MCEE-WHO Methods and Data Sources for Child Causes of Death 2000-2017

MCEE-WHO methods and data sources for child causes of death 2000-2017

Department of Evidence, Information and Research (WHO, Geneva) and Maternal Child Epidemiology Estimation (MCEE)

December 2018

Global Health Estimates Technical Paper WHO/HMM/IER/GHE/2018.4

Acknowledgments

This Technical Paper was prepared by Colin Mathers, with inputs from Dan Hogan, Diana Yeung, Li Liu, and Shefali Oza. Country estimates of child deaths by cause for years 2000-2017 were primarily prepared by Diana Yeung, Yue Chu, Li Liu, Jamie Perin, and Bob Black (Johns Hopkins University) and Shefali Oza, Simon Cousens, and Joy Lawn (London School of Hygiene and Tropical Medicine) of the Maternal and Child Epidemiology Estimation (MCEE) group, and Daniel Hogan, Doris Ma Fat, Jessica Ho and Colin Mathers, of the Mortality and Health Analysis unit in the WHO Department of Information, Evidence 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 John Aponte, Marta Gacic-Dodo, Lucia Hug, Mary Mahy, Kim Marsh, Abdisalan Noor, Minal Patel, 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].

Table of Contents

Acknowledgments ...... i Table of Contents ...... ii 1 Introduction ...... 1 2 All-cause mortality and population estimates for years 2000-2017 ...... 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 ...... 3 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 Measles ...... 8 4.4 Conflict and natural disasters ...... 9 5 Uncertainty of estimates ...... 9 References ...... 9 Annex Table A. Methods used for estimation of child causes of death, by country, 2000-2017 ...... 12 Annex Table B.1. First-level categories for analysis of neonatal child causes of death...... 17 Annex Table B.2. First-level categories for analysis of postneonatal child causes of death ...... 18

1 Introduction This document, Global Health Estimates Technical Paper WHO/HMM/IER/GHE/2018.4, is an update to the previous WHO/HMM/IER/GHE/2018.1, which described estimation methodology for child causes of death (COD) for 2000-2016. This updated version is edited to reflect an update in which child causes of death are estimated for years 2000-2017. The underlying methodological approaches are similar to those used to derive child COD estimates for years 2000-2012, which were published in May 2014, child COD estimates for 2000-2013, which were published in September 2014, and child COD estimates for 2000-2015, which were published in February 2016.

Cause-specific estimates of deaths for children under age 5 were estimated for 14 cause categories for years 2000-2017 using methods similar to those described elsewhere by Liu et al. (1,2) and on the WHO website (http://www.who.int/healthinfo/global_burden_disease/en/). These estimates were prepared by the WHO Department of Information, Evidence 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 2000-2017 supersede previously published estimates for child causes of death for years 2000-2010, 2000-2012, 2000-2013, 2000-2015 and 2000- 2016. The estimation framework is similar to that used for the previous estimates (3), although some methodological components have been improved along with updated inputs for child mortality levels (4) as well as cause-specific estimates for HIV, malaria, and measles deaths (as described in Section 4). Deaths due to pertussis were not prepared for this estimation round. 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 November 2018, 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 2000-2017

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 (4). 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

1 www.childmortality.org

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used as the source of data for the estimation of trends in neonatal, infant and child mortality. For 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 (5). 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 (6). 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 2017 (7). 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 1990-2017 using data and methods as described below in Section 4.4, 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 4.3, 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 (1,2,8). 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 (1,9). 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.

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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 (10). The number of countries reporting data using the 10th revision of the International Classification of Diseases (ICD-10) (1) 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 for 76 countries (Annex Table A). This includes using VR directly for estimating cause of death distributions for Andorra, Cyprus, Ecuador, Nicaragua 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, Denmark and Portugal) reporting mortality data to WHO do not provide the breakdown for the neonatal vs postneonatal period across all years. In these cases, 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. 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

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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 2017 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 has developed two models for separately estimating neonatal deaths by cause for early (0-6 days) and late (7-27 days) neonatal periods. These cause categories are defined in Annex Table B. The model outputs are a set of cause fractions that add to 1. These are fitted within the HIV-free “envelope”, the total neonatal deaths estimated by UN-IGME with neonatal HIV deaths subtracted. For this update, the cause fractions estimated for the 2017 update for years 2000-2016 were projected one year forward to provide cause fractions for years 2000-2017. Low mortality countries Death registration data were used to directly calculate cause of death distributions for 76 countries considered to have reliable information as described in Section 3.1. Data from these 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 38 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 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 78 high mortality countries the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies (1,2). 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 (8). The VAMCM model was used to estimate eight cause categories for the 78 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 of 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 (9) for details.

