Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study

Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study Fassou Mathias GROVOGUI, Nafissatou Dioubate, Hawa Manet, Pepe Kpogomou, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4926298/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Abstract We modelled geographic accessibility, coverage, and wealth-based inequalities for childbirth care in Grand Conakry, Guinea. We assembled boundaries, health facilities, wealth index, road network, elevation, landcover, and geo-traced travel speeds. Using least-cost path algorithm, we computed travel times to the nearest facility by level and sector. We quantified the coverage (% of women of childbearing age) within 15, 30, and 60-minutes of the nearest facility and its variation by wealth index. Average travel speeds ranged from 14 to 28 km/h. Travel to any facility took 8-minutes, increasing to 22 for public hospitals (range from 5 to 33-minutes across communes). Coverage was 100% within 30-minutes of any facility, dropping to 82% for public hospitals, varying across communes. Slower speeds due to traffic substantially increased travel time and reduced coverage. Pro-rich inequalities emerged, especially in peri-urban communes with longer travel times. Targeted interventions are needed to reach equitable access to childbirth care. Health sciences/Health care/Public health Social science/Geography Health sciences/Health care/Health policy Health sciences/Health care/Health services Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Global progress in reducing maternal mortality has stagnated since 2016. It is unlikely that the Sustainable Development Goal (SDG) target of 70 maternal deaths per 100,000 livebirths will be achieved by 2030 [ 1 ]. The highest maternal mortality ratio (MMR) is in sub-Saharan Africa (SSA) − 545 deaths per 100,000 livebirths; 70% of all maternal deaths globally. The highest risk of maternal and perinatal death is at the time of childbirth, from causes including severe bleeding, hypertensive disorders, obstructed labour, and sepsis [ 2 ]. Most of the nearly 300,000 maternal and 5 million perinatal (stillbirths and newborn) deaths can be prevented if women receive adequate care during pregnancy and give birth in facilities able to manage complications [ 3 ]. During childbirth, pregnant women and their (unborn) babies are most at risk of poor outcomes, meaning that access to high quality care is critical and can save lives [ 4 ]. For example, half of maternal deaths and three-quarters of intrapartum stillbirths are preventable with timely access to high-quality emergency obstetric care [ 5 ]. Availability of basic and comprehensive facilities within 2–3 hours is currently considered a reasonable standard [ 6 , 7 ]. Therefore, substantial efforts have been directed into characterizing travel time and distance, to locating hotspots and vulnerable areas for interventions [ 8 ], and to understanding the role of physical access on maternal and perinatal health outcomes [ 9 , 10 ]. Most research on physical accessibility to health facilities has been focused on rural areas [ 10 , 11 ] due to the role of geographic distance, lack of roads and transport, and lower density of health facilities. However, urban settings face specific challenges in ensuring accessible, equitable and high-quality maternal and newborn care. Two-thirds of the world’s population is expected to live in urban areas by 2050. Nearly 90% of these additional 2.5 billion urban residents will concentrate in Africa and Asia [ 12 ]. The massive rural-urban migration leads to constrained infrastructure in the urban areas resulting in slums, informal settlements, exacerbation of inequalities, infectious diseases and non-communicable disease. Emerging evidence shows that the urban health advantage is diminishing in terms of health outcomes, including perinatal outcomes, across SSA countries [ 13 , 14 ]. There is a recognition that potentially long travel times are obfuscated by short geographic distances to health facilities in urban areas due to poor road infrastructure and traffic congestion [ 9 , 15 – 17 ]. Only a handful of studies have been conducted in urban areas of LMICs including 15 cities in Nigeria [ 4 , 18 , 19 ], Nairobi, Kenya [ 20 ], and Cali in Colombia [ 21 , 22 ]. Two studies focused on disaggregating accessibility metrics by the degree of urbanization [ 23 ] and by informal settlements [ 24 ] across SSA. While useful for national and regional comparison, both studies used a database of health facilities curated between 2012 and 2018 capturing only public health facilities [ 25 ]. Further, the analysis disaggregating by urbanization [ 23 ] relied on secondary maps of accessibility that were limited in terms of informing healthcare metrics [ 26 ]. To date, there has been no comprehensive evaluation of geographic accessibility to facilities offering childbirth care nor linking these metrics to socio-economic indicators in major urban areas of Guinea. This is despite Guinea being one of the countries globally with very high MMR of 553 per 100,000 live births [ 1 ]. This translated to an estimated more than 2,500 maternal deaths in 2020 in the country. Modelled outputs in 2018 suggested that about 40% of women of children bearing were marginalized from hospital care, i.e. living outside a 2-hours radius from the nearest health facility based on generic travel speeds that were homogenous in rural and urban areas [ 24 ]. In this study, we aimed to comprehensively assess geographic accessibility in the Grand Conakry metropolitan area in the Republic of Guinea. Specifically, our objectives are to i) estimate travel time to facilities that offer childbirth care, disaggregated by facility level and sector, using least-cost path algorithm at a 10m spatial resolution; ii) compute the percentage of women of child bearing age (WoCBA) and pregnant women within 15, 30, and 60 minutes of the nearest facility; and iii) assess the intersection between geographic marginalization and poverty (relative wealth index) in the metropolitan area. For policy relevance, the estimates are also presented by sub-national units (commune and district). Methods Study context The Republic of Guinea is divided into eight administrative regions, including the special region of Conakry. These regions are further subdivided into 33 prefectures in the countryside and ten communes in Conakry (Kaloum, Dixinn, Matam, Gbéssia, Matoto, Tombolia, Ratoma, Lambagny, Sonfonia, and Kassa island). The setting of the study is the Grand Conakry, a metropolitan area encompassing the core urban part of Conakry city (excluding Kassa island) and the peri-urban area around the city ( Figure 1 ). Grand Conakry had an estimated population of over 2.6 million (49.5% of whom are women) in 2024. This represents about a fifth of the Guinean population [27]. In Guinea, the proportion of the population living in urban areas has steadily increased from 10% in 1960 to 37% in 2022. More than half of this urban population (54%) is based in Conakry city [27]. In 2024, Grand Conakry consisted of 14 urban communes: nine in Conakry city; two in Dubréka prefecture (Kagbélen and Dubréka); and three in Coyah prefecture (Coyah, Sanoyah and Manéah). Grand Conakry is bounded to the west by the Atlantic Ocean, to the south by the islands of Kaback, Kakossa and Matakang, to the north by the rural zone of Dubréka prefecture, and to the east by the rural part of Coyah prefecture. The landscape of the area is mainly characterized by hills and coastal plains. Roads are the primary means of transportation used in Grand Conakry. However, many roads are in poor condition, making travel difficult, particularly during periods of heavy rain. Traffic jams are common in Grand Conakry and have a substantial impact on socioeconomic activities. For example, in 2017, traffic jams were estimated to cost 5% of Guinea's GDP[28]. Health care in Grand Conakry is provided by both public and private sectors. According to the Ministry of Health and Public Hygiene, Grand Conakry has 155 health facilities: 58 public, 84 private, and 13 private-not-for-profit. Maternal care is mainly provided by the public sector. Antenatal care visits and basic emergency obstetric care (BEmOC) are offered at health posts, health centers, and private-not-for-profit centers. Comprehensive emergency obstetric care (CEmOC) is available mainly in public hospitals (district, regional, and national) and in a few private hospitals. Healthcare utilization for maternal health services is high; according to the 2018 Demographic and Health Survey (DHS)[30], 90% of births in Conakry took place in health facilities (25% in public hospitals) [31]. The total fertility rate was 3.8 in all urban areas of Guinea and 3.2 in Conakry [30]. Overall methodological approach We undertook four steps to model travel time, extract geographic coverage metrics and link the modelled travel time to relative wealth index ( Figure 2 ). First, we defined the boundaries of Grand Conakry. Second, we assembled health facilities that offer childbirth care, factors that affect travel time including road network, elevation, landcover and travel speeds. Third, we used a geospatial framework to compute travel time to the nearest heath facility disaggregated by level and sector for five travel speeds scenarios. In the last step, we linked the travel time to population distribution (WoCBA and pregnant women) and relative wealth index. Data a) Grand Conakry boundaries We considered the boundaries of the main city (Conakry) and the adjoining suburbs that form the Grand Conakry. However, there were no open access vector files of these boundaries. Therefore, we defined and digitized the external boundaries of Grand Conakry and its subdivisions (communes) based on secondary data sources. We loaded a hard copy map showing the Grand Conakry based on the Urban Sector Review [32] on ArcGIS Pro v 3.3.1 (ESRI, Redlands, CA, USA). The hard copy map was overlaid on an OpenStreetMap (OSM) and digitized. To digitize the boundaries of the communes we used information from a presidential decree creating new communes in Grand-Conakry [33]. The digitized boundaries ( Figure 1 ) were discussed with team members from the African Center of Excellence for the Prevention and Control of Communicable Diseases (CEA-PCMT), Gamal Abdel Nasser University, Conakry, Republic of Guinea and authorities in Conakry for contextual validation. b) Health facilities providing childbirth service We aimed to define a geocoded list of health facilities which provide childbirth care within the defined boundaries of Grand Conakry. To achieve this, we obtained two lists of health facilities covering Conakry, Coyah and Dubréka health districts from the Ministry of Health and Public Hygiene through the Strategic and Development Office (BSD) . One list showed all facilities while the second list indicated the annual births per facility. The two lists were harmonized to create a master list only showing facilities which offered childbirth care in 2023 and their attributes (district, facility name, level, and ownership). Public health facilities included health posts, health centres, and hospitals (district, regional and national) while private sector included clinics and faith-based facilities (managed by religious groups). We geocoded the list using a variety of approaches. We extracted coordinates from the open SSA database [25], validated them while at the same time geocoding other facilities not in the SSA lists based on online Gazetteers and basemaps (Google Maps, GeoNames, OpenStreetMap, Bing Maps, and HERE Map). For the remaining facilities which could not be geocoded through SSA list or online sources, we collected their GPS coordinates positioned at the entrance of the facility. The final list contained 86 facilities. The number, level and sector of health facilities in Grand Conakry providing childbirth care are shown in Table 1 and their locations are shown on Figure 3 . Table 1. The distribution of health facilities providing childbirth care by level, sector and district that were included in the analysis in Grand Conakry. Travel times and inequalities were assessed for the four categories of health facilities shown in Table 1. Those include i) public and private facilities providing childbirth care (the most non restricted scenario), ii) public facilities providing childbirth care, iii) public and private hospitals providing childbirth care and iv) public hospitals (the most restricted scenario) which provide care for complications free of charge. This allowed analysis of both access and equity to primary and hospital care within the metropolitan area. a) Factors that affect travel time We used publicly available geospatial data of factors that could affect travel time to facilities offering childbirth care. These included road networks from OpenStreetMap (OSM) from 2023 [34], Sentinel-2 land cover at 10m spatial resolution from 2023 [35], Shuttle Radar Topography Mission digital elevation model (DEM) at 30 m spatial resolution [36], and travel barriers (water bodies and flooded vegetation) [35]. The roads were reclassified into trunk, trunk link, primary, secondary, tertiary, residential, other roads (minor), and non-motorized, based on the road attributes