MCEE-WHO methods and data sources
for child causes of death 2000-2015
Department of Evidence, Information and Research (WHO, Geneva)
and Maternal Child Epidemiology Estimation (MCEE)
February 2016
Global Health Estimates Technical Paper WHO/HIS/IER/GHE/2016.1
Acknowledgments
This Technical Paper was prepared by Daniel Hogan, with inputs from Li Liu, Colin Mathers, Shefali Oza
and Yue Chu. Country estimates of child deaths by cause for years 2000-2015 were primarily prepared
by Li Liu, Shefali Oza, Bob Black, Simon Cousens, Joy Lawn, Yue Chu and Jamie Perin, of the Maternal and
Child Epidemiology Estimation (MCEE) group, and Dan Hogan, Doris Ma Fat and Colin Mathers, of the
Mortality and Health Analysis unit in the WHO Department of Evidence, information and Research, with
advice and inputs from other members of MCEE, WHO Departments, collaborating UN Agencies, and
other WHO expert advisory groups and academic collaborators.
The Maternal and Child Health Estimation group has been supported by a grant from the Bill & Melinda
Gates Foundation.
These estimates make considerable use of the all-cause mortality estimates developed by the
Interagency Group on Child Mortality Estimation (UN-IGME), and births estimates from the UN
Population Division, as well as 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 for this update provided
by Leontine Alkema, Ties Boerma, Cynthia Boschi-Pinto, Richard Cibulskis, Marta Gacic-Dodo, Cristin
Fergus, Robert Jakob, Mary Mahy, and Danzhen You.
Estimates and analysis are available at:
http://www.who.int/healthinfo/global_burden_disease/en/
For further information about the estimates and methods, please contact: [email protected].
i
Table of Contents
Acknowledgments.......................................................................................................................................... i
Table of Contents .......................................................................................................................................... ii
1 Introduction ………………………………………………………………………………………………………………………………………1
2 All-cause mortality and population estimates for years 2000-2015 ....................................................... 1
2.1 Estimation of neonatal and under-5 mortality rates ....................................................................... 1
2.2 Population size and births estimates ............................................................................................... 2
2.3 Mortality shocks – epidemics, conflicts and disasters ..................................................................... 2
3 Child mortality by cause .......................................................................................................................... 2
3.1 Death registration data .................................................................................................................... 2
3.2 Modeling causes of neonatal death (ages 0-27 days) ..................................................................... 4
3.3 Modeling causes of postneonatal deaths (ages 1-59 months) ........................................................ 5
3.5 Causes of child death for China and India ....................................................................................... 6
4 Methods for cause-specific revisions and updates.................................................................................. 7
4.1 HIV/AIDS........................................................................................................................................... 7
4.2 Malaria ............................................................................................................................................. 7
4.3 Whooping cough .............................................................................................................................. 8
4.4 Measles ............................................................................................................................................ 9
4.5 Conflict and natural disasters .......................................................................................................... 9
5 Uncertainty of estimates ....................................................................................................................... 10
References ………………….. ............................................................................................................................ 11
Annex Table A. Methods used for estimation of child causes of death, by country, 2000-2015 .............. 14
Annex Table B.1. First-level categories for analysis of neonatal child causes of death.............................. 19
Annex Table B.2. First-level categories for analysis of postneonatal child causes of death ...................... 20
ii
1
Introduction
This document, Global Health Estimates Technical Paper WHO/HIS/IER/GHE/2014.6.2, is an update to
the original document Global Health Estimates Technical Paper WHO/HIS/HSI/GHE/2014.6, which
described estimation methodology for child causes of death (COD) for 2000-2012. This updated version
(2016.1) is edited to reflect an update to those estimates in which child causes of death are estimated for
years 2000-2015. The underlying methodological approaches are similar to those used to derive child
COD estimates for years 2000-2012, which were published in May 2014, and child COD estimates for
2000-2013, which were published in September 2014.
Cause-specific estimates of deaths for children under age 5 were estimated for 15 cause categories for
years 2000-2015 using methods similar to those described elsewhere by Liu et al. (1) and on the WHO
website (2). These estimates were prepared by the WHO Department of Evidence, Information and
Research and the Maternal and Child Epidemiology Estimation (MCEE) group, with inputs and assistance
from other WHO Departments and UN Agencies. These child cause of death estimates for years 20002015 supersede previously published estimates for child causes of death for years 2000-2010, 20002012 and 2000-2013. The estimation framework is similar to that used for the previous estimates,
although some methodological components have been improved along with updated inputs for child
mortality levels (3) as well as cause-specific estimates for HIV, malaria, measles and pertussis 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 represent the best estimates of WHO and MCEE, based on the
evidence available to them up until October 2015, 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 about data sources and
methods for preparing child mortality estimates by cause. Data files and statistical code that allow
interested readers to replicate the child cause of death estimates can be found at
http://www.who.int/healthinfo/global_burden_disease/en/.
2
All-cause mortality and population estimates for years 2000-2015
2.1
Estimation of neonatal and under-5 mortality rates
Methods for estimating time series of neonatal (0-27 days), infant (0-365 days) 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 estimate country-specific trends in under-five mortality per 1000 live births (U5MR)
over the past few decades (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
1
www.childmortality.org
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countries with incomplete death registration, all available census and survey data sources that meet
quality criteria are used. UN-IGME methods are documented in a series of papers published in a
collection in 2012 (4).
