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Fine-scale malaria risk mapping from routine aggregated case data

Fine-scale malaria risk mapping from routine aggregated case data
Fine-scale malaria risk mapping from routine aggregated case data
Background: Mapping malaria risk is an integral component of efficient resource allocation. Routine health facility data are convenient to collect, but without information on the locations at which transmission occurred, their utility for predicting variation in risk at a sub-catchment level is presently unclear.

Methods: Using routinely collected health facility level case data in Swaziland between 2011-2013, and fine scale environmental and ecological variables, this study explores the use of a hierarchical Bayesian modelling framework for downscaling risk maps from health facility catchment level to a fine scale (1 km x 1 km). Fine scale predictions were validated using known household locations of cases and a random sample of points to act as pseudo-controls.

Results: Results show that fine-scale predictions were able to discriminate between cases and pseudo-controls with an AUC value of 0.84. When scaled up to catchment level, predicted numbers of cases per health facility showed broad correspondence with observed numbers of cases with little bias, with 84 of the 101 health facilities with zero cases correctly predicted as having zero cases.

Conclusions: This method holds promise for helping countries in pre-elimination and elimination stages use health facility level data to produce accurate risk maps at finer scales. Further validation in other transmission settings and an evaluation of the operational value of the approach is necessary.
1475-2875
421
Sturrock, Hugh J.W.
466a210d-9012-4603-a261-0afc0a8b62bb
Cohen, Justin M.
7de99049-a4c3-4fa1-8ff8-cc1bc5dcdfc9
Keil, Petr
d93eebc2-632d-49d6-91ee-4d4167acb79a
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Le Menach, Arnaud
31494ace-95c8-488f-bf84-42747d9d72a7
Ntshalintshali, Nyasatu E.
ac0e8cc0-c305-43ad-9cc1-83b78f330c28
Hsiang, Michelle S.
0370cbd3-3379-4af8-8856-87416dfdce61
Gosling, Roland D.
7c1ac561-ab78-4328-b0cb-c5fe442f6416
Sturrock, Hugh J.W.
466a210d-9012-4603-a261-0afc0a8b62bb
Cohen, Justin M.
7de99049-a4c3-4fa1-8ff8-cc1bc5dcdfc9
Keil, Petr
d93eebc2-632d-49d6-91ee-4d4167acb79a
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Le Menach, Arnaud
31494ace-95c8-488f-bf84-42747d9d72a7
Ntshalintshali, Nyasatu E.
ac0e8cc0-c305-43ad-9cc1-83b78f330c28
Hsiang, Michelle S.
0370cbd3-3379-4af8-8856-87416dfdce61
Gosling, Roland D.
7c1ac561-ab78-4328-b0cb-c5fe442f6416

Sturrock, Hugh J.W., Cohen, Justin M., Keil, Petr, Tatem, Andrew J., Le Menach, Arnaud, Ntshalintshali, Nyasatu E., Hsiang, Michelle S. and Gosling, Roland D. (2014) Fine-scale malaria risk mapping from routine aggregated case data. Malaria Journal, 13 (1), 421. (doi:10.1186/1475-2875-13-421).

Record type: Article

Abstract

Background: Mapping malaria risk is an integral component of efficient resource allocation. Routine health facility data are convenient to collect, but without information on the locations at which transmission occurred, their utility for predicting variation in risk at a sub-catchment level is presently unclear.

Methods: Using routinely collected health facility level case data in Swaziland between 2011-2013, and fine scale environmental and ecological variables, this study explores the use of a hierarchical Bayesian modelling framework for downscaling risk maps from health facility catchment level to a fine scale (1 km x 1 km). Fine scale predictions were validated using known household locations of cases and a random sample of points to act as pseudo-controls.

Results: Results show that fine-scale predictions were able to discriminate between cases and pseudo-controls with an AUC value of 0.84. When scaled up to catchment level, predicted numbers of cases per health facility showed broad correspondence with observed numbers of cases with little bias, with 84 of the 101 health facilities with zero cases correctly predicted as having zero cases.

Conclusions: This method holds promise for helping countries in pre-elimination and elimination stages use health facility level data to produce accurate risk maps at finer scales. Further validation in other transmission settings and an evaluation of the operational value of the approach is necessary.

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Published date: 3 November 2014
Organisations: Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 370704
URI: http://eprints.soton.ac.uk/id/eprint/370704
ISSN: 1475-2875
PURE UUID: 3f7a41eb-0681-4e3d-9655-304b4b97cea3
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 04 Nov 2014 11:39
Last modified: 15 Mar 2024 03:43

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Contributors

Author: Hugh J.W. Sturrock
Author: Justin M. Cohen
Author: Petr Keil
Author: Andrew J. Tatem ORCID iD
Author: Arnaud Le Menach
Author: Nyasatu E. Ntshalintshali
Author: Michelle S. Hsiang
Author: Roland D. Gosling

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