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Background: Despite a global reduction of about 38% in maternal mortality rate between 2000 and 2017, sub-Saharan Africa is still experiencing high mortality among women. Access to high quality care before, during and after childbirth has been described as one of the effective means of reducing such mortality. In the sub-region, only 52% of women receive at least four antenatal visits. This study examined the factors influencing antenatal care utilization in sub-Saharan Africa. Methods: Data from Demographic and Health Surveys (DHS) of 31 countries involving 235,207 women age 15–49 years who had given birth to children within 5 years of the surveys were used in the study. Multinomial logistic regression model was applied in the analysis. Results: About 13% of women in sub-Saharan Africa did not utilize antenatal care while 35 and 53% respectively partially and adequately utilized the service. Adequate utilization of antenatal care was highest among women age 25–34 years (53.9%), with secondary or higher education (71.3%) and from the richest households (54.4%). The odds of adequate antenatal care utilization increased for women who are educated up to secondary or higher education level, from richest households, working, living in urban areas, exposed to media and did not experience problem getting to health facility or obtaining permission to visit health facility. Conclusions: This study has revealed information not only on women who did not utilize antenatal care but also on women who partially and adequately utilized the service. The study concluded that the correlates of antenatal care utilization in sub-Saharan Africa include socioeconomic and demographic factors, getting permission to visit health facility, unwillingness to visit health facility alone and problem encountered in reaching the health facility.
This study used data from Demographic and Health Surveys (DHS) of 31 sub-Saharan African countries which were conducted between 2010 and 2018. The surveys are cross-sectional and obtained information on health and other related issues from women of reproductive age (15–49 years). Sample selection in the surveys involved a two-stage stratified sampling method. Each country was divided into clusters. In the first stage, enumeration areas (EAs) were selected in each cluster and a household listing exercise was conducted in in all selected enumeration areas. The list of households was used as a basis for household selection. In the second stage, households were selected from each enumeration area. In each selected household, women within 15–49 years of age who were either permanent residents or visitors in the night preceding the survey were selected and interviewed. Such women were engaged in a face-to-face interview by field workers who recorded the information in questionnaires. Issues covered in each questionnaire included socioeconomic characteristics, reproductive history, antenatal, delivery and postnatal care, breastfeeding, domestic violence, childhood vaccinations and illnesses, among others. In this study, 235,207 women who have had at least one birth within five years preceding the surveys were involved. The number of women involved in the study and the years of surveys for each country are presented in Table 1. Year of survey, number of women and antenatal care utilization in Sub-Saharan Africa using Demographic and Health Surveys 2010–2018 Outcome variable in this study is antenatal care utilization which was measured as not utilized, partially utilized and adequately utilized. The ‘not utilized’ category involved women who did not attend antenatal clinic at all when they were pregnant. While the ‘partially utilized’ category included women who attended antenatal clinic less than 4 times, the ‘adequately utilized’ category involved women who attended antenatal clinic 4 or more times during their pregnancy period. In this study, the following independent variables were considered: age, education, household wealth, residence, employment, media exposure, parity, getting permission to use health facility, distance to health facility and unwillingness to visit health facility alone. Age was categorized as 15–24 years, 25–34 years and 35 years and above. Education was defined as none, primary and secondary or higher. Household wealth was measured through the ownership of household items such as radio, television, car, bicycle, agricultural land, farm animals and housing characteristics such as toilet facilities, water source, flooring/roofing materials, etc. Households were awarded scores based on the number of items available in the households. The scoring was done using principal component analysis. The result was thereafter expressed in five quintiles namely, poorest, poorer, middle, richer and richest. Residence was grouped into urban and rural. Employment was defined as working for those who engaged in one economic activity or another and not working for those who did not engage in economic activities. Media exposure was categorized as exposed and not exposed. Parity was categorized from 1 to 5 or more. Getting permission to use health facility, distance to health facility and unwillingness to visit health facility alone were measured as problem for women who experienced difficulty in respect of each of the variables and not a problem for those who did not experience any difficulty. Analysis in this study was carried out in three stages. The first stage involved pooling data sets of the 31 countries together in order to have a single data set for sub-Saharan Africa. To ensure that under-enumeration and over-enumeration in the surveys were adequately adjusted for, a weighting factor (v0051000000) was applied to the data. The data were further defined as survey data using the svyset command. In the second stage, Chi-Square test was used to describe the relationships between antenatal care utilization and the independent variables. The third stage involved multivariate analysis where multinomial logistic regression was applied. This is a statistical technique used when the outcome variable has more than two categories. The multinomial logistic regression model is given as: Where j = 1, 2, …, J − 1 (categories of the outcome variable) and J is the base outcome; αj represent the intercepts and βj1, … βjp represent the logit coefficients; and X1…Xp represent the independent variable [13]. Base outcome in the analysis is none (women who did not attend antenatal clinic). Odds ratios including their corresponding 95% confidence intervals were thereafter obtained. Three α levels (0.05, 0.01 and 0.001) were specified for the interpretation of statistical significance. Stata 14 statistical package was used to perform all the statistical operations.
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