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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

Map of the location of UVLs in the municipality of Itaboraí, State of Rio de Janeiro, Brazil
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Fig1: Map of the location of UVLs in the municipality of Itaboraí, State of Rio de Janeiro, Brazil

Mentions: The Itaboraí municipality (latitude 22° 44’ 51” South, longitude 42° 51’ 21” West) is situated in the metropolitan region of Rio de Janeiro and has an area of 430.373 km2, which corresponds to 8.08 % of the region (Fig. 1). The population is 218,008 inhabitants, and the demographic density is 506.56/km2. Itaboraí is 17 m above sea level and is located 40 km from the municipality of Rio de Janeiro and 75,57 Km from the coastline and the Eastern region of the state. It has 79 neighborhoods (Ibge 2010) that are divided into 8 districts: Centro (thirty-three neighborhoods), Porto de Caixas (two neighborhoods), Itambi (eight neighborhoods), Sambaetiba (six neighborhoods), Visconde de Itaboraí (seven neighborhoods), Cabuçu (seven neighborhoods), Manilha (thirteen neighborhoods), and Pacheco (three neighborhoods). The districts of Cabuçu and Pachecos are predominantly rural with small urban nuclei and are characterized as rural–urban areas.Fig. 1


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)

Map of the location of UVLs in the municipality of Itaboraí, State of Rio de Janeiro, Brazil
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Map of the location of UVLs in the municipality of Itaboraí, State of Rio de Janeiro, Brazil
Mentions: The Itaboraí municipality (latitude 22° 44’ 51” South, longitude 42° 51’ 21” West) is situated in the metropolitan region of Rio de Janeiro and has an area of 430.373 km2, which corresponds to 8.08 % of the region (Fig. 1). The population is 218,008 inhabitants, and the demographic density is 506.56/km2. Itaboraí is 17 m above sea level and is located 40 km from the municipality of Rio de Janeiro and 75,57 Km from the coastline and the Eastern region of the state. It has 79 neighborhoods (Ibge 2010) that are divided into 8 districts: Centro (thirty-three neighborhoods), Porto de Caixas (two neighborhoods), Itambi (eight neighborhoods), Sambaetiba (six neighborhoods), Visconde de Itaboraí (seven neighborhoods), Cabuçu (seven neighborhoods), Manilha (thirteen neighborhoods), and Pacheco (three neighborhoods). The districts of Cabuçu and Pachecos are predominantly rural with small urban nuclei and are characterized as rural–urban areas.Fig. 1

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