Assessing the impact of geographical access to health facilities on maternal healthcare utilization: Evidence from the Burkina Faso demographic and health survey 2010

listen audio

Study Justification:
– Improving maternal and child health (MCH) is a significant challenge in developing countries.
– Geographical accessibility to health facilities is a suspected obstacle to the utilization of appropriate MCH services.
– This study aims to assess the impact of geographical access to health facilities on maternal healthcare utilization in Burkina Faso.
Highlights:
– The study used data from the Burkina Faso demographic and health survey (DHS) 2010.
– Distance from residential areas to the closest health facility was measured using Geographic Information System (GIS) data.
– Regression analysis revealed that increased distance to the closest health center decreased the likelihood of receiving appropriate maternal healthcare services.
– Specifically, a one kilometer increase in distance reduced the odds of receiving four or more antenatal care visits by 0.05 and reduced the odds of delivering with the assistance of a skilled birth attendant by 0.267.
– The study concludes that improving geographical access to health facilities can increase the use of appropriate healthcare services during pregnancy and childbirth.
Recommendations:
– Prioritize investment in transport infrastructure to improve geographical access to health facilities.
– Focus on reducing the distance between residential areas and the closest health center to increase maternal healthcare utilization.
Key Role Players:
– Ministry of Health: Responsible for implementing policies and strategies to improve maternal healthcare utilization.
– Ministry of Infrastructure: Involved in infrastructure development, including transport infrastructure.
– Health Centers: Provide healthcare services and may need to expand their reach to improve geographical access.
– Community Leaders: Play a role in advocating for improved healthcare services and infrastructure in their communities.
Cost Items for Planning Recommendations:
– Road Construction and Maintenance: Budget for building and maintaining roads to improve accessibility.
– Transportation Services: Allocate funds for transportation services, such as ambulances or community transport, to facilitate access to health facilities.
– Health Center Expansion: Consider the cost of expanding existing health centers or building new ones to ensure adequate coverage.
– Training and Capacity Building: Invest in training healthcare professionals to provide quality maternal healthcare services.
– Monitoring and Evaluation: Allocate resources for monitoring and evaluating the impact of interventions on maternal healthcare utilization.
Please note that the cost items provided are general categories and may vary depending on the specific context and requirements of Burkina Faso.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used a nationally representative survey with a large sample size and conducted multivariate logistic regressions to estimate the effects of distance on maternal healthcare utilization. The results showed a clear relationship between distance to the closest health center and the likelihood of receiving appropriate maternal healthcare services. The study also provided detailed information on the methodology used, including the data sources and analysis techniques. However, to improve the evidence, the abstract could include more information on the statistical significance of the regression results and the magnitude of the effect sizes. Additionally, it would be helpful to mention any limitations of the study, such as potential confounding factors or biases. Finally, the abstract could provide recommendations for policy or practice based on the findings, such as specific interventions to improve geographical access to health facilities in Burkina Faso.

Background: Improving maternal and child health (MCH) remains a serious challenge for many developing countries. Geographical accessibility from a residence to the nearest health facility is suspected to be an important obstacle hampering the use of appropriate services for MCH especially in Sub-Sharan African countries. In Burkina Faso, a landlocked country in the Sahel region of West Africa, women’s use of proper healthcare services during pregnancy and childbirth is still low. This study therefore assessed the impact of geographical access to health facilities on maternal healthcare utilization in Burkina Faso. Methods: We used the Burkina Faso demographic and health survey (DHS) 2010 dataset, with its sample of 10,364 mothers aged 15-49 years. Distance from residential areas to the closest health facility was measured by merging the DHS dataset with Geographic Information System data on the location of health centers in Burkina Faso. Multivariate logistic regressions were conducted to estimate the effects of distance on maternal healthcare utilization. Results: Regression results revealed that the longer the distance to the closest health center, the less likely it is that a woman will receive appropriate maternal healthcare services. The estimates show that one kilometer increase in distance to the closest health center reduces the odds that a woman will receive four or more antenatal care by 0.05 and reduces by 0.267 the odds that she will deliver her baby with the assistance of a skilled birth attendant. Conclusions: Improving geographical access to health facilities increases the use of appropriate healthcare services during pregnancy and childbirth. Investment in transport infrastructure should be a prioritized target for further improvement in MCH in Burkina Faso.

