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Background: Complications during pregnancy and childbirth are the leading causes of maternal and child deaths and disabilities, particularly in low- and middle-income countries. Timely and frequent antenatal care prevents these burdens by promoting existing disease treatments, vaccination, iron supplementation, and HIV counseling and testing during pregnancy. Many factors could contribute to optimal ANC utilization remaining below targets in countries with high maternal mortality. This study aimed to assess the prevalence and determinants of optimal ANC utilization by using nationally representative surveys of countries with high maternal mortality. Methods: Secondary data analysis was done using recent Demographic and Health Surveys (DHS) data of 27 countries with high maternal mortality. The multilevel binary logistic regression model was fitted to identify significantly associated factors. Variables were extracted from the individual record (IR) files of from each of the 27 countries. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) and p-value of ≤0.05 in the multivariable model were used to declare significant factors associated with optimal ANC utilization. Result: The pooled prevalence of optimal ANC utilization in countries with high maternal mortality was 55.66% (95% CI: 47.48–63.85). Several determinants at the individual and community level were significantly associated with optimal ANC utilization. Mothers aged 25–34 years, mothers aged 35–49 years, mothers who had formal education, working mothers, women who are married, had media access, households of middle-wealth quintile, richest household, history of pregnancy termination, female household head, and high community education were positively associated with optimal ANC visits in countries with high maternal mortality, whereas being rural residents, unwanted pregnancy, having birth order 2–5, and birth order >5 were negatively associated. Conclusion and recommendations: Optimal ANC utilization in countries with high maternal mortality was relatively low. Both individual-level factors and community-level factors were significantly associated with ANC utilization. Policymakers, stakeholders, and health professionals should give special attention and intervene by targeting rural residents, uneducated mothers, economically poor women, and other significant factors this study revealed.
The Demography and Health Surveys employed a cross-sectional study design to collect the data. In this study, we only included countries with high maternal mortality and have publically available DHS data (26). This study is a secondary data analysis using the DHS data conducted in 27 countries. The DHS is a nationally representative survey that is conducted in low- and middle-income countries globally. We used individual record (IR) files to extract the study participants of this study. We weighted the sample using the individual weight of women (v005) to produce the proper representation. Hence, sample weights were generated by dividing (v005) by 1,000,000, and the total weighted sample size from the pooled data was 209,538 (Table 1). Maternal mortality, category, and year of the survey by the country. High, 300–499; Very high, 500–999; Extremely high, >1,000. Women aged 15–49 years with a birth in the last 5 years receiving antenatal care from a skilled provider for the most recent birth were the study population. Sample weight was used to correct for over- and under-sampling and generalizability of the findings. Antenatal care visit was the outcome variable for this study. We dichotomized the ANC visits as inadequate and adequate according to the WHO classification (11). Inadequate ANC is <4 visits, whereas optimal if women had four and more visits. Potential explanatory variables associated with completing optimal ANC visits were considered on two levels. Variables such as mother's age, maternal educational status, parity, marital status, sex of the household head, birth order, and wealth index were used at the individual level, whereas residence, community-level education, community-level poverty, and community-level media exposure were used as community-level variables. Community-level media usage is the proportion of women in the community who use radio, TV, and newsletter, and it was categorized as low community-level media usage and high community-level media usage. “Low” refers to communities in which <50% of respondents had media access, while “high” indicates communities in which ≥50% of respondents had media access. Community-level women's education refers to the proportion of women in the community who have formal education. It was categorized as low if communities in which < 50% of respondents had formal education and high if ≥50% of respondents had attended formal education. Community-level poverty refers to the proportion of women in the community who had low-wealth quintiles. It was categorized as low if the proportion of low-wealth quintile households was < 50% and high if the proportion was ≥50%. STATA version 14.2 was used to clean, recode, and analyze the data. A multilevel binary logistic regression model was fitted to identify significantly associated factors. Both community- and individual-level variables with a p-value of ≤0.2 in the bi-variable analysis were included in the multivariable model. Adjusted OR (AOR) with 95% CI and p < 0.05 were applied to determine significantly associated factors. Four models were applied, comprising the null model (model 0) containing no variables, which is used to check the variability of ANC visits in the community and provide evidence to assess random effect using the interclass correlation coefficient (ICC). Model I was adjusted for individual-level variables, Model II with community-level factors, and Model III with variables from both individual- and community-level variables were fitted with the outcome variable. The fixed effect is a measure of association that estimates the association between independent variables and ANC and is stated as AOR with a 95% confidence interval. The Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and proportional change in variance (PCV) were computed to assess the clustering effect/variability.
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