Under-five and infant mortality rates are estimated from data inputs using a multi-level penalized spline
regression model that accommodates sources of bias across input data sources. Neonatal mortality
rates (NMR) are then estimated in a second estimation process which models NMR as a function of
U5MR, with splines used to capture country-specific data trends (3). For countries where child mortality
is strongly affected by HIV, U5MR and NMR are estimated initially using neonatal and child mortality
observations for non-AIDS deaths, calculated by subtracting from total death rates the estimated HIV
death rates in the neonatal and 1-59 month periods respectively, and then AIDS deaths are added back
on to the non-HIV deaths to compute the total estimated U5MR and NMR.
2.2
Population size 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 World
Population Prospects 2015 (5). They may thus differ slightly from official national estimates for
corresponding years.
2.3
Mortality shocks – epidemics, conflicts and disasters
Country-specific estimates of deaths for organized conflicts and major natural disasters were prepared
for years 1990-2015 using data and methods as described below in Section 4.5, with large mortality
shocks due to conflicts and disasters added to the all-cause child mortality estimates from the UN-IGME.
As described in Section 4.4, deaths due to measles outbreaks were identified and also added to the UNIGME estimates for total child deaths.
3
Child mortality by cause
Final cause of death estimates for children under 5 are the result of separate estimation processes for
causes of death during the neonatal (0-27 days) and postneonatal (1 to 59 months) periods (1,6). The
neonatal period is further divided into two periods, 0-6 days and 7-27 days, to allow for more accurate
modeling within these time periods, between which cause of death distributions can change significantly
in many countries (1,7). The approach used for estimating cause of death distributions for early
neonatal, late neonatal, and postneonatal periods varied depending on a country’s data availability and
under-five mortality rate. Three general estimation strategies were employed. First, for countries with
high-quality vital registration (VR) data, cause distributions were estimated directly from the vital
registration data. Second, for lower mortality countries lacking high-quality vital registration data, cause
of death distributions were predicted from a regression fit to data from countries with high-quality VR
data. Third, for higher mortality countries without high-quality VR data, cause of death distributions
were predicted from a regression fit to data assembled from studies of causes of death in high-mortality
countries, which typically relied on verbal autopsy. These approaches are augmented by UN program
estimates of certain diseases, such as HIV and measles. The following sections explain the
methodological details of the estimation process.
3.1
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 (8). The number of countries
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reporting data using the 10th revision of the International Classification of Diseases (ICD-10) (9) has
continued to increase.
Death registration data were used directly for estimating cause fractions of neonatal and postneonatal
child deaths for countries with high-quality vital registration data and population coverage of >80%. VR
data were considered to be high 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 these estimates, VR data was used for directly estimating cause of
death distributions in the neonatal period for 65 countries and in the postneonatal period for 67
countries (Annex Table A). VR data was also used directly for estimating cause of death distributions for
Brunei Darussalam and Turkey for recent years for which VR data are available, with trends estimated
from the low-mortality multi-cause model (described in 3.2 and 3.3) used to project backwards to 2000
for years before high-quality data are available.
Annual data from 2000 to the latest available year were incorporated for country estimates. For small
countries with a 2012 population of less than 1 million and fewer than 50 neonatal or postneonatal
annual deaths, a three-year moving average was computed to obtain a more stable estimate of
mortality by cause. In cases where data on causes of death were missing for some years, local logistic
regressions fit to a country’s cause of death time series were used to impute missing numbers of deaths
by cause. A few countries (Canada, Portugal and Switzerland) reporting mortality data to WHO do not
provide the breakdown for the neonatal period across all years. In these cases, 1-59 months deaths by
cause were imputed from totals for 0-4 years, using information on the average cause-specific ratio of
neonatal to postneonatal deaths from other parts of the country’s time series and data from other
countries in the same region. Neonatal cause distributions for these countries were estimated with the
low-mortality multi-cause model (see Section 3.2).
The category “Preterm” includes all the specific codes for complications of preterm birth and the related
obstetric causes codes for preterm labour as cause of death. Less than 1% of these deaths were
attributed to term small for gestational age (SGA) as cause of death. Almost all (99%) deaths in this
category were coded as due to complications of preterm birth. This is in line with data reported from
industrialized countries.
Respiratory distress syndrome (RDS) with ICD-10 codes P22 and P27 and intraventricular haemorrhage
code P52 were assigned to preterm birth since they are almost a distinctive characteristic of preterm
birth. In some developing countries, it has been noted that the proportion of neonatal deaths coded to
RDS is relatively high compared to developed countries. This may be due to certification habits inherent
to the medical profession in these countries and the application of ICD-10 rules for the determination of
the underlying cause of death. One of these rules stipulates that when the death certificate has other
conditions listed together with prematurity, the coders should code to the other conditions including
RDS. (Reference ICD-10 rule P1, ICD-10 vol2 section 4.3.5.) Alternatively, this may be a real result of
limited intensive care for babies with RDS in some countries, especially transitional countries.
The ICD-10 provides a chapter on 'Congenital malformations, deformations and chromosomal
abnormalities' which captures most of the deaths among neonates due to congenital abnormalities. In
addition neonatal deaths classified in other chapters of the ICD-10 such as endocrine, nutritional and
metabolic diseases, and diseases of the nervous, digestive, circulatory, musculoskeletal and
genitourinary systems were reassigned to congenital abnormalities as these are consequences of
congenital malformations. Neonatal deaths classified to the diseases of the respiratory symptoms are
included in the acute respiratory infections for this analysis.
