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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 Kernel estimator of the dengue Bayesian rate (epidemic range of 2008) and the main highways, Itaboraí municipality, Rio de Janeiro State, Brazil
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Fig5: Map of the Kernel estimator of the dengue Bayesian rate (epidemic range of 2008) and the main highways, Itaboraí municipality, Rio de Janeiro State, Brazil

Mentions: In the Kernel estimate analysis, we observed that the highest dengue risk regions were located in the UVLs that had the highest population densities; these regions also happen to be located along major highways. Four nuclei were identified as high risk. The first nucleus is a large central nucleus composed of three UVLs (ita08, ita13, and ita15); is located along highway BR 101; has a concentrated population of 72,439 inhabitants, which represents 34 % of the population of the municipality in 2008 (211,697 inhabitants); and has a demographic density that varied from 11,172.65 to 3,053.52/ Km2. The second nucleus (Nucleus 2), which is located to the right of the first, is in the northeast region of the municipality along state highway RJ 116 and is composed of three UVLs, one rural (ita07) and two urban (ita11 and ita14). These three units together contain 34,900 inhabitants, approximately 17 % of the total population, and have a demographic density between 110.08 and 1,102.64/ Km2. The third nucleus (Nucleus 3) is located left of the first nucleus; is composed of two UVLs, with a high population density (ita09 and ita18); and is close to highways BR 101, BR493, and RJ 104. The total population is 48,325 inhabitants, which represents 23 % of the population of the city. The fourth nucleus (Nucleus 4), which is of lesser intensity, is composed of the urban UVL ita06, is located on the border of the highway João Batista Caffara Campos, and has a population of 4,293 inhabitants and a demographic density of 470.72/Km2 (Fig. 5).Fig. 5


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 Kernel estimator of the dengue Bayesian rate (epidemic range of 2008) and the main highways, 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

Fig5: Map of the Kernel estimator of the dengue Bayesian rate (epidemic range of 2008) and the main highways, Itaboraí municipality, Rio de Janeiro State, Brazil
Mentions: In the Kernel estimate analysis, we observed that the highest dengue risk regions were located in the UVLs that had the highest population densities; these regions also happen to be located along major highways. Four nuclei were identified as high risk. The first nucleus is a large central nucleus composed of three UVLs (ita08, ita13, and ita15); is located along highway BR 101; has a concentrated population of 72,439 inhabitants, which represents 34 % of the population of the municipality in 2008 (211,697 inhabitants); and has a demographic density that varied from 11,172.65 to 3,053.52/ Km2. The second nucleus (Nucleus 2), which is located to the right of the first, is in the northeast region of the municipality along state highway RJ 116 and is composed of three UVLs, one rural (ita07) and two urban (ita11 and ita14). These three units together contain 34,900 inhabitants, approximately 17 % of the total population, and have a demographic density between 110.08 and 1,102.64/ Km2. The third nucleus (Nucleus 3) is located left of the first nucleus; is composed of two UVLs, with a high population density (ita09 and ita18); and is close to highways BR 101, BR493, and RJ 104. The total population is 48,325 inhabitants, which represents 23 % of the population of the city. The fourth nucleus (Nucleus 4), which is of lesser intensity, is composed of the urban UVL ita06, is located on the border of the highway João Batista Caffara Campos, and has a population of 4,293 inhabitants and a demographic density of 470.72/Km2 (Fig. 5).Fig. 5

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