Global Burden of Disease:Methods and Implications for Indonesia

26 February 2014

Sarah Wulf, MPH, PhD candidate

Research Associate

2

Is Mary healthier than Rosa?

3

Outline: Burden of Disease

MotivationWhy do we care about it?

MethodsHow do we measure it?

ImplicationsWhat do we do with the results?

Motivation: summarize population health

4

Primary goal: to have an accurate, comprehensive, and comparable summary of a population’s health

Historically, heavy dependence ono Mortality rates

o Life expectancy

for key decision making and planning

Do these metrics achieve the goal?

Then why this dependence?

Motivation: better health burden evidence

5

Understanding disease determinants and outcomes

Set research and

development priorities

Manage program

implementation

Establish health agendas

Monitor progress Evaluate what works and what does not

1

2

3

Motivation: Global Burden of Disease

6

• GBD is a systematic, scientific effort to quantify the comparative magnitude of health loss to diseases, injuries, and risk factors by age, sex, and geography over time

• GBD approacho Analyze all available sources of information and correct problems

with the data

o Measure health loss using a common metric for a comprehensive set of diseases, injuries, and risk factors

o Decouple epidemiological assessment and advocacy

o Inject non-fatal health outcomes into health policy debate

Teaser: Global Burden of Disease results

7

http://viz.healthmetricsandevaluation.org

http://viz.healthmetricsandevaluation.org/gbd-compare/

8

Teaser: Top 10 causes of Death in Indonesia

9

Teaser: Top 10 causes of DALYs in Indonesia

10

Outline: Burden of Disease

MotivationWhy do we care about it?

MethodsHow do we measure it?

ImplicationsWhat do we do with the results?

11

DALYs = YLLs + YLDs

Overall health loss

Health loss due to premature

mortality

Health loss due to living with disability

Methods: Overview

• GBD approach to measurement is different than many single disease, injury or risk factor studies.

• Differences are philosophical and technical. o Comprehensive Comparisons

o Estimating and Communicating Uncertainty

o Internal Consistency

o Iterative Approach to Estimation

12

A. Communicable, maternal, neonatal, and nutritional conditions

B. Non-communicable causes including cancers, diabetes, cardiovascular disorders and chronic respiratory diseases

C. Injuries, both unintentional and intentional

Three broad groups of causes of health loss

13

Cause List

o List of causes used to produce estimates of mortality, morbidity and burden

o Hierarchical structure of diseases and injuries

o 5 levels of aggregation

o Mutually exclusive categories in each level: add up to 100% of burden

o GBD 2010o 291 diseases and injuries and 1,160 sequelae

o Cause of death for 235 diseases and injuries

o Non-fatal estimates for 290 diseases and injuries and 1160 sequelae

14

Methods

1. Demographics

Population and all-cause mortality

2. Covariates

3. Cause of death burden

Years of Life Lost due to premature mortality (YLLs)

4. Non-fatal health burden by cause

Years Lived with Disability (YLDs)

5. Total burden

Disability-Adjusted Life Years (DALYs)

6. Risk factor attribution

15

Methods: Demographics

• Mortality rate = (deaths in an age group in a year) (population in an age group at the midpoint of the year)

• Commonly reported probabilities of death

1) 1q0 – infant mortality ‘rate’, the probability of death between birth and exact age 1.

2) 5q0 – child mortality, the probability of death between birth and exact age 5.

3) 45q15 – adult mortality, the probability of death between age 15 and exact age 60 conditional on being alive at age 15.

16

1

SWEDEN

Val

ue

Year1750 177518001825185018751900192519501975 2000

0

0.1

0.2

0.3

0.4

0.5

Male 5q0Female 5q0

Val

ue

Year1750 177518001825185018751900192519501975 2000

0

0.2

0.4

0.6

0.8

1

Male 45q15Female 45q15

250 Years of Child and Adult Mortality

17

1

Alternative Mortality Measurement Methods

1) Complete birth histories

2) Summary birth histories

3) Sibling survival

4) Household deaths in the last 12 months

5) Demographic surveillance systems

18

1

19

1

20

1

21

1

Methods

1. Demographics

Population and all-cause mortality

2. Covariates

3. Cause of death burden

YLLs

4. Non-fatal health burden by cause

YLDs

5. Total burden

DALYs

6. Risk factor attribution

22

Methods: Covariates

• A major component of GBD is making estimates if data are sparse or conflicting data from multiple sources

• Covariates help in modeling

• Database of 84 covariate topic areas and 179 variants of the covariates*

• Missing data addressed using spatial-temporal regression and Gaussian process regression

23

2

* The full list of covariates can be found in the supplementary appendix to the Lancet comment "GBD 2010: design, definition, and metrics" (http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(12)61899-6/fulltext)

Covariate: Lag-Distributed Income (LDI)• A composite of 7 different GDP

serieso IMF ID (2005 base year)

o Penn ID (2005 base year)

o WB ID (2005 base year)

o Maddison ID (1990 base year)

o WB USD (2005 base year)

o IMF USD (2005 base year)

o UN USD (2005 base year)

• Composite GDP smoothed over preceding 10 years to produce LDI

• Ref: James et al (http://www.pophealthmetrics.com/content/10/1/12)

IMF= International Monetary Fund

Penn=University of Pennsylvania

Meddison : Angus Maddison’s research homepage at the University of Groningen Department of Economics 24

1000

015

000

2000

025

000

3000

035

000

LD

I (I

$ pe

r ca

pita

)1940 1960 1980 2000 2020

Year

Lag Distributed Income Per Capita in Australia

2

25

Covariate:Alcohol (liters per capita)

FAO Food Balance Sheets, World Drink Trends

2

Methods

1. Demographics

Population and all-cause mortality

2. Covariates

3. Cause of death burden

YLLs

4. Non-fatal health burden by cause

YLDs

5. Total burden

DALYs

6. Risk factor attribution

26

Methods: Cause of death burden

27

3

1) Identify and obtain all published and unpublished sources of data on causes of death.

2) Assess and enhance data quality and comparability

3) Develop and apply models for 235 individual causes of death

4) CoDCorrect – develop final estimates for each age-sex-country-year where the sum of the 235 individual causes of death equals the age-sex specific all-cause mortality rate

Cause of Death Ensemble modeling

1) “CODEm” -- used for most causes

2) Develop a large range of plausible models for each cause – all combination of selected covariates tested. Models retained that are significant with coefficients in the expected direction. All permutations tested for four families of models: mixed effects log rates, mixed effects logit cause fractions, ST-GPR log rates, ST-GPR logit cause fractions.

3) Create combinations ‘ensembles’ of the best performing models

4) Statistical tests of out-of-sample predictive validity all models

5) Select the best performing model or ensemble of models

28

3

CoDCorrect Algorithm

oEstimates for each age-sex-country-year for the 235 causes are constrained to equal the demographic estimate of all cause mortality for that age-sex-country-year.

oThis rescaling is repeated1000 times to propagate the uncertainty in the estimates for each cause into the final results

29

3

YLL calculation

30

YLLs X = deaths X * e X

Years of life lost due to premature

mortality

Number of deaths at age x

Standard life expectancy at

age x

3

31

Standard life expectancy

Based on lowest mortality rates at each age observed in any population of 5M or more.

Most estimates for Japanese women

Same standard for men and women

Age Life expectancy (years)0 86•021 85•215 81•25

10 76•2715 71•2920 66•3525 61•4030 54•4635 51•5340 46•6445 41•8050 37•0555 32•3860 27•8165 23•2970 18•9375 14•8080 10•9985 7•6490 5•0595 3•31

100 2•23105 1•63

3

32

3

33

3

Methods

1. Demographics

Population and all-cause mortality

2. Covariates

3. Cause of death burden

YLLs

4. Non-fatal health burden by cause

YLDs

5. Total burden

DALYs

6. Risk factor attribution

34

Methods: Non-fatal burden by cause

35

YLDs = Prev * DW

Years lived with disability

Prevalence of condition

Disability weight

4

Methods: Non-fatal burden by cause

• Incidence rate = (number of new cases of a disease) (person-time of observation)

• Prevalence rate = (number of individuals with a disease) (population)

• Prevalence ≈ Incidence * Durationo assuming incidence/remission/mortality rates are relatively stable

over time and/or duration is short

36

4

37

Challenges of YLD estimation

Data sources

Uncertainty

• No single source of data for YLDs from all conditions

• Inconsistency and gaps in information

• Uncertainty from data itself, lack of data, disability weights

Process specifications

• Complex disease epidemiology

• Severity distributions of health states

• Comorbidity

4

38

YLD calculation

𝑌𝐿𝐷𝑠𝑑𝑖𝑠𝑒𝑎𝑠𝑒= ∑𝑠𝑒𝑞𝑢𝑒𝑙𝑎=𝑖

𝑗

𝑃𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒𝑖∗𝐷𝑖𝑠𝑎𝑏𝑙𝑖𝑡𝑦 h𝑊𝑒𝑖𝑔 𝑡𝑖

Prevalence:

─ Estimates of country-/year-/age-/sex-specific disease sequela prevalence

─ Identify and pool all usable data sources

Disability weights (DWs):

─ Estimates of the disability associated with each health state

─ GBD Disability Survey, 2012

4

39

Data sources

• Systematic literature reviews

• Population surveys

• Cancer registries

• Renal replacement therapy registries

• Hospital data

• Outpatient data

• Cohort follow-up studies

• Disease surveillance systems

4

40

Data adjustments

Data issue Adjustment

Inconsistent case definition

Measurement instrument bias

Non-representative population bias

Incompleteness

Selection bias

Outlier studies

Correct for at-risk population

Downweight

Adjust upwards

Crosswalk

4

41

Methods

• DisMod-MR

• Natural history models

• Geospatial models

• Back-calculation models

• Registration completeness models

4

42

DisMod

• Bayesian Disease Modeling statistical tool

• Performs crosswalks to adjust for methodological variation

• Incorporates assumptions to inform the model

• Borrows strength using covariates and super-region, region, and country random effects to inform regions/countries with little or no data

• Forces consistency among disease parameters

4

43

Three estimation strategies with DisMod

Direct estimation of disease sequelae

Maternal sepsis

Disability envelopes for etiological attribution

Otitis media Congenital Meningitis Other causes

Hearing loss

Disability envelopes for disease sequelae Diabetes mellitus

Diabetic neuropathy

Diabetic foot ulcer

Diabetic amputation

Uncomplicated diabetes

Diabetic retinopathy

4

44

DisMod output

• Epidemiological parameters estimated by:

oCountry

oYear

oAge

oSex

• Estimates repeated 1,000 times to define uncertainty

Need to build in reality of comorbidity

4

Comorbidity adjustment

45

1 Simulate comorbidity distribution

• Use prevalence and disability weights across hypothetical 20,000 people in each demographic group

2 Calculate combined disability weights (CDW)

where n = number of health states observed for individual i

3 Reaggregate by disease sequela

• Apportion CDWs to each of the contributing sequelae in proportion to the DW of a sequela on its own

4 Quantify uncertainty

• Repeat 1,000 times to estimate uncertainty

Comorbidity-adjusted YLDs with uncertainty

4

Methods

1. Demographics

Population and all-cause mortality

2. Covariates

3. Cause of death burden

YLLs

4. Non-fatal health burden by cause

YLDs

5. Total burden

DALYs

6. Risk factor attribution

46

47

DALYs = YLLs + YLDs

Overall health loss

Health loss due to premature

mortality

Health loss due to living with disability

Methods: Total burden5

Methods

1. Demographics

Population and all-cause mortality

2. Covariates

3. Cause of death burden

YLLs

4. Non-fatal health burden by cause

YLDs

5. Total burden

DALYs

6. Risk factor attribution

48

Methods: Risk factor attribution

49

6

GBD 2010 – risks quantifiedUnimproved water and sanitation

Unimproved water

Unimproved sanitation

Air pollution

Ambient particulate matter pollution

Household air pollution from solid fuels

Ambient ozone pollution

Other environmental risks

Residential radon

Lead exposure

Child and maternal undernutrition

Suboptimal breastfeeding

Non-exclusive breastfeeding

Discontinued breastfeeding

Childhood underweight

Iron deficiency

Vitamin A deficiency

Zinc deficiency

Tobacco smoking and secondhand smoke

Tobacco smoking

Second-hand smoke

Alcohol and other drugs

Alcohol use

Drug use (opioids, cannabis, amphetamines)

Physiological risks for chronic diseases

High fasting plasma glucose

High total cholesterol

High systolic blood pressure

High body mass index

Low bone mineral density

Sexual abuse and violence

Childhood sexual abuse

Intimate partner violence

50

6

Dietary risk factors and physical inactivity

Diet low in fruits

Diet low in vegetables

Diet low in whole grains

Diet low in nuts/seeds

Diet low in milk

Diet high in unprocessed red meat

Diet high in processed meat

Sugar-sweetened beverages

Diet low in fibre

Diet low in calcium

Diet low in seafood omega-3

Diet low in polyunsaturated fatty acid (PUFA)

Diet high in trans fatty acids

Diet high in sodium

Physical inactivity and low physical activity

Occupational exposures

Occupational exposure to asbestos

Occupational exposure to arsenic

Occupational exposure to benzene

Occupational exposure to beryllium

Occupational exposure to cadmium

Occupational exposure to chromium

Occupational exposure to diesel

Occupational exposure to formaldehyde

Occupational exposure to nickel

Occupational exposure to polycyclic aromatic hydrocarbons

Occupational exposure to second hand smoke

Occupational exposure to silica

Occupational exposure to sulfuric acid

Occupational exposure to asthmagens

Occupational exposure to particulates and gases

Occupational noise

Occupational risk factors for injury

Occupational low back pain

GBD 2010 – risks quantified (2)

51

6

Calculating risk factor burden

1. Select risk-outcome pairs;

2. Estimate exposure distributions to each risk factor in the population;

3. Estimate cause effect sizes: relative risk per unit of exposure for each risk-outcome pair;

4. Choose a counterfactual exposure distribution: theoretical minimum risk exposure distribution (TMRED); and

5. Compute attributable burden, including uncertainty.

52

6

Risk-outcome inclusion criteria

1. Likely importance of a risk factor to disease burden or policy;

2. Availability of sufficient data and methods to enable estimation of exposure distributions by country for at least one of the study periods;

3. Sufficient evidence for causal effect (convincing or probable evidence) and to estimate outcome-specific effect sizes; and

4. Evidence to support generalizability of effect sizes to populations other than those included in epidemiological studies.

53

6

Strengths of GBD methods

• Comprehensive Comparisons

• Estimating and Communicating Uncertainty

• Internal Consistency

• Iterative Approach to Estimation

54

55

Outline: Burden of Disease

MotivationWhy do we care about it?

MethodsHow do we measure it?

ImplicationsWhat do we do with the results?

56

Using GBD to Answer Four Questions

Comparable results across countries and over time allow for examination of 4 questions:

1) What are the main causes of health loss in a country today?

2) What causes are getting worse and which are improving?

3) Compared to a set of relevant countries, what causes have rates that are substantially higher (or lower)?

4) Compared to the lowest rates for each disease in the set of relevant countries, where is the greatest potential to reduce burden?

57

58

59

60

61

62

Key findings in Indonesia

• Strokeo Top cause of burden

o Opportunities for intervention: hypertension management, smoking cessation, dietary risks

• Tuberculosiso Second highest cause of burden, despite reductions in mortality

o Opportunities for intervention: increased case detection, prevention strategies

• Road traffic injurieso Third highest cause of burden, still increasing

o Opportunities for intervention: road engineering, helmet/seatbelt law enforcement, vehicle safety standards

• Diabetes and Chronic Kidney Diseaseo Rapid rise in both since 1990

o Opportunities for intervention: improved prevention and management in primary care

63

Implications for Indonesia

• Large variation across geography and socioeconomic status in Indonesia warrants a closer look to determine the people and communities at highest risk of health burden.

• Indonesia needs better control of major risk factors, especially dietary risk factors like low fruit intake.

• Road traffic injuries continue to contribute substantially to national health burden and require efforts by the health and transportation sectors to reduce burden.

• Communicable diseases need to be addressed also, especially tuberculosis, diarrhea, vaccine-preventable diseases, typhoid, malaria, and HIV.

GBD goes on…

• Shifting to annual updates of burden – next one: 2014 with estimates up to 2013

• Continue to update the data, methods and computational infrastructure

• IHME works closely with countries to update, revise and increase the utility of GBD measurements for policy making

• Critical next step for countries: transition to subnational measurements of burden of disease

• Planning to add health expenditure by disease

64

…to subnational Indonesia!

• Subnational estimates for burden of disease by province in Indonesia will be included in GBD 2015.

• Subset of diseases will get extra focus:

65

• Stroke• Ischemic heart disease• Tuberculosis• Diabetes• Cancer• COPD• Road traffic injuries• Lower respiratory infections (and

etiologies)

• Diarrhea (and etiologies)

• Maternal conditions• Malaria• Dengue• HIV• Leprosy• Yaws• Perhaps more . . .