Global Burden of Disease - Pakistan Presentation

UNIVERSITY OF WASHINGTON

The Global Burden of Diseases, Injuries, and Risk Factors Study

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Presentation Outline

• Goal and key attributes

• Project structure and partners

• Mortality

• Causes of Death

• Systematic reviews

• Analysis of disease-specific data for calculating YLD

• Disability Weights measurement

• Future work

3

GBD Goal

• To produce new, robust, and reliable estimates of burden for all major diseases, injuries, and risks that are widely disseminated, understood, and easily used by policymakers, researchers, funders, and practitioners.

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Key Attributes

• Producing specific DALY, YLL, and YLD estimates for over 300+ diseases/injuries and 40+ risk factors by age and sex for 21 regions for the years 1990, 2005, and 2010.

• Providing a consistent time trend (methods for current ‘00, ’02, ‘04 estimates are not comparable to ‘90).

• Providing first comprehensive revision of Disability weights since 1996. Many Burden estimates done after the original study had used ad hoc DW based on Dutch study.

• Providing improved analytical tools to facilitate Burden estimates and policy use.

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• Mortality

• Causes of Death




• Future work

Organizational Structure

Core TeamExternal

Advisory Board

COD Sub-Team Rafael Lozano

CRA Sub-TeamMajid Ezzati

DW Sub-TeamJosh Salomon

Mortality Sub-TeamChris Murray and

Alan Lopez

YLD Sub-TeamRafael Lozano

and Colin Mathers

Cluster A CVD, COPD, Cancer

Majid EzzatiHarvard University

Cluster B Child/Maternal

Bob BlackJohns Hopkins

University

Cluster C Injuries and Mental

HealthTheo Vos

University of Queensland

Cluster D Communicable

DiseasesNeff Walker

Johns Hopkins University

Cluster ENoncommunicable

DiseasesCatherine MichaudHarvard University

• Vision, decision-making, and leadership are handled by the core team, a group of 12 key individuals from the collaborating institutions.

• Specific analytical tasks are grouped into (1) Causes of Death, (2) Comparative Risk Assessment, (3) Disability Weights, and (4) Mortality Estimation. Each of these “subteams” are led by 1 or 2 members of the core team to guide each category’s specific scientific progress and analysis.

• Management of diseases, injuries and risks are organized into clusters and are led by 1 member of the core team who oversees the cluster’s expert groups.

• Expert groups are comprised of knowledgeable specialists of a disease, injury, or risk.

Organizational structure

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Johns Hopkins University

Harvard University

University of Queensland

Institute for Health Metrics and Evaluation

World Health Organization

44 expert groups, with

over 800 members worldwide

Collaborating Partners

Expert Groups

CancersCardiovascular DiseasesChronic Respiratory DiseasesClimate ChangesIndoor Air PollutionMetabolic RisksNutritional RisksOutdoor Air PollutionPhysical InactivitySocioeconomic FactorsTobacco

ARI Meningitis SepsisChild NutritionCongenital and NeonatalDiarrheaMalariaMaternal ConditionsSelected Vaccine Preventable DiseasesWater, Sanitation, Hygiene

Alcohol UseCollective ViolenceIllicit Drug UseIntimate Partner and Sexual ViolenceLead ExposureMental DisordersMusculoskeletalNeurological DisordersOccupational RisksOther InjuriesRoad Traffic Accidents

HepatitisHIV/AIDSParasitic &Vector DiseasesSTIsTuberculosisUnsafe Sex

DentalDiabetesGastrointestinalGenitourinary DiseasesHearing LossHemoglobinopathiesSkin DiseasesVision Loss

