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Introduction: The use of antenatal care (ANC) plays a major role in minimizing maternal and child mortality through effective and appropriate screening, preventive, or treatment intervention. Even though almost all pregnancy-related mortalities are largely preventable through adequate use of ANC, sub-Saharan Africa (SSA), particularly East African Countries, continues to share the largest share of global maternal, and newborn mortality. Therefore, this study assesses if educational attainment is associated with optimal ANC utilization in East Africa. Methods: A secondary data analysis was done based on Demographic and Health Surveys (DHS) from 2010 to 2018 in the 11 East African Countries. A total weighted sample of 84,660 women who gave birth in the 5 years preceding each country’s DHS survey and had ANC visits were included in this study. Data processing and analysis were performed using STATA 15 software. A multilevel mixed-effect logistic regression model was fitted to examine the association of educational attainment and ANC utilization. Variables with a p-value <0.05 were declared as significant factors associated with ANC utilization. Model comparison was done based on Akaike and Bayesian Information Criteria (AIC and BIC). Results: The magnitude of optimal ANC utilization in East African Countries was 56.37% with 95% CI (56.03, 56.69) with the highest optimal ANC utilization in Zimbabwe (80.96%) and the lowest optimal ANC utilization in Rwanda (44.31%). Women who had higher education levels were more likely to have optimal ANC utilization, compared to those with no education (AOR = 2.34; 95 and CI; 2.11–2.59). Women who had media exposure were more likely to have optimal ANC utilization than those who have no media exposure (AOR = 1.07; 95% CI; 1.03, 1.10). Conclusion: Antenatal care utilization was low in East African countries. Educational attainment, maternal age, wealth index, birth order, media exposure, and living countries were factors associated with ANC utilization. Efforts to improve antenatal care and other maternal health service utilization in East Africa must take into account these factors. Specifically, working on the access to mass media by women may also improve antenatal care utilization.
The United Nations (UN) Statistics Division has subdivided the African continent into five regions. Among these countries, East Africa is the largest region that includes 19 countries (Burundi, Comoros, Djibouti, Ethiopia, Eritrea, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Reunion, Rwanda, Seychelles, Somalia, Somaliland, Tanzania, Uganda, Zambia, and Zimbabwe). This study was a secondary data analysis based on Demographic and Health Surveys (DHS). Of these 19 East African countries, 13 countries have DHS data, whereas 6 (Djibouti, Somalia, Somaliland, Seychelles and Mauritius, Reunion). Among these 13 countries that have DHS data, 2 countries have DHS data that was conducted before 2010 (Eritrea-2002 and Madagascar-2008). In this study, we included 11 countries’ DHS data that was conducted after 2010. The data of these 11 East African countries were accessed from the demographic health survey (DHS) program official database www.measuredhs.com after authorization was granted through an online request by explaining the goal of our study. We used the individual Record (IR file) data set and extracted the dependent and independent variables. To collect knowledge that is comparable across countries in the world, the DHS program adopts standardized methods involving uniform questionnaires, manuals, and field procedures. DHS is a nationally representative household survey that offers data from a wide variety of population, health, and nutrition tracking and effect assessment measures with face-to-face interviews of women aged 15 to 49. Stratified, multi-stage, random sampling is used in the surveys. In each country, information was obtained from qualified women aged 15 to 49 years. Detailed survey techniques and methods of sampling used to collect data have been recorded elsewhere.26 There are a total of 89,991 women who gave birth in the 5 years preceding each country’s DHS survey. Of these, 84,660 women were reported using ANC service 5 years before the survey, and they are being eligible for our study in which this implies that around 5% of the women in East Africa did not attend ANC at all. The response (outcome) variable of this study was ANC utilization. The response variable is binary, and it is coded as 1 if women received ANC from skilled healthcare providers (doctors, midwives, nurses, and health officers) at least four times and 0 otherwise. Independent variables are classified as community and individual-level factors. Community-level variables: Country (11 countries in East Africa) and residence (urban and rural). The individual-level variables: Age (maternal age was categorized by 5 years interval as follows: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years), level of education (no education, primary education, secondary, and higher education), distance from a health facility (big problem and not a big problem), birth order (1st, 2nd–4th, and ≥5th), mass media exposure was recorded as the frequency of reading newspaper, listening to radio and watching TV (categorized as Has exposure or Has no exposure), and wealth index. Household wealth is represented by the wealth index (in five categories: poorest, poorer, middle, richer, and richest). Wealth index was constructed using data on a households’ ownership of selected assets, such as television and bicycles, materials used for housing construction, and types of water access and sanitation facilities. The index placed individual households on a continuous scale relative to their wealth status.27 Data processing and analysis were performed using STATA 15 software. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the representativeness of the survey and to tell the STATA to take into account the sampling design when calculating standard errors to get reliable statistical estimates. Cross tabulations and summary statistics were conducted to describe the study population. In the first step, bi-variable analysis and cross-tabulations by Pearson’s chi-squared test were used to examine if educational attainment was associated with ANC utilization. Later, since the DHS data have a hierarchical nature, women within a cluster may be more similar to each other than women in the other cluster. Due to this, the assumption of independent observations and equal variance across clusters might be violated. Therefore, an advanced statistical model is required to take into account the between cluster variability to get a reliable standard error and unbiased estimate. Furthermore, by taking into account the dichotomous nature of the outcome variable, multilevel mixed-effect logistic regression was fitted. Model comparison was done based on Akaike and Bayesian Information Criteria (AIC and BIC). A mixed-effect model with the lowest Information Criteria (AIC and BIC) was selected. The individual and community-level variables associated with ANC utilization were checked independently in the bi-variable multilevel mixed-effect logistic regression model, and variables that were statistically significant at p-value 0.20 in the bi-variable multilevel mixed-effects logistic regression analysis were considered for the final individual and community-level model adjustments. In the multivariable multilevel mixed-effect analysis, variables with a p-value≤0.05 were declared as significant determinants of ANC utilization. Intra-class correlation coefficients (ICC) were used to check whether or not the multilevel model is appropriate and how much of the overall variation in the response is explained by clustering. Four models were fitted. The first was the null model that did not include exposure variables, which were used to verify community variance and provide evidence to assess random effects at the community level. Then, Model-I was the multivariable model adjustment for individual-level variables, and Model-II was adjusted for community-level factors. In Model-III, the outcome variable was equipped with potential candidate variables from both individual and community-level variables. The fixed effects (a measure of association) were used to estimate the association between the optimal ANC utilization and explanatory variables and expressed as an odds ratio with a 95% confidence interval. Regarding the measures of variation (random-effects), community-level variance with standard deviation and intra-cluster correlation coefficient (ICC) was used.
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