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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

Rafael Lozano1, Mohsen Naghavi1, Kyle J Foreman2  +193 more•Institutions (95)
15 Dec 2012-The Lancet (Elsevier)-Vol. 380, Iss: 9859, pp 2095-2128

TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex, using the Cause of Death Ensemble model.

Abstract: Summary Background Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. Methods We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. Findings In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45–54% since 1990; ischaemic heart disease and stroke YLLs increased by 17–28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. Interpretation Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. Funding Bill & Melinda Gates Foundation.

Summary (6 min read)

Jump to: [Introduction] – [Data and methods] – [Database development] – [Population-based cancer registries] – [Police reports] – [Health facility data] – [Cause of Death Ensemble modeling (CODEm)] – [Negative binomial models] – [Fixed proportion models] – [Natural History Models] – [Mortality Shock Regressions] – [Combining Results for Individual Causes of Death to Generate Final Estimates] – [Ranking lists] – [Epidemiological Factors] – [Global Causes of Death] – [Discussion] – [Author Contributions] – [Figures and Tables] and [70 Bates M, O’Grady J, Mudenda V, Shibemba A, Zumla A. New global estimates of malaria deaths.]

Introduction

  • Cause-specific mortality is arguably one of the most fundamental metrics of population health.
  • For the remaining deaths which are not medically certified, many different data sources and diagnostic approaches must be used from surveillance systems, demographic research sites, surveys, censuses, disease registries, and police records to construct a consolidated picture of causes of death in various populations.
  • These efforts often include very specific steps undertaken for different data sources and are frequently poorly documented.
  • GBD revisions for 1999, 2000, 2001, 2002, 2004, and 2008 have used these compositional models to allocate deaths according to three broad cause groups: communicable, maternal, neonatal, and nutritional causes; noncommunicable diseases; and injuries.
  • 38 Given the profusion of statistical modeling options, an important innovation has been the reporting of out-of-sample predictive validity to document the performance of complex models.

Data and methods

  • Some general aspects of the analytical framework such as the creation of the 21 GBD regions and the full hierarchical cause list including the mapping of the International Classification of Diseases and Injuries (ICD) to the GBD cause list are reported elsewhere.
  • While results are reported in this paper at the regional level for 1990 and 2010, the cause of death analysis has been undertaken at the country level for 187 countries from 1980 to 2010.
  • Using longer time series improves the performance of many types of estimation models; data prior to 1980, however, are much sparser for developing countries so the authors have restricted the analysis to 1980-2010.

Database development

  • Over the five year duration of the GBD 2010 study, the authors have sought to identify all published and unpublished data sources relevant to estimating causes of death for 187 countries from 1980 to 2010.
  • Web Table 1a provides a summary of the siteyears of data identified by broad type of data system and, similarly, Web Table 1b illustrates the number of site-years by GBD region.
  • Of the GBD regions, subSaharan Africa Central has the most limited evidence base with data on only 27 causes from at least one country.
  • In addition, there is country to country variation in the detail used to report causes of death included in national reporting lists, namely the basic tabulation list for ICD9, the ICD10 tabulation list, three digit and four digit detail, and special reporting lists.
  • Verbal autopsy data collected through sample registration systems, demographic surveillance systems, or surveys Verbal autopsy (VA) is a means for ascertaining the cause of death of individuals and the cause-specific mortality fractions in populations with incomplete vital registration systems.

Population-based cancer registries

  • Population-based cancer registries provide an important source of data on incidence of cancers in various countries.
  • The authors identified 2,715 site-years of cancer registry data across 93 countries.
  • The log of the MI ratio has been estimated as a function of national income per capita with random effects for country, year, and age.
  • The estimated mortality to incidence ratios have been used to map cancer registry data on incidence to expected deaths which have been incorporated into the database.
  • MI ratios by country, age, and sex are available on request.

Police reports

  • In most countries, police and crime reports are an important source of information for some types of injuries, notably road injuries and inter-personal violence.
  • The police reports used in this analysis were collected from published studies, national agencies, and institutional surveys such as the United Nations Crime Trends survey and the WHO Global Status Report on Road Safety Survey.30,41.
  • The authors identified 32 site-years of burial and mortuary data in 11 countries from ministries of health, published reports, and mortuaries themselves.
  • The authors also identified 52 surveys/censuses covering injury mortality across 65 survey/census years.
  • Surveillance data on the number of maternal deaths, or the maternal mortality ratio multiplied by births, were converted into cause fractions by dividing by the total number of deaths estimated in the reproductive age groups.

Health facility data

  • The authors chose to only incorporate deaths due to injury from this source because of known bias.
  • In settings where a data source does not capture all deaths in a population, the cause composition of deaths captured may be different than those that are not.
  • At the global level, the number of deaths estimated in 2010 for ARI and diarrhea for example differ by 0·9% and 1·2%, respectively, between models that include all data and those that exclude data where under-five death registration is below 70% complete.
  • Garbage codes are causes of death that should not be identified as underlying causes of death but have been entered as the underlying cause of death on death certificates.
  • For the GBD 2010, the authors have identified causes that should not be assigned as underlying cause of death at a much more detailed level.

Cause of Death Ensemble modeling (CODEm)

  • For all major causes of death except for HIV/AIDS and measles, the authors have used cause of death ensemble modeling – 133 causes in the cause list and three other special aggregates.
  • In addition, four families of statistical models are developed using covariates: mixed effects linear models of the log of the death rate, mixed effects linear models of the logit of the cause fraction, spatial-temporal Gaussian process regression (ST-GPR) models of the log of the death rate, and ST-GPR of the logit of the cause fraction.
  • 3) Based on out-of-sample predictive validity, the best performing model or ensemble is selected.
  • Web Table 6 summarizes the performance of the CODEm models developed for 133 causes in the cause list for which the authors exclusively use CODEm and three special aggregates in the GBD 2010.
  • In all cases the out-of-sample performance is worse (larger RMSE) than the in-sample performance.

Negative binomial models

  • For 13 causes, the number of deaths observed in the database is too low to generate stable estimates of out-of-sample predictive validity.
  • For these causes, the authors developed negative binomial models using plausible covariates.
  • These causes are identified in Web Table 5.
  • For these negative binomial models, standard model building practice was followed where plausible covariates were included in the model development and reverse stepwise procedures followed for covariate inclusion.
  • Uncertainty distributions were estimated using both uncertainty in the regression betas for the covariates and from the gamma distribution of the negative binomial.

