DETERMINAN KEJADIAN KISTA OVARIUM PADA WANITA USIA SUBUR DI KABUPATEN BALANGAN MENGGUNAKAN REGRESI LOGISTIK BINER - Dhea Arinda; Dewi Anggraini; Meitria Syahadatina Noor (Universitas Lambung Mangkurat, 2022)
Ovarian cysts are the most common gynecologic cases of many gynecologic cancers. Ovarian cyst is a disease that causes many deaths. This high mortality rate is due to the fact that the disease is initially asymptomatic and only causes complaints when metastases have occurred so that 60-70% of patients come at an advanced stage. Based on the results of the 2007 Basic Health Research survey, the number of patients with ovarian cysts in South Kalimantan was 1,2% of 56 respondents. This study took a case study in a district in South Kalimantan, namely Balangan Regency with the aim of explaining the characteristics of the distribution of ovarian cysts and the factors that influence the incidence of ovarian cysts in women of childbearing age in Balangan Regency using binary logistic regression method. Based on descriptive statistical analysis, it was found that the distribution characteristics of ovarian cyst sufferers were from 59 people who had checked for cyst symptoms at Balangan Hospital, 46 people were known to have cysts, while 13 people were not known to have cysts. Based on binary logistic regression analysis, the factors that influence the incidence of ovarian cysts for data on the incidence of ovarian cysts in Balangan Hospital are parity and employment status, while the age factor has no significant effect. Using the Odss Ratio (OR) parity value, patients with nulliparous status had a 0,033 higher risk of developing ovarian cysts than patients with multiparous status. using the OR value of the occupational status patients who had a job had a 0,014 higher risk of developing ovarian cysts than patients who did not have a job. Keywords: Ovarian cysts, Logistic binary, Odds Ratio.