Cluster C Injuries and Mental Health

Theo VosUniversity of Queensland

Cluster A CVD, COPD, Cancer

Majid EzzatiHarvard University

Cluster B Child/Maternal

Bob BlackJohns Hopkins University

Cluster ENoncommunicable Diseases

Catherine MichaudHarvard University

Cluster D Communicable Diseases

Neff WalkerJohns Hopkins University

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10

Systematic reviews of

prevalence/ incidence

Peer Review

2nd Revision internal

consistency analysis by

disease

Internal consistency analysis by

disease

Causes of death

consistency analysis

Revised internal

consistency by disease

Systematic reviews of disabling sequelae

Peer ReviewRevision of disabling sequelae

Functional health status

Disability weights

Start

First Consultative Review Meeting

Identify diseases and disabling sequelae

Data input from1. Vital registration

deaths by age,sex, and cause

2. Disease registries3. Surveys- DHS, WHS

Data input fromAll cause mortality

by age and sex from vital registration data, survey data, and other sources

YLD

GBD Operational Components

Causes of death

YLL

Shapes Key Colors Key

Inputs Process Outputs Data input fromHealth state

measurement in representative samples

by sequelae

Expert Groups

Core Team

Peer Review

Expert Groups & Core Team

Expert Group Meetings

Estimation of

valuation function

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Start

First Consultative Review Meeting

Identify diseases and disabling sequelae

GBD Operational Components – Comparative Risk Assessment

Shapes Key Colors Key

Inputs Process Outputs

Expert Groups

Core Team

Peer Review

Expert Groups & Core Team

Expert Group Meetings

Revised exposure

data

Revision ofdisease end-

points andhazard

magnitude

Peer Review

Peer Reviewof

hazardous effects

Exposure metric &

assessment of systematic

bias

Systematic reviews of exposure data and

hazardous effects

Data input from1. Health

examination surveys2. Risk factor indicator

registries (FAO, WHO, etc)

PAF(Individual& multiple

risks)

Addictive substances

• Tobacco use

• Alcohol use

• Illicit drug use

Environmental

• Unsafe water, sanitation, and hygiene

• Urban ambient air pollution

• Household air pollution from solid fuel use

• Lead exposure

• Passive smoking / Environmental tobacco smoke

• Food contamination

• Road and vehicle safety

Violence related

• Sexual violence

• Intimate partner violence

• Collective violence

• Possession of firearms

Undernutrition (child and maternal)

• Folic acid deficiency

• Anaemia and/or iron deficiency

• Small-for-gestational age

• Growth retardation

• Suboptimal breasfeeding

• Vitamin A deficiency

• Zinc deficiency

Reproductive and sexual

• Unsafe sex

• Unwanted pregnancies

Risks related to medical practice

Genetic

Systemic

• Global climate change

• Socioeconomic factors

Other selected risks to health

• Osteoporosis

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Risk Factors Occupational

• Risks for injuries

• Carcinogens

• Airborne particulates

• Ergonomic stressors

• Noise

• Pesticides

• Other

Metabolic, nutritional and lifestyle

• High blood pressure

• High cholesterol

• High blood glucose

• Dietary fats

• High BMI

• Low intake of fruit and vegetable

• Physical inactivity

• Other nutritional

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21 GBD Regions

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Age Groups for Results

• <1 month

• 1 – 11 months

• 1 – 4 years

• 5 – 9 years

• 10 – 14 years

• 15 – 19 years

• 20 – 24 years

• 25 – 34 years

• 35 – 44 years

• 45 – 54 years

• 55 – 64 years

• 65 – 74 years

• 75 – 84 years

• 85+ years

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• Mortality

• Causes of Death



• Disability weights measurement

• Future work

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• Generating regional estimates for age and sex

• All cause mortality based on demographic sources recording the event of death.

• The sum of all cause specific deaths for any age-sex group must equal, and not exceed, the overall mortality envelope for that age-sex group.

Mortality

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Mortality Estimation

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Mortality estimation: Synthesis

Gaussian Process Regression:

•Synthesizes discordant time series of mortality estimates into a best estimate of smooth trend

• Assigns probabilities to different functions according to how likely they are to be the true function

• Uses prior beliefs, the data and the uncertainty in the data to inform those probabilities

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Child Mortality in Nicaragua: Example of Using VR and Survey and Census Data

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Child Death Numbers: East Sub-Saharan Africa

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Child Death Numbers: Global

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GPR for Adult mortality: Nicaragua

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GPR for Adults: Zimbabwe

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Using 5q0 and 45q15 to a Complete Lifetable

WHO uses modified logit lifetable system (Murray et al 2003) to generate complete lifetables.

HIV mortality is modeled separately from demographic sources and added on after demographic estimation.

We wanted to improve the performance of model life table systems and avoid modeling strategies that are not empirically based.

Over 18 months, developed a semi-parametric approach to the modified logit lifetable system that improves performance and captures the empirical impact of HIV.

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Key Attributes of Mortmatch

Based on 5q0, 45q15 and HIV sero-prevalence searches database of (nearly 8000 lifetables) for nearest matches using Mahalanobis distance.

