Non-Fatal Health Outcomes: years lived with disability

Non-Fatal Health Outcomes:

Years Lived with Disability

Findings and implications of the Global Burden of Disease Study 2010Royal Society, London, 14 December 2012

Professor Theo Vos

School of Population Health

Summary of methods

Results

Reflections

Outline

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GBD 2010 Previous method

Prevalence * DW

“True” systematic reviews and synthesis of all available data

Consistency check between disease parameters

Adjustments for comorbidity

Uncertainty quantified

DWs: paired comparisons; population surveys

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Incidence * duration * DW

Choice of single data set for a given population/time

Consistency check between disease parameters

Comorbidity ignored

No uncertainty

DWs: panel of health experts; person trade off

New approach

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Analytical steps

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Systematic review

Dismod-MR

Covariates:‒ Study characteristics

• Definition• Study type• Representative?

‒ Country characteristics• GDP• Access to health

services• Conflict

‒ Adjustment data points

‒ Pooling info‒ Predicting “gaps” ‒ Consistency between

parameters

Prevalence

DisMod-MRBayesian meta-regression

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5

Example of inconsistent data: osteoarthritis knee

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DisMod-MRBayesian meta-regression

Prevalence

Disability weight surveys

DWs

Severity distribution

YLDsSystematic

reviewDisMod-MR

Covariates:‒ Study characteristics

• Definition• Study type• Representative?

‒ Country characteristics.• GDP• Access to health

services• Conflict

‒ Adjustment data points

‒ Pooling info‒ Predicting “gaps” ‒ Consistency between

parameters

7

Analytical steps

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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Heat maps paired comparisons

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Best Worst

Best

Worst

First sequela in pair

Second sequela in pair

High agreement in choices between very healthy vs. unhealthy outcomes (>90%)

… or vice versa (<10%)

Split responses for similar outcomes (~50%)

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Comparisons between surveys

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Survey and pooled results

High degree of consistency across diverse cultural settings and respondent characteristics

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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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Summary of methods

Results

Reflections

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Outline

Global YLDs per person by age and sex, 1990 and 2010

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15

-50%

-25%

0%

25%

50%

all causes Group 1 NCD Injuries

Drivers of change in YLDs1990–2010

% change 1990-2000 % change due to change in rates

% change due to ageing % change due to population growth

33%40%

38%

5%

Percentage of YLDs in 2010by cause and age

Males Females

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Percentage of YLDs in 2010by cause and region

Global YLDs ranks, 1990 and 2010

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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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Outline

Summary of methods

Results

Reflections

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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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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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