Findings and implications of the Global Burden of Disease Study 2010 Royal Society, London, 14 December 2012 Joshua A Salomon Harvard School of Public Health

2. Global patterns and trends in healthy life expectancy
• Background
• Summary of methods
• Key findings
• Interpretation, limitations, conclusions
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3. Healthy life expectancy
% Surviving
100
90
80
70
60
50
40
30
20
10
0
0 20 40 60 80 100
Age
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4. Healthy life expectancy
Life expectancy =
% Surviving
Area under survival curve
100
90
80
70
60
50
40
30
20
10
0
0 20 40 60 80 100
Age
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5. Healthy life expectancy
Life expectancy =
% Surviving Weight
0
Area under survival curve
100
90
1
80 Healthy life expectancy
70
B‘Not (dichotomous) =
60
Area A only
50 healthy’
40
30 A
20
‘Healthy’
10
0
0 20 40 60 80 100
Age
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6. Healthy life expectancy
Life expectancy =
% Surviving Weight
0
Area under survival curve
100
90
80 Healthy life expectancy
70
B 1 (dichotomous) =
60
Area A only
50
40
30 A Healthy life expectancy
20 (continuous) =
10 Weighted area under
0
0 20 40 60 80 100
survival curve
Age
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7. Healthy life expectancy and compression of morbidity
• By examining life expectancy, healthy life expectancy, and the
difference between the two over time, we can evaluate
competing hypotheses about relationships between changes in
mortality and morbidity
 Compression of morbidity: healthy life expectancy rises faster than life
expectancy
 Expansion of morbidity: life expectancy rises faster than healthy life
expectancy
• A number of studies have evaluated compression vs.
expansion using health expectancy measures
 e.g. Crimmins et al. 1997, Mathers et al. 1999, Robine et al. 1999, 2009;
Crimmins and Beltrán-Sánchez 2010.
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8. Study objectives
• Assess healthy life expectancy for 187 countries, in 1990 and
2010, using comparable methods, to:
 Characterize current levels and patterns
 Evaluate change over time
 Consider evidence for compression or expansion of morbidity
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9. Global patterns and trends in healthy life expectancy
• Background
• Summary of methods
• Key findings
• Interpretation, limitations, conclusions
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10. Inputs for calculating healthy life expectancy in GBD
Age-specific mortality
Prevalence of nonfatal
outcomes
Disability weights
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11. Inputs for calculating healthy life expectancy in GBD
• Life tables by sex, country, and year in
GBD 2010 based on
Age-specific mortality • Estimation of mortality between birth
and age five (5q0)
• Estimation of mortality between ages 15
and 60 (45q15)
Prevalence of nonfatal • Model life tables to translate into full set
outcomes of age-specific mortality rates
Disability weights
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12. Inputs for calculating healthy life expectancy in GBD
Age-specific mortality
• Prevalence of 1,160 disabling sequelae by
age, sex, country, year based on:
Prevalence of nonfatal • Systematic review of available data
outcomes sources from published literature
• Bayesian meta-regression approach to
estimate prevalence across populations
and over time
Disability weights
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13. Inputs for calculating healthy life expectancy in GBD
Age-specific mortality
Prevalence of nonfatal • 1,160 sequelae mapped into 220 unique
outcomes
health states, and disability weights
estimated for each of these
• Disability weights capture health loss on
scale from 0 (no health loss) to 1
Disability weights
(equivalent to death)
• Estimates based on new survey data
collected among 30,000 respondents
from diverse settings
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14. Computing healthy life expectancy
• Information on prevalence of all disabling sequelae, and
disability weights attached to these, integrated into a
continuous index of average disability including comorbidity
 Simulation used to capture exposure to multiple disabilities
 Combined disability weight for an individual with multiple morbidities
computed multiplicatively (assumes independence)
 Individual results aggregated by age to yield ‘YLD rates’ which capture
severity-weighted disability prevalence
• Sullivan’s method used to incorporate YLD rates into life table
 ‘Healthy years’ lived in an age interval estimated by multiplying years
lived by (1 – YLD rate)
