Global Burden of Disease Study - 2010

Global Burden of Disease Study – 2010

Dr. Rizwan S A, M.D.,

Outline of presentation
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Beginnings
Prior studies
What is GBD?
Need for GBDS – 2010
Methodology of GBDS – 2010
Analytical components of GBDS – 2010
Five critical choices
Results overview
Articles published

Beginnings
• Original GBD 1990 Study was commissioned by the
World Bank in 1991 to provide a comprehensive
assessment of the burden of 107 diseases and
injuries and ten selected risk factors for the world
and eight major regions in 1990
• The disability-adjusted life year (DALY), a time-based
measure that combined years of life lost due to
premature mortality and years of life lost due to time
lived in health states less than ideal health, was
developed to assess the burden of disease.

Prior studies
• Global Burden of Disease Study 1990 – World bank
commissioned (for 131 diseases and injuries and 10
risk factors)
• Global Burden of Disease Study 2000 (GBD 2000)
• Results for 1999, 2000, 2001, and 2002 were published
in World Health Reports 1999-2003
• WHO published estimates for 2004
• WHO also issued updates for estimates in 2009
• National Burden of Disease studies in at least 37
countries

What is GBD ? - 1
• There are some 6 billion people in the world, and hundreds of
millions experience disease or injury each year.
• Taken as a whole, the combined pain, suffering, loss of
productivity and unrealised hopes and dreams are our world’s
burden of disease !
• In other words, the burden of disease is a measurement of
the gap between the current health of a population and an
ideal scenario where everyone completes their full life
expectancy in full health
• The Global Burden of Disease project attempts to measure
this total disease burden

What is GBD? - 2
• Comparison is at the heart of the GBD approach
• Comparison requires consistent definitions over
time and across place;
• Comparison across diseases for non-fatal health
outcomes requires a system of disability eights
that produce meaningful summary metrics across
hundreds or thousands of specific disease sequelae.
• metrics that quantify types of outcomes in
commensurable units.

What is GBD? - 3
• One needs estimates for all diseases, injuries, and
risk factors, not just those for which there are the
best data in a place
• Poorly studied health problems may in fact be
some of the most important in some places and
neglected because no one has collected good data
in the past
• All age-groups are included in the GBD so that
comparisons across age and sex groups are also a
key advantage

What is GBD? - 4
• GBD construct of the burden of disease is health
loss, not income or productivity loss
• Health loss does not capture all the key dimensions
of health events;
• GBD treats a unit of health loss as equally important
across individuals whether rich or poor, old or young,
male or female.
• Incorporate advances in epidemiology, demography,
statistics, and other quantitative disciplines.
• Completely revamped and improved tools.

What is GBD? - 5
• An approach to global descriptive epidemiology
• It is a systematic, scientific effort to quantify the comparative
magnitude of health loss due to diseases, injuries, and risk
factors by age, sex, and geographies for specific points in time
• Provides an opportunity to see the big picture, to compare
diseases, injuries, and risk factors, and to understand in a
given place, time, and age-sex group, what are the most
important contributors to health loss.

GBD 2010 Team
486 authors from 302 institutions in 50 countries

Seven institutions: IHME as the coordinating center, the University of Queensland, the
Harvard School of Public Health, the Johns Hopkins Bloomberg School of Public Health,
the University of Tokyo, Imperial College London, and the World Health Organization.
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Key Activities
1. Produced valid, unbiased, and comparable estimates of
prevalence of disease and injury cases or episodes and
relevant disabling sequelae at the population level for the
GBD regions
2. Produced cause-specific mortality by region that collectively
sums to all-cause mortality estimates globally
3. Revised the health state severity weight system, a highly
debated component of past GBD studies that seeks to
measure health state severity
4. Produced estimates of YLDs, YLLs, death, and DALYs for
diseases, injuries, and risk factors for 21 regions for 20 age
groups and both sexes, for 1990, 2005, and 2010

Scope of the 2010 study
• 291 diseases and injuries
• 1,160 disabling sequelae of these diseases and
injuries
• 67 risk factors
• 20 age groups, two sexes
• 187 countries
• 1990-2010
• Improved methods for the estimation of health state
severity weights
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Study objectives
• Two major objectives
– Revising figures for 1990 given new data and
improved techniques and generating new
estimates for 2005 along with projections for
2010.
– Developing various sets of tools, each tailored to a
specific audience, to standardize and broaden the
field of burden research and analysis.

