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      Neonatal mortality and associated factors among newborns in Mogadishu, Somalia: a multicenter hospital-based cross-sectional study

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          Abstract

          Introduction

          Neonatal mortality is a significant public health problem in Sub-Saharan Africa, particularly in Somalia, where limited data exists about this. Mogadishu, the densely populated capital, faces a high rate of neonatal mortality, but this has not been widely studied on a national level. Healthcare providers and policymakers are working to reduce newborn deaths, but a comprehensive understanding of the contributing factors is crucial for effective strategies. Therefore, this study aims to determine the magnitude of neonatal death and identify factors associated with it in Mogadishu, Somalia.

          Method

          A multicenter hospital-based cross-sectional study was conducted to collect data from participants at 5 purposively selected hospitals in Mogadishu, Somalia. A well-structured, reliable, self-developed, validated questionnaire containing socio-demographic, maternal, and neonatal characteristics was used as a research tool. Descriptive statistics were used for categorical and continuous variables presented. Chi-square and logistic regression were used to identify factors associated with neonatal mortality at a significant level of α = 0.05.

          Results

          A total of 513 participants were recruited for the study. The prevalence of neonatal mortality was 26.5% [95%CI = 22.6–30.2]. In a multivariable model, 9 variables were found: female newborns (AOR = 1.98, 95%CI = 1.22–3.19), those their mothers who did not attend ANC visits (AOR = 2.59, 95%CI = 1.05–6.45), those their mothers who did not take tetanus toxoid vaccination (AOR = 1.82, 95%CI = 1.01–3.28), those their mothers who delivered in instrumental assistant mode (AOR = 3.01, 95%CI = 1.38–6.56), those who had neonatal sepsis (AOR = 2.24, (95%CI = 1.26–3.98), neonatal tetanus (AOR = 16.03, 95%CI = 3.69–69.49), and pneumonia (AOR = 4.06, 95%CI = 1.60–10.31) diseases during hospitalization, premature (AOR = 1.99, 95%CI = 1.00–3.94) and postmature (AOR = 4.82, 95%CI = 1.64–14.16) neonates, those with a birth weight of less than 2500 gr (AOR = 4.82, 95%CI = 2.34–9.95), those who needed resuscitation after delivery (AOR = 2.78, 95%CI = 1.51–5.13), and those who did not initiate early breastfeeding (AOR = 2.28, 95%CI = 1.12–4.66), were significantly associated with neonatal mortality compared to their counterparts.

          Conclusion

          In this study, neonatal mortality was high prevalence. Therefore, the intervention efforts should focus on strategies to reduce maternal and neonatal factors related to neonatal mortality. Healthcare workers and health institutions should provide appropriate antenatal, postnatal, and newborn care.

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          Most cited references66

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          Purposeful selection of variables in logistic regression

          Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
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            4 million neonatal deaths: when? Where? Why?

            The proportion of child deaths that occurs in the neonatal period (38% in 2000) is increasing, and the Millennium Development Goal for child survival cannot be met without substantial reductions in neonatal mortality. Every year an estimated 4 million babies die in the first 4 weeks of life (the neonatal period). A similar number are stillborn, and 0.5 million mothers die from pregnancy-related causes. Three-quarters of neonatal deaths happen in the first week--the highest risk of death is on the first day of life. Almost all (99%) neonatal deaths arise in low-income and middle-income countries, yet most epidemiological and other research focuses on the 1% of deaths in rich countries. The highest numbers of neonatal deaths are in south-central Asian countries and the highest rates are generally in sub-Saharan Africa. The countries in these regions (with some exceptions) have made little progress in reducing such deaths in the past 10-15 years. Globally, the main direct causes of neonatal death are estimated to be preterm birth (28%), severe infections (26%), and asphyxia (23%). Neonatal tetanus accounts for a smaller proportion of deaths (7%), but is easily preventable. Low birthweight is an important indirect cause of death. Maternal complications in labour carry a high risk of neonatal death, and poverty is strongly associated with an increased risk. Preventing deaths in newborn babies has not been a focus of child survival or safe motherhood programmes. While we neglect these challenges, 450 newborn children die every hour, mainly from preventable causes, which is unconscionable in the 21st century.
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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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                Author and article information

                Contributors
                pamornsri.sri@mfu.ac.th
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                19 June 2024
                19 June 2024
                2024
                : 24
                : 1635
                Affiliations
                [1 ]Department of Public Health, School of Health Science, Mae Fah Luang University, ( https://ror.org/00mwhaw71) Chiang Rai Province, Thailand
                [2 ]Department of Neonatal Intensive Care Unit, Yardimeli Hospital, Mogadishu, Somalia
                [3 ]School of Medicine, Mae Fah Luang University, ( https://ror.org/00mwhaw71) Chiang Rai Province, Thailand
                Article
                19149
                10.1186/s12889-024-19149-7
                11186222
                38898456
                04cdf22a-ffa9-4cbb-b5a8-1b9fa9a70e53
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 25 June 2023
                : 14 June 2024
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                © BioMed Central Ltd., part of Springer Nature 2024

                Public health
                associated factors,neonatal mortality,newborns,prevalence,mogadishu,somalia
                Public health
                associated factors, neonatal mortality, newborns, prevalence, mogadishu, somalia

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