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Years lived with disability: Methods and Key Findings
This document outlines the methods and findings related to years lived with disability (YLD), emphasizing the challenges in estimating YLD due to data inconsistencies and uncertainties. It highlights that significant shifts occurred between 1990 and 2010, with a slow decline in YLD rates and a notable increase in the burden from non-communicable diseases. Overall, it underscores the reality that while people are living longer, they are experiencing more disabilities.
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Years lived with disability: Methods and Key Findings
- 1. Years lived withdisability: 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 YLDestimation 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: ─ Estimatesof 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 • Systematicliterature 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 issueAdjustment 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 • Naturalhistory models • Geospatial models • Back-calculation models • Registration completeness models 7
- 8. DisMod-MR • Bayesian DiseaseModeling 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 strategieswith 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 • Epidemiologicalparameters 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 Simulatecomorbidity 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.
- 13. 13 Global YLD ratesby age, 1990 and 2010
- 14. 14 Global YLDs bycause/age, 2010
- 15. Global top 10causes of YLDs, 1990 to 2010 15 Females Males Note: Rankings are based on age-standardized YLD rates.
- 16. 16 % YLDs bycause and region, 2010
- 17. 17 % YLDs bycause and region, 2010
- 18. 18 % YLDs causedby cancers, 2010
- 19. 19 % YLDs causedby cancers, 2010 Females age 30-34
- 20. 20 Major shifts inglobal 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
Editor's Notes
- #2 179 covariates220 DWs289 causes of disability1160sequelae41 diseases have severity distributions
- #7 All bias at once. What do we do about them? Typical is crosswalk.
- #8 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
- #10 After modeling all the disease sequela, we have output that consists of . . .
- #11 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