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Spatial analysis of infection by the human immunodeficiency virus among pregnant women

OBJECTIVES: to analyze the spatial distribution of reported cases of pregnant women infected by the human immunodeficiency virus and to identify the urban areas with greater social vulnerability to the infection among pregnant women. METHOD: ecological study, developed by means of spatial analysis t...

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Detalles Bibliográficos
Autores principales: de Holanda, Eliane Rolim, Galvão, Marli Teresinha Gimeniz, Pedrosa, Nathália Lima, Paiva, Simone de Sousa, de Almeida, Rosa Lívia Freitas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Escola de Enfermagem de Ribeirão Preto / Universidade de São Paulo 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547067/
https://www.ncbi.nlm.nih.gov/pubmed/26155005
http://dx.doi.org/10.1590/0104-1169.0481.2574
Descripción
Sumario:OBJECTIVES: to analyze the spatial distribution of reported cases of pregnant women infected by the human immunodeficiency virus and to identify the urban areas with greater social vulnerability to the infection among pregnant women. METHOD: ecological study, developed by means of spatial analysis techniques of area data. Secondary data were used from the Brazilian National Disease Notification System for the city of Recife, Pernambuco. Birth data were obtained from the Brazilian Information System on Live Births and socioeconomic data from the 2010 Demographic Census. RESULTS: the presence of spatial self-correlation was verified. Moran's Index was significant for the distribution. Clusters were identified, considered as high-risk areas, located in grouped neighborhoods, with equally high infection rates among pregnant women. A neighborhood located in the Northwest of the city was distinguished, considered in an epidemiological transition phase. CONCLUSION: precarious living conditions, as evidenced by the indicators illiteracy, absence of prenatal care and poverty, were relevant for the risk of vertical HIV transmission, converging to the grouping of cases among disadvantaged regions.