Risk factor mapping and case map of environmentally based disease in Yogyakarta (Faculty of Medicine Universitas Indonesia, 2019)
BACKGROUND A geographic information system (GIS) is required to guide interventions into prevent ARI and reduce the incidence of cases. The purpose of this study is to find out whether there is spatial autocorrelation in the spread of ARI; to obtain spatial information about the ARI risk factors, the ARI case map, and the factors related to the occurrence of ARI. METHODS This study is a quantitative research study with case-control study design.The sampling technique was purposive sampling. Spatial analysis techniques used were buffers and spatial clustering. The measurement of spatial autocorrelation was calculated by Moranâs Index method. RESULTS The risk factors for ARI based on the history of ARI disease were cough and cold in the last one year, and cough and cold lasting more than two weeks (OR = 15.691; 95% CI = 6.558-37.546 and OR = 6.645; 95% CI = 3.013-14.652). The risk factors for ARI based on the house physical environment were the room density, existence of glass windows on the house roof, electricity as a light source, presence of family members who smoke, and proximity to pollution exposure and waste disposal. Moran's Index value shows positive spatial autocorrelation. CONCLUSIONS GIS produces ARI distribution patterns. Based on the results of the cluster, the incidence of ARI cases in this region are interrelated or one case with another case is closely related, due to its close position.