GHME 2013 Conference Session: Global Burden of Diseases, Injuries, and Risk Factors Study 2010: workshop on methods and key findings Date: June 18 2013 Presenter: Haidong Wang Institute: Institute for Health Metrics and Evaluation (IHME), University of Washington

1. UNIVERSITY OF WASHINGTON
Mortality analysis for Global Burden
of Diseases, Injuries, and Risk
Factors Study 2010
June 18, 2013
Haidong Wang, PhD
Assistant Professor of Global Health
on behalf of the Demographics Research Team for GBD 2010

2. Outline
 Overview of the mortality process for GBD 2010
 Mortality data analysis and synthesis
 New model life table system
 GBD 2010: summary results
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3. 3

4. GBD mortality process
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5. Outline
 Overview of the mortality process for GBD 2010
 Mortality data analysis and synthesis
 New model life table system
 GBD 2010: summary results
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6. Updated tools for mortality data analysis
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7. Updated tools for mortality data analysis
 Updated Summary birth history
method that generates child
mortality estimates even for the
most recent five-year time
period before the survey
 Validation shows over 40%
reduction in mean relative error
and more significant
improvement for the period right
before the survey
 Of great importance for policy
makers who need the most
current mortality assessment
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8. Updated tools for mortality data analysis
 Dealt with biases inherent
in sibling survival method:
survival bias, zero
survivor bias, and recall
bias
 Provided crucial
information on adult
mortality in areas without
vital registration systems
 Provided estimates
comparable to other
independent sources of
mortality data
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9. Updated tools for mortality data analysis
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10. Empirical mortality databases
 25,054 data points for child mortality analysis [1950-2011]
from complete birth history (19.2%), household death
recall (0.6%), summary birth history (57.7%), and vital
registration and other sample registration systems (22.5%)
 14,211 data points for adult mortality analysis [1950-2011]
from household death recall (1.9%), sibling survival
method (21.3%), and Vital Registration/Sample
Registration System/Disease Surveillance Points (76.9%).
 7,294 empirical life tables observed post-1950
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11. Data synthesis methods
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12. Data synthesis methods: Gaussian
process regression
 For each country, model qt (the probability of dying in
year t) as:
 Instead of specifying one function, specify a distribution of
functions
 M is a function of time capturing the average, underlying
trend in the country. For both 5q0 and 45q15
estimations, we use spatio-temporal regressions to
provide this mean trend.
 C encodes smoothness in the trend and correlation of
mortality rates over time.
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f ~ GP(M, C)
qt = f(t) + εt

13. Data synthesis methods: spatio-temporal
regression
Spatio-temporal regression is used to provide prior, or the
mean trend, for Gaussian process regression.
1. Predict a trend based on covariates
2. Calculate the unexplained residual difference
(difference between the data and the predicted trend)
3. Smooth the residual differences over countries and
across time
4. Add the smoothed differences to the predicted trend
from step 1
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14. Data synthesis example 1: child mortality in Nicaragua
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15. Data synthesis example 2: child mortality in Turkey
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16. Outline
 Overview of the mortality process for GBD 2010
 Mortality data analysis and synthesis
 New model life table system
 GBD 2010: summary results
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17. Model life table system: desirable attributes
 Should be parsimonious and require only a few entry
parameters to generate a full life table
 Adequately captures the range of age patterns of mortality
observed in real populations and yields high predictive
validity, not just measured by summary indices such as life
expectancy at birth, but more importantly, by age-specific
mortality rates
 Provides satisfactory estimates of age-specific mortality for
countries with high levels of mortality, especially those
plagued by the HIV/AIDS epidemic
 Generates age-specific mortality with a plausible time trend;
the partial derivative of entry parameters such as 5q0 and
45q15 should be positive with respect to age-specific mortality
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18. New model life table system
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The new model life table system is essentially a two-step
process:
1. We first estimate a set of HIV counterfactual entry
parameters (5q0 and 45q15) using covariates: education,
GDP, and crude death rates from HIV/AIDS by age
group.
2. We then estimate an HIV/AIDS-free life table using the
estimated child and adult mortality rates. We do this in
the logit space, where the estimated life table is based on
a selected standard life table and the differences in child
and adult mortality rates between the two life tables.
3. The effects of HIV/AIDS by age/sex are then added to the
HIV-free life table from step two.

19. Outline
 Overview of the mortality process for GBD 2010
 Mortality data analysis and synthesis
 New model life table system
 GBD 2010: summary results
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Underestimating progress in under-5 mortality

21. Changes in global age-specific mortality
rate, 1970-2010
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23. Global mortality envelope by age, 1970-2010
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24. Changes in mean age at death by
region, 1970-2010
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25. Conclusions
 We assembled comprehensive databases on child
mortality, adult mortality, and life tables.
 We have completely updated a suite of formal
demographic models in analyzing mortality information
from censuses, vital registration, and surveys.
 The application of state-of-the-art data synthesis methods
generates more robust estimates, even for extrapolation.
 We propagated 95% uncertainty intervals for every metric
estimated throughout the whole mortality process.
 Detailed estimates of mortality rate, life expectancy, and
death counts for 187 countries between 1970 and 2010
show drastic demographic transition in the past four
decades.
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26. UNIVERSITY OF WASHINGTON
Thank you!