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Improving imperfect data from health management information systems in Africa using space-time geostatistics

Improving imperfect data from health management information systems in Africa using space-time geostatistics
Improving imperfect data from health management information systems in Africa using space-time geostatistics
Background
Reliable and timely information on disease-specific treatment burdens within a health system
is critical for the planning and monitoring of service provision. Health management information
systems (HMIS) exist to address this need at national scales across Africa but are failing to
deliver adequate data because of widespread underreporting by health facilities. Faced with
this inadequacy, vital public health decisions often rely on crudely adjusted regional and
national estimates of treatment burdens.
Methods and Findings
This study has taken the example of presumed malaria in outpatients within the largely
incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that
can predict values for all data that are missing through space and time. The resulting complete
set can then be used to define treatment burdens for presumed malaria at any level of spatial
and temporal aggregation. Validation of the model has shown that these burdens are
quantified to an acceptable level of accuracy at the district, provincial, and national scale.
Conclusions
The modelling framework presented here provides, to our knowledge for the first time,
reliable information from imperfect HMIS data to support evidence-based decision-making at
national and sub-national levels.
health care, Africa, health managament information systems
1549-1277
825-831
Gething, Peter W.
6afb7d8c-8816-4c03-ae73-55951c8b197f
Noor, Abdisalan M.
06d32991-29fe-47a5-a62b-fe584c753414
Gikandi, Priscilla W.
86483393-ae74-4f5d-8ec8-34fb2d8c09d7
Ogara, Esther A. A.
ada41b2f-c980-4729-be88-f4e8ff0435f9
Hay, Simon I.
471d3ae4-a3c1-4d29-93e3-a90d44471b00
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Snow, Robert S.
13ff6a95-3aa1-4b5e-91db-3980b238ef2e
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Gething, Peter W.
6afb7d8c-8816-4c03-ae73-55951c8b197f
Noor, Abdisalan M.
06d32991-29fe-47a5-a62b-fe584c753414
Gikandi, Priscilla W.
86483393-ae74-4f5d-8ec8-34fb2d8c09d7
Ogara, Esther A. A.
ada41b2f-c980-4729-be88-f4e8ff0435f9
Hay, Simon I.
471d3ae4-a3c1-4d29-93e3-a90d44471b00
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Snow, Robert S.
13ff6a95-3aa1-4b5e-91db-3980b238ef2e
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Gething, Peter W., Noor, Abdisalan M., Gikandi, Priscilla W., Ogara, Esther A. A., Hay, Simon I., Nixon, Mark S., Snow, Robert S. and Atkinson, Peter M. (2006) Improving imperfect data from health management information systems in Africa using space-time geostatistics. PLoS Medicine, 3 (6), 825-831. (doi:10.1371/journal.pmed.0030271).

Record type: Article

Abstract

Background
Reliable and timely information on disease-specific treatment burdens within a health system
is critical for the planning and monitoring of service provision. Health management information
systems (HMIS) exist to address this need at national scales across Africa but are failing to
deliver adequate data because of widespread underreporting by health facilities. Faced with
this inadequacy, vital public health decisions often rely on crudely adjusted regional and
national estimates of treatment burdens.
Methods and Findings
This study has taken the example of presumed malaria in outpatients within the largely
incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that
can predict values for all data that are missing through space and time. The resulting complete
set can then be used to define treatment burdens for presumed malaria at any level of spatial
and temporal aggregation. Validation of the model has shown that these burdens are
quantified to an acceptable level of accuracy at the district, provincial, and national scale.
Conclusions
The modelling framework presented here provides, to our knowledge for the first time,
reliable information from imperfect HMIS data to support evidence-based decision-making at
national and sub-national levels.

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More information

Submitted date: 29 November 2005
Published date: 6 June 2006
Keywords: health care, Africa, health managament information systems
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 262858
URI: https://eprints.soton.ac.uk/id/eprint/262858
ISSN: 1549-1277
PURE UUID: d4989648-fc61-42ae-9bb1-4d5d48f3fe72
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 21 Jul 2006
Last modified: 08 Oct 2019 00:57

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