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Background: The world is making progress toward achieving maternal and child health (MCH) related components of the Sustainable Development Goals. Nevertheless, the progress of many countries in Sub-Saharan Africa is lagging. Geographical accessibility from residence to health facilities is considered a major obstacle hampering the use of appropriate MCH services. Benin, a country where the southern and northern parts belong to different geographical zones, has among the highest maternal mortality rate in the world. Adequate use of MCH care is important to save lives of women and their babies. This study assessed the effect of geographical accessibility to health facilities on antenatal care and delivery services utilization in Benin, with an emphasis on geographical zones. Methods: We pooled two rounds of Benin Demographic and Health Surveys (BDHS). The sample included 18,105 women aged 15–49 years (9111 from BDHS-2011/2012 and 8994 from BDHS-2017/2018) who had live births within five years preceding the surveys. We measured the distance and travel time from residential areas to the closest health center by merging the BDHS datasets with Benin’s geographic information system data. Multivariate logistic regression analysis was performed to estimate the effect of geographical access on pregnancy and delivery services utilization. We conducted a propensity score-matching analysis to check for robustness. Results: Regression results showed that the distance to the closest health center had adverse effects on the likelihood of a woman receiving appropriate maternal healthcare. The estimates showed that one km increase in straight-line distance to the closest health center reduces the odds of the woman receiving at least one antenatal care by 0.042, delivering in facility by 0.092, and delivering her baby with assistance of skilled birth attendants by 0.118. We also confirmed the negative effects of travel time and altitude of women’s residence on healthcare utilization. Nonetheless, these effects were mainly seen in the northern part of Benin. Conclusions: Geographical accessibility to health facilities is critically important for the utilization of antenatal care and delivery services, particularly in the northern part of Benin. Improving geographical accessibility, especially in rural areas, is significant for further use of maternal health care in Benin.
Benin is located in West Africa on the Gulf of Guinea, and covers an area of 114.763 square km. Benin consists of 12 departments: Alibori, Atacora, Atlantique, Borgou, Collines, Couffo, Donga, Littoral, Mono, Ouémé, Plateau, and Zou [17]. These departments are divided into 77 municipalities and then subdivided into 546 districts [17]. In terms of maternal and neonatal healthcare, Benin has two national hospitals, six Departmental Hospital Centers, 28 Zone Hospitals, 12 other hospitals, 76 municipality maternity units and more than 825 district maternity units [18]. Benin is a country where the southern and northern parts belong to different geographical zones. Benin’s geographic gradient is well-marked from south to north. The southern part of the country is in the coastal zone, whereas the northern part is mountainous. The Atakora mountain chain culminates at 658 m above sea level. Regarding geographical accessibility within Benin, the main mode of transport used are two-wheelers (personal motorbikes and motorbike taxis) and personal cars for road transport [19]. The ownership rate of cars per household was low, at 4.0% in 2011/2012 and 3.9% in 2017/2018 [3]. On the other hand, the ownership rate of motorbikes per household was comparatively higher, increasing from 58.4% in 2011/2012 to 66.3% in 2017/2018 [3, 20]. Road infrastructure consists of “classified roads” (6076 km) and “rural roads” (23,000 km) [19]. “Classified roads” include national roads (3898 km) and interstate national roads (2178 km), but only 30% of “classified roads” are paved [19]. Regarding “rural roads”, only 1/3 (a total of 7400 km) are properly constructed [19]. The low percentage of asphalted roads in the country leads to the high ownership of intermediate means of transport among households, such as motorcycles, tricycles, animal-drawn carts, rickshaws, bicycles etc., which are flexible and efficient on rough roads [19, 21]. We used two latest cross-sectional data from the Benin Demographic and Health Survey (BDHS), 2017/2018 [3] and 2011/2012 [20]. Table Table11 shows the summary of the surveys. Regarding the sampling design, in the first stage of BDHS 2017/2018, 555 primary sampling units (clusters) were drawn from the list of 12,633 enumeration areas [3]. In BDHS 2011/2012, 750 clusters were selected from 7352 enumeration areas [20]. In the second stage, 26 households and 24 households per cluster were selected in BDHS 2017/2018 and BDHS 2011/2012, respectively. BDHS 2017/2018 surveyed 14,156 households and 15,928 women [3], while BDHS 2011/2012 surveyed 17,422 households and 16,599 women [20]. We used data of 18,105 women, 8994 women from BDHS 2017/2018 and 9111 women from BDHS 2011/2012, who had live births within five years preceding the surveys as a study sample. Summary of the surveys Our main explanatory variable is geographical accessibility. Geographical accessibility is defined as “the physical distance or travel time from the service delivery point to the user” [22]. In addition, several studies have treated the altitude of women’s residence as a significant aspect of geographical accessibility [23, 24]. Accordingly, our study analyzed distance, travel time, and the altitude of women’s residence as important aspects of geographical accessibility. We used the GIS module of the BDHS and the ArcGIS software to calculate these