Years lived with disability:
Methods and key findings
June 18, 2013
Sarah Wulf, MPH
PhD student, Global Health
Research As...
2
DALYs = YLLs + YLDs
Overall
health loss
Health loss due
to premature
mortality
Health loss due to
living with
disability
Challenges of YLD estimation
3
Data sources
Uncertainty
• No single source of data for YLDs from all
conditions
• Inconsis...
4
YLD calculation
Prevalence:
─ Estimates of country-/year-/age-/sex-specific disease sequela prevalence
─ Identify and po...
Data sources
• Systematic literature reviews
• Population surveys
• Cancer registries
• Renal replacement therapy registri...
Data adjustments
6
Data issue Adjustment
Inconsistent case definition
Measurement instrument bias
Non-representative popul...
Methods
• DisMod-MR
• Natural history models
• Geospatial models
• Back-calculation models
• Registration completeness mod...
DisMod-MR
• Bayesian Disease Modeling Meta-Regression tool
• Negative binomial statistical model
• Performs crosswalks to ...
Three estimation strategies with DisMod-MR
9
Direct estimation of
disease sequelae
Maternal sepsis
Disability envelopes
fo...
DisMod-MR output
10
• Epidemiological parameters estimated for:
o187 countries
oYears 1990, 2005, 2010
oSingle-year age gr...
Comorbidity adjustment
11
1 Simulate comorbidity distribution
• Use prevalence and disability weights across hypothetical ...
Key findings
12
13
Global YLD rates by age, 1990 and 2010
14
Global YLDs by cause/age, 2010
Global top 10 causes of YLDs, 1990 to 2010
15
Females
Males
Note: Rankings are based on age-standardized YLD rates.
16
% YLDs by cause and region, 2010
17
% YLDs by cause and region, 2010
18
% YLDs caused by cancers, 2010
19
% YLDs caused by cancers, 2010
Females age 30-34
20
Major shifts in global YLDs, 1990 to 2010
1) Very slow decline in YLD rates relative to YLL rates.
2) Steady shift towa...
Thank you
Sarah Wulf, MPH
swulf@uw.edu
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Years lived with disability: Methods and Key Findings

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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

Published in: Health & Medicine
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  • 179 covariates220 DWs289 causes of disability1160sequelae41 diseases have severity distributions
  • All bias at once. What do we do about them? Typical is crosswalk.
  • We used four sets of alternative methods for some disorders because of variation in the types of data available and the complexity of their spatial and temporal distributions.Natural history- HIV/AIDS- Measles- PertussisGeospatial - Ascariasis- Trichuriasis- Hookworm- SchistosomiasisBack-calculation- Diptheria- Tetanus- RabiesRegistration completeness- Tuberculosis- Dengue
  • After modeling all the disease sequela, we have output that consists of . . .
  • First study to:Have country-level estimates for all these conditionsDefine uncertainty around all estimatesAdjust estimates for comorbidity.by comorbidity, I mean . . .. . . A person can have multiple conditions at the same time.Important because . . .. . . Burden not additive Need to adjust for that
  • Years lived with disability: Methods and Key Findings

    1. 1. Years lived with disability: Methods and key findings June 18, 2013 Sarah Wulf, MPH PhD student, Global Health Research Associate, IHME
    2. 2. 2 DALYs = YLLs + YLDs Overall health loss Health loss due to premature mortality Health loss due to living with disability
    3. 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. 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. 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. 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. 7. Methods • DisMod-MR • Natural history models • Geospatial models • Back-calculation models • Registration completeness models 7
    8. 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. 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. 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. 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. 12. Key findings 12
    13. 13. 13 Global YLD rates by age, 1990 and 2010
    14. 14. 14 Global YLDs by cause/age, 2010
    15. 15. Global top 10 causes of YLDs, 1990 to 2010 15 Females Males Note: Rankings are based on age-standardized YLD rates.
    16. 16. 16 % YLDs by cause and region, 2010
    17. 17. 17 % YLDs by cause and region, 2010
    18. 18. 18 % YLDs caused by cancers, 2010
    19. 19. 19 % YLDs caused by cancers, 2010 Females age 30-34
    20. 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. 21. Thank you Sarah Wulf, MPH swulf@uw.edu

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