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Background: Non-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015. Methods: We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores. Findings: We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4–19·2 billion) and diarrhoeal diseases (2·39 billion, 2·30–2·50 billion). Eight causes of chronic disease and injury each affected more than 10% of the world's population in 2015: permanent caries, tension-type headache, iron-deficiency anaemia, age-related and other hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that affected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35–2·37 billion) individuals affected. The second and third leading impairments by number of individuals affected were hearing loss and vision loss, respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause included in our analysis. For low SDI geographies, Group 1 causes typically accounted for 20–30% of total disability, largely attributable to nutritional deficiencies, malaria, neglected tropical diseases, HIV/AIDS, and tuberculosis. Lower back and neck pain was the leading global cause of disability in 2015 in most countries. The leading cause was sense organ disorders in 22 countries in Asia and Africa and one in central Latin America; diabetes in four countries in Oceania; HIV/AIDS in three southern sub-Saharan African countries; collective violence and legal intervention in two north African and Middle Eastern countries; iron-deficiency anaemia in Somalia and Venezuela; depression in Uganda; onchoceriasis in Liberia; and other neglected tropical diseases in the Democratic Republic of the Congo. Interpretation: Ageing of the world's population is increasing the number of people living with sequelae of diseases and injuries. Shifts in the epidemiological profile driven by socioeconomic change also contribute to the continued increase in years lived with disability (YLDs) as well as the rate of increase in YLDs. Despite limitations imposed by gaps in data availability and the variable quality of the data available, the standardised and comprehensive approach of the GBD study provides opportunities to examine broad trends, compare those trends between countries or subnational geographies, benchmark against locations at similar stages of development, and gauge the strength or weakness of the estimates available.
Global annualised rate of change in age-standardised years of life lost (YLLs) and years lived with disability (YLDs) for Level 3 causes between 1990 and 2015 TB=tuberculosis. HIV=HIV/AIDS. Diarrhoea=diarrhoeal diseases. Intest Inf=intestinal infectious diseases. LRI=lower respiratory infections. URI=upper respiratory infections. Otitis=otitis media. Whooping=whooping cough. Varicella=varicella and herpes zoster. Chagas=chagas disease. Cysticer=cysticercosis. LF=lymphatic fi lariasis. Oncho=onchocerciasis. Trachoma=trachoma. Dengue=dengue. Yellow Fev=yellow fever.Nematode=intestinal nematode infections. Mat hem=maternal haemorrhage. Mat sepsis=maternal sepsis and other maternal infections. Mat HTN=maternal hypertensive disorders. Obst labour=maternal obstructed labour and uterine rupture. Comp abort=maternal abortion, miscarriage, and ectopic pregnancy. Oth mat=other maternal disorders. NN preterm=neonatal preterm birth complications. NN enceph=neonatal encephalopathy due to birth asphyxia and trauma. NN sepsis=neonatal sepsis and other neonatal infections. NN haemol=haemolytic disease and other neonatal jaundice. Oth NN=other neonatal disorders. PEM=protein-energy malnutrition. Iodine=iodine defi ciency. Oth nutr=other nutritional defi ciencies. STD=sexually transmitted diseases excluding HIV. Hep=hepatitis. Stomach C=stomach cancer. Melanoma=malignant skin melanoma. Skin C=non-melanoma skin cancer. Cervix C=cervical cancer. Uterus C=uterine cancer. Prostate C=prostate cancer. Testis C=testicular cancer. Kidney C=kidney cancer. Thyroid C=thyroid cancer. Hodgkin=Hodgkin lymphoma. Lymphoma=non-Hodgkin lymphoma. Myeloma=multiple myeloma. Oth neopla=Other neoplasms. RHD=rheumatic heart disease. Stroke=cerebrovascular disease. HTN HD=hypertensive heart disease. PVD=peripheral vascular disease. COPD=chronic obstructive pulmonary disease. Asthma=asthma. ILD=interstitial lung disease and pulmonary sarcoidosis. Oth resp=other chronic respiratory diseases. PUD=peptic ulcer disease. Gastritis=gastritis and duodenitis. Hernia=inguinal, femoral, and abdominal hernia. Oth digest=other digestive diseases. Parkinson=Parkinson's disease. Schiz=schizophrenia. Drugs=drug use disorders. AGN=acute glomerulonephritis. Congenital=congenital anomalies. Skin=skin and subcutaneous diseases. Road inj=road injuries. Oth trans=other transport injuries. Drown=drowning. Fire=fi re, heat, and hot substances. Poison=poisonings. Mech=exposure to mechanical forces. Med treat=adverse eff ects of medical treatment. Animal=animal contact. F body=foreign body. Heat & cold=environmental heat and cold exposure. Oth unint=other unintentional injuries. Violence=interpersonal violence. War=collective violence and legal intervention.