data from OSM, local context information and travel speeds data [37]. The Sentinel land use/cover was used to represent areas where no roads existed while the DEM was used to adjusting the walking speeds uphill and downhill [38]. The water bodies and flooded vegetation derived from the land use cover map [35] were treated as barriers except in presence of bridge. The maps of these factors are shown in Supplementary information - Figure S1 . b) Travel speed We derived novel speeds across different types of roads through geo-tracing trajectories within Grand Conakry. Specifically, we used tablets and smartphones with KoboCollect App [39] geotrace option which marks global positioning system (GPS) points along trajectories at specific time intervals and precisions as researchers travelled throughout Grand Conakry in their routine research activities. Time intervals were set at 1 minute and the precision at 20 meters (i.e., a GPS point would be recorded every 1 minute if the precision was less than or equal to 20 meters). Each submission/form was designed to represent one trajectory; a total of 176 forms were completed and submitted. Researchers were prompted to enter on the form the exact time before and after completing the trajectory. Additional details on mode of transportation, road quality, travel speed, and other characteristics of the trajectory were entered in either open or closed-ended questions. Researchers were encouraged to take a variety of road types and modes of transportation to ensure that a variety of scenarios were covered. To address the issue of time entries occurring after the end of the trajectory, time information from the data collector was compared to the number of geopoints for each trajectory. Submissions with time a difference of more than 20 minutes (n=72) were excluded. Data was collected by 18 volunteers working at the CEA-PCMT between May 13 th and June 3 rd , 2024 (dry season) at different times of the day. Based on this information, the average speed on each road segment was computed by dividing the total distance travelled by the time difference between start and end of the journey. These speeds were linked to OSM road network. The speed by road type were summarized to obtain the average speed, the maximum and minimum speed and the 25 th and 75 th percentile. These five scenarios represent a continuum from the lowest (due to severe traffic jams) and highest travel speeds (substantially low traffic jam or weekend travel) within Grand Conakry. c) Relative wealth index We used Meta’s Relative Wealth Index (RWI) [40]. The RWI estimates relative wealth of the people living in each micro-region of 2.4km by 2.4km relative to others in the same country. The estimates were constructed through machine-learning based on data from satellite imagery, mobile phone networks, topographic and as aggregated and deidentified connectivity data from Facebook. The models are trained based on data from DHS Program [41]. The data was available at humanitarian data exchange portal [42]. d) Population distribution: Women of childbearing age and pregnancies We obtained the constrained version of total number of females per 100m grid broken down by 5-year groupings in 2020 for Guinea from the WorldPop portal [29,43]. We summed the 5-age groups that constitute women of childbearing age (15–49 years) and clipped to the extents of Grand Conakry. To obtain the 2024 estimates, we projected the 2020 estimates of WoCBA based on 2019-2020 growth rates that had been derived using similar age disaggregated data. To obtain an equivalent surface showing the distribution of pregnancies, we converted the 2024 estimates of WoCBA population to a surface of pregnancies following the methodology described by James et al. in 2018 [44]. We did this by multiplying each 5-year population group by its corresponding age-specific fertility rate for women in urban areas [30]. The spatially distributed number of births was summed and multiplied by a constant (1.0382) [45–49] to account for pregnancy loss and multiples in the calculation of the spatially distributed number of pregnancies. Cost distance algorithm modelling We used a least-cost path algorithm to model a hybrid travelling scenario that included walking and motorized transport to the nearest facility. We generated 20 sets of models from the travel time estimates using four sets of destinations for health-seeking: all facilities providing childbirth care and three subsets: public facilities only; all hospitals; public hospitals. For each set of destinations, we applied five travel times: average, minimum, maximum, and 25 th (lower quartile) and 75 th percentile (upper quartile). We present the average travel time scenario as the main results and refer to the ranges provided by the interquartile ranges and minimum-maximum speeds as best/worst case scenarios. Specifically, we implemented the least-cost path algorithm in AccessMod version 5.8.0 [50,51]. AccessMod is a World Health Organization (WHO) tool used to model how geographically accessible existing health services are to the target population including other functionalities [50]. First, we merged the road network and land cover including water bodies and flooded vegetation using the “Merge land cover” in AccessMod resulting to a merged gridded surface. The merged gridded surface, travel speeds (per scenario) and location of facilities (by level and sector) were used to compute cumulative travel time from each raster cell (10m by 10m) to the nearest heath facility considering least cost (cost measured in terms of time). Slope derived from the DEM was used to adjust walking speeds based on Tobler’s formulation [38]. The formulation decreases the up-slope walking speed as the slope increases, while slightly increasing the speed for a slightly negative slope when walking down-slope. The anisotropic travel times were computed towards the health facility. The "knight's move" was specified which allowed consideration of 16 neighbour cells for higher accuracy. Finally, we extracted the average travel time for Grand Conakry and commune for all 20 scenarios. Travel time and inequalities Based on the assembled population distribution maps and modelled travel time, we extracted the percentage of WoCBA age and pregnant women, who were within 15, 30, and 60 minutes of the nearest facility for all the 20 scenarios. The extraction was done for the entire Grand Conakry and for each of its 14 communes. Finally, we linked the travel time for each scenario with the RWI. Before linking, we resampled the travel time raster to 240m given the course resolution of RWI. The resultant 4,464 raster cells of travel time were linked to RWI value that was spatially closest to it. Based on RWI, we created five wealth quintiles and generated equiplots to summarize travel time by wealth quintile for Grand Conakry and three districts (Conakry, Coyah, and Dubréka) in R using ggplot2 package [52–54]. Ethics This study used secondary publicly available data and primary data (travel speeds, location of health facilities) which did not require ethical approval. Results The data used to compute travel speeds were based on 102 trajectories. Over 1,255 km were covered in total, with each trajectory covering a median of 11.9 km [lower quartile= 5.5km, upper quartile=18.5km]. The estimated travel speeds, by road type, are shown in Table 2 . Overall, the travel speeds in Grand Conakry were slow, regardless of the road type, all under 60 km/h. The average travel speeds ranged from 14 to 28 km/h, depending on road type. However, there was large variability in speeds, spanning from just above 2 km/h (highest traffic – worst scenario) to 60km/h (lowest traffic – best scenario). These speeds were used within five travel scenarios to estimate travel times. Walking speeds adopted for different landcover types were based on previous published studies [18,55,56]. Travel times to the nearest facility for the 20 scenarios are summarised by the average and by the minimum and maximum speeds (extreme margins) or lower and upper quartile (moderate margins) for Grand Conakry metropolitan area, and by district and commune . We describe the results of two scenarios here (best and worst scenario) shown in Table 2 and Figure 3, while the results of the other 18 scenarios are presented in the Supplementary file – Table S1 . At the pixel level, on average, travel time to the nearest public or private health facility offering childbirth care ranged from 0 to 35 minutes. The longest travel time was 126 minutes when considering the slowest speed. When these estimates were aggregated at the level of Grand Conakry, the average travel time was 8 minutes. This could increase 4-fold to 30 minutes in the scenario with minimum speed. Across the 14 communes, the average travel time was heterogenous and ranged from 3 minutes (8 minutes in case of slowest speed) in the Conakry communes of Kaloum, Matam and Dixinn, to 15 minutes (19 minutes in case of slowest speed) in Manéah (Coyah district) ( Table 2 ). On the other hand, in the most restricted scenario incorporating travel to the nearest public hospital only, average travel time ranged from 0 to 68 minutes at the pixel level and increased to 222 minutes for the slowest speed. The aggregated average travel time at the level of Grand Conakry was 22 minutes, with an estimate of 80 minutes with minimum speed. Across the communes, this ranged from 5 minutes (inner communes of Conakry district) to over 33 minutes in some communes of Coyah and Dubréka districts. In the minimum speed scenario, travel time to the nearest public hospital exceeded 60 minutes in seven of the 14 communes of Grand Conakry ( Table 2 ). Geographic coverage (proportion of WoCBA within specified travel time thresholds: 15, 30, and 60 minutes) are shown in Table 3 to any nearest health facility and public hospital by district and commune. The rest of the results including geographic coverage for pregnant women are presented in Supplementary information – Table S2 and Table SX3 . Approximately 94% of WoCBA living in Grand Conakry were within 15 minutes of the nearest health facility based on the average speed scenario. Under the same conditions, at a threshold of 30 minutes, all WoCBA were within this threshold. However, this coverage reduced to 32% (15 minutes) and 70% (30 minutes) considering the minimum travel speeds ( Table 3 ). At the commune level, geographic coverage at a threshold of 15 minutes was universal in 12 out of the 14 communes, mainly in the district of Conakry. However, in the minimum speed scenario, this proportion dropped below 30% in five communes (outside Conakry district) and below 50% in three communes. Based on the average speed, all WoCBA in Grand Conakry were within 30 minutes of the nearest health facility providing childbirth care. Nonetheless, when the minimum speed was considered, there was highly variability ranging from 31% in Manéah (Coyah District) and 40% in Kagbélen (Dubréka District) to 100% in several communes within Conakry district. On the other hand, when considering geographic coverage for the nearest public hospital, 44%, 82%, and 100% WoCBA were within 15, 30 and for 60 minutes, respectively using the average speed. These percentages drop to 2%, 7%, and 30% under the minimum travel speed scenario. Substantial variations were observed when the estimates were summarized by communes across the three (15, 30 and 60 minutes) travel time thresholds. For example, no WoCBA in Sanoyah (Coyah) and Kagbélen (Dubréka) was within 15 minutes to a public hospital, while at least 67% (Gbéssia) to 100% of women in Kaloum, Matam, Dixinn, and Ratoma (district of Conakry), considering the average speed. Additionally, all WoCBA in Kaloum, Matam, and Dixinn were within 60 minutes of the nearest public hospital, regardless of the speed scenario. In contrast, fewer than 2% of WoCBA living in the communes of Sanoyah (Coyah) and Kagbélen (Dubréka) were within 60 minutes of the nearest public hospital under the minimum speed scenario. Figure 5 shows the distribution of travel time by wealth quintiles, for the three scenarios of minimum, average and maximum travel speeds, by the four types of health facilities and in the three districts. Results for lower and upper quintile speeds are shown in supplementary information – Figure S2 . Pro-rich inequalities existed and varied in magnitude by facility type and travel speed. Overall, in Grand Conakry, the inequality gap was largest with minimum speed, followed by average and maximum speed respectively. Inequalities in travel time to hospitals were larger than to any facility offering childbirth care. For example, travel time to nearest public facility was 22 mins for Q5 (richest) and 52 mins for Q1 (poorest) (30 mins difference), while travel time to nearest public hospital was 54 mins for Q5 and 105 mins for Q1 (51 mins difference) under the minimum speed scenario. When the results were further disaggregated by district, the level of inequality in favour of the rich varied, with Conakry having the lowest differences between the richest and poorest quintiles consistently by travel speed, level and sector of facility ( Figure 5 ). The lowest inequalities were observed when estimated travel time to nearest public/private facility was based on maximum