who-h il 2000-2011 · i a k wl g this technical paper was written by colin mathers and li liu with...

WHO-CHERG methods and data

sources for child causes of death

2000-2011

Department of Health Statistics and Information Systems (WHO, Geneva)

and WHO-UNICEF Child Health Epidemiology Reference Group (CHERG)

June 2013

Global Health Estimates Technical Paper WHO/HIS/HSI/GHE/2013.2

i

Acknowledgments

This Technical Paper was written by Colin Mathers and Li Liu with inputs and assistance from Wahyu

Retno Mahanani, Jessica Ho and Doris Ma Fat. Estimates of regional child deaths by cause for years

2000-2011 were primarily prepared by Li Liu, Bob Black, Simon Cousens and Joy Lawn, of the Child

Health Epidemiology Reference Group (CHERG), and Colin Mathers and Doris Ma Fat (from the Mortality

and Burden of Disease Unit in the WHO Department of Health Statistics and Information Systems), with

advice and inputs from other members of CHERG, WHO Departments, collaborating UN Agencies, and

other WHO expert advisory groups and academic collaborators.

The Child Health Epidemiology Reference Group has been supported by a grant from the Bill & Melinda

Gates Foundation to the US Fund for UNICEF for CHERG.

These estimates make considerable use of the all-cause mortality estimates developed by the

Interagency Group on Child Mortality Estimation (UN-IGME), and the births estimates of the UN

Population Division, as well as specific inputs for certain vaccine-preventable diseases developed under

the oversight of the WHO Quantitative Immunization and Vaccines Related Research (QUIVER) Advisory

Group. While it is not possible to name all those who provided advice, assistance or data, both inside

and outside WHO, we would particularly like to note the assistance and inputs provided by Diego

Bassani, Ties Boerma, Cynthia Boschi-Pinto, Tony Burton, Harry Campbell, Richard Cibulskis, Cristina

Garcia, Peter Ghys, Mie Inoue, Robert Jakob, Prabhat Jha, Hope Johnson, Mikkel Oestergaard, Igor

Rudan, Emily Simons, Karen Stanecki, Peter Strebel, Tessa Wardlaw, and Danzhen You.

Estimates and analysis are available at:

http://www.who.int/gho/mortality_burden_disease/en/index.html

For further information about the estimates and methods, please contact [email protected]

In this series

1. WHO methods and data sources for life tables 1990-2011 (Global Health Estimates Technical Paper

WHO/HIS/HSI/GHE/2013.1)

2. WHO-CHERG methods and data sources for child causes of death 2000-2011 (Global Health Estimates

Technical Paper WHO/HIS/HSI/GHE/2013.2)

3. WHO methods and data sources for global causes of death 2000-2011 (Global Health Estimates

Technical Paper WHO/HIS/HSI/GHE/2013.3)

ii

Table of Contents

Acknowledgments .......................................................................................................................................... i

Table of Contents .......................................................................................................................................... ii

1 Introduction……………… ............................................................................................................................. 1

2 Population and all-cause mortality estimates for years 2000-2011 ........................................................ 1

2.1 Estimation of neonatal, infant and under-5 mortality rates............................................................ 1

2.2 Population and births estimates ...................................................................................................... 2

2.3 Mortality shocks – epidemics, conflicts and disasters ..................................................................... 2

3 Child mortality by cause .......................................................................................................................... 3

3.1 Causes of under 5 death in countries with good death registration data ....................................... 3

3.2 Causes of neonatal death (deaths at less than 28 days of age) ....................................................... 3

3.3 Causes of child death at ages 1-59 months –low mortality countries ............................................. 4

3.4 Causes of child death at ages 1-59 months –high mortality countries ........................................... 4

3.5 Causes of child death for China and India ....................................................................................... 5

4 Methods for cause-specific revisions and updates.................................................................................. 5

4.1 HIV/AIDS........................................................................................................................................... 5

4.2 Malaria ............................................................................................................................................. 5

4.3 Whooping cough .............................................................................................................................. 6

4.4 Measles ............................................................................................................................................ 6

4.5 Conflict and natural disasters .......................................................................................................... 7

5 Uncertainty of estimates ......................................................................................................................... 7

References………………………………………………………………………………………………………………………………………………8

Annex Table A Methods used for estimation of child causes of death, by country, 2000-2011 .............. 10

Annex Table B First-level categories for analysis of child causes of death ............................................... 15

Annex Table C Re-assignment of ICD-10 codes for certain neonatal deaths ........................................... 16

Annex Table D Country groupings used for regional tabulations ............................................................. 18

D.1 WHO Regions and Member States ................................................................................................ 18

D.2 Countries grouped by WHO Region and average income per capita* .......................................... 19

D.3 World Bank income grouping* ...................................................................................................... 20

D.4 World Bank Regions ....................................................................................................................... 21

D.5 Millennium Development Goal (MDG) Regions ............................................................................ 22

World Health Organization Page 1

1 Introduction

Cause-specific estimates of deaths for children under age 5 were estimated for 17 cause categories for

years 2000-2010 using methods described elsewhere by Liu et al. (1) and on the WHO website (2). These

estimates were prepared by the WHO Department of Health Statistics and Information Systems and the

Child Health Epidemiology Reference Group (CHERG), with inputs and assistance from other WHO

Departments and UN Agencies. These previously published estimates for years 2000-2010 have been

updated at regional level by WHO and CHERG to take account of revisions in child mortality levels (3), as

well as cause-specific estimates for HIV, tuberculosis, measles and malaria deaths (as described in

Section 4). Inputs to the multivariate cause composition models were also updated as described below

in Section 3.

These estimates of child deaths by cause also form an input to the more general regional estimates of

deaths by cause, age and sex also released on the WHO website in June 2013 as a part of the WHO

Global Health Estimates (GHE) (4). Both the child causes of death and the Global Health Estimates will be

updated to years 2000-2012 later in 2013, and released at country level following consultation with

WHO Member States.

These estimates of child deaths by cause represent the best estimates of WHO and CHERG, based on the

evidence available to them up until May 2013, rather than representing the official estimates of

Member States, and have not necessarily been endorsed by Member States. They have been computed

using standard categories, definitions and methods to ensure cross-national comparability and may not

be the same as official national estimates produced using alternate, potentially equally rigorous

methods. The following sections of this document provide explanatory notes on data sources and

methods for preparing child mortality estimates by cause.