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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 (9,12).

3.3 Modeling causes of postneonatal deaths (ages 1-59 months) Low mortality countries For 37 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 2012 estimation round (8). 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 (1,8,13): 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 (2,4) include: • a complete update of all covariate time series used in the model, and • the use of death registration data for the years 1990 to 2017, which includes a total of 1,698 country-year observations from 81 countries. High mortality countries For 79 high mortality countries the cause of death distribution was estimated using a multinomial model applied to (largely) verbal autopsy data from research studies (1,2,8,13,14). A total of 218 sets of data points from 129 VA studies and 41 countries that met the inclusion criteria2 were included. 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

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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were excluded from the multinomial model. Measles and HIV/AIDS deaths were separately estimated as described in Section 4. Covariates for the postneonatal VAMCM were the same as those for the previous round (4), which 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) (2). 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. Key revisions to the previous VAMCM model (2,4) include: • a complete update of all covariate time series used in the model, and • a complete update of all intervention coverage time series used in the model. 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 (15). 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 the 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 numbers 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 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. For years 2016 and 2017, we applied the national cause-of-death distribution in 2015 to the all-cause number of deaths and mortality envelope in 2016 and 2017 for neonatal and 1-59-month age group separately to derive age- and cause-specific estimates. 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 2000-2016 (1,2,8). 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 postneonatal causes of death, input data on cause of death distributions were restricted to the Million Deaths Study for each of the 22 major states (16), as well as data from published community studies and INDEPTH sites in India. 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

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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 (1,2,8,14,16). The parameterized model was subsequently applied to a set of state-level cause-specific covariates for years 2000-2016 to derive cause of death estimates for all 35 states for the 17-year period. Finally, the state-level estimates were collapsed to obtain the national child cause of death distribution for 2000-2016 for major causes of postneonatal death, including pneumonia, diarrhea, meningitis, injuries, congenital malformations, causes arising from the neonatal period, and other causes. For year 2017, we applied the national cause-of-death distribution in 2016 to the all-cause number of deaths and mortality envelope in 2017 for neonatal and 1-59-month age group separately to derive age- and cause-specific estimates.

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 data. For other countries, estimates were based on UNAIDS estimates of HIV/AIDS mortality (17). It was assumed based on advice from UNAIDS that 1% of HIV deaths under age 5 occurred in the neonatal period. 4.2 Malaria Countries 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 were 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 in 2004 as estimated in the WHO Global Burden of Disease update (18), 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 (19-21) 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 (22). The number of cases reported by a Ministry of Health was 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. The procedure combines data reported by the National Malaria Control Program (reported cases, reporting completeness and likelihood that cases are parasite positive) with data

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

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obtained from nationally representative household surveys on health-service such as Demographic and Health Survey or Multiple Indicator Cluster Survey which are typically conducted every three to five years. The total number of cases is obtained by adding the cases in public sector with estimated cases in private sector and cases where no treatment is sought, as follows:

퐶푎푠푒푠푝푢푏푙푖푐 푠푒푐푡표푟 = (퐶푎푠푒푠푐표푛푓푖푟푚푒푑 + 퐶푎푠푒푠푝푟푒푠푢푚푒푑 × 푇푒푠푡 푝표푠푖푡푖푣푖푡푦 푟푎푡푒)⁄푅푒푝표푟푡푖푛푔 푐표푚푝푙푒푡푒푛푒푠푠

퐶푎푠푒푠푝푟푖푣푎푡푒 푠푒푐푡표푟 = 퐶푎푠푒푠푝푢푏푙푖푐 푠푒푐푡표푟 × 푃푟표푝. 푠푒푒푘푖푛푔 푐푎푟푒푝푟푖푣푎푡푒 푠푒푐푡표푟 ⁄푃푟표푝. 푠푒푒푘푖푛푔 푐푎푟푒푝푢푏푙푖푐 푠푒푐푡표푟