GBD advantages - 1
1. Evidence-based evaluations
– standard measures to ensure that all conditions
receive systematic, objective analysis.

2. Changing awareness and validity of different
global health conditions
– visibility and legitimacy to conditions like
depression which cause great suffering with little
associated mortality, and to conditions like road
traffic accidents, formerly outside the scope of
public health.

GBD advantages - 2
3. Cost effectiveness analysis
– allows for lives in every part of the world to be
valued equally and creates a common unit of
currency for making decisions about the costs and
benefits of various health interventions.
– using standard units of measurement, such as
disability-adjusted life years (DALYs).

4. Engaging researchers, experts, and
policymakers

Need for GBDS (2010) - 1
• While various groups have published partial
updates of GBD rankings, there has not yet
been a comprehensive and systematic
revision.
• As a result, burden estimates today contain
some outdated, and often, inconsistent
information.

• A core principle of the GBD is to facilitate unbiased
comparisons across diseases, injuries, and risk
factors.
• Disability weights are the mechanism through which
time spent with a disease or injury sequela is made
commensurate with other disease and injury
sequelae and with time lost due to premature
mortality.
• Disability weights had not been empirically revised
since the GBD 1990.

• Demand for more detailed information and
estimation of the burden of disease from diarrhea,
pneumonia, and meningitis broken down by specific
pathogen
• Fourth, GBD 1990 and GBD 2000 results were based
on different datasets and methods, but many users
of the information have compared the results to
examine trends for key outcomes.
• A reassessment of 1990 burden using strictly
comparable methods to the current time period was
needed

• Methods innovation across the spectrum from
mortality measurement, cause of death analysis, and
meta-regression for epidemiological synthesis
provided opportunities to modernize the GBD
approach
• Combined with a global movement towards
enhanced transparency and access to data, the
empirical basis for assessing the epidemiology of
diseases is much richer than in the past

Disease and injury causes, sequelae,
and health states
• Cause list has a hierarchical structure
• At each cause level, the list provides a set of mutually
exclusive and collectively exhaustive categories
• Added causes on the basis of three criteria:
potentially large, health policy interest, feasibility of
measurement.
• Four levels of diseases and injuries and a fifth level
for sequela,

Causes, sequelae, and health states - 1
• The four levels provide different degrees of
disaggregation of the causes:
• Three level 1 causes, 21 level 2 causes, 155 level 3
causes, and 112 level 4 causes
• Of the level 3 causes 129 have no level 4 causes below
them in the hierarchy;
• At the most disaggregated level there are, therefore,
241 mutually exclusive and collectively exhaustive
causes
• The three level 1 causes are communicable, maternal,
neonatal, and nutritional disorders; non-communicable
diseases; and injuries.

• List of sequelae is designed to capture the direct
consequences of disease or injury that are not
otherwise captured elsewhere in the cause list
• Number of sequelae evaluated has increased for all
three broad cause groups, leading to 1,160 in total
• A given outcome only appears once in the cause
sequela list to avoid double counting

• For the GBD 2010 an important innovation has been to
identify which disease sequelae are common across
conditions; these common disease sequelae are called
health states

• Shorter list of health states which facilitates the
disability weight measurement component of the study
• For each of the health states, a lay description was
developed for use in the empirical assessment of
disability weights