speed, with all five wealth quintiles having estimates under 10 minutes in the three sub-districts. The largest inequality in travel time to nearest public/private health facility with minimum speed was observed in Coyah, with a travel time of 21 minutes for Q5 (richest) that goes up to 53 min for Q1 (poorest). Limiting the destination to public health facilities did not lead to major changes in observed inequalities. Restricting to the nearest public hospital increased the inequalities and reversed the direction of inequalities in some districts. For example, the shortest travel time to the nearest hospital was consistently observed for Q4 in Dubréka, while the longest was among Q1 (poorest) (75 minutes vs. 143 minutes with minimum speed). Inequalities generally increased when limiting the destination to public hospitals in the three districts. For example, in Conakry travel time with minimum speed to the nearest public hospital is 52 mins for Q5(richest) and 79 mins for Q1(poorest). In Coyah, Q3(middle) had the shortest travel time to the nearest public hospital regardless of the speed. Discussion We assessed spatial accessibility and geographic coverage to health facilities providing childbirth care based on geotraced travel speeds within the Grand Conakry metropolitan area. We identified spatial heterogeneities by sector (private and public) and facility level (hospital and lower level) and linked these indices with relative wealth index. Most WoCBA need on average 15 minutes to reach the nearest facility offering childbirth care in any of the communes, however, under heavy traffic conditions, this increased to about 50 minutes. Under the later conditions, accessibility declined rapidly when WoCBA are in need of affordable emergency obstetric care, which is mostly available in public hospitals, with travel time exceeding two hours in communes of Dubréka district. All WoCBA in Grand Conakry appear to reside within half an hour travel time to the nearest facility; however, when traffic was heavy, this may reduce to less than 60% in four of the 14 communes. Similarly, despite all women being within an hour of a public hospital; in six communes, only 3 in 10 women are within the same threshold under lowest travel speeds. Private health facilities contributed considerably to increasing geographic coverage, especially in the peri-urban Coyah district. Finally, while we showed that women from wealthier areas were, on average, closer to any health facility, paradoxically, poorer women were closer to public hospitals. When compared to travel time from other cities in SSA such as Nigeria [ 4 , 57 ], the estimates from Grand Conakry are not substantially different under the average travel speeds scenario. Spatial access to public comprehensive emergency obstetric care varied between 10 minutes and 41 minutes across 15 Nigerian cities, while in Grand Conakry travel time to public hospitals varied between 5 minutes and 33 minutes across the 14 communes [ 4 , 57 ]. Similarly, under high traffic, 71% of the WoCBA were within 1 hour in Grand Conakry while it ranged from 83–100% across the 15 most populated cities in Nigeria [ 4 ]. Our study has several applications and implications both for research and implementation in improving access to childbirth care which we discuss in detail. Irrespective of the approach used to estimate travel time in SSA [ 58 ], a frequent issue is the lack of observed data representing the journey to facilities offering childbirth care to parametrise travel time models [ 59 ]. Among these observed data is travel speed, arguably the most important factor that contributes to magnitude of the estimated travel time. Due to lack of observed travel speeds, most studies spanning subnational to continental levels in SSA rely on generic speeds [ 8 , 18 , 55 , 60 ] which may lead to spurious results. Overall, the travel speeds we measured in the Grand Conakry metropolitan area were slower than previously described in literature [ 8 , 60 ]. Travel speeds within Grand Conakry did not vary substantially between main road types such as trunk and primary roads, with an average speed of less than 28km/hr and a maximum speed of 60km/hr. Yet, average speeds of 100km/hr have been used previously for major roads [ 8 , 60 ]. Therefore, it is likely that previous estimates of geographic accessibility to healthcare which included the Grand Conakry overestimated travel time. The low speeds observed in Grand Conakry could be explained by its geographical configuration. Grand Conakry is among the African metropolises facing the most complex challenges regarding land use and transport, primarily due to its geographical location and linear shape [ 61 ]. It is a peninsula, extending 40 km in length and barely 5 km in width, with spatial growth constrained by Mount Kakoulima to the east and mangroves to the west, north and south. Further, administrative functions are predominantly centralised within the commune of Kaloum, the narrowest part of the city with a few decentralised offices in Dixinn and Matam. Consequently, during rush hours, a significant portion of the population either travels to or departs from Kaloum, which is the most constrained and inflexible area of the capital in terms of road infrastructure. This results in traffic congestion that can last for hours during peak times and beyond. Under heavy traffic scenario, the average travel time to the nearest facility and nearest public hospital offering childbirth care was approximately 30 minutes and 80 minutes, respectively. Within the metropolitan area, there was high heterogeneity, 12 to 48 minutes for all facilities and 21–123 minutes for public hospitals by commune. Women residing in these communes are within the recommended 2 hours radius, as proposed by the Ending Preventable Maternal Mortality strategies [ 7 , 62 ]. However, at high resolution some pixels fell outside the 2 hour threshold, highlighting the need to prioritise these hotspots. The communes within Dubréka and Coyah districts had the longest travel times and fewest percentage of WoCBA within either 30 minutes or 1 hour, especially under heavy traffic conditions. This could be due to a number of reasons. First, the geographical distribution of health facilities is skewed, with these two districts having fewer facilities and a substantial number of WoCBA. For instance, Kaloum (Conakry district) represents 0.15% of the population of Grand Conakry but hosts 6.9% of the all facilities (18% of all public hospitals). In contrast, Kagbélen (Dubréka district), with over 17% of the population, has only 7.0% of the facilities (no public hospital), and all at the primary level concentrated in the eastern part of the commune. Even more striking are Sanoyah and Manéah communes (Coyah district), representing about 10% of the population each but hosting only one public health facility each (1.2%) and no private facilities. These disparities are even wider when focusing on public hospitals which provide emergency obstetric functions such as caesarean section and blood transfusion, free of charge according to the national guidelines. In fact, over half of the nine referral hospitals (55.6%) are in the three smallest communes of Grand Conakry where only 1.3% WoCBA reside. Second, these districts with poor geographic access metrics are in the peri-urban area or relatively newly urbanised. Kaloum is the oldest urbanised commune while Kagbélen, Sanoyah, and Manéah are newer and are a key urbanisation frontier where growth happens. These peri-urban areas have a poor road network making it harder to reach the health service providers where roads are not well developed. Third, the assessment of geographic access to healthcare in peri-urban areas is further compounded by the fact that open data on road network are likely to be incomplete due to fewer efforts of volunteered geographic information in the suburbs [ 18 ] compared to the developed core urban area of Conakry city. The disadvantage in the peripheral areas compared to the inner city was also observed in cities across Nigeria [ 4 ] and Cali, Colombia [ 22 ]. Fourth, one of these communes in the peri urban areas (Kagbélen), in particular, is an industrialised zone with numerous cement manufacturers and a poor road network predominantly consisting of tertiary and residential roads frequented by many trucks [ 61 ]. Long travel times result in delays in accessing life-saving care for both women and their babies in an appropriate environment. Finally, in these peri-urban areas with poor road connectivity, some sections of the journey that entail walking are at a high elevation based on the topography, thus increasing the difficulty of walking; which could be particularly challenging for pregnant women before/during labour, or those that have to be referred postpartum. Despite the private facilities charging for services, when accounted for in our analyses they improve geographic access to care in some of the communes. For example, in Kagbélen commune in Dubréka district about 45% of the WoCBA are within 30 minutes of public hospitals, however when both public and private hospitals are considered, the coverage improves to 68%. In 2022, the private sector accounted for 2% of the overall health infrastructure in Guinea. However, the distribution of health infrastructure is different within urban areas. In Grand Conakry, 28% of the health facilities providing childbirth care are private wile more than half (54%) of the hospitals are also private. According to a report from the Ministry of Health (MoH), these private facilities provide about 30 to 40% of the maternal, neonatal, and infant care [ 63 ]. While the high number of private health facilities in Grand Conakry could provide alternatives and reduce travel time needed to access childbirth care, on the other hand, it could also be contribute to increasing the inequality gap. This is because, only those who can afford to pay for care in private sector will have access. In fact, the ability to access geographically and pay for health services in the private sector will not improve the availability and the quality of care unless the private facility are fully aligned with the government guidelines in terms of content and quality of care to provide to women during pregnancy and childbirth. Travel time to any health facility was lowest in the wealthiest 20% of the population, indicating pro-rich inequalities irrespective of the speed scenario. The inequalities were smaller in Conakry while WoCBA in the peri-urban communes face further challenges and were particularly more vulnerable facing widespread inequalities. Similar findings were observed in Nigerian cities[ 19 ] and in Cali, Colombia [ 22 ]. While the same pattern was observed for public hospitals, there were a few exceptions where paradoxically the poorer were closer to public hospitals in Grand Conakry. This can be explained by the fact that public hospitals are often located in the historical urban centres where the poorest residents live. In contrast, those in the top three wealth quintiles tend to move to newly urbanised areas with modern buildings, less crowding, reduced noise, better sanitation systems, and schools. In Conakry, this pattern is evident in communes such as Ratoma, Lambagny and Sonfonia, where there is only one public hospital providing comprehensive emergency newborn and obstetric care. Building of new hospitals has not been keeping up with the population expansion of the city, thus these new neighbourhoods are marginalised. However, private-for-profit health providers see opportunities in these areas, mostly because people who live in these newer neighbourhoods might be able to afford to pay for care. This in turn increases the overall number of health facilities and creating a wealth-related inequality in access to healthcare. Strengths and limitations Our analyses have several strengths. First, we used a validated and updated health facility list from the Ministry of Health and Public Hygiene, ensuring accuracy regarding whether facilities provide childbirth care and their geographic locations. Therefore, the geocoded list we created provides a much-needed update of the SSA database [ 25 ] in this metropolitan area. Further, we incorporated public and private facilities whereas majority of the previous studies have relied on public health facilities because of the ease of mapping and validating these public facilities. Secondly, we defined Grand Conakry metropolitan area using urbanization trends and the planning by the government. We captured the core urban and the suburbs surrounding the core city [ 64 ] reflecting the lived experiences of WoCBA. Third, we captured and applied realistic travel speeds capturing the local context. We provided measures of uncertainty around the average estimates, that’s two extremes (minimum and maximum speeds) and two conservative estimates (25th and 75th percentile). This approach of collecting data is cost effective especially when embedded with other routine tasks and projects and has the potential to contribute to more realistic estimates advancing the frontier of spatial accessibility [ 65 ]. The approach compliments and advances other approaches where speeds are elicited from local experts to provide an informed guess of speeds across different