… 
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www.thelancet.com Vol 388 October 8, 2016
1545
Articles
Global, regional, and national incidence, prevalence, and
years lived with disability for 310 diseases and injuries,
1990–2015: a systematic analysis for the Global Burden of
Disease Study 2015
GBD 2015 Disease and Injury Incidence and Prevalence Collaborators*
Background Non-fatal outcomes of disease and injury increasingly detract from the ability of the world’s population to
live in full health, a trend largely attributable to an epidemiological transition in many countries from causes aff ecting
children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases,
Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with
disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015.
Methods We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated
and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources,
updates to literature reviews for 85 causes, and the identifi cation and inclusion of additional studies published up to
November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources.
Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the
DisMod-MR Bayesian meta-regression tool fi rst developed for GBD 2010 and GBD 2013. For some causes, we used
alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence
and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines
measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to
compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores.
Findings We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for
causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess
of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4–19·2 billion) and diarrhoeal
diseases (2·39 billion, 2·30–2·50 billion). Eight causes of chronic disease and injury each aff ected more than 10% of the
world’s population in 2015: permanent caries, tension-type headache, iron-defi ciency anaemia, age-related and other
hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that
aff ected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35–2·37 billion) individuals aff ected.
The second and third leading impairments by number of individuals aff ected were hearing loss and vision loss,
respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on
a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates
were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause
included in our analysis. For low SDI geographies, Group 1 causes typically accounted for 20–30% of total disability,
largely attributable to nutritional defi ciencies, malaria, neglected tropical diseases, HIV/AIDS, and tuberculosis. Lower
back and neck pain was the leading global cause of disability in 2015 in most countries. The leading cause was sense
organ disorders in 22 countries in Asia and Africa and one in central Latin America; diabetes in four countries in
Oceania; HIV/AIDS in three southern sub-Saharan African countries; collective violence and legal intervention in two
north African and Middle Eastern countries; iron-defi ciency anaemia in Somalia and Venezuela; depression in Uganda;
onchoceriasis in Liberia; and other neglected tropical diseases in the Democratic Republic of the Congo.
Interpretation Ageing of the world’s population is increasing the number of people living with sequelae of diseases
and injuries. Shifts in the epidemiological profi le driven by socioeconomic change also contribute to the continued
increase in years lived with disability (YLDs) as well as the rate of increase in YLDs. Despite limitations imposed by
gaps in data availability and the variable quality of the data available, the standardised and comprehensive approach
of the GBD study provides opportunities to examine broad trends, compare those trends between countries or
subnational geographies, benchmark against locations at similar stages of development, and gauge the strength or
weakness of the estimates available.
Funding Bill & Melinda Gates Foundation.
Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license.