2 Population and all-cause mortality estimates for years 2000-2011

2.1 Estimation of neonatal, infant and under-5 mortality rates

Methods for estimating time series for neonatal, infant and under-5 mortality rates have been

developed and agreed upon within the Inter-agency Group for Child Mortality Estimation (UN-IGME)

which is made up of WHO, UNICEF, UN Population Division, World Bank and academic groups. UN-IGME

annually assesses and adjusts all available surveys, censuses and vital registration data, to then estimate

the country-specific trends in under-five mortality per 1000 live births (U5MR) over the past few

decades in order to predict the rates for the reference years (3). All data sources and estimates are

documented on the UN-IGME website.1 For countries with complete recording of child deaths in death

registration systems, these are used as the source of data for the estimation of trends in neonatal, infant

and child mortality. For countries with incomplete death registration, all other available census and

survey data sources, which meet quality criteria, are used. UN-IGME methods are documented in a

series of papers published in a collection in 2012 (5).

For data from civil registration, the neonatal mortality per 1000 live births (NMR) is calculated as the

number of neonatal death divided by the live births reported from the country when available. For

household surveys, child and neonatal mortality rates are calculated from the full birth history (FBH)

data, where women are asked for the date of birth of each of their children, whether the child is still

1 www.childmortality.org


alive, and if not the age at death FBH data, collected by all DHS surveys, allow the calculation of child

mortality indicators for specific time periods in the past; DHS publishes child mortality estimates for five

5-year periods before the survey, that is, 0 to 4, 5 to 9, 10 to 14 etc.

A database consisting of pairs of NMRs and U5MRs was compiled. For a given year, NMR and U5MR

were included in the database when data for both of these were available. To ensure consistency with

U5MR estimates produced by UN-IGME, U5MR and NMR data points were rescaled for all years to

match the UN-IGME estimates.

For countries where child mortality is strongly affected by HIV, the NMR was estimated initially using

neonatal and child mortality observations for non-AIDS deaths, calculated by subtracting from total

death rates the estimates HIV death rates in the neonatal and 1-59 month periods respectively, and

then AIDS neonatal deaths be added back on to the non-HIV neonatal deaths to compute the total

estimated neonatal death rate.

The following statistical model was used to estimate NMR:

log(NMR/1000) = α0+ β1*log(U5MR/1000) + β2*([log(U5MR/1000)] 2)

with additional random effect intercept parameters for both country and region. For countries with

good vital registration data covering the period 1990-2011, random effects parameters for slope or

trend parameters were also added. Based on predictive performance evaluation using ten-fold cross-

validation, the statistical model fitted to data point for 1990 onwards were retained and only the most

recent data point from each survey was included (6).

2.2 Population and births estimates

Total deaths by age and sex were estimated for each country by applying the UN-IGME estimates of

neonatal and under 5 mortality rates to the estimated total births and de facto resident population

estimates for children under age 5 prepared by the United Nations Population Division in its 2010

revision (7). They may thus differ slightly from official national estimates for corresponding years. Child

causes of death will be updated in their next revision to take account of revisions to population

estimates included in the World Population Prospects 2012 (released mid-June 2013) (8).

2.3 Mortality shocks – epidemics, conflicts and disasters

Country-specific estimates of deaths for organized conflicts and major natural disasters were prepared

for years 1990-2011 using data and methods documented in Section 4.5. For country-years where total

all-age death rates from these conflicts and disasters exceeded 1 per 10,000 population, neonatal and

child mortality rates were adjusted by adding the estimated age-specific components of the conflict and

disaster deaths.

As described in Section 4.4, deaths due to measles outbreaks and epidemics were identified and also

added to the smoothed all-cause envelopes estimated from the UN-IGME estimates for neonatal and

under 5 deaths.


3 Child mortality by cause

3.1 Causes of under 5 death in countries with good death registration data

Cause-of-death statistics are reported to WHO on an annual basis by country, year, cause, age and sex.

Most of these statistics can be accessed in the WHO Mortality Database (9). The number of countries

reporting data using the 10th revision of the International Classification of Diseases (ICD-10) (10) has

continued to increase. For these estimates, a total of 114 countries had data covering 80% or more of

deaths in the country, of which 93 countries were reporting data coded to the third or fourth character

of ICD-10 and 59 countries had data for years 2010 or 2011.

Death registration data were used directly for estimating causes of neonatal and under 5 child deaths

for countries with good quality vital registration (VR) data with population coverage of >80%. VR data

were considered as of good quality if the following criteria were met: (a) reasonable distribution of

deaths by cause were reported without excessive use of implausible codes or certain codes, and (b)

sufficient details of the coding was provided so that deaths could be grouped into appropriate

categories used in the analysis.

For countries with adequate death registration, data on causes of child deaths were extracted from the

WHO mortality database, adjusted for coverage incompleteness where needed, and grouped according

to the standard International Classification of Diseases, 10th revision (ICD-10). For earlier years when

ICD-9 codes were used, a mapping system was applied to convert them into ICD-10 codes (1,

webappendix). Certain neonatal codes were re-assigned from ill-defined codes to more plausible codes

(see Annex Table C). Annual data for years 2000 to the latest available year were included with data

closest to the estimating year used where possible. Where the latest year available was earlier than

2011, the cause distribution for the latest available year was assumed to apply for subsequent year(s),

which was then applied to the age-specific total number of child deaths.

3.2 Causes of neonatal death (deaths at less than 28 days of age)

The CHERG neonatal working group undertook an extensive exercise to derive mortality estimates for six

causes of neonatal death, including preterm birth, asphyxia, severe infection, diarrhoea, congenital

malformation and other causes (1). These cause categories are defined in Annex Table B.

Death registration data were used directly for 61 countries considered to have reliable information. For

another 51 low mortality countries, the cause distribution was estimated using a multinomial model

applied to death registration data. For 80 high mortality countries the cause distribution was estimated

using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies (1). A total

of 90 studies in 34 countries in high mortality populations met the inclusion criteria. The multinomial

model for high mortality countries was generally used for countries with average U5MR>35 for the

period 2000-2010.

A separate cause category for neonatal pneumonia is included in the model, and the neonatal sepsis

category includes a number of neonatal infections, such as meningitis and tetanus, not separately

identified. The number of tetanus deaths was also modeled separately in a single cause model using

using a logistic regression model with percent of women who were literate, percent of births with skilled

attendant, and percent protected at birth by tetanus toxoid vaccine as covariates. The resulting cause-

specific inputs were adjusted country-by-country to fit the estimated neonatal death envelopes for

corresponding years.