Fixed proportion models

  • In 28 cases where death is a rare event, the authors have first modeled the parent cause in the GBD hierarchy using CODEm and then allocated deaths to specific causes using a fixed proportion.
  • Proportions have been computed using all available data in the database and are fixed over time, but, depending on data density, allowed to vary by region, age, or sex.
  • Finally, cellulitis, decubitus ulcer, other skin and subcutaneous diseases, abscess, impetigo, and other bacterial skin diseases all varied by age and sex.
  • The meta-regression have generated region-age-sex estimates with uncertainty of etiological fractions for diarrhea, LRI, meningitis, chronic kidney disease, maternal conditions, cirrhosis.
  • In the cases of cirrhosis, liver cancer, maternal conditions, and chronic kidney disease, the studies or datasets on etiology identify primary cause as assessed clinically; for diarrhea, LRI, and meningitis, etiology is based on laboratory findings.

Natural History Models

  • For a few selected causes, there is evidence that cause of death data systems do not capture sufficient information for one of two reasons.
  • Second, there are reasons to believe that there is systematic misclassification of deaths in cause of death data sources, particularly for congenital syphilis,54,55 whooping cough,56 measles,57 and HIV/AIDS.58.
  • In the case of HIV/AIDS, a hybrid approach has been used.
  • For 36 countries, with complete and high quality vital registration systems, the authors have used CODEm, in consultation with UNAIDS.
  • For the remaining countries, the authors have used the estimates with uncertainty by age and sex provided directly by UNAIDS based on their 2012 revision.

Mortality Shock Regressions

  • To estimate deaths directly due to natural disasters or collective violence, the authors use a different approach.
  • Details of this approach are outlined in Murray et al.59.
  • To develop the covariate on battle deaths during collective violence, the authors used data from the Armed Conflict Database from the International Institute for Strategic Studies (1997-2011), the Uppsala Conflict Data Program(UCDP)/PRIO Armed Conflict Dataset (1946-present), and available data from complete VR systems.
  • The relationships between under-five mortality and adult mortality and the disaster and collective violence covariates are estimated using 43 empirical observations for disasters and 206 empirical observations for collective violence (only years with over 1 per 10,000 crude death rate from shocks are kept in this analysis).
  • The relationship is estimated for excess mortality from these data sources by first subtracting from observed mortality rates the expected death rates in shock years using the methods outlined in Murray et al.59.

Combining Results for Individual Causes of Death to Generate Final Estimates

  • Given that the authors develop single cause models, it is imperative as a final step to ensure that individual cause estimates sum to the all-cause mortality estimate for each age-sex-country-year group.
  • The authors use a simple algorithm called CoDCorrect; at the level of each draw from the posterior distribution of each cause, they proportionately rescale each cause such that the sum of the cause-specific estimates equals the number of deaths from all causes generated from the demographic analysis.47.
  • The authors have chosen levels for each cause based on consideration of the amount and quality of available data.
  • Because there are substantially more data on all cardiovascular causes from verbal autopsy studies than for specific cardiovascular causes, the authors have designated “all cardiovascular” as a level 1 cause for CoDCorrect.

Ranking lists

  • For the presentation of leading causes of death, the level at which one ranks causes is subject to debate.
  • Given the GBD cause list tree structure, multiple options are possible such as all cancers versus sitespecific cancers.
  • The authors have opted to produce tables of rankings using the level of disaggregation that seems most relevant for public health decision-making.
  • The reference standard has been constructed using the lowest observed death rate in each age group across countries with a population greater than five million (see Murray et al39 for details).
  • Because the all-cause mortality analysis is undertaken, however, for more detailed age-groups up to age 110, the authors are able to take into account the mean age of death over 80 in each country-year-sex group in computing YLLs.

Epidemiological Factors

  • To help understand the drivers of change in the numbers of deaths by cause or region, the authors have decomposed change from 1990 to 2010 into growth in total population, change in population age- and sex-structure, and change in age- and sex-specific rates.
  • The difference between 2010 deaths and the population growth and aging scenario is the difference in death numbers due to epidemiological change in age- and sex-specific death rates.
  • Each of these three differences is also presented as a percent change with reference to the 1990 observed death number.
  • Further details on the data and methods used for specific causes of death is available on request.

Global Causes of Death

  • The GBD cause list divides causes into three broad groups.
  • With declining age-specific death rates from all three groups of causes, including noncommunicable diseases, the global shift towards noncommunicable diseases and injuries as leading causes of death is being driven by population growth and aging, and not by increases in age-sex-cause specific death rates.
  • Among communicable diseases, notably lower respiratory infections (194 thousand), diarrhea (77 thousand) and meningitis (46 thousand) account for the remaining neonatal deaths.
  • A number of causes have much larger uncertainty intervals than adjacent causes in the rank list.
  • At both time periods, there is substantial variation across regions in the relative importance of different causes, with communicable diseases and related causes being much more important in parts of sub-Saharan Africa and parts of Asia than in North Africa, and vascular diseases and cancer predominating in most other regions.

Discussion

  • The GBD 2010 is the most comprehensive and systematic analysis of causes of death undertaken to date.
  • The global health community can now draw on annual estimates of mortality, by age and sex, for 21 regions of the world, for each year from 1980 to 2010, for 235 separate causes, each with 95% uncertainty intervals to aid interpretation.
  • An innovative dimension of the GBD 2010 has been the addition of estimates of deaths due to different diarrhea and lower respiratory infection (LRI) pathogens.
  • First, cause of death data even in settings with medical certification may not always accurately capture the underlying cause of death.

Author Contributions

  • CJLM, ADL, and RL prepared the first draft.
  • RL, MN, KF, SL, KS, ADL, and CJLM finalized the draft based on comments from other authors and reviewer feedback.
  • ADL and CJLM conceived of the study and provided overall guidance.
  • All other authors developed cause-specific models, reviewed results, provided guidance on the selection of key covariates, and reviewed the manuscript.

