Mortality analysis for Global Burden of Diseases, Injuries, and Risk Factors Study 2010


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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

Published in: Health & Medicine, Technology
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  • . Walk through the mortality estimation process. setting up the background for us to understand the examples I’m going to use: Turkey and South Africa. . How do we make estimates of age specific mortality rate: data analysis tools, data synthesis tools and model life table.
  • UNICEF estimates for 2007 changed by about 1∙1 million deaths in 3 years of updates. Estimates from GBD also decreased by 748 000 between estimate series from 2010 and estimate series from 2012. In the newly released UNICEF 2012 estimates, the global under-5 deaths estimate for 2010 was 466 000 lower than it was for the same year in the 2011 report. These declines in the estimated number of deaths in children younger than 5 years globally for the same year originate from new data for trends in child mortality and from the effect of advances in estimation methods. New data for child mortality and fertility have tended to show greater declines than estimated by models. The models, particularly the Loess model for the most recent time period, tend to be conservative about recent time trends. The implication of this finding is that when estimates of the achievement of MDG 4 (reduce child mortality) are made in 2015, we are likely to underestimate progress because of this historical trend. The problem of underestimation of true progress on MDG 4 is likely to be even greater at the country level than at the global level, where some countries have had much sharper declines than expected on the basis of income, educational attainment, efforts towards fighting HIV/AIDS, or other factors.
  • A simple summary measure of these demographic and epidemiological factors is the mean age at death. Population ageing and changes in age specific death rates have led to profound changes in the mean age at death in different regions. This figure here compares the mean age at death in 1970 with that in 2010. All regions, including those in sub-Saharan Africa most affected by HIV/AIDS, have had increases in the mean age at death. In some regions, especially east Asia, but also south Asia, southeast Asia, and Latin America, the mean age at death increased by at least an average of 1 year for every 2 calendar years since 1970. Three out of four Latin American regions (central, tropical, and Andean), east Asia, and north Africa and the Middle East had increases in mean ages at death of more than 29 years.
  • Mortality analysis for Global Burden of Diseases, Injuries, and Risk Factors Study 2010

    1. 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. 2. Outline  Overview of the mortality process for GBD 2010  Mortality data analysis and synthesis  New model life table system  GBD 2010: summary results 2
    3. 3. 3
    4. 4. GBD mortality process 4
    5. 5. Outline  Overview of the mortality process for GBD 2010  Mortality data analysis and synthesis  New model life table system  GBD 2010: summary results 5
    6. 6. Updated tools for mortality data analysis 6
    7. 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 7
    8. 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 8
    9. 9. Updated tools for mortality data analysis 9
    10. 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 10
    11. 11. Data synthesis methods 11
    12. 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. 12 f ~ GP(M, C) qt = f(t) + εt
    13. 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 13
    14. 14. Data synthesis example 1: child mortality in Nicaragua 14
    15. 15. Data synthesis example 2: child mortality in Turkey 15
    16. 16. Outline  Overview of the mortality process for GBD 2010  Mortality data analysis and synthesis  New model life table system  GBD 2010: summary results 16
    17. 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 17
    18. 18. New model life table system 18 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. 19. Outline  Overview of the mortality process for GBD 2010  Mortality data analysis and synthesis  New model life table system  GBD 2010: summary results 19
    20. 20. 20 Underestimating progress in under-5 mortality
    21. 21. Changes in global age-specific mortality rate, 1970-2010 21
    22. 22. 22
    23. 23. Global mortality envelope by age, 1970-2010 23
    24. 24. Changes in mean age at death by region, 1970-2010 24
    25. 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. 25
    26. 26. UNIVERSITY OF WASHINGTON Thank you!