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Objectives Improving access to emergency obstetrical and neonatal care (EmONC) is a key strategy for reducing maternal and neonatal mortality. Access is shaped by several factors, including service availability and geographical accessibility. In 2013, the Ministry of Health (MoH) of Togo used service availability and other criteria to designate particular facilities as EmONC facilities, facilitating efficient allocation of limited resources. In 2018, the MoH further revised and rationalised this health facility network by applying an innovative methodology using health facility characteristics and geographical accessibility modelling to optimise timely access to EmONC services. This study compares the geographical accessibility of the network established in 2013 and the smaller network developed in 2018. Design We used data regarding travel modes and speeds, geographical barriers and topographical and urban constraints, to estimate travel times to the nearest EmONC facilities. We compared the EmONC network of 109 facilities established in 2013 with the one composed of 73 facilities established in 2018, using three travel scenarios (walking and motorised, motorcycle-taxi and walking-only). Results When walking and motorised travel is considered, the 2013 EmONC network covers 81% and 96.6% of the population at the 1-hour and 2-hour limit, respectively. These figures are slightly higher when motorcycle-taxis are considered (82.8% and 98%), and decreased to 34.7% and 52.3% for the walking-only scenario. The 2018 prioritised EmONC network covers 78.3% (1-hour) and 95.5% (2-hour) of the population for the walking and motorised scenario. Conclusions By factoring in geographical accessibility modelling to our iterative EmONC prioritisation process, the MoH was able to decrease the designated number of EmONC facilities in Togo by about 30%, while still ensuring that a high proportion of the population has timely access to these services. However, the physical access to EmONC for women unable to afford motorised transport remains inequitable.
Togo is one of the smallest African countries in terms of landmass (approximately 56 785 km2), with a population of approximately 7.9 million in 2018,22 and is bordered by Benin to the east, Ghana to the west and Burkina Faso to the north. More than 40% of the total population live in the southern part of the country (in the region of Maritime and in the capital city of Lomé), with 42% of this population living in urban areas.23 The 2017 Multiple Indicator Cluster Survey for Togo24 determined that there is a strong correlation between poverty and maternal mortality and morbidity. Indeed, the probability of giving birth assisted by qualified personnel is found to be highly correlated to socioeconomic status and to place of residence. The aforementioned survey determined that 98% of women in the richest quintile are assisted at birth by skilled health personal, compared with only 52% of the poorest quintile. It also highlighted the fact that 98% of women living in urban settings receive skilled birth attendance, as compared with 65% of women living in rural settings. Three-quarters of maternal deaths were found to be due to direct obstetrical causes. This suggests that access to timely EmONC is essential for reducing maternal mortality in Togo. The 2017–2022 national plan for health sector development in Togo25 highlights that morbidity and mortality rates for mothers and newborns are still very high, and notes that this is due to insufficient supply of obstetrical and neonatal care and by barriers to service access, including financial barriers. Despite some progress over the last decades, maternal and newborn mortality are still high in Togo, respectively, with 396 (80% CI: 270 to 557) maternal deaths per 100 000 live births in 201725 and 25 neonatal deaths per 1000 live births in 2018.2 Improving access to quality EmONC services has been a priority for the government since the EmONC needs assessment of 2012. In 2013, the MoH used facility data to identify 109 priority health facilities designated to provide EmONC services, and launched a process to monitor them on a quarterly basis to assess and address gaps in availability and quality of care. This national network encompassed both governmental and non governmental facilities, and its selection was done by considering several pieces of information on each facility: high obstetrical activity (ie, favouring those with more than 30 deliveries/month), qualified human resources, capacity to organise referrals, gaps in EmONC signal functions and adequate geographical distribution. EmONC facility functionality requires enough qualified providers for 24 hours/7 days services and the availability of key equipment, consumables and medication for performing the key medical interventions (or signal functions) that are used to treat the direct obstetrical complications that cause the vast majority of maternal deaths. While the focus on a prioritised set of health facilities led to some progress in the availability of services, for example, the increase of the proportion of EmONC facilities applying vacuum extractions from 24% in 2014 to 43% in 2016, the MoH decided in 2018 to further focus their efforts and resource allocation by further decreasing the number of priority