Improving imperfect data from health management information systems in Africa using space-time geostatistics
<p>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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Public Library of Science
2006
|
| Subjects: |
| _version_ | 1826299064884396032 |
|---|---|
| author | Gething, P Noor, A Gikandi, P Ogara, E Hay, S Nixon, M Snow, R Atkinson, P |
| author_facet | Gething, P Noor, A Gikandi, P Ogara, E Hay, S Nixon, M Snow, R Atkinson, P |
| author_sort | Gething, P |
| collection | OXFORD |
| description | <p>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.</p><p>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.</p><p>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.</p> |
| first_indexed | 2024-03-07T04:56:14Z |
| format | Journal article |
| id | oxford-uuid:d6b20b84-1198-46c9-8d7b-99622b2f9588 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:56:14Z |
| publishDate | 2006 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:d6b20b84-1198-46c9-8d7b-99622b2f95882022-03-27T08:35:28ZImproving imperfect data from health management information systems in Africa using space-time geostatisticsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d6b20b84-1198-46c9-8d7b-99622b2f9588Zoological sciencesEcology (zoology)Public HealthEpidemiologyMalariaTropical medicineAfricaGeographyEnglishOxford University Research Archive - ValetPublic Library of Science2006Gething, PNoor, AGikandi, POgara, EHay, SNixon, MSnow, RAtkinson, P<p>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.</p><p>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.</p><p>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.</p> |
| spellingShingle | Zoological sciences Ecology (zoology) Public Health Epidemiology Malaria Tropical medicine Africa Geography Gething, P Noor, A Gikandi, P Ogara, E Hay, S Nixon, M Snow, R Atkinson, P Improving imperfect data from health management information systems in Africa using space-time geostatistics |
| title | Improving imperfect data from health management information systems in Africa using space-time geostatistics |
| title_full | Improving imperfect data from health management information systems in Africa using space-time geostatistics |
| title_fullStr | Improving imperfect data from health management information systems in Africa using space-time geostatistics |
| title_full_unstemmed | Improving imperfect data from health management information systems in Africa using space-time geostatistics |
| title_short | Improving imperfect data from health management information systems in Africa using space-time geostatistics |
| title_sort | improving imperfect data from health management information systems in africa using space time geostatistics |
| topic | Zoological sciences Ecology (zoology) Public Health Epidemiology Malaria Tropical medicine Africa Geography |
| work_keys_str_mv | AT gethingp improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT noora improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT gikandip improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT ogarae improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT hays improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT nixonm improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT snowr improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics AT atkinsonp improvingimperfectdatafromhealthmanagementinformationsystemsinafricausingspacetimegeostatistics |