roads [ 18 , 66 ] or the use of application programming interfaces (APIs) to indirectly account for realistic travel speeds [ 4 , 18 – 22 , 57 ]. However, our findings have limitations. First, we acknowledge that we did not integrate elements of referral – i.e., that some proportion of women giving birth will first seek care at a lower-level health facility and will then be referred to a hospital. This means that we might have underestimated the time to reach appropriate care. Second, we could not account for “waiting time” in terms of waiting for transport/for money – so travel times are likely also underestimated. Third, we computed the travel time using the nearest health facility, while some people may bypass the nearest facility for personal preferences and perceptions. Further we collected data during the dry season only. Therefore, accounting for bypassing and/or rainy season, our modelled travel times could have been longer than we currently estimated. Fourth, due to incomplete data on road network in the suburbs [ 18 ] compared to the developed core urban area, the estimated travel time might be an overestimate. Finally, in the equity analysis, we assumed same travel speeds for everyone – but this is probably not the case – i.e., richer people more likely to travel by private car and poorer by shared transport (taxi, motorcycle). Conclusions In Grand Conakry, there are major disparities in travel times and therefore geographic access to healthcare facilities providing childbirth care, is driven by the skewed spatial distribution of health facilities, heavy traffic, and socio-economic marginalisation. The average travel time to facilities is generally within the internationally defined thresholds under normal traffic conditions. However, during heavy traffic the situation becomes critical, with travel times exceeding two hours in some areas. Such long delays present an obstacle for women in need of emergency obstetric care. The peri-urban communes (Dubréka and Coyah districts) are almost medical deserts, due to low number of facilities, particularly public hospitals providing comprehensive emergency obstetric care services. Further, wealthier populations live closer to facilities providing childbirth care, while, surprisingly, poorer populations living in older, more centrally located urban areas benefit from greater proximity to public hospitals. The findings are useful for healthcare planning within Grand Conakry in terms of geographic distribution of health facilities offering childbirth care vis-a-vis the WoCBA and their corresponding socio-economic situation. For example, private health facilities improved geographic access in under-served areas. However, the effectiveness of this integration requires the alignment of private health facilities with public health standards for maternal health services and the financial accessibility of these services to the broader population. This study underscores the need for targeted interventions to address these accessibility gaps, particularly in the peri-urban districts of Coyah and Dubréka where infrastructure development lags behind. For example, building new roads, and enhancing public transportation could mitigate some of these challenges, ensuring that WoCBA in Grand Conakry have timely access to childbirth care, regardless of their socio-economic status or the time of day. Declarations Acknowledgements The authors thank the National Director of the Strategic and Development Office of the Ministry of Health of Guinea, Souleymane Diakité, who facilitated the acquisition of health facility and birth datasets for Grand Conakry. they also extend our gratitude to Abdoul Karim Nabé and Bintou Cissé from the same Directorate, who provided technical support and guidance during the cleaning and updating of the datasets. The authors are also grateful to Alseny Camara from the Health District Office of Dubréka, who helped validate the boundaries of the urban zone of Dubréka. Additionally, they acknowledge the support from the team at the African Center of Excellence for the Prevention and Control of Communicable Diseases (CEA-PCMT), Gamal Abdel Nasser University, Conakry (Karifa Kourouma, Aissata Tounkara, Aly Badara Touré, Armand Saloun Kamano, Christiane Tolno, Youssouf Keita, Mohamed Aly Bangoura, Angèle Soua Kolié, Diarouga Baldé, Akodegnon Honora Djidonou, Elie Beavogui) for their contribution to the travel data collection. Data The datasets used for this analysis are publicly available. These include a database of health facilities obtained from the Ministry of Health and Public Hygiene, while links for population relative wealth index and factors affecting travel to healthcare (road network, digital elevation model and land use) are listed in the manuscript. Ethical approval This study used secondary publicly available data and primary data (Travel speeds, location of health facilities) which did not require ethical approval. Funding The study was funded by Fonds voor Wetenschappelijk Onderzoek (FWO) Grant ID: G074724N and The Belgian Federal Directorate-General for Development Cooperation Humanitarian Aid (DGD) Competing interests The authors declare they have no competing interests. Authors’ contribution Term Authors Conceptualization LB, PMM, AD, AS, FMG Methodology LB, PMM, AD, AS, FMG, Software PMM, FMG, AS Validation LB, PM, AD, AS, FMG, ND, HM, TMM, AD Formal analysis PM, FMG, AS Investigation ND, HM, PK, FMG, PMM, AS Resources FMG, ND, PMM, AS Data Curation FMG, ND, PMM Writing - Original Draft FMG, AS, LB, PMM, PK, HM, ND, TMM, AD Writing - Review & Editing FMG, AS, LB, PMM, PK, HM, ND, TMM, AD Visualization FMG, PMM, AS Supervision PMM, LB, AD Project administration PMM, LB, AD Funding acquisition LB, AD References WHO, UNICEF, UNFPA, et al. Trends in maternal mortality 2000 to 2020. Geneva 2023. Kassebaum NJ, Bertozzi-Villa A, Coggeshall MS, et al. 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Travel scenario workshops for geographical accessibility modeling of health services: A transdisciplinary evaluation study. Front Public Health . 2023;10. doi: 10.3389/fpubh.2022.1051522 Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.docx Tables.docx Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4926298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342827143,"identity":"3f29e4be-61f1-46d6-995f-1aa043f8737f","order_by":0,"name":"Fassou Mathias 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Delamou","email":"","orcid":"","institution":"African Center of Excellence for the Prevention and Control of Communicable Diseases (CEA-PCMT), Gamal Abdel Nasser University, Conakry, Republic of Guinea","correspondingAuthor":false,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Delamou","suffix":""},{"id":342827151,"identity":"317eadec-9363-4327-b240-a44b142b54d2","order_by":8,"name":"Peter Macharia","email":"","orcid":"https://orcid.org/0000-0003-3410-1881","institution":"Institute of Tropical Medicine Antwerp","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Macharia","suffix":""}],"badges":[],"createdAt":"2024-08-16 16:40:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4926298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4926298/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44284-025-00220-2","type":"published","date":"2025-04-23T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64582128,"identity":"68d7e090-df7d-4feb-aca9-83eadb900a75","added_by":"auto","created_at":"2024-09-16 06:39:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":625167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommunes and population density \u003c/strong\u003e[29]\u003cstrong\u003e within Grand Conakry metropolitan area, Guinea\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFootnote to Figure 1\u003c/strong\u003e\u003c/em\u003e: The map was plotted by the authors in ArcGIS Pro V 3.3 (ESRI, Redlands, CA, USA). The Grand Conakry boundaries were digitised based on secondary data as described under the data sub section while the boundaries of Guinea were downloaded from HDX portal with CC BY 4.0 License (\u003ca href=\"https://data.humdata.org/\"\u003ehttps://data.humdata.org/\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/f2d715ac70ae92e63395a158.png"},{"id":64582561,"identity":"101a20d3-6336-424f-a8e9-cc3fb48b52fb","added_by":"auto","created_at":"2024-09-16 06:47:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":256752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe analytical flow of estimating travel time to health facilities and linking it with population, relative wealth index and subnational boundaries in the Grand Conakry, Guinea\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/f04f033bf991d323605c8a65.png"},{"id":64582832,"identity":"a2db4459-057c-429a-8d37-4f3f3d7880b4","added_by":"auto","created_at":"2024-09-16 06:55:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe spatial distribution of health facilities by level, sector that were included in the analysis in Grand Conakry metropolitan area, Guinea\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/a8777c75eabf9018e600d4e6.png"},{"id":64582133,"identity":"158c9f7d-e7b9-41bc-9877-fd580fc14485","added_by":"auto","created_at":"2024-09-16 06:39:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":514873,"visible":true,"origin":"","legend":"\u003cp\u003eTrave time to nearest any facility (upper panel) and public hospitals (lower panel) based on average, minimum/maximum and lower/upper quartile travel speeds\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/1769cc4fd95497b33ce3a8e6.png"},{"id":64582562,"identity":"510ea4b1-1a9a-4796-9bec-b8319f0c7082","added_by":"auto","created_at":"2024-09-16 06:47:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":277279,"visible":true,"origin":"","legend":"\u003cp\u003eEquiplots of travel time (geographic accessibility) by relative wealth index by different travel speeds, facility type and subnational area.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/4dd052acf062b0544b4c980d.png"},{"id":81264477,"identity":"9c338040-5df6-4440-9771-18997293c672","added_by":"auto","created_at":"2025-04-24 07:13:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2849594,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/b96fb9ae-e109-42a4-abe3-ff50b8cc4b38.pdf"},{"id":64582130,"identity":"5992c2ef-99fe-4713-a5c2-f7de0ad52fde","added_by":"auto","created_at":"2024-09-16 06:39:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":733652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/df8df578c7e51a203336d9cf.docx"},{"id":64582132,"identity":"01bfa215-7a96-4337-b5e1-76da9b963958","added_by":"auto","created_at":"2024-09-16 06:39:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":386628,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4926298/v1/5fa05f1df2918cf37528e1f9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal progress in reducing maternal mortality has stagnated since 2016. It is unlikely that the Sustainable Development Goal (SDG) target of 70 maternal deaths per 100,000 livebirths will be achieved by 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The highest maternal mortality ratio (MMR) is in sub-Saharan Africa (SSA) \u0026minus;\u0026thinsp;545 deaths per 100,000 livebirths; 70% of all maternal deaths globally. The highest risk of maternal and perinatal death is at the time of childbirth, from causes including severe bleeding, hypertensive disorders, obstructed labour, and sepsis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Most of the nearly 300,000 maternal and 5\u0026nbsp;million perinatal (stillbirths and newborn) deaths can be prevented if women receive adequate care during pregnancy and give birth in facilities able to manage complications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring childbirth, pregnant women and their (unborn) babies are most at risk of poor outcomes, meaning that access to high quality care is critical and can save lives [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For example, half of maternal deaths and three-quarters of intrapartum stillbirths are preventable with timely access to high-quality emergency obstetric care [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Availability of basic and comprehensive facilities within 2\u0026ndash;3 hours is currently considered a reasonable standard [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, substantial efforts have been directed into characterizing travel time and distance, to locating hotspots and vulnerable areas for interventions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and to understanding the role of physical access on maternal and perinatal health outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost research on physical accessibility to health facilities has been focused on rural areas [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] due to the role of geographic distance, lack of roads and transport, and lower density of health facilities. However, urban settings face specific challenges in ensuring accessible, equitable and high-quality maternal and newborn care. Two-thirds of the world\u0026rsquo;s population is expected to live in urban areas by 2050. Nearly 90% of these additional 2.5\u0026nbsp;billion urban residents will concentrate in Africa and