Spatial modelling of healthcare utilisation for treatment of fever in Namibia

Abstract

Background: Health 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.

Method: This 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.

Results: Overall, 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.

Conclusion: 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. These methods could be extended to other African countries where detailed mapping of health facilities exists.

Figure 1

Location of the 2009 MIS clusters shown as red dots (N = 120) in relation to public health facilities shown as blue dots (N = 245) in the nine northern provinces of Namibia where the 2009 MIS was undertaken (Kunene, Omusati, Oshana, Ohangwena, Otjozondjupa, Omaheke, Kavango and Caprivi). Subsequent analysis was restricted to only these nine regions.

Figure 2

Probability decay function for the MIS survey showing probability of attendance (y-axis) for treatment seeking group against increasing travel times (x-axis); Y = C/(1 + e^(A-x)/B) where C (0.766) is the limiting function on the y-axis; A (3.736) is the asymptote factor at the inflection point of the model; B (-0.609) is the decay parameter. The model was run using log transformed travel time (x-axis) and later back transformed for presentation purposes. The attendance pattern (1 = attendance and 0 = non-attendance) is also superimposed on the decay curve. The coefficient of all parameters were significant at p < 0.001.

Figure 3

Map of probability of attendance for treatment fever by children under the age of five years at the nearest health facility based on the MIS 2009. The map shows the 9 regions in north Namibia where MIS was carried out namely; Kunene, Omusati, Oshana, Ohangwena, Otjozondjupa, Omaheke, Kavango and Caprivi. The lowest probability was 0.02 and the highest probability was 0.76.

Figure 4

Number of children under the age of five years (y-axis) against an increasing probability of attendance for fever (x-axis) at the nearest public health facility. Majority of children were at a probability greater than 0.5 with maximum probability of attendance of 0.76.

Figure 5

Map of northern Namibia showing health facility catchment areas developed using the modelled travel time to the nearest public health facility overlaid with the probability of attendance of a public health facility by children less than five years of when sick with fever. The health facilities are shown as blue dots. Darker shades of red represent increasing probability.