Matched lifetables used to establish standard life table.

Modified logit transformation including bend factors used to estimate full survivorship curve based on matched standard.

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Validation Results-1

01

2

0 1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100

excludes outside values

Entry parameters from HMD: Males only

relative error in lx: MORTMatch relative error in lx: modmatch

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• Mortality

• Causes of Death




• Future work

Sources: Gathering COD Data

Types of sources

o Verbal Autopsies

o Household Surveys

o Hospital Records

o Sentinel Registration

o Demographic Surveillance Systems

o Sample Registration Systems

o Vital Registration with Certification of Cause of Death

Data providers

• WHO mortality database (Geneva)

• PAHO, EMRO, WPRO mortality databases

• National Ministries of Health

• Networks: INDEPTH, Matlab, India, etc.

• Researchers

• Literature Review

Almost all information related with causes of death is useful.

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> 5,000 country-years observed

• VR data don’t get more than 100 countries per year• VA and maternal deaths added important value

Data for more of 180 countries and territories

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Almost 1 billion deaths from 1950 to 2008, only 34% of total expected

Sub_Saharan_Africa_West

Oceania

Latin_America_Andean

Asia_South

Sub_Saharan_Africa_Southern

Caribbean

Asia_Central

Latin_America_Southern

North_Africa_Middle_East

Asia_East

Australasia

Latin_America_Tropical

Asia_Southeast

Asia_Pacific_High_Income

Europe_Central

Europe_Eastern

Latin_America_Central

North_America_High_Income

Europe_Western

1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

39,085

463,434

5,754,853

5,785,132

5,933,272

6,163,084

11,626,166

15,112,752

16,329,319

18,095,408

18,738,326

36,770,945

41,879,227

60,659,801

66,331,482

76,332,493

77,616,744

124,404,521

285,614,616

%Developed Countries 55Eastern & Central Europe 18Latin-America and Carb 16Asia & Oceania 7.5 Middle Eats & North Afr 1.9S.S.A. 0.7

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VA Literature Search Process

Search “Verbal Autopsy”

• 971 studies from Google Scholar• 320 studies from PubMed• 687 studies from Google-country search• 1978 total articles culled

Check for duplicates• 473 duplicate studies between Google Scholar and

PubMed

Screen studies using criteria

• 818 total studies screened from Google Scholar• 234 total studies screened from Google• 5 reviewers for studies from PubMed• 3 reviewers for studies from Google Scholar• 2 reviewers for studies from Google search

Extract data from selected studies • 172 studies from which data was extracted

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VA Literature Screening Criteria

• Four criteria:

1. Population based study

2. Using verbal autopsy method

3. Open to any age group

4. Open to any set of causes

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All Child Maternal 0

10

20

30

40

50

60

70

80

Types of Studies in the Cause of Death Verbal Autopsy Database

Study Category

Nu

mb

er o

f S

tud

ies

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1.3 million deaths from VA studies by GBD region and source

Europe Western

Europe Eastern

Caribbean

Asia Central

Oceania

LA Central

LA Southern

SSA Central

Asia East

SSA Southern

NA Miiddle East

Asia Southast

SSA West

SSA East

Asia South

10 100 1,000 10,000 100,000 1,000,000

Turkey

Andra P

Cherg

India DHS

INDEPTH

Tanzania Nat VA

Matlab

India SRS

India SCD

Sist Rew

1,000 10,000 100,000 1,000,000

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1.3 million deaths from VA studies

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Assess •Evaluate basic attributes: ICD format, coverage, age groupings, administrative regions, etc.•Check for consistency: totals/subtotals, clumped data, double counting

Correct •Correct restriction violations, redistribute unknown age and unknown sex deaths•Calculate remainder codes to reconcile totals and subtotals

Mapping •Based in list of 39 causes for adults and 26 for children under five•Comparable among countries and across ICD revision (ICD 1st to ICD 10th)•Also map to GBD Cause list 317 causes (280 are CoD)

Disaggregate •Disaggregate tabulated codes•Break up large age groups

Redistribute •Identifying Garbage Codes and Targets•Redistribute garbage codes

Cleaning: Preparing VR Data for Analysis

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Name of List and Number of Causes

* Causes of death

317(290)*

GBD 2005 Cause List (ICD 10 4 digit)

3

24

39

CodMod I

CodMod II

CodMod B

• ICD 1,2,3,4,5,6,7,8,9,10

• ICD 9 BTL, ICD 10 Tab A

• China (ICD 9 and 10)