퐶푎푠푒푠푁표푡 푠푒푒푘푖푛푔 푡푟푒푎푡푚푒푛푡 = 퐶푎푠푒푠푝푢푏푙푖푐 푠푒푐푡표푟 × 푃푟표푝. 푛표푡 푠푒푒푘푖푛푔 푐푎푟푒⁄푃푟표푝. 푠푒푒푘푖푛푔 푐푎푟푒푝푢푏푙푖푐 푠푒푐푡표푟. Uncertainty around the estimates was simulated from the distribution of the parameters (test positivity rate, reporting completeness, proportion seeking care in private and in public sector) (22). 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) (1,8). 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. 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. 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 updated in 2018 by the Malaria Atlas Project at Oxford University in close collaboration with WHO and used to update both study level and national level covariates in the VAMCM model. 4.3 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 (24) 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 (25). 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 (26-28) 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 (29). The estimates used here are from an update to those in (29) and (308) that take into account trends in case notifications and vaccine coverage up to and including the year 2017.

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 (3). 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

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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. 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. 4.4 Conflict and natural disasters Estimated deaths due to major crises were derived from various data sources from 1990 to 2017, and incorporated into the all-cause mortality estimates produced by UN-IGME. Natural disasters were obtained from the CRED International Disaster Database (31), with under-5 proportions estimated as described elsewhere (32), and conflict deaths were taken from UCDP/PRIO datasets as well as reports prepared by the UN and other organizations (33). 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.

References (1) Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE, et al. Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet. 2015;385(9966):430-40. (2) Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the Sustainable Development Goals. Lancet. 2016;388(10063):3027-35.

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(3) United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels & Trends in Child Mortality. New York: UNICEF, 2018. (available from: http://www.childmortality.org). (4) World Health Organization. MCEE-WHO methods and data sources for child causes of death 2000-2016. Global Health Estimates Technical Paper WHO/HMM/IER/GHE/2018.1 (available from: http://www.who.int/healthinfo/global_burden_disease/en/). (5) The PLoS Medicine Collection on Child Mortality Estimation Methods. 2012. http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/iss ue.pcol.v07.i19 (accessed 15 November 2013). (6) Alexander M, Alkema L. Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model. 2016. (available from: https://arxiv.org/abs/1612.03561). (7) United Nations Department of Economic and Social Affairs Population Division. World Population Prospects - the 2017 Revision. New York: United Nations, 2017. (8) Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn JE, et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet. 2012;379(9832):2151-61. (9) Oza S, Lawn JE, Hogan DR, Mathers C, Cousens SN. Neonatal cause-of-death estimates for the early and late neonatal periods for 194 countries: 2000-2013. Bull World Health Organ. 2015;93(1):19-28. (10) World Health Organization. WHO Mortality Database. Geneva: 2018. (available from: http://www.who.int/healthinfo/mortality_data/en/). (11) World Health Organization. International Statistical Classification of Diseases and Related Health Problems – 10th Revision. Geneva: 1992. (12) Save the Children. Surviving the First Day: State of the World's Mothers 2013. 2013. (13) Johnson HL, Liu L, Fischer-Walker C, Black RE. Estimating the distribution of causes of death among children age 1-59 months in high-mortality countries with incomplete death certification. Int J Epidemiol. 2010;39(4):1103-14. (14) Black RE, Cousens S, Johnson HL, Lawn JE, Rudan I, Bassani DG, et al. Global, regional, and national causes of child mortality in 2008: a systematic analysis. Lancet. 2010;375(9730):1969-87. (15) He C, Liu L, Chu Y, Perin J, Dai L, Li X, et al. National and subnational all-cause and cause-specific child mortality in China, 1996-2015: a systematic analysis with implications for the Sustainable Development Goals. Lancet Glob Health. 2017;5(2):e186- e97. (16) Bassani DG, Kumar R, Awasthi S, Morris SK, Paul VK, Shet A, et al. Causes of neonatal and child mortality in India: a nationally representative mortality survey. Lancet. 2010;376(9755):1853-60. (17) UNAIDS. UNAIDS Data 2018. Geneva: UNAIDS, 2018.

