Abstract
BackgroundHealth care utilization is affected by several factors including geographic accessibility. Empirical data on utilization of health facilities is important to understanding geographic accessibility and defining health facility catchments at a national level. Accurately defining catchment population improves the analysis of gaps in access, commodity needs and interpretation of disease incidence. Here, empirical household survey data on treatment seeking for fever were used to model the utilisation of public health facilities and define their catchment areas and populations in northern Namibia.MethodThis study uses data from the Malaria Indicator Survey (MIS) of 2009 on treatment seeking for fever among children under the age of five years to characterize facility utilisation. Probability of attendance of public health facilities for fever treatment was modelled against a theoretical surface of travel times using a three parameter logistic model. The fitted model was then applied to a population surface to predict the number of children likely to use a public health facility during an episode of fever in northern Namibia.ResultsOverall, from the MIS survey, the prevalence of fever among children was 17.6% CI [16.0-19.1] (401 of 2,283 children) while public health facility attendance for fever was 51.1%, [95%CI: 46.2-56.0]. The coefficients of the logistic model of travel time against fever treatment at public health facilities were all significant (p < 0.001). From this model, probability of facility attendance remained relatively high up to 180 minutes (3 hours) and thereafter decreased steadily. Total public health facility catchment population of children under the age five was estimated to be 162,286 in northern Namibia with an estimated fever burden of 24,830 children. Of the estimated fevers, 8,021 (32.3%) were within 30 minutes of travel time to the nearest health facility while 14,902 (60.0%) were within 1 hour.ConclusionThis study demonstrates the potential of routine household surveys to empirically model health care utilisation for the treatment of childhood fever and define catchment populations enhancing the possibilities of accurate commodity needs assessment and calculation of disease incidence. These methods could be extended to other African countries where detailed mapping of health facilities exists.
Highlights
Health care utilization is affected by several factors including geographic accessibility
This study demonstrates the potential of routine household surveys to empirically model health care utilisation for the treatment of childhood fever and define catchment populations enhancing the possibilities of accurate commodity needs assessment and calculation of disease incidence
The coefficients of the logistic model, fitted for the modelled travel times against attendance of the public health facilities for treatment of fever by children under the age of five years reported during the Malaria Indicator Survey (MIS), were all significant with P < 0.001 (Figure 2)
Summary
Health care utilization is affected by several factors including geographic accessibility. The cost surface based on travel times, showed closer agreement with the pattern of use in rural South Africa when modelled as a logistic function [31] These forms of distance measurement have been used to analyse utilisation by using metrics such as number of health facilities within a certain predefined distance of the facility, the average distance to n number of health facilities and the gravity model [6,32]. The gravity model is a spatial interaction model analogous to Newton’s law of gravity where the force of attraction between two bodies varies proportionally to the product of their masses and inversely to distance between them [33,34] In this form, patient interaction with healthcare is denoted by flow from patient origin to the health service while the masses are represented by various utilization effects such as cost, size of health facility or propensity of patient groups to use healthcare [15]. The other distance metrics either ignore the interaction with other possible providers within the considered region or may assume that patients always use the nearest facility [35]
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