Disease and injury ranking list
• Because the leading causes of burden tend to have some influence
on the perception of disease control priorities, the choice of
aggregation is at once important
• Provides information at the second level cause (21 causes)
• A ranking list with 176 causes selected to distinguish and cluster
conditions together that may have programmatic or public health
significance
• Results in the tables are provided for all 291 causes; the ranking list is
used only for the leading cause figures
• The 176 causes along with the excluded residual categories are
also mutually exclusive and collectively exhaustive

Risks rather than specific diseases
• An alternative way of looking at ill health is to consider risks for disease
rather than diseases themselves
• A risk is defined as the probability of an adverse health outcome or a
factor that raises this probability
• A single risk factor may underlie several diseases; e.g. unsafe sex is a risk
for HIV/AIDS, many other sexually transmitted diseases and teenage
pregnancy
• It is often more useful to direct health interventions against risks rather
than specific diseases
• DALYs have been assigned to health risks as well as diseases

Risk factor list
• Comparative Risk Assessment 2000 list of 26 risk
factors and subtracted and added risk factors based
on consideration of four criteria:
• Convincing or probable evidence on relative risks
by cause, sufficient data to estimate exposure by
region, potential size of the attributable burden
of disease, and the extent to which burden is
modifiable
• A hierarchy with three levels

Risk factor list
• Level 1 risks
– Groups of risk factors that are related by mechanism,
biology, or potential policy intervention.

• Most risks are presented at level 2, but in some
cases, such as occupational carcinogens
calculations, are done at a third level
• The category diet and physical inactivity clusters
together some key behavioral risks. This category
has been substantially expanded to include 14
components of diet.

Risk factor list
• For some of the level 1 clusters of risk factors, there are
analytical challenges for computation related to risk factors
that operate through each other.
• The problem of mediation is particularly challenging for
the physiological risks. For example, BMI operates in part
through blood pressure and cholesterol changes.
• In this analysis, we have not been able to estimate this
level 1 risk factor, so that results are presented for only 67
of the 69 risks or clusters of risks
• As with diseases and injuries, we have identified a list of
43 risk factors for use in developing tables and figures of
leading causes of risk factors.

Regions
• Basis of two criteria
– Epidemiological homogeneity and geographic contiguity

• Maximize the between-region variance in outcomes and
minimize the within-region variance in outcomes
• The process was iterative based on examination of levels
of under-5 mortality and adult mortality (as measured by
the probability of death between ages 15 and 60)
• 21 regions were made.
• For some types of analysis, created seven super-regions
• For some Bayesian estimation procedures used in the
analysis, super-regions are useful in the presence of
sparse data.

Subgroups - 1
• Early neonatal, late neonatal, post-neonatal, 1-4,
5-9, 10-14, …..70-74, 75-79, and 80+
• In the demographic analysis of age-specific
mortality, estimated deaths and death rates for
age-groups up to 100.
• Due to limitations of data at older ages, however,
80+ used as the terminal age-group for the cause of
death and disease and injury sequelae analysis.

Subgroups - 2
• All estimates are produced separately for males and females
• Not been able to include estimation of fetal deaths; this is
an important area for future quantitative work.
• A critical dimension of the GBD 2010 is the addition of
multiple time periods assessed using strictly comparable data
and methods
• For three years - 1990, 2005, and 2010.
• All results for causes of death have in the computation been
estimated for single years as well

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Covariate database
Age-specific mortality rates
Cause of death database
Estimating causes of death
Ensuring consistency between cause-specific estimates and all-cause mortality
estimates
Disease sequelae epidemiology database
Estimating disease sequela prevalence, incidence, and duration
Impairment levels
Nature and external cause of injury analysis
Disability weights for 220 health states
Comorbidity simulation
Healthy life expectancy
Computation of YLLs, YLDs, and DALYs from diseases and injuries with uncertainty
Risk factor exposure database
Estimating the prevalence of exposure
Estimating relative risks for risk-disease pairs
Theoretical minimum risk exposure
Computation of attributable burden for each risk factor with uncertainty

Covariate database
• In settings where there are sparse data or conflicting data
from multiple sources
• This estimation task is facilitated by the use of appropriate
covariates that can be used as independent variables in the
modeling process
• A database of 84 covariate topic areas and 179 variants
of the covariates has been developed for this purpose
• Missing data in covariates have been addressed using spatialtemporal regression and Gaussian process regression (GPR)

Age-specific mortality rates
• Complete re-analysis of the evidence on levels of
age-specific mortality has been undertaken for
187 countries from 1970 to 2010
• This re-analysis takes advantage of improvements
in demographic estimation methods for various
types of data on child and adult mortality
• All available data on child and adult mortality
have been combined into a time series of best
estimates

Cause of death database
• Information on causes of death comes primarily from vital
registration systems and verbal autopsy studies
• IHME has systematically created a cause of death database
spanning the period 1980 to 2010, including mapping data
across various revisions and national variants of the ICD
• Algorithms have also been developed and applied to deal with
the problem of ill-defined coding in vital registration and
verbal autopsy data where deaths are assigned to unknown
causes, intermediate causes, or immediate causes of death
rather than the underlying cause of death

Estimating causes of death
• For 133 of 235 causes, cause of death ensemble modeling
(CODEm) used as the primary strategy to develop causespecific estimates;
• Three key aspects to this approach
– To develop and test a diverse set of plausible models instead
of a single analyst-preferred model;
– To objectively test the prediction performance of these
models by holding out some of the data from the analysis
and then comparing model prediction to the held out data
– In the CODEm approach, predictions for the GBD are based
on a pool of the best models.

• For some sub-causes, such as diarrhea etiologies,
HIV/AIDS, and for some causes that rarely cause death,
alternative methods have been used

Ensuring consistency between cause-specific
estimates and all-cause mortality estimates
• Cause of death estimation proceeds in two
stages.
– First, models are developed for each cause of
death in isolation
– Second, the estimates for each cause are
combined
into estimates
for all causes
simultaneously that sum to the demographic
estimates of all-cause mortality.

• This simple algorithm leads to greater change
in the estimates for more uncertain causes

Disease sequelae epidemiology
database
• To support estimation for each region of disease
sequelae incidence, prevalence, duration, remission,
and excess mortality, a database of available
published and unpublished data has been developed

Estimating disease sequela
prevalence, incidence, and duration
• Developing estimates for disease incidence,
prevalence, duration, and excess mortality estimates
for 21 regions for three time periods by age and sex, a
meta-regression tool was used (DisMod-MR)
• Dealing with selection bias intrinsic to some data
sources, such as hospital discharges
• For some causes where more complicated models
capturing more stages of disease progression are
necessary, such as HIV, more elaborate natural history
models were used, refined, or developed.

Impairment levels
• The impairment analysis has been undertaken for
vision loss, hearing loss, anemia, infertility, heart
failure, intellectual disability, and epilepsy
• In these cases, the quality and extent of data on the
overall impairment level have been determined to
be stronger than on disease specific impairments.

Nature and external cause of injury
analysis
• Dual nature of injury assessment. Each injury can be characterized by
the nature of injury such as a femur fracture or head trauma and
the external cause of injury such as a road injury or a fall.
• To assess disability, data is required on the frequency of the nature
of injury as well as follow-up data on reduced health functioning at
certain points in time after the injury.
• capture data on the matrix between the nature of injury
incidence and the external causes leading to these injuries
• first time included survey data on the incidence of external causes
and, perhaps more importantly, multiple sources of long-term
follow-up data to estimate the fraction of individuals with each nature
of injury progressing to permanent disability

Disability weights for 220 health states
• Disability weights are the key mechanism in the GBD approach through
which disease and injury sequelae are made comparable with each
other and with time lost due to premature mortality.
• we have switched to the construct of disability weights for health
states as assessed by the general public.
• To achieve this, simple lay descriptions of the 220 unique health states
across the 1,160 disease and injury sequelae were developed.
• Disability weights were assessed using general population surveys in
five countries to capture a diverse set of views and through an
Internet survey.
• Disability weights were elicited using pairwise comparisons where the
respondent is asked to identify which health state represents a higher
level of health.

Comorbidity simulation
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Combinations of these sequelae change the likely health experience of
the individual.

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Comorbidity is taken into account in three stages.
– First, for each age-sex group, a micro-simulation is developed to estimate the number of
individuals who will have combinations of disease conditions.
– Second, the combined disability weight for individuals with
estimated assuming a multiplicative model.

multiple

conditions

is

– Finally, the overall individual health loss captured in the disability weight is assigned to the
different sequelae experienced by an individual proportionate to the disability weights of
individual conditions on their own.

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The micro-simulation process is repeated for each country-age-sex-year 1000
times to propagate uncertainty in the prevalences and disability weights into
the comorbidity estimates.

Healthy life expectancy
• Healthy life expectancy is a single summary metric of population
health that is not affected by population age structure
• It can be thought of as equivalent to the expectation of life at birth
in full health given the currently observed age-specific death rates
and prevalence of disease and injury sequelae
• Used the life tables developed in component 2 for each country in
1990 and 2010, the disability weights and the output of the
comorbidity simulations to estimate healthy life expectancy for 187
countries

Computation of YLLs, YLDs, and DALYs from
diseases and injuries with uncertainty
• Each quantity is estimated with uncertainty by
generating 1,000 draws from the posterior
distribution
• Given the large number of diseases and injuries and
their sequelae, this task is computationally intensive

Risk factor exposure database
• Assesses the attributable burden of disease using
four components:
– Database on risk factor exposure,
– Estimating the prevalence of risk factor exposure by age
and sex
– Relative risk for a specific disease,
– Counterfactual distribution of risk exposure called the
theoretical minimum risk distribution.

Estimating the prevalence of exposure
• Exposure estimates have been developed for many
risks using the GBD Bayesian meta-regression tool,
DisMod-MR
• Related Bayesian methods have been used in
published analyses for the metabolic risks
• For some risks, alternative modeling strategies have
been used

Estimating relative risks for riskdisease pairs
• For each risk-disease pair, relative risks for specific
disease outcomes have been taken from published
meta-analyses or updated meta-analyses that have
been undertaken as part of this study.
• Special analyses have been undertaken for water and
sanitation, all sources of particulates less than 2·5
microns, and the components of diet

Theoretical minimum risk exposure
• The tradition in risk factor epidemiology is to assess
attributable burden by estimating a counterfactual scenario
where exposure to a risk factor in the past is set to a
counterfactual distribution
• Following the framework for comparative risk assessment,
counterfactuals are set to the lowest risk distribution that has
been observed or is theoretically possible – the theoretical
minimum risk exposure distribution (TMRED)

Computation of attributable burden for
each risk factor with uncertainty
• Risk factor burden is assessed using the distributions
of burden for each disease associated with a risk
factor
• Uncertainty from the prevalence of exposure,
relative risks, and where appropriate the TMRED, are
all propagated into the final risk factor uncertainty

Five critical choices
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New standard reference life table
Comorbidity
Incidence versus prevalence
Discounting for time
Age-weighting

New standard reference life table - 1
• The standard reference life table for the GBD 1990 was based
on the highest observed life expectancy at the time, Japanese
females with a life expectancy at birth close to 82·5 years
• Given the progress in extending life expectancy in the last 20
years, it is appropriate to extend the normative goal for life
expectancy
• Developed a new reference standard life table by identifying
the lowest observed death rate for any age-group in countries
with more than five million in population

New standard reference life table - 2
• At the Critical Ethical Choices for DALYs meeting, there was a
consensus that using a different standard for males and
females was inappropriate
• First, the empirically observed gap in life expectancy between
males and females continues to narrow
• Within high-income countries, the gaps between male and
female life expectancies especially for the lowest mortality
communities have also narrowed
• Second, there is no reason that society should have lower
aspirations for health for males than females

Comorbidity
• A year of life lived with a disabling sequela such as
blindness should contribute the same amount to the
burden of disease regardless of whether it occurs in
someone with another condition or not.
• Critical Ethical Choices for DALYs meeting viewed that
burden should be assessed individual by individual.
Individuals with the same functional health loss should be
treated as like regardless of whether that functional health
loss came from one or several contributing conditions.