The demographic and health survey (DHS) of Burkina Faso 2010 was used for the empirical analysis in our study [27]. The DHS is a nationally representative survey that applies a stratified two-stage cluster sampling design. The sample is stratified into urban and rural areas to represent both areas. In the sampling, the primary survey units (“clusters”) are first selected from larger 13 regional units based on 2006 General Population and Housing Census and then individual households are randomly selected within each cluster. The data were collected between May 2010 and January 2011. In all, 14,947 households from 574 clusters (398 from rural areas and 176 from urban areas) were sampled, and then 14,424 households were actually surveyed. (The response rate was 99.2%). The study population for our analysis was 10,364 women (15–49 years old) who lived in these households and had a live birth in the 5 years preceding the survey [27]. Our main explanatory variable was distance from a residential area to the closest health center. Because the DHS household questionnaires contained no information regarding distance or travel time to health facilities, we used the Geographical Information System (GIS) module of the DHS to calculate these distances. We also obtained geographic information on roads and health centers through the Ministry of Infrastructure of Burkina Faso (for road information) and the Centre National de Recherche Scientifique et Technologique of Burkina Faso (for health center information). For measuring the distance between a residence and the closest health facility, a previous study conducted in Ghana employed the following methods [28]: 1) Euclidean distance (km), the straight-line distance from a residence to the closest health facility; 2) network distance (km), the distance along the road network from a residence to the closest health facility plus the Euclidean distance to the road network from a residence and from the road network to the health facility; 3) network travel time (hour), the distance along the road network from a residence to the closet health facility plus the Euclidean distance multiplied by off-road walking speed to the road network from the residence and from the road network to the health facility; and 4) raster-based travel time (hour), the travel time from a residence to the closest health facility, assuming mechanized or non-mechanized travel on roads and non-mechanized travel on- or off-road depending on the land cover speed [28]. Results obtained by these methods were similar in Ghana [28]. We chose the Euclidean distance to measure the distance from a cluster centroid to the closest health center and then used it as a proxy for the geographical access to a health center from a residential area. The advantage of this method is that it can be generalized for other similar topography and cultural contexts in West Africa [28]. Figure ​Figure11 shows a map of Burkina Faso, divided into 352 communes. (Burkina Faso is divided into 13 administrative regions, which are subdivided into 45 provinces; the provinces are subdivided into 352 communes) Fig. ​Fig.22 shows the country’s network of roads. In Fig. ​Fig.3,3, the red circles indicate the 574 clusters from which households were randomly selected for the survey. Figure ​Figure44 shows the location of 1520 health centers (purple circles). We did not distinguish the level of health services provided by each center. Finally, we used Quantum-GIS software to calculate the Euclidean distance (km) from each cluster to the closest health center. Burkina Faso divided by communes Road network GIS points of clusters GIS points of health centers In addition to the distance to health facilities, we analyzed if the availability of means of transport at the community-level was associated with the use of maternal healthcare. DHS did not include information on public transport, but asked questions about whether or not the household owned a bicycle and motorbike, which are the popular means of transport in Burkina Faso even among women in seeking healthcare during pregnancy and childbirth [23]. We thus calculated the ownership rates of bicycles and motorbikes per cluster and used them as proxy variables for the community-level availability of means of transport. Multivariate logistic regressions were conducted to analyze the effect of distance to the closest health center on maternal healthcare utilization. Data analysis was carried out using Stata 14.0. Because DHS applied a two-stage cluster sampling design, we used the svy (survey) commands of Stata to correct for unequal sampling probability, clustering and stratification in calculating descriptive statistics and performing regression analysis. We used the following outcome variables: 1) whether the woman made at least one ANC visit during her latest pregnancy (“Received any ANC”); 2) whether the woman made four or more ANC visits during her latest pregnancy (“Received ≥ 4 ANC”); 3) whether the woman used a health facility at the birth-delivery (“Facility delivery”); and 4) whether the woman was attended by a professional health worker, i.e., doctor, nurse, auxiliary nurse or midwife at the birth-delivery (“Delivery by SBA” (skilled birth attendant)). Because all the outcome variables were binary, they were coded 1 if the mother had received appropriate healthcare (ANC, Facility delivery, or SBA) during her pregnancy or at childbirth, or 0 otherwise. Outcome variables for birth-delivery, i.e. 3) “Facility delivery” and 4) “Delivery by SBA, included all the births (14,996) that had taken place during the five years preceding the survey. Therefore, we included mother-level random intercepts into multivariate logistic regressions for 3) “Facility delivery” and 4) “Delivery by SBA to adjust for the correlation of births to the same mother. We used demographic and socioeconomic characteristics at the mother, household, and community levels as control variables. The mother-level variables consisted of age and educational achievement (no education, primary, secondary, and higher). The household-level variables included the religion of the household head (no religion, Muslim, Catholic, Protestant, and traditional religion/animist), asset quintiles. The community-level variables included area dummies (rural or urban). In addition, region dummies (Boucle du Mouhoun, Cascades, Centre, Centre-Est, Centre-Ouest, Centre-Nord, Centre-Sud, Est, Hauts Basins, Nord, Plateau Central, Sahel, and Sud-Ouest) were included to consider regional differences within the country.

