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

Vargas WP, Kawa H, Sabroza PC, Soares VB, Honório NA, de Almeida AS - BMC Public Health (2015)

Bottom Line: The higher risk areas were those that were close to the main highways.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.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.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Endemias Samuel Pessoa, Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rua Leopoldo Bulhões, 1480, 6° andar, Manguinhos, CEP 21041-210, Rio de Janeiro, RJ, Brazil. waldemir.vargas@ensp.fiocruz.br.

ABSTRACT

Background: 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.

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Related in: MedlinePlus

Maps of the sociodemographic indicators related to the property water supply (piped and well) and garbage disposal, which characterize the urban infrastructure, 2010 Demographic Census, Itaboraí municipality, Rio de Janeiro State, Brazil
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Fig4: Maps of the sociodemographic indicators related to the property water supply (piped and well) and garbage disposal, which characterize the urban infrastructure, 2010 Demographic Census, Itaboraí municipality, Rio de Janeiro State, Brazil

Mentions: By mapping the water supplies according to the UVLs, we can observe the precarious water supply on an intra-municipal scale where most UVLs, in which houses are linked to the piped supply, do not surpass the second interval of the class distribution (0.01–20.0). However, almost all of these UVLs contain houses that use wells as their water supply (Fig. 4). Considering the municipality as a whole, it can be observed that only 27.9 % of the properties are linked to the piped water supply and that approximately 80 % of the houses use a well to obtain their water.Fig. 4


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

Vargas WP, Kawa H, Sabroza PC, Soares VB, Honório NA, de Almeida AS - BMC Public Health (2015)

Maps of the sociodemographic indicators related to the property water supply (piped and well) and garbage disposal, which characterize the urban infrastructure, 2010 Demographic Census, Itaboraí municipality, Rio de Janeiro State, Brazil
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526415&req=5

Fig4: Maps of the sociodemographic indicators related to the property water supply (piped and well) and garbage disposal, which characterize the urban infrastructure, 2010 Demographic Census, Itaboraí municipality, Rio de Janeiro State, Brazil
Mentions: By mapping the water supplies according to the UVLs, we can observe the precarious water supply on an intra-municipal scale where most UVLs, in which houses are linked to the piped supply, do not surpass the second interval of the class distribution (0.01–20.0). However, almost all of these UVLs contain houses that use wells as their water supply (Fig. 4). Considering the municipality as a whole, it can be observed that only 27.9 % of the properties are linked to the piped water supply and that approximately 80 % of the houses use a well to obtain their water.Fig. 4

Bottom Line: The higher risk areas were those that were close to the main highways.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.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.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Endemias Samuel Pessoa, Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rua Leopoldo Bulhões, 1480, 6° andar, Manguinhos, CEP 21041-210, Rio de Janeiro, RJ, Brazil. waldemir.vargas@ensp.fiocruz.br.

ABSTRACT

Background: 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.

Show MeSH
Related in: MedlinePlus