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Background: Every day in 2017, approximately 810 women died from preventable causes related to pregnancy and childbirth, with 99% of these maternal deaths occurring in low and lower-middle-income countries. Sub-Saharan Africa (SSA) alone accounts for roughly 66%. If pregnant women gained recommended ANC (Antenatal Care), these maternal deaths could be prevented. Still, many women lack recommended ANC in sub-Saharan Africa. This study aimed at determining the pooled prevalence and determinants of recommended ANC utilization in SSA. Methods: We used the most recent standard demographic and health survey data from the period of 2006 to 2018 for 36 SSA countries. A total of 260,572 women who had at least one live birth 5 years preceding the survey were included in this study. A meta-analysis of DHS data of the Sub-Saharan countries was conducted to generate pooled prevalence, and a forest plot was used to present it. A multilevel multivariable logistic regression model was fitted to identify determinants of recommended ANC utilization. The AOR (Adjusted Odds Ratio) with their 95% CI and p-value ≤0.05 was used to declare the recommended ANC utilization determinates. Results: The pooled prevalence of recommended antenatal care utilization in sub-Saharan Africa countries were 58.53% [95% CI: 58.35, 58.71], with the highest recommended ANC utilization in the Southern Region of Africa (78.86%) and the low recommended ANC utilization in Eastern Regions of Africa (53.39%). In the multilevel multivariable logistic regression model region, residence, literacy level, maternal education, husband education, maternal occupation, women health care decision autonomy, wealth index, media exposure, accessing health care, wanted pregnancy, contraceptive use, and birth order were determinants of recommended ANC utilization in Sub-Saharan Africa. Conclusion: The coverage of recommended ANC service utilization was with high disparities among the region. Being a rural residence, illiterate, low education level, had no occupation, low women autonomy, low socioeconomic status, not exposed to media, a big problem to access health care, unplanned pregnancy, not use of contraceptive were determinants of women that had no recommended ANC utilization in SSA. This study evidenced the existence of a wide gap between SSA regions and countries. Special attention is required to improve health accessibility, utilization, and quality of maternal health services.
Thirty-six sub-Saharan Africa countries’ most recent Demographic and Health Surveys (DHS) data were used for this study (Table 1). The countries were given a unique identification number and appended together to have a single dataset that represents the sub-Saharan Africa countries. The DHS dataset is representative of each nation in the sub-Saharan Africa countries. The detail of the DHS dataset was found from our previously published work [19]. Pooled Demographic and Health Surveys (DHS) data from 36 sub-Saharan countries, 2006–2018 The DHS data had different datasets. For this study, Individual records (IR dataset) were used. The dataset includes marriage and sexual activity, fertility, fertility preference, family planning, anthropometry and anemia in women, malaria prevention for women, HIV/AIDS, women’s empowerment, adult and maternal mortality, and domestic violence. The detail of the dataset was published elsewhere [20]. The two-stage stratified sampling technique was used to select the study participants in the DHS dataset. We appended 36 subiSaharan Africa countries after unique IDs were given for each country. Pooled analysis was done after sampling weight. A total of 260,572 reproductive-age women who gave at least one birth in the 5 years preceding each country survey was included in this study. The outcome variable for this study was whether a woman had four and above antenatal care visits or not. The variable is generated using WHO-recommended antenatal Care service. We coded “1” if women had four and above antenatal care visit service and”0″ otherwise [9]. Based on known facts and literature [17, 21–23], the explanatory variables included in this study were region, residence, age group, maternal education, husband education, maternal occupational status, women autonomy on health care, wealth index, media exposure, accessing health care, wanted pregnancy, contraceptive utilization, and birth order. The theoretical literature review help establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. The following diagram was created to clearly define the relationship between recommended ANC utilization and variables using solid and broken lines. The solid line indicates a direct relationship, and the broken line indicates an indirect relationship. The figure presented that factors such as community-level characteristics, socio-demographic characteristics, pregnancy-related characteristics, media exposure, and maternal health service characteristics could affect recommended ANC utilization. More ever, the figure illustrated the theoretical relationship between recommended ANC utilization across sub-Saharan Africa countries (Fig. 1). Theoretical review of the relationship between recommended ANC utilization and variables in SSA from 2006 to 2018 The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the representativeness of the survey and tell the STATA to consider the sampling design when calculating standard errors to get reliable statistical estimates. Descriptive and summary statistics were conducted using STATA version 14 software. The pooled prevalence of antenatal care utilization with a 95% Confidence Interval (CI) was reported for sub- Saharan Africa Countries from 2006 to 2018. The detail of the data management was found from our previously published work [19]. The DHS data had a hierarchical structure, which violates the independent assumptions. Women are nested within clusters, and women within the same cluster are more similar than the rest of the cluster. This nature of the DHS data needs to take into account the between cluster variability using appropriate statistical modeling. Four models were fitted null model (models without the explanatory variables), a model I (models include community-level variables, model II (models include individual-level variable)) and Model III (models include both individual and community level variables) were fitted to select the best fit model for the data using Log-Likelihood Ratio (LLR) and Deviance [24, 25]. Model III, which includes both individual and community level variable, was selected because of its highest LLR and Smallest deviance (Table 3). Multilevel multivariable logistic regression model analysis result of recommended antenatal care visit in Sub-Saharan Africa from 2006 to 2018 * = significant at alpha 5% The fixed effect analysis was done using included variables in the model, both individual and community-level variables. The random effect analysis was done by considering variations between clusters (EAs) assessed by computing the Intra-class correlation coefficient (ICC), a proportional change in variance (PCV), and median odds ratio (MOR) [25–27]. The ICC is the proportion of variance explained by the grouping structure in the population. It was computed as ICC= σμ2σμ2+π2/3; Where: the standard logit distribution has a variance of π2/3, σμ2 indicates the cluster variance. Whereas PCV measures the total variation attributed by individual level and community level factors in the multilevel model as compared to the null model. It was computed as: varianceofnullmodel−varianceoffullmodelvarianceofnullmodel. MOR is defined as the odds ratio’s median value between the cluster at high risk and cluster at lower risk of recommended ANC utilization when randomly picking out two clusters (EAs). It was computed as: MOR = exp. (2∗σμ2∗0.6745) ~ MOR = exp. (0.95 ∗ σμ). Permission to get access to the data was obtained from the measure DHS program online request from http://www.dhsprogram.com.website, and the data used were publicly available with no personal identifier.
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