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For the analyses of neonatal deaths, deaths that were reported as due to ill-defined causes (ICD-9
Chapter XVI , ICD-10 Chapter XVIII, on symptoms, signs and abnormal clinical and laboratory findings,
not elsewhere classified) as well as the codes P92, P95 and P96 were proportionately reassigned to
other defined causes including external causes of injuries. However for the analyses of the deaths aged
1-59 months of age, only those ill-defined causes coded to R codes were proportionately reassigned to
the natural causes.
Final country time series for 2000 to 2015 of the proportion of deaths by cause for neonatal and
postneonatal periods from high-quality VR data were multiplied by UN-IGME envelopes to obtain
estimates of numbers of deaths by cause.
3.2
Modeling causes of neonatal death (ages 0-27 days)
The MCEE neonatal working group undertook an extensive exercise to derive mortality estimates for
dominant causes of neonatal death, defined as deaths occurring at less than 28 days of age. These cause
categories are defined in Annex Table B. Estimates were derived separately for early (0-6 days) and late
(7-27 days) neonatal periods.
Low mortality countries
Death registration data were used to directly calculate cause of death distributions for 67 countries
considered to have reliable information as described in Section 3.1. Data from these 67 countries were
then used to fit a multinomial logistic regression model (separately for early and late neonatal periods),
which was then employed to predict cause distributions for 46 low mortality countries without highquality VR data. This vital registration multi-cause model (VRMCM) was used to estimate seven broad
cause categories in these 46 countries: complications of preterm birth (“preterm”), intrapartum-related
complications (“intrapartum”, which includes birth asphyxia and birth trauma), congenital disorders,
pneumonia, sepsis and other severe infections (“sepsis”), injuries, and other causes.
High mortality countries
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 119 studies from 39 countries
in high mortality populations met the inclusion criteria. The high mortality, verbal-autopsy based
multinomial model (VAMCM) was used for countries that were classified as high mortality based on an
average U5MR>35 from 2000-2010 in an earlier estimation round (6). The VAMCM model was used to
estimate eight cause categories for the 80 high mortality countries (separately for early and late
neonatal periods): preterm, intrapartum, congenital disorders, pneumonia, diarrhea, neonatal tetanus,
sepsis, and other causes.
Covariates for each for the four models (VRMCM and VAMCM for early and late neonatal periods) were
selected separately via cross-validation. The final set of covariates included in at least one of the four
models was: female literacy, Gini coefficient, neonatal mortality rate, infant mortality rate, under 5
mortality rate, low birth weight, GNI per capita (PPP, $international), antenatal care coverage,
percentage of births with skilled birth attendance, general fertility rate, BGC vaccine coverage, PAB
vaccine coverage, and indicator variables for world regions. Linear, quadratic and restricted cubic spline
formulations were considered for each covariate with the regression. See supplementary appendix in (7)
for details.
The estimated proportional distribution of causes of death predicted from the models were then
combined with information on causes of death from WHO program estimates as described in Section 4,
and applied to numbers of neonatal deaths derived from UN-IGME estimates of neonatal mortality
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rates. For modeled countries, it was assumed that 74% of neonatal deaths occurred in the early period
and 26% occurred in the late period (7,10).
3.3
Modeling causes of postneonatal deaths (ages 1-59 months)
Low mortality countries
For 43 low mortality countries without available vital registration data on postneonatal causes of death,
the cause distribution was estimated using a multinomial logistic regression model applied to death
registration data from countries with reliable VR information. The multinomial model applied to death
registration data was generally used for countries that were classified as low mortality based on an
average U5MR<35 from 2000-2010 in the previous estimation round (6).
The multinomial logistic regression model was developed using death registration data from countries
with >80% complete cause of death certification for the year closest to 2008 to estimate the proportion
of deaths due to pneumonia, diarrhea, meningitis, injuries, perinatal, congenital anomalies, other noncommunicable diseases (NCDs) and other causes. The model included the following covariates, which
were previously identified as being predictive of cause of death distributions for the postneonatal period
(1,6,11): under five mortality rates (U5MRs), under five population size, GNI per capita (PPP,
$international), human development index (HDI), Gini coefficient, an education index, percentage of
births with skilled birth attendance, percent urbanization, percent with access to an improved drinkingwater, DTP3 vaccine coverage, Hib3 vaccine coverage, year, and WHO region. The proportional
distribution of causes of death predicted from the model was then applied to UN-IGME mortality
envelope for children 1-59 months of age, after removing some program specific causes (see Section 4).
Key revisions to the previous VRMCM model used for the 2000-2015 estimates (1) include the use of
death registration data for the years 1990 to 2014, which includes a total of 1,364 country-year
observations from 68 countries, and updated covariate time series.
High mortality countries
For 81 high mortality countries the cause of death distribution was estimated using a multinomial model
applied to (largely) verbal autopsy data from research studies (1,6,11,12). For these estimates for years
2000-2015, the multicause model for deaths at ages 1-59 months (1) was further updated to include
recent community-based VA studies published between May 28, 2013 and February 12, 2015, as well as
national VA surveys. A total of 218 sets of data points from 129 VA studies and 41 countries that met the
inclusion criteria 2 were included. Among them 90 data points were either new or updated from the last
round of estimates for 2000-2013. These studies were predominantly from lower income high mortality
countries. Mortality estimates for eight cause categories of postneonatal death were derived:
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. Malnutrition deaths were included in the “other” cause of death category. Deaths due
to measles, unknown causes, and HIV/AIDS were excluded from the multinomial model. Measles and
HIV/AIDS deaths were separately estimated as described in Section 4.
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.
