Increased suicide risk among younger women in winter during full moon in northern Europe. An artifact or a novel finding?
Bibliographic record
Abstract
Available evidence suggests that there is no effect of moon phases on suicidal behavior. However, a Finnish study recently reported elevated suicide rates during full-moon, but only among premenopausal women and only in winter. This could not be replicated in an Austrian study and stirred a discussion about whether the Finnish finding was false-positive or if there are unaccounted moderator variