Years lived with disability:Methods and key findings

June 18, 2013

Sarah Wulf, MPH

PhD student, Global Health

Research Associate, IHME

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DALYs = YLLs + YLDs

Overall health loss

Health loss due to premature

mortality

Health loss due to living with disability

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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

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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

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Data sources

• Systematic literature reviews

• Population surveys

• Cancer registries

• Renal replacement therapy registries

• Hospital data

• Outpatient data

• Cohort follow-up studies

• Disease surveillance systems

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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

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Methods

• DisMod-MR

• Natural history models

• Geospatial models

• Back-calculation models

• Registration completeness models

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DisMod-MR

• Bayesian Disease Modeling Meta-Regression tool

• Negative binomial statistical model

• 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

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Three estimation strategies with DisMod-MR

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

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DisMod-MR output

• Epidemiological parameters estimated for:

o187 countries

oYears 1990, 2005, 2010

oSingle-year age groups

oBoth sexes

• Estimates repeated 1,000 times to define uncertainty

Need to build in reality of comorbidity

Comorbidity adjustment

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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

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Key findings

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Global YLD rates by age, 1990 and 2010

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Global YLDs by cause/age, 2010

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Global top 10 causes of YLDs, 1990 to 2010

Females

Males

Note: Rankings are based on age-standardized YLD rates.

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% YLDs by cause and region, 2010

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% YLDs by cause and region, 2010

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% YLDs caused by cancers, 2010

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% YLDs caused by cancers, 2010 Females age 30-34

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Major shifts in global YLDs, 1990 to 2010

1) Very slow decline in YLD rates relative to YLL rates.

2) Steady shift toward a larger share of burden from YLDs.

3) The main causes of YLDs are non-communicable diseases.

4) People are living longer but with more disability.

Thank you

Sarah Wulf, MPH

swulf@uw.edu