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      Individual and community-level determinants, and spatial distribution of institutional delivery in Ethiopia, 2016: Spatial and multilevel analysis

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          Abstract

          Background

          Institutional delivery is an important indicator in monitoring the progress towards Sustainable Development Goal 3.1 to reduce the global maternal mortality ratio to less than 70 per 100,000 live births. Despite the international focus on reducing maternal mortality, progress has been low, particularly in Sub-Saharan Africa (SSA), with more than 295,000 mothers still dying during pregnancy and childbirth every year. Institutional delivery has been varied across and within the country. Therefore, this study aimed to investigate the individual and community level determinants, and spatial distribution of institutional delivery in Ethiopia.

          Methods

          A secondary data analysis was done based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total weighted sample of 11,022 women was included in this study. For spatial analysis, ArcGIS version 10.6 statistical software was used to explore the spatial distribution of institutional delivery, and SaTScan version 9.6 software was used to identify significant hotspot areas of institutional delivery. For the determinants, a multilevel binary logistic regression analysis was fitted to take to account the hierarchical nature of EDHS data. The Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), Proportional Change in Variance (PCV), and deviance (-2LL) were used for model comparison and for checking model fitness. Variables with p-values<0.2 in the bi-variable analysis were fitted in the multivariable multilevel model. Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) were used to declare significant determinant of institutional delivery.

          Results

          The spatial analysis showed that the spatial distribution of institutional delivery was significantly varied across the country [global Moran’s I = 0.04 (p<0.05)]. The SaTScan analysis identified significant hotspot areas of poor institutional delivery in Harari, south Oromia and most parts of Somali regions. In the multivariable multilevel analysis; having 2–4 births (AOR = 0.48; 95% CI: 0.34–0.68) and >4 births (AOR = 0.48; 95% CI: 0.32–0.74), preceding birth interval ≥ 48 months (AOR = 1.51; 95% CI: 1.03–2.20), being poorer (AOR = 1.59; 95% CI: 1.10–2.30) and richest wealth status (AOR = 2.44; 95% CI: 1.54–3.87), having primary education (AOR = 1.47; 95% CI: 1.16–1.87), secondary and higher education (AOR = 3.44; 95% CI: 2.19–5.42), having 1–3 ANC visits (AOR = 3.88; 95% CI: 2.77–5.43) and >4 ANC visits (AOR = 6.53; 95% CI: 4.69–9.10) were significant individual-level determinants of institutional delivery while being living in Addis Ababa city (AOR = 3.13; 95% CI: 1.77–5.55), higher community media exposure (AOR = 2.01; 95% CI: 1.44–2.79) and being living in urban area (AOR = 4.70; 95% CI: 2.70–8.01) were significant community-level determinants of institutional delivery.

          Conclusions

          Institutional delivery was low in Ethiopia. The spatial distribution of institutional delivery was significantly varied across the country. Residence, region, maternal education, wealth status, ANC visit, preceding birth interval, and community media exposure were found to be significant determinants of institutional delivery. Therefore, public health interventions should be designed in the hotspot areas where institutional delivery was low to reduce maternal and newborn mortality by enhancing maternal education, ANC visit, and community media exposure.

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          Most cited references58

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          Global causes of maternal death: a WHO systematic analysis.