facilities; the MoH ultimately selected 73 health facilities that should provide EmONC services in the country. This selection was guided by a methodology developed by United Nations Population Fund (UNFPA) and formalised and described in UNFPA’s EmONC Network Development Implementation Manual.26 In brief, this methodology, based on WHO, UNFPA and UNICEF recommendations,5 helps countries to identify a network of EmONC facilities by selecting a number of EmONC facilities that do not exceed the international norm of five EmONC facilities per 500 000 population and, by prioritising facilities that can most feasibly be supported to actually provide all the relevant EmONC signal functions 24 hours/7 days (see table 1) with quality care, during the next programmatic cycle of the MoH (ie, in the next 3–4 years). The selection of health facilities is done by considering both health facility characteristics (eg, number of deliveries, human resources, gaps in EmONC signal functions, infrastructure) and two spatially explicit indicators20: the population living within 1-hour and/or 2-hour maximum travel time from the nearest EmONC facility (ie, population coverage), and the quality of each referral linkage between basic EmONC (BEmONC) health facilities and their closest comprehensive EmONC (CEmONC) health facility. BEmONC health facilities should provide seven EmONC signal functions 24 hours/7 days while CEmONC health facilities provide nine EmONC signal functions (see table 1). An EmONC facility is considered as functioning when it provides services 24 hours/7 days, and when it has performed all seven (BEmONC) or all nine (CEmONC) signal functions in the 3 months prior to data collection.5 A non-functioning EmONC facility has failed to provide one or more EmONC signal functions during that same period. Emergency obstetrical and neonatal care (EmONC) signal functions We used AccessMod, a WHO stand-alone and open-source geospatial tool,11 27 to analyse the physical accessibility to, and the population coverage of, the national networks of EmONC health facilities in Togo in 2013 and 2018. AccessMod is based on a least-cost path algorithm that computes the fastest route between any location and the nearest health service (for details, see11). It can consider several sequential modes of travel (eg, walking to a road, where one catches a motorised vehicle), several types of barriers to movement, health facility capacity and population, when determining catchment areas. Moreover, AccessMod considers the direction of movement by applying an anisotropic analysis (ie, considering the slope of the terrain to accurately model bicycling and walking speeds). Contrary to many accessibility modelling approaches that are based on a perfectly routable road data set (such as Nichols et al28), AccessMod can consider off-road travel in addition to road travel. On-road and off-road travel speeds and modes of transport are user-defined in a travel scenario that assigns these travel constraints to each land cover and/or road category found in the input data. For the movements of our target population, we defined three distinct travel scenarios from home to the nearest EmONC facility: (1) a walking and motorised scenario, (2) a motorcycle-taxi scenario and (3) a walking-only scenario. The first scenario assumes that patients walk to the nearest road and then use a motorised mode of transportation (car, minibus or other motorised vehicle, either private or public), immediately available, to continue their journey. In the second scenario, patients use only a motorcycle-taxi to travel on-road and off-road, with different speeds depending on the land cover. Finally, the third scenario assumes patients are walking or being carried (eg, on stretchers, carts) at walking speed (or lower). The choice for these three scenarios and their associated modes and speeds of travel were decided through an iterative process of two workshops in Togo in 2016–2018. A first 3-day workshop took place in May 2016 to strengthen national capacity to analyse the geographical accessibility of health facilities in Togo by using AccessMod. The 30 Togolese participants were technical experts from the MoH, the national Togolese Institute for Statistics, Economic and Demographic studies (INSEED), non-governmental organizations, United Nations (UN) agencies and other key stakeholders (eg, regional health director, monitoring expert) from all regions of Togo. The pool of experts discussed and agreed on the specifics of how women with obstetrical emergency typically travel when they need to reach an EmONC facility in Togo. Average speeds of travel on all road categories were estimated by consensus, with the walking and motorised scenario deemed to be the most frequent. During a second workshop in May 2018, health stakeholders from each of the six regions of Togo gathered to define which health facilities should comprise their EmONC facility regional network, using the prioritisation methodology described above to select a number of EmONC facilities per region not exceeding the international norm of five EmONC facilities per 500 000 population. These participants further refined the travel scenarios, and agreed that bicycles and public transport were rarely used, with the exception of a public bus system operating in greater Lomé.29 In both workshops, participants