Figures and Tables

  • Decomposition analysis of the change of global death numbers by broad cause groups from 1990 to 2010 into total population growth, population aging and changes in age-,sex-and cause-specific death rates.
  • Percentage of global deaths for females and males in 1990 and 2010 by cause and age.
  • The colors represent the various level one causes; blue is for non-communicable diseases, red is for communicable, maternal, neonatal and nutritional conditions, and green is for injuries.
  • The dashed lines signify descending order in rank, while the solid lines signify ascending order in rank.
  • Some cause abbreviations used in the figure are lower respiratory infections (“LRI”); ischemic heart disease (“IHD”); chronic obstructive pulmonary disorder (“COPD”); protein energy malnutrition (“PEM”); tuberculosis (“TB”); neonatal encephalopathy (“N Enceph”); neonatal sepsis (“N Sepsis”); road injuries (“Road Inj”); cancer (“CA”); and chronic kidney disease (“CKD”).

70 Bates M, O’Grady J, Mudenda V, Shibemba A, Zumla A. New global estimates of malaria deaths.

  • Global Enterics Mutli-Center Study (GEMS): University of Maryland School of Medicine.
  • Differences in the etiology of communityacquired pneumonia according to site of care: A population-based study.
  • Respiratory syncytial virus infection in elderly and high-risk adults.
  • Lancet 2011; 377: 1438–47. 92 Beaglehole R, Yach D. Globalisation and the prevention and control of non-communicable disease: the neglected chronic diseases of adults.
  • Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets.