Pending further revisions of the neonatal tetanus model to estimate longer-term trends in neonatal

tetanus deaths, estimates for 2011 and 2000 were based on projection and back-projection of the 2008


estimates using estimates of trends in tetanus deaths from the Institute for Health Metrics and

Evaluation (IHME) Global Burden of Disease (GBD) 2010 study (11).

3.3 Causes of child death at ages 1-59 months –low mortality countries

For 51 low mortality countries without VR data or with VR data not meeting quality criteria (see Section

3.1), the cause distribution was estimated using a multinomial model applied to death registration data.

This multinomial model applied to death registration data was generally used for countries with average

U5MR<35 for the period 2000-2010.

For the estimates for years 2000-2011, the previous vital registration-based multicause model (VRMCM)

was revised to include additional death registration data and to update time series for covariates and

extend them to 2011. The choice of covariates included in the model was not revisited for this regional-

level update. The multinomial logistic regression model was estimated using death registration data

from countries with >80% complete cause of death (CoD) certification for years 1990-2011 to estimate

the proportion of deaths due to pneumonia, diarrhea, meningitis, injuries, perinatal, congenital

anomalies, other non-communicable diseases (NCDs) and other causes.

The current version of the model used death registration data for the years 1990 to 2011, including

1,123 data points, representing 63 countries. The model included the following covariates that were

determined a priori: U5MR, GNI per capita (PPP, $international), WHO European and American regions.

Adjustments for the scaling-up of Hib vaccine occurred within the model. The proportional distribution

of causes of death was then applied to the HIV-free and measles-free envelope for children 1-59 months

of age. Jack-knife and Monte Carlo simulation methods were used to estimate uncertainty.

3.4 Causes of child death at ages 1-59 months –high mortality countries

For 79 high mortality countries (average U5MR>35 for the period 2000-2010), the cause distribution was

estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies

(1, 12, 13). The verbal autopsy-based multicause model (VAMCM) for deaths at ages 1-59 months was

used to derive mortality estimates for seven causes of postneonatal death, including pneumonia,

diarrhea, malaria, meningitis, injuries, congenital malformations, causes arising in the perinatal period

(prematurity, birth asphyxia and trauma, sepsis and other conditions of the newborn), and other causes,

based on 113 data points from 74 studies of postneonatal deaths from 33 countries that met inclusion

criteria2. Studies were predominantly from lower income high mortality countries. Malnutrition deaths

were included in the other cause of death category. Deaths due to unknown causes were excluded from

the analysis. Deaths due to measles and HIV/AIDS were estimated separately.

The resulting cause-specific inputs were adjusted country-by-country to fit the estimated 1-59 month

death envelopes (excluding HIV and measles deaths) for corresponding years and then estimates were

further adjusted for intervention coverage (pneumonia and meningitis estimates adjusted for use of Hib

vaccine; malaria estimates adjusted for insecticide treated mosquito nets (ITNs)). This method was used

for countries without useable death registration data and with U5MR>26 and gross national income per

capita less than $7,510.

2 Studies conducted in year 1980 or later, a multiple of 12 months in study duration, cause of death available for

more than a single cause, with at least 25 deaths in children <5 years of age, each death represented once, and less

than 25% of deaths due to unknown causes were included. Studies conducted in sub-groups of the study population

(e.g. intervention groups in clinical trials) and verbal autopsy studies conducted without use of a standardized

questionnaire or the methods could not be confirmed were excluded from the analysis.


3.5 Causes of child death for China and India

In order to estimate trends in under 5 causes of death for India, the previously developed subnational

analyses were further refined and used to develop national estimates for years 2000-2011 (14). For

neonates, a verbal autopsy multi-cause model (VAMCM) based on 37 sub-national Indian community-

based VA studies was used to predict the cause distribution of deaths at state level. The resulting cause-

specific proportions were applied to the estimated total number of neonatal deaths to obtain the

estimated number of deaths by cause at state level prior to summing to obtain national estimates.

For children who died in the ages of 1-59 months in India, the previously developed VAMCM was rerun

for years 2000-2011 using a total of 23 sub-national community-based VA studies plus 22 sets of

observations for the Indian states derived from the Million Death Study (15). Nine cause categories were

specified, including measles plus the eight specified in the post-neonatal VAMCM for other countries.

State-level measles deaths were then normalized to fit the national measles estimates produced by the

WHO IVB. State-level AIDS and malaria estimates were provided by UNAIDS and WHO malaria program,

respectively. All cause fractions were adjusted to sum to one. The state-level estimates were collapsed

to obtain national estimates at the end.

For China, updated IGME U5MR estimates in 2000-2011 were applied to the VA-based national cause-

specific models developed by Rudan and colleagues (16) to derive cause-fractions annually in this

period. Together with cause-specific inputs from WHO technical programmes and UNAIDS for measles,

meningitis, malaria and AIDS, the resulting cause-specific inputs for China were adjusted to fit the

estimated total deaths at ages 0-1 month and 1-59 months, respectively.

4 Methods for cause-specific revisions and updates

4.1 HIV/AIDS

For countries with death registration data, HIV/AIDS mortality estimates were generally based on the

most recently available vital registration data except where there was evidence of miscoding of

HIV/AIDS deaths. In such cases, a time series analysis of causes where there was likely miscoding of

HIV/AIDS deaths was carried out to identify and re-assign miscoded HIV/AIDS deaths. For other

countries, estimates were based on UNAIDS estimated HIV/AIDS mortality (17). It was assumed based

on advice from UNAIDS that 1% of HIV deaths under age 5 occurred in the neonatal period.

4.2 Malaria

Countries outside the WHO African Region and low transmission countries in Africa3.

Estimates of the number of cases were made by adjusting the number of reported malaria cases for

completeness of reporting, the likelihood that cases are parasite-positive and the extent of health

service use. The procedure, which is described in the World Malaria Report 2012 (18), combines data

reported by National Malaria Control Programs (reported cases, reporting completeness, likelihood that

cases are parasite positive) with those obtained from nationally representative household surveys on

health service use. If data from more than one household survey was available for a country, estimates

of health service use for intervening years were imputed by linear regression. If only one household

3 Botswana, Cape Verde, Eritrea, Madagascar, Namibia, Swaziland, South Africa, and Zimbabwe


survey was available then health service use was assumed to remain constant over time; analysis

summarized in the World Malaria Report 2008 (19) indicated that the percentage of fever cases seeking

treatment in public sector facilities varies little over time in countries with multiple surveys. Such a

procedure results in an estimate with wide uncertainty intervals around the point estimate.

