Global mortality landscape

November 1, 2010

Christopher J.L. Murray

Institute Director

Outline

• Overview of data for all-cause mortality estimation

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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• Most developing countries do not have vital registration systems that capture all deaths

• Alternative methods have evolved to track all-cause mortality in these countries:

1)Correcting incomplete vital registration systems using “death distribution methods”

2)Using sample registration systems with or without correction for incompleteness

3)Census data on deaths in the last 12 months

4)Survey data on sibling survival

Measuring Adult Mortality Can Be Challenging

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• We focus time on all-cause mortality measurement because it is a key input to measuring any cause of adult female mortality especially maternal mortality.

• A major difference between WHO and IHME estimation of maternal mortality is due to different assessments of adult female reproductive-aged mortality

Levels of Adult Female Mortality Are a Key Input to Measuring Maternal Mortality

Mortality estimation at IHME

• Goals:

o Identify all available data sources

o Correct these data sources for known biases using the best methods

o Apply a modeling algorithm that allows for the synthesis of multiple data sources

o Provide uncertainty estimates

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Mortality Estimation

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GBD Epidemiological Regions

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New Tools for Mortality Data Analysis

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New Tools for Synthesizing Data Sources

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• Gaussian process regression: used in adult and child models

o Allows for more flexibility in functional form of mortality over time

o Informed by prior beliefs, data, and uncertainty

o Better captures both sampling and nonsampling uncertainty in empirical data sources

• Spatial-temporal regression: used with adult model only

o Borrows strength from other related observations from another time point or related area

o The method (a two-stage approach):

─ Runs a linear regression

─ Smoothes the residuals over space and time

─ Adds smoothed residuals back into the first stage prediction

New Tools for Synthesizing Data Sources:Methods

Outline

• Overview of data for all-cause mortality estimation

o Data for the estimation of child mortality

o Data for the estimation of adult mortality

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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Data

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Empirical Measurements

Outliers

Vital Registration 3549 77

Sample Registration Systems 53 1

Complete Birth Histories 1447 8

Summary Birth Histories 9870 685

Household Deaths 62 17

Country-Specific Surveys 10 5

Disease Surveillance Points 12 0

Murray, Laakso, Shibuya et al Original Database

1075 241

Reports and Publications 96 0

Total 16174 1034

Neonatal, Post-neonatal, Childhood Model

Fewer data sources provide information on the breakdown of under-five deaths by month of death required to measure NN, PNN or CHD rates.

Data (147 countries): o 1234 VR country-years

o 526 DHS complete birth history country-years

We estimate a hierarchical model relating the probability of an under-5 death occurring during the NN, PNN or CHD period to the level of under-5 mortality using a random intercept and slope.

We tested multiple specifications for this model and used the model with the highest predictive validity.

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Predictive Validity for Neonatal, Post-neonatal, Childhood Model

Design Age Mean Relative Error Median Relative Error

In Sample Neonatal 9.3% 6.2%

Post-neonatal 10.9% 8.0%

Child 11.4% 7.8%

Dropping 20% of data Neonatal 9.8% 6.7%

Post-neonatal 11.8% 8.7%

Child 11.9% 8.1%

Dropping 20% of countries

Neonatal 11.4% 7.1%

Post-neonatal 12.9% 9.1%

Child 14.3% 8.9%

Dropped last 10 years Neonatal 14.4% 11.4%

Post-neonatal 21.0% 15.1%

Child 19.2% 13.3%

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Outline

• Overview of data for all-cause mortality estimation

o Data for the estimation of child mortality

o Data for the estimation of adult mortality

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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Systematic search of official, survey and published literature

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Rajaratnam et al, Lancet 2010

Assessing completeness of death reporting• For each vital registration data point, census or survey recall of

deaths in recent time periods, and sample registration data, evaluated completeness using new death distribution methods.

• Assessment of completeness for adults takes into account the results of three different death distribution methods and the independently assessed completeness of the VR system for child mortality.

• Completeness of adult VR is better than previously believed in many middle-income countries.

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Sibling History Data Needs to be Corrected for Survivor Bias

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• DHS sibling histories provide underestimates in most cases of adult mortality due to survivor bias and recall bias.

• We re-analyzed all sibling history data to take into account these corrections.

