Spatial accessibility to basic public health services in South Sudan

  • Peter M. Macharia | pmacharia@kemri-wellcome.org Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
  • Paul O. Ouma Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
  • Ezekiel G. Gogo Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
  • Robert W. Snow Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom.
  • Abdisalan M. Noor Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom.

Abstract

At independence in 2011, South Sudan’s health sector was almost non-existent. The first national health strategic plan aimed to achieve an integrated health facility network that would mean that 70% of the population were within 5 km of a health service provider. Publically available data on functioning and closed health facilities, population distribution, road networks, land use and elevation were used to compute the fraction of the population within 1 hour walking distance of the nearest public health facility offering curative services. This metric was summarised for each of the 78 counties in South Sudan and compared with simpler metrics of the proportion of the population within 5 km of a health facility. In 2016, it is estimated that there were 1747 public health facilities, out of which 294 were non-functional in part due to the on-going civil conflict. Access to a service provider was poor with only 25.7% of the population living within one-hour walking time to a facility and 28.6% of the population within 5 km. These metrics, when applied sub-nationally, identified the same high priority, most vulnerable counties. Simple metrics based upon population distribution and location of facilities might be as valuable as more complex models of health access, where attribute data on travel routes are imperfect or incomplete and sparse. Disparities exist in South Sudan among counties and those with the poorest health access should be targeted for priority expansion of clinical services.

Downloads

Download data is not yet available.

Author Biographies

Peter M. Macharia, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi
Assistant Research Officer, Malaria Public Health and Epidemiology Group
Paul O. Ouma, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi
Assistant Research Officer, Assistant Research Officer, Malaria Public Health and Epidemiology Group
Ezekiel G. Gogo, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi
Assistant Research Officer, Malaria Public Health and Epidemiology Group
Robert W. Snow, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford
Profffesor (Principal Investigator); Malaria Public Health and Epidemiology Group
Abdisalan M. Noor, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford
Dr.(Principal Investigator) Malaria Public Health and Epidemiology Group
Published
2017-05-11
Section
Original Articles
Keywords:
South Sudan, Health facilities, Spatial accessibility
Statistics
Abstract views: 2650

PDF: 737
APPENDIX: 756
HTML: 955
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
Macharia, P., Ouma, P., Gogo, E., Snow, R., & Noor, A. (2017). Spatial accessibility to basic public health services in South Sudan. Geospatial Health, 12(1). https://doi.org/10.4081/gh.2017.510