Improving the spatial accessibility of healthcare in North Kivu, Democratic Republic of Congo. (August 2020) - Pu, Qiang; Yoo, Eun-Hye; Rothstein, David H.; Cairo, Sarah; Malemo, Luc
Abstract: Accessibility to healthcare has a direct bearing on the overall well-being of the population. Poor access to healthcare has serious consequences particularly in low and low-middle income countries (LMIC) in sub-Saharan Africa. The lack of detailed and up-to-date spatial data and health information in these regions further challenges both the accurate assessment of spatial accessibility and the determination of optimal locations of healthcare facilities that would improve health service planning. In the present study, we proposed a systematic approach to assess the spatial accessibility to healthcare and to identify optimal locations for additional healthcare facilities based on the accessibility measures. Results from a raster-based accessibility measurement showed that majority of population could not reach the nearest hospitals within 2-hours; only 25%, 50%, and 44% of population reached the nearest hospital within 2-hours under walking, motor and, bus travel scenarios, respectively. Our results also showed that the five newly proposed hospitals whose optimal locations were determined using a location-allocation model could potentially increase 11.41%, 8.29%, and 8.95% of additional population coverage for the three travel scenarios. The proposed health system evaluation approach and the health care planning based on open-source data derived from remote sensing and crowdsourcing and the spatial modeling approach has the potential to be useful in LMIC to improve overall population health. Highlights: A systematic approach to evaluate spatial accessibility in resource-poor regions using open-source spatial datasets. Integrating spatial accessibility and location-allocation model for healthcare planning under specific travel scenarios. A first attempt to investigate overall spatial accessibility to healthcare in North Kivu, Democratic Republic of Congo. Model validation by comparing the model-based travel-time estimates to field survey responses.