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Background: In many low and middle-income countries (LMICs), timely access to emergency healthcare services is limited. In urban settings, traffic can have a significant impact on travel time, leading to life-threatening delays for time-sensitive injuries and medical emergencies. In this study, we examined travel times to hospitals in Nairobi, Kenya, one of the largest and most congested cities in the developing world. Methods: We used a network approach to estimate average minimum travel times to different types of hospitals (e.g. ownership and level of care) in Nairobi under both congested and uncongested traffic conditions. We also examined the correlation between travel time and socioeconomic status. Results: We estimate the average minimum travel time during uncongested traffic conditions to any level 4 health facility (primary hospitals) or above in Nairobi to be 4.5 min (IQR 2.5–6.1). Traffic added an average of 9.0 min (a 200% increase). In uncongested conditions, we estimate an average travel time of 7.9 min (IQR 5.1–10.4) to level 5 facilities (secondary hospitals) and 11.6 min (IQR 8.5–14.2) to Kenyatta National Hospital, the only level 6 facility (tertiary hospital) in the country. Traffic congestion added an average of 13.1 and 16.0 min (166% and 138% increase) to travel times to level 5 and level 6 facilities, respectively. For individuals living below the poverty line, we estimate that preferential use of public or faith-based facilities could increase travel time by as much as 65%. Conclusion: Average travel times to health facilities capable of providing emergency care in Nairobi are quite low, but traffic congestion double or triple estimated travel times. Furthermore, we estimate significant disparities in timely access to care for those individuals living under the poverty line who preferentially seek care in public or faith-based facilities.
Kenya is divided into 47 counties, the most populous of which is Nairobi county, whose borders are synonymous with those of the nation’s capital city of Nairobi. The metro area has a rapidly-growing population of greater than 6.5 million people. While communicable diseases remain the most common cause of death in Kenya, non-communicable diseases are becoming more prominent as Kenya goes through its epidemiologic transition. Time-sensitive conditions such as ischemic heart disease (5.0% of deaths), stroke (4.8% of deaths), and injury (7.7% of deaths) have seen a relative increase and are likely to continue to grow in the future [14]. Health facilities in Kenya are a mix of public and private. Facilities are assigned levels as per the Kenya Essential Packages for Health (KEPH) based on capacity and services [15]. We considered levels 4 and above as viable candidates to provide emergency care [16,17]. Level 4 facilities are primary/first level hospitals, which should provide Basic Life Support. Level 5, or secondary/second level hospitals, should provide emergency services, including Advanced Life Support. Finally, level 6, or tertiary level facilities provide a full complement of tertiary care services. However, the actual level of care provided may vary [18]. Nairobi contains a large number of hospitals, including one public level 6 hospital, Kenyatta, and four level 5 facilities, only one of which is public [19]. Facility data were downloaded from the Kenyan Ministry of Health website [19]. We selected KEPH level 4, 5, and 6 facilities that were in Nairobi, or the surrounding counties of Kiambu, Machakos, or Kajiado. We included facilities from surrounding counties in the event that those were the closest facilities for individuals living on the edge of Nairobi. We excluded specialty facilities, e.g., maternal, eye, mental, or those that were only dispensaries or only saw outpatients. This left us with 70 facilities (Fig. 1) to consider as part of the analysis. Kenyan health facilities, levels 4–6, in Nairobi and surrounding counties. Street network data was obtained from Open Street Map, an open-source database of street maps maintained by a global community (https://www.openstreetmap.org/). Population data in 100 m by 100 m squares in 2015 and percent of the population below the poverty line at the 1 km by 1 km square level in 2008 were obtained from the Afripop database (http://www.afripop.org/). The poverty dataset used the Alkire Foster method, where someone was defined as living in poverty if they were deprived in at least one third of ten indices encompassing health, education, and standard of living. Road network data were stored in a PostgresSQL database and converted to a query-able geographic database using PostGIS and pgrouting, extensions to PostgresSQL that allow databases to store geographic data and use algorithms to do different types of routing. The shapefile for Nairobi was downloaded from the Kenyan Elections portal via the Humanitarian Data Exchange (https://data.humdata.org/dataset/kenya-elections). Our analysis utilized a method similar to that used by Lee et al. to estimate driving time to eye care services in the United States [20]. Using the network data from Open Street Maps, we created a query-able geographic database, consisting of vertices and edges that correspond to intersections and streets. Because Open Street Maps lacked speed limit information for most streets, we assigned speeds for both uncongested and congested traffic using the speeds suggested by Avner and Lall [21]. Speed limits that were available in the data set were used unless they exceeded the maximum of motorway speed of 110 km/h. For congested speeds in roads not covered by Avner and Lall, we used 2/3 of the uncongested speed. We created a grid of 0.5 × 0.5 km squares to use as samples, resulting in 2903 sample points throughout the city of Nairobi. This size was selected because it could both show variation over small areas and was computationally manageable. For each grid square centroid, we found the nearest node in the traffic network and the nodes nearest to each of the 70 facilities. The minimum time between each centroid node and each facility node was calculated using Dijkstra’s algorithm, where cost was the time it took to travel down each edge [22]. Due to inconsistencies in traffic network data, such as disconnected nodes or closed street loops, our approach yielded a small number (0.4%) of missing values, which were interpolated using simple kriging. This resulted in a raster for each facility that contained travel times to that facility. We then found the minimum value for each pixel in the raster to the closest level 4, 5 or 6 health facility. To examine the percentage of the population within a certain travel time of a facility, we aggregated the Afripop population dataset to be the same size squares as our sample grid. Then we resampled to align the grids and scaled to maintain the same overall population total. We followed a similar process for the percent of the population below the poverty line (see Fig. 2 for the resulting rasters). Combined, these two rasters allowed us to estimate the number of people living in each section of the grid, and the number of them living below the poverty line. Rasters of Nairobi population density and poverty. All database queries, data analyses, and data visualizations were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org).
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