Lancet 2016; 388: 1545–602
See Editorial page 1447
See Comment pages 1448
and 1450
*Collaborators listed at the end
of the Article
Correspondence to:
Prof Theo Vos, Institute for
Health Metrics and Evaluation,
Seattle, WA 98121, USA
tvos@uw.edu
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Introduction
Although substantial progress has been made toward
reducing mortality and extending life expectancy
throughout the world over the past few decades, the
epidemiological transition is manifest in the growing
importance of non-fatal diseases, outcomes, and injuries
which pose, partly as a consequence of decreasing death
rates, a rising challenge to the ability of the world’s
population to live in full health. Complementing
information on deaths by age, sex, cause, geography, and
time with equally detailed information on disease
incidence, prevalence, and severity is key to a balanced
debate in health policy. For this reason, the Global
Burden of Disease (GBD) Study uses the disability-
adjusted life-year (DALY), combining years of life lost
(YLLs) due to mortality and years lived with disability
(YLDs) in a single metric. One DALY can be thought of
as one lost year of healthy life. The sum of DALYs in a
population can be thought of as the gap between the
population’s present health status and an ideal situation
where the entire population lives to an advanced age,
free of disease. Assessments of how diff erent diseases
lead to multimorbidity and reductions in functional
health status are important for both health system
planning1 and a broader range of social policy issues
such as the appropriate age for retirement in some
countries.2,3 Many challenges in making standardised
estimates of non-fatal health outcomes are similar to
those aff ecting mortality estimates (including variations
in case defi nitions, data collection methods, variable
quality of data collection, confl icting data, and missing
data) but are compounded by more sparse and varied
data sources, the need to characterise each disease by its
disabling sequelae or consequence(s), and the need to
quantify the severity of these consequences. The
standardised approach of the annual GBD updates
addresses these measurement problems to enhance
comparability between causes by geography and
over time.
The estimates from GBD 2013 drew attention to large
increases in the number of YLDs over the previous
decade, whereas rates of YLDs for most causes remained
stable or showed only small decreases.4 The GBD 2013
assessment largely attributed increases in the number
of YLDs to musculoskeletal disorders, mental and
substance use disorders, neurological disorders, and
chronic respiratory diseases, as well as population growth
and ageing. GBD 2013 also brought attention to increased
diff erences in trends between mortality and morbidity
for many causes. YLDs as a proportion of DALYs
increased globally, a manifestation of the continuing
epidemiological transition in low-income and middle-
income countries. Decreases in mortality from diseases
such as pneumonia, diarrhoea, maternal and neonatal
disorders, and an absence of progress in reducing YLD
rates continued to drive a transition toward a greater
global number of YLDs.
Along with broad recognition that data from some
regions were sparse and that more and higher quality data
in general would probably improve estimation, useful
debates on the GBD results have been published. These
debates have focused on the analysis or presentation of
individual diseases, such as changes over time in GBD
estimates of dementia,5 the accuracy of HIV incidence
estimates,6,7 the absence of sepsis as a disease,8,9 the quality
of some cancer registry data,10 and the absence of mental
disorders as sequelae of neglected tropical diseases.11 The
GBD empirical approach to measuring the public’s view of
health state severity has generated substantial interest with
questions about the relative importance of diff erent
dimensions of health,12,13 the quantifi cation of health loss,14,15
and discussions of the transferability of judgments about
relative health to conventional notions of disability and
dependence.5 In each cycle of the GBD, we seek to improve
the estimates, refl ecting published and unpublished
critique through the acquisition of new data, expansion of
the network of collaborators, changes in how data are
corrected for bias, advances in modelling techniques, and
the targeted expansion of the GBD cause list.
The primary objective of this component of the GBD
was to use all available data of suffi cient quality to
generate reliable and valid assessments of disease and
injury sequelae incidence, prevalence, and YLDs for all
310 causes in the GBD cause hierarchy for 591 locations
in the GBD study during 1990–2015. We describe the
change over time and between populations in relation to
where countries fall on the development continuum.16
Continuing eff orts to improve data and code transparency
are an important part of the GBD cycle. These results
thus supersede any previous publications about the GBD
on disease incidence, prevalence, and YLDs.
Methods
Overall approach
We estimated incidence and prevalence by age, sex, cause,
year, and geography using a wide range of updated and
standardised analytical procedures. The overall logic of
our analytical approach is shown for the entire non-fatal
estimation process in fi gure 1. The appendix provides a
single source for detail of inputs, analytical processes,
and outputs and methods specifi c to each cause. This
study complies with the Guidelines for Accurate and
Transparent Health Estimates Reporting (GATHER)
recommendations (methods appendix pp 1, 608–10).17
Geographies in GBD 2015
The geographies included in GBD 2015 have been
arranged into a set of hierarchical categories composed of
seven super-regions and a further nested set of 21 regions
containing 195 countries and territories. Eight additional
subnational assessments were done for Brazil, China,
India, Japan, Kenya, Saudi Arabia, South Africa, Sweden,
and the USA (methods appendix pp 611–24). For this
study we present data at the national and territory level.
