Prediction premenstrual syndrome (PMS) with anxiety, and depression in female students

Abstract

Background: Premenstrual syndrome (PMS) is a prevalent and multifaceted disorder affecting women of childbearing age, characterized by emotional, physical, and behavioral symptoms. The psychological dimensions, particularly anxiety and depression, exacerbate the burden of PMS. This research aimed to find predictive factors of PMS using ordinal logistic regression models (OLR). Materials and methods: 624 female university students in Ilam, Iran, participated in this cross-sectional survey. The DASS-42 and the Premenstrual Symptoms Screening Tool (PSST) were used to measure the severity of PMS, anxiety, and depression. OLR models were used to predict PMS. Results: Prevalence of low, moderate, and severe PMS according to DSM-5 was 33.4%, 52%, and 14.6%, respectively. Positive and substantial correlation coefficients were found between the overall PMS score and the total depression and anxiety scores (P < 0.001). Multivariate OLR showed that when depression levels rose from mild to moderate or moderate to severe, the risk of PMS increased by 41% (OR = 1.41, 95% CI [1.21, 1.65], P < 0.001), and the risk of anxiety increased by 51% (OR = 1.51, 95% CI [1.29, 1.76], P < 0.001). Higher sleeping hours were an independent risk factor for PMS severity (OR = 1.40, 95% CI [1.11, 1.77], P = 0.005). Conclusion: Results advocate integrating mental health screening into PMS management protocols, particularly using tools like the DASS-42-5. OLR regression emerged as a statistically robust method for modeling ordinal PMS outcomes, offering clinical and research relevance for targeted interventions in reproductive-aged populations.