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1
Global and regional mortality from 235 causes of death for 20 age-
groups in 1990 and 2010: A systematic analysis.
Rafael Lozano, Mohsen Naghavi, Kyle Foreman, Stephen Lim, Kenji Shibuya, Victor Aboyans*, Tim
Adair*, Rakesh Aggarwal*, Stephanie Ahn*, Miriam Alvarado*, Kathryn Andrews*, H. Ross Anderson*,
Charles Atkinson*, Derrick Bennett*, Robert J. Berry*, Kavi Bhalla*, Boris Bikbov*, Ian Bolliger*, Chiara
Bucello*, Christine Budke*, Peter Burney*, Charles Canter*, Jonathan Carapetis*, Honglei Chen*, David
Chou*, Salim Chowdhury*, Sumeet Chugh*, Luc Coffeng*, Samantha Colquhoun*, Katherine Colson*,
Leslie Cooper*, Matthew Corriere*, Monica Cotrinovis*, Karen Courville de Vaccaro*, William Couser*,
Benjamin Cowie*, Michael Criqui*, Kaustubh Dabhadkar*, Nabila Dahodwala*, Diego De Leo*, Louisa
Degenhardt*, Allyne Delossantos*, Herbert Duber*, Don Des Jarlais*, E. Ray Dorsey*, Patricia
Espindola*, Patricia Erwin*, Majid Ezzati*, Abraham D. Flaxman*, Mohammad H. Forouzanfar*, Richard
Franklin*, Michael K. Freeman*, Emmanuela Gakidou*, Flavio Gaspari*, Diego Gonzalez-Medina*, Yara
Halasa*, Diana Haring*, James Harrison*, Rasmus Havmoeller*, Roderick Hay*, Peter Hotez*, Damian
Hoy*, Kathryn Jacosben*, Spencer L. James*, Rashmi Jasrasaria*, Rachel Jenkins*, Nicole Johns*,
Ganesan Karthikeyan*, Nicholas Kassebaum*, Andre Keren*, Rita Krishnamurthi*, Steven Lipshultz*,
Michael F. MacIntyre*, Leslie Mallinger*, Lyn March*, Guy Marks*, Robin Marks*, Akira Matsumori*,
Richard Matzopoulos*, Bongani Mayosi*, Mary McDermott*, John McGrath*, George Mensah*, Tony
Merriman*, Catherine Michaud*, Matthew Miller*, Ali A. Mokdad*, Andrew Moran*, Luigi Naldi*, K.M.
Venkat Narayan*, Kiumarss Nasseri*, Paul Norman*, Summer Lockett Ohno*, Saad B. Omer*, Katrina
Ortblad*, Richard Osborne*, Doruk Ozgediz*, Bishnu Pahari*, Andrea Panozo Rivero*, Rogelio Perez
Padilla*, Fernando Perez-Ruiz*, Norberto Perico*, David Phillips*, Kelsey Pierce*, C. Arden Pope III*,
Esteban Porrini*, Murugesan Raju*, Dharani Ranganathan*, Juergen Rehm*, David Rein*, Guiseppe
Remuzzi*, Fred Rivara*, Thomas Roberts*, Felipe Rodriguez De Leòn*, Lisa Rosenfeld*, Joshua
Salomon*, Uchechukwu Sampson*, Ella Sanman*, David Schwebel*, Donald Shepard*, Jessica
Singleton*, Andrew Steer*, Bernadette Thomas*, Imad Tleyjeh*, Thomas Truelsen*, Jaakko
Tuomilehto*, Eduardo Undurraga*, Lakshmi Vijayakumar*, Theo Vos*, Gregory Wagner*, Mengru
Wang*, Kerrianne Watt*, Martin Weinstock*, Robert Weintraub*, James Wilkinson*, Anthony Woolf*,
Sarah Wulf*, Paul Yip*, Azadeh Zabtian*, Alan D. Lopez†, Christopher JL Murray†
,§
*Listed alphabetically
†Senior authors
§ Corresponding author
2
Abstract
Background
Reliable and timely information on the leading causes of death in populations, and how these are
changing, is a critical input into health policy debates. We aimed to estimate annual deaths for the world
and 21 regions over the period 1980-2010 for 235 causes, with uncertainty intervals, separately by age
and sex.
Methods
We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from
vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and
mortuaries. Data quality was assessed for completeness, diagnostic accuracy, missing data, stochastic
variations and probable causes of death. We applied six different modeling strategies to estimate cause-
specific mortality trends depending on the strength of the data. For 133 causes and three special
aggregates we utilized the Cause of Death Ensemble model (CODEm) approach, which utilizes four
families of statistical models testing a large set of different models using different permutations of
covariates. Model ensembles were developed from these component models. Model performance was
assessed using rigorous out-of-sample testing of prediction error and the validity of 95% uncertainty
intervals. For nine causes with low observed numbers of deaths, we developed negative binomial
models with plausible covariates. For 28 causes where death is rare, we modeled the higher level cause
in the GBD cause hierarchy and then allocated deaths across component causes proportionately,
estimated from all available data in the database. For selected causes (African trypanosomiasis,
congenital syphilis, whooping cough, measles and HIV) we used natural history models based on
information on incidence, prevalence, and case-fatality. We separately estimated etiological fractions
for diarrhea, lower respiratory infections, meningitis, chronic kidney disease, maternal conditions,
cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality
shock regressions. For every cause, we estimated 95% uncertainty intervals that capture both
parameter estimation uncertainty and uncertainty due to model specification where CODEm has been
used. We constrained cause-specific fractions within each age-sex group to sum to total mortality
based on draws from the uncertainty distributions.
Findings
In 2010 there were a total of 52·8 million deaths globally. At the most aggregate level, communicable,
maternal, neonatal, and nutritional causes (CMNN) were 24·8% of deaths worldwide in 2010, down from
34% in 1990. This was largely due to declines in mortality from diarrheal disease (2·5 to 1·4 million),
lower respiratory infections (3·4 to 2·8 million), neonatal conditions (3·1 to 2·2 million), measles (0·63 to
0·13 million), and tetanus (0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in
1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality has also risen by an
estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in
2010. Deaths from noncommunicable diseases (NCDs) rose by just under eight million deaths between
1990 and 2010, accounting for two out of every three deaths (34·5 million) worldwide by 2010. Eight
million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million
(19%) were from trachea, bronchus, and lung cancer. Ischemic heart disease (IHD) and stroke
3
collectively killed 12.9 million people in 2010, or one in four deaths worldwide, compared with one in
five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global
deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two
decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents
(1·3 million in 2010) and a rise in deaths from falls. IHD, stroke, chronic obstructive pulmonary disease
(COPD), lower respiratory infections (LRI), lung cancer, and HIV/AIDS were the leading causes of death in
2010. IHD, LRI, stroke, diarrheal disease, malaria, and HIV/AIDS being the leading causes of years of life
lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990 (HIV/AIDS added,
preterm conditions dropped). YLLs from LRI and diarrhea have declined by 45-54% since 1990; IHD and
stroke YLLs increased by 17-28%. Regional variations in leading causes of death are
substantial. Communicable, maternal, and neonatal causes still accounted for 50% of premature
mortality in sub-Saharan Africa in 2010. Age standardized death rates from some key condition have
risen (HIV/AIDS, Alzheimer’s disease, diabetes mellitus, and chronic kidney disease in particular), but for
the vast majority of diseases, death rates have fallen over the past two decades; including major
vascular diseases, COPD, all forms of cancer, liver cirrhosis, and maternal conditions. For others, notably
malaria, prostate cancer, and injuries, there has been little change.
Interpretation
Population growth, increased average age of the world’s population, and largely declining age, sex, and
cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal and
nutritional causes towards noncommunicable diseases. Nevertheless, communicable, maternal,
neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on
this general pattern of the epidemiological transition there is marked regional variation in many causes
including interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African
trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound
epidemiological assessments of the causes of death on a regular basis.
Introduction
Cause-specific mortality is arguably one of the most fundamental metrics of population health. The rates
and numbers of people who die, where, at what age, and from what, is a critical input into policy
debates and planning current interventions, as well as prioritizing research for new health technologies.