Incidence versus prevalence
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Two types of DALYs were calculated in the GBD 1990: prevalence DALYs and
incidence DALYs.
Murray argued that the incidence perspective had two advantages: mortality
rates used to compute YLLs are by definition incidence rates; using incidence
for YLDs means that both non-fatal and fatal outcomes are analyzed in a
consistent fashion.
several disadvantages - individuals living with reductions in health functioning
who need health and other social services may not be reflected in burden
estimates if incidence has declined substantially.
If the incidence of ischemic heart disease is designated to start with plaque
formation or detectable myocardial ischemia will profoundly change the number
of discounted YLDs. This arbitrariness to the construct of when a disease
process starts is not present in a prevalence perspective.
incorporation of comorbidity is more straightforward in a prevalence
approach than an incidence approach.
Incidence approach - information intensive and computationally challenging.

Discounting for time
• Anand and Hanson argued that there was no intrinsic
reason to value a year of health as less important
simply because it is in the future
• One cannot expect that the value of a year of healthy
life to society will be less in the future;
• There is no expectation of declining marginal utility
of years of healthy life
• Chosen to compute the base case of DALYs for the
with a discount rate of 0%

Age-weighting
• Some have argued that deaths at particularly young ages or
even fetal death represents less of a loss of health because
individuals do not have fully developed life plans
• “the well-being of some age groups is instrumental in making
society flourish; therefore, collectively we may be more
concerned with improving health status for individuals in
these age groups”
• We recognize, however, that there is still an important set of
issues about social priority that should be attached to
preventing fetal death compared to death after a live birth
that have not been addressed here

The disability transition: progressive shift in burden
from premature mortality to years lived with disability
1990

2010

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• 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
• Six diﬀerent modeling strategies to estimate cause-speciﬁc
mortality trends depending on the strength of the data

Methods
• For 133 causes and three special aggregates - 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. model performance was assessed with rigorous out-of-sample
testing of prediction error and the validity of 95% UIs.
• For 13 causes with low observed numbers of deaths - negative binomial
models with plausible covariates.
• For 27 causes for which death is rare - modelled the higher level 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), - natural history models based on information
on incidence, prevalence, and case-fatality.

Methods
• 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

Sources of data
• Identified 25 variants of cause of death reporting lists in use
from 1980 to 2010 across all sources of vital registration.
• Identified 2798 site-years of data from 130 countries between
1980 and 2010
• Verbal autopsy data were collected through sample
registration systems, demographic surveillance systems, or
surveys. identified 486 site-years of published and
unpublished verbal autopsy data across 66 countries, of which
10% were nationally representative.
• We identified 2715 site-years of cancer registry data across 93
countries.

Sources of data
• Police and crime reports are an important source of information for some
types of injuries, notably road injuries and interpersonal violence.
• The police reports used in this analysis were obtained from published
studies, national agencies, and institutional surveys such as the UN Crime
Trends survey and the WHO Global Status Report on Road Safety Survey.
• 1129 site-years of police reports from 122 countries
• 32 site-years of burial and mortuary data in 11 countries
• identified 1557 survey years with sibling history data, and a further 52
household survey years or census years of data covering 61 countries.
• We also identified 56 surveys or censuses with information on injury
mortality across 65 survey years or census years.
• identified eight countries with nationally representative maternal
mortality surveillance systems covering 83 site-years
• 21 site-years of data based on deaths in health facilities.