The number of deaths was estimated by multiplying the estimated number of P. falciparum malaria

cases by a fixed case fatality rate for each country as described in the World Malaria Report 2012 (18).

This method is used for all countries outside the African Region and for countries within the African

Region where estimates of case incidence were derived from routine reporting systems and where

malaria causes less than 5% of all deaths in children under 5. A case fatality rate of 0·45% is applied to

the estimated number of P. falciparum cases for countries in the African Region and a case fatality rate

of 0·3% for P. falciparum cases in other Regions. In situations where the fraction of all deaths due to

malaria is small, the use of a case fatality rate in conjunction with estimates of case incidence was

considered to provide a better guide to the levels of malaria mortality than attempts to estimate the

fraction of deaths due to malaria.

Somalia, Sudan and high transmission countries in the WHO African Region.

Child malaria deaths were estimated using the VAMCM described in Section 3.4. The VAMCM derives

mortality estimates for malaria, as well as 7 other causes (pneumonia, diarrhea, congenital

malformation, causes arising in the perinatal period, injury, meningitis, and other causes) using

multinomial logistic regression methods to ensure that all 8 causes are estimated simultaneously with

the total cause fraction summing to 1. Malaria deaths were retrospectively adjusted for coverage of ITNs

and use of Haemophilus influenzae type b vaccine (1).

4.3 Whooping cough

An updated model of whooping cough (pertussis) mortality is being developed by the WHO Department

of Immunization, Vaccines and Biologicals (IVB). This model has not been finalized in time for the release

of these regional-level estimates but will be used to update the GHE estimates at country level later in

2013. In the interim, whooping cough mortality estimates from the IHME GBD 2010 (11) have been used

as an input to the WHO-CHERG analysis of child causes of death under age five (see Section 3.2).

4.4 Measles

To estimate deaths attributable to measles, a new model of measles mortality developed by WHO

Department of Immunization, Vaccines and Biologicals (IVB) was used to first estimate country-and-

year-specific cases using surveillance data (20). The improved statistical model firstly estimates measles

cases by country and year using surveillance data and making explicit projections about dynamic

transitions over time as well as overall patterns in incidence.

The age distribution of measles cases are then estimated using a logistic regression function fitted to

172,191 measles cases with data on age at infection from 102 countries over 2005-2009 extracted from

WHO's monthly measles case-based reporting system. Two explanatory variables were included in the

regression: 1) the 5 year rolling average of estimated MCV1 coverage, categorized in <60%, 60-84%, and

85-100%; and 2) geographic region classified in to 7 groups.

Country-specific measles case-fatality ratios (CFRs) for children 1-4 years of age were taken from a

comprehensive review of community-based studies (21). This review included 102 field studies

conducted in 29 countries during the period 1974-2007. The set of CFRs were revised for two countries

(India and Nepal) where additional studies have been published subsequent to the review (22, 23). The


same CFRs were used for infants and for children aged 1-4 years. For the period 2000-2011, we assumed

that age-specific CFRs are not declining over time.

Age-specific deaths are aggregated to derive measles deaths for all children below five and for ages five

and over. The new method takes into account herd immunity and produces results that are fairly

consistent with previous ones (24). Uncertainty is estimated by bootstrap sampling from the

distribution of incidence and age distribution estimates. Updated estimates of measles deaths by age

and country for years 2000-2011 were prepared using the above methods at the end of 2012 and

summary results published in the Weekly Epidemiological Record (25).These were used for this update

of GHE causes of death for years 2000-2011.

For countries experiencing measles outbreaks, the measles deaths were split into outbreak and endemic

deaths, the latter of which were smoothed using local regression (26). For the ages of 1-59 months, the

endemic measles deaths and AIDS deaths were added to the measles- and AIDS-free all-cause deaths for

which the VAMCM derived cause fractions were applied. The measles outbreak deaths were added back

at the end. In places where the outbreak deaths resulted in an increase in the all-cause deaths by 10% or

more, the original survey data were screened to examine whether a real increase in child mortality was

indicated for the outbreak year. If there were survey data available for the years around the outbreak

but no evidence of an increased mortality, the measles outbreak deaths were truncated at 10% of the

all-cause deaths. This was only necessary in few countries, almost all of which are in Africa and all

occurred in the early 2000s when more measles deaths were estimated.

4.5 Conflict and natural disasters

Country-specific estimates of war and conflict deaths were updated using information on conflict

intensity, time trends, and mortality obtained from a variety of published and unpublished war mortality

databases (27, section 3.4). Estimated deaths for major natural disasters were obtained from the

OFDA/CRED International Disaster Database (28). For the few countries where estimated all-age deaths

due to conflict and disasters exceeded 1 per 10,000 population, the estimated under 5 deaths were

added to the estimated deaths for injuries and all causes.

5 Uncertainty of estimates

Previously published country-level estimates for child causes of death 2000-2010 include 95%

uncertainty ranges. These were calculated from the uncertainty ranges for the estimated neonatal

deaths and deaths in the 1-59 month period, assuming zero correlation across the age groups.

For each of the multicause models for neonatal and 1-59 month deaths, a bootstrap approach was used.

One thousand bootstrap samples were drawn from the input data sets and the parameters of the

multinomial model re-estimated using the bootstrap sample. The re-estimated model was then used to

predict the country-specific cause distributions. The uncertainty ranges presented represent the 2.5th

and 97.5th centiles from the 1000 bootstrap samples.

For the next update of child causes of death at country level for years 2000-2012, cause-specific

uncertainty ranges will be revised. For the regional results released in June 2013, provisional country-

level estimates are aggregated to regional groupings. Uncertainty ranges for specific priority causes are

available in relevant cited publications.


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Schimek MG, editors. Statistical theory and computational aspects of smoothing. New York,

Springer, 1996, 10-49.

(27) World Health Organization. WHO methods and data sources for life tables 1990-2011 . Global

Health Estimates Technical Paper. Geneva, World Health Organization, 2013

(WHO/HIS/HSI/GHE/2013.1).