• Corrections change mortality rate on average by more than 30%

Obermeyer et al PLoS Medicine

Outline

• Overview of data process for all-cause mortality estimation

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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Under-5 mortality by GBD region

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Global under-5 death numbers compared to other sources

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Global trends in neonatal, post-neonatal and child mortality rates

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Under-5 mortality rate by GBD region

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Under-5 mortality rate, 2010

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Annualized rate of decline in U5MR 1990-2010

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Annualized percent decline in under-5 mortality by Region

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-1 0 1 2 3 4 51990-2000

Asia Pacific, High Income

Australasia

Europe, Western

Latin America, Southern

North America, High Income

Asia, Central

Europe, Central

Europe, Eastern

Asia, East

Asia, Southeast

Oceania

Asia, South

Caribbean

Latin America, Andean

Latin America, Central

Latin America, Tropical

North Africa/Middle East

Sub-Saharan Africa, Central

Sub-Saharan Africa, East

Sub-Saharan Africa, Southern

Sub-Saharan Africa, West

Key findings

• Global under-five mortality has dropped from 11.9 million in 1990 to 7.9 million in 2008, a 34% reduction in 28 years. U5 death number is estimated to be 7.7 million in 2010

• Majority of the under-five deaths occurred in developing countries. Around 87% of the U5 deaths occurred in South Asia and Sub-Saharan Africa in 2008

• Under-five mortality rates are declining among all 21 GBD regions. Accelerated decline is observed in 13 of the 21 GBD regions, including Sub-Saharan Africa

• Trends of change in under-five mortality at country level differ within GBD regions

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Outline

• Overview of data process for all-cause mortality estimation

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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Adult Mortality

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Global trend in adult mortality, 1970-2010

Trend in adult mortality by GBD region

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Adult Mortality, 2010

Women

Men

Annualized rate of decline in adult mortality, 1970-2010

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Women

Men

Countries with the lowest risks of adult mortality in 1970 and 2010, by sex

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Key findings

• Trends in adult mortality differ by country and sex

• IHME adult mortality estimates are quite different from those provided by United Nations Population Division

• The new method provides estimates of adult mortality for countries without empirical data

• Annual rate of change in adult mortality has wide range. Rapid decline in adult mortality is possible and has been observed in many countries

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Outline

• Overview of data process for all-cause mortality estimation

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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Outline

• Overview of data process for all-cause mortality estimation

• Child estimates

• Adult estimates

• Country 45q15 results

• Age-specific all-cause death counts

• Maternal estimates

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Model Life Table Systems

• What we want: a full life table (age specific mortality rates) for a population where we only know 5q0, 45q15 and HIV seroprevalence

• What we have: a set of empirical life tables of high quality from other populations

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Model Life Table Systems

• Model Life Table Systems are built on the observation that age specific probabilities of death from two life tables from similar populations are linearly related (after some transformation)

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Establishing the Standard Life Table

• Use 5q0 and 45q15 (and other ‘entry-parameters’) to identify a life table from a population with similar mortality situation within your empirical data set

o The selected empirical life table is referred to as the “standard” life table

o For example, you might select the 1934 life table if you wished to obtain a life table for 1935

• One could also select a set of empirical life tables and average them together to get a standard life table

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Basic mechanics

• Transform 5q0 and 45q15 to the life table indicators which will be related to the standard life table

• Relate observed life table indicators to the standard life table by estimating a linear equation

• Use the rest of the age-specific probabilities from the standard life table and the linear equation to then obtain the whole life table for the country-year of interest

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Current Method: Establishing the standard life table• Expanded database of empirical life tables

o A total of 8,682 country-years including 526 life tables from Africa and Asia (in addition to Asia/High Income countries)

• Improved matching process to identify life tables from similar populations

o For countries where an empirical life table from the same country is available within a 15-year radius in the database, use the observed life tables as standard

o For all other countries, use entry parameters: 5q0, 45q15 to match life tables out of the database (priorities are given to life tables from the same country/GBD region)

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Current Method: Specification of Model

• Two-pronged approach

• 1) Countries with abundant data

o Age specific models that predict the difference in mortality between the country-year of interest and the standard based on differences in 5q0 and 45q15 are developed

o Models are then used with single-year closest to the year of interest to predict the full life table

• 2) Countries with limited data

o Age specific models that predict the difference in mortality between the country-year of interest and the standard based on differences in 5q0 and 45q15 are developed

o Models are used to predict mortality in the absence of HIV

o Rate of change models are used to add on HIV component of mortality

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Conclusions

Advantages of the current model:

