Barriers to healthcare access and healthcare seeking for childhood illnesses among childbearing women in Burundi

PLoS ONE, Volume 17, No. 9 September, Year 2022

Interpretation

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.

AI Digest

Study Justification:

The study aimed to investigate the barriers to healthcare access and health seeking for childhood illnesses among childbearing women in Burundi. This research is important because poor health seeking behavior remains a significant challenge in accessing healthcare in sub-Saharan Africa, despite the availability of effective treatments for most childhood illnesses. Understanding the barriers to healthcare access can help inform policies and interventions to improve healthcare utilization and ultimately reduce child morbidity and mortality.

Study Highlights:

1. Less than 50% of children in Burundi who were ill two weeks before the survey obtained healthcare.
2. Children of mothers who perceived getting money for medical care and going for medical care alone as big problems had lower odds of getting healthcare.
3. Children of mothers with three or four children were more likely to get healthcare for childhood illnesses compared to those with one child.
4. Children of mothers with single birth children were less likely to get healthcare compared to those with multiple births.

Recommendations for Lay Readers:

1. The government and non-governmental health organizations should strengthen women’s healthcare accessibility for child healthcare services and health seeking behaviors.
2. The Burundian government, through multi-sectoral partnerships, should strengthen health systems for maternal health and address structural determinants of women’s health.
3. Efforts should be made to improve the status of women and foster their overall socioeconomic well-being.
4. Free child healthcare policies in Burundi should be strengthened to enhance the utilization of child healthcare services.

Recommendations for Policy Makers:

1. Increase investment in improving healthcare accessibility for child healthcare services and health seeking behaviors among childbearing women.
2. Strengthen health systems for maternal health, including infrastructure, staffing, and availability of essential medicines and supplies.
3. Implement policies and programs that address the structural determinants of women’s health, such as poverty, education, and gender inequality.
4. Enhance the implementation and effectiveness of free child healthcare policies to ensure equitable access to healthcare services for all children.

Key Role Players:

1. Burundian government
2. Non-governmental health organizations
3. Ministry of Health
4. Community health workers
5. Women’s organizations
6. International development partners

Cost Items for Planning Recommendations:

1. Infrastructure development and improvement
2. Training and capacity building for healthcare providers
3. Procurement and supply of essential medicines and supplies
4. Health education and awareness campaigns
5. Monitoring and evaluation systems
6. Research and data collection on healthcare access and health seeking behaviors
Please note that the cost items provided are general categories and not actual cost estimates. The actual cost will depend on the specific context and implementation strategies.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a nationally representative survey and utilizes a large sample size. The study design is clearly described, and statistical analyses are conducted to generate results. However, to improve the evidence, the abstract could provide more details on the sampling process, such as the sampling frame and the specific sampling technique used. Additionally, it would be helpful to include information on the response rate of the survey and any potential limitations of the study.

Abstract

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.

Materials

N/A

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Mobile Health (mHealth) Solutions: Implementing mobile health technologies, such as SMS reminders for prenatal care appointments, can help improve access to maternal health services. These reminders can be sent directly to pregnant women’s mobile phones, ensuring they don’t miss important appointments.

2. Telemedicine: Introducing telemedicine services can enable pregnant women in remote or underserved areas to access healthcare professionals remotely. Through video consultations, pregnant women can receive medical advice, monitor their health, and receive necessary care without the need for travel.

3. Community Health Workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and pregnant women in rural areas. These workers can provide education, support, and basic healthcare services to pregnant women, improving access to maternal health services.

4. Financial Assistance Programs: Developing and implementing financial assistance programs can help address the barrier of financial constraints. These programs can provide subsidies or vouchers for maternal health services, making them more affordable and accessible for women in need.

5. Maternal Health Clinics: Establishing dedicated maternal health clinics can create a centralized and specialized hub for pregnant women to receive comprehensive care. These clinics can offer a range of services, including prenatal care, antenatal classes, and postnatal support, all in one location.

6. Public Awareness Campaigns: Launching public awareness campaigns can help educate communities about the importance of maternal health and the available services. These campaigns can address cultural beliefs, dispel myths, and encourage women to seek timely and appropriate care during pregnancy.

