Association between Hormonal Contraceptive Use and Prevalence of Premenstrual Symptoms
Bibliographic record
Abstract
Background Hormonal contraceptive (HC) use may be associated with a reduction in some premenstrual symptoms, however, the evidence remains equivocal. Objective To determine the prevalence of premenstrual symptoms in a multiethnic population of women and to investigate the association between hormonal contraceptive use and premenstrual symptoms. Methods 1048 women aged 20–29 years participating in the Toronto Nutrigenomics and Health Study provided data on their premenstrual symptoms and HC use. Severity of symptoms was classified as none, mild, moderate, or severe. Frequencies of premenstrual symptoms were determined as a total and by ethnicity. Logistic regressions were used to calculate the odds ratio (OR) and 95% confidence interval (CI) to analyze the associations between symptoms and use of HCs, adjusting for ethnicity. Results The prevalence of individual symptoms differed as follows: cramps (75%), bloating (74%), mood swings (72%), increased appetite (64%), acne (61%), fatigue (55%), sexual desire (50%), anxiety (35%), desire to be alone (33%), depression (28%), headache (26%), confusion (21%), clumsiness (15%), nausea (15%), and insomnia (11%). Prevalence of cramps, bloating, acne, sexual desire, headache, confusion, and nausea differed between ethnicities (P<0.05). For example, cramps were reported by 79% of Caucasians and 84% of South Asians, but only 67% of East Asians. The OR (95% CIs) for experiencing the following moderate/severe symptoms in women using HCs compared with those not using HCs were: cramps 0.48 (0.33–0.70), fatigue 0.64 (0.44–0.95), depression 0.35 (0.19–0.65), desire to be alone 0.32 (0.18–0.58), clumsiness 0.22 (0.06–0.74), confusion 0.21 (0.08–0.54), and anxiety 0.41 (0.24–0.70). Premenstrual symptoms of acne, mood swings, bloating, increased appetite, headache, insomnia, nausea, and sexual desire were not associated with HC use. Conclusion This study demonstrates that the prevalence of premenstrual symptoms varies widely between symptoms and ethnicities. Our findings also indicate that HC use is associated with a lower risk of experiencing many, but not all premenstrual symptoms. Support or Funding Information Research support: A.C.J. is a recipient of a Natural Sciences and Engineering Research Council Graduate Scholarship
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".