The Influence of Depression on Biased Diagnosis of Premenstrual Syndrome and Premenstrual Dysphoric Disorder by the PSST Inventory

Development and validation of risk prediction model for premenstrual syndrome in nurses: results from the nurses-based the TARGET cohort study

Objective

Premenstrual syndrome (PMS) stands as a significant concern within the realm gynecological disorders, profoundly impacting women of childbearing age in China. However, the elusive nature of its risk factors necessitates investigation. This study, therefore, is dedicated to unraveling the intricacies of PMS by focusing on nurses, a cohort with unique occupational stressors, to develop and validate a predictive model for assessing the risk of PMS.

Methods

This investigation employed a multi-center cross-sectional analysis drawing upon data from the TARGET Nurses' health cohort. Utilizing online survey versions of the Premenstrual Syndrome Scale (PMSS), a comprehensive dataset encompassing physiological, social, psychological, occupational, and behavioral variables was collected from 18,645 participants. A stepwise multivariate logistic regression analysis was conducted to identify independent risk factors for PMS. Furthermore, a refined variable selection process was executed, combining the Least Absolute Shrinkage and Selection Operator (LASSO) method with 10-fold cross-validation. The visualization of the risk prediction model was achieved through a nomogram, and its performance was evaluated using the C index, receiver operating characteristic (ROC) curves, and the calibration curves.

Results

Among the diverse variables explored, this study identified several noteworthy predictors of PMS in nurses, including tea or coffee consumption, sleep quality, menstrual cycle regularity, intermenstrual bleeding episodes, dysmenorrhea severity, experiences of workplace bullying, trait coping style, anxiety, depression and perceived stress levels. The prediction model exhibited robust discriminatory power, with an area under the curve of 0.765 for the training set and 0.769 for the test set. Furthermore, the calibration curve underscored the model's high degree of alignment with observed outcomes.

Conclusion

The developed model showcases exceptional accuracy in identifying nurses at risk of PMS. This early alert system holds potential to significantly enhance nurses' well-being and underscore the importance of professional support.

The longitudinal predictive effect of self-reported frequency of premenstrual syndrome on depression: Findings from the Australian Longitudinal Study on Women's Health

Background

Previous studies have revealed a high comorbidity between premenstrual syndrome (PMS) and depression; however, whether PMS can longitudinally predict depression has not been examined in large sample studies.

Methods

This study surveyed 8,133 women from the 1973-78 cohort of the Australian Longitudinal Study on Women's Health. Participants completed seven repeated measurements at 3-year intervals starting in 2000 (aged 22-27 years). Binary logistic and multivariate ordered logistic regression models were used to examine the predictive role of self-reported frequency of PMS symptoms in 2000 on self-reported diagnosis of depression and frequency of depressive symptoms, respectively, for each follow-up survey.

Results

Self-reported frequency of PMS symptoms in the year 2000 predicted self-reported diagnosis of depression in most follow-up surveys. Specifically, compared to women who reported "never" had PMS symptoms in 2000, those who reported "often" had them were more likely to report a diagnosis of depression in 2006 (OR = 1.72), 2012 (OR = 1.88), 2015 (OR = 1.49), and 2018 (OR = 1.90); and those who reported "sometimes" had PMS symptoms in 2000 were more likely to report a diagnosis of depression in 2012 (OR =1.37) and 2018 (OR = 1.59). Furthermore, self-reported frequency of PMS symptoms in 2000 predicted self-reported frequency of depressive symptoms in each follow-up survey. Compared to women who reported "never" had PMS symptoms in 2000, those who reported "sometimes", or "often", had PMS symptoms reported depressive symptoms more frequently.

Conclusion

Self-reported frequency of PMS can predict the self-reported frequency of depressive symptoms and the subsequent diagnosis of depression.

Development and Psychometric Testing of a New Short-Form of the Premenstrual Symptoms Questionnaire (PSQ-S)

Purpose

The Premenstrual Symptoms Questionnaire (PSQ) is a patient-reported outcome measurement tool for premenstrual symptoms. Although the PSQ is a very useful tool with 14 items to screen for a wide variety of premenstrual symptoms, not everyone will respond to all the questions. Fewer questions would be less burdensome on the respondents. We aimed to develop and analyze the psychometric properties of a short-form of the PSQ (PSQ-S).

Patients and Methods

The study participants were from an earlier study with a sample of 1388 female students. We reanalyzed data collected from 922 students with regular menstrual cycles who completed the PSQ, premenstrual dysphoric disorder (PMDD) scale, Somatic Symptom Scale-8 (SSS-8), and numerical rating scale (NRS) for menstrual pain. First, we selected nine items for the PSQ-S based on the results of the corrected item-total correlation analysis. The PSQ-S was then analyzed for reliability and validity using the classical test theory. Moreover, item response theory was applied to test the psychometric properties of the PSQ-S.

Results

Cronbach's α for the PSQ-S score was 0.93. Principal component factor analysis revealed a one-factor model. Confirmatory factor analysis showed that the one-factor model was a good fit. The PSQ-S total score was strongly correlated with the PSQ total score, PMDD scale score, and SSS-8 score (r=0.978, 0.854, and 0.648, respectively) and moderately correlated with the NRS (r=0.437). Item response theory analyses showed that the constructs and items of the PSQ-S had satisfactory discrimination, difficulty parameters, item information curves, and test information curves. Receiver operating characteristic curve analysis revealed a cut-off score of ≥22 for suspected premenstrual disorders based on the total PSQ-S score.

Conclusion

The PSQ-S, consisting of nine items from the PSQ, had sufficient reliability and validity and could be a convenient assessment tool for premenstrual symptoms in routine clinical practice.