Examining barriers to healthcare access and utilization of antenatal care services: evidence from demographic health surveys in sub-Saharan Africa

Background: Antenatal care utilization is one of the means for reducing the high maternal mortality rates in sub-Saharan Africa. This study examined the association between barriers to healthcare access and implementation of the 2016 WHO antenatal care services model among pregnant women seeking antenatal care in selected countries in sub-Saharan Africa. Methods: This study considered only Demographic and Health Survey data collected in 2018 in sub-Saharan Africa. Hence, the Demographic and Health Survey data of four countries in sub-Saharan Africa (Nigeria, Mali, Guinea and Zambia) were used. A sample of 6761 from Nigeria, 1973 from Mali, 1690 from Guinea and 1570 from Zambia was considered. Antenatal care visits, categorized as < 8 visits or ≥8 visits, and time of the first antenatal care visit, categorized as ≤3 months or > 3 months (as per the WHO recommendations) were the outcome variables for this study. Both descriptive statistics and ordinal logistic regression were used to analyze the data. Crude odds ratios (cOR) and adjusted odds ratios (aOR) and p-values < 0.05 were used for the interpretation of results. Results: With timing of antenatal care visits, getting money needed for treatment (aOR = 1.38, 95% CI = 1.03–1.92) influenced early timing of antenatal care visits in Mali whereas getting permission to visit the health facility (aOR = 1.62, 95% CI = 1.15–2.33) motivated women to have early timing of antenatal care visits in Guinea. We found that women who considered getting money needed for treatment as not a big problem in Nigeria were more likely to have the recommended number of antenatal care visits (aOR = 1.38, 95% CI= 1.11–1.73). On the contrary, in Guinea, Zambia and Mali, getting permission to visit health facilities, getting money for treatment, distance to the health facility and not wanting to go alone were not barriers to having ≥ 8 antenatal care visits. Conclusion: Our study has emphasized the role played by barriers to healthcare access in antenatal care utilization across sub-Saharan African countries. There is the need for governmental and non-governmental organizations to ensure that policies geared towards improving the quality of antenatal care and promoting good interaction between health care seekers and health care providers are integrated within the health system. SSA is the portion of the continent of Africa that lies south of the Sahara. According to the United Nations, the region consists of all African countries and territories that are fully or partially south of the Sahara and it geographically consists of 46 countries, including Nigeria, Guinea, Mali and Zambia [19]. SSA is home to over 500 million women who account for about half of the continent’s population and 14% of the female population worldwide and approximately 47% of them are of reproductive age (15–49, [20]). Despite an overall improvement in maternal survival and a 45% decrease in maternal mortality rate globaly since 1990, women in SSA continue to bear an unacceptable health burden [21]. This has often been attributed to a number of factors including the lack of universal access to essential services and interventions and maternal health related services are not an exception [22]. Hence, millions of women in SSA are not accessing maternal health services, and undergo their pregnancies and childbirths outside the health system and this explains sub-Saharan African countries' excessive share of the global burden of disease and death, particularly as it relates to maternal and reproductive health [23]. This study only considered Demographic and Health Survey (DHS) data collected in 2018 in SSA. The inclusion criteria were such that all countries with datasets that were more than a year after the November 2016 WHO new guidelines for ANC services were considered for analyses. We used all datasets available for SSA countries collected in the year 2018 or later considering 9–10 months gestation period of pregnancy. Thus, the DHS data of Nigeria, Mali, Guinea and Zambia were used. The DHS is a standardized cross-sectional survey carried out in over 90 low-to-middle income countries with the aim of providing current estimates on a number of health indicators and to track countries' progress on the SDGs. The surveys employ a two-stage stratified sampling in sampling the research participants, where countries are grouped into urban and rural areas. The first stage involves the selection of clusters usually called enumeration areas (EAs) and the second stage consists of the selection of household for the survey. To ensure consistency in data collection across countries, the DHS uses a standard questionnaire comparable across countries for data collection, and the questionnaire is often translated into the major local languages of the countries involved [24]. To ensure validity of the translated questionnaires, the DHS reports that the translated questionnaires together with the version in English are pretested in English and the local dialect. After that, the pretest field staff actively discussed the questionnaires and made suggestions to modify all versions. Following field practice, a debriefing session is held with the pretest field staff, and modifications to the questionnaires were made based on lessons drawn from the exercise. Details of the sampling methods, procedures and implementation can be found on the DHS website in each country final report [25–28]. Table 1 provides detailed information on the period of data collection and sample sizes for each eligible country. To assess factors associated with the WHO recommendations (ANC visit ≥8 and ANC timing ≤3 months), we extracted information on all currently married women, who gave birth in the last 0–12 months prior to the month of interview, responded to questions on ANCvisits and timing and had