Sistem Informasi Geografis pada Kasus Demam Berdarah Dengue di Kabupaten Sidoarjo Tahun 2019 - Rafdi Ghazi Iriyanto Putra (Media Gizi Kesmas, 2023)
Background: The development of information technology in the health sector is becoming increasingly complex and diverse, one of which is the Geographic Information System (GIS). GIS in the health sector is widely known as a surveillance tool, which can be used to assess health risks and threats in the community; know the spread of disease and outbreak investigations; planning and implementing health service programs, as well as evaluation and monitoring. In 2019, 367 cases of Dengue Hemorrhagic Fever (DHF) were found in Sidoarjo Regency, where the number of cases found has increased from the previous year.   Objectives: This study aims to describe the distribution of DHF case data in each region and to analyze the factors that influence the number of DHF cases in Sidoarjo Regency in 2019. Methods: This study used a cross-sectional design. This study population were all sub-districts in Sidoarjo Regency in 2019, which were 18 sub-districts. The sample was the total population. The dependent variable was the number of DHF cases in Sidoarjo District in 2019, while the independent variables were population density per km2, percentage of drinking water facilities that meet health requirements, number of public places that meet health requirements, and number of families with access to healthy latrines. This study used secondary data, namely Sidoarjo District map and Sidoarjo District Health Profile 2019. The analysis used is mapping analysis and multiple linear regression with GeoDa. Results: The distribution of the highest number of DHF cases was found in Sukodono, Candi, Reinforcement and Taman Subdistricts. The results of the analysis showed that population density had no significant effect on the number of DHF cases (p=0.26206), the percentage of drinking water facilities that met the requirements had a significant effect on the number of DHF cases (p=0.00654), the number of public places that met health requirements had an effect significantly to the number of DHF cases (p=0.04448), and the number of families with proper access to sanitation facilities (healthy latrines) has a significant effect on the number of DHF cases (p=0.03526). Conclusions: Factors that influence the number of DHF cases are the percentage of drinking water facilities that meet the requirements, the number of public places that meet health requirements, and the number of families with access to healthy latrines. It is expected to modify the investigation technique for finding DHF cases early by utilizing spatial and time data.