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      Causes of death identified in neonates enrolled through Child Health and Mortality Prevention Surveillance (CHAMPS), December 2016 –December 2021

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      1 , * , , 1 , 1 , 1 , 1 , 2 , 3 , 3 , 4 , 4 , 5 , 5 , 6 , 5 , 5 , 5 , 7 , 8 , 9 , 10 , 11 , 8 , 12 , 7 , 8 , 7 , 8 , 13 , 14 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 21 , 22 , 21 , 23 , 24 , 25 , 26 , 27 , 27 , 28 , for the CHAMPS Consortium
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          Abstract

          Each year, 2.4 million children die within their first month of life. Child Health and Mortality Prevention Surveillance (CHAMPS) established in 7 countries aims to generate accurate data on why such deaths occur and inform prevention strategies. Neonatal deaths that occurred between December 2016 and December 2021 were investigated with MITS within 24–72 hours of death. Testing included blood, cerebrospinal fluid and lung cultures, multi-pathogen PCR on blood, CSF, nasopharyngeal swabs and lung tissue, and histopathology examination of lung, liver and brain. Data collection included clinical record review and family interview using standardized verbal autopsy. The full set of data was reviewed by local experts using a standardized process (Determination of Cause of Death) to identify all relevant conditions leading to death (causal chain), per WHO recommendations. For analysis we stratified neonatal death into 24-hours of birth, early (1-<7 days) and late (7-<28 days) neonatal deaths. We analyzed 1458 deaths, 41% occurring within 24-hours, 41% early and 18% late neonatal deaths. Leading underlying causes of death were complications of intrapartum events (31%), complications of prematurity (28%), infections (17%), respiratory disorders (11%), and congenital malformations (8%). In addition to the underlying cause, 62% of deaths had additional conditions and 14% had ≥3 other conditions in the causal chain. The most common causes considering the whole causal chain were infection (40%), prematurity (32%) and respiratory distress syndrome (28%). Common maternal conditions linked to neonatal death were maternal hypertension (10%), labour and delivery complications (8%), multiple gestation (7%), placental complications (6%) obstructed labour and chorioamnionitis (5%, each). CHAMPS’ findings showing the full causal chain of events that lead to death, in addition to maternal factors, highlights the complexities involved in each death along with the multiple opportunities for prevention. Highlighting improvements to prenatal and obstetric care and infection prevention are urgently needed in high-mortality settings.

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          Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals

          Summary Background Despite remarkable progress in the improvement of child survival between 1990 and 2015, the Millennium Development Goal (MDG) 4 target of a two-thirds reduction of under-5 mortality rate (U5MR) was not achieved globally. In this paper, we updated our annual estimates of child mortality by cause to 2000–15 to reflect on progress toward the MDG 4 and consider implications for the Sustainable Development Goals (SDG) target for child survival. Methods We increased the estimation input data for causes of deaths by 43% among neonates and 23% among 1–59-month-olds, respectively. We used adequate vital registration (VR) data where available, and modelled cause-specific mortality fractions applying multinomial logistic regressions using adequate VR for low U5MR countries and verbal autopsy data for high U5MR countries. We updated the estimation to use Plasmodium falciparum parasite rate in place of malaria index in the modelling of malaria deaths; to use adjusted empirical estimates instead of modelled estimates for China; and to consider the effects of pneumococcal conjugate vaccine and rotavirus vaccine in the estimation. Findings In 2015, among the 5·9 million under-5 deaths, 2·7 million occurred in the neonatal period. The leading under-5 causes were preterm birth complications (1·055 million [95% uncertainty range (UR) 0·935–1·179]), pneumonia (0·921 million [0·812 −1·117]), and intrapartum-related events (0·691 million [0·598 −0·778]). In the two MDG regions with the most under-5 deaths, the leading cause was pneumonia in sub-Saharan Africa and preterm birth complications in southern Asia. Reductions in mortality rates for pneumonia, diarrhoea, neonatal intrapartum-related events, malaria, and measles were responsible for 61% of the total reduction of 35 per 1000 livebirths in U5MR in 2000–15. Stratified by U5MR, pneumonia was the leading cause in countries with very high U5MR. Preterm birth complications and pneumonia were both important in high, medium high, and medium child mortality countries; whereas congenital abnormalities was the most important cause in countries with low and very low U5MR. Interpretation In the SDG era, countries are advised to prioritise child survival policy and programmes based on their child cause-of-death composition. Continued and enhanced efforts to scale up proven life-saving interventions are needed to achieve the SDG child survival target. Funding Bill & Melinda Gates Foundation, WHO.
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            National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis

            Summary Background Reducing neonatal mortality is an essential part of the third Sustainable Development Goal (SDG), to end preventable child deaths. To achieve this aim will require an understanding of the levels of and trends in neonatal mortality. We therefore aimed to estimate the levels of and trends in neonatal mortality by use of a statistical model that can be used to assess progress in the SDG era. With these estimates of neonatal mortality between 1990 and 2017, we then aimed to assess how different targets for neonatal mortality could affect the burden of neonatal mortality from 2018 to 2030. Methods In this systematic analysis, we used nationally-representative empirical data related to neonatal mortality, including data from vital registration systems, sample registration systems, and household surveys, to estimate country-specific neonatal mortality rates (NMR; the probability of dying during the first 28 days of life) for all countries between 1990 (or the earliest year of available data) and 2017. For our analysis, we used all publicly available data on neonatal mortality from databases compiled annually by the UN Inter-agency Group for Child Mortality Estimation, which were extracted on or before July 31, 2018, for data relating to the period between 1950 and 2017. All nationally representative data were assessed. We used a Bayesian hierarchical penalised B-splines regression model, which allowed for data from different sources to be weighted differently, to account for variable biases and for the uncertainty in NMR to be assessed. The model simultaneously estimated a global association between NMR and under-5 mortality rate and country-specific and time-specific effects, which enabled us to identify countries with an NMR that was higher or lower than expected. Scenario-based projections were made at the county level by use of current levels of and trends in neonatal mortality and historic or annual rates of reduction that would be required to achieve national targets. The main outcome that we assessed was the levels of and trends in neonatal mortality and the global and regional NMRs from 1990 to 2017. Findings Between 1990 and 2017, the global NMR decreased by 51% (90% uncertainty interval [UI] 46–54), from 36·6 deaths per 1000 livebirths (35·5–37·8) in 1990, to 18·0 deaths per 1000 livebirths (17·0–19·9) in 2017. The estimated number of neonatal deaths during the same period decreased from 5·0 million (4·9 million–5·2 million) to 2·5 million (2·4 million–2·8 million). Annual NMRs vary widely across the world, but west and central Africa and south Asia had the highest NMRs in 2017. All regions have reported reductions in NMRs since 1990, and most regions accelerated progress in reducing neonatal mortality in 2000–17 versus 1990–2000. Between 2018 and 2030, we project that 27·8 million children will die in their first month of life if each country maintains its current rate of reduction in NMR. If each country achieves the SDG neonatal mortality target of 12 deaths per 1000 livebirths or fewer by 2030, we project 22·7 million cumulative neonatal deaths by 2030. More than 60 countries need to accelerate their progress to reach the neonatal mortality SDG target by 2030. Interpretation Although substantial progress has been made in reducing neonatal mortality since 1990, increased efforts to improve progress are still needed to achieve the SDG target by 2030. Accelerated improvements are most needed in the regions and countries with high NMR, particularly in sub-Saharan Africa and south Asia. Funding Bill & Melinda Gates Foundation, United States Agency for International Development.
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              Intrapartum-related neonatal encephalopathy incidence and impairment at regional and global levels for 2010 with trends from 1990

