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Objective We examined the national prevalence as well as the individual and contextual factors associated with maternal healthcare utilisation in Mali. Setting The study was conducted in Mali. Participants We analysed data on 6335 women aged 15-49 years from Mali’s 2018 Demographic and Health Survey. Outcome variable Maternal healthcare utilisation comprising antenatal care (ANC) attendant, skilled birth attendant (SBA), and postnatal care (PNC) attendant, was our outcome variable. Results Prevalence of maternal healthcare utilisation was 45.6% for ANC4+, 74.7% for SBA and 25.5% for PNC. At the individual level, ANC4 + and SBA utilisation increased with increasing maternal age, level of formal education and wealth status. Higher odds of ANC4 + was found among women who are cohabiting (adjusted OR (aOR)=2.25, 95% CI 1.16 to 4.37) and delivered by caesarean section (aOR=2.53, 95% CI 1.72 to 3.73), while women who considered getting money for treatment (aOR=0.72, 95% CI 0.60 to 0.88) and distance to health facility (aOR=0.73, 95% CI 0.59 to 0.90) as a big problem had lower odds. Odds to use PNC was higher for those who were working (aOR=1.22, 95% CI 1.01 to 1.48) and those covered by health insurance (aOR=1.87, 95% CI 1.36 to 2.57). Lower odds of SBA use were associated with having two (aOR=0.48, 95% CI 0.33 to 0.71), three (aOR=0.37, 95% CI 0.24 to 0.58), and four or more (aOR=0.38, 95% CI 0.24 to 0.59) children, and residing in a rural area (aOR=0.35, 95% CI 0.17 to 1.69). Listening to the radio and watching TV were associated with increased maternal healthcare utilisation. Conclusion The government should increase availability, affordability and accessibility to healthcare facilities by investing in health infrastructure and workforce to achieve Sustainable Development Goal 3.4 of reducing maternal morality to less than 70 deaths per 100 000 live births by 2030. It is important to ascertain empirically why PNC levels are astonishingly lower relative to ANC and SBA.
We analysed a cross-sectional data from the 2018 DHS of Mali. The DHS is a nationally representative and comparative survey conducted in over 85 LMICs worldwide.27 A structured questionnaire was used to collect data from the respondents on health indicators such as maternal healthcare utilisation.27 The respondents were sampled using a two-stage cluster sampling technique. Detailed sampling technique has been highlighted in a study by Aliaga and Ruilin.28 In the present study, a total of 6335 women of reproductive age (15–49 years) were included in the analysis. The data set is freely available for download on the DHS platform.29 In drafting this manuscript, we relied on the Strengthening the Reporting of Observational Studies in Epidemiology statement guidelines.30 Mali is a West-African country with a population of 20 548 743.31 It has a pyramidal healthcare system, the community health system, requiring entry into the health system from community health centres.32 This decentralised system operates at five levels, namely national, regional, district, health area and community.32 The major healthcare financing mechanism is out-of-pocket payments for services including maternal healthcare services. Existing insecurities and internal displacement of people have also exacerbated inequalities in health infrastructure and access.33 No patients were involved in this study as we used secondary data. ANC, SBA and PNC were the outcome variables in this study. To assess ANC, the respondents were asked about the number of antenatal visits they made during their recent pregnancy. The response options recoded into 0–3=0 (<4 ANC attendance) and ≥4=1 (≥4 ANC attendance). With SBA, the respondents were asked ‘Who assisted (NAME) during delivery?’. Those whose response options included any category of health professionals were classified as ‘having SBA’ while those who were assisted by traditional birth attendants and others were grouped as ‘not having SBA’. PNC on the other hand was assessed using the question, ‘Did (NAME) go for postnatal checks within 2 months?’. The response options were 0=no; 1=yes; and 8=don’t know. Those whose response option was ‘don’t know’ were dropped. We, therefore, used the dichotomised responses in the final analysis. The coding and classification were informed by literature that used the DHS data sets.34–37 We considered 17 explanatory variables in this study. These variables were selected based on their availability in the DHS data sets as well as their significant association with the outcome variables in the study.34 38 39 The variables were ground into individual level (age of the respondent, educational level, marital status, religion, current working status, parity, national health insurance coverage, delivery by caesarean section, frequency of listening to radio, frequency of watching television, frequency of reading newspaper or magazine, getting medical help for self: permission to go; getting medical help for self: distance to health facility, and getting medical help for self: getting money for treatment) and contextual level (wealth index, place of residence and region). We maintained the existing coding in the DHS data set for current working status, national health insurance, delivery by caesarean section, frequency of listening to radio, frequency of watching television, frequency of reading newspaper/magazine, getting medical help for self: permission to go; getting medical help for self: distance to health facility, and getting medical help for self: getting money for treatment, wealth index, place of residence and region. The age of the respondent was recoded into ‘15–19’, ‘20–24’, ’25–29’, ’30–34’ and ‘35 and above’. The level of education of the respondent was recoded into ‘no education’, ‘primary’ and ‘secondary or higher’. Marital status was coded as ‘never married’, ‘married’, ‘cohabiting’ and ‘widowed or divorced or separated’. Religious affiliation was coded as ‘Christianity’, ‘Islamic’, ‘African Traditional or no religion or others’. Parity was coded as ‘one birth’, ‘two births’, ‘three births’ and ‘four or more births’. Stata software V.16.0 was used to perform the statistical analysis. All the analyses were weighted. We used percentages to summarise the prevalence of ANC, SBA and PNC as shown in figure 1. Later, cross-tabulation and χ2 tests were performed to examine the distribution of the outcome variables across the explanatory variables. Corresponding p values from the χ2 test were used to determine the statistically significant association between the outcome variables and the explanatory variables. All the variables that showed significance were placed in the regression model. We built four models under multilevel regression analysis to examine the association between each of the outcome variables and the explanatory variables. The first model (Model O) was fitted to show the variance in the outcome variables attributed to the clustering of the primary sampling units and the explanatory variables. Model I was fitted to include the individual-level variables against each of the outcome variables. Model II contained the contextual-level variables. Model III was fitted to include all the explanatory variables against each of the outcome variables. We used Akaike’s Information Criterion to test for model fitness and model comparison. The result of the regression analysis was presented using adjusted ORs (aORs) with their 95% CIs. We applied the women’s sample weights (v005/1000 000) to obtain unbiased estimates, according to the DHS guidelines and the Stata survey command ‘svy’ was used to adjust for the complex sampling structure of the data in all the analyses. Prevalence of maternal healthcare utilisation among women in Mali. ANC, antenatal care; PNC, postnatal care; SBA, skilled birth attendant. Since our analysis was based on publicly available data, no further ethical permission was necessary. Ethical guidelines regarding the usage of secondary data for publication were adhered to. Further information about DHS data usage and ethical standards are available at http://googl/ny8T6X.
