, 2016).
1. World Population Prospects - Population Division - United Nations (accessed April 5, 2016).
1. Hill K, Zimmerman L, Jamison DT. Mortality risks in children aged 5–14 years in low-income and middle-income countries: a systematic empirical analysis. Lancet Glob Health. 2015;3:e609–e616.
1. World Health Organization (WHO) WHO methods and data sources for country-level causes of death 2000–2012. WHO; Geneva, Switzerland: 2014.
1. World Health Organization (WHO) WHO methods and data sources for global burden of disease estimates 2000–2011. WHO; Geneva, Switzerland: 2013.
1. WHO | Estimates for 2000–2012. WHO. (accessed June 11, 2016).

Source: PubMed

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

Abstract

Figures

References

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies