GHME 2013 Conference Session: Global Burden of Diseases, Injuries, and Risk Factors Study 2010: workshop on methods and key findings Date: June 18 2013 Presenter: Sarah Wulf Institute: Institute for Health Metrics and Evaluation (IHME), University of Washington

1. Years lived with disability:
Methods and key findings
June 18, 2013
Sarah Wulf, MPH
PhD student, Global Health
Research Associate, IHME

2. 2
DALYs = YLLs + YLDs
Overall
health loss
Health loss due
to premature
mortality
Health loss due to
living with
disability

3. Challenges of YLD estimation
3
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. 4
YLD calculation
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

5. Data sources
• Systematic literature reviews
• Population surveys
• Cancer registries
• Renal replacement therapy registries
• Hospital data
• Outpatient data
• Cohort follow-up studies
• Disease surveillance systems
5

6. Data adjustments
6
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

7. Methods
• DisMod-MR
• Natural history models
• Geospatial models
• Back-calculation models
• Registration completeness models
7

8. 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
8

9. Three estimation strategies with DisMod-MR
9
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

10. DisMod-MR output
10
• 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

11. Comorbidity adjustment
11
1 Simulate comorbidity distribution
• Use prevalence and disability weights across hypothetical 20,000
people in each demographic group
2 Calculate combined disability weights (CDW)
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

12. Key findings
12

13. 13
Global YLD rates by age, 1990 and 2010

14. 14
Global YLDs by cause/age, 2010

15. Global top 10 causes of YLDs, 1990 to 2010
15
Females
Males
Note: Rankings are based on age-standardized YLD rates.

16. 16
% YLDs by cause and region, 2010

17. 17
% YLDs by cause and region, 2010

18. 18
% YLDs caused by cancers, 2010

19. 19
% YLDs caused by cancers, 2010
Females age 30-34

20. 20
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.

21. Thank you
Sarah Wulf, MPH
swulf@uw.edu