Global Burden of Diseases, Injuries, and Risk Factors Study 2010: Comorbidity

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GHME 2013 Conference
Session: Global and national Burden of Disease IV
Date: June 18 2013
Presenter: Theo Vos
Institute:
Institute for Health Metrics and Evaluation (IHME)
University of Washington

Published in: Health & Medicine, Education
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Global Burden of Diseases, Injuries, and Risk Factors Study 2010: Comorbidity

  1. 1. UNIVERSITY OF WASHINGTON Global Burden of Diseases, Injuries, and Risk Factors Study 2010: Comorbidity June 18, 2013 Theo Vos Professor of Global Health
  2. 2. 2 Outline Exploration of comorbidity in Medical Expenditure Panel Surveys (MEPS) in USA Comorbidity simulation: “COMO”
  3. 3. Comorbidity in MEPS 3 Medical Expenditure Panel Surveys (MEPS) o New panel starts every year o 5 data collection points over two years for each panel o Main focus on expenditure of any health service contact o 2000 to 2009 o 192,806 observations from 108,522 individuals o Diagnostic info on 158 GBD disease and injury categories o Health status information by SF-12, twice over two years
  4. 4. Mapping SF-12 to GBD disability weights • Convenience sample of 60 IHME staff who had not worked on GBD • Asked to fill in SF-12 for a random pick of 50 out of 60 health states spanning the spectrum from very mild to most severe in the disability weight surveys Very mild: “has some difficulty with distance vision, for example reading signs, but no other problems with eyesight” Most severe: “hears and sees things that are not real and is afraid, confused, and sometimes violent. The person has great difficulty with communication and daily activities, and sometimes wants to harm or kill himself (or herself) • Respondents asked to fill in SF-12 for an individual as described in the lay descriptions presented 4
  5. 5. Mapping SF-12 to GBD disability weights • 394 observations (18% of total) excluded from further analysis as they were more than two standard deviations from the median • Loess regression of remaining SF-12 scores and disability weights 5
  6. 6. Parsing overall DWs into DWs for each health state • 6
  7. 7. Parsing overall DWs into DWs for each health state • 7
  8. 8. Age and comorbidity 8
  9. 9. Population-level predictions 9
  10. 10. 10 Dependent and independent comorbidity for diabetes
  11. 11. 11 Major depression COPD Asthma Migraine
  12. 12. 12 Conclusions from MEPS o Age is no longer a major predictor of comorbidity if a large number of health states are accounted for o A multiplicative model of “combining” disability weights derived for all ages replicates the age pattern of levels of disability reported by individuals on SF-12 (and translated by us into GBD disability weights) o After correcting for independent comorbidities, adding dependent probabilities of co-occurring conditions makes little difference
  13. 13. 13 Outline Exploration of comorbidity in Medical Expenditure Panel Surveys (MEPS) in USA Comorbidity simulation: “COMO”
  14. 14. Disability in a comorbid case: individual perspective • The experience of living with multiple diseases: o Disability weights are multiplicative, not additive o Cumulative (multiplicative) weight is lower than additive 14
  15. 15. Population perspective oSimulate hypothetical populations of 10,000 for each age, sex, year, country: 0.25 billion people simulated! oUse prevalence of each of 1120 health states as probabilities oDetermine for each individual if they have 0, 1, 2 …n comorbid health states oUse multiplicative function to get “comorbidity corrected” total DW for each individual oProportionately reduce the value of each comorbid health state’s DW for that individual oAverage all DWs for all individuals with a health state after the correction 15
  16. 16. Comorbidity correction by age 16
  17. 17. 17 Conclusions oUseful new insights on comorbidity from dataset with rich diagnostic and health status information oSearch for similar non-USA datasets, preferably in LMIC, to replicate these analyses: potential candidates in China and Turkey oDecision to seriously address comorbidity in GBD was most compelling reason to abandon the previous approach of incidence YLD Incidence

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