GIS applications for mapping and spatial modeling of urban-use water quality: a case study in District of Cuiabá, Mato Grosso, Brazil

A cross-sectional study utilizing spatial analysis techniques was conducted to study water quality problems and risk of waterborne enteric diseases in a lower-middle-class urban district of Cuiabá, the capital of Mato Grosso State, Brazil. Field surveys indicate high rates of supply water contaminat...

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Bibliographic Details
Published in:Cadernos de saúde pública 2007-04, Vol.23 (4), p.875-884
Main Authors: Zeilhofer, Peter, Zeilhofer, Liliana Victorino Alves Corrêa, Hardoim, Edna Lopes, Lima, Zoraidy Marques de, Oliveira, Catarina Silva
Format: Article
Language:English
Subjects:
Brazil - epidemiology
Child
Enterobacteriaceae Infections - epidemiology
Enterobacteriaceae Infections - etiology
Epidemiologic Methods
Geographic Information Systems
Humans
Intestinal Diseases, Parasitic - epidemiology
Intestinal Diseases, Parasitic - etiology
PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Socioeconomic Factors
Topography, Medical
Urban Population
Water Microbiology
Water Supply - analysis
Water Supply - standards
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Summary:A cross-sectional study utilizing spatial analysis techniques was conducted to study water quality problems and risk of waterborne enteric diseases in a lower-middle-class urban district of Cuiabá, the capital of Mato Grosso State, Brazil. Field surveys indicate high rates of supply water contamination in domiciles and, conspicuously, in public and private schools. Logistic regression models developed for the variables turbidity, Escherichia coli, total coliforms, and intestinal parasite infection did not identify singular explanatory factors for the supply water conditions and elevated incidences of enteric diseases among children. The contamination problems were found to be the result of precarious conditions involving both public infrastructure and in-building sanitary installations and their maintenance. GIS methods were successfully applied to create spatial datasets for logistic regression model building and to construct risk maps using regression coefficients.
ISSN:0102-311X
1678-4464
0102-311X
DOI:10.1590/S0102-311X2007000400015