Modelling distances travelled to government health services in Kenya

Trop Med Int Health. 2006 Feb;11(2):188-96. doi: 10.1111/j.1365-3156.2005.01555.x.


Objective: To systematically evaluate descriptive measures of spatial access to medical treatment, as part of the millennium development goals to reduce the burden of HIV/AIDS, tuberculosis and malaria.

Methods: We obtained high-resolution spatial and epidemiological data on health services, population, transport network, topography, land cover and paediatric fever treatment in four Kenyan districts to develop access and use models for government health services in Kenya. Community survey data were used to model use of government health services by febrile children. A model based on the transport network was then implemented and adjusted for actual use patterns. We compared the predictive accuracy of this refined model to that of Euclidean distance metrics. RESULTS Higher-order facilities were more attractive to patients (54%, 58% and 60% in three scenarios) than lower-order ones. The transport network model, adjusted for competition between facilities, was most accurate and selected as the best-fit model. It estimated that 63% of the population of the study districts were within the 1 h national access benchmark, against 82% estimated by the Euclidean model.

Conclusions: Extrapolating the results from the best-fit model in study districts to the national level shows that approximately six million people are currently incorrectly estimated to have access to government health services within 1 h. Simple Euclidean distance assumptions, which underpin needs assessments and against which millennium development goals are evaluated, thus require reconsideration.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Child, Preschool
  • Cost of Illness
  • Fever / epidemiology
  • Fever / therapy
  • Geographic Information Systems
  • Health Facilities*
  • Health Services Accessibility* / economics
  • Humans
  • Kenya
  • Models, Organizational
  • Population Surveillance / methods
  • Time Factors
  • Transportation / economics
  • Travel*