Spatial modelling of healthcare utilisation for treatment of fever in Namibia
Spatial modelling of healthcare utilisation for treatment of fever in Namibia
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
6-[25pp]
Alegana, Victor
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Wright, J.A.
94990ecf-f8dd-4649-84f2-b28bf272e464
Petrina, Uusiku
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Noor, Abdisalan
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Snow, Robert
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Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
15 February 2012
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Wright, J.A.
94990ecf-f8dd-4649-84f2-b28bf272e464
Petrina, Uusiku
1a742f74-3274-45b0-9132-c93a87543fe3
Noor, Abdisalan
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Snow, Robert
0792eb8a-6d13-4ac4-beef-03301e1c0c27
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Alegana, Victor, Wright, J.A., Petrina, Uusiku, Noor, Abdisalan, Snow, Robert and Atkinson, Peter M.
(2012)
Spatial modelling of healthcare utilisation for treatment of fever in Namibia.
International Journal of Health Geographics, 11 (6), .
(doi:10.1186/1476-072X-11-6).
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
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Published date: 15 February 2012
Organisations:
WorldPop, Geography & Environment, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 210990
URI: http://eprints.soton.ac.uk/id/eprint/210990
ISSN: 1476-072X
PURE UUID: c7ad37f3-bd0c-418a-bf7c-da6d00d89115
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Date deposited: 17 Feb 2012 11:13
Last modified: 15 Mar 2024 03:20
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Contributors
Author:
Uusiku Petrina
Author:
Abdisalan Noor
Author:
Robert Snow
Author:
Peter M. Atkinson
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