Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/202661 |
Resumo: | Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components — longevity, education, and income) with TB spatial distribution. Results: During the study period (2011–2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036). Conclusions: We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs. |
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Gehlen, MirelaNicola, Maria Rita CastilhosDalla Costa, Elis ReginaCabral, Vagner KunzQuadros, Everton Luís Luz deChaves, Caroline OliveiraLahm, Regis AlexandreNicolella, AlbertoRossetti, Maria LúciaSilva, Denise Rossato2019-12-18T03:59:24Z20191876-035Xhttp://hdl.handle.net/10183/202661001106333Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components — longevity, education, and income) with TB spatial distribution. Results: During the study period (2011–2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036). Conclusions: We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs.application/pdfengJournal of infection and public health. Oxford. Vol. 12 (2019), p. 681–689TuberculoseEpidemiologiaIncidênciaAnálise espacialMapeamento geográficoAnálise por conglomeradosIndicadores de desenvolvimentoBrasilTuberculosisGeospatial intelligenceGeographic information systemsDisease hotspotsClusterGeospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in BrazilEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001106333.pdf.txt001106333.pdf.txtExtracted Texttext/plain34534http://www.lume.ufrgs.br/bitstream/10183/202661/2/001106333.pdf.txteabdf01df2e15b5ae41ee9d20460be55MD52ORIGINAL001106333.pdfTexto completo (inglês)application/pdf2581105http://www.lume.ufrgs.br/bitstream/10183/202661/1/001106333.pdfff0dc25cf7372384e1d61011b6598562MD5110183/2026612019-12-19 04:59:36.040765oai:www.lume.ufrgs.br:10183/202661Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2019-12-19T06:59:36Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
title |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
spellingShingle |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil Gehlen, Mirela Tuberculose Epidemiologia Incidência Análise espacial Mapeamento geográfico Análise por conglomerados Indicadores de desenvolvimento Brasil Tuberculosis Geospatial intelligence Geographic information systems Disease hotspots Cluster |
title_short |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
title_full |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
title_fullStr |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
title_full_unstemmed |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
title_sort |
Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil |
author |
Gehlen, Mirela |
author_facet |
Gehlen, Mirela Nicola, Maria Rita Castilhos Dalla Costa, Elis Regina Cabral, Vagner Kunz Quadros, Everton Luís Luz de Chaves, Caroline Oliveira Lahm, Regis Alexandre Nicolella, Alberto Rossetti, Maria Lúcia Silva, Denise Rossato |
author_role |
author |
author2 |
Nicola, Maria Rita Castilhos Dalla Costa, Elis Regina Cabral, Vagner Kunz Quadros, Everton Luís Luz de Chaves, Caroline Oliveira Lahm, Regis Alexandre Nicolella, Alberto Rossetti, Maria Lúcia Silva, Denise Rossato |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Gehlen, Mirela Nicola, Maria Rita Castilhos Dalla Costa, Elis Regina Cabral, Vagner Kunz Quadros, Everton Luís Luz de Chaves, Caroline Oliveira Lahm, Regis Alexandre Nicolella, Alberto Rossetti, Maria Lúcia Silva, Denise Rossato |
dc.subject.por.fl_str_mv |
Tuberculose Epidemiologia Incidência Análise espacial Mapeamento geográfico Análise por conglomerados Indicadores de desenvolvimento Brasil |
topic |
Tuberculose Epidemiologia Incidência Análise espacial Mapeamento geográfico Análise por conglomerados Indicadores de desenvolvimento Brasil Tuberculosis Geospatial intelligence Geographic information systems Disease hotspots Cluster |
dc.subject.eng.fl_str_mv |
Tuberculosis Geospatial intelligence Geographic information systems Disease hotspots Cluster |
description |
Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components — longevity, education, and income) with TB spatial distribution. Results: During the study period (2011–2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036). Conclusions: We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs. |
publishDate |
2019 |
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2019-12-18T03:59:24Z |
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2019 |
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Estrangeiro info:eu-repo/semantics/article |
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http://hdl.handle.net/10183/202661 |
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1876-035X |
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001106333 |
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Journal of infection and public health. Oxford. Vol. 12 (2019), p. 681–689 |
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