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Covariates for the postneonatal VAMCM were selected via cross-validation. The final model included the
following covariates: under five mortality rate, malaria parasite prevalence rate (PfPR), presence of a
meningitis epidemic, GNI per capita (PPP, $international), measles vaccine coverage, skilled birth
attendance coverage, percent of the population living in urban areas, percent of children who are
underweight, and year.
Estimates of deaths by cause were adjusted for intervention coverage (pneumonia and meningitis
estimates adjusted for the use and effectiveness of Hib and pneumococcal vaccines; diarrhea estimates
adjusted for the use and effectiveness of rotavirus vaccine) (1). Final cause-specific estimates for the
proportion of deaths due to each cause were multiplied by the estimated 1-59 month death envelopes
(excluding HIV and measles deaths) for corresponding years to obtain estimates of number of deaths by
cause.
3.5
Causes of child death for China and India
China
The number of deaths by cause and live births data were obtained from China Maternal and Child
Surveillance System (MCMSS) for years 2000-2015 by age-sex-residency-region strata. Causes of death,
which were coded according to ICD-10, were categorized into broader MCEE cause categories (Annex
Table B). Live births were adjusted based on the sampling probability of China MCMSS. Three-year
moving average of live births fractions by strata were computed to obtain stable estimates and these
were applied to UN total number of live births to calculate subnational live births. Total number of
deaths were estimated based on subnational live births and MCMSS strata-specific mortality rates
smoothed using a three-year moving average, and normalized to fit IGME all-cause number of death
estimates. Cause-specific death proportions from MCMSS, smoothed using a 7-year moving average,
were applied to the estimated total number of deaths to obtain the estimated number of deaths by
cause by strata prior to summing to obtain national estimates. These estimates are based on provisional
analyses, which are subject to change (13).
India
In order to estimate trends in under 5 causes of death for India, previously developed subnational
analyses were further refined and used to develop national estimates for years 2000-2015 (1,6). For
neonatal causes of death, Indian states were modeled separately within the high mortality, verbal
autopsy multi-cause model described in Section 3.2. The resulting cause-specific proportions were
applied to the estimated 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 between ages 1-59 months, an updated systematic review was conducted to
identified new studies published between May 28, 2013 and February 12, 2015, which identified 28
study data points based on a set of inclusion criteria (11). Cause of death distribution derived from the
Million Death Studies were also included for each of the 22 major states (14). A set of cause-specific
covariates were abstracted for each of a total of 50 study data points either from the studies themselves
or from other sub-national data sources, such as the National Family and Health Survey (NFHS) and the
District-Level Health Survey (DLHS). Based on these study-level data, a multi-cause model was
constructed applying a multinomial logistic regression framework (1,6,12,14). The parameterized model
was subsequently applied to a set of state-level cause-specific covariates for years 2000-2015 to derive
cause of death estimates for all 35 states for the 16-year period. Finally, the state-level estimates were
collapsed to obtain the national child cause of death distribution for 2000-2015 for eight causes of
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postneonatal death, including pneumonia, diarrhea, malaria, meningitis, injuries, congenital
malformations, causes arising from the neonatal period, and other causes.
4
Methods for cause-specific revisions and updates
4.1
HIV/AIDS
For countries with death registration data that met the usability criteria in Section 3.2, HIV/AIDS
mortality estimates were generally based on the most recently available vital registration. For other
countries, estimates were based on UNAIDS estimates of HIV/AIDS mortality (15). 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 with high quality VR data
For countries in which death reporting is estimated to capture > 50% of all deaths and a high proportion
of malaria cases are parasitologically confirmed, reported malaria deaths are adjusted for completeness
of death reporting. For countries in elimination programme phase, reported malaria deaths are adjusted
for completeness of case reporting.
Countries without high quality VR data
For countries (i) outside the African Region in which death reporting is estimated to capture ≤ 50% of all
deaths or a high proportion of malaria cases are not parasitologically confirmed, or (ii) in the African
Region where estimates of case incidence were derived from routine reporting systems and where
malaria comprises less than 5% of all deaths in children under 5 as described in the Global Burden of
Disease Incremental Revision for 2004 (WHO, 2008), 3 case fatality rates are used to derive number of
deaths from case estimates. A case fatality rate of 0·256% is applied to the estimated number of P.
falciparum cases, being the average of case fatality rates reported in the literature (16-18) and
unpublished data from Indonesia, 2004-2009 (correspondence with Dr. Ric Price, Menzies School of
Health Research). A case fatality rate of 0.0375% is applied to the estimated number of P. vivax cases,
representing the mid-point of the range of case fatality rates reported in a study by (19).
The number of cases reported by a Ministry of Health is adjusted to take into account (i) incompleteness
in reporting systems (ii) patients seeking treatment in the private sector, self-medicating or not seeking
treatment at all, and (iii) potential over-diagnosis through the lack of laboratory confirmation of cases.
Generally, the total number of cases, M, lies within the range:
M upper =
M lower =
C + (s × P )
r
C + (s × P )
r
×
1
u
×
(1 − n)
u
Where:
3
Algeria, Botswana, Cape Verde, Comoros, Eritrea, Ethiopia, Madagascar, Namibia, Sao Tome and Principe, South
Africa, Swaziland, and Zimbabwe
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C =
Reported number of confirmed malaria cases in a year.
P =
Reported number of probable or unconfirmed cases in a year.
s =
The slide positivity rate (and the proportion of unconfirmed cases expected to be positive for
malaria parasites).
r =
Completeness of health facility reporting.
u
= The proportion of the population with fever (or suspected malaria) that uses health facilities
that are covered by the health facility reporting system.
n =
The proportion of the population with fever (or suspected malaria) that does not seek
treatment.
Information on C, P, s and r is supplied by Ministries of Health. Information on u and n is obtained from
nationally representative household surveys which are generally conducted by the National Statistical
Office within a malaria endemic country. The most common source is a Demographic and Health Survey
or Multiple Indicator Cluster Survey which are typically conducted every three to five years. Malaria
mortality estimates were projected forward to 2014 and 2015. This was accomplished using the results
of country-specific segmented regression analyses, an approach that has been used to identify points of
change in disease trends over time (20-23). The trend line from the most recent segment of years was
extrapolated to project cases and deaths for 2014 and 2015.
For countries in the African Region where malaria comprises 5% or more of all deaths in children under
5, child malaria deaths were estimated using a verbal autopsy multi-cause model (VAMCM) (1,6). The
VAMCM estimates cause fractions for malaria along with 7 other cause categories (pneumonia,
diarrhea, meningitis, congenital malformation, causes arising in the perinatal period, injury, and other
causes) using multinomial logistic regression to ensure that all 8 causes are estimated simultaneously
with the total cause fraction summing to 1. New to this estimation round, the regression equation for
malaria deaths includes malaria parasite prevalence (PfPR) as a covariate. The estimate of PfPR provides
a continuous classification of malaria risk over space (5 kilometer squared units) and time (2000-2015),
which replaces the three category Malaria Risk in Africa (MARA) map used previously. The estimates of
PfPR are derived from a geostatistical model that incorporates changes in coverage of malaria
interventions (insecticide treated bednets, indoor residual spraying, antimalarial treatment) over time to
produce a risk map of parasite prevalence for each year. These estimates have been produced by the
Malaria Atlas Project at Oxford University in close collaboration with WHO.
4.3
Whooping cough
In an effort to better characterize the global burden of pertussis, the World Health Organization has
developed a series of global pertussis models. Recognizing the limited data to support model inputs, in
2009, the World Health Organization’s Department of Immunization Vaccines and Biologicals’
Quantitative Immunization and Vaccines Related Research (QUIVER), recommended that a revised
pertussis model be developed to specifically address uncertainty in the model inputs and parameter
values. Inputs to the current model are country- and year-specific estimates of population by single year
of age (24) and estimated pertussis immunization coverage (25). Age-, country-, and immunization
history- specific estimates of the probability of initial infection, probability that an infected individual
develops typical symptoms of a case of pertussis and the probability that a case of pertussis will die
were estimated using structured expert judgment (26-28). Annual deaths attributable to pertussis
infection during the neonatal period (5% of estimated pertussis deaths 0-11 months of age), from age 1World Health Organization
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11 months of age (estimated as 95% of deaths 1-11 months of age) and 12-59 months of age were
estimated for each country for the years 2000 – 2012. For these cause of death estimates, the pertussis
cause fraction was assumed to be constant to extrapolate forward to 2015.
4.4
Measles
In May 2010, WHO established targets for measles vaccine coverage, incidence and mortality as
milestone towards measles eradication. This created a requirement to report annually on these statistics,
and to address this need WHO has worked with technical experts and its QUIVER advisory group (29) to
develop an improved statistical model that firstly estimates measles cases by country and year using
surveillance data. The estimation uses the Kalman Filter method in order to make explicit projections
about dynamic transitions over time as well as overall patterns in incidence (30). The cases are then
stratified by age classes based on a model fitted to data stratified by national GDP and vaccine coverage.
The results are applied to age-specific case fatality ratios for each country (31-33) and then aggregated
again to produce overall measles deaths. Uncertainty is estimated by bootstrap sampling from the
distribution of incidence and age distribution estimates. This method was published in the Lancet in
2012 (34). The estimates used here are from an updated to take into account trends in case notifications
and vaccine coverage up to and including the year 2014 (35).
Inclusion of measles deaths within the all-cause envelope for child mortality
Estimated measles deaths in countries experiencing measles outbreaks can vary substantially from yearto-year, whereas the all-cause mortality envelopes for deaths at ages 1-59 months vary smoothly from
year to year due to the use of regression smoothing techniques applied to the available under 5
mortality observations (36). For countries without good death registration data, child mortality
observations from surveys and censuses can have considerable variability which may reflect real
changes (such as due to a measles outbreak) or be mainly due to measurement errors and variations in
survey sampling and quality. In order to include the measles deaths within the all-cause envelope
without creating fluctuations in death rates for other causes, the measles deaths for each country were
split into "outbreak" deaths and a smoothly varying endemic measles component. The latter was
estimated by fitting a regression of the log of measles deaths on time, after withholding observations
that differed by more than 25% from the average trend as identified with a Loess regression. To project
forward to 2015, the endemic cause fraction was assumed to be constant.
For high mortality countries, the endemic measles mortality component and HIV deaths were subtracted
from all-cause deaths in the age range 1-59 months in order to estimate the HIV- and measles-free
envelope to which the verbal autopsy based multi-cause model cause fractions were applied. The
outbreak deaths were then added back to the measles deaths, and all-cause deaths.