See Online for appendix
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List of causes and sequelae
The GBD cause and sequelae list is organised
hierarchically (methods appendix 625–53). At Level 1
there are three cause groups: communicable, maternal,
neonatal, and nutritional diseases (Group 1 diseases);
non-communicable diseases; and injuries. These Level 1
aggregates are subdivided at Level 2 of the hierarchy into
21 cause groupings. The disaggregation into Levels 3
and 4 contains the fi nest level of detail for causes
captured in GBD 2015. Sequelae of diseases and injuries
are organised at Levels 5 and 6 of the hierarchy. The
nest detail for all sequelae estimated in GBD is at
Level 6 and is aggregated into summary sequelae
categories (Level 5) for causes with large numbers of
sequelae. Sequelae in GBD are mutually exclusive and
collectively exhaustive, and thus our YLD estimates at
each level of the hierarchy sum to the total of the level
above. Prevalence aggregations are estimated at the level
of individuals who might have more than one sequela or
disease and therefore are not additive.
The cause and sequelae list was expanded based upon
feedback after the release of GBD 2013 and input from
GBD 2015 collaborators. Nine causes for which
non-fatal outcomes are estimated were added: Ebola
virus disease, motor-neuron disease, environmental
heat and cold exposure, four subtypes of leukaemia,
and two subtypes of non-melanoma skin cancer
(methods appendix pp 625–53). The incorporation of
these changes expanded the cause list from the
301 causes with non-fatal estimates examined in
GBD 2013, to 310 causes with non-fatal estimates and
from 2337 to 2619 unique sequelae at Level 6 of the
hierarchy. At the newly created Level 5 of the hierarchy
there were 154 summary sequela categories. The
methods appendix (pp 654–61) provides a list of
International Classifi cation of Diseases version 9
(ICD-9) and version 10 (ICD-10) codes used in the
extraction of hospital and claims data, mapped to GBD
2015 non-fatal causes, impairments, and nature of
injury categories.
Input data
Process
Results
Database
Non-fatal estimation process
Raw data source
Data adjustment
Database
Alternative disease modelling
Impairment and underlying cause
Post DisMod-MR
DisMod-MR
Injury modelling
Disability weights
Final burden estimates
Colours of non-fatal estimation
Case notifications
1 Data sources 2 Data adjustment 3b Alternative disease modelling strategies (details figure 1B)
3c Injury modelling strategy
5 Severity distribution
6 Disability weights
7 Comorbidity
8 YLDs
3a DisMod-MR
2.1 estimation
4 Impairment and underlying cause estimation
Expansion factors for
case notifications
Population-at-risk
data
Seroprevalence data
Disease registries
Birth registries
Active screening
Intervention coverage
Vital registration
Surveillance
Community surveys
National surveys
Outpatient hospital
data
Claims data:
outpatient visits
Inpatient hospital
data
SMR data from
cohort studies
Cohort study
MEPS
Expert estimates
duration untreated
injuries
Sequelae mapped
to health states
Household surveys
Open access web-
based survey
GBD collaborator
advice
Surveys with
diagnostic information
and SF-12
Opportunistic surveys
by IHME to fill SF-12
for 60 lay descriptions
Cohort follow-up
studies
Claims data:
inpatient visits
Adjustment for
under-reporting
HIV/AIDS and
TB Malaria Seroprevalence
to incidence
models
Case fatality
proportion and
cause of death
rate models
Apply aetiology or
severity proportions to
disease or impairment
morbidity estimates
Scale impairment
prevalence by
underlying cause or
severity to envelope
Neonatal
disorders Cancer
Age–sex
splitting
Add study-level
covariaties
Pre DisMod bias
correction
Adjustment for
multiple outpatient
visits per prevalent or
incident case based
on claims data
Adjustment from
primary code to all
code based on claims
data for causes
with long duration
Adjustment for
multiple admissions
in same individual Generate cause–nature
of injury matrices
with negative
binomial models
Determine most
severe nature of
injury category in
any individual
Compute excess
mortality before
from available
incidence or
prevalence and
CSMR data
Adjusted
input