(28) CRED. EM-DAT: The CRED International Disaster Database. Belgium, Université Catholique de

Louvain, 2012. Available at: http://www.emdat.be/database


Annex Table A Methods used for estimation of child causes of death, by

country, 2000-2011

Country Method

Afghanistan VA (high mortality) multicause models

Albania VR multicause models

Algeria VA (high mortality) multicause models

Andorra VR multicause models

Angola VA (high mortality) multicause models

Antigua and Barbuda VR data (WHO tabulations)

Argentina VR data (WHO tabulations)

Armenia VR multicause models

Australia VR data (WHO tabulations)

Austria VR data (WHO tabulations)

Azerbaijan VA (high mortality) multicause models

Bahamas VR data (WHO tabulations)

Bahrain VR data (WHO tabulations)

Bangladesh VA (high mortality) multicause models

Barbados VR data (WHO tabulations)

Belarus VR multicause models

Belgium VR data (WHO tabulations)

Belize VR data (WHO tabulations)

Benin VA (high mortality) multicause models

Bhutan VA (high mortality) multicause models

Bolivia VA (high mortality) multicause models

Bosnia and Herzegovina VR multicause models

Botswana VA (high mortality) multicause models

Brazil VR data (WHO tabulations)

Brunei Darussalam VR multicause models

Bulgaria VR data (WHO tabulations)

Burkina Faso VA (high mortality) multicause models

Burundi VA (high mortality) multicause models

Cambodia VA (high mortality) multicause models

Cameroon VA (high mortality) multicause models

Canada VR multicause models plus VR data for 0-4 years

Cape Verde VR multicause models

Central African Republic VA (high mortality) multicause models

Chad VA (high mortality) multicause models

Chile VR data (WHO tabulations)

China National VA model based on subnational Chinese studies only

Colombia VR data (WHO tabulations)


Comoros VA (high mortality) multicause models

Congo VA (high mortality) multicause models

Cook Islands VR multicause models

Costa Rica VR data (WHO tabulations)

Cote d'Ivoire VA (high mortality) multicause models

Croatia VR data (WHO tabulations)

Cuba VR data (WHO tabulations)

Cyprus VR multicause models

Czech Republic VR data (WHO tabulations)

Democratic People's Republic of Korea VA (high mortality) multicause models

Democratic Republic of the Congo VA (high mortality) multicause models

Denmark VR data (WHO tabulations)

Djibouti VA (high mortality) multicause models

Dominica VR data (WHO tabulations)

Dominican Republic VA (high mortality) multicause models

Ecuador VR multicause models

Egypt VR multicause models

El Salvador VR multicause models

Equatorial Guinea VA (high mortality) multicause models

Eritrea VA (high mortality) multicause models

Estonia VR data (WHO tabulations)

Ethiopia VA (high mortality) multicause models

Fiji VR multicause models

Finland VR data (WHO tabulations)

France VR data (WHO tabulations)

Gabon VA (high mortality) multicause models

Gambia VA (high mortality) multicause models

Georgia VR multicause models

Germany VR data (WHO tabulations)

Ghana VA (high mortality) multicause models

Greece VR data (WHO tabulations)

Grenada VR data (WHO tabulations)

Guatemala VA (high mortality) multicause models

Guinea VA (high mortality) multicause models

Guinea-Bissau VA (high mortality) multicause models

Guyana VR data (WHO tabulations)

Haiti VA (high mortality) multicause models

Honduras VR multicause models

Hungary VR data (WHO tabulations)

Iceland VR data (WHO tabulations)

India State-level Indian-specific VA model

Indonesia VA (high mortality) multicause models


Iran (Islamic Republic of) VA (high mortality) multicause models

Iraq VA (high mortality) multicause models

Ireland VR data (WHO tabulations)

Israel VR data (WHO tabulations)

Italy VR data (WHO tabulations)

Jamaica VR multicause models

Japan VR data (WHO tabulations)

Jordan VR multicause models

Kazakhstan VA (high mortality) multicause models

Kenya VA (high mortality) multicause models

Kiribati VA (high mortality) multicause models

Kuwait VR multicause models plus VR data for 0-4 years

Kyrgyzstan VA (high mortality) multicause models

Lao People's Democratic Republic VA (high mortality) multicause models

Latvia VR data (WHO tabulations)

Lebanon VR multicause models

Lesotho VA (high mortality) multicause models

Liberia VA (high mortality) multicause models

Libyan Arab Jamahiriya VR multicause models

Lithuania VR data (WHO tabulations)

Luxembourg VR data (WHO tabulations)

Madagascar VA (high mortality) multicause models

Malawi VA (high mortality) multicause models

Malaysia VR multicause models

Maldives VR multicause models

Mali VA (high mortality) multicause models

Malta VR data (WHO tabulations)

Marshall Islands VA (high mortality) multicause models

Mauritania VA (high mortality) multicause models

Mauritius VR data (WHO tabulations)

Mexico VR data (WHO tabulations)

Micronesia (Federated States of) VA (high mortality) multicause models

Monaco VR multicause models

Mongolia VA (high mortality) multicause models

Montenegro VR data (WHO tabulations)

Morocco VA (high mortality) multicause models

Mozambique VA (high mortality) multicause models

Myanmar VA (high mortality) multicause models

Namibia VA (high mortality) multicause models

Nauru VA (high mortality) multicause models

Nepal VA (high mortality) multicause models

Netherlands VR data (WHO tabulations)


New Zealand VR data (WHO tabulations)

Nicaragua VR multicause models

Niger VA (high mortality) multicause models

Nigeria VA (high mortality) multicause models

Niue VR multicause models

Norway VR data (WHO tabulations)

Oman VR multicause models

Pakistan VA (high mortality) multicause models

Palau VR multicause models

Panama VR data (WHO tabulations)

Papua New Guinea VA (high mortality) multicause models

Paraguay VR multicause models

Peru VR multicause models

Philippines VA (high mortality) multicause models

Poland VR data (WHO tabulations)

Portugal VR multicause models plus VR data for 0-4 years

Qatar VR multicause models

Republic of Korea VR multicause models plus VR data for 0-4 years

Republic of Moldova VR data (WHO tabulations)

Romania VR data (WHO tabulations)

Russian Federation VR multicause models

Rwanda VA (high mortality) multicause models

Saint Kitts and Nevis VR data (WHO tabulations)

Saint Lucia VR multicause models plus VR data for 0-4 years

Saint Vincent and the Grenadines VR data (WHO tabulations)

Samoa VR multicause models

San Marino VR multicause models plus VR data for 0-4 years

Sao Tome and Principe VA (high mortality) multicause models

Saudi Arabia VR multicause models

Senegal VA (high mortality) multicause models

Serbia VR data (WHO tabulations)

Seychelles VR multicause models

Sierra Leone VA (high mortality) multicause models

Singapore VR data (WHO tabulations)

Slovakia VR data (WHO tabulations)

Slovenia VR data (WHO tabulations)

Solomon Islands VA (high mortality) multicause models

Somalia VA (high mortality) multicause models

South Africa VA (high mortality) multicause models

Spain VR data (WHO tabulations)