4.5
Conflict and natural disasters
Estimated deaths due to major crises were derived from various data sources from 1990 to present, and
incorporated into the all-cause mortality estimates produced by UN-IGME. Natural disasters were
obtained from the CRED International Disaster Database (37), with under-5 proportions estimated as
described elsewhere (38), and conflict deaths were taken from UCDP/PRIO datasets as well as reports
prepared by the UN and other organizations. Additional child deaths due to major crises were added to
the estimated background mortality rate if they met the following criteria:
1. The crisis was isolated to a few years
World Health Organization
Page 9
2. Under-five crisis deaths were >10% of under-five non-crisis deaths
3. Crisis U5MR > 0.2 per 1,000
4. Number of under-five crisis deaths >10 deaths.
For child causes of death, these deaths were assumed to be caused by injuries.
5
Uncertainty of estimates
The methodological approach to computing 95% confidence intervals around estimates for child causes
of death was similar to that used for estimating uncertainty for the child COD estimates for 2000-2013
(1). Country-level all-cause mortality envelope uncertainty for neonatal and postneonatal periods was
simulated from the posterior draws for neonatal and under-five mortality rates as produced by IGME. To
estimate uncertainty by cause within the neonatal and postneonatal envelopes, a bootstrap procedure
was used to compute 95% confidence intervals around predicted cause fractions from each of the multicause models for neonatal and 1-59 month deaths. These bootstrapped draws were combined with
draws obtained for program estimates to simulate posterior distributions for all 15 cause fractions,
which were in turn applied to the simulated draws for the envelopes. Uncertainty around cause
fractions for countries with high-quality VR data was simulated using poisson distributions.
World Health Organization
Page 10
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World Health Organization
Page 13
Annex Table A. Methods used for estimation of child causes of death, by
country, 2000-2015
Country
Neonatal method
Postneonatal Method
Afghanistan
VAMCM
VAMCM
Albania
VRMCM
VRMCM
Algeria
VAMCM
VAMCM
Andorra
VRMCM
VRMCM
Angola
VAMCM
VAMCM
Antigua and Barbuda
VR data
VR data
Argentina
VR data
VR data
Armenia
VRMCM
VRMCM
Australia
VR data
VR data
Austria
VR data
VR data
Azerbaijan
VAMCM
VAMCM
Bahamas
VR data
VR data
Bahrain
VR data
VR data
Bangladesh
VAMCM
VAMCM
Barbados
VR data
VR data
Belarus
VRMCM
VRMCM
Belgium
VR data
VR data
Belize
VR data
VR data
Benin
VAMCM
VAMCM
Bhutan
VAMCM
VAMCM
Bolivia (Plurinational State of)
VAMCM
VAMCM
Bosnia and Herzegovina
VRMCM
VRMCM
Botswana
VAMCM
VAMCM
Brazil
VR data
VR data
Brunei Darussalam
VR data
VR data
Bulgaria
VR data
VR data
Burkina Faso
VAMCM
VAMCM
Burundi
VAMCM
VAMCM
Cambodia
VAMCM
VAMCM
Cameroon
VAMCM
VAMCM
Canada
VRMCM
VR data
Cabo Verde
VRMCM
VRMCM
Central African Republic
VAMCM
VAMCM
Chad
VAMCM
VAMCM
Chile
VR data
VR data
China
Sample VR
Sample VR
Colombia
VR data
VR data
World Health Organization
Page 14
Comoros
VAMCM
VAMCM
Congo
VAMCM
VAMCM
Cook Islands
VRMCM
VRMCM
Costa Rica
VR data
VR data
Croatia
VR data
VR data
Cuba
VR data
VR data
Cyprus
VRMCM
VRMCM
Czech Republic
VR data
VR data
Côte d'Ivoire
VAMCM
VAMCM
Democratic People's Republic of Korea
VAMCM
VAMCM
Democratic Republic of the Congo
VAMCM
VAMCM
Denmark
VR data
VR data
Djibouti
VAMCM
VAMCM
Dominica
VR data
VR data
Dominican Republic
VAMCM
VAMCM
Ecuador
VRMCM
VRMCM
Egypt
VRMCM
VRMCM
El Salvador
VRMCM
VRMCM
Equatorial Guinea
VAMCM
VAMCM
Eritrea
VAMCM
VAMCM
Estonia
VR data
VR data
Ethiopia
VAMCM
VAMCM
Fiji
VRMCM
VRMCM
Finland
VR data
VR data
France
