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A local space–time kriging approach applied to a national outpatient malaria data set

A local space–time kriging approach applied to a national outpatient malaria data set
A local space–time kriging approach applied to a national outpatient malaria data set
Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date information on a range of health metrics and this requirement is generally addressed by a Health Management Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases often contain only a small proportion of the expected records. In this paper, we take an important health metric in Kenya (the proportion of outpatient treatments for malaria (MP)) from the national HMIS database and predict the values of MP at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space–time prediction approach, and (b) the replacement of a globally stationary with a locally varying random function model. Space–time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than conventional spatial-only kriging. A modification of space–time kriging that allowed space–time variograms to be recalculated for every prediction location within a spatially local neighbourhood resulted in a larger decrease in mean imprecision over ordinary kriging (18.3%) although the mean bias was reduced less (87.5%).
space–time geostatistics, local kriging, malaria, public health, Kenya
0098-3004
1337-1350
Gething, P.W.
82a5722c-21cc-462c-bdaf-7af4d50a6219
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Noor, A.M.
241236c3-43df-47b0-bcab-ff7c25318cc6
Gikandi, P.W.
1952a0cc-9b84-4d50-bffe-5242118c78f1
Hay, S.I.
18d621e0-2813-4c05-b2b7-09df3f24aca7
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Gething, P.W.
82a5722c-21cc-462c-bdaf-7af4d50a6219
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Noor, A.M.
241236c3-43df-47b0-bcab-ff7c25318cc6
Gikandi, P.W.
1952a0cc-9b84-4d50-bffe-5242118c78f1
Hay, S.I.
18d621e0-2813-4c05-b2b7-09df3f24aca7
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Gething, P.W., Atkinson, P.M., Noor, A.M., Gikandi, P.W., Hay, S.I. and Nixon, M.S. (2007) A local space–time kriging approach applied to a national outpatient malaria data set. Computers & Geosciences, 33 (10), 1337-1350. (doi:10.1016/j.cageo.2007.05.006).

Record type: Article

Abstract

Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date information on a range of health metrics and this requirement is generally addressed by a Health Management Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases often contain only a small proportion of the expected records. In this paper, we take an important health metric in Kenya (the proportion of outpatient treatments for malaria (MP)) from the national HMIS database and predict the values of MP at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space–time prediction approach, and (b) the replacement of a globally stationary with a locally varying random function model. Space–time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than conventional spatial-only kriging. A modification of space–time kriging that allowed space–time variograms to be recalculated for every prediction location within a spatially local neighbourhood resulted in a larger decrease in mean imprecision over ordinary kriging (18.3%) although the mean bias was reduced less (87.5%).

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More information

Submitted date: 14 March 2006
Published date: October 2007
Keywords: space–time geostatistics, local kriging, malaria, public health, Kenya
Organisations: Remote Sensing & Spatial Analysis

Identifiers

Local EPrints ID: 264890
URI: http://eprints.soton.ac.uk/id/eprint/264890
ISSN: 0098-3004
PURE UUID: 832c16a0-0ce6-481b-a29e-a63c44ff1843
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for M.S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 23 Nov 2007 16:40
Last modified: 19 Nov 2019 02:04

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