Built upon a database with over 8,000 empirical life tables

Improved predictive validity

More versatile

Provides plausible estimates for CYs affected by HIV/AIDS

Provides age specific mortality rates that follow the trends in 5q0 and 45q15

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Outline

• Overview of data process for all-cause mortality estimation

• Child estimates

• Adult estimates

• Country 45q15 estimates

• Age-specific all-cause death counts

• Maternal estimates

o The rest of the workshop will offer an opportunity to discuss in great detail the data and methods used to arrive at the following estimates

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Global Maternal Deaths

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Global Maternal Deaths

Year Number of maternal deaths (Uncertainty interval)

1980 526,300 (446,400 – 629,600)

1990 441,500 (376,200 – 535,100)

2000 417,200 (365,700 – 479,200)

2008 342,900 (302,100 – 394,300)

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Births by Region, 1980-2008

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Global Maternal Mortality Ratio

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Maternal Deaths by Region, 1980-2008

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Regional trends in the MMR

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Regional trends in the MMR

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MMR with and without HIV

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MMR per 100,000 live births, 2008

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Top 21 countries: maternal deaths

Order Country Deaths in 1000s (UI) Deaths (%) Cumulative % Births (%) Cumulative %1 India 68.3 (41.6-106.2) 19.9 19.9 19.7 19.72 Nigeria 36.7 (22.4-57.0) 10.7 30.6 4.4 24.13 Pakistan 20.1 (12.3-31.3) 5.9 36.5 3.9 28.04 Afghanistan 20.0 (7.5-43.1) 5.8 42.3 0.9 28.95 Ethiopia 18.2 (11.1-28.8) 5.3 47.6 2.3 31.26 Congo, the Democratic Republic of the 15.4 (9.0-24.7) 4.5 52.1 2.1 33.37 Bangladesh 11.6 (6.7-18.7) 3.4 55.5 2.5 35.88 Indonesia 9.6 (5.6-16.0) 2.8 58.3 3.1 38.99 Tanzania, United Republic of 8.0 (4.8-12.8) 2.3 60.6 1.3 40.2

10 China 7.3 (6.4-8.3) 2.1 62.7 13.3 53.511 Malawi 6.8 (4.0-10.9) 2.0 64.7 0.4 53.912 Côte d'Ivoire 6.8 (4.1-10.8) 2.0 66.7 0.5 54.413 Kenya 6.2 (3.6-10.2) 1.8 68.5 1.1 55.514 Chad 5.3 (3.3-8.2) 1.5 70.0 0.4 55.915 Mozambique 5.2 (3.1-8.4) 1.5 71.5 0.6 56.516 Uganda 5.2 (3.1-8.2) 1.5 73.0 1.1 57.617 Cameroon 5.0 (2.8-8.1) 1.4 74.4 0.5 58.118 Niger 4.7 (3.0-7.3) 1.4 75.8 0.6 58.719 Angola 4.6 (1.8-9.9) 1.3 77.1 0.6 59.320 Sudan 4.0 (2.5-6.0) 1.2 78.3 0.9 60.221 Mali 3.6 (2.3-5.5) 1.1 79.4 0.4 60.6

All other countries (160) 70.3 (43.0-112.2) 20.5 100.0 39.3 100.0Total 342.9 100.0 100.0 100.0 100.0

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Annualized Rate of Decline in MMR, 1990 to 2008

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Annualized Rate of Decline in MMR, excluding HIV, 1990 to 2008

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Gaining Ground

• Global maternal deaths down to 342,900 in 2008

• Global trend is a 1.4% decline per year since 1990

• 23 countries are on track to meet MDG 5, achieving an annual rate of decline of 5.5%

o Includes Egypt, Albania, Tunisia, El Salvador, Romania

• Other countries are achieving substantial progress

o Including China, Bolivia, Ecuador, Peru, Rwanda

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Progress Undocumented But Not Unexpected• Global total fertility rate has dropped from 3.70 in 1980 to 2.56

in 2008

• Income per capita has been rising over the period, particularly in Asia and Latin America

• Maternal education has been increasing as well

o In sub-Saharan Africa, the average years of schooling for women aged 25-44 rose from 1.5 years in 1980 to 4.4 years in 2008

• The steady, slow rise in skilled birth attendance coverage may also have contributed

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Adverse Impact of HIV

• Progress on reducing maternal mortality would have been much greater in the absence of HIV, especially in sub-Saharan Africa

• Important implications for intervention policy

o Interventions for treating pregnant women with HIV would include antiretrovirals, not part of the set of interventions targeting HIV-negative women

• Critical to track HIV-related maternal mortality, but challenging in settings without vital registration

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