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Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda

BACKGROUND: Spatial epidemiological analyses primarily depend on spatially-indexed medical records. Some countries have devised ways of capturing patient-specific spatial details using ZIP codes, postcodes or personal numbers, which are geocoded. However, for most resource-constrained African countr...

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Autores principales: Aturinde, Augustus, Rose, Nakasi, Farnaghi, Mahdi, Maiga, Gilbert, Pilesjö, Petter, Mansourian, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842149/
https://www.ncbi.nlm.nih.gov/pubmed/31703685
http://dx.doi.org/10.1186/s12911-019-0949-y
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author Aturinde, Augustus
Rose, Nakasi
Farnaghi, Mahdi
Maiga, Gilbert
Pilesjö, Petter
Mansourian, Ali
author_facet Aturinde, Augustus
Rose, Nakasi
Farnaghi, Mahdi
Maiga, Gilbert
Pilesjö, Petter
Mansourian, Ali
author_sort Aturinde, Augustus
collection PubMed
description BACKGROUND: Spatial epidemiological analyses primarily depend on spatially-indexed medical records. Some countries have devised ways of capturing patient-specific spatial details using ZIP codes, postcodes or personal numbers, which are geocoded. However, for most resource-constrained African countries, the absence of a means to capture patient resident location as well as inexistence of spatial data infrastructures makes capturing of patient-level spatial data unattainable. METHODS: This paper proposes and demonstrates a creative low-cost solution to address the issue. The solution is based on using interoperable web services to capture fine-scale locational information from existing “spatial data pools” and link them to the patients’ information. RESULTS: Based on a case study in Uganda, the paper presents the idea and develops a prototype for a spatially-enabled health registry system that allows for fine-level spatial epidemiological analyses. CONCLUSION: It has been shown and discussed that the proposed solution is feasible for implementation and the collected spatially-indexed data can be used in spatial epidemiological analyses to identify hotspot areas with elevated disease incidence rates, link health outcomes to environmental exposures, and generally improve healthcare planning and provisioning.
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spelling pubmed-68421492019-11-14 Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda Aturinde, Augustus Rose, Nakasi Farnaghi, Mahdi Maiga, Gilbert Pilesjö, Petter Mansourian, Ali BMC Med Inform Decis Mak Research Article BACKGROUND: Spatial epidemiological analyses primarily depend on spatially-indexed medical records. Some countries have devised ways of capturing patient-specific spatial details using ZIP codes, postcodes or personal numbers, which are geocoded. However, for most resource-constrained African countries, the absence of a means to capture patient resident location as well as inexistence of spatial data infrastructures makes capturing of patient-level spatial data unattainable. METHODS: This paper proposes and demonstrates a creative low-cost solution to address the issue. The solution is based on using interoperable web services to capture fine-scale locational information from existing “spatial data pools” and link them to the patients’ information. RESULTS: Based on a case study in Uganda, the paper presents the idea and develops a prototype for a spatially-enabled health registry system that allows for fine-level spatial epidemiological analyses. CONCLUSION: It has been shown and discussed that the proposed solution is feasible for implementation and the collected spatially-indexed data can be used in spatial epidemiological analyses to identify hotspot areas with elevated disease incidence rates, link health outcomes to environmental exposures, and generally improve healthcare planning and provisioning. BioMed Central 2019-11-08 /pmc/articles/PMC6842149/ /pubmed/31703685 http://dx.doi.org/10.1186/s12911-019-0949-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Aturinde, Augustus
Rose, Nakasi
Farnaghi, Mahdi
Maiga, Gilbert
Pilesjö, Petter
Mansourian, Ali
Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda
title Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda
title_full Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda
title_fullStr Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda
title_full_unstemmed Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda
title_short Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda
title_sort establishing spatially-enabled health registry systems using implicit spatial data pools: case study – uganda
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842149/
https://www.ncbi.nlm.nih.gov/pubmed/31703685
http://dx.doi.org/10.1186/s12911-019-0949-y
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