Sri Lanka VR multicause models

Sudan VA (high mortality) multicause models


Suriname VR data (WHO tabulations)

Swaziland VA (high mortality) multicause models

Sweden VR data (WHO tabulations)

Switzerland VR data (WHO tabulations)

Syrian Arab Republic VR multicause models

Tajikistan VA (high mortality) multicause models

Thailand VR multicause models

The former Yugoslav Republic of Macedonia VR multicause models plus VR data for 0-4 years

Timor-Leste VA (high mortality) multicause models

Togo VA (high mortality) multicause models

Tonga VR multicause models

Trinidad and Tobago VR data (WHO tabulations)

Tunisia VR multicause models

Turkey VR multicause models

Turkmenistan VA (high mortality) multicause models

Tuvalu VR multicause models

Uganda VA (high mortality) multicause models

Ukraine VR multicause models

United Arab Emirates VR multicause models

United Kingdom VR data (WHO tabulations)

United Republic of Tanzania VA (high mortality) multicause models

United States VR data (WHO tabulations)

Uruguay VR data (WHO tabulations)

Uzbekistan VA (high mortality) multicause models

Vanuatu VR multicause models

Venezuela VR data (WHO tabulations)

Viet Nam VR multicause models

Yemen VA (high mortality) multicause models

Zambia VA (high mortality) multicause models

Zimbabwe VA (high mortality) multicause models


Annex Table B First-level categories for analysis of child causes of

death

Cause name ICD-10 code

All causes A00-Y89

I. Communicable, maternal,

perinatal and nutritional

conditionsa

A00-B99, D50-D53, D64.9, E00-E02, E40-E64, G00, G03-G04, H65-H66, J00-J22, J85, N30, N34, N390, N70-N73, O00-P96, U04

HIV/AIDS B20-B24

Diarrhoeal diseases A00-A09

Pertussis A37

Tetanus A33-A35

Measles B05

Meningitis/encephalitis A39, A83-A87, G00, G03, G04

Malaria B50-B54, P37.3, P37.4

Acute respiratory infections H65-H66, J00-J22, J85, P23

Prematurity P01.0, P01.1, P07, P22, P25-P28, P61.2, P77

Birth asphyxia & birth trauma P01.7-P02.1, P02.4-P02.6, P03, P10-P15, P20-P21, P24, P50, P90-P91

Sepsis and other infectious conditions of the newborn

P35-P39 (exclude P37.3, P37.4)

Other Group I Remainder

II. Noncommunicable diseasesa C00-C97, D00-D48, D55-D64 (exclude D64.9), D65-D89, E03-E34, E65-E88, F01-

F99, G06-G98, H00-H61, H68-H93, I00-I99, J30-J84, J86-J98, K00-K92, L00-L98, M00-M99, N00-N28, N31-N32, N35-N64 (exclude N39.0), N75-N98, Q00-Q99

Congenital anomalies Q00-Q99

Other Group II Remainder

III. Injuries V01-Y89

a Deaths coded to “Symptoms, signs and ill-defined conditions” (780-799 in ICD-9 and R00-R99 in ICD-10) are distributed

proportionately to all causes within Group I and Group II.


