Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model
OBJECTIVE: To study the relationship between the risk of dengue and sociodemographic variables through the use of spatial regression models fully Bayesian in the municipalities of Espírito Santo in 2010. METHOD: This is an ecological study and exploration that used spatial analysis tools in pre...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Associação Brasileira de Pós-Graduação em Saúde Coletiva
2014-01-01
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| Series: | Revista Brasileira de Epidemiologia |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2014000600150&lng=en&tlng=en |
| _version_ | 1828443232172769280 |
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| author | Taizi Honorato Priscila Pagung de Aquino Lapa Carolina Maia Martins Sales Barbara Reis-Santos Ricardo Tristão-Sá Adelmo Inácio Bertolde Ethel Leonor Noia Maciel |
| author_facet | Taizi Honorato Priscila Pagung de Aquino Lapa Carolina Maia Martins Sales Barbara Reis-Santos Ricardo Tristão-Sá Adelmo Inácio Bertolde Ethel Leonor Noia Maciel |
| author_sort | Taizi Honorato |
| collection | DOAJ |
| description | OBJECTIVE: To study the relationship between the risk of dengue and sociodemographic variables through the use of spatial regression models fully Bayesian in the municipalities of Espírito Santo in 2010. METHOD: This is an ecological study and exploration that used spatial analysis tools in preparing thematic maps with data obtained from SinanNet. An analysis by area, taking as unit the municipalities of the state, was performed. Thematic maps were constructed by the computer program R 2.15.00 and Deviance Information Criterion (DIC), calculated in WinBugs, Absolut and Normalized Mean Error (NMAE) were the criteria used to compare the models. RESULTS: We were able to geocode 21,933 dengue cases (rate of 623.99 cases per 100 thousand habitants) with a higher incidence in the municipalities of Vitória, Serra and Colatina; model with spatial effect with the covariates trash and income showed the best performance at DIC and Nmae criteria. CONCLUSION: It was possible to identify the relationship of dengue with factors outside the health sector and to identify areas with higher risk of disease. |
| first_indexed | 2024-12-10T21:25:49Z |
| format | Article |
| id | doaj.art-e69489995e754b5eabe41c1942290955 |
| institution | Directory Open Access Journal |
| issn | 1980-5497 |
| language | English |
| last_indexed | 2024-12-10T21:25:49Z |
| publishDate | 2014-01-01 |
| publisher | Associação Brasileira de Pós-Graduação em Saúde Coletiva |
| record_format | Article |
| series | Revista Brasileira de Epidemiologia |
| spelling | doaj.art-e69489995e754b5eabe41c19422909552022-12-22T01:32:59ZengAssociação Brasileira de Pós-Graduação em Saúde ColetivaRevista Brasileira de Epidemiologia1980-54972014-01-0117suppl 215015910.1590/1809-4503201400060013S1415-790X2014000600150Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian modelTaizi HonoratoPriscila Pagung de Aquino LapaCarolina Maia Martins SalesBarbara Reis-SantosRicardo Tristão-SáAdelmo Inácio BertoldeEthel Leonor Noia MacielOBJECTIVE: To study the relationship between the risk of dengue and sociodemographic variables through the use of spatial regression models fully Bayesian in the municipalities of Espírito Santo in 2010. METHOD: This is an ecological study and exploration that used spatial analysis tools in preparing thematic maps with data obtained from SinanNet. An analysis by area, taking as unit the municipalities of the state, was performed. Thematic maps were constructed by the computer program R 2.15.00 and Deviance Information Criterion (DIC), calculated in WinBugs, Absolut and Normalized Mean Error (NMAE) were the criteria used to compare the models. RESULTS: We were able to geocode 21,933 dengue cases (rate of 623.99 cases per 100 thousand habitants) with a higher incidence in the municipalities of Vitória, Serra and Colatina; model with spatial effect with the covariates trash and income showed the best performance at DIC and Nmae criteria. CONCLUSION: It was possible to identify the relationship of dengue with factors outside the health sector and to identify areas with higher risk of disease.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2014000600150&lng=en&tlng=enEpidemiologia e BioestatísticaDengueModelos linearesDeterminantes sociais da saúdeAnálise espacialInferência Bayesiana |
| spellingShingle | Taizi Honorato Priscila Pagung de Aquino Lapa Carolina Maia Martins Sales Barbara Reis-Santos Ricardo Tristão-Sá Adelmo Inácio Bertolde Ethel Leonor Noia Maciel Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model Revista Brasileira de Epidemiologia Epidemiologia e Bioestatística Dengue Modelos lineares Determinantes sociais da saúde Análise espacial Inferência Bayesiana |
| title | Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model |
| title_full | Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model |
| title_fullStr | Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model |
| title_full_unstemmed | Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model |
| title_short | Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model |
| title_sort | spatial analysis of distribution of dengue cases in espirito santo brazil in 2010 use of bayesian model |
| topic | Epidemiologia e Bioestatística Dengue Modelos lineares Determinantes sociais da saúde Análise espacial Inferência Bayesiana |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2014000600150&lng=en&tlng=en |
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