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(18) WHO. The global burden of disease: 2004 update. WHO, Geneva, 2008. Available from: http://www.who.int/evidence/bod. (19) Meek SR. Epidemiology of malaria in displaced Khmers on the Thai-Kampuchean border. Southeast Asian J Trop Med Public Health. 1988;19(2):243-52. (20) Luxemburger C, Ricci F, Nosten F, Raimond D, Bathet S, White NJ. The epidemiology of severe malaria in an area of low transmission in Thailand. Trans R Soc Trop Med Hyg. 1997;91(3):256-62. Epub 1997/05/01. (21) Alles HK, Mendis KN, Carter R. Malaria mortality rates in South Asia and in Africa: implications for malaria control. Parasitol Today. 1998;14(9):369-75. (22) Douglas NM, Pontororing GJ, Lampah DA, Yeo TW, Kenangalem E, Poespoprodjo J, et al. Mortality attributable to Plasmodium vivax malaria: a clinical audit from Papua, Indonesia. BMC Med. 2014;12(1):217. (23) World Health Organization. World Malaria Report 2017. Geneva: WHO, 2017. (24) World Health Organization. Report on the WHO Quantitative Immunization and Vaccines Related Research (QUIVER) Advisory Committee Meeting 5-7 October 2010. Geneva: World Health Organization, 2011 WHO/IVB/11.06. (25) Chen S, Fricks J, Ferrari MJ. Tracking measles infection through non-linear state space models. Journal of the Royal Statistical Society Series C-Applied Statistics. 2012;61:117-34. (26) Wolfson LJ, Strebel PM, Gacic-Dobo M, Hoekstra EJ, McFarland JW, Hersh BS. Has the 2005 measles mortality reduction goal been achieved? A natural history modelling study. Lancet. 2007;369(9557):191-200. (27) Sudfeld CR, Halsey NA. Measles Case Fatality Ratio in India: A Review of Community Based Studies. Indian Pediatrics. 2009;46(11):983-9. (28) Joshi AB, Luman ET, Nandy R, Subedi BK, Liyanage JBL, Wierzba TF. Measles deaths in Nepal: estimating the national case-fatality ratio. Bulletin of the World Health Organization. 2009;87(6):456-65. (29) Simons E, Ferrari M, Fricks J, Wannemuehler K, Anand A, Burton A, et al. Assessment of the 2010 global measles mortality reduction goal: results from a model of surveillance data. Lancet. 2012;379(9832):2173-8. (30) World Health Organization. Global progress towards regional measles elimination, worldwide, 2000-2013. Weekly Epidemiological Record. 2014;89(46):509-16. (31) CRED. EM-DAT: The CRED International Disaster Database. Belgium: Université Catholique de Louvain, 2012. (32) World Health Organization. WHO methods and data sources for global causes of death 2000-2011 (Global Health Estimates Technical Paper WHO/HIS/HSI/GHE/2013.3). Geneva: World Health Organization, 2013. (available from: http://www.who.int/healthinfo/statistics/GHE_TR2013-3_COD_MethodsFinal.pdf). (33) World Health Organization. WHO methods and data sources for country-level causes of death 2000-2016. Global Health Estimates Technical Paper WHO/HMM/IER/GHE/2018.3 (available from: http://www.who.int/healthinfo/global_burden_disease/en/).

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Annex Table A. Methods used for estimation of child causes of death, by country, 2000-2017 Country Neonatal method Postneonatal method Afghanistan VAMCM VAMCM Albania VRMCM VRMCM Algeria VAMCM VAMCM Andorra VR data VR data 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 Cabo Verde VRMCM VRMCM Cambodia VAMCM VAMCM Cameroon VAMCM VAMCM Canada VR data VR data Central African Republic VAMCM VAMCM Chad VAMCM VAMCM Chile VR data VR data China Sample VR Sample VR

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Colombia VR data VR data Comoros VAMCM VAMCM Congo VAMCM VAMCM Cook Islands VRMCM VRMCM Costa Rica VR data VR data Cote d'Ivoire VAMCM VAMCM Croatia VR data VR data Cuba VR data VR data Cyprus VR data VR data Czechia VR data VR data 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 VR data VR data 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

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Indonesia VAMCM VAMCM 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 VR data VR data Japan VR data VR data Jordan VRMCM VRMCM Kazakhstan VAMCM VAMCM Kenya VAMCM VAMCM Kiribati VAMCM VAMCM Kuwait VR data VR data Kyrgyzstan VR data VR data 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

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Netherlands VR data VR data New Zealand VR data VR data Nicaragua VR data VR data 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 VR data 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 VR data VR data 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

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Sri Lanka VRMCM VRMCM 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 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