data
Non-fatal database:
prevalence, incidence, excess
mortality rate, RR, SMR,
duration, remission,
severity proportions, and
intermediary modelling
variables
CSMR from
CoDCorrect
Study
covariates
Disability weights for
235 health states
Country
covariates
Apply cause–nature
injury of matrices
Regression to estimate
disability weight by
cause in survey
respondents controlling
for comorbidity
YLD to YLL ratio for
12 residual
causes without
primary data
Analysis of paired
comparison and
population health
equivalence responses
Incidence by cause
of injury code
Prevalence and
incidence by disease
or impairment
Proportion of
disease or impairment
sequelae or causes
Scaled proportion of
disease or impairment
sequelae or underlying
causes
Proportion by
sequelae
Prevalence and
incidence of
sequelae from
alternative models
Prevalence and
incidence of
sequelae
Unadjusted YLD
by sequelae
Probability of
long-term
disability
Estimate duration
of short-term
disability
DisMod-MR 2.1
Meta-analysis
Short-term incidence
by cause–nature
and inpatient
or outpatient
Short-term prevalence
by cause–nature and
inpatient or outpatient
Lay descriptions for
235 health states
Long-term incidence
by cause–nature
combination
Long-term prevalence
by cause–nature
and inpatient or
outpatient
Map EQ5D
to SF-12
DisMod-MR
2.1
Scale to
100%
Scale to
100%
Map SF-12 to
GBD disability
weights
Meta-analysis
proportion by
severity level
DisMod analysis
proportion by
severity level
Comorbidity
correction
(COMO)
Prevalence and incidence
of sequelae of impairment
or diseases (by severity
or underlying cause)
YLLs residual
causes without
primary data
YLDs for each disease
and injury by age, sex,
year, and country
Figure 1: Analytical fl ow chart for the estimation of cause-specifi c YLDs by location, age, sex, and year for GBD 2015
Ovals represent data inputs, square boxes represent analytical steps, cylinders represent databases, and parallelograms represent intermediate and fi nal results. The fl ow chart is colour-coded by major
estimation component: raw data sources, in pink; data adjustments, in yellow; DisMod-MR 2.1 estimation, in purple; alternative modelling strategies, in light green; injury modelling strategy, in dark
green; estimation of impairments and underlying causes, in brown; post-DisMod-MR and comorbidity correction, in blue; disability weights, in orange; and cause of death and demographic inputs, in
grey. GBD=Global Burden of Disease. TB=tuberculosis. SF-12=Short Form 12 questions. MEPS=Medical Expenditure Panel Surveys. CSMR=cause-specifi c mortality rate. SMR=standardised mortality
ratio. YLDs=years lived with disability. YLLs=years of life lost. IHME=Institute for Health Metrics and Evaluation.
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Period of analysis
A complete set of age-specifi c, sex-specifi c, cause-
specifi c, and geography-specifi c incidence and prevalence
numbers and rates were computed for the years 1990,
1995, 2000, 2005, 2010, and 2015. In this study we focus
on trends for main and national results over the past
decade, from 2005 to 2015, together with more detailed
results for 2015. Online data visualisations at vizhub
provide access to results for all GBD metrics.
Non-fatal modelling strategies vary substantially
between causes. Figure 1 outlines the general process of
non-fatal outcome estimation from data inputs to
nalisation of YLD burden results; step 3b of that process
identifi es alternative modelling approaches used for
specifi c causes (methods appendix pp 603, 604). The
starting point for non-fatal estimation is the compilation
of data sources identifi ed through systematic analysis
and extractions based on predetermined inclusion and
exclusion criteria (methods appendix p 603). As part of
the inclusion criteria, we defi ned disease-specifi c or
injury-specifi c reference case defi nitions and study
methods, as well as alternative allowable case defi nitions
and study methods which were adjusted for if we detected
a systematic bias. We used 15 types of primary data
sources representing disease prevalence, incidence,
mortality risk, duration, remission, or severity in the
estimation process (oval shapes in fi gure 1).
