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Introduction Poor health seeking behaviour continues to be major challenge in accessing healthcare in sub-Saharan Africa despite the availability of effective treatment for most childhood illnesses. The current study investigated the barriers to healthcare access and health seeking for childhood illnesses in Burundi. Methods The study utilized data from the 2016-17 Burundi Demographic and Health Survey (BDHS). A total of 2173 children under five of childbearing women were included in our study. The outcome variable for the study was healthcare seeking for childhood illnesses (diarrhea and fever/cough). Barriers to healthcare access were the explanatory variables and maternal and child factors were the control variables. Chi-square test of independence and a binary logistic regression modelling were carried out to generate the results. Results Overall, less than 50% of children in Burundi who were ill two weeks before the survey obtained healthcare. We found that children of mothers who perceived getting money for medical care for self as a big problem [aOR = 0.75; CI = 0.60-0.93] and considered going for medical care alone as a big problem [aOR = 0.71; CI = 0.55-0.91] had lower odds of getting healthcare, compared to those of mothers who considered these indicators as not a big problem. The results also showed that children of mothers who had three [aOR = 1.48; 1.02-2.15] and four [aOR = 1.62; 1.10-2.39], children were more likely to get healthcare for childhood illnesses compared to those whose mothers had one child. Children of mothers with single birth children were less likely to get healthcare compared to those whose mothers had multiple births. Conclusion Findings of the low prevalence of healthcare for childhood illnesses in Burundi suggest the need for government and non-governmental health organizations to strengthen women’s healthcare accessibility for child healthcare services and health seeking behaviours. The Burundian government through multi-sectoral partnership should strengthen health systems for maternal health and address structural determinants of women’s health by creating favourable conditions to improve the status of women and foster their overall socioeconomic well-being. Free child healthcare policies in Burundi should be strengthened to enhance the utilization of child healthcare services in Burundi.
The study employed a cross-sectional study design and used data from the 2016–17 Burundi Demographic and Health Survey (DHS). Specifically, data from the birth recode file, which has one record for every child ever born to interviewed women was used. The DHS is a nationally representative survey that is conducted in over 85 low-and middle-income countries globally. The survey focuses on essential maternal and child health markers including “health seeking behaviour” [18]. The study by Aliaga and Ruilin [19] provides details of the sampling process. The surveys employ a two-stage stratified sampling technique, which makes the survey data nationally representative [19]. The first stage involves the generation of a sampling frame from enumeration areas (EAs) that covered the given country. The EAs are mostly generated from the most recent national census data in the country. Each EA is subsequently segmented into standard size segments of about 100–500 households per segment. The second stage involves a systematic selection of households from the EAs and an in-person interviews in selected households with the various target populations: women (15–49) and men (15–64). The number of selected households per EA ranged from 30 to 40 households/women per rural cluster and from 20 to 25 households/women per urban cluster. A total of 2173 children under five of childbearing women who had complete information on all the variables of interest were included in our study. Since the authors used a secondary data, they were not directly involved in the data collection. However, data collection was done by trained field staff who were responsible for data collection for the survey in Burundi. Fig 1 shows how we arrived at the sample. The outcome variable for the study was health seeking behaviour for childhood illnesses. It was derived as a composite variable from two questions, “Did [NAME] receive treatment for diarrhea?”, and “Did [NAME] receive treatment for fever/cough?” The responses were “yes” and “no”. Women whose children suffered from either diarrhea or fever/cough two weeks prior to the survey responded to these questions. Women who responded that they sought healthcare for either treatment for diarrhea or fever/cough or both were considered as seeking healthcare for childhood illnesses and were given the code 1 = yes while those who responded that they neither sought for treatment for diarrhea nor fever/cough were considered as those who never sought healthcare for childhood illnesses and were coded as 0 = no. The study looked at barriers in accessing healthcare as the explanatory variable. In the DHS, barriers in accessing healthcare was generated by asking women if they had serious problems in accessing healthcare for themselves when they are sick. The problems were difficulty with distance to the facility, difficulty in getting money for treatment, difficulty with getting permission to visit health facility, and difficulty in not wanting to go for medical help alone. For each of these questions, the responses were ‘big problem’ and ‘not a big problem’. Although these indicators are asked of women and are not linked to healthcare seeking for the child, we consider these indicators as proxy for accessing barriers women go through when seeking healthcare for the child. Fourteen variables were considered in the study as covariates. The variables were age, marital status, employment status, parity, religion, exposure to mass media (radio, television and newspaper), size of child at birth, birth order, twin status, and sex of child. The other variables were sex of household head, community literacy level, community socio-economic status, and place of residence. The variables were not determined a priori; instead, based on parsimony, theoretical relevance and practical significance with health seeking behaviour for childhood illnesses [11,20]. Marriage was recoded into “never married (0)”, “married (1)”, “cohabiting (2)”, “widowed (3)”, and “divorced (4)”. We recoded parity (birth order) as “one birth (1)”, “two births (2)”, “three births (3)”, and “four or more births (4)”; religion as “Christianity (1)”, “Islam (2)”, “Traditionalist (3)”, and “no religion (4)”; size of child at birth as “larger than average”, “average”, and “smaller than average”; and twin status as “single birth” and “multiple birth”. Exposure to media was coded as yes and no, signifying whether a woman reads newspaper, listens to radio or watches television or not. The data were analysed with Stata version 14.2. The analyses were done in three steps. The first step was the computation of the prevalence of women’s health seeking behaviour for childhood illnesses in Burundi. The second step was a bivariate analysis using Pearson’s chi-square test of independence that calculated the prevalence and proportions of health seeking behaviour for childhood illnesses across the independent variables with their significance levels. Statistical significance was considered at a p-value less than 0.20. The choice of a P < 0.20, instead of the usual P ≤ 0.05, were influenced by two main reasons (a) the purpose of the bivariate analyses was to identify potential predictor variables for the multivariate analyses rather than testing hypothesis, and b) it would minimize the risk of excluding variables with a biological (theoretical) plausibility from the multivariate analyses due to reasons, including confounding [21,22]. However, the statistical significance of the results of the binary logistic regression analysis was determined at P ≤ 0.05, because of its common usage in medical research. Before conducting the binary logistic regression analysis, a multi-collinearity test was carried out among all the statistically significant variables to determine if there was evidence of multicollinearity between them. Using the variance inflation factor (VIF), the multicollinearity test showed that there was no evidence of collinearity among the explanatory variables (Mean VIF = 1.20, Max VIF = 1.53, Minimum = 1.03). In all, two models were generated from the binary logistic regression analysis. The first model (Model I) was the bivariate analysis between each of the explanatory variables, covariates, and health seeking behavior for childhood illnesses. Model II which is the complete model, was a multivariate logistic regression analysis where all the variables were used against the dependent variable. The results of the regression analyses were presented as crude odds ratio (cOR) and adjusted odds ratio (aOR). A sample weight (v005/1,000,000) to correct for over and under sampling was applied and the “svy” command to account for the complex survey design and generalizability of the findings was also used. In this study, we relied on the Strengthening the Reporting of Observational Studies in Epidemiology’ (STROBE) statement in writing the manuscript [23]. This study used secondary data and therefore no further approval was required because the data is available in the public domain. However, the authors sought permission to use the data by applying to MEASURE DHS and obtained approval to use the data.
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