VR data
VR data
Gabon
VAMCM
VAMCM
Gambia
VAMCM
VAMCM
Georgia
VRMCM
VRMCM
Germany
VR data
VR data
Ghana
VAMCM
VAMCM
Greece
VR data
VR data
Grenada
VR data
VR data
Guatemala
VAMCM
VAMCM
Guinea
VAMCM
VAMCM
Guinea-Bissau
VAMCM
VAMCM
Guyana
VR data
VR data
Haiti
VAMCM
VAMCM
Honduras
VRMCM
VRMCM
Hungary
VR data
VR data
Iceland
VR data
VR data
India
VAMCM state level
VAMCM state level
Indonesia
VAMCM
VAMCM
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Page 15
Iran (Islamic Republic of)
VAMCM
VAMCM
Iraq
VAMCM
VAMCM
Ireland
VR data
VR data
Israel
VR data
VR data
Italy
VR data
VR data
Jamaica
VRMCM
VRMCM
Japan
VR data
VR data
Jordan
VRMCM
VRMCM
Kazakhstan
VAMCM
VAMCM
Kenya
VAMCM
VAMCM
Kiribati
VAMCM
VAMCM
Kuwait
VR data
VR data
Kyrgyzstan
VAMCM
VAMCM
Lao People's Democratic Republic
VAMCM
VAMCM
Latvia
VR data
VR data
Lebanon
VRMCM
VRMCM
Lesotho
VAMCM
VAMCM
Liberia
VAMCM
VAMCM
Libya
VRMCM
VRMCM
Lithuania
VR data
VR data
Luxembourg
VR data
VR data
Madagascar
VAMCM
VAMCM
Malawi
VAMCM
VAMCM
Malaysia
VRMCM
VRMCM
Maldives
VRMCM
VRMCM
Mali
VAMCM
VAMCM
Malta
VR data
VR data
Marshall Islands
VAMCM
VAMCM
Mauritania
VAMCM
VAMCM
Mauritius
VR data
VR data
Mexico
VR data
VR data
Micronesia (Federated States of)
VAMCM
VAMCM
Monaco
VRMCM
VRMCM
Mongolia
VAMCM
VAMCM
Montenegro
VR data
VR data
Morocco
VAMCM
VAMCM
Mozambique
VAMCM
VAMCM
Myanmar
VAMCM
VAMCM
Namibia
VAMCM
VAMCM
Nauru
VAMCM
VAMCM
Nepal
VAMCM
VAMCM
Netherlands
VR data
VR data
World Health Organization
Page 16
New Zealand
VR data
VR data
Nicaragua
VRMCM
VRMCM
Niger
VAMCM
VAMCM
Nigeria
VAMCM
VAMCM
Niue
VRMCM
VRMCM
Norway
VR data
VR data
Oman
VRMCM
VRMCM
Pakistan
VAMCM
VAMCM
Palau
VRMCM
VRMCM
Panama
VR data
VR data
Papua New Guinea
VAMCM
VAMCM
Paraguay
VRMCM
VRMCM
Peru
VRMCM
VRMCM
Philippines
VAMCM
VAMCM
Poland
VR data
VR data
Portugal
VRMCM
VR data
Qatar
VRMCM
VRMCM
Republic of Korea
VR data
VR data
Republic of Moldova
VR data
VR data
Romania
VR data
VR data
Russian Federation
VRMCM
VRMCM
Rwanda
VAMCM
VAMCM
Saint Kitts and Nevis
VR data
VR data
Saint Lucia
VR data
VR data
Saint Vincent and the Grenadines
VR data
VR data
Samoa
VRMCM
VRMCM
San Marino
VRMCM
VRMCM
Sao Tome and Principe
VAMCM
VAMCM
Saudi Arabia
VRMCM
VRMCM
Senegal
VAMCM
VAMCM
Serbia
VR data
VR data
Seychelles
VRMCM
VRMCM
Sierra Leone
VAMCM
VAMCM
Singapore
VR data
VR data
Slovakia
VR data
VR data
Slovenia
VR data
VR data
Solomon Islands
VAMCM
VAMCM
Somalia
VAMCM
VAMCM
South Africa
VR data
VAMCM
South Sudan
VAMCM
VAMCM
Spain
VR data
VR data
Sri Lanka
VRMCM
VRMCM
World Health Organization
Page 17
Sudan
VAMCM
VAMCM
Suriname
VR data
VR data
Swaziland
VAMCM
VAMCM
Sweden
VR data
VR data
Switzerland
VRMCM
VR data
Syrian Arab Republic
VRMCM
VRMCM
Tajikistan
VAMCM
VAMCM
Thailand
VRMCM
VRMCM
The former Yugoslav Republic of Macedonia
VR data
VR data
Timor-Leste
VAMCM
VAMCM
Togo
VAMCM
VAMCM
Tonga
VRMCM
VRMCM
Trinidad and Tobago
VR data
VR data
Tunisia
VRMCM
VRMCM
Turkey
VR data
VR data
Turkmenistan
VAMCM
VAMCM
Tuvalu
VRMCM
VRMCM
Uganda
VAMCM
VAMCM
Ukraine
VRMCM
VRMCM
United Arab Emirates
VRMCM
VRMCM
United Kingdom
VR data
VR data
United Republic of Tanzania
VAMCM
VAMCM
United States of America
VR data
VR data
Uruguay
VR data
VR data
Uzbekistan
VAMCM
VAMCM
Vanuatu
VRMCM
VRMCM
Venezuela (Bolivarian Republic of)
VR data
VR data
Viet Nam
VRMCM
VRMCM
Yemen
VAMCM
VAMCM
Zambia
VAMCM
VAMCM
Zimbabwe
VAMCM
VR data = tabulations of vital registration data from WHO Mortality Database
VRMCM = multi-cause model based on vital registration data
VAMCM = multi-cause model based on verbal autopsy studies
World Health Organization
VAMCM
Page 18
Annex Table B.1. First-level categories for analysis of neonatal child
causes of death
Cause category
ICD-10 code
ICD-9 code
All causes
A00-Y89
001-999
I.
