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Objective This study aimed to assess the determinants of accessing healthcare among reproductive-age women in Sub-Saharan Africa (SSA). Design, setting and analysis Cross-sectional data were sourced from recent Demographic and Health Surveys in 36 SSA countries. We employed mixed-effect analysis to identify the determinants of accessing healthcare in SSA. OR and its 95% CI were reported for determinants associated with accessing healthcare. Outcome The outcome for this study was whether accessing healthcare was a € big problem’ or € not a big problem’. Responses to these questions were categorised as a big problem and not a big problem. Participants A total weighted sample of 500 439 reproductive-age (15-49 years) women from each country’s recent Demographic and Health Surveys from 2006 to 2018 were included in this study. Results The pooled prevalence of healthcare access among reproductive-age women in SSA was 42.56% (95% CI 42.43% to 42.69%). The results of the mixed-effect analysis revealed that the determinants of accessing healthcare were urban residence (adjusted OR (AOR)=1.25, 95% CI 1.34 to 1.73), ability to read and write (AOR=1.15, 95% CI 1.22 to 1.28), primary education (AOR=1.08, 95% CI 1.07 to 1.12), secondary education and above (AOR=1.12, 95% CI 1.10 to 1.14), husband with primary education (AOR=1.06, 95% CI 1.07 to 1.1.12), husband with secondary education and above (AOR=1.22, 95% CI 1.18 to 1.27), middle wealth index (AOR=1.43, 95% CI 1.40 to 1.47), rich wealth index (AOR=2.19, 95% CI 2.13 to 2.24) and wanted pregnancy (AOR=1.27, 95% CI 1.19 to 1.29). Conclusion Healthcare access in SSA was found at 42.56%, which is very low even if Sustainable Development Goal 3.8 targeted universal health coverage for everyone so they can obtain the health services they need. The major determinants of healthcare access among reproductive-age women in SSA were urban residence, higher educational level, higher wealth index and wanted pregnancy. The findings of this study suggest and recommend strengthening and improving healthcare access for women who reside in the countryside, women with low level of education and women of low socioeconomic status.
Data for this study were sourced from the most recent surveys in 36 SSA countries from 2006 to 2018. The DHS programme collects data that are comparable across low-income and middle-income countries. The programme designs the same manual, code, value level, variable name and procedure in more than 90 countries across the world. The SSA countries included in this study are listed in table 1. Details can be found in our previously published work.19–21 The inclusion and exclusion criteria for SSA countries are shown in figure 1. Data were collected from each country’s survey year 5 years preceding the survey. The DHS collects data on HIV/AIDS, nutrition, child health, child nutrition, reproductive health, family planning, marriage, fertility and mortality. Individual record files were used in this study. Diagrammatic representation of Sub-Saharan African countries included in the study. DHS, Demographic and Health Survey. Pooled Demographic and Health Survey (DHS) data from 36 Sub-Saharan African countries A two-stage stratified sampling method was used to select study participants. First, the enumeration area was selected based on each country frame developed from the previous census conducted. Second, households from each enumeration area were selected. The full sampling procedure is found elsewhere.22 A total of 500 439 reproductive-age women were eligible for this study. Due to the observational nature of the study, the Strengthening the Reporting of Observational Studies in Epidemiology checklist was used and is provided in online supplemental file 1. bmjopen-2021-054397supp001.pdf The outcome variable was accessibility. Most studies have ignored travel time and transport cost when looking at access to health facilities. In the DHS data, women were asked whether a range of factors would be a big problem for them when accessing healthcare. We generated a composite outcome variable using each country’s DHS standard question. The questions included the following: The responses to the questions asked are ‘big problem’ and ‘not a big problem’. If a woman faces at least one problem, access to healthcare is considered a big problem and is coded 1 or 0 otherwise. After reviewing different types of literature,12 13 17 23–25 variables were retrieved from the DHS data set. Variables at the individual, community and regional levels were considered in this study. Individual-level variables include age group, literacy level, women’s educational status, marital status, husband’s educational status, maternal occupation status, media exposure, wealth status, birth order and wanted pregnancy, whereas residence was a community-level variable and region a regional-level variable. In this study, both descriptive and inferential analyses were done. The survey year and the number of reproductive-age women in each country are presented in the tables. The weighted number of reproductive-age women and the weighted percentage of women for each sociodemographic variable are presented in table 2. Model comparison is presented in table 3. The results of the multivariable generalised mixed-effect model are presented to see the effect size of the association between the outcome and the independent variables. Socioeconomic and demographic characteristics of reproductive-age women in Sub-Saharan Africa Model comparison and random-effect results for the final model GLMM, generalised linear mixed effect model; ICC, intraclass correlation coefficient; LLR, log-likelihood ratio; LR test, likelihood ratio test; MOR, median OR. STATA V.14 software was used for analysis. First, each country was given a code and then appended together to create a single data set that represents the SSA countries. There are individual-level and community-level variables in the data set. The nature of the DHS data set is hierarchical and needs advanced statistical techniques to account for variability. The generalised linear mixed-effect model was fitted. Both fixed and random estimates were reported. For fixed-effect estimates, adjusted OR (AOR) and its 95% CI were reported to see the effect size of the association between healthcare access problem and the independent variables (table 4). For random-effect estimates, intraclass correlation and median OR were reported (table 3). First, in the bivariable analysis, variables with a p value less than 0.2 were taken as a candidate variable for the final model. Variables in the final model with a p value less than 0.005 were declared as determinants significantly associated with accessing healthcare in SSA. Multivariable mixed-effect logistic regression analysis of determinants of healthcare access in Sub-Saharan Africa *significant at alpha 0.05, **significant at alpha 0.01 and ***significant at alpha 0.001 AOR, adjusted odds ratio; COR, crude odds ratio. There is no direct public and patient involvement in the design and conduct of this research.