complete response for all variables considered. Country and sample size details aSample size by design are women aged 15–49 currently married, who gave birth in the last 0–12 months prior to the month of interview and responded to questions on antenatal care visits and timing bSelected women sample are women with complete response for all variables considered For this study, two ANC variables (timing of ANC visits and number of ANC visits) were investigated: Four key variables, that measure barriers to healthcare access were considered as key explanatory variables. These were getting permission to visit health facilities, getting money for treatment, distance to the health facility and not wanting to go alone. Each of these variables were categorized into ‘big problem’ and ‘not a big problem’. Our interest was to find out whether those who considered each of the barriers as ‘not a big problem’ will attend the ≥8 visits and receive their first ANC ≤3 months during pregnancy. Apart from the key independent variables, other factors considered relevant with ANC utilization were also included in the analyses as covariates. These include age of women at childbirth (≤19, 20–24, 25–29, 30–34, 35–39, ≥40), residence (urban vs rural), religion (Christians, Muslims and others), birth order (1–2, 3–4, ≥5), pregnancy intention (no vs yes) and polygyny (monogamous, polygamous as first wife, polygamous as second wife or latter). Other relevant variables selected were health insurance coverage (no vs yes), wealth quintile (poorest, poorer, middle, richer, richest), husband’s highest educational level (no education, primary, secondary, tertiary) and the difference in age between husband and wife (wife older or same age with husband, husband 1–5 years older, husband 6–10 years older, husband more than 10 years older). The selection of these variables was influenced by the Health Care Services Utilisation Model by Andersen and Newman [29]. The model is a behavioural model that explains the conditions that either promote or hinder the utilisation of health care services [29]. This model identified three main conditions or factors that influence an individual to or not use a health care service. These factors are the Predisposing factors, Enabling factors and Need for care factors. Predisposing factors refer to the demographic, social structure and health belief characteristics. The demographic characteristics of the individual that affect the decision to use or not use a health care service. Social Structure consist of factors surrounding education and occupation and health belief factors consist of values, attitudes of health care service providers, and knowledge about health [29]. In this study, the predisposing factors were age of women at childbirth, religion, birth order, polygyny, husband’s highest educational level and the difference in age between husband and wife. Enabling factors are the resources or means that is available to an individual to seek health care services. Enabling factors are measured at the household level, thus, the availability of income and the community level, thus, the availability and location of health care facilities in the community [29]. In this study, the enabling factors included getting permission to visit health facilities, getting money for treatment, distance to the health facility, not wanting to go alone, residence and wealth quintile. Need for care factors refer to how an individual perceives his own general health and functional condition, as well as their familiarity with the signs and symptoms of ill health, agony and concerns about their health [29]. The need for care factors is influenced by the predisposing factors and the enabling factors of an individual. In this study, the need for care factors included pregnancy intention and health insurance coverage (Fig. 1). Conceptual Framework Statistical analyses were performed using Stata 14.0. Data on respondents who had complete information and responses for all variables considered in this study were used for the analyses. Data were first summarized by presenting the frequency distributions of the variables using descriptive statistics of frequency and percentages for each of the four countries considered in this study. Next, number of women with at least 8 ANC visits and early timing of first ANC visit (≤3 months of gestation) by the four measures of barriers to healthcare access were presented as frequency distributions and corresponding 95% confidence intervals (CI) at p< 0.05. This was followed by a crude ordinal logistic regression to ascertain the relationship between number and timing of ANC and the independent variables, with the results presented as crude odds ratios (cOR). The final step of the analyses involved the use of an ordinal logistic regression to examine the relationship between number and timing of ANC and the variables for measuring barriers to healthcare access, while adjusting for other covariates, with the results presented as adjusted odds ratios (aOR). We used weighting, clustering and stratification to adjust for the complex survey design. P-values < 0.05 were used for interpretation of results. Ethical permissions were not required for this study since we used DHS datasets already publicly available. The DHS reports that ethical procedures were the responsibility of the institutions that commissioned, funded, or managed the surveys. All DHS surveys are approved by Inner City Fund (ICF) international as well as an Institutional Review Board (IRB) in respective countries to ensure that the protocols are in compliance with the U.S. Department of Health and Human Services regulations for the protection of human subjects. In compliance with the declaration of Helsinki on human research, the consent for participation were duly obtained from respondents before data collection. The dataset for countries where DHS is conducted can be accessed for free after due permission from the DHS Measures.