The time required to travel to healthcare facilities influences whether individuals seek care when it is needed. This relationship is most problematic in low-income settings, where long travel times are also associated with higher relative transport costs. Thus, effectively characterizing travel time can assist in identifying communities that would most benefit from additional healthcare resources.
In this study, we produce the first high-resolution global maps of travel time to hospitals and clinics. The results show that 91.1% of the world’s population can reach a hospital or clinic within an hour if they have access to motorized transportation, but only 56.7% can do so by walking alone. This means 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.
Policy-makers may benefit from the travel time to healthcare maps as these highlight areas most in need of additional personnel and resources. By increasing the efficiency of resource allocation, the maps could help to increase health equity without requiring additional resources. Critically, by freely providing the tools to make custom travel time maps, we also enable public health professionals to characterize accessibility to specialized services such as emergency care.
This work was done in collaboration with collaborators from:
- Department of Natural Resources, ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
- Swiss Federal Institute of Technology Lausanne (École Polytechnique Fédérale de Lausanne), Lausanne, Switzerland.
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, California, USA.
- Institute for Disease Modeling, Bellevue, WA, USA.
- Stanford University, Palo Alto, CA, USA.
- Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA.
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
# Full Citation
D.J. Weiss, A. Nelson, C.A. Vargas-Ruiz, K. Gligorić, S. Bavadekar, E. Gabrilovich, A. Bertozzi-Villa, J. Rozier, H.S. Gibson, T. Shekel, C. Kamath, A. Lieber, K. Schulman, Y. Shao, V. Qarkaxhija, A.K. Nandi, S.H. Keddie, S. Rumisha, P. Amratia, R. Arambepola, E.G. Chestnutt, J.J. Millar, T.L. Symons, E. Cameron, K.E. Battle, S. Bhatt, and P.W. Gething. Global maps of travel time to healthcare facilities. (2020). Nature Medicine. doi:10.1038/s41591-020-1059-1
# Data Visualisation
- MAP Explorer – please select the surface from the Layer Catalogue
Please note that these downloads are large and may take some time to complete.
- Walking only Travel Time to Healthcare (raster data)
- Motorized Travel Time to Healthcare (raster data)
- Walking only Friction Surface (raster data)
- Motorized Friction Surface (raster data)
# Example Code for Accessibility Mapping
- Generic Accessibility Mapping Script – R script
This work is licensed under a Creative Commons Attribution 4.0 International License.
Contains data from OpenStreetMap © OpenStreetMap contributors.