Trends in causes of death provide an important geographical summary of whether society is or is not
making progress in reducing the burden of premature (and especially avoidable) mortality and where
renewed efforts are required. If a health information system is not providing timely and accurate
information on causes of death by age and sex, major reforms are required to provide health planners
with this essential health intelligence.
Despite the importance of tracking causes of death and the tradition since 1893 of standardizing
definitions and coding for causes of death in the International Classification of Diseases and Injuries
4
(ICD), global assessments of causes of death are a major analytical challenge. Vital registration systems
that include medical certification of the cause of death captured approximately 18·8 million deaths out
of an as estimated annual total of 51·7 million deaths in 2005, which is the latest year for which the
largest number of countries reported deaths by a vital registration system. Even for these deaths, the
comparability of findings on the leading causes of death is affected by variation in certification skills
among physicians, the diagnostic and pathological data available at the time of completing a death
certificate, variations in medical culture in choosing the underlying cause, and legal and institutional
frameworks for governing mortality reporting.
1–5
For the remaining deaths which are not medically
certified, many different data sources and diagnostic approaches must be used from surveillance
systems, demographic research sites, surveys, censuses, disease registries, and police records to
construct a consolidated picture of causes of death in various populations. Because of the variety of data
sources and their associated biases, cause of death assessments are inherently uncertain and subject to
vigorous debate.
6–8
Efforts to develop global assessments for selected causes began in the 1980s.
9–11
These efforts were
motivated in part because the sum of various disease-specific estimates substantially exceeded the
estimated number of deaths in the world, particularly for children.
12
Lopez and Hull
11
attempted to
develop a set of estimates of under-five mortality by cause consistent with all-cause mortality data in
1983. The GBD 1990 was the first comprehensive attempt to do so, and included 134 causes covering all
age groups. The GBD 1990 cause of death approach has been applied with some refinements to yield
estimates of causes of death for 1999, 2000, 2001, 2002, 2004, and 2008.
13–17
Over this period, special
attention has been paid to priority diseases such as malaria, HIV/AIDS, and tuberculosis. The Child
Health Epidemiology Reference Group (CHERG) has also produced estimates of under-five mortality
from 16 causes that sum to estimates of under-five deaths for 2000-2003, 2008, and 2010
18–20
partially
using the GBD 1990 approach combined with other methods, and putting special emphasis on the use of
verbal autopsy as a source of data in low-income settings. In addition to these comprehensive
approaches, the tradition of disease-specific analyses that began in the 1980s with global cancer
mortality has continued and intensified. In the last five years, for example, papers and reports have
been published on global mortality from maternal causes,
21–24
malaria,
25,26
tuberculosis,
27,28
HIV/AIDS,
29
road traffic accidents,
30
site-specific cancers,
31,32
and diabetes,
33
among others.
34,35
These assessments of
individual causes are based on diverse epidemiological approaches of varying scientific rigor, and
moreover are not constrained to sum to estimates of all-cause mortality from demographic sources.
Global cause of death assessments can be characterized in four dimensions: the universe of raw data
identified and examined, efforts to evaluate and enhance quality and comparability of data, the
statistical modeling strategy, and whether causes of death are constrained to sum to all-cause mortality.
In terms of the universe of data, the various iterations of the GBD and CHERG analysis of under-five
deaths have made substantial use of data on causes of death from systems that attempt to capture the
event of death. Other single-cause analyses, such as the annual UNAIDS efforts to estimate HIV related
deaths, measles estimates,
34
the World Malaria Report,
26
the WHO Global TB Control Report,
28
and
many others have used data on disease incidence or prevalence and data on case-fatality rates
combined in a natural history progression model. Perhaps the area of greatest variation in the published
5
studies is the efforts to assess and enhance the quality and comparability of available data. These efforts
often include very specific steps undertaken for different data sources and are frequently poorly
documented. Third, over the last two decades, efforts to develop statistical models for causes of death
have become more sophisticated. Compositional models that estimate cause fractions for several causes
at once were first applied to global health by Salomon and Murray
36
and have been used extensively by
CHERG but only for a subset of causes. GBD revisions for 1999, 2000, 2001, 2002, 2004, and 2008 have
used these compositional models to allocate deaths according to three broad cause groups:
communicable, maternal, neonatal, and nutritional causes; noncommunicable diseases; and injuries.
More recently, the array of modeling strategies used for causes of death has been broadened to include
spatial-temporal Gaussian process regression,
22,37
mixed effects hierarchical models, and ensemble
models.
38
Given the profusion of statistical modeling options, an important innovation has been the
reporting of out-of-sample predictive validity to document the performance of complex models.
22,38
Given the developments in the field of mortality and cause of death estimation, for the GBD 2010 we
have completely re-evaluated all aspects of the GBD analytical strategy, including demographic
estimation of all-cause mortality.
39,40
Because of the huge increase in published verbal autopsy studies
and the availability in the public domain of cause of death data from government vital registration
sources (130 countries), the universe of data has expanded substantially. Assessing and enhancing the
quality and comparability of data can now take into account time trends in cause of death data from
1980 to 2010 that provide important insights into changes in certification and coding. Borrowing from
other scientific fields, we have changed our analytical approach (see below) to an ensemble modeling
strategy in order to generate more realistic uncertainty intervals and more accurate predictions.
38
These
innovations have been used in estimating mortality for an expanded GBD cause list of 291 causes
compared with 134 in the GBD 1990 Study; of the 291 causes, 235 are causes of mortality, while the
remaining causes account for years lived with disability (YLDs) but not deaths. We use a unified
framework for all causes such that the sum of cause-specific estimates equals the number of deaths
from all causes in each country or region, period, age group, and sex. This creates a link between the
systematic analysis of data on all-cause mortality reported by Wang et al
40
and results by cause
presented here. In this paper, we provide a summary overview of the vast array of data and methods
that have gone into this revision of the GBD, as well as what we believe are the key global and regional
findings of importance for health priority debates.
Data and methods
Some general aspects of the analytical framework such as the creation of the 21 GBD regions and the
full hierarchical cause list including the mapping of the International Classification of Diseases and
Injuries (ICD) to the GBD cause list are reported elsewhere.
39
While results are reported in this paper at
the regional level for 1990 and 2010, the cause of death analysis has been undertaken at the country
level for 187 countries from 1980 to 2010. Using longer time series improves the performance of many
types of estimation models; data prior to 1980, however, are much sparser for developing countries so
we have restricted the analysis to 1980-2010.
Citations
More filters
Journal Article•DOI•
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

