Worldwide prevalence of dysmenorrhea: a systematic review and meta-analysis across 70 countries
Bibliographic record
Abstract
ABSTRACT: Dysmenorrhea is menstrual pain of uterine origin that can be classified as primary (PD) or secondary (SD). The worldwide prevalence of dysmenorrhea has been estimated in previous systematic reviews; however, these results are often limited by the focus on specific populations, or the inclusion of studies published only in English. Therefore, we estimated the worldwide prevalence of dysmenorrhea of both PD and SD. In this systematic review, we searched 6 databases for studies reporting the prevalence of dysmenorrhea published between 2000 and 2024, without language restriction. The risk of bias of the included studies was assessed using the Joana Briggs Institute tool. Meta-analysis was conducted in Rstudio. The heterogeneity within meta-analyses was evaluated by I 2 statistics. Subgroup analyses were performed by PD, SD, age group, and study setting to investigate sources of heterogeneity as well. The certainty of evidence was assessed using GRADE modified for prevalence studies. A total of 336 studies were included in this systematic review. Most of them were conducted in Asia (49.4%). The pooled worldwide prevalence of dysmenorrhea, PD, and SD were 71.3% (95% CI 68.7%-73.8%), 73% (95% CI 68%-78%), and 35% (95% CI 19%-56%), respectively. Dysmenorrhea was more prevalent in Central America (89.6%), Sri Lanka (97.7%), among adults (73.3%), and at universities (78.4%). All subgroup analyses showed high heterogeneity (I 2 = 99.5%-100%) with very low certainty of evidence. This high prevalence of dysmenorrhea worldwide highlights the need for healthcare providers and public health organizations to address menstrual pain's global burden.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".