Data sources
For this iteration of the study, we updated data searches
through systematic data and literature reviews for
85 causes published up to Oct 31, 2015. For other
causes, input from GBD collaborators resulted in
the identifi
cation and inclusion of a small number of
additional studies published after January, 2013. Data were
systematically screened from household surveys archived
in the Global Health Data Exchange, sources suggested
to us by in-country experts, and surveys identifi ed in
major multinational survey data catalogues and Ministry
of Health and Central Statistical Offi ce websites. Case
notifi cations reported to WHO were updated up to and
including 2015. Citations for all data sources used for non-
fatal estimation in GBD 2015 are provided in searchable
form through a new web tool. A description of the
search terms used for cause-specifi c systematic reviews,
inclusion and exclusion criteria, and the preferred and
alternative case defi nitions and study methods are detailed
by cause in the methods appendix (pp 26–601).
Hospital inpatient data were extracted from
284 country-year and 976 subnational-year combinations
from 27 countries in North America, Latin America,
Europe, and New Zealand. Outpatient encounter data
were available from the USA, Norway, Sweden, and
Canada for 48 country-years. For GBD 2015, we also
accessed aggregate data derived from claims information
in a database of US private and public insurance schemes
for the years 2000, 2010, and 2012. From the linked
claims data, we generated several correction factors to
account for bias in health service encounter data from
elsewhere, which were largely available to us aggregated
by ICD code and by primary diagnosis only. First, for
chronic disorders, we estimated the ratio between
prevalence from primary diagnoses and prevalence from
all diagnoses associated with a claim. Second, we used
the claims data to generate the average number of
outpatient visits per disorder. Similarly, we generated per
person discharge rates from hospital inpatient data in the
USA and New Zealand, the only sources with unique
patient identifi ers available for GBD 2015.
In GBD 2013, we calculated a geographical and temporal
data representativeness index (DRI) of non-fatal data
sources for each cause or impairment. The DRI represents
the fraction of countries for which any incidence,
prevalence, remission, or mortality risk data were available
for a cause. This metric quantifi es data availability, not
data quality.
The overall DRI and period-specifi c DRI
measures for each cause and impairment are presented in
the methods appendix (pp 662–68). DRI ranged from 90%
for nine
causes, including tuberculosis and measles, to
less than 5% for acute hepatitis C and the category of
other exposures to mechanical forces. Required case
reporting resulted in high
DRI values for notifi able
infectious diseases; the network of population-based
registries for cancers resulted in a DRI of above 50%.
DRI values
ranged from 6·1% in North Korea to 91·3%
in the USA.
Many high-income countries, as well as
Brazil, India, and China, had DRI values above 63%; data
availability was low in several countries, including
Equatorial Guinea, Djibouti, and South Sudan.
Non-fatal disease models
In addition to the corrections applied to claims and
hospital data, a number of other adjustments were applied
including age–sex splitting, bias correction, adjustments
for under-reporting of notifi cation data, and computing
expected values of excess mortality. In GBD 2013, we
estimated expected values of excess mortality from
prevalence or incidence and cause-specifi c mortality
rate data for a few causes only, including tuberculosis
and chronic obstructive pulmonary disease. In order to
achieve greater consistency between our cause of death
and non-fatal data, we adopted this strategy systematically
for GBD 2015. We matched every prevalence data point
(or incidence datapoint for short duration disorders) with
the cause-specifi c mortality rate value corresponding to
the age range, sex, year, and location of the datapoint.
The ratio of cause-specifi c mortality rate to prevalence is
conceptually equivalent to an excess mortality rate.