              Background: Intrapartum hypoxic events (“birth asphyxia”) may result in stillbirth, neonatal or postneonatal mortality, and impairment. Systematic morbidity estimates for the burden of impairment outcomes are currently limited. Neonatal encephalopathy (NE) following an intrapartum hypoxic event is a strong predictor of long-term impairment. Methods: Linear regression modeling was conducted on data identified through systematic reviews to estimate NE incidence and time trends for 184 countries. Meta-analyses were undertaken to estimate the risk of NE by sex of the newborn, neonatal case fatality rate, and impairment risk. A compartmental model estimated postneonatal survivors of NE, depending on access to care, and then the proportion of survivors with impairment. Separate modeling for the Global Burden of Disease 2010 (GBD2010) study estimated disability adjusted life years (DALYs), years of life with disability (YLDs), and years of life lost (YLLs) attributed to intrapartum-related events. Results: In 2010, 1.15 million babies (uncertainty range: 0.89–1.60 million; 8.5 cases per 1,000 live births) were estimated to have developed NE associated with intrapartum events, with 96% born in low- and middle-income countries, as compared with 1.60 million in 1990 (11.7 cases per 1,000 live births). An estimated 287,000 (181,000–440,000) neonates with NE died in 2010; 233,000 (163,000–342,000) survived with moderate or severe neurodevelopmental impairment; and 181,000 (82,000–319,000) had mild impairment. In GBD2010, intrapartum-related conditions comprised 50.2 million DALYs (2.4% of total) and 6.1 million YLDs. Conclusion: Intrapartum-related conditions are a large global burden, mostly due to high mortality in low-income countries. Universal coverage of obstetric care and neonatal resuscitation would prevent most of these deaths and disabilities. Rates of impairment are highest in middle-income countries where neonatal intensive care was more recently introduced, but quality may be poor. In settings without neonatal intensive care, the impairment rate is low due to high mortality, which is relevant for the scale-up of basic neonatal resuscitation.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Data curation
                Role: Methodology
                Role: Methodology
                Role: ConceptualizationRole: InvestigationRole: Methodology
                Role: Methodology
                Role: ConceptualizationRole: Investigation
                Role: Methodology
                Role: Investigation
                Role: ConceptualizationRole: Investigation
                Role: ConceptualizationRole: Investigation
                Role: Methodology
                Role: Methodology
                Role: Methodology
                Role: ConceptualizationRole: Investigation
                Role: Data curation
                Role: Methodology
                Role: Investigation
                Role: Methodology
                Role: Investigation
                Role: Investigation
                Role: Data curation
                Role: Investigation
                Role: Data curation
                Role: Conceptualization
                Role: Data curation
                Role: Data curation
                Role: Data curation
                Role: Data curation
                Role: Investigation
                Role: Conceptualization
                Role: Conceptualization
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: ConceptualizationRole: Funding acquisition
                Role: ConceptualizationRole: Funding acquisition
                Role: Formal analysis
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: Visualization
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLOS Glob Public Health
                PLOS Glob Public Health
                plos
                PLOS Global Public Health
                Public Library of Science (San Francisco, CA USA )
                2767-3375
                20 March 2023
                2023
                : 3
                : 3
                : e0001612
                Affiliations
                [1 ] South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, South Africa
                [2 ] Kenya County Department of Health, Kisumu, Kenya
                [3 ] Centers for Disease Control and Prevention, Kisumu, Kenya
                [4 ] Kenya Medical Research Institute-Center for Global Health Research (KEMRI-CGHR), Kisumu, Kenya
                [5 ] International Center for Diarrhoeal Diseases Research (icddr,b), Dhaka, Bangladesh
                [6 ] Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [7 ] ISGlobal—Hospital Clínic, Unversitat de Barcelona, Barcelona, Spain
                [8 ] Centro de Investigação em Saúde de Manhiça [CISM], Maputo, Mozambique
                [9 ] Institutó Catalana de Recerca I Estudis Avançats [ICREA], Barcelona, Spain
                [10 ] Hospital Sant Joan de Déu, Universitat de Barcelona, Esplugues, Barcelona, Spain
                [11 ] Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública [CIBERESP], Madrid, Spain
                [12 ] Instituto Nacional de Saúde [INS], Maputo, Mozambique
                [13 ] Centre pour le Développement des Vaccins (CVD-Mali), Ministère de la Santé, Bamako, Mali
                [14 ] Department of Pediatrics, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
                [15 ] Department of Pathological Anatomy and Cytology, University Hospital of Point G, Bamako, Mali
                [16 ] Crown Agents, Freetown, Sierra Leone
                [17 ] World Health Organization–Sierra Leone, Freetown, Sierra Leone
                [18 ] Ola During Children’s Hospital, Freetown, Sierra Leone
                [19 ] St. Luke’s University Health Network, Easton, Pennsylvania, United States of America
                [20 ] University of Ghana Medical School, Accra, Ghana
                [21 ] College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia
                [22 ] Department of Infectious Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [23 ] Bacterial and Mycology Unit, Ethiopian Public Health Institute, Addis Ababa, Ethiopia
                [24 ] Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [25 ] Infectious Diseases Pathology Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [26 ] Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
                [27 ] Center for Global Health, US Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [28 ] Emory Global Health Institute, Emory University, Atlanta, Georgia, United States of America
                Jhpiego, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                ¶ Membership of CHAMPS Consortium is provided in S1 Text.

                Author information
                https://orcid.org/0000-0002-2606-6951
                https://orcid.org/0000-0002-7629-0636
                https://orcid.org/0000-0002-8648-9403
                https://orcid.org/0000-0003-0875-7596
                https://orcid.org/0000-0003-3681-5976
                https://orcid.org/0000-0002-0670-6026
                https://orcid.org/0000-0001-9686-1187
                https://orcid.org/0000-0003-0341-2329
                https://orcid.org/0000-0002-8348-4965
                https://orcid.org/0000-0002-7099-2936
                https://orcid.org/0000-0002-5667-9660
                https://orcid.org/0000-0002-1253-198X
                https://orcid.org/0000-0002-1056-3216
                Article
                PGPH-D-22-01311
                10.1371/journal.pgph.0001612
                10027211
                36963040
                a59ba81e-5df5-47d1-a1da-65086e08420c
                © 2023 Mahtab et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 August 2022
                : 27 January 2023
                Page count
                Figures: 10, Tables: 4, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award ID: OPP1126780
                Award Recipient :
                This work was supported by the Bill & Melinda Gates Foundation [OPP1126780 to CW]. The funder participated in discussions of study design and data collection. They did not participate in the conduct of the study; the management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
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                CHAMPS data are open access and datasets are available for download or request on www.champshealth.org.

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