Findings and implications of the Global Burden of Disease Study 2010 Royal Society, London, 14 December 2012 Professor Theo Vos School of Population Health

1. Non-Fatal Health Outcomes:
Years Lived with Disability
Findings and implications of the Global Burden of Disease Study 2010
Royal Society, London, 14 December 2012
Professor Theo Vos
School of Population Health

2. Outline
Summary of methods
Results
Reflections
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3. New approach
GBD 2010 Previous
method
Prevalence * DW Incidence * duration * DW
“True” systematic reviews and Choice of single data set for a
synthesis of all available data given population/time
Consistency check between Consistency check between
disease parameters disease parameters
Adjustments for comorbidity Comorbidity ignored
Uncertainty quantified No uncertainty
DWs: paired comparisons; DWs: panel of health experts;
population surveys person trade off
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4. Analytical steps
Systematic
Dismod-MR Prevalence
review
Covariates: ‒ Adjustment data
‒ Study characteristics points
• Definition ‒ Pooling info
• Study type ‒ Predicting “gaps”
• Representative? ‒ Consistency between
‒ Country characteristics parameters
• GDP
• Access to health
services
• Conflict

5. DisMod-MR
Bayesian meta-regression
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6. DisMod-MR
Bayesian meta-regression
Example of inconsistent data: osteoarthritis knee
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7. Analytical steps
Severity
distribution
Systematic
DisMod-MR Prevalence YLDs
review
Covariates: ‒ Adjustment data
‒ Study characteristics points DWs
• Definition ‒ Pooling info
• Study type ‒ Predicting “gaps”
• Representative? ‒ Consistency between
parameters
‒ Country characteristics. Disability weight
• GDP surveys
• Access to health
services
• Conflict
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8. GBD 2010 disability weights
 Large empirical effort
– In-person surveys in Indonesia, Bangladesh, Tanzania, and Peru
– Telephone survey in US
– Internet survey
 Parsimonious set of 220 health states presented as short
lay descriptions prepared with expert groups
 Pair-wise comparisons: “Who is the healthier?”
 Random set of 15 pairs for each respondent
 Some of the web survey respondents answered
population health equivalence questions to help anchor
on scale 0-1
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9. Heat maps paired comparisons
High agreement in choices between very
healthy vs. unhealthy outcomes (>90%)
Worst
Second
sequela
in pair
Split responses for similar
outcomes (~50%) … or vice versa
Best (<10%)
Best Worst
First sequela in pair
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10. Comparisons between surveys
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11. Survey and pooled results
6 6 6
r = 0.90 r = 0.94 r = 0.97
4 4 4
United States
Indonesia
Peru
2 2 2
0 0 0
-2 -2 -2
-2 0 2 4 6 -2 0 2 4 6 -2 0 2 4 6
Pooled Pooled Pooled
6 6 6
r = 0.75 r = 0.94 r = 0.98
4 4 4
Bangladesh
Tanzania
Web
2 2 2
0 0 0
-2 -2 -2
-2 0 2 4 6 -2 0 2 4 6 -2 0 2 4 6
Pooled Pooled Pooled
 High degree of consistency across diverse cultural
settings and respondent characteristics
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12. Special analytical cases
 Impairments such as vision loss and intellectual disability
‒ Outcome from many diseases and injuries
‒ Measure total  distribution by underlying cause  constrain to
total
 Injuries
‒ Cause of injury (road traffic accident or fall)
‒ Nature of injury that causes disability (head injury or fracture)
‒ Short-term and long-term disabling consequences
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13. Outline
Summary of methods
Results
Reflections
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14. Global YLDs per person by age
and sex, 1990 and 2010
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15. Drivers of change in YLDs
1990–2010
50%
40%
33% 38%
25%
5%
0%
-25%
-50%
all causes Group 1 NCD Injuries
% change 1990-2000 % change due to change in rates
% change due to ageing % change due to population growth
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16. Percentage of YLDs in 2010
by cause and age
Males Females
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17. Percentage of YLDs in 2010
by cause and region
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18. Global YLDs ranks, 1990 and 2010
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19. Prevalence and DW for top 5
conditions
Prevalence Average DW
Back pain 9% 0.14
Depression 4% 0.23
Anaemia 14% 0.04
Neck pain 5% 0.11
COPD 5% 0.10
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20. Outline
Summary of methods
Results
Reflections
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21. Advances
 Much more data-driven process
 Less researcher „choices‟
 Uncertainty
 Greater involvement by disease/injury experts and
understanding of methods
– …. old adagio of GBD “decoupling epidemiology from advocacy”
more acute than ever ….
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22. Challenges
 Large heterogeneity
– True variation in disease experience
– Methodological differences
 Plea for greater standardisation in data collections
 Data gaps
– “Underserved” world regions
– “Underserved” diseases
– Surprising lack of data on severity and often not comparable
Plea for representative large data collections with diagnostic and
severity information to allow co-morbidity adjusted severity
measures
Mapping from patient derived severity measures to our “DW space”
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