• Russia (ICD 9 and 10)

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Mapping GBD Cause List with ICD Revisions and Other Tabulated List

BTL1 2 3 4 5 Tab B 6,7 Tab A 89 tab

9 VA 10Tab

10

GBD 2005 Cause List (317 )

GBD 1990 Cause List (100)

CODMOD level 2 (24)

CODMOD level B(39)

ICD and other formats1900 2000

CODMOD II

3

24

CODMOD I

Intentional injuries C24

Unintentional injuriesC23

Congenital anomaliesB22

Genitourinary diseases---Skin diseases----Musculoskeletal diseasesB21

Digestive diseases---Oral conditionsB20

Respiratory diseasesB19

Cardiovascular and circulatory diseasesB18

Mental and behavioral disorders--- Neurological conditions-- Sense organ diseasesB17

Endocrine, nutritional, blood and immune disorders B16

Diabetes mellitusB15

Malignant neoplasm and B. Other neoplasmB14

Small poxA13

Nutritional deficienciesA12

Perinatal and infant causesA11

Maternal conditionsA10

Respiratory infectionsA9

Meningitis and encephalitis and Hepatitis and Other infectious diseasesA8

Parasitic and vector diseasesA7

MalariaA6

Selected Vaccine Preventable Childhood DiseasesA5

Intestinal infectious diseasesA4

STDs excluding HIVA3

HIV/AIDSA2

TuberculosisA1

This level can be presented with 24 causes and subgroups and 3 big groups

CODMOD II

CODMOD B

3

24

39

CODMOD I

CODMOD II

CODMOD B

24.3War and civil conflict and Legally sanctioned deaths

24.2 Interpersonal violence

24.1 Self-inflicted injuries

23.8 Accidental exposure to other and unspecified factors 23.7 Accidental poisoning by and exposure to noxious substances (acute or chronic)

23.6 Exposure to smoke, fire and flaes, contact with heat and hot substances

23.5 Accidental drowning and submersion

23.3 Falls

23.1Transport Injures

20.2 Other digestive diseases

20.1 Cirrhosis of the liver

18.5 Other circulatory diseases

18.4 Cerebrovascular disease

18.2 Ischaemic heart disease

14.9 Other malignant and benign neoplasm

14.6 Cervix and Corpus uteri cancer

14.5 Breast cancer

14.4 Larynx , Trachea, bronchus and lung cancers

14.2 Stomach cancer

14.1 Esophagus cancer

8.3 Other infectious diseases

8.1 Meningitis and encephalitis

From the 24 causes we are dividing Malignant Neoplasm, CVD and Injuries

42

Evolution of Garbage Codes in GBD Studies

• 1990: ill defined; heart failure and atherosclerosis; cancer without defined site and injuries ill defined

• 2000: same codes of 1990 with better methods of redistribution

• 2005: Completely different approach, based on new concepts and methodso More garbage codes and more targetso Sequences for redistributiono Methods of redistribution

Distribution of Garbage Codes by Type and Region

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

SSA Asia LA Europe C&E ALL Europe W Caribbean N.America Australasia

SpecialsImmediateSequelaeIntermediateI&D UNSCancerIll Def

% o

f G

C

• ~20% total deaths from VR are GCs

• 10 causes accumulate 75%

• Intermediate causes are the most important Garbage Codes

Causes ICD 10 %

Ill-defined R00-R99 26.0

Heart Failure I50 18.0

Renal Failure N18 6.4

Atherosclerosis I70 6.0Malignant neoplasm without specification of site C80 4.8

Septicaemia A41 4.2

Essential (primary) hypertension I10 3.0

Exposure to Unspecified factor X59 2.7

Pulmonary embolism I26 2.2

Respiratory Failure J96 2.0

Percent of deaths with garbage codesSelect Countries of the Americas, circa 2005

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Causes of Death Modeling Strategy Challenges• Dependent variable: age-specific rates or age-specific cause-

specific mortality fractions.

• Model each cause as a function of critical covariates available for most countries/sites: GDP, education, tobacco consumption, HIV sero-prevalence, TFR, DTP coverage, SBA, water and sanitation, war, disasters ….

• Covariates only explain 30-40% of the variance depending on cause.