Communicable, maternal,
perinatal and nutritional
conditionsa
A00-B99, D50-D53, D64.9, E00-E02,
E40-E64, G00-G09, H65-H66, J00J22, J85, N30, N34, N390, N70-N73,
O00-P96, U04
001-139, 243, 260-269, 279.5-279.6,
280, 281, 285.9, 320-326, 381-382,
460-466, 480-487, 513, 614-616, 630676, 760-779
HIV/AIDS
B20-B24
042, 279.5-279.6
Diarrhoeal diseases
A00-A09
001-009
Pertussis
A37
033
Tetanus
A33-A35
037, 771.3
Meningitis/encephalitis
A20.3, A32.1, A39, A83-A87, G00,
G03-G04
036, 047, 062-064, 320, 322-323
Malaria
B50-B54, P37.3, P37.4
084
Acute respiratory infections
A36, J, P23
032, 460-466, 470-487, 490, 491.9496, 501-518.0, 519
Prematurity
P01.0, P01.1, P07, P22, P25-P28,
P52, P61.2, P77
434.9 518, 761.0-761.1, 765, 769,
770.0, 770.2-770.9, 772.1, 774.2,
776.6, 777.5-777.6, 786.3
Birth asphyxia & birth trauma
P01.7-P02.1, P02.4-P02.6, P03, P10P15, P20-P21, P24, P50, P90-P91
348.1-348.9,
437.1-437.9,
761.7762.1, 762.4-762.6, 763, 767-768,
770.1, 772.2, 779.0-779.2
Sepsis and other infectious
conditions of the newborn
A10-A20.2, A20.4-A32.0, A32.2-A32.9,
A38.0-A38.9, A40-A82, A88-A99, B
(exclude B20-B24, B50-54), G01-G02,
G05-G09, P36-P39 (exclude P37.3,
P37.4)
010-031, 034-035, 038-041, 045-046,
048-055, 057, 061, 065-083, 085-133,
324-326, 491, 730, 771.0-771.2,
771.4-771.8, 780.6, 785.4
Other Group I
Remainder
Remainder
C00-C97, D00-D48, D55-D64 (exclude
D64.9), D65-D89, E03-E34, E65-E88,
F01-F99, G10-G98, H00-H61, H68H93, I00-I99, J30-J84, J86-J98, K00K92, L00-L98, M00-M99, N00-N28,
N31-N32, N35-N64 (exclude N39.0),
N75-N98, Q00-Q99
140- 242, 244-259, 270-279, 282-285,
286-319, 330-380, 383-459, 470-478,
490- 512, 514-611, 617- 629, 680- 759
(exclude 279.5-279.6, 285.9)
Congenital anomalies
D55-D68, E01-E07, E70-E84, G10G99, H, I, K, L, M, N, P35, P76, Q00Q99
056, 240-243, 245-259, 272-277,
279.3-279.4, 279.8-286, 288.2, 303,
330-348.0, 349-426, 427.5, 428-434.0,
435-437.0, 438-451, 453.2, 456.1,
458.9, 520-729, 733-759, 775.2,
777.0-777.4, 784.0
Other Group II
Remainder
Remainder
S01-Y89
800-999
II. Noncommunicable
diseasesa
III. Injuries
a
b
Deaths coded to “Symptoms, signs and ill-defined conditions” (780-799 in ICD-9; R00-R99 in ICD-10) are distributed proportionately to all causes.
Also referred to as “intrapartum-related complications”
World Health Organization
Page 19
Annex Table B.2. First-level categories for analysis of postneonatal child
causes of death
Cause category
ICD-10 code
ICD-9 code
All causes
A00-Y89
001-999
I.
Communicable, maternal,
perinatal and nutritional
conditionsa
A00-B99, D50-D53, D64.9, E00-E02,
E40-E64, G00-G09, H65-H66, J00J22, J85, N30, N34, N390, N70-N73,
O00-P96, U04
001-139, 243, 260-269, 279.5-279.6,
280, 281, 285.9, 320-326, 381-382,
460-466, 480-487, 513, 614-616, 630676, 760-779
HIV/AIDS
B20-B24
279.5-279.6, 042
Diarrhoeal diseases
A00-A09
001-009
Pertussis
A37
033
Tetanus
A33-A35
037, 771.3
Measles
B05
055
Meningitis/encephalitis
A20.3, A32.1, A39.1, G00–G09
036, 320, 322-326
Malaria
B50-B54, P37.3, P37.4
084
Acute respiratory infections
H65-H66, J00-J22, J85, P23, U04
460-466,
770.0
Prematurity
P01.0, P01.1, P07, P22, P25-P28,
P52, P61.2, P77
761.0-761.1, 765, 769, 770.2-770.9,
772.1, 774.2, 776.6, 777.5-777.6,
Birth asphyxia & birth traumab
P01.7-P02.1, P02.4-P02.6, P03, P10P15, P20-P21, P24, P50, P90-P91
761.7-762.1, 762.4-762.6, 763, 767768, 770.1, 772.2, 779.0-779.2
Sepsis and other infectious
conditions of the newborn
P35-P39 (exclude P37.3, P37.4)
Other Group I
Remainder
Remainder
C00-C97, D00-D48, D55-D64 (exclude
D64.9), D65-D89, E03-E34, E65-E88,
F01-F99, G10-G98, H00-H61, H68H93, I00-I99, J30-J84, J86-J98, K00K92, L00-L98, M00-M99, N00-N28,
N31-N32, N35-N64 (exclude N39.0),
N75-N98, Q00-Q99
140- 242, 244-259, 270-279, 282-285,
286-319, 330-380, 383-459, 470-478,
490- 512, 514-611, 617- 629, 680- 759
(exclude 279.5-279.6, 285.9)
Congenital anomalies
Q00-Q99
740-759
Other Group II
Remainder
Remainder
V01-Y89
E800-E999
II. Noncommunicable
diseasesa
III. Injuries
480-487,
381-382,
513,
771.0-771.2, 771.4-771.8
a
Deaths coded to “Symptoms, signs and ill-defined conditions” (780-799 in ICD-9 and R00-R99 in ICD-10) are distributed proportionately across Group
I and Group II cause of postneonatal deaths.
b
Also referred to as “intrapartum-related complications”
World Health Organization
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