Asia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The massive rural-urban migration leads to constrained infrastructure in the urban areas resulting in slums, informal settlements, exacerbation of inequalities, infectious diseases and non-communicable disease. Emerging evidence shows that the urban health advantage is diminishing in terms of health outcomes, including perinatal outcomes, across SSA countries [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a recognition that potentially long travel times are obfuscated by short geographic distances to health facilities in urban areas due to poor road infrastructure and traffic congestion [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Only a handful of studies have been conducted in urban areas of LMICs including 15 cities in Nigeria [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Nairobi, Kenya [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Cali in Colombia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Two studies focused on disaggregating accessibility metrics by the degree of urbanization [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and by informal settlements [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] across SSA. While useful for national and regional comparison, both studies used a database of health facilities curated between 2012 and 2018 capturing only public health facilities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Further, the analysis disaggregating by urbanization [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] relied on secondary maps of accessibility that were limited in terms of informing healthcare metrics [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, there has been no comprehensive evaluation of geographic accessibility to facilities offering childbirth care nor linking these metrics to socio-economic indicators in major urban areas of Guinea. This is despite Guinea being one of the countries globally with very high MMR of 553 per 100,000 live births [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This translated to an estimated more than 2,500 maternal deaths in 2020 in the country. Modelled outputs in 2018 suggested that about 40% of women of children bearing were marginalized from hospital care, i.e. living outside a 2-hours radius from the nearest health facility based on generic travel speeds that were homogenous in rural and urban areas [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aimed to comprehensively assess geographic accessibility in the Grand Conakry metropolitan area in the Republic of Guinea. Specifically, our objectives are to i) estimate travel time to facilities that offer childbirth care, disaggregated by facility level and sector, using least-cost path algorithm at a 10m spatial resolution; ii) compute the percentage of women of child bearing age (WoCBA) and pregnant women within 15, 30, and 60 minutes of the nearest facility; and iii) assess the intersection between geographic marginalization and poverty (relative wealth index) in the metropolitan area. For policy relevance, the estimates are also presented by sub-national units (commune and district).\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy context\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe Republic of Guinea is divided into eight administrative regions, including the special\u0026nbsp;region of Conakry. These regions are further subdivided into 33\u0026nbsp;prefectures in the countryside and ten communes in Conakry (Kaloum, Dixinn, Matam, Gb\u0026eacute;ssia, Matoto, Tombolia, Ratoma, Lambagny, Sonfonia, and Kassa island). The setting of the study is the Grand Conakry, a metropolitan area encompassing the core urban part of Conakry city (excluding Kassa island) and the peri-urban area around the city (\u003cstrong\u003e\u003cu\u003eFigure 1\u003c/u\u003e\u003c/strong\u003e). Grand Conakry had an estimated population of over 2.6 million (49.5% of whom are women) in 2024. This represents about a fifth of the Guinean population\u0026nbsp;[27]. In Guinea, the proportion of the population living in urban areas has steadily increased from 10% in 1960 to 37% in 2022. More than half of this urban population (54%) is based in Conakry city\u0026nbsp;[27].\u003c/p\u003e\n\u003cp\u003eIn 2024, Grand Conakry consisted of 14 urban communes: nine in Conakry city; two in Dubr\u0026eacute;ka prefecture (Kagb\u0026eacute;len and Dubr\u0026eacute;ka); and three in Coyah prefecture (Coyah, Sanoyah and Man\u0026eacute;ah). Grand Conakry is bounded to the west by the Atlantic Ocean, to the south by the islands of Kaback, Kakossa and Matakang, to the north by the rural zone of Dubr\u0026eacute;ka prefecture, and to the east by the rural part of Coyah prefecture. The landscape of the area is mainly characterized by hills and coastal plains. Roads are the primary means of transportation used in Grand Conakry. However, many roads are in poor condition, making travel difficult, particularly during periods of heavy rain. Traffic jams are common in Grand Conakry and have a substantial impact on socioeconomic activities. For example, in 2017, traffic jams were estimated to cost 5% of Guinea\u0026apos;s GDP[28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealth care in Grand Conakry is provided by both public and private sectors. According to the Ministry of Health and Public Hygiene, Grand Conakry has 155 health facilities: 58 public, 84 private, and 13 private-not-for-profit. Maternal care is mainly provided by the public sector. Antenatal care visits and basic emergency obstetric care (BEmOC) are offered at health posts, health centers, and private-not-for-profit centers. Comprehensive emergency obstetric care (CEmOC) is available mainly in public hospitals (district, regional, and national) and in a few private hospitals. Healthcare utilization for maternal health services is high; according to the 2018 Demographic and Health Survey (DHS)[30], 90% of births in Conakry took place in health facilities (25% in public hospitals) [31]. The total fertility rate was 3.8 in all urban areas of Guinea and 3.2 in Conakry [30].\u003c/p\u003e\n\u003ch2\u003eOverall methodological approach\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe undertook four steps to model travel time, extract geographic coverage metrics and link the modelled travel time to relative wealth index (\u003cstrong\u003e\u003cu\u003eFigure 2\u003c/u\u003e\u003c/strong\u003e). First, we defined the boundaries of Grand Conakry. Second, we assembled health facilities that offer childbirth care, factors that affect travel time including road network, elevation, landcover and travel speeds. Third, we used a geospatial framework to compute travel time to the nearest heath facility disaggregated by level and sector for five travel speeds scenarios. In the last step, we linked the travel time to population distribution (WoCBA and pregnant women) and relative wealth index.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003ea)\u0026nbsp;\u0026nbsp;Grand Conakry boundaries\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eWe considered the boundaries of the main city (Conakry) and the adjoining suburbs that form the Grand Conakry. However, there were no open access vector files of these boundaries. Therefore, we defined and digitized the external boundaries of Grand Conakry and its subdivisions (communes) based on secondary data sources. We loaded a hard copy map showing the Grand Conakry based on the Urban Sector Review [32] on ArcGIS Pro v 3.3.1 (ESRI, Redlands, CA, USA). The\u0026nbsp;hard copy map was overlaid on an OpenStreetMap (OSM) and digitized. To digitize the boundaries of the communes we used information from a presidential decree creating new communes in Grand-Conakry [33].\u0026nbsp;The digitized boundaries (\u003cstrong\u003e\u003cu\u003eFigure 1\u003c/u\u003e\u003c/strong\u003e) were discussed with team members from the African Center of Excellence for the Prevention and Control of Communicable Diseases (CEA-PCMT), Gamal Abdel Nasser University, Conakry, Republic of Guinea and authorities in Conakry for contextual validation.\u003c/p\u003e\n\u003ch3\u003eb)\u0026nbsp;\u0026nbsp;Health facilities providing childbirth service\u003c/h3\u003e\n\u003cp\u003eWe aimed to define a geocoded list of health facilities which provide childbirth care within the defined boundaries of Grand Conakry. To achieve this, we obtained two lists of health facilities covering Conakry, Coyah and Dubr\u0026eacute;ka health districts from the Ministry of Health and Public Hygiene through the Strategic and Development Office (BSD)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eOne list showed all facilities while the second list indicated the annual births per facility. The two lists were harmonized to create a master list only showing facilities which offered childbirth care in 2023 and their attributes (district, facility name, level, and ownership). Public health facilities included health posts, health centres, and hospitals (district, regional and national) while private sector included clinics and faith-based facilities (managed by religious groups).\u003c/p\u003e\n\u003cp\u003eWe geocoded the list using a variety of approaches. We extracted coordinates from the open SSA database [25], validated them while at the same time geocoding other facilities not in the SSA lists based on online Gazetteers and basemaps (Google Maps, GeoNames, OpenStreetMap, Bing Maps, and HERE Map). For the remaining facilities which could not be geocoded through SSA list or online sources, we collected their GPS coordinates positioned at the entrance of the facility. The final list contained 86 facilities. The number, level and sector of health facilities in Grand Conakry providing childbirth care are shown in \u003cstrong\u003e\u003cu\u003eTable 1\u003c/u\u003e\u003c/strong\u003e and their locations are shown on \u003cstrong\u003e\u003cu\u003eFigure 3\u003c/u\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. The\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edistribution\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of health facilities\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eproviding childbirth care\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eby level, sector and district that were included in the analysis in Grand Conakry.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTravel times and inequalities were assessed for the four categories of health facilities shown in Table 1. Those include i) public and private facilities providing childbirth care (the most non restricted scenario), ii) public facilities providing childbirth care, iii) public and private hospitals providing childbirth care and iv) public hospitals (the most restricted scenario) which provide care for complications free of charge. This allowed analysis of both access and equity to primary and hospital care within the metropolitan area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea) \u0026nbsp; Factors that affect travel time\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used publicly available geospatial data of factors that could affect travel time to facilities offering childbirth care. These included road networks from OpenStreetMap (OSM) from 2023 [34], Sentinel-2 land cover at 10m spatial resolution from 2023 [35], Shuttle Radar Topography Mission digital elevation model (DEM) at 30 m spatial resolution [36], and travel barriers (water bodies and flooded vegetation) [35]. The roads were reclassified into trunk, trunk link, primary, secondary, tertiary, residential, other roads (minor), and non-motorized, based on the road attributes data from OSM, local context information and travel speeds data [37]. The Sentinel land use/cover was used to represent areas where no roads existed while the DEM was used to adjusting the walking speeds uphill and downhill [38]. The water bodies and flooded vegetation derived from the land use cover map [35] were treated as barriers except in presence of bridge. The maps of these factors are shown in\u003cstrong\u003e\u0026nbsp;\u003cu\u003eSupplementary information - Figure S1\u003c/u\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) \u0026nbsp; Travel speed\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe derived novel speeds across different types of roads through geo-tracing trajectories within Grand Conakry. Specifically, we used tablets and smartphones with KoboCollect App [39] \u003cem\u003egeotrace\u003c/em\u003e option which marks global positioning system (GPS) points along trajectories at specific time intervals and precisions as researchers travelled throughout Grand Conakry in their routine research activities. Time intervals were set at 1 minute and the precision at 20 meters (i.e., a GPS point would be recorded every 1 minute if the precision was less than or equal to 20 meters).