7. Partnerships and Collaborations: Strengthening partnerships between government agencies, non-governmental organizations, and private sector entities can lead to innovative solutions and resource sharing. Collaborative efforts can help improve infrastructure, training, and service delivery, ultimately enhancing access to maternal health services.

It’s important to note that the specific context and needs of Burundi should be considered when implementing these innovations.

AI Innovations Description

Based on the description provided, the study identified several barriers to healthcare access and healthcare seeking for childhood illnesses in Burundi. The study found that children of mothers who perceived getting money for medical care for themselves as a big problem and considered going for medical care alone as a big problem had lower odds of receiving healthcare for childhood illnesses. Additionally, children of mothers with three or four children were more likely to receive healthcare compared to those whose mothers had only one child.

Based on these findings, the study recommends the following to improve access to maternal health in Burundi:

1. Strengthen women’s healthcare accessibility: The government and non-governmental health organizations should work together to improve women’s access to healthcare services for their children. This can be achieved by increasing the availability and affordability of healthcare facilities, especially in rural areas where access is limited.

2. Address structural determinants of women’s health: The Burundian government, through multi-sectoral partnerships, should address the underlying factors that hinder women from seeking healthcare for their children. This includes creating favorable conditions to improve the status of women and their overall socioeconomic well-being. This may involve initiatives such as improving education, employment opportunities, and social support systems for women.

3. Strengthen free child healthcare policies: The existing policies that provide free child healthcare services in Burundi should be strengthened to enhance the utilization of these services. This can be done by raising awareness about the availability and benefits of free healthcare services for children, as well as addressing any logistical or administrative barriers that may prevent women from accessing these services.

By implementing these recommendations, it is hoped that access to maternal health in Burundi will be improved, leading to better healthcare outcomes for children and their mothers.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthen healthcare accessibility: The government and non-governmental health organizations should work together to improve women’s access to healthcare services for child healthcare. This can be achieved by increasing the number of healthcare facilities, particularly in rural areas, and ensuring that these facilities are equipped with the necessary resources and trained healthcare professionals.

2. Address financial barriers: Efforts should be made to address financial barriers that prevent women from seeking healthcare for their children. This can include implementing or strengthening free child healthcare policies, providing financial assistance or subsidies for healthcare expenses, and improving health insurance coverage for maternal and child health services.

3. Improve awareness and education: Enhancing awareness and education about the importance of maternal and child healthcare can help overcome barriers to seeking healthcare. This can be done through community outreach programs, health education campaigns, and the use of mass media to disseminate information about available healthcare services and their benefits.

4. Empower women: Efforts should be made to empower women and improve their overall socioeconomic well-being. This can include initiatives to improve women’s education, economic opportunities, and decision-making power within their families and communities. Empowered women are more likely to prioritize their own and their children’s health and seek appropriate healthcare when needed.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify specific indicators that can measure the impact of the recommendations on access to maternal health. For example, indicators could include the percentage of women seeking healthcare for their children, the average distance to the nearest healthcare facility, or the percentage of women who perceive financial barriers as a problem.

2. Collect baseline data: Gather data on the current status of access to maternal health in the target population. This can be done through surveys, interviews, or analysis of existing data sources such as health records or demographic surveys.

3. Develop a simulation model: Create a simulation model that incorporates the identified indicators and factors influencing access to maternal health. This model should consider the potential impact of the recommendations on these factors and how they interact with each other.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. This can involve adjusting variables such as the number of healthcare facilities, the availability of financial assistance, or the level of awareness and education.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on access to maternal health. This can include assessing changes in the identified indicators and comparing them to the baseline data.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate its accuracy by comparing the simulated outcomes with real-world data or expert opinions.

7. Communicate findings: Present the findings of the simulation analysis in a clear and concise manner, highlighting the potential benefits of implementing the recommendations to improve access to maternal health. This can help inform decision-making and resource allocation for maternal health interventions.

It is important to note that the methodology described above is a general framework and can be adapted based on the specific context and available data.

Author

Dima Health

Identifiers:

DOI: 10.1371/journal.pone.0274547

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Burundi

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female