| Summary: | Background: Environmental diseases remained a dominant health problem in the
modern society, particularly in Indonesia. This is evident from the high incidence
rate and health facility visits associated with such diseases. Of the environmental
diseases, asthma accounts for a great proportion of the morbidity rates. Prevalence
of pediatric asthma in Indonesia remained relatively high, particularly urban areas,
namely, ranging from 3,7%-16,4%. (1)
Objective: The study aims at describing the spatial distribution of asthma in
pediatric patients and finding out factors related to the incidence of bronchial
asthma in pediatric patients in Sragen Sub-district.
Method: A study was conducted using case control design with Geographical
Information System (GIS) modeling by means of spatial analytic approach.
Subjects of the research were selected using Registry Based Study or Hospital
Based approach. The sample was selected by matching the cases and controls, in
terms of age and sex. Total population of 82 respondents was taken as the sample.
Data that had been collected were analyzed in univariate, bivariate, and
multivariate ways and with spatial analysis using overlay, clustering and buffering
functions in Geographical Information System (GIS) modeling.
Results: The research showed that risk factors that were found to have a
relationship with asthma in pediatric patients, respectively based on the degree of
domination, were family history of asthma (OR=22.2, 95% CI=5.987-82.188,
p=0.000), the distance between residence and highway �250 meters (OR=5.2,
95% CI=1.210-22.399, p=0.027), and possession of feathered animals, such as
dog, cat, rabbit, hamster, bird and poultry (OR=4.4, 95% CI=1.164-16.827,
p=0.029). Spatial analysis with Geographical Information System (GIS) modeling
showed a tendency to cluster in the patients whose resident was �250 meters from
the highway.
Conclusion: The most dominant risk factor for the incidence of pediatric asthma
was family history of asthma. In addition, Geographical Information System
(GIS) modeling using spatial analytic approach clearly describes the spatial
distribution and location of the cases, with a clustering tendency, to identify
spatial vulnerability to asthma.
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