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach submission/form was designed to represent one trajectory; a total of 176 forms were completed and submitted. Researchers were prompted to enter on the form the exact time before and after completing the trajectory. Additional details on mode of transportation, road quality, travel speed, and other characteristics of the trajectory were entered in either open or closed-ended questions. Researchers were encouraged to take a variety of road types and modes of transportation to ensure that a variety of scenarios were covered. To address the issue of time entries occurring after the end of the trajectory, time information from the data collector was compared to the number of geopoints for each trajectory. Submissions with time a difference of more than 20 minutes (n=72) were excluded. Data was collected by 18 volunteers working at the CEA-PCMT between May 13\u003csup\u003eth\u003c/sup\u003e and June 3\u003csup\u003erd\u003c/sup\u003e, 2024 (dry season) at different times of the day.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on this information, the average speed on each road segment was computed by dividing the total distance travelled by the time difference between start and end of the journey. These speeds were linked to OSM road network. The speed by road type were summarized to obtain the average speed, the maximum and minimum speed and the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentile. These five scenarios represent a continuum from the lowest (due to severe traffic jams) and highest travel speeds (substantially low traffic jam or weekend travel) within Grand Conakry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec)\u0026nbsp; \u0026nbsp;Relative wealth index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Meta\u0026rsquo;s Relative Wealth Index (RWI) [40]. The RWI estimates relative wealth of the people living in each micro-region of 2.4km by 2.4km relative to others in the same country. The estimates were constructed through machine-learning based on data from satellite imagery, mobile phone networks, topographic and as aggregated and deidentified connectivity data from Facebook. The models are trained based on data from DHS Program [41]. The data was available at humanitarian data exchange portal [42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed) \u0026nbsp; Population distribution: Women of childbearing age and pregnancies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained the constrained version of total number of females per 100m grid broken down by 5-year groupings in 2020 for Guinea from the WorldPop portal [29,43]. We summed the 5-age groups that constitute women of childbearing age (15\u0026ndash;49 years) and clipped to the extents of Grand Conakry. To obtain the 2024 estimates, we projected the 2020 estimates of WoCBA based on 2019-2020 growth rates that had been derived using similar age disaggregated data.\u003c/p\u003e\n\u003cp\u003eTo obtain an equivalent surface showing the distribution of pregnancies, we converted the 2024 estimates of WoCBA population to a surface of pregnancies following the methodology described by James et al. in 2018 [44]. We did this by multiplying each 5-year population group by its corresponding age-specific fertility rate for women in urban areas [30]. The spatially distributed number of births was summed and multiplied by a constant (1.0382) [45\u0026ndash;49] to account for pregnancy loss and multiples in the calculation of the spatially distributed number of pregnancies.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCost distance algorithm modelling\u003c/h2\u003e\n\u003cp\u003eWe used a least-cost path algorithm to model a hybrid travelling scenario that included walking and motorized transport to the nearest facility. We generated 20 sets of models from the travel time estimates using four sets of destinations for health-seeking: all facilities providing childbirth care and three subsets: public facilities only; all hospitals; public hospitals. For each set of destinations, we applied five travel times: average, minimum, maximum, and 25\u003csup\u003eth\u003c/sup\u003e (lower quartile) and 75\u003csup\u003eth\u003c/sup\u003e percentile (upper quartile). We present the average travel time scenario as the main results and refer to the ranges provided by the interquartile ranges and minimum-maximum speeds as best/worst case scenarios.\u003c/p\u003e\n\u003cp\u003eSpecifically, we implemented the least-cost path algorithm in AccessMod version 5.8.0 [50,51]. AccessMod is a World Health Organization (WHO) tool used to model how geographically accessible existing health services are to the target population including other functionalities [50]. First, we merged the road network and land cover including water bodies and flooded vegetation using the \u0026ldquo;Merge land cover\u0026rdquo; in AccessMod resulting to a merged gridded surface. The merged gridded surface, travel speeds (per scenario) and location of facilities (by level and sector) were used to compute cumulative travel time from each raster cell (10m by 10m) to the nearest heath facility considering least cost (cost measured in terms of time).\u003c/p\u003e\n\u003cp\u003eSlope derived from the DEM was used to adjust walking speeds based on Tobler\u0026rsquo;s formulation [38]. The formulation decreases the up-slope walking speed as the slope increases, while slightly increasing the speed for a slightly negative slope when walking down-slope. The anisotropic travel times were computed towards the health facility. The \u0026quot;knight\u0026apos;s move\u0026quot; was specified which allowed consideration of 16 neighbour cells for higher accuracy. Finally, we extracted the average travel time for Grand Conakry and commune for all 20 scenarios.\u003c/p\u003e\n\u003ch2\u003eTravel time and inequalities\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eBased on the assembled population distribution maps and modelled travel time, we extracted the percentage of WoCBA age and pregnant women, who were within 15, 30, and 60 minutes of the nearest facility for all the 20 scenarios. The extraction was done for the entire Grand Conakry and for each of its 14 communes. Finally, we linked the travel time for each scenario with the RWI. Before linking, we resampled the travel time raster to 240m given the course resolution of RWI. The resultant 4,464 raster cells of travel time were linked to RWI value that was spatially closest to it. Based on RWI, we created five wealth quintiles and generated equiplots to summarize travel time by wealth quintile for Grand Conakry and three districts (Conakry, Coyah, and Dubr\u0026eacute;ka) in R using ggplot2 package\u0026nbsp; [52\u0026ndash;54].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used secondary publicly available data and primary data (travel speeds, location of health facilities) which did not require ethical approval.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe data used to compute travel speeds were based on 102 trajectories. Over 1,255 km were covered in total, with each trajectory covering a median of 11.9 km [lower quartile= 5.5km, upper quartile=18.5km]. The estimated travel speeds, by road type, are shown in \u003cstrong\u003e\u003cu\u003eTable 2\u003c/u\u003e\u003c/strong\u003e. Overall, the travel speeds in Grand Conakry were slow, regardless of the road type, all under 60 km/h. The average travel speeds ranged from 14 to 28 km/h, depending on road type. However, there was large variability in speeds, spanning from just above 2 km/h (highest traffic \u0026ndash; worst scenario) to 60km/h (lowest traffic \u0026ndash; best scenario). These speeds were used within five travel scenarios to estimate travel times. Walking speeds adopted for different landcover types were based on previous published studies [18,55,56].\u003c/p\u003e\n\u003cp\u003eTravel times to the nearest facility for the 20 scenarios are summarised by the average and by the minimum and maximum speeds (extreme margins) or lower and upper quartile (moderate margins) for Grand Conakry metropolitan area, and by district and commune\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eWe describe the results of two scenarios here (best and worst scenario) shown in \u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 3,\u003c/strong\u003e while the results of the other 18 scenarios are presented in the \u003cstrong\u003e\u003cu\u003eSupplementary file \u0026ndash; Table S1\u003c/u\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the pixel level, on average, travel time to the nearest public or private health facility offering childbirth care ranged from 0 to 35 minutes. The longest travel time was 126 minutes when considering the slowest speed. When these estimates were aggregated at the level of Grand Conakry, the average travel time was 8 minutes. This could increase 4-fold to 30 minutes in the scenario with minimum speed. Across the 14 communes, the average travel time was heterogenous and ranged from 3 minutes (8 minutes in case of slowest speed) in the Conakry communes of Kaloum, Matam and Dixinn, to 15 minutes (19 minutes in case of slowest speed) in Man\u0026eacute;ah (Coyah district) (\u003cstrong\u003e\u003cu\u003eTable 2\u003c/u\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOn the other hand, in the most restricted scenario incorporating travel to the nearest public hospital only, average travel time ranged from 0 to 68 minutes at the pixel level and increased to 222 minutes for the slowest speed. The aggregated average travel time at the level of Grand Conakry was 22 minutes, with an estimate of 80 minutes with minimum speed. Across the communes, this ranged from 5 minutes (inner communes of Conakry district) to over 33 minutes in some communes of Coyah and Dubr\u0026eacute;ka districts. In the minimum speed scenario, travel time to the nearest public hospital exceeded 60 minutes in seven of the 14 communes of Grand Conakry (\u003cstrong\u003e\u003cu\u003eTable 2\u003c/u\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGeographic coverage (proportion of WoCBA within specified travel time thresholds: 15, 30, and 60 minutes) are shown in \u003cstrong\u003e\u003cu\u003eTable 3\u003c/u\u003e\u003c/strong\u003e to any nearest health facility and public hospital by district and commune. The rest of the results including geographic coverage for pregnant women are presented in \u003cstrong\u003e\u003cu\u003eSupplementary information \u0026ndash; Table S2 and Table SX3\u003c/u\u003e\u003c/strong\u003e. Approximately 94% of WoCBA living in Grand Conakry were within 15 minutes of the nearest health facility based on the average speed scenario. Under the same conditions, at a threshold of 30 minutes, all WoCBA were within this threshold. However, this coverage reduced to 32% (15 minutes) and 70% (30 minutes) considering the minimum travel speeds (\u003cstrong\u003e\u003cu\u003eTable 3\u003c/u\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAt the commune level, geographic coverage at a threshold of 15 minutes was universal in 12 out of the 14 communes, mainly in the district of Conakry. However, in the minimum speed scenario, this proportion dropped below 30% in five communes (outside Conakry district) and below 50% in three communes. Based on the average speed, all WoCBA in Grand Conakry were within 30 minutes of the nearest health facility providing childbirth care. Nonetheless, when the minimum speed was considered, there was highly variability ranging from 31% in Man\u0026eacute;ah (Coyah District) and 40% in Kagb\u0026eacute;len (Dubr\u0026eacute;ka District) to 100% in several communes within Conakry district.\u003c/p\u003e\n\u003cp\u003eOn the other hand, when considering geographic coverage for the nearest public hospital, 44%, 82%, and 100% WoCBA were within 15, 30 and for 60 minutes, respectively using the average speed. These percentages drop to 2%, 7%, and 30% under the minimum travel speed scenario. Substantial variations were observed when the estimates were summarized by communes across the three (15, 30 and 60 minutes) travel time thresholds. For example, no WoCBA in Sanoyah (Coyah) and Kagb\u0026eacute;len (Dubr\u0026eacute;ka) was within 15 minutes to a public hospital, while at least 67% (Gb\u0026eacute;ssia) to 100% of women in Kaloum, Matam, Dixinn, and Ratoma (district of Conakry), considering the average speed. Additionally, all WoCBA in Kaloum, Matam, and Dixinn were within 60 minutes of the nearest public hospital, regardless of the speed scenario. In contrast, fewer than 2% of WoCBA living in the communes of Sanoyah (Coyah) and Kagb\u0026eacute;len (Dubr\u0026eacute;ka) were within 60 minutes of the nearest public hospital under the minimum speed scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFigure 5\u003c/u\u003e\u003c/strong\u003e shows the distribution of travel time by wealth quintiles, for the three scenarios of minimum, average and maximum travel speeds, by the four types of health facilities and in the three districts. Results for lower and upper quintile speeds are shown in \u003cstrong\u003esupplementary information \u0026ndash; Figure S2\u003c/strong\u003e. Pro-rich inequalities existed and varied in magnitude by facility type and travel speed. Overall, in Grand Conakry, the inequality gap was largest with minimum speed, followed by average and maximum speed respectively. Inequalities in travel time to hospitals were larger than to any facility offering childbirth care. For example, travel time to nearest public facility was 22 mins for Q5 (richest) and 52 mins for Q1 (poorest) (30 mins difference), while travel time to nearest public hospital was 54 mins for Q5 and 105 mins for Q1 (51 mins difference) under the minimum speed scenario.