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

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
Outline
Exploration of comorbidity in Medical Expenditure
Panel Surveys (MEPS) in USA
Comorbidity simulation: “COMO”

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. 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. 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. Parsing overall DWs into DWs for each
health state
•
6

7. Parsing overall DWs into DWs for each
health state
•
7

8. Age and comorbidity
8

9. Population-level predictions
9

10. 10
Dependent and independent comorbidity
for diabetes

11. 11
Major depression COPD
Asthma Migraine

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
Outline
Exploration of comorbidity in Medical Expenditure
Panel Surveys (MEPS) in USA
Comorbidity simulation: “COMO”

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. 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. Comorbidity correction by age
16

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