Suicidal Risk in Women with Premenstrual Syndrome and Premenstrual Dysphoric Disorder: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Women with premenstrual dysphoric disorder (PMDD) and premenstrual syndrome (PMS) experience substantial functional impairment and decreased quality of life. While previous research has highlighted a relationship between premenstrual disturbances and suicide risk, no meta-analysis has been conducted to quantitatively assess the findings. Methods: A systematic review and meta-analysis was conducted by searching the literature in three databases (Pubmed, PsycINFO, and EMBASE) on July 15, 2020. Studies that assessed the relationship between suicidality (attempt, ideation, and/or plan) and premenstrual disturbance (PMDD, PMS, and/or premenstrual symptoms) were included. Results: Thirteen studies were included in the qualitative review ( n = 10 included in meta-analysis). Results revealed that women with PMDD are almost seven times at higher risk of suicide attempt (OR: 6.97; 95% CI: 2.98–16.29, p < 0.001) and almost four times as likely to exhibit suicidal ideation (OR: 3.95; 95% CI: 2.97–5.24, p < 0.001). Similarly, women with PMS are also at increased risk of suicidal ideation (OR: 10.06; 95% CI: 1.32 to −76.67, p = 0.03), but not for suicide attempt (OR: 1.85; 95% CI: 0.77 to −4.46, p = 0.17). Conclusions: Women with PMDD and PMS are at higher risk of suicidality compared with women without premenstrual disturbances. These findings support routine suicidal risk assessments for women who suffer from moderate-to-severe premenstrual disturbance. Furthermore, psychosocial treatments for women diagnosed with PMS/PMDD should consider and target suicidality to minimize risk and improve well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".