• Sparse data for some developing regions

• Compositional bias, data in each time period reflects a changing set of countries/sites

• Small numbers – VA studies and small countries have huge sampling and non-sampling variation

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Causes of Death Modeling Strategy – 3 Steps• Step 1 – run outlier resistant models using basic covariates

including negative binomial regression, quantile (L1) regression.

• Evaluate residuals – drop outliers using Box-plot methods, assess correlations over space and time in residuals using heatmaps.

• Use local regression methods (two-dimensional Loess) to model residuals. Space dimension relatedness is based on the observed correlation structure in the heatmaps.

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48

Using VR data for 2005

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50

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Validation of Models

• Many variants possible at each stage. How to choose most valid predictive models and how to pool results across a range of models.

• Three tests of predictive validity:

1) Exclude 20% of country-years at random and predict for them out of sample

2) Exclude last 10 years of sequence for all countries and predict them out of sample

3) Exclude 20% of countries and predict them entirely out of sample.

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• Mortality

• Causes of Death




• Future work

Systematic Reviews

• Objective:o To evaluate and interpret all available research evidence relevant

to a particular condition

• To date we have: o Recruited over 800 experts worldwide

o Worked with experts and Core team to revise the cause list

o Begun processing epidemiological reviews from experts

• Next steps:o Upcoming expert group meeting May 2010

o Complete systematic epidemiological reviews

o Peer review

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54

Cardiomyopathy Epi Review ProcessInitial search of primary databases (n=28,382)

Duplicates removed

Eligibility screening (n=301)Articles evaluated by 2 or more

abstractors and excluded by criteria on closer review (n=174)

Data abstractionRegional experts review

articles and collect data on standardized collection sheets.

Adjustments for bias and missing data is reviewed.

(n=pending)Incidence

Mortality

Case fatality

Prevalence

Inclusion criteria :1. diagnostic methods2. ICD coding3. epidemiological factors,4. population-based demographics

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Cardiomyopathy Epi Review Data

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• Mortality

• Causes of Death




• Future work

57

Analysis of Disease-Specific Data for YLD

YLD = Disability Weight x Incidence x Duration

The GBD links losses of health to disease and injury causes through the concepts of cases and sequelae.

For incident cases of a given disease or injury in the population, there will be a distribution of current and future health states in the population, and the GBD maps this distribution of health states to a small set of discrete entities for which epidemiological estimates and YLD calculations are made.

Case definitions are based upon expert group guidance

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Cardiomyopathy Disease Model

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• DisMod III uses a compartmental model of disease progression to infer consistent epidemiological parameters from sparse and noisy data.

Generic Model of Disease

DisMod III

StatesS: healthy (susceptible)C: diseased (condition of interest) D: dead from the diseaseM: dead from all other causes

Transition ratesi: incidencer: remissionƒ: case fatalitym: all other mortality

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DisMod III Analysis: CardiomyopathyCardiomyopathy for males in Asia Pacific High Income region in 2005

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DisMod III Analysis: CardiomyopathyCardiomyopathy for males in Asia Pacific High Income region in 2005

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Cardiomyopathy Prevalence

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• Mortality

• Causes of Death




• Future work

64

• Objectives:o Derive disability weights for ~250 sequelae, which capture the

major health consequences of all of the causes in the GBD Study

o Address criticisms of previous approaches:─ Focus on valuations from community respondents in a “Disability Weights

Measurement Survey”

─ Use of techniques that are well-matched to the intended measurement construct

o Provide transparent, standardized and replicable approach that will easily accommodate additions or amendments

Disability Weights Measurement

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• Disability weights provide the bridge between mortality and non-fatal outcomes in disability adjusted life years (DALYs)

• Disability weights quantify overall health levels associated with different states, on a continuum between perfect health (which has a value of 0) and death (which has a value of 1)o Construct reflects decrements from perfect health, distinct from broader

notions of well-being or social value

o Must be measured on meaningful cardinal scale

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• Survey componentso Community surveys in 6 sites (Tanzania, Indonesia, Bangladesh, Peru,

South Africa, United States), focusing on random paired comparison and time trade-off questions for 108 sequelae

o Open access Web-based surveys including all sequelae, and paired comparison, time trade-off and population equivalence questions

o Community surveys are using computer-assisted personal interview approach with laptops

Household interview in Pemba, TZ 10/23/2009

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• Mortality

• Causes of Death




• Future work

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Global Burden of Disease - Pakistan Presentation

Health & Medicine