[...]

Stephen S Lim1, Theo Vos, Abraham D. Flaxman1, Goodarz Danaei2  +207 more•Institutions (92)
15 Dec 2012-The Lancet
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.
Abstract: Methods We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent eff ects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. W e estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specifi c deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. Findings In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2–7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5–7·0]), and alcohol use (5·5% [5·0–5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8–9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6–8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4–6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2–10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily aff ect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient defi ciencies, fell in rank between 1990 and 2010, with unimproved water

8,301 citations

Journal Article•DOI•
Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013

[...]

Marie Ng1, Tom P Fleming1, Margaret Robinson1, Blake Thomson1, Nicholas Graetz1, Christopher Margono1, Erin C Mullany1, Stan Biryukov1, Cristiana Abbafati2, Semaw Ferede Abera3, Jerry Abraham4, Niveen M E Abu-Rmeileh, Tom Achoki1, Fadia AlBuhairan5, Zewdie Aderaw Alemu6, Rafael Alfonso1, Mohammed K. Ali7, Raghib Ali8, Nelson Alvis Guzmán9, Walid Ammar, Palwasha Anwari10, Amitava Banerjee11, Simón Barquera, Sanjay Basu12, Derrick A Bennett8, Zulfiqar A Bhutta13, Jed D. Blore14, N Cabral, Ismael Ricardo Campos Nonato, Jung-Chen Chang15, Rajiv Chowdhury16, Karen J. Courville, Michael H. Criqui17, David K. Cundiff, Kaustubh Dabhadkar7, Lalit Dandona18, Lalit Dandona1, Adrian Davis19, Anand Dayama7, Samath D Dharmaratne20, Eric L. Ding21, Adnan M. Durrani22, Alireza Esteghamati23, Farshad Farzadfar23, Derek F J Fay19, Valery L. Feigin24, Abraham D. Flaxman1, Mohammad H. Forouzanfar1, Atsushi Goto, Mark A. Green25, Rajeev Gupta, Nima Hafezi-Nejad23, Graeme J. Hankey26, Heather Harewood, Rasmus Havmoeller27, Simon I. Hay8, Lucia Hernandez, Abdullatif Husseini28, Bulat Idrisov29, Nayu Ikeda, Farhad Islami30, Eiman Jahangir31, Simerjot K. Jassal17, Sun Ha Jee32, Mona Jeffreys33, Jost B. Jonas34, Edmond K. Kabagambe35, Shams Eldin Ali Hassan Khalifa, Andre Pascal Kengne36, Yousef Khader37, Young-Ho Khang38, Daniel Kim39, Ruth W Kimokoti40, Jonas Minet Kinge41, Yoshihiro Kokubo, Soewarta Kosen, Gene F. Kwan42, Taavi Lai, Mall Leinsalu22, Yichong Li, Xiaofeng Liang43, Shiwei Liu43, Giancarlo Logroscino44, Paulo A. Lotufo45, Yuan Qiang Lu21, Jixiang Ma43, Nana Kwaku Mainoo, George A. Mensah22, Tony R. Merriman46, Ali H. Mokdad1, Joanna Moschandreas47, Mohsen Naghavi1, Aliya Naheed48, Devina Nand, K.M. Venkat Narayan7, Erica Leigh Nelson1, Marian L. Neuhouser49, Muhammad Imran Nisar13, Takayoshi Ohkubo50, Samuel Oti, Andrea Pedroza, Dorairaj Prabhakaran, Nobhojit Roy51, Uchechukwu K.A. Sampson35, Hyeyoung Seo, Sadaf G. Sepanlou23, Kenji Shibuya52, Rahman Shiri53, Ivy Shiue54, Gitanjali M Singh21, Jasvinder A. Singh55, Vegard Skirbekk41, Nicolas J. C. Stapelberg56, Lela Sturua57, Bryan L. Sykes58, Martin Tobias1, Bach Xuan Tran59, Leonardo Trasande60, Hideaki Toyoshima, Steven van de Vijver, Tommi Vasankari, J. Lennert Veerman61, Gustavo Velasquez-Melendez62, Vasiliy Victorovich Vlassov63, Stein Emil Vollset41, Stein Emil Vollset64, Theo Vos1, Claire L. Wang65, Xiao Rong Wang66, Elisabete Weiderpass, Andrea Werdecker, Jonathan L. Wright1, Y Claire Yang67, Hiroshi Yatsuya68, Jihyun Yoon, Seok Jun Yoon69, Yong Zhao70, Maigeng Zhou, Shankuan Zhu71, Alan D. Lopez14, Christopher J L Murray1, Emmanuela Gakidou1 •
University of Washington1, Sapienza University of Rome2, Mekelle University3, University of Texas at San Antonio4, King Saud bin Abdulaziz University for Health Sciences5, Debre markos University6, Emory University7, University of Oxford8, University of Cartagena9, United Nations Population Fund10, University of Birmingham11, Stanford University12, Aga Khan University13, University of Melbourne14, National Taiwan University15, University of Cambridge16, University of California, San Diego17, Public Health Foundation of India18, Public Health England19, University of Peradeniya20, Harvard University21, National Institutes of Health22, Tehran University of Medical Sciences23, Auckland University of Technology24, University of Sheffield25, University of Western Australia26, Karolinska Institutet27, Birzeit University28, Brandeis University29, American Cancer Society30, Ochsner Medical Center31, Yonsei University32, University of Bristol33, Heidelberg University34, Vanderbilt University35, South African Medical Research Council36, Jordan University of Science and Technology37, New Generation University College38, Northeastern University39, Simmons College40, Norwegian Institute of Public Health41, Boston University42, Chinese Center for Disease Control and Prevention43, University of Bari44, University of São Paulo45, University of Otago46, University of Crete47, International Centre for Diarrhoeal Disease Research, Bangladesh48, Fred Hutchinson Cancer Research Center49, Teikyo University50, Bhabha Atomic Research Centre51, University of Tokyo52, Finnish Institute of Occupational Health53, Heriot-Watt University54, University of Alabama at Birmingham55, Griffith University56, National Center for Disease Control and Public Health57, University of California, Irvine58, Johns Hopkins University59, New York University60, University of Queensland61, Universidade Federal de Minas Gerais62, National Research University – Higher School of Economics63, University of Bergen64, Columbia University65, Shandong University66, University of North Carolina at Chapel Hill67, Fujita Health University68, Korea University69, Chongqing Medical University70, Zhejiang University71
30 Aug 2014-The Lancet
TL;DR: The global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013 is estimated using a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs).
Abstract: Summary Background In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013. Methods We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19 244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Findings Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m 2 or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4–29·3) to 36·9% (36·3–37·4) in men, and from 29·8% (29·3–30·2) to 38·0% (37·5–38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9–24·7) of boys and 22·6% (21·7–23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7–8·6) to 12·9% (12·3–13·5) in 2013 for boys and from 8·4% (8·1–8·8) to 13·4% (13·0–13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Interpretation Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Funding Bill & Melinda Gates Foundation.

7,968 citations

Journal Article•
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010 (vol 380, pg 2197, 2012)

[...]

Christopher J L Murray, Theo Vos, Rafael Lozano, Mohammad A. AlMazroa, Ziad A. Memish 
01 Jan 2013-The Lancet
TL;DR: In this article, a comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study, and the authors aimed to calculate disease burden globally and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time.
Abstract: BACKGROUND Measuring disease and injury burden in populations requires a composite metric that captures both premature mortality and the prevalence and severity of ill-health. The 1990 Global Burden of Disease study proposed disability-adjusted life years (DALYs) to measure disease burden. No comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study. We aimed to calculate disease burden worldwide and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time. METHODS We calculated DALYs as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were calculated for 291 causes, 20 age groups, both sexes, and for 187 countries, and aggregated to regional and global estimates of disease burden for three points in time with strictly comparable definitions and methods. YLLs were calculated from age-sex-country-time-specific estimates of mortality by cause, with death by standardised lost life expectancy at each age. YLDs were calculated as prevalence of 1160 disabling sequelae, by age, sex, and cause, and weighted by new disability weights for each health state. Neither YLLs nor YLDs were age-weighted or discounted. Uncertainty around cause-specific DALYs was calculated incorporating uncertainty in levels of all-cause mortality, cause-specific mortality, prevalence, and disability weights. FINDINGS Global DALYs remained stable from 1990 (2·503 billion) to 2010 (2·490 billion). Crude DALYs per 1000 decreased by 23% (472 per 1000 to 361 per 1000). An important shift has occurred in DALY composition with the contribution of deaths and disability among children (younger than 5 years of age) declining from 41% of global DALYs in 1990 to 25% in 2010. YLLs typically account for about half of disease burden in more developed regions (high-income Asia Pacific, western Europe, high-income North America, and Australasia), rising to over 80% of DALYs in sub-Saharan Africa. In 1990, 47% of DALYs worldwide were from communicable, maternal, neonatal, and nutritional disorders, 43% from non-communicable diseases, and 10% from injuries. By 2010, this had shifted to 35%, 54%, and 11%, respectively. Ischaemic heart disease was the leading cause of DALYs worldwide in 2010 (up from fourth rank in 1990, increasing by 29%), followed by lower respiratory infections (top rank in 1990; 44% decline in DALYs), stroke (fifth in 1990; 19% increase), diarrhoeal diseases (second in 1990; 51% decrease), and HIV/AIDS (33rd in 1990; 351% increase). Major depressive disorder increased from 15th to 11th rank (37% increase) and road injury from 12th to 10th rank (34% increase). Substantial heterogeneity exists in rankings of leading causes of disease burden among regions. INTERPRETATION Global disease burden has continued to shift away from communicable to non-communicable diseases and from premature death to years lived with disability. In sub-Saharan Africa, however, many communicable, maternal, neonatal, and nutritional disorders remain the dominant causes of disease burden. The rising burden from mental and behavioural disorders, musculoskeletal disorders, and diabetes will impose new challenges on health systems. Regional heterogeneity highlights the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account. Because of improved definitions, methods, and data, these results for 1990 and 2010 supersede all previously published Global Burden of Disease results. FUNDING Bill & Melinda Gates Foundation.