recognised that during the wet season the speeds of travel generally decrease in many areas. However, they decided to use only a dry season scenario to guide planning as this is the longest season in Togo, around 8 months. The final travel speeds chosen for the three scenarios are found in online supplemental file 1. bmjopen-2020-045891supp001.pdf Road speeds in urban areas were adjusted in order to simulate slower motorised travel occurring in intra-urban contexts due to various factors such as stoplights, traffic, pedestrians and other hazards. For the walking and motorised scenario, we lowered travel speeds on asphalted roads, secondary roads and tracks to 40 km/hour, 30 km/hour and 10 km/hour, respectively. For the motorcycle-taxi scenario, these average travel speeds were lowered to 30 km/hour, 15 km/hour and 10 km/hour. These speed corrections were applied on all road segments falling within the extent of the urban areas informed by the Global Rural–Urban Mapping Project.30 To account for uncertainty in travel speeds, we additionally ran each consensus travel scenario with 20% slower or 20% faster speeds compared with the consensus scenario, following the methodology developed in Ouma et al17 and in Stewart et al31. This translated to lower and upper uncertainty bounds on the reported output statistics. To further disentangle the effects of uncertainties on the speeds on roads and off roads, we ran scenario 1 by keeping on-road speeds constant (at consensus values) and varying off-road speeds by the plus or minus 20%. Conversely, we ran this scenario keeping off-road speeds constant and varying on-road speeds. Finally, to test the model’s sensitivity to the consideration of topography, we also ran each consensus travel speed scenario without considering slope correction (ie, isotropic mode). Two uncertainty indicators were produced: (1) lower and upper uncertainty bounds on the variation in percentage (per region and for the whole country) of the coverage of population living less than 1 hour and less than 2 hours away from the closest EmONC facility, and (2) a series of spatially-explicit uncertainty maps. In these uncertainty maps, pixel values indicate the extent of the uncertainty of travel time to the nearest EmONC facility, obtained by subtracting the travel time grid resulting from the ‘-20%’ travel speeds scenario by the one resulting from the ‘+20%’ travel speeds scenario. This highlights the areas in the country where the travel time estimates have the most uncertainty. Various data sets were assembled and prepared to run the geospatial analyses in AccessMod. Details of the preparation steps are found in online supplemental file 2. We considered barriers to terrestrial movements composed of quarries and bodies of water, such as rivers, lakes and damned areas. The barriers file was created by the Togolese Ministry of Agriculture, Livestock and Hydraulics32 (figure 1A). We used the road network created by the Direction de la Cartographie nationale et du cadastre of Togo, classified in three hierarchical categories: asphalted roads, secondary roads and tracks33 (figure 1B). We obtained the Digital Elevation Model (DEM) of Togo from NASA’ Shuttle Radar Topography Mission34 (figure 1C). Slopes were derived from the DEM directly in AccessMod. Vector and raster data sets used. (A) Barriers to movement (rivers, waterbodies, lakes); (B) road network; (C) Digital Elevation Model (DEM); (D) 2013 population density raster; (E) 2018 population density raster; (F) landcover raster; (G) 2013 EmONC facilities; (H) 2018 EmONC facilities; (I) administrative boundaries (prefectures, regions and Global Rural-Urban Mapping Project (GRUMP) urban extents) with region names. BEmONC, basic EmONC; CEmONC, comprehensive EmONC; EmONC, emergency obstetrical and neonatal care. We used the population density data set from the Worldpop project,35 provided with UN-adjusted population count from 2013 (figure 1D) and 2018 (figure 1E). We assumed that the target population of women with obstetrical complications is uniformly distributed across the overall population. Land cover information was provided by INSEED, with a land cover data set composed of 12 categories (figure 1F). We obtained the names and geographical coordinates of the 109 EmONC facilities composing the initial national network of EmONC health facilities in 2013 from INSEED (figure 1G). We further verified these names and coordinates with the help of the MoH. For the EmONC health facilities network of 2018 (figure 1H), the final designated set of 73 facilities was obtained through the MoH.36 Finally, the most recent administrative boundaries at the prefectural, regional and national levels were created and made available by INSEED using data collected during the 2010 general census37 (see figure 1I). These data sets were used to determine the percentage of population coverage for each administrative unit. There was no patient or public involvement in this study. Health facility names and geographical coordinates, as well as administrative boundaries, were shared by INSEED. The road network was shared by the Direction de la Cartographie nationale et du cadastre of Togo. The barriers to movement data sets were provided by the Ministère de l’agriculture, de la production animale et halieutique. All other geospatial data were publicly available.