Results
• Population growth, increased average age of the
world’s population, and largely decreasing agespecific, sex-specific, and cause-specific death rates
combine to drive a broad shift from communicable,
maternal, neonatal, and nutritional causes towards
non-communicable diseases.

http://www.healthmetricsandevaluation.org/gbd/visualizations/gbd-2010-leading-causesand-risks-region-heat-map

What is DALY?
DALY= YLL + YLD
Years of Lost Life (due to premature mortality)
Years Lost to Disability (due to injury or illness)

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The DALY is the internationally-accepted measure of death and disability and is increasingly cited as
a powerful tool for decision makers in international health.

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It is the sum of the number of years lost to premature mortality and the number of productive
years lost to disability. One DALY is equivalent to one lost year of healthy life.

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Calculation of DALYs is based on the assumption that everyone in the world has a right to the best
life expectancy in the world. The only differences in the rating of a death or disability should be due
to age and sex and not to income, culture, location or social class.

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It’s attractiveness lies in the fact that it combines information about mortality and morbidity in a
single number. DALY allows the losses due to disability and the losses due to premature death to be
expressed in the same unit. Hence, DALYs facilitate comparisons of different health states or health
outcomes.

Sample “DALY” calculations for a disease
Example A:
• 100,000 children are stricken for 1 week with a disease with a disability weighting*
of 0.3; 2% die at 1 year old.
• DALYs
= YLL + YLD
= (2000 X 80) + (100,000 X 7/365 X 0.3)
= 160,000 + 575
= 160,600

Example B:
• 100,000 adults are stricken for 2 years with a disease with disability weighting* of
0.6; 20% die at age 80 years.
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DALYs

= YLL + YLD
= (20,000 x 2) + (100,000 x 2 x 0.6)
= 40,000 + 120,000
= 160,000

*There are STANDARDISED DISABILITY RATINGS for various conditions
e.g deafness= 0.33, Down syndrome=0.5, Diarrhoea=0.12

Why are DALYs important?
DALYs attempt to provide an appropriate, balanced attention to the effects of
non-fatal as well as fatal diseases on overall health. In the absence of such
assessments, conditions which cause decrements in function but not
mortality tend to be neglected.
DALYs help to inform debates on priorities for health service delivery, research
and planning. For example, DALYs can be used to:

• Compare the health of one population with another – and allow decision
makers to focus on health systems with the worst performance
• Compare the health of the same population at different points in time

• Compare the health of subgroups within a population - to identify health
inequalities

Disability weights, discounting and age
weighting
• Egalitarian principles are explicitly built into the DALY metric,
and the global burden of disease studies apply these to all
regions of the world.
• The studies use the same "ideal" life expectancy for all
population subgroups and exclude all non-health
characteristics (such as race, socioeconomic status or
occupation) apart from age and sex from consideration in
calculating lost years of healthy life.
• Most importantly, they use the same "disability weight" for
everyone living a year in a specified health state.

Disability weights, discounting and age
weighting
• Disability weights
– A disability weight is a weight factor that reflects the severity of
the disease on a scale from 0 (perfect health) to 1 (equivalent to
death). Years Lost due to Disability (YLD) are calculated by
multiplying the incident cases by duration and disability weight
for the condition
• Age weighting and discounting
– 3% discounting and non-uniform age weighting was used in the
original GBD 1990 study. These adjustments result in less weight
given to years lived at young and older ages
– The GBD 2001-2 study used 3% discounting but uniform age
weighting. GBD 2004 update used the original 3% discounting
and non-uniform age weighting

DALYs: Social values
1. How to compare years lost due to death with years
lived in poor health?

essential

 Disability Weights values between 0 and 1
2. Value of health year of life equal at all ages?
 age weights
3. Value of future years of life?
 discounting

http://www.who.int/healthinfo/global_burden_disease/daly_disability_weight/en/index.html

optional

Disability Weights
 In GBD, non-fatal consequences of diseases and injuries understood
as transitions through different ‘health states’
 YLD calculation requires aggregate assessments of the overall
decrements in health associated with particular health states 
disability weights
 DWs are measures of overall levels of health rather than contribution
of health to overall welfare

