Risk factor mapping and case map of environmentally based disease in Yogyakarta
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, t...
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doaj-87d6799e46234cf5b94a99bb2abbcc462020-11-25T02:39:23ZengFaculty of Medicine Universitas Indonesia Medical Journal of Indonesia0853-17732252-80832019-08-0128210.13181/mji.v28i2.30933093Risk factor mapping and case map of environmentally based disease in YogyakartaHariza Adnani0Achmad Arman Subiyanto1Diffah Hanim2Endang Sutisna Sulaeman3Graduate Programs in Health Promotion and Comunity Empowerment, Universitas Sebelas Maret, Surakarta, IndonesiaMasters Program in Family Medicine, Universitas Sebelas Maret, Surakarta, IndonesiaMaster Program in Nutrition, Universitas Sebelas Maret, Surakarta, IndonesiaMaster Program in Public Health, Universitas Sebelas Maret, Surakarta, Indonesia 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 Moran’s 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. http://mji.ui.ac.id/journal/index.php/mji/article/view/3093case studygeographic information systemhealth risk assessmentinfectious diseasesmap |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hariza Adnani Achmad Arman Subiyanto Diffah Hanim Endang Sutisna Sulaeman |
spellingShingle |
Hariza Adnani Achmad Arman Subiyanto Diffah Hanim Endang Sutisna Sulaeman Risk factor mapping and case map of environmentally based disease in Yogyakarta Medical Journal of Indonesia case study geographic information system health risk assessment infectious diseases map |
author_facet |
Hariza Adnani Achmad Arman Subiyanto Diffah Hanim Endang Sutisna Sulaeman |
author_sort |
Hariza Adnani |
title |
Risk factor mapping and case map of environmentally based disease in Yogyakarta |
title_short |
Risk factor mapping and case map of environmentally based disease in Yogyakarta |
title_full |
Risk factor mapping and case map of environmentally based disease in Yogyakarta |
title_fullStr |
Risk factor mapping and case map of environmentally based disease in Yogyakarta |
title_full_unstemmed |
Risk factor mapping and case map of environmentally based disease in Yogyakarta |
title_sort |
risk factor mapping and case map of environmentally based disease in yogyakarta |
publisher |
Faculty of Medicine Universitas Indonesia |
series |
Medical Journal of Indonesia |
issn |
0853-1773 2252-8083 |
publishDate |
2019-08-01 |
description |
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 Moran’s 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.
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topic |
case study geographic information system health risk assessment infectious diseases map |
url |
http://mji.ui.ac.id/journal/index.php/mji/article/view/3093 |
work_keys_str_mv |
AT harizaadnani riskfactormappingandcasemapofenvironmentallybaseddiseaseinyogyakarta AT achmadarmansubiyanto riskfactormappingandcasemapofenvironmentallybaseddiseaseinyogyakarta AT diffahhanim riskfactormappingandcasemapofenvironmentallybaseddiseaseinyogyakarta AT endangsutisnasulaeman riskfactormappingandcasemapofenvironmentallybaseddiseaseinyogyakarta |
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1724786438526992384 |