Annex Table C Re-assignment of ICD-10 codes for certain neonatal

deaths

Cause Recode Cause Recode Cause Recode Cause Recode Cause Recode

A153 P370 D649 P614 I471 P291 J698 P249 K760 P788

A162 P370 D65 P60 I472 P291 J70 P24 K761 P788

A165 P370 D696 D694 I479 P291 J709 P249 K762 P788

A169 P370 D699 P549 I48 P29 J80 P22 K763 P788

A170 P370 E101 P702 I490 P291 J840 P258 K767 P788

A180 P370 E102 P702 I494 P291 J841 P258 K768 P788

A320 P372 E110 P702 I498 P291 J848 P258 K769 P788

A321 P372 E112 P702 I499 P291 J849 P258 K819 P788

A327 P372 E116 P702 I50 P29 J85 P28 K82 P78

A328 P372 E117 P702 I500 P290 J850 P288 K828 P788

A329 P372 E140 P702 I501 P290 J851 P288 K830 P788

A35 A33 E144 P702 I509 P290 J852 P288 K831 P788

A40 P36 E145 P702 I517 Q248 J860 P288 K838 P788

A401 P360 E147 P702 I518 Q248 J869 P288 K839 P788

A402 P361 E149 P702 I519 Q249 J90 P28 K85 P78

A403 P361 E343 P051 I60 P52 J930 P251 K868 P788

A408 P361 E86 P74 I603 P525 J931 P251 K869 P788

A409 P361 E87 P74 I607 P525 J938 P251 K904 P788

A41 P36 E870 P742 I608 P525 J939 P251 K909 P788

A410 P362 E871 P742 I609 P525 J940 P288 K920 P540

A412 P363 E872 P740 I61 P52 J941 P288 K922 P543

A413 P368 E874 P748 I610 P524 J942 P548 K928 P788

A415 P368 E875 P743 I612 P524 J948 P288 K929 P789

A418 P368 E876 P743 I615 P524 J96 P28 N133 Q620

A419 P369 E877 P744 I616 P524 J960 P285 N139 Q623

B00 P35 E878 P744 I618 P524 J961 P285 N17 P96

B000 P352 F322 P914 I619 P524 J969 P285 N170 P960

B004 P352 F328 P914 I620 P528 J980 P288 N171 P960

B007 P352 F329 P914 I629 P529 J981 P281 N172 P960

B008 P352 F439 P209 I632 P529 J982 P250 N179 P960

B009 P352 G91 Q03 I633 P529 J984 P288 N180 P960

B01 P35 G911 Q039 I634 P529 J985 P288 N188 P960

B010 P358 G912 Q039 I635 P529 J986 P288 N189 P960

B011 P358 G913 Q039 I638 P529 J988 P288 N19 P96

B012 P358 G919 Q039 I639 P529 J989 P289 N359 Q643

B018 P358 G930 Q046 I64 P52 K220 Q395 N390 P393

B019 P358 G931 P219 I671 I607 K311 Q400 N433 P835

B059 P358 G936 P524 J12 P23 K44 Q79 N883 P010

B060 P350 G952 P025 J120 P230 K440 Q790 R001 P209

B068 P350 I050 Q232 J121 P230 K441 Q790 R011 P298


Cause Recode Cause Recode Cause Recode Cause Recode Cause Recode

B069 P350 I059 Q238 J128 P230 K449 Q790 R030 P292

B09 P35 I071 Q228 J129 P230 K561 Q438 R040 P548

B25 P35 I080 Q238 J13 P23 K562 Q438 R042 P269

B250 P351 I340 Q233 J14 P23 K565 Q433 R048 P548

B251 P351 I348 Q238 J15 P23 K566 P769 R049 P548

B258 P351 I35 Q23 J150 P236 K57 Q43 R05 P28

B259 P351 I350 Q230 J151 P235 K593 Q431 R060 P228

B270 P358 I351 Q231 J152 P232 K625 P542 R064 P228

B370 P375 I352 Q238 J153 P233 K631 P780 R068 P228

B371 P375 I359 Q238 J154 P236 K633 P788 R090 P219

B372 P375 I370 Q221 J155 P234 K65 P78 R092 P285

B373 P375 I379 Q223 J156 P236 K650 P781 R160 Q447

B374 P375 I38 I42 J157 P236 K659 P781 R162 Q447

B375 P375 I42 I42 J158 P236 K660 Q433 R230 Q249

B376 P375 I420 I424 J159 P236 K661 P548 R509 P819

B377 P375 I421 Q248 J16 P23 K720 P788 R568 P90

B378 P375 I422 I424 J18 P23 K729 P788 R571 P741

B379 P375 I429 I424 J180 P239 K732 P788 R58 P54

B509 P373 I442 Q246 J181 P239 K745 P788 R601 P833

B54 P37 I443 Q246 J188 P239 K746 P788 R628 P059

B582 P371 I455 Q246 J189 P239 K750 P788 R629 P059

B589 P371 I458 Q246 J386 Q318 K752 P788 R630 P929

D500 P549 I459 Q246 J439 P250 K758 P788 R638 P929

D609 D610 I460 P291 J69 P24 K759 P788 R75 B24

D62 P61 I469 P291 J690 P249


Annex Table D Country groupings used for regional tabulations

D.1 WHO Regions and Member States

WHO African Region

Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African

Republic, Chad, Comoros, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Equatorial Guinea,

Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar,

Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and

Principe, Senegal, Seychelles, Sierra Leone, South Africa, South Sudan, Swaziland, Togo, Uganda, United

Republic of Tanzania, Zambia, Zimbabwe

WHO Region of the Americas

Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Brazil,

Canada, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador,

Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru,

Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago,

United States of America, Uruguay, Venezuela (Bolivarian Republic of)

WHO South-East Asia Region

Bangladesh, Bhutan, Democratic People's Republic of Korea, India, Indonesia, Maldives, Myanmar,

Nepal, Sri Lanka, Thailand, Timor-Leste

WHO European Region

Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria,

Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece,

Hungary, Iceland, Ireland, Israel, Italy, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Luxembourg, Malta,

Monaco, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Romania, Russian

Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Tajikistan, The former

Yugoslav Republic of Macedonia, Turkey, Turkmenistan, Ukraine, United Kingdom, Uzbekistan

WHO Eastern Mediterranean Region

Afghanistan, Bahrain, Djibouti, Egypt, Iran (Islamic Republic of), Iraq, Jordan, Kuwait, Lebanon, Libya,

Morocco, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syrian Arab Republic, Tunisia, United

Arab Emirates, Yemen

WHO Western Pacific Region

Australia, Brunei Darussalam, Cambodia, China, Cook Islands, Fiji, Japan, Kiribati, Lao People's

Democratic Republic, Malaysia, Marshall Islands, Micronesia (Federated States of), Mongolia, Nauru,

New Zealand, Niue, Palau, Papua New Guinea, Philippines, Republic of Korea, Samoa, Singapore,

Solomon Islands, Tonga, Tuvalu, Vanuatu, Viet Nam


D.2 Countries grouped by WHO Region and average income per capita*

High income

Andorra, Australia, Austria, Bahamas, Bahrain, Barbados, Belgium, Brunei Darussalam Canada, Croatia,

Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany Greece, Hungary, Iceland, Ireland,

Israel, Italy, Japan, Kuwait, Luxembourg, Malta, Monaco, Netherlands, ,New Zealand, Norway, Oman, Poland,

Portugal, Qatar, Republic of Korea, Saint Kitts and Nevis, San Marino, Saudi Arabia, Singapore, Slovakia,

Slovenia, Spain, Sweden, Switzerland, Trinidad and Tobago, United Arab Emirates, United Kingdom, United

States of America

Low and middle income

WHO African Region

Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic,

Chad, Comoros, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Equatorial Guinea**, Eritrea,

Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali,

Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal,

Seychelles, Sierra Leone, South Africa, South Sudan, Swaziland, Togo, Uganda, United Republic of Tanzania,

Zambia, Zimbabwe

WHO Region of the Americas

Antigua and Barbuda, Argentina, Belize, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Costa Rica,

Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada Guatemala, Guyana, Haiti, Honduras,

Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Lucia, Saint Vincent and the Grenadines,

Suriname, Uruguay, Venezuela (Bolivarian Republic of)

WHO South-East Asia Region

Bangladesh, Bhutan, Democratic People's Republic of Korea, India, Indonesia, Maldives, Myanmar, Nepal, Sri

Lanka, Thailand, Timor-Leste

WHO European Region

Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Georgia, Kazakhstan, Kyrgyzstan,

Latvia, Lithuania, Montenegro, Republic of Moldova, Romania, Russian Federation, Serbia, Tajikistan, The

former Yugoslav Republic of Macedonia, Turkey, Turkmenistan, Ukraine, Uzbekistan

WHO Eastern Mediterranean Region

Afghanistan, Djibouti, Egypt, Iran (Islamic Republic of), Iraq, Jordan, Lebanon, Libya, Morocco, Pakistan,

Somalia, Sudan, Syrian Arab Republic, Tunisia, Yemen

WHO Western Pacific Region

Cambodia, China, Cook Islands, Fiji, Kiribati, Lao People's Democratic Republic, Malaysia, Marshall Islands,

Micronesia (Federated States of), Mongolia, Nauru***, Niue***, Palau, Papua New Guinea, Philippines,

Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Viet Nam

* This regional grouping classifies WHO Member States according to the World Bank income and geographic regional categories

for the year 2011 (World Bank list of economies, July 2012)

** Equatorial Guinea is classified by the World Bank as high income, it is kept here with upper middle income to avoid a

regional grouping containing only one country and because its mortality profile is not dissimilar to neighbouring countries.