GBD1990 :
 DW elicited from panel of health professionals following explicit
protocol evaluating 22 indicator conditions in an intensive group
exercise with ‘deliberative phase’ using person trade-off (PTO)
method. Responses averaged across participants

New Disability Weight Project

The new DW will have a greater emphasis on paired
comparisons, anchored by time trade-off methods.
It also aims to engage members of the general community
(including those in developing countries) to a greater degree.

The DW project is being carried out in two stages:

 a community household survey in selected regions, and
 an online open-access survey.

New Disability Weight Project
1.

Community surveys
•
•
•

2.

(Pemba, Bangladesh, Indonesia, Peru, USA)
Cultural/SES diversity >> representativeness
Paired comparisons

Internet survey
•
•
•
•

the primary source of data for the final DW
will include random(?) selections from all ~230 sequelae
open to all interested in participating (open-access
includes a variety of measurements paired comparisons, ranking,
visual analogue scale, time trade-off, population equivalence) to
anchor the scale (paired comparisons, ranking, visual analogue scale,
time trade-off, population equivalence)

http://gbdsurvey.org/

Discounting
Discounting common practice in economic analyses.

Assumes that individuals value their health more now
than at some point in the future. So the further in the
future health loss occurs the more they are
discounted.
•

GBD 1990 used 3% discounting

Discounting
Why discount?
1.
Consistency with cost-effectiveness analyses
2.
Prevent giving ‘excessive’ weight to deaths at younger ages

Age weighting


Used to reflect a social preference that values a year
lived by young adult more highly than that of young
children or the elderly.

• Eg. An Australian survey found that respondents considered saving four
20-year olds as important as saving ten 60-year olds (Nord et al, 1996
and 1998)


Not related to productivity but ‘social’ role in caring
for the young and old

Age weighting
Arguments against:

1.6

•

•

relative value

1.2

Unacceptable on
equity grounds
Does not reflect actual
social values

0.8

But
• Everyone potentially
lives through every age
 not inequitable

0.4

0.0
0

20

40

60

age

80

100

Source: Murray and Lopez, 2006. Chapter 5

Terminology used GBDS
• Disability : Disability refers to any short-term or long-term health loss.
• Sequelae :In the GBD 2010 cause list there are 291 diseases and injuries,
of which 289 cause disability. In total, 1160 sequelae of these diseases and
injuries. For example, diabetic neuropathy is a sequela of diabetes
mellitus. To avoid double counting, a sequela can only be counted in the
cause list once even if the same outcome might be caused by more than
one disease.
• Health state :Across the 1160 sequelae, 220 unique health states. For
example, both malaria and hookworm have mild anaemia as a sequela.
Mild anaemia is a unique health state. The list of unique health states
serves two purposes: to allow assessment of the total burden of some
health states such as anaemia across various causes, and to simplify the
task of measuring disability weights for sequelae.

Terminology used GBDS
• Disability weights : A quantiﬁcation of the severity of health loss
associated with the 220 unique health states on a scale from 0 to 1,
when 0 is commensurate with perfect health and 1 is
commensurate with death.
• Years lived with disability (YLDs) : For the GBD 2010, YLDs per
person from a sequela are equal to the prevalence of the sequela
multiplied by the disability weight for the health state associated
with that sequela. YLDs for a disease or injury are the sum of the
YLDs for each sequela associated with the disease or injury.
• Impairments : In the GBD 2010 we estimated the prevalence and
burden of several unique health states that are sequelae for
multiple diseases including anaemia, heart failure, vision loss,
seizures, hearing loss, infertility, and intellectual disability. These
are referred to as impairments.

Global Burden of Disease Study - 2010

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