6,449 citations

Journal Article•DOI•
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

[...]

Christopher J L Murray1, Theo Vos2, Rafael Lozano1, Mohsen Naghavi1  +366 more•Institutions (141)
15 Dec 2012-The Lancet
TL;DR: The results for 1990 and 2010 supersede all previously published Global Burden of Disease results and highlight the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account.
Abstract: Summary Background Measuring disease and injury burden in populations requires a composite metric that captures both premature mortality and the prevalence and severity of ill-health. The 1990 Global Burden of Disease study proposed disability-adjusted life years (DALYs) to measure disease burden. No comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study. We aimed to calculate disease burden worldwide and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time. Methods We calculated DALYs as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were calculated for 291 causes, 20 age groups, both sexes, and for 187 countries, and aggregated to regional and global estimates of disease burden for three points in time with strictly comparable definitions and methods. YLLs were calculated from age-sex-country-time-specific estimates of mortality by cause, with death by standardised lost life expectancy at each age. YLDs were calculated as prevalence of 1160 disabling sequelae, by age, sex, and cause, and weighted by new disability weights for each health state. Neither YLLs nor YLDs were age-weighted or discounted. Uncertainty around cause-specific DALYs was calculated incorporating uncertainty in levels of all-cause mortality, cause-specific mortality, prevalence, and disability weights. Findings Global DALYs remained stable from 1990 (2·503 billion) to 2010 (2·490 billion). Crude DALYs per 1000 decreased by 23% (472 per 1000 to 361 per 1000). An important shift has occurred in DALY composition with the contribution of deaths and disability among children (younger than 5 years of age) declining from 41% of global DALYs in 1990 to 25% in 2010. YLLs typically account for about half of disease burden in more developed regions (high-income Asia Pacific, western Europe, high-income North America, and Australasia), rising to over 80% of DALYs in sub-Saharan Africa. In 1990, 47% of DALYs worldwide were from communicable, maternal, neonatal, and nutritional disorders, 43% from non-communicable diseases, and 10% from injuries. By 2010, this had shifted to 35%, 54%, and 11%, respectively. Ischaemic heart disease was the leading cause of DALYs worldwide in 2010 (up from fourth rank in 1990, increasing by 29%), followed by lower respiratory infections (top rank in 1990; 44% decline in DALYs), stroke (fifth in 1990; 19% increase), diarrhoeal diseases (second in 1990; 51% decrease), and HIV/AIDS (33rd in 1990; 351% increase). Major depressive disorder increased from 15th to 11th rank (37% increase) and road injury from 12th to 10th rank (34% increase). Substantial heterogeneity exists in rankings of leading causes of disease burden among regions. Interpretation Global disease burden has continued to shift away from communicable to non-communicable diseases and from premature death to years lived with disability. In sub-Saharan Africa, however, many communicable, maternal, neonatal, and nutritional disorders remain the dominant causes of disease burden. The rising burden from mental and behavioural disorders, musculoskeletal disorders, and diabetes will impose new challenges on health systems. Regional heterogeneity highlights the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account. Because of improved definitions, methods, and data, these results for 1990 and 2010 supersede all previously published Global Burden of Disease results. Funding Bill & Melinda Gates Foundation.

6,252 citations

Journal Article•DOI•
Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

[...]

Emelia J. Benjamin, Michael J. Blaha, Stephanie E. Chiuve, Mary Cushman, Sandeep R Das, Rajat Deo, Sarah D. de Ferranti, James S. Floyd, Myriam Fornage, Cathleen Gillespie, Carmen R. Isasi, Monik C. Jim'nez, Lori Chaffn Jordan, Suzanne E. Judd, Daniel T. Lackland, Judith H. Lichtman, Lynda D. Lisabeth1, Simin Liu, Chris T. Longenecker, Rachel H. Mackey, Kunihiro Matsushita2, Dariush Mozaffarian, Michael E. Mussolino, Khurram Nasir, Robert W. Neumar1, Latha Palaniappan, Dilip K. Pandey, Ravi R. Thiagarajan, Mathew J. Reeves, Matthew D. Ritchey, Carlos J. Rodriguez, Gregory A. Roth, Wayne D. Rosamond, Comilla Sasson, Amytis Towfghi, Connie W. Tsao, Melanie B. Turner, Salim S. Virani, Jenifer H. Voeks, Joshua Z. Willey, John T. Wilkins, Jason H Y Wu, Heather M. Alger, Sally S. Wong, Paul Muntner •
University of Michigan1, Johns Hopkins University2
07 Mar 2017-Circulation
TL;DR: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Jiménez, ScD, SM Lori Chaffin Jordan,MD, PhD Suzanne E. Judd, PhD
Abstract: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

6,097 citations

References
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Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008.

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Jacques Ferlay1, Hai-Rim Shin1, Freddie Bray1, David Forman1, Colin Mathers2, Donald Maxwell Parkin3 •
International Agency for Research on Cancer1, World Health Organization2, University of Oxford3
15 Dec 2010-International Journal of Cancer
TL;DR: The results for 20 world regions are presented, summarizing the global patterns for the eight most common cancers, and striking differences in the patterns of cancer from region to region are observed.
Abstract: Estimates of the worldwide incidence and mortality from 27 cancers in 2008 have been prepared for 182 countries as part of the GLOBOCAN series published by the International Agency for Research on Cancer. In this article, we present the results for 20 world regions, summarizing the global patterns for the eight most common cancers. Overall, an estimated 12.7 million new cancer cases and 7.6 million cancer deaths occur in 2008, with 56% of new cancer cases and 63% of the cancer deaths occurring in the less developed regions of the world. The most commonly diagnosed cancers worldwide are lung (1.61 million, 12.7% of the total), breast (1.38 million, 10.9%) and colorectal cancers (1.23 million, 9.7%). The most common causes of cancer death are lung cancer (1.38 million, 18.2% of the total), stomach cancer (738,000 deaths, 9.7%) and liver cancer (696,000 deaths, 9.2%). Cancer is neither rare anywhere in the world, nor mainly confined to high-resource countries. Striking differences in the patterns of cancer from region to region are observed.

