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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...

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Autores principales: Alegana, Victor A, Wright, Jim A, Pentrina, Uusiku, Noor, Abdisalan M, Snow, Robert W, Atkinson, Peter M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292929/
https://www.ncbi.nlm.nih.gov/pubmed/22336441
http://dx.doi.org/10.1186/1476-072X-11-6
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author Alegana, Victor A
Wright, Jim A
Pentrina, Uusiku
Noor, Abdisalan M
Snow, Robert W
Atkinson, Peter M
author_facet Alegana, Victor A
Wright, Jim A
Pentrina, Uusiku
Noor, Abdisalan M
Snow, Robert W
Atkinson, Peter M
author_sort Alegana, Victor A
collection PubMed
description 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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spelling pubmed-32929292012-03-05 Spatial modelling of healthcare utilisation for treatment of fever in Namibia Alegana, Victor A Wright, Jim A Pentrina, Uusiku Noor, Abdisalan M Snow, Robert W Atkinson, Peter M Int J Health Geogr Research 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. BioMed Central 2012-02-15 /pmc/articles/PMC3292929/ /pubmed/22336441 http://dx.doi.org/10.1186/1476-072X-11-6 Text en Copyright ©2012 Alegana et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Alegana, Victor A
Wright, Jim A
Pentrina, Uusiku
Noor, Abdisalan M
Snow, Robert W
Atkinson, Peter M
Spatial modelling of healthcare utilisation for treatment of fever in Namibia
title Spatial modelling of healthcare utilisation for treatment of fever in Namibia
title_full Spatial modelling of healthcare utilisation for treatment of fever in Namibia
title_fullStr Spatial modelling of healthcare utilisation for treatment of fever in Namibia
title_full_unstemmed Spatial modelling of healthcare utilisation for treatment of fever in Namibia
title_short Spatial modelling of healthcare utilisation for treatment of fever in Namibia
title_sort spatial modelling of healthcare utilisation for treatment of fever in namibia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292929/
https://www.ncbi.nlm.nih.gov/pubmed/22336441
http://dx.doi.org/10.1186/1476-072X-11-6
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