Association among house infestation index, dengue incidence, and sociodemographic indicators: surveillance using geographic information system

dc.creatorVargas, Waldemir Paixão
dc.creatorKawa, Hélia
dc.creatorSabroza, Paulo Chagastelles
dc.creatorSoares, Valdenir Bandeira
dc.creatorHonório, Nildimar Alves
dc.creatorAlmeida, Andréa Sobral de
dc.date2016-03-03T11:57:39Z
dc.date2016-03-03T11:57:39Z
dc.date2015
dc.date.accessioned2023-09-26T23:13:17Z
dc.date.available2023-09-26T23:13:17Z
dc.identifierVARGAS, Waldemir Paixão: et al. Association among house infestation index, dengue incidence, and sociodemographic indicators: surveillance using geographic information system. BMC Public Health, v.15:746, 25p, 2015.
dc.identifier1471-2458
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/12949
dc.identifier10.1186/s12889-015-2097-3
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8888175
dc.descriptionBackground: We identified dengue transmission areas by using the Geographic Information Systems located at local surveillance units of the Itaboraí municipality in state of Rio de Janeiro. We considered the association among the house infestation index, the disease incidence, and sociodemographic indicators during a prominent dengue outbreak in 2007 and 2008. Methods: In this ecological study, the Local Surveillance Units (UVLs) of the municipality were used as spatial pattern units. For the house analysis, we used the period of higher vector density that occurred previous to the larger magnitude epidemic range of dengue cases. The average dengue incidence rates calculated in this epidemic range were smoothed using the Bayesian method. The associations among the House Infestation Index (HI), the Bayesian rate of the average dengue incidence, and the sociodemographic indicators were evaluated using a Pearson’s correlation coefficient. The areas that were at a higher risk of dengue occurrence were detected using a kernel density estimation with the kernel quartic function. Results: The dengue transmission pattern in Itaboraí showed that the increase in the vector density preceded the increase in incidence. The HI was positively correlated to the Bayesian dengue incidence rate (r = 0.641; p = 0.01). The higher risk areas were those that were close to the main highways. In the Kernel density estimation analysis, we observed that the regions that were at a higher risk of dengue were those that were located in the UVLs and had the highest population densities; these locations were typically located along major highways. Four nuclei were identified as epicenters of high risk. Conclusions: The spatial analysis units used in this research, i.e., UVLs, served as a methodological resource for examining the compatibility of different information sources concerning the disease, the vector indices, and the municipal sociodemographic aspects and were arranged in distinct cartographic bases. Dengue is a multi-scale geographic phenomenon, and using the UVLs as analysis units made it possible to differentiate the dengue occurrence throughout the municipality. The methodological approach used in this research helped improve the Itaboraí municipality monitoring activities and the local territorial monitoring in other municipalities that are affected by this public health issue.
dc.formatapplication/pdf
dc.languageeng
dc.publisherBioMed Central
dc.rightsopen access
dc.subjectDengue
dc.subjectInfestation index
dc.subjectGeographic Information Systems
dc.subjectEpidemiological surveillance
dc.subjectDengue
dc.subjectVigilância Epidemiológica
dc.subjectSistemas de Informação Geográfica
dc.titleAssociation among house infestation index, dengue incidence, and sociodemographic indicators: surveillance using geographic information system
dc.typeArticle


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