UNIVERSITY OF WASHINGTON

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

June 18, 2013

Haidong Wang, PhDAssistant 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

3

4

GBD mortality process

5

Outline

Overview of the mortality process for GBD 2010

Mortality data analysis and synthesis

New model life table system

GBD 2010: summary results

6

Updated tools for mortality data analysis

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

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

9

Updated tools for mortality data analysis

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

11

Data synthesis methods

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

Data synthesis methods: spatio-temporal regressionSpatio-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

14

Data synthesis example 1: child mortality in Nicaragua

15

Data synthesis example 2: child mortality in Turkey

16

Outline

Overview of the mortality process for GBD 2010

Mortality data analysis and synthesis

New model life table system

GBD 2010: summary results

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

18

New model life table systemThe 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

20

Underestimating progress in under-5 mortality

21

Changes in global age-specific mortality rate, 1970-2010

22

23

Global mortality envelope by age, 1970-2010

24

Changes in mean age at death by region, 1970-2010

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.

UNIVERSITY OF WASHINGTON

Thank you!