          Data for the causes of maternal deaths are needed to inform policies to improve maternal health. We developed and analysed global, regional, and subregional estimates of the causes of maternal death during 2003-09, with a novel method, updating the previous WHO systematic review. We searched specialised and general bibliographic databases for articles published between between Jan 1, 2003, and Dec 31, 2012, for research data, with no language restrictions, and the WHO mortality database for vital registration data. On the basis of prespecified inclusion criteria, we analysed causes of maternal death from datasets. We aggregated country level estimates to report estimates of causes of death by Millennium Development Goal regions and worldwide, for main and subcauses of death categories with a Bayesian hierarchical model. We identified 23 eligible studies (published 2003-12). We included 417 datasets from 115 countries comprising 60 799 deaths in the analysis. About 73% (1 771 000 of 2 443 000) of all maternal deaths between 2003 and 2009 were due to direct obstetric causes and deaths due to indirect causes accounted for 27·5% (672 000, 95% UI 19·7-37·5) of all deaths. Haemorrhage accounted for 27·1% (661 000, 19·9-36·2), hypertensive disorders 14·0% (343 000, 11·1-17·4), and sepsis 10·7% (261 000, 5·9-18·6) of maternal deaths. The rest of deaths were due to abortion (7·9% [193 000], 4·7-13·2), embolism (3·2% [78 000], 1·8-5·5), and all other direct causes of death (9·6% [235 000], 6·5-14·3). Regional estimates varied substantially. Between 2003 and 2009, haemorrhage, hypertensive disorders, and sepsis were responsible for more than half of maternal deaths worldwide. More than a quarter of deaths were attributable to indirect causes. These analyses should inform the prioritisation of health policies, programmes, and funding to reduce maternal deaths at regional and global levels. Further efforts are needed to improve the availability and quality of data related to maternal mortality. © 2014 World Health Organization; licensee Elsevier. This is an Open Access article published without any waiver of WHO's privileges and immunities under international law, convention, or agreement. This article should not be reproduced for use in association with the promotion of commercial products, services, or any legal entity. There should be no suggestion that WHO endorses any specific organisation or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.
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            • Article: not found

            Use of mass media campaigns to change health behaviour.

            Mass media campaigns are widely used to expose high proportions of large populations to messages through routine uses of existing media, such as television, radio, and newspapers. Exposure to such messages is, therefore, generally passive. Such campaigns are frequently competing with factors, such as pervasive product marketing, powerful social norms, and behaviours driven by addiction or habit. In this Review we discuss the outcomes of mass media campaigns in the context of various health-risk behaviours (eg, use of tobacco, alcohol, and other drugs, heart disease risk factors, sex-related behaviours, road safety, cancer screening and prevention, child survival, and organ or blood donation). We conclude that mass media campaigns can produce positive changes or prevent negative changes in health-related behaviours across large populations. We assess what contributes to these outcomes, such as concurrent availability of required services and products, availability of community-based programmes, and policies that support behaviour change. Finally, we propose areas for improvement, such as investment in longer better-funded campaigns to achieve adequate population exposure to media messages. Copyright © 2010 Elsevier Ltd. All rights reserved.
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              A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena.

              In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression. The aim of this study was to present measures of variation appropriate for the logistic case in a didactic rather than a mathematical way. Data were used from the health survey conducted in 2000 in the county of Scania, Sweden, that comprised 10 723 persons aged 18-80 years living in 60 areas. Conducting multilevel logistic regression different techniques were applied to investigate whether the individual propensity to consult private physicians was statistically dependent on the area of residence (that is, intraclass correlation (ICC), median odds ratio (MOR)), the 80% interval odds ratio (IOR-80), and the sorting out index). The MOR provided more interpretable information than the ICC on the relevance of the residential area for understanding the individual propensity of consulting private physicians. The MOR showed that the unexplained heterogeneity between areas was of greater relevance than the individual variables considered in the analysis (age, sex, and education) for understanding the individual propensity of visiting private physicians. Residing in a high education area increased the probability of visiting a private physician. However, the IOR showed that the unexplained variability between areas did not allow to clearly distinguishing low from high propensity areas with the area educational level. The sorting out index was equal to 82%. Measures of variation in logistic regression should be promoted in social epidemiological and public health research as efficient means of quantifying the importance of the context of residence for understanding disparities in health and health related behaviour.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                12 November 2020
                2020
                : 15
                : 11
                : e0242242
                Affiliations
                [1 ] Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
                [2 ] Department of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
                Sheffield Hallam University, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6812-1659
                Article
                PONE-D-19-33251
                10.1371/journal.pone.0242242
                7660564
                33180845
                2a03fc95-349e-4f4f-b561-101208487918
                © 2020 Tesema 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
                : 1 December 2019
                : 28 October 2020
                Page count
                Figures: 6, Tables: 5, Pages: 22
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Birth
                Labor and Delivery
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Birth
                Labor and Delivery
                People and Places
                Geographical Locations
                Africa
                Ethiopia
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Antenatal Care
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Maternal Mortality
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                People and Places
                Population Groupings
                Ethnicities
                African People
                Somalian People
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Custom metadata
                All relevant data are within the manuscript. The data we used is available online and you can access it from www.measuredhs.com.

                Uncategorized
                Uncategorized

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