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      Number of antenatal care visits and associated factors among reproductive age women in Sub-Saharan Africa using recent demographic and health survey data from 2008–2019: A multilevel negative binomial regression model

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          Abstract

          Background

          Antenatal care is one of the best strategies for maternal and neonatal mortality reduction. There is a paucity of evidence on the mean number of ANC visits and associated factors in Sub-Saharan Africa (SSA). This study aimed to investigate the mean number of ANC visits and associated factors among reproductive-age women in Sub-Saharan Africa using the Demographic and Health Survey conducted from 2008 to 2019.

          Method

          A total of 256,425 weighted numbers of women who gave birth five years before the survey were included. We used STATA version 14 for data management and analysis. A multilevel negative binomial regression model was fitted. Finally, the Adjusted Incident Rate Ratio (AIRR) with its 95% CI confidence interval was reported. Statistical significance was declared at P-value < 0.05.

          Results

          The mean number of ANC visits among women who gave birth five years before the survey in SSA was 3.83 (95% CI = 3.82, 3.84) Individual-level factors such as being aged 36–49 years (AIRR = 1.20, 95% CI = 1.18,1.21), having secondary education &above (AIRR = 1.44, 95% CI = 1.42, 1.45), having rich wealth status (AIRR = 1.08, 95% CI = 1.07, 1.09), media exposure (AIRR = 1.10, 95% CI = 1.09,1.11), and grand multiparity (AIRR = 0.90, 95% CI = 0.89, 0.91) were significantly associated with the number of ANC visits. Furthermore, rural residence (AIRR = 0.90, 95% CI = 0.89, 0.91), Western SSA region (AIRR = 1.19, 95% CI = 1.18, 1.20) and being from a middle-income country (AIRR = 1.09, 95% CI = 1.08, 1.10) were community-level factors that had a significant association with the number of ANC visits.

          Conclusion

          The mean number of ANC visits in SSA approximates the minimum recommended number of ANC visits by the World Health Organization. Women’s educational status, women’s age, media exposure, parity, planned pregnancy, wealth status, residence, country’s income, and region of SSA had a significant association with the frequency of ANC visits. This study suggests that addressing geographical disparities and socio-economic inequalities will help to alleviate the reduced utilization of ANC services.

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          Most cited references47

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          Advanced cervical dilatation as a predictor for low emergency cesarean delivery: a comparison between migrant and non-migrant Primiparae – secondary analysis in Berlin, Germany

          Background Cesarean rates are higher in women admitted to labor ward during early stages rather than at later stages of labor. In a study in Germany, crude cesarean rates among Turkish and Lebanese immigrant women were low compared to non-immigrant women. We evaluated whether these immigrant women were admitted during later stages of labor, and if so, whether this explains their lower cesarean rates. Methods We enrolled 1413 nulliparous women with vertex pregnancies, singleton birth, and 37+ week of gestation, excluding elective cesarean deliveries, in three Berlin obstetric hospitals. We applied binary logistic regression to adjust for social and obstetric factors; and standardized coefficients to rank predictors derived from the regression model. Results At the time of admission to labor ward, a smaller proportion of Turkish migrant women was in the active phase of labor (cervical dilation: 4+ cm), compared to women of Lebanese origin and non-immigrant women. Rates of cesarean deliveries were lower in women of Turkish and Lebanese origin (15.8 and 13.9%) than in non-immigrant women (23.9%). In the logistic regression analysis, more advanced cervical dilatation was inversely associated with the outcome cesarean delivery (OR: 0.76, 95%CI: 0.70–0.82). In addition, higher maternal age (OR: 1.06, 95%CI: 1.04–1.09), application of oxytocic agents (OR: 0.55, 95%CI: 0.42–0.72), and obesity (OR: 2.25, 95%CI: 1.51–3.34) were associated with the outcome. Ranking of predictors indicate that cervical dilatation is the most relevant predictor derived from the regression model. Conclusions Advanced cervical dilatation at the time of admission to labor ward does not explain lower emergency cesarean delivery rates in Turkish and Lebanese migrant women, despite the fact that this is the strongest among the predictors for emergency cesarean delivery identified in this study.
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            The association of neighbourhood and individual social capital with consistent self-rated health: a longitudinal study in Brazilian pregnant and postpartum women

            Background Social conditions, social relationships and neighbourhood environment, the components of social capital, are important determinants of health. The objective of this study was to investigate the association of neighbourhood and individual social capital with consistent self-rated health in women between the first trimester of pregnancy and six months postpartum. Methods A multilevel cohort study in 34 neighbourhoods was performed on 685 Brazilian women recruited at antenatal units in two cities in the State of Rio de Janeiro, Brazil. Self-rated health (SRH) was assessed in the 1st trimester of pregnancy (baseline) and six months after childbirth (follow-up). The participants were divided into two groups: 1. Good SRH – good SRH at baseline and follow-up, and, 2. Poor SRH – poor SRH at baseline and follow-up. Exploratory variables collected at baseline included neighbourhood social capital (neighbourhood-level variable), individual social capital (social support and social networks), demographic and socioeconomic characteristics, health-related behaviours and self-reported diseases. A hierarchical binomial multilevel analysis was performed to test the association between neighbourhood and individual social capital and SRH, adjusted for covariates. Results The Good SRH group reported higher scores of social support and social networks than the Poor SRH group. Although low neighbourhood social capital was associated with poor SRH in crude analysis, the association was not significant when individual socio-demographic variables were included in the model. In the final model, women reporting poor SRH both at baseline and follow-up had lower levels of social support (positive social interaction) [OR 0.82 (95% CI: 0.73-0.90)] and a lower likelihood of friendship social networks [OR 0.61 (95% CI: 0.37-0.99)] than the Good SRH group. The characteristics that remained associated with poor SRH were low level of schooling, Black and Brown ethnicity, more children, urinary infection and water plumbing outside the house. Conclusions Low individual social capital during pregnancy, considered here as social support and social network, was independently associated with poor SRH in women whereas neighbourhood social capital did not affect women’s SRH during pregnancy and the months thereafter. From pregnancy and up to six months postpartum, the effect of individual social capital explained better the consistency of SRH over time than neighbourhood social capital.
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              Intermediate and advanced topics in multilevel logistic regression analysis

              Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: 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
                27 December 2022
                2022
                : 2
                : 12
                : e0001180
                Affiliations
                [1 ] Department of Epidemiology, Gambella Regional Health Bureau, Gambella, Ethiopia
                [2 ] Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
                [3 ] Department of Human Nutrition, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
                [4 ] Department of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia
                Janaki Medical College, Tribhuvan University, NEPAL
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-1150-6333
                Article
                PGPH-D-22-00897
                10.1371/journal.pgph.0001180
                10022079
                36962803
                4bfd4e58-76c3-45d7-b512-f1aa31d120b0
                © 2022 Gebeyehu 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
                : 27 May 2022
                : 27 November 2022
                Page count
                Figures: 0, Tables: 3, Pages: 14
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Antenatal Care
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Maternal Mortality
                People and Places
                Geographical Locations
                Africa
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                Health Surveys
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Regression Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Regression Analysis
                Custom metadata
                The dataset is available from the DHS program official database www.measuredhs.com.

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