 Life table recomputed for healthy life years
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15. Global patterns and trends in healthy life expectancy
• Background
• Methods for measuring healthy life expectancy (HALE) in the
Global Burden of Disease 2010
• Key findings
• Interpretation, limitations, conclusions
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16. Global life expectancy and healthy life expectancy
• In 2010, global life expectancy at birth was
 For males: 67.5 (95% uncertainty interval 66.9 to 68.1)
 For females: 73.3 (72.8 to 73.8)
• In 2010, global healthy life expectancy was
 For males: 58.3 (56.7 to 59.8)
 For females: 61.8 (60.1 to 63.4)
• Between 1990 and 2010, life expectancy at birth increased
 For males, by 4.7 (3.9 to 5.4) years
 For females, by 5.1 (4.5 to 5.8 years)
• Between 1990 and 2010, HALE at birth increased
 For males, by 3.9 (3.3 to 4.5) years
 For females, by 4.0 (3.5 to 4.5 years)
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17. Highest and lowest healthy life expectancy at birth, 2010
Highest male healthy life expectancy Highest female healthy life expectancy
Japan Japan
Singapore South Korea
Switzerland Spain
Spain Singapore
Italy Taiwan
Lowest male healthy life expectancy Lowest female healthy life expectancy
Mozambique Mozambique
Swaziland Swaziland
Central… Lesotho
Lesotho Central…
Haiti Haiti
0 20 40 60 80 0 20 40 60 80
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18. Male healthy life expectancy at birth, 2010
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19. Female healthy life expectancy at birth, 2010
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20. Changes in healthy life expectancy at birth, 1990-2010
Largest increases, males Largest increases, females
Ethiopia Rwanda
Angola Maldives
Rwanda Ethiopia
Niger Angola
Maldives Bhutan
Largest decreases, males Largest decreases, females
South Africa South Africa
Zimbabwe Zimbabwe
Swaziland Haiti
Lesotho Swaziland
Haiti Lesotho
-20 0 20 -20 0 20
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21. Compression or expansion of morbidity?
Life expectancy – healthy life expectancy = healthy years lost to disability
Males Females
1. Across countries, higher life expectancy is associated with more
healthy years lost to disability in 1990
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22. Compression or expansion of morbidity?
Life expectancy – healthy life expectancy = healthy years lost to disability
Males Females
1. Across countries, higher life expectancy is associated with more
healthy years lost to disability in 1990 and in 2010
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23. Compression or expansion of morbidity?
Life expectancy – healthy life expectancy = healthy years lost to disability
Males Females
2. Over time (1990 to 2010), increases in life expectancy are
associated with increases in healthy years lost to disability
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24. Drivers of change in healthy life expectancy
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25. Advances and limitations
• Strengths of the approach used include
 Systematic use of multiple data sources including biomarkers and functional
measurements where appropriate and available
 Comparable methods applied to two time points
 Accounting for independent comorbidity
• Limitations of the approach include
 Lack of vital registration data in many countries, and limited basis for estimating
adult mortality
 Limitations in available information on prevalence over time for some
conditions, and Bayesian estimation approach may underestimate variability
where evidence on regional differences is weak
 Estimates of disability weights depend on validity of descriptions, and no
allowance for changing disability weights over time may miss some effects of
intervention
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26. Summary and discussion
• Healthy life expectancy has increased more slowly than life
expectancy over the last 20 years, which means:
 Gains in survivorship have added healthy years but also unhealthy ones
 The world’s population loses more healthy years to disability today than 20
years ago
 This increase has profound implications for healthcare needs and costs
• Most gains in healthy life expectancy since 1990 have been
attributable to reductions in child and adult mortality
• Major drivers of nonfatal losses of healthy life include mental and
behavioral disorders and musculoskeletal disorders, which have not
seen major improvements globally over last 20 years
• More attention needed on reducing prevalence and impact of major
causes of disability
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