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen the results were further disaggregated by district, the level of inequality in favour of the rich varied, with Conakry having the lowest differences between the richest and poorest quintiles consistently by travel speed, level and sector of facility (\u003cstrong\u003e\u003cu\u003eFigure 5\u003c/u\u003e\u003c/strong\u003e). The lowest inequalities were observed when estimated travel time to nearest public/private facility was based on maximum speed, with all five wealth quintiles having estimates under 10 minutes in the three sub-districts. The largest inequality in travel time to nearest public/private health facility with minimum speed was observed in Coyah, with a travel time of 21 minutes for Q5 (richest) that goes up to 53 min for Q1 (poorest). Limiting the destination to public health facilities did not lead to major changes in observed inequalities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRestricting to the nearest public hospital increased the inequalities and reversed the direction of inequalities in some districts. For example, the shortest travel time to the nearest hospital was consistently observed for Q4 in Dubr\u0026eacute;ka, while the longest was among Q1 (poorest) (75 minutes vs. 143 minutes with minimum speed). Inequalities generally increased when limiting the destination to public hospitals in the three districts. For example, in Conakry travel time with minimum speed to the nearest public hospital is 52 mins for Q5(richest) and 79 mins for Q1(poorest). In Coyah, Q3(middle) had the shortest travel time to the nearest public hospital regardless of the speed.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe assessed spatial accessibility and geographic coverage to health facilities providing childbirth care based on geotraced travel speeds within the Grand Conakry metropolitan area. We identified spatial heterogeneities by sector (private and public) and facility level (hospital and lower level) and linked these indices with relative wealth index. Most WoCBA need on average 15 minutes to reach the nearest facility offering childbirth care in any of the communes, however, under heavy traffic conditions, this increased to about 50 minutes. Under the later conditions, accessibility declined rapidly when WoCBA are in need of affordable emergency obstetric care, which is mostly available in public hospitals, with travel time exceeding two hours in communes of Dubr\u0026eacute;ka district.\u003c/p\u003e \u003cp\u003eAll WoCBA in Grand Conakry appear to reside within half an hour travel time to the nearest facility; however, when traffic was heavy, this may reduce to less than 60% in four of the 14 communes. Similarly, despite all women being within an hour of a public hospital; in six communes, only 3 in 10 women are within the same threshold under lowest travel speeds. Private health facilities contributed considerably to increasing geographic coverage, especially in the peri-urban Coyah district. Finally, while we showed that women from wealthier areas were, on average, closer to any health facility, paradoxically, poorer women were closer to public hospitals.\u003c/p\u003e \u003cp\u003eWhen compared to travel time from other cities in SSA such as Nigeria [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], the estimates from Grand Conakry are not substantially different under the average travel speeds scenario. Spatial access to public comprehensive emergency obstetric care varied between 10 minutes and 41 minutes across 15 Nigerian cities, while in Grand Conakry travel time to public hospitals varied between 5 minutes and 33 minutes across the 14 communes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Similarly, under high traffic, 71% of the WoCBA were within 1 hour in Grand Conakry while it ranged from 83\u0026ndash;100% across the 15 most populated cities in Nigeria [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Our study has several applications and implications both for research and implementation in improving access to childbirth care which we discuss in detail.\u003c/p\u003e \u003cp\u003eIrrespective of the approach used to estimate travel time in SSA [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], a frequent issue is the lack of observed data representing the journey to facilities offering childbirth care to parametrise travel time models [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Among these observed data is travel speed, arguably the most important factor that contributes to magnitude of the estimated travel time. Due to lack of observed travel speeds, most studies spanning subnational to continental levels in SSA rely on generic speeds [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] which may lead to spurious results. Overall, the travel speeds we measured in the Grand Conakry metropolitan area were slower than previously described in literature [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Travel speeds within Grand Conakry did not vary substantially between main road types such as trunk and primary roads, with an average speed of less than 28km/hr and a maximum speed of 60km/hr. Yet, average speeds of 100km/hr have been used previously for major roads [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Therefore, it is likely that previous estimates of geographic accessibility to healthcare which included the Grand Conakry overestimated travel time.\u003c/p\u003e \u003cp\u003eThe low speeds observed in Grand Conakry could be explained by its geographical configuration. Grand Conakry is among the African metropolises facing the most complex challenges regarding land use and transport, primarily due to its geographical location and linear shape [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. It is a peninsula, extending 40 km in length and barely 5 km in width, with spatial growth constrained by Mount Kakoulima to the east and mangroves to the west, north and south. Further, administrative functions are predominantly centralised within the commune of Kaloum, the narrowest part of the city with a few decentralised offices in Dixinn and Matam. Consequently, during rush hours, a significant portion of the population either travels to or departs from Kaloum, which is the most constrained and inflexible area of the capital in terms of road infrastructure. This results in traffic congestion that can last for hours during peak times and beyond.\u003c/p\u003e \u003cp\u003eUnder heavy traffic scenario, the average travel time to the nearest facility and nearest public hospital offering childbirth care was approximately 30 minutes and 80 minutes, respectively. Within the metropolitan area, there was high heterogeneity, 12 to 48 minutes for all facilities and 21\u0026ndash;123 minutes for public hospitals by commune. Women residing in these communes are within the recommended 2 hours radius, as proposed by the Ending Preventable Maternal Mortality strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. However, at high resolution some pixels fell outside the 2 hour threshold, highlighting the need to prioritise these hotspots. The communes within Dubr\u0026eacute;ka and Coyah districts had the longest travel times and fewest percentage of WoCBA within either 30 minutes or 1 hour, especially under heavy traffic conditions. This could be due to a number of reasons.\u003c/p\u003e \u003cp\u003eFirst, the geographical distribution of health facilities is skewed, with these two districts having fewer facilities and a substantial number of WoCBA. For instance, Kaloum (Conakry district) represents 0.15% of the population of Grand Conakry but hosts 6.9% of the all facilities (18% of all public hospitals). In contrast, Kagb\u0026eacute;len (Dubr\u0026eacute;ka district), with over 17% of the population, has only 7.0% of the facilities (no public hospital), and all at the primary level concentrated in the eastern part of the commune. Even more striking are Sanoyah and Man\u0026eacute;ah communes (Coyah district), representing about 10% of the population each but hosting only one public health facility each (1.2%) and no private facilities. These disparities are even wider when focusing on public hospitals which provide emergency obstetric functions such as caesarean section and blood transfusion, free of charge according to the national guidelines. In fact, over half of the nine referral hospitals (55.6%) are in the three smallest communes of Grand Conakry where only 1.3% WoCBA reside.\u003c/p\u003e \u003cp\u003eSecond, these districts with poor geographic access metrics are in the peri-urban area or relatively newly urbanised. Kaloum is the oldest urbanised commune while Kagb\u0026eacute;len, Sanoyah, and Man\u0026eacute;ah are newer and are a key urbanisation frontier where growth happens. These peri-urban areas have a poor road network making it harder to reach the health service providers where roads are not well developed. Third, the assessment of geographic access to healthcare in peri-urban areas is further compounded by the fact that open data on road network are likely to be incomplete due to fewer efforts of volunteered geographic information in the suburbs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] compared to the developed core urban area of Conakry city. The disadvantage in the peripheral areas compared to the inner city was also observed in cities across Nigeria [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and Cali, Colombia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, one of these communes in the peri urban areas (Kagb\u0026eacute;len), in particular, is an industrialised zone with numerous cement manufacturers and a poor road network predominantly consisting of tertiary and residential roads frequented by many trucks [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Long travel times result in delays in accessing life-saving care for both women and their babies in an appropriate environment. Finally, in these peri-urban areas with poor road connectivity, some sections of the journey that entail walking are at a high elevation based on the topography, thus increasing the difficulty of walking; which could be particularly challenging for pregnant women before/during labour, or those that have to be referred postpartum.\u003c/p\u003e \u003cp\u003eDespite the private facilities charging for services, when accounted for in our analyses they improve geographic access to care in some of the communes. For example, in Kagb\u0026eacute;len commune in Dubr\u0026eacute;ka district about 45% of the WoCBA are within 30 minutes of public hospitals, however when both public and private hospitals are considered, the coverage improves to 68%. In 2022, the private sector accounted for 2% of the overall health infrastructure in Guinea. However, the distribution of health infrastructure is different within urban areas. In Grand Conakry, 28% of the health facilities providing childbirth care are private wile more than half (54%) of the hospitals are also private. According to a report from the Ministry of Health (MoH), these private facilities provide about 30 to 40% of the maternal, neonatal, and infant care [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the high number of private health facilities in Grand Conakry could provide alternatives and reduce travel time needed to access childbirth care, on the other hand, it could also be contribute to increasing the inequality gap. This is because, only those who can afford to pay for care in private sector will have access. In fact, the ability to access geographically and pay for health services in the private sector will not improve the availability and the quality of care unless the private facility are fully aligned with the government guidelines in terms of content and quality of care to provide to women during pregnancy and childbirth.\u003c/p\u003e \u003cp\u003eTravel time to any health facility was lowest in the wealthiest 20% of the population, indicating pro-rich inequalities irrespective of the speed scenario. The inequalities were smaller in Conakry while WoCBA in the peri-urban communes face further challenges and were particularly more vulnerable facing widespread inequalities. Similar findings were observed in Nigerian cities[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and in Cali, Colombia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While the same pattern was observed for public hospitals, there were a few exceptions where paradoxically the poorer were closer to public hospitals in Grand Conakry. This can be explained by the fact that public hospitals are often located in the historical urban centres where the poorest residents live. In contrast, those in the top three wealth quintiles tend to move to newly urbanised areas with modern buildings, less crowding, reduced noise, better sanitation systems, and schools. In Conakry, this pattern is evident in communes such as Ratoma, Lambagny and Sonfonia, where there is only one public hospital providing comprehensive emergency newborn and obstetric care. Building of new hospitals has not been keeping up with the population expansion of the city, thus these new neighbourhoods are marginalised. However, private-for-profit health providers see opportunities in these areas, mostly because people who live in these newer neighbourhoods might be able to afford to pay for care. This in turn increases the overall number of health facilities and creating a wealth-related inequality in access to healthcare.