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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].

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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

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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 2000-2015 supersede previously published estimates for child causes of death for years 2000-2010, 2000-2012 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

World Health Organization Page 1


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 UN-IGME 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


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.


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 high-quality 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


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 non-communicable 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 drinking-water, 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 criteria2 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.


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


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:

ur

PsCM upper

1)(×

×+=

u

nr

PsCM lower

)1()( −×

×+=

Where:

3 Algeria, Botswana, Cape Verde, Comoros, Eritrea, Ethiopia, Madagascar, Namibia, Sao Tome and Principe, South Africa, Swaziland, and Zimbabwe


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 1-


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 year-to-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


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 multi-cause 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.


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Annex Table A. Methods used for estimation of child causes of death, by country, 2000-2015

Country Neonatal method Postneonatal Method Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia (Plurinational State of) Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cabo Verde Central African Republic Chad Chile China Colombia

VAMCM VRMCM VAMCM VRMCM VAMCM VR data VR data VRMCM VR data VR data VAMCM VR data VR data VAMCM VR data VRMCM VR data VR data VAMCM VAMCM VAMCM VRMCM VAMCM VR data VR data VR data VAMCM VAMCM VAMCM VAMCM VRMCM VRMCM VAMCM VAMCM VR data Sample VR VR data

VAMCM VRMCM VAMCM VRMCM VAMCM VR data VR data VRMCM VR data VR data VAMCM VR data VR data VAMCM VR data VRMCM VR data VR data VAMCM VAMCM VAMCM VRMCM VAMCM VR data VR data VR data VAMCM VAMCM VAMCM VAMCM VR data VRMCM VAMCM VAMCM VR data Sample VR VR data


Comoros Congo Cook Islands Costa Rica Croatia Cuba Cyprus Czech Republic Côte d'Ivoire Democratic People's Republic of Korea Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia

VAMCM VAMCM VRMCM VR data VR data VR data VRMCM VR data VAMCM VAMCM VAMCM VR data VAMCM VR data VAMCM VRMCM VRMCM VRMCM VAMCM VAMCM VR data VAMCM VRMCM VR data VR data VAMCM VAMCM VRMCM VR data VAMCM VR data VR data VAMCM VAMCM VAMCM VR data VAMCM VRMCM VR data VR data VAMCM state level VAMCM

VAMCM VAMCM VRMCM VR data VR data VR data VRMCM VR data VAMCM VAMCM VAMCM VR data VAMCM VR data VAMCM VRMCM VRMCM VRMCM VAMCM VAMCM VR data VAMCM VRMCM VR data VR data VAMCM VAMCM VRMCM VR data VAMCM VR data VR data VAMCM VAMCM VAMCM VR data VAMCM VRMCM VR data VR data VAMCM state level VAMCM


Iran (Islamic Republic of) Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia (Federated States of) Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands

VAMCM VAMCM VR data VR data VR data VRMCM VR data VRMCM VAMCM VAMCM VAMCM VR data VAMCM VAMCM VR data VRMCM VAMCM VAMCM VRMCM VR data VR data VAMCM VAMCM VRMCM VRMCM VAMCM VR data VAMCM VAMCM VR data VR data VAMCM VRMCM VAMCM VR data VAMCM VAMCM VAMCM VAMCM VAMCM VAMCM VR data

VAMCM VAMCM VR data VR data VR data VRMCM VR data VRMCM VAMCM VAMCM VAMCM VR data VAMCM VAMCM VR data VRMCM VAMCM VAMCM VRMCM VR data VR data VAMCM VAMCM VRMCM VRMCM VAMCM VR data VAMCM VAMCM VR data VR data VAMCM VRMCM VAMCM VR data VAMCM VAMCM VAMCM VAMCM VAMCM VAMCM VR data


New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Republic of Korea Republic of Moldova Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka

VR data VRMCM VAMCM VAMCM VRMCM VR data VRMCM VAMCM VRMCM VR data VAMCM VRMCM VRMCM VAMCM VR data VRMCM VRMCM VR data VR data VR data VRMCM VAMCM VR data VR data VR data VRMCM VRMCM VAMCM VRMCM VAMCM VR data VRMCM VAMCM VR data VR data VR data VAMCM VAMCM VR data VAMCM VR data VRMCM

VR data VRMCM VAMCM VAMCM VRMCM VR data VRMCM VAMCM VRMCM VR data VAMCM VRMCM VRMCM VAMCM VR data VR data VRMCM VR data VR data VR data VRMCM VAMCM VR data VR data VR data VRMCM VRMCM VAMCM VRMCM VAMCM VR data VRMCM VAMCM VR data VR data VR data VAMCM VAMCM VAMCM VAMCM VR data VRMCM


Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Thailand The former Yugoslav Republic of Macedonia Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United Republic of Tanzania United States of America Uruguay Uzbekistan Vanuatu Venezuela (Bolivarian Republic of) Viet Nam Yemen Zambia Zimbabwe

VAMCM VR data VAMCM VR data VRMCM VRMCM VAMCM VRMCM VR data VAMCM VAMCM VRMCM VR data VRMCM VR data VAMCM VRMCM VAMCM VRMCM VRMCM VR data VAMCM VR data VR data VAMCM VRMCM VR data VRMCM VAMCM VAMCM VAMCM

VAMCM VR data VAMCM VR data VR data VRMCM VAMCM VRMCM VR data VAMCM VAMCM VRMCM VR data VRMCM VR data VAMCM VRMCM VAMCM VRMCM VRMCM VR data VAMCM VR data VR data VAMCM VRMCM VR data VRMCM VAMCM VAMCM 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


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, J00-J22, 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, 630-676, 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, 491.9-496, 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, P10-P15, P20-P21, P24, P50, P90-P91

348.1-348.9, 437.1-437.9, 761.7-762.1, 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

II. Noncommunicable diseasesa

C00-C97, D00-D48, D55-D64 (exclude D64.9), D65-D89, E03-E34, E65-E88, F01-F99, G10-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

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, G10-G99, H, I, K, L, M, N, P35, P76, Q00-Q99

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

III. Injuries S01-Y89 800-999

a 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. b Also referred to as “intrapartum-related complications”


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, J00-J22, 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, 630-676, 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, 480-487, 381-382, 513, 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, P10-P15, P20-P21, P24, P50, P90-P91

761.7-762.1, 762.4-762.6, 763, 767-768, 770.1, 772.2, 779.0-779.2

Sepsis and other infectious conditions of the newborn

P35-P39 (exclude P37.3, P37.4) 771.0-771.2, 771.4-771.8

Other Group I Remainder Remainder

II. Noncommunicable diseasesa

C00-C97, D00-D48, D55-D64 (exclude D64.9), D65-D89, E03-E34, E65-E88, F01-F99, G10-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

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

III. Injuries V01-Y89 E800-E999 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”


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