To estimate non-fatal health outcomes in previous
iterations of GBD, most diseases and impairments were
modelled in DisMod-MR, a Bayesian meta-regression tool
originally developed for GBD 2010 (step 3a in fi gure 1).18
DisMod-MR was designed to address statistical challenges
in estimation of non-fatal health outcomes, and for
For Global Health Data Exchange
see http://ghdx.healthdata.org
For data in GBD 2015 see http://
ghdx.healthdata.org/global-
burden-disease-study-2015
For data visualisations at
vizhub see http://vizhub.
healthdata.org/gbd-compare
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synthesis of often sparse and heterogeneous epidemio-
logical data. For GBD 2015, the computational engine of
DisMod-MR 2.1 remained unchanged, but we substantially
rewrote the code that organises the fl ow of data and
settings at each level of the analytical cascade. The
sequence of estimation occurs at fi ve levels: global, super-
region, region, country, and where applicable, subnational
locations (appendix pp 611–24). At each level of the cascade,
the DisMod-MR 2.1 computational engine enforces
consistency between all disease parameters. For GBD 2015,
we generated fi ts for the years 1990, 1995, 2000, 2005, 2010,
and 2015. We log-linearly interpolated estimates for the
intervening years in each 5-year period. Greater detail on
DisMod-MR 2.1 is available at Global Health Data
Exchange and the methods appendix (pp 7–11).
In previous iterations of GBD, custom models were
created for a short list of causes for which the compartment
model underpinning DisMod (susceptible, diseased, and
dead) was insuffi cient to capture the complexity of the
disease or for which incidence and prevalence needed to
be derived from other data. Step 3b of fi gure 1 describes
the development of custom models with greater detail
shown in the methods appendix fi gure 1B (p 604, and
for associated write-ups pp 26–601) for HIV/AIDS,
tuberculosis, malaria, cancer, neonatal disorders,
infectious diseases for which we derived incidence from
seroprevalence data, and infectious diseases for which we
derived incidence from cause of death rates and pooled
estimates of the case fatality proportion.
In GBD 2013, we estimated the country–age–sex–year
prevalence of nine impairments (step 4 of fi gure 1).
Impairments in GBD are disorders or specifi c domains of
functional health loss that are spread across many GBD
causes as sequelae and for which there are better data to
estimate the occurrence of the overall impairment than for
each sequela based on the underlying cause. Overall
impairment prevalence was estimated with DisMod-MR 2.1
except for anaemia, for which spatiotemporal Gaussian
Process regression methods were applied. We constrained
cause-specifi c estimates of impairments, such as in the
19 causes of blindness, to sum to the total prevalence
estimated for that impairment. Anaemia, epilepsy, hearing
loss, heart failure, and intellectual disability were estimated
at diff erent levels of severity.
Severity distributions
In step 5, sequelae were further defined in terms
of
severity for 194 causes at Level 4 of the hierarchy
(fi gure 1A). We generally followed the same
approach for
estimating the distribution of severity as in GBD 2013.
For Ebola virus disease, we created a health state for the
infectious disease episode with duration derived from
average hospital admission times, and a health state for
ongoing postinfection malaise and joint problems based
on four follow-up studies19–22 from which we derived an
average duration. The health states for the subtypes of
leukaemia and non-melanoma skin cancer were the
same as the general cancer health states. For motor-
neuron disease we accessed the Pooled Resource Open-
Access ALS Clinical Trials (PROACT) database containing
detailed information on symptoms and impairments for
more than 8500 patients who took part in the trials.23
Disability weights
We used the same disability weights as in GBD 2013 (see
methods appendix pp 669–94 for a complete listing of
the lay descriptions and values for the 235 health states
used in GBD 2015).
Comorbidity
In step 7, we estimated the co-occurrence of diff erent
diseases by simulating 40 000 individuals in each
geography–age–sex–year combination as exposed to the
independent
probability of having any of the sequelae
included
in GBD 2015 based on disease prevalence. We
tested the contribution of dependent and independent
comorbidity in the US Medical Expenditure Panel Surveys
(MEPS) data, and found that independent comorbidity
was the dominant factor even though there are well
known examples of dependent comorbidity. Age was
the main predictor of comorbidity such that age-specifi c
microsimulations accommodated most of the required
comorbidity correction. Taking dependent comorbidity