Spatial analysis of diarrheal diseases and its relationship with the environmental monitoring

ABSTRACT

Overall, urbanization in Brazil causes many environmental and social problems, among which we highlight the public health impacts. This study examined the use of geoprocessing tools that can help understand the relationship between the quality of water in rivers and the incidence of acute diarrheal diseases. In a Brazilian municipality, we carried out environmental monitoring in points of a river, then sought information as to waterborne diseases, and used the following geoprocessing tools: spatial distribution analysis with distance nearest tool and kernel density estimation; and cluster analysis with the average nearest neighbour and the K Ripley function. Among the results are the following: the point with the worst water quality groups most of the cases of diarrheal diseases, corroborating the classical association that links this result to the worst living conditions; among the tools used, the kernel density estimation enabled spatial visualization of clustering of cases of diarrhea; and Ripley’s K function statistically demonstrated this agglomeration. The information derived from the environmental monitoring of rivers contributes to identify the possible causes of diarrheal diseases. The spatial analyses conducted can be replicated in other municipalities, in order to identify risk areas to diarrheal diseases and to allow preventive actions.

Keywords:
spatial distribution; cluster analysis; kernel; water quality; Ripley function