Modelling distances travelled to government health services in Kenya

Abstract

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.

Figure 1

Plots showing the pattern of patients' choice of health services in a HC–D, D–H and HC–H relationship. The black line plots the patient's choice between any pair of relationship. The grey lines represent the limits of the 95% confidence interval, which give the extents of the overlap area. The position on the y-axis corresponding to a value of 0.5 on the x-axis provides the boundary displacement factor.

Figure 2

A scatter plot of travel time comparing the Euclidean model (EM) and the competition-adjusted transport network model (TNM) for 668 children who were treated at government health services. The dashed line represents the trend-line of the scatter plot. The solid line represents the theoretical line of complete agreement between the two models.

Figure 3

Graph showing the percentage of population within 1 h to the nearest government health facilities for both the Euclidean and the transport network model before and after adjustment for competition between facilities. The interval bars represent the lowest and highest proportion of population within 1 h of government health services attributed to each model across the four districts.

Figure 4

(a–c) Maps of Kwale showing access to government health services based on travel time (hours) for the Euclidean, the transport network and the adjusted transport network models. = government health facility; = catchment area; = 0-0.5 hours; = >0.5–1 h; = >1 hour. All maps are at the same scale as shown on Figure 4a. The North direction is towards the top of the page.