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Developing geostatistical space–time models to predict outpatient treatment burdens from incomplete national data

Developing geostatistical space–time models to predict outpatient treatment burdens from incomplete national data
Developing geostatistical space–time models to predict outpatient treatment burdens from incomplete national data
Basic health system data such as the number of patients utilizing different health facilities and the types of illness for which they are being treated are critical for managing service provision. These data requirements are generally addressed with some form of national Health Management Information System (HMIS), which coordinates the routine collection and compilation of data from national health facilities. HMIS in most developing countries are characterized by widespread underreporting. Here we present a method to adjust incomplete data to allow prediction of national outpatient treatment burdens. We demonstrate this method with the example of outpatient treatments for malaria within the Kenyan HMIS. Three alternative modeling frameworks were developed and tested in which space–time geostatistical prediction algorithms were used to predict the monthly tally of treatments for presumed malaria cases (MC) at facilities where such records were missing. Models were compared by a crossvalidation exercise and the model found to most accurately predict MC incorporated available data on the total number of patients visiting each facility each month. A space–time stochastic simulation framework to accompany this model was developed and tested in order to provide estimates of both local and regional prediction uncertainty. The level of accuracy provided by the predictive model, and the accompanying estimates of uncertainty around the predictions, demonstrate how this tool can mitigate the uncertainties caused by missing data, substantially enhancing the utility of existing HMIS data to health-service decision makers.
0016-7363
167-188
Gething, Peter
ca9898f8-55b0-42bd-8554-b7393e1bb267
Noor, Absilan
7cf5f586-9d1c-4e44-b7d6-820be9e7bbc9
Gikandi, Priscilla
07abb99a-cef9-4aaf-b784-1670899a0ffa
Hay, Simon
085df25d-03f1-4685-b981-e6792f034496
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Gething, Peter
ca9898f8-55b0-42bd-8554-b7393e1bb267
Noor, Absilan
7cf5f586-9d1c-4e44-b7d6-820be9e7bbc9
Gikandi, Priscilla
07abb99a-cef9-4aaf-b784-1670899a0ffa
Hay, Simon
085df25d-03f1-4685-b981-e6792f034496
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b

Gething, Peter, Noor, Absilan, Gikandi, Priscilla, Hay, Simon, Nixon, Mark and Atkinson, Peter (2008) Developing geostatistical space–time models to predict outpatient treatment burdens from incomplete national data. Geographical Analysis, 40 (2), 167-188. (doi:10.1111/j.1538-4632.2008.00718.x).

Record type: Article

Abstract

Basic health system data such as the number of patients utilizing different health facilities and the types of illness for which they are being treated are critical for managing service provision. These data requirements are generally addressed with some form of national Health Management Information System (HMIS), which coordinates the routine collection and compilation of data from national health facilities. HMIS in most developing countries are characterized by widespread underreporting. Here we present a method to adjust incomplete data to allow prediction of national outpatient treatment burdens. We demonstrate this method with the example of outpatient treatments for malaria within the Kenyan HMIS. Three alternative modeling frameworks were developed and tested in which space–time geostatistical prediction algorithms were used to predict the monthly tally of treatments for presumed malaria cases (MC) at facilities where such records were missing. Models were compared by a crossvalidation exercise and the model found to most accurately predict MC incorporated available data on the total number of patients visiting each facility each month. A space–time stochastic simulation framework to accompany this model was developed and tested in order to provide estimates of both local and regional prediction uncertainty. The level of accuracy provided by the predictive model, and the accompanying estimates of uncertainty around the predictions, demonstrate how this tool can mitigate the uncertainties caused by missing data, substantially enhancing the utility of existing HMIS data to health-service decision makers.

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Published date: April 2008

Identifiers

Local EPrints ID: 150975
URI: https://eprints.soton.ac.uk/id/eprint/150975
ISSN: 0016-7363
PURE UUID: d0abbdf2-b556-468d-9e7b-ab4304e4e449
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 06 May 2010 15:16
Last modified: 19 Jun 2018 00:37

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Contributors

Author: Peter Gething
Author: Absilan Noor
Author: Priscilla Gikandi
Author: Simon Hay
Author: Mark Nixon ORCID iD
Author: Peter Atkinson

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