variables. First, we used the “Euclidean distance (km)”—a straight-line distance between two points—to measure the distance from one’s residence to the closest health facility [25]. The advantage of using this method is that it can be generalized for other similar topography and cultural contexts in Sub-Saharan Africa [25]. Several previous studies used the Euclidean distance to estimate geographical accessibility to health facilities in Sub-Saharan Africa [12, 26, 27]. However, using straight-line distance to assess geographical accessibility is sometimes regarded as less accurate than applying travel distance because it is the simplest measure [28–30]. In addition, Euclidean distance may lead to an underestimation of travel distance [26]. Despites these limitations, Euclidean distance is considered a valid measure of accessibility in both rural and urban areas in Sub-Saharan Africa [25, 31]. Second, we used the “road network distance” as a more realistic measure. It is the distance along road infrastructure from a residence to the closest health facility. It can be defined as the Euclidean distance from the residence to the road network, plus a distance from the road network to a health facility [25]. To estimate the road network distance, we used the Benin Road Infrastructure dataset from the World Bank website that contains shapefiles of all roads as of 2017 [32]. We also used the geolocation points of the clusters provided by the BDHS and those of health facilities from the World Health Organization (WHO) website [33]. Third, we calculated travel time (in minutes: by walking and via car) from a residence to the closest health facility. The walking time was calculated from the women’s residence to the closest health center, following paths and roads for pedestrians. Walking speed was set at 5 km/h. Considering the types of roads, the driving time was calculated from the women’s residence to the closest health center. Driving speed was set at 80 km/h for “classified roads” and 60 km/h for “rural roads.” We used Dijkstra’s algorithm of ArcGIS, which can estimate the driving time from the residence to the closest health center by using the fastest route. Fourth, we examined the effects of the geographic altitude of a residence (in meters) on women’s utilization of maternal healthcare resources. It was assumed that it would be more difficult for women living in highland areas to reach health facilities as confirmed in Ethiopia [23]. We used ArcGIS to conduct the abovementioned analysis. Finally, we analyzed whether the availability of means of transport at the community level was associated with maternal healthcare utilization. The BDHS contains questions about whether the household owned a bicycle or motorbike. Because bicycles and motorbikes are popular means of transport in Benin, they can be used to travel when seeking maternal healthcare at health facilities [34]. Since the ownership of motorbikes and bicycles at the household level seems less important than its availability, we used the ownership rates of them per cluster (community), which would reflect more realistic situation of Benin. Thus, we calculated the ownership rates of bicycles and motorbikes per cluster and used them as proxy variables for community-level availability of means of transport. We applied multivariate logistic regressions to analyze the impact of distance to the closest health center on maternal healthcare utilization. Data analysis was performed using Stata version 14. Because BDHS applied a two-stage cluster sampling design, we used the svy (survey) commands of Stata to correct for unequal sampling probability, clustering, and stratification to calculate descriptive statistics and perform regression analysis. Additionally, we conducted propensity score matching (PSM) analysis to check the robustness of the logistic regressions. The PSM attempts to estimate the effects of a specific policy or treatment in observational studies by reducing the bias arising from confounding factors that might predict outcome variables. It matches treated and untreated units based on a set of basic characteristics and attempts to balance both groups. According to the National Health Development Plan (NHDP) 2009–2018 of Benin, geographical accessibility to healthcare services in Benin was defined as “the percentage of the population living within 5 km of the closest health center” [35]. Therefore, we created two groups, the treatment group (women living more than 5 km from the closest health center) and a comparison group (women living within 5 km from the closest health facility) to assess whether the threshold (5 km from the closest health center) had adverse effects on women’s use of appropriate maternal healthcare. We used the following outcome variables: (1) whether the woman made at least one ANC visit during her latest pregnancy (“any ANC”); (2) whether the woman made four or more ANC visits during her latest pregnancy (“ ≥ 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 birth (“delivery by SBA”). Because all the outcome variables were binary, they were coded 1 if the mother had received appropriate healthcare (ANC, facility delivery, or SBA) during pregnancy or childbirth, and 0 otherwise. We used mother-, household-, and community-level characteristics as control variables. Mother-level variables comprised age and educational achievement (no education, primary, secondary/higher). Household-level variables included religion of the household head (Muslim, Protestant, Catholic, Vodoum/other traditional, and No religion/others) and asset quintiles. Community-level variables included geographical zones of a residence (south or north of the country), as well as the ownership rates of bicycles and motorbikes.
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