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOur analyses have several strengths. First, we used a validated and updated health facility list from the Ministry of Health and Public Hygiene, ensuring accuracy regarding whether facilities provide childbirth care and their geographic locations. Therefore, the geocoded list we created provides a much-needed update of the SSA database [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] in this metropolitan area. Further, we incorporated public and private facilities whereas majority of the previous studies have relied on public health facilities because of the ease of mapping and validating these public facilities. Secondly, we defined Grand Conakry metropolitan area using urbanization trends and the planning by the government. We captured the core urban and the suburbs surrounding the core city [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] reflecting the lived experiences of WoCBA.\u003c/p\u003e \u003cp\u003eThird, we captured and applied realistic travel speeds capturing the local context. We provided measures of uncertainty around the average estimates, that\u0026rsquo;s two extremes (minimum and maximum speeds) and two conservative estimates (25th and 75th percentile). This approach of collecting data is cost effective especially when embedded with other routine tasks and projects and has the potential to contribute to more realistic estimates advancing the frontier of spatial accessibility [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The approach compliments and advances other approaches where speeds are elicited from local experts to provide an informed guess of speeds across different roads [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] or the use of application programming interfaces (APIs) to indirectly account for realistic travel speeds [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, our findings have limitations. First, we acknowledge that we did not integrate elements of referral \u0026ndash; i.e., that some proportion of women giving birth will first seek care at a lower-level health facility and will then be referred to a hospital. This means that we might have underestimated the time to reach appropriate care. Second, we could not account for \u0026ldquo;waiting time\u0026rdquo; in terms of waiting for transport/for money \u0026ndash; so travel times are likely also underestimated. Third, we computed the travel time using the nearest health facility, while some people may bypass the nearest facility for personal preferences and perceptions. Further we collected data during the dry season only. Therefore, accounting for bypassing and/or rainy season, our modelled travel times could have been longer than we currently estimated. Fourth, due to incomplete data on road network in the suburbs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] compared to the developed core urban area, the estimated travel time might be an overestimate. Finally, in the equity analysis, we assumed same travel speeds for everyone \u0026ndash; but this is probably not the case \u0026ndash; i.e., richer people more likely to travel by private car and poorer by shared transport (taxi, motorcycle).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn Grand Conakry, there are major disparities in travel times and therefore geographic access to healthcare facilities providing childbirth care, is driven by the skewed spatial distribution of health facilities, heavy traffic, and socio-economic marginalisation. The average travel time to facilities is generally within the internationally defined thresholds under normal traffic conditions. However, during heavy traffic the situation becomes critical, with travel times exceeding two hours in some areas. Such long delays present an obstacle for women in need of emergency obstetric care. The peri-urban communes (Dubr\u0026eacute;ka and Coyah districts) are almost medical deserts, due to low number of facilities, particularly public hospitals providing comprehensive emergency obstetric care services. Further, wealthier populations live closer to facilities providing childbirth care, while, surprisingly, poorer populations living in older, more centrally located urban areas benefit from greater proximity to public hospitals.\u003c/p\u003e \u003cp\u003eThe findings are useful for healthcare planning within Grand Conakry in terms of geographic distribution of health facilities offering childbirth care vis-a-vis the WoCBA and their corresponding socio-economic situation. For example, private health facilities improved geographic access in under-served areas. However, the effectiveness of this integration requires the alignment of private health facilities with public health standards for maternal health services and the financial accessibility of these services to the broader population. This study underscores the need for targeted interventions to address these accessibility gaps, particularly in the peri-urban districts of Coyah and Dubr\u0026eacute;ka where infrastructure development lags behind. For example, building new roads, and enhancing public transportation could mitigate some of these challenges, ensuring that WoCBA in Grand Conakry have timely access to childbirth care, regardless of their socio-economic status or the time of day.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the National Director of the Strategic and Development Office of the Ministry of Health of Guinea, Souleymane Diakit\u0026eacute;, who facilitated the acquisition of health facility and birth datasets for Grand Conakry. they also extend our gratitude to Abdoul Karim Nab\u0026eacute; and Bintou Ciss\u0026eacute; from the same Directorate, who provided technical support and guidance during the cleaning and updating of the datasets. The authors are also grateful to Alseny Camara from the Health District Office of Dubr\u0026eacute;ka, who helped validate the boundaries of the urban zone of Dubr\u0026eacute;ka. Additionally, they acknowledge the support from the team at the African Center of Excellence for the Prevention and Control of Communicable Diseases (CEA-PCMT), Gamal Abdel Nasser University, Conakry (Karifa Kourouma, Aissata Tounkara, Aly Badara Tour\u0026eacute;, Armand Saloun Kamano, Christiane Tolno, Youssouf Keita, Mohamed Aly Bangoura, Ang\u0026egrave;le Soua Koli\u0026eacute;, Diarouga Bald\u0026eacute;, Akodegnon Honora Djidonou, Elie Beavogui) for their contribution to the travel data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used for this analysis are publicly available. These include a database of health facilities obtained from the Ministry of Health and Public Hygiene, while links for population relative wealth index and factors affecting travel to healthcare (road network, digital elevation model and land use) are listed in the manuscript.\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThis study used secondary publicly available data and primary data (Travel speeds, location of health facilities) which did not require ethical approval.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study was funded by Fonds voor Wetenschappelijk Onderzoek (FWO) Grant ID: G074724N and The Belgian Federal Directorate-General for Development Cooperation Humanitarian Aid (DGD)\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contribution\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"374\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eTerm\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eAuthors\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eConceptualization\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eLB, PMM, AD, AS, FMG\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eMethodology\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eLB, PMM, AD, AS, FMG,\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eSoftware\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003ePMM, FMG, AS\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eValidation\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eLB, PM, AD, AS, FMG, ND, HM, TMM, AD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eFormal analysis\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003ePM, FMG, AS\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eInvestigation\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eND, HM, PK, FMG, PMM, AS\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eResources\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eFMG, ND, PMM, AS\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eData Curation\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eFMG, ND, PMM\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eWriting - Original Draft\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eFMG, AS, LB, PMM, PK, HM, ND, TMM, AD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eWriting - Review \u0026amp; Editing\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eFMG, AS, LB, PMM, PK, HM, ND, TMM, AD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eVisualization\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eFMG, PMM, AS\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eSupervision\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003ePMM, LB, AD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eProject administration\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003ePMM, LB, AD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.57219251336898%\" valign=\"top\"\u003eFunding acquisition\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"60.42780748663102%\" valign=\"top\"\u003eLB, AD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO, UNICEF, UNFPA, \u003cem\u003eet al.\u003c/em\u003e Trends in maternal mortality 2000 to 2020. 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Advancing the frontiers of geographic accessibility to healthcare services. \u003cem\u003eCommunications Medicine\u003c/em\u003e. 2023;3:158. doi: 10.1038/s43856-023-00391-w\u003c/li\u003e\n\u003cli\u003eMolenaar L, Hierink F, Brun M, \u003cem\u003eet al.\u003c/em\u003e Travel scenario workshops for geographical accessibility modeling of health services: A transdisciplinary evaluation study. \u003cem\u003eFront Public Health\u003c/em\u003e. 2023;10. doi: 10.3389/fpubh.2022.1051522\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4926298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4926298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe modelled geographic accessibility, coverage, and wealth-based inequalities for childbirth care in Grand Conakry, Guinea. We assembled boundaries, health facilities, wealth index, road network, elevation, landcover, and geo-traced travel speeds. Using least-cost path algorithm, we computed travel times to the nearest facility by level and sector. We quantified the coverage (% of women of childbearing age) within 15, 30, and 60-minutes of the nearest facility and its variation by wealth index. Average travel speeds ranged from 14 to 28 km/h. Travel to any facility took 8-minutes, increasing to 22 for public hospitals (range from 5 to 33-minutes across communes). Coverage was 100% within 30-minutes of any facility, dropping to 82% for public hospitals, varying across communes. Slower speeds due to traffic substantially increased travel time and reduced coverage. Pro-rich inequalities emerged, especially in peri-urban communes with longer travel times. Targeted interventions are needed to reach equitable access to childbirth care.\u003c/p\u003e","manuscriptTitle":"Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 06:39:14","doi":"10.21203/rs.3.rs-4926298/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cities","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcities","sideBox":"Learn more about [Nature Cities](https://www.springer.com/journal/44284)","snPcode":"44284","submissionUrl":"","title":"Nature Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e548cb20-189a-4eff-9dec-3d10d9a8a5a9","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36322845,"name":"Health sciences/Health care/Public health"},{"id":36322846,"name":"Social science/Geography"},{"id":36322847,"name":"Health sciences/Health care/Health policy"},{"id":36322848,"name":"Health sciences/Health care/Health services"},{"id":36322849,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-04-24T07:13:26+00:00","versionOfRecord":{"articleIdentity":"rs-4926298","link":"https://doi.org/10.1038/s44284-025-00220-2","journal":{"identity":"nature-cities","isVorOnly":false,"title":"Nature Cities"},"publishedOn":"2025-04-23 04:00:00","publishedOnDateReadable":"April 23rd, 2025"},"versionCreatedAt":"2024-09-16 06:39:14","video":"","vorDoi":"10.1038/s44284-025-00220-2","vorDoiUrl":"https://doi.org/10.1038/s44284-025-00220-2","workflowStages":[]},"version":"v1","identity":"rs-4926298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4926298","identity":"rs-4926298","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}