*** Nauru and Niue have been classified into income groups using gross domestic product.


D.3 World Bank income grouping*

Low income

Afghanistan, Bangladesh, Benin, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad

Comoros, Democratic People's Republic of Korea, Democratic Republic of the Congo, Eritrea, Ethiopia

Gambia, Guinea, Guinea-Bissau, Haiti, Kenya, Kyrgyzstan, Liberia, Madagascar, Malawi, Mali, Mauritania,

Mozambique, Myanmar, Nepal, Niger Rwanda, Sierra Leone, Somalia, Tajikistan, Togo, Uganda, United

Republic of Tanzania, Zimbabwe

Lower middle income

Albania, Armenia, Belize, Bhutan, Bolivia (Plurinational State of), Cameroon, Cape Verde, Congo, Côte

d'Ivoire, Djibouti, Egypt, El Salvador, Fiji, Georgia, Ghana, Guatemala, Guyana, Honduras, India,

Indonesia, Iraq, Kiribati, Lao People's Democratic Republic, Lesotho, Marshall Islands, Micronesia

(Federated States of), Mongolia, Morocco, Nicaragua, Nigeria, Pakistan, Papua New Guinea, Paraguay,

Philippines, Republic of Moldova, Samoa, Sao Tome and Principe, Senegal, Solomon Islands, South

Sudan, Sri Lanka, Sudan, Swaziland, Syrian Arab Republic, Timor-Leste, Tonga, Ukraine, Uzbekistan,

Vanuatu, Viet Nam, Yemen Zambia

Upper middle income

Algeria, Angola, Antigua and Barbuda, Argentina, Azerbaijan, Belarus, Bosnia and Herzegovina,

Botswana, Brazil, Bulgaria, Chile, China, Colombia, Cook Islands, Costa Rica, Cuba, Dominica, Dominican

Republic, Ecuador, Equatorial Guinea**, Gabon, Grenada, Iran (Islamic Republic of), Jamaica, Jordan,

Kazakhstan, Latvia, Lebanon, Libya, Lithuania, Malaysia, Maldives, Mauritius, Mexico Montenegro,

Namibia, Nauru***, Niue***, Palau, Panama, Peru, Romania, Russian Federation, Saint Lucia, Saint

Vincent and the Grenadines, Serbia, Seychelles, South Africa, Suriname, Thailand, The former Yugoslav

Republic of Macedonia, Tunisia, Turkey, Turkmenistan, Tuvalu, Uruguay, Venezuela (Bolivarian Republic

of)

High income



Israel, Italy, Japan, Kuwait, Luxembourg, Malta, Monaco, Netherlands, ,New Zealand, Norway, Oman,

Poland, Portugal, Qatar, Republic of Korea, Saint Kitts and Nevis, San Marino, Saudi Arabia, Singapore,

Slovakia, Slovenia, Spain, Sweden, Switzerland, Trinidad and Tobago, United Arab Emirates, United

Kingdom, United States of America

* This regional grouping classifies WHO Member States according to the World Bank income categories for the year 2011

(World Bank list of economies, July 2012)



*** Nauru and Niue have been classified into income groups using gross domestic product.


D.4 World Bank Regions

High income



Israel, Italy, Japan, Kuwait, Luxembourg, Malta, Monaco, Netherlands, ,New Zealand, Norway, Oman,

Poland, Portugal, Qatar, Republic of Korea, Saint Kitts and Nevis, San Marino, Saudi Arabia, Singapore,

Slovakia, Slovenia, Spain, Sweden, Switzerland, Trinidad and Tobago, United Arab Emirates, United

Kingdom, United States of America

East Asia and Pacific

Cambodia, China, Cook Islands, Democratic People's Republic of Korea, Fiji, Indonesia, Kiribati, Lao

People's Democratic Republic, Malaysia, Marshall Islands, Micronesia (Federated States of), Mongolia,

Myanmar, Nauru, Niue, Palau, Papua New Guinea, Philippines, Samoa, Solomon Islands, Thailand,

Timor-Leste, Tonga, Tuvalu, Vanuatu, Viet Nam

Europe and Central Asia

Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Georgia, Kazakhstan,

Kyrgyzstan, Latvia, Lithuania, Montenegro Republic of Moldova, Romania, Russian Federation, Serbia,

Tajikistan, The former Yugoslav Republic of Macedonia, Turkey, Turkmenistan, Ukraine, Uzbekistan

Latin America and Caribbean

Antigua and Barbuda, Argentina, Belize, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Costa

Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti,

Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Lucia, Saint Vincent and the

Grenadines, Suriname, Uruguay, Venezuela (Bolivarian Republic of)

Middle East and North Africa

Algeria, Djibouti, Egypt, Iran (Islamic Republic of), Iraq, Jordan, Lebanon ,Libya, Morocco, Syrian Arab

Republic, Tunisia, Yemen

South Asia

Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka

Sub-Saharan Africa

Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad,

Comoros, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Equatorial Guinea**, Eritrea,

Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi,

Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe,

Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Togo, Uganda,

United Republic of Tanzania, Zambia, Zimbabwe




D.5 Millennium Development Goal (MDG) Regions

Developed regions

Albania, Andorra, Australia, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Canada,

Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland,

Ireland, Israel, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Monaco, Montenegro, Netherlands,

New Zealand, Norway, Poland, Portugal, Republic of Moldova, Romania, Russian Federation, San

Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, The former Yugoslav Republic of

Macedonia, Ukraine, United Kingdom, United States of America

Developing regions

Caucasus and Central Asia

Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan

Eastern Asia China, Democratic People's Republic of Korea, Mongolia, Republic of Korea

Latin America and the Caribbean

Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Brazil, Chile,

Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala,

Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis,

Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela

(Bolivarian Republic of)

Northern Africa Algeria, Egypt, Libya, Morocco, Tunisia

Oceania

Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, Niue, Palau, Papua

New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu

South-eastern Asia

Brunei Darussalam, Cambodia, Indonesia, Lao People's Democratic Republic, Malaysia, Myanmar,

Philippines, Singapore, Thailand, Timor-Leste, Viet Nam

Southern Asia

Afghanistan, Bangladesh, Bhutan, India, Iran (Islamic Republic of), Maldives, Nepal, Pakistan, Sri Lanka

Sub-Saharan Africa

Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad,

Comoros, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Djibouti, Equatorial Guinea, Eritrea,

Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi,

Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe,

Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Togo, Uganda,

United Republic of Tanzania, Zambia, Zimbabwe

Western Asia Bahrain, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar Saudi Arabia, Syrian Arab

Republic, Turkey, United Arab Emirates, Yemen

who-h il 2000-2011 · i a k wl g this technical paper was written by colin mathers and li liu with...

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