OBJECTIVE: To further understand the disease behavior of dengue by analyzing studies on dengue and geoprocessing, as well as socioeconomic and environmental indicators. METHOD: MEDLINE, SciELO, and Lilacs databases, as well as the CAPES dissertation databank, were searched using the following key words: dengue, geographic information system, spatial analysis, geoprocessing, remote sensing, and socioenvironmental indicators. A manual search of the bibliographies of select articles was also performed. All studies published in English, Portuguese, or Spanish, through December 2007, that focused on dengue, geoprocessing, and socioeconomic and environmental indicators were included. The relevant articles were grouped according to type (serologic surveys or secondary data analyses) and spatial analysis unit (municipality, health district, neighborhood, administrative region, census tracts, and city blocks). RESULTS: Twenty-two studies from Latin America (19 from Brazil) were evaluated. Six were serologic surveys and 16 employed secondary data. Geographic information systems were employed in one survey, and 11 used secondary data analyses. Spatial clustering was similar in both types of studies. Poverty was not a major risk factor for the disease. Spatial heterogeneity of living conditions and incidence was reported by 15 of 16 studies with secondary data. CONCLUSIONS: Since the complexity of dengue is closely tied to the ecological characteristics of the environment, studies based on spatial clusters plus local environmental determinants provide a more comprehensive view of the disease. These studies also allow for the identification of spatial heterogeneity, shown to be a key to understanding how dengue epidemics develop.
OBJECTIVE: To further understand the disease behavior of dengue by analyzing studies on dengue and geoprocessing, as well as socioeconomic and environmental indicators. METHOD: MEDLINE, SciELO, and Lilacs databases, as well as the CAPES dissertation databank, were searched using the following key words: dengue, geographic information system, spatial analysis, geoprocessing, remote sensing, and socioenvironmental indicators. A manual search of the bibliographies of select articles was also performed. All studies published in English, Portuguese, or Spanish, through December 2007, that focused on dengue, geoprocessing, and socioeconomic and environmental indicators were included. The relevant articles were grouped according to type (serologic surveys or secondary data analyses) and spatial analysis unit (municipality, health district, neighborhood, administrative region, census tracts, and city blocks). RESULTS: Twenty-two studies from Latin America (19 from Brazil) were evaluated. Six were serologic surveys and 16 employed secondary data. Geographic information systems were employed in one survey, and 11 used secondary data analyses. Spatial clustering was similar in both types of studies. Poverty was not a major risk factor for the disease. Spatial heterogeneity of living conditions and incidence was reported by 15 of 16 studies with secondary data. CONCLUSIONS: Since the complexity of dengue is closely tied to the ecological characteristics of the environment, studies based on spatial clusters plus local environmental determinants provide a more comprehensive view of the disease. These studies also allow for the identification of spatial heterogeneity, shown to be a key to understanding how dengue epidemics develop.
Authors: Trevon L Fuller; Guilherme Calvet; Camila Genaro Estevam; Jussara Rafael Angelo; Gbenga J Abiodun; Umme-Aiman Halai; Bianca De Santis; Patricia Carvalho Sequeira; Eliane Machado Araujo; Simone Alves Sampaio; Marco Cesar Lima de Mendonça; Allison Fabri; Rita Maria Ribeiro; Ryan Harrigan; Thomas B Smith; Claudia Raja Gabaglia; Patrícia Brasil; Ana Maria Bispo de Filippis; Karin Nielsen-Saines Journal: PLoS One Date: 2017-11-16 Impact factor: 3.240
Authors: Jefferson Pereira Caldas Santos; Nildimar Alves Honório; Christovam Barcellos; Aline Araújo Nobre Journal: Int J Environ Res Public Health Date: 2020-09-08 Impact factor: 3.390