Global maps of travel time to healthcare facilities

Abstract

Access to healthcare is a requirement for human well-being that is constrained, in part, by the allocation of healthcare resources relative to the geographically dispersed human population 1-3 . Quantifying access to care globally is challenging due to the absence of a comprehensive database of healthcare facilities. We harness major data collection efforts underway by OpenStreetMap, Google Maps and academic researchers to compile the most complete collection of facility locations to date. Leveraging the geographically variable strengths of our facility datasets, we use an established methodology 4 to characterize travel time to healthcare facilities in unprecedented detail. We produce maps of travel time with and without access to motorized transport, thus characterizing travel time to healthcare for populations distributed across the wealth spectrum. We find that just 8.9% of the global population (646 million people) cannot reach healthcare within one hour if they have access to motorized transport, and that 43.3% (3.16 billion people) cannot reach a healthcare facility by foot within one hour. Our maps highlight an additional vulnerability faced by poorer individuals in remote areas and can help to estimate whether individuals will seek healthcare when it is needed, as well as providing an evidence base for efficiently distributing limited healthcare and transportation resources to underserved populations both now and in the future.Access to healthcare is a measure of human well-being that is constrained by numerous geographically varying factors 1-3 , the most immediate of which is the time it takes individuals to travel to a properly equipped and adequately staffed healthcare facility. Due to spatial clustering of healthcare facilities in densely populated areas, individuals living in rural regions often face increased travel times and thus cost when seeking healthcare. This situation can be exacerbated by poor transportation infrastructure and lack of motorized transport, which further increase the time required for travel and could disproportionally affect lower-income populations. As such, people facing long travel times to healthcare facilities are less likely to seek care when it is needed [5][6][7][8][9] , and the consequences of failing to seek care include increased mortality and morbidity from treatable conditions 10,11 .

Toward a geography of community health workers in Niger: a geospatial analysis

“…Our estimates of accessibility coverage are lower than those from Weiss and colleagues 34 which estimated 61·8% and 80·7% of the population in Niger was within 60 minutes of the nearest health facility in 2020 based on a walking scenario and motorized transportation scenario, respectively. Paradoxically the health facility dataset used in Weiss and colleagues 34 included 568 fewer health service delivery locations (n=2821 in Weiss and colleagues 34 for 2020, n=3389 in our model for 2013). The higher estimates of accessibility coverage in Weiss and colleagues 34 may be explained by three factors.…”

“…The higher estimates of accessibility coverage in Weiss and colleagues 34 may be explained by three factors. First, travel speeds for motorized transportation used by Weiss and colleagues 34 are higher than those used in our model. Second, the walking model by Weiss and colleagues 34 assumes a travel speed of 5km per hour across all landcover classes, whereas our model varies walking travel speed from 5km per hour to 0 km per hour (impassable), according to land cover class and is based on realistic estimates of travel speeds from previous studies in Niger and the region.…”

“…Paradoxically the health facility dataset used in Weiss and colleagues 34 included 568 fewer health service delivery locations (n=2821 in Weiss and colleagues 34 for 2020, n=3389 in our model for 2013). The higher estimates of accessibility coverage in Weiss and colleagues 34 may be explained by three factors. First, travel speeds for motorized transportation used by Weiss and colleagues 34 are higher than those used in our model.…”

Estimating the local spatio‐temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

“…Data on stocks of malaria supplies is commonly available as part of the national HMIS. Moreover, a local characterization of the main drivers of health care utilization over space and time can be obtained elsewhere using available maps of geographic accessibility to health facilities (available at the global level at a 1 km × 1 km resolution [61]), data from patients coming to health facilities for diseases other than malaria, and institutional knowledge about the timing and geographic extent of interventions that can have major impacts on health care utilization (e.g. user fee exemptions, health insurance, etc.…”

Wearable Triboelectric Nanogenerators for Therapeutics

“…As the world marches into the era of 5G and the internet of things (IoT), access to personalized healthcare (see Glossary) is becoming a requirement to ensure the well-being of each individual [1,2]. However, more than 40% of the world population cannot reach a medical facility on foot within 1 h [3]. Delayed and inadequate medical services will bring considerable inconvenience to groups who live in rural areas and in developing countries.…”