Predicting suicidal thoughts using depression, anxiety and premenstrual syndrome patterns in female students of Ilam universities

Suicide is one of the major mental health problems. One of the most important ways to prevent suicide is to identify people with suicidal thoughts and also to identify the factors that can affect suicidal thoughts. The aim of this study was to predict suicidal thoughts in female students of Ilam universities.

In this cross-sectional study, 331 female students of Ilam universities in May to August 2020 participated by cluster sampling using an online questionnaire. Suicidal thoughts was measured using Beck Suicide Scale Thoughts, Premenstrual Syndrome (PMS) was measured using 19 indicators of Premenstrual Symptoms Screening Tool (PSST) and depression and anxiety using DASS42 questionnaire. Data were analyzed using chi-square test. Linear and logistic regression models were used to predict suicidal thoughts.

Suicidal thoughts had a significant relationship with depression, anxiety, and premenstrual symptoms (P<0.01). The results of univariate logistic showed that the prevalence of suicidal thoughts in people with extremely severe and severe depression was 33 and 13 times higher than those without depression and in people with extremely severe and severe anxiety was 9 and 4 times higher than in people without anxiety (P<0.001). Suicidal thoughts were 3 times more common in people with severe PMS symptoms than in those with mild symptoms (P=0.002).

The results showed that anxiety, depression, and PMS have a direct and significant relationship with suicidal thoughts in female  students of Ilam Universities. Therefore, by providing mental health services and screening for mental disorders and premenstrual symptoms, an effective step can be taken to prevent suicide among students.