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Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda

BACKGROUND: Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. METHODS: GIS is used to evaluate the feasibility of m-Healt...

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Autores principales: Larocca, Alberto, Moro Visconti, Roberto, Marconi, Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075756/
https://www.ncbi.nlm.nih.gov/pubmed/27776516
http://dx.doi.org/10.1186/s12936-016-1546-5
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author Larocca, Alberto
Moro Visconti, Roberto
Marconi, Michele
author_facet Larocca, Alberto
Moro Visconti, Roberto
Marconi, Michele
author_sort Larocca, Alberto
collection PubMed
description BACKGROUND: Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. METHODS: GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1) malaria affects the largest number of people; (2) the application of m-Health protocol based on the mobile network has the highest potential impact. RESULTS: About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende) could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network. CONCLUSIONS: The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.
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spelling pubmed-50757562016-10-28 Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda Larocca, Alberto Moro Visconti, Roberto Marconi, Michele Malar J Research BACKGROUND: Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. METHODS: GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1) malaria affects the largest number of people; (2) the application of m-Health protocol based on the mobile network has the highest potential impact. RESULTS: About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende) could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network. CONCLUSIONS: The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising. BioMed Central 2016-10-24 /pmc/articles/PMC5075756/ /pubmed/27776516 http://dx.doi.org/10.1186/s12936-016-1546-5 Text en © The Author(s) 2016 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
Larocca, Alberto
Moro Visconti, Roberto
Marconi, Michele
Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
title Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
title_full Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
title_fullStr Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
title_full_unstemmed Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
title_short Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda
title_sort malaria diagnosis and mapping with m-health and geographic information systems (gis): evidence from uganda
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075756/
https://www.ncbi.nlm.nih.gov/pubmed/27776516
http://dx.doi.org/10.1186/s12936-016-1546-5
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