Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil

Detalhes bibliográficos
Autor(a) principal: Gehlen, Mirela
Data de Publicação: 2019
Outros Autores: 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
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.
id UFRGS-2_732ef7e3a8e70934e44680c66f357bb8
oai_identifier_str oai:www.lume.ufrgs.br:10183/202661
network_acronym_str UFRGS-2
network_name_str Repositório Institucional da UFRGS
repository_id_str
spelling 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
dc.date.accessioned.fl_str_mv 2019-12-18T03:59:24Z
dc.date.issued.fl_str_mv 2019
dc.type.driver.fl_str_mv Estrangeiro
info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/202661
dc.identifier.issn.pt_BR.fl_str_mv 1876-035X
dc.identifier.nrb.pt_BR.fl_str_mv 001106333
identifier_str_mv 1876-035X
001106333
url http://hdl.handle.net/10183/202661
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Journal of infection and public health. Oxford. Vol. 12 (2019), p. 681–689
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRGS
instname:Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
instname_str Universidade Federal do Rio Grande do Sul (UFRGS)
instacron_str UFRGS
institution UFRGS
reponame_str Repositório Institucional da UFRGS
collection Repositório Institucional da UFRGS
bitstream.url.fl_str_mv http://www.lume.ufrgs.br/bitstream/10183/202661/2/001106333.pdf.txt
http://www.lume.ufrgs.br/bitstream/10183/202661/1/001106333.pdf
bitstream.checksum.fl_str_mv eabdf01df2e15b5ae41ee9d20460be55
ff0dc25cf7372384e1d61011b6598562
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)
repository.mail.fl_str_mv
_version_ 1801224980559560704