N/A

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to remote areas and provide maternal healthcare services, including antenatal care and skilled birth attendance.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in remote areas with healthcare professionals who can provide virtual consultations and guidance throughout their pregnancy.

3. Community health workers: Training and deploying community health workers who can provide basic maternal healthcare services, education, and referrals in underserved areas.

4. Transportation infrastructure improvement: Investing in transportation infrastructure, such as roads and bridges, to improve access to health facilities for pregnant women in remote areas.

5. Incentives for healthcare providers: Implementing incentives, such as financial rewards or career advancement opportunities, to encourage healthcare providers to work in rural and underserved areas, thus increasing the availability of skilled birth attendants.

6. Public-private partnerships: Collaborating with private sector organizations to establish and maintain healthcare facilities in underserved areas, ensuring access to maternal healthcare services.

7. Health education programs: Developing and implementing health education programs that focus on maternal health, targeting both women and their communities to increase awareness and knowledge about the importance of seeking appropriate healthcare during pregnancy and childbirth.

8. Maternal health insurance schemes: Introducing or expanding maternal health insurance schemes to provide financial protection and ensure affordable access to maternal healthcare services for all women, regardless of their socioeconomic status.

9. Integration of technology: Leveraging technology, such as mobile applications or SMS reminders, to provide pregnant women with timely information, appointment reminders, and access to healthcare resources.

10. Empowerment of women: Promoting women’s empowerment and involvement in decision-making regarding their own healthcare, as well as advocating for their rights and access to quality maternal healthcare services.

These innovations aim to address the barriers related to geographical access and improve the utilization of maternal healthcare services in Burkina Faso.
AI Innovations Description
The recommendation based on the study is to improve geographical access to health facilities in order to increase the utilization of maternal healthcare services. This can be achieved through investment in transport infrastructure, such as roads and transportation systems, to reduce the distance and travel time to the nearest health center. Additionally, ensuring the availability of means of transport at the community level, such as bicycles and motorbikes, can also improve access to maternal healthcare. By addressing these barriers, more women will be able to access appropriate healthcare services during pregnancy and childbirth, leading to improved maternal and child health outcomes.
AI Innovations Methodology
To improve access to maternal health, here are some potential recommendations:

1. Mobile clinics: Implementing mobile clinics that can travel to remote areas to provide maternal healthcare services. These clinics can bring essential medical equipment and skilled healthcare professionals to areas where there is limited access to health facilities.

2. Telemedicine: Utilizing telemedicine technology to provide remote consultations and medical advice to pregnant women in underserved areas. This can help overcome geographical barriers by connecting women with healthcare professionals through video calls or phone consultations.

3. Community health workers: Training and deploying community health workers who can provide basic maternal healthcare services in their communities. These workers can educate women about prenatal care, assist with deliveries, and provide postnatal support.

4. Transportation support: Improving transportation infrastructure and providing transportation support to pregnant women in remote areas. This can include subsidizing transportation costs or establishing transportation networks specifically for maternal healthcare purposes.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be as follows:

1. Define the target population: Identify the specific population group that will benefit from the recommendations, such as pregnant women in rural areas of Burkina Faso.

2. Collect baseline data: Gather data on the current access to maternal healthcare services in the target population, including the number of women receiving antenatal care, facility deliveries, and skilled birth attendants.

3. Simulate the implementation of recommendations: Use modeling techniques to simulate the impact of implementing the recommendations. This can involve creating scenarios where mobile clinics, telemedicine, community health workers, and transportation support are introduced to the target population.

4. Measure the outcomes: Assess the impact of the simulated recommendations on access to maternal healthcare. Measure indicators such as the increase in the number of women receiving antenatal care, facility deliveries, and skilled birth attendants.

5. Analyze the results: Analyze the data to determine the effectiveness of each recommendation and identify any potential challenges or limitations.

6. Refine the recommendations: Based on the results, refine the recommendations to optimize their impact on improving access to maternal health. This may involve adjusting the implementation strategies or identifying additional interventions that can further enhance access.

7. Monitor and evaluate: Continuously monitor and evaluate the implementation of the recommendations to ensure their sustained impact on improving access to maternal health. This can involve collecting data on key indicators and conducting regular assessments to identify areas for improvement.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different innovations on improving access to maternal health and make informed decisions on implementing the most effective strategies.

Share this:
Facebook
Twitter
LinkedIn
WhatsApp
Email