20,379 citations

The Global Burden of Disease Study

[...]

Christopher J L Murray, Alan D. Lopez
01 Jan 2003

11,134 citations

Journal Article•DOI•
Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization

[...]

Alan S. Go1, Glenn M. Chertow, Dongjie Fan, Charles E. McCulloch, Chi-yuan Hsu •
Kaiser Permanente1
23 Sep 2004-The New England Journal of Medicine
TL;DR: The longitudinal glomerular filtration rate was estimated among 1,120,295 adults within a large, integrated system of health care delivery in whom serum creatinine had been measured between 1996 and 2000 and who had not undergone dialysis or kidney transplantation.
Abstract: Background End-stage renal disease substantially increases the risks of death, cardiovascular disease, and use of specialized health care, but the effects of less severe kidney dysfunction on these outcomes are less well defined. Methods We estimated the longitudinal glomerular filtration rate (GFR) among 1,120,295 adults within a large, integrated system of health care delivery in whom serum creatinine had been measured between 1996 and 2000 and who had not undergone dialysis or kidney transplantation. We examined the multivariable association between the estimated GFR and the risks of death, cardiovascular events, and hospitalization. Results The median follow-up was 2.84 years, the mean age was 52 years, and 55 percent of the group were women. After adjustment, the risk of death increased as the GFR decreased below 60 ml per minute per 1.73 m2 of body-surface area: the adjusted hazard ratio for death was 1.2 with an estimated GFR of 45 to 59 ml per minute per 1.73 m2 (95 percent confidence interval, 1....

8,832 citations

Journal Article•DOI•
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

[...]

Stephen S Lim1, Theo Vos, Abraham D. Flaxman1, Goodarz Danaei2  +207 more•Institutions (92)
15 Dec 2012-The Lancet
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.
Abstract: Methods We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent eff ects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. W e estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specifi c deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. Findings In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2–7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5–7·0]), and alcohol use (5·5% [5·0–5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8–9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6–8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4–6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2–10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily aff ect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient defi ciencies, fell in rank between 1990 and 2010, with unimproved water

8,301 citations

Book•
The global burden of disease: a comprehensive assessment of mortality and disability from diseases injuries and risk factors in 1990 and projected to 2020.

[...]

Christopher J L Murray, Alan D. Lopez
01 Jan 1996
TL;DR: This is the first in a planned series of 10 volumes that will attempt to "summarize epidemiological knowledge about all major conditions and most risk factors" and use historical trends in main determinants to project mortality and disease burden forward to 2020.
Abstract: This is the first in a planned series of 10 volumes that will attempt to "summarize epidemiological knowledge about all major conditions and most risk factors;...generate assessments of numbers of deaths by cause that are consistent with the total numbers of deaths by age sex and region provided by demographers;...provide methodologies for and assessments of aggregate disease burden that combine--into the Disability-Adjusted Life Year or DALY measure--burden from premature mortality with that from living with disability; and...use historical trends in main determinants to project mortality and disease burden forward to 2020." This first volume includes chapters summarizing results from the project as a whole. (EXCERPT)

7,084 citations

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Frequently Asked Questions (15)
Q1. What have the authors contributed in "Global and regional mortality from 235 causes of death for 20 age- groups in 1990 and 2010: a systematic analysis" ?

In this paper, the authors proposed a method to provide timely and accurate information on causes of death by age and sex. 

Improved estimation of mortality from HIV/AIDS including uncertainty in the future will come both from continued progress in the estimation of the time course of the HIV epidemic by UNAIDS as well as further data on the levels of adult mortality in some key countries such as Nigeria. These are important both for the prioritization of existing treatments, such as rotavirus or pneumococcal vaccines, but also for the development of future technologies. When large multi-center studies such as GEMS publish their results this will be an important addition to the analysis ; future revisions of the GBD should make use of these results as they become available. The authors believe that for causes where the magnitude of these corrections is comparatively large, future research should be targeted to trying to build a better understanding of the strengths and weaknesses of the various data sources, whether epidemiological or demographic. 

Because of known bias in the epidemiological composition of burial and mortuary data, the authors only use information on the fraction of injuries due to specific sub-causes from these sources. 

Much could be learned about causes of death in countries where death certification is poor through the more widespread testing and application of recent advances in verbal autopsy methods which greatly reduce heterogeneity in diagnostic practices across populations where VA is currently used. 

By the post-neonatal period, causes of death are dominated by diarrhea, LRI, and other infectious diseases such as measles, among others. 

Because of the variety of data sources and their associated biases, cause of death assessments are inherently uncertain and subject to vigorous debate. 

The ambition to estimate mortality from 235 causes with uncertainty for 187 countries over time from 1980 to 2010 means that many choices about data sources, quality adjustments to data and modeling strategies had to be made. 

Although the authors report more disaggregated causes, because of considerations related to public health programs, the authors have chosen to include diarrheal diseases, lower respiratory infections, maternal causes, cerebrovascular disease, liver cancer, cirrhosis, drug use, road injury, exposure to mechanical forces, animal contact, homicide, and congenital causes in the ranking list. 

In addition, four families of statistical models are developed using covariates: mixed effects linear models of the log of the death rate, mixed effects linear models of the logit of the cause fraction, spatial-temporal Gaussian process regression (ST-GPR) models of the log of the death rate, and ST-GPR of the logit of the cause fraction. 

60–62The relationships between under-five mortality and adult mortality and the disaster and collective violence covariates are estimated using 43 empirical observations for disasters and 206 empirical observations for collective violence (only years with over 1 per 10,000 crude death rate from shocks are kept in this analysis). 

For 13 causes, the number of deaths observed in the database is too low to generate stable estimates of out-of-sample predictive validity. 

Opportunities for strengthening death registration, cause of death certification, and the more widespread use of verbal autopsy exist. 

The coefficients from these regressions and the disaster and collective violence covariates are used to predict excess deaths from these two causes. 

There are reasons, however, to also be concerned that deaths recorded in systems with low coverage may be biased towards selected causes that are more likely to occur in hospital. 

Although at the draw level the same scalar is applied to all causes, the net effect of CoDCorrect is to change the size of more uncertain causes by more than is done for more certain causes, a desirable property.