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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, A00-B99, D50-D53, E01-E02, E40- 001-139, 243, 260-269, 279.5-279.6, perinatal and nutritional E64, G00-G09, J00-J99, N29-N30, 280, 281, 285.9, 320-326, 381-382, conditionsa N33-N34, N39.0, N70-N74, P00-P91 460-466, 480-487, 513, 614-616, 630- (exclude P76), P93-P94, U04 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, 036, 047, 062-064, 320, 322-323 G03-G04

Malaria B50-B54, P37.3, P37.4 084

Acute respiratory infections A36, J00-J99, P23 032, 460-466, 470-487, 490, 491.9- 496, 501-518.0, 519

Prematurity P01.0, P01.1, P07, P22, P25-P28, 434.9 518, 761.0-761.1, 765, 769, P52, P61.2, P77 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- 348.1-348.9, 437.1-437.9, 761.7- P15, P20-P21, P24, P50, P90-P91 762.1, 762.4-762.6, 763, 767-768, 770.1, 772.2, 779.0-779.2

Sepsis and other infectious A15-A20.2, A20.7-A32.0, A32.7-A32.9, 010-031, 034-035, 038-041, 045-046, conditions of the newborn A38, A40-A82, A88-A99, B00-B99 048-055, 057, 061, 065-083, 085-133, (exclude B20-B24, B50-54), G01-G02, 324-326, 491, 730, 771.0-771.2, G05-G09, P35-P39 (exclude P37.3, 771.4-771.8, 780.6, 785.4 P37.4)

Other Group I Remainder Remainder

II. Noncommunicable C00-C97, D00-D48, D55-89, E00, 140- 242, 244-259, 270-279, 282-285, a diseases E03-E35, E65-E90, G10-G99, H00- 286-319, 330-380, 383-459, 470-478, H95, I00-I99, K00-K93, L00-L99, M00- 490- 512, 514-611, 617- 629, 680- 759 M99, N00-N28, N31-N32, N35-N64 (exclude 279.5-279.6, 285.9) (exclude N39.0), N75-N98, P76, Q00- Q99

Congenital anomalies D55-D68, E00, E03-E07, E70-E84, 056, 240-243, 245-259, 272-277, G10-G99, H00-H95, I00-I99, K00-K93, 279.3-279.4, 279.8-286, 288.2, 303, L00-L99, M00-M99, N00-N28, N31- 330-348.0, 349-426, 427.5, 428-434.0, N32, N35-N64 (exclude N39.0), N75- 435-437.0, 438-451, 453.2, 456.1, N98, P76, Q00-Q99 458.9, 520-729, 733-759, 775.2, 777.0-777.4, 784.0

Other Group II Remainder Remainder

III. Injuries V01-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. Similarly, P92, P95-96, and F00-F99 codes in ICD-10 are distributed proportionally to all causes. b Also referred to as “intrapartum-related complications”

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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, A00-B99, D50-D53, D64.9, E00-E02, 001-139, 243, 260-269, 279.5-279.6, perinatal and nutritional E40-E64, G00-G09, H65-H66, J00- 280, 281, 285.9, 320-326, 381-382, conditionsa J22, J85, N30, N34, N390, N70-N73, 460-466, 480-487, 513, 614-616, 630- O00-P96, U04 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, 761.0-761.1, 765, 769, 770.2-770.9, P52, P61.2, P77 772.1, 774.2, 776.6, 777.5-777.6,

Birth asphyxia & birth traumab P01.7-P02.1, P02.4-P02.6, P03, P10- 761.7-762.1, 762.4-762.6, 763, 767- P15, P20-P21, P24, P50, P90-P91 768, 770.1, 772.2, 779.0-779.2

Sepsis and other infectious P35-P39 (exclude P37.3, P37.4) conditions of the newborn 771.0-771.2, 771.4-771.8

Other Group I Remainder Remainder

II. Noncommunicable C00-C97, D00-D48, D55-D64 (exclude 140- 242, 244-259, 270-279, 282-285, a diseases D64.9), D65-D89, E03-E34, E65-E88, 286-319, 330-380, 383-459, 470-478, F01-F99, G10-G98, H00-H61, H68- 490- 512, 514-611, 617- 629, 680- 759 H93, I00-I99, J30-J84, J86-J98, K00- (exclude 279.5-279.6, 285.9) K92, L00-L98, M00-M99, N00-N28, N31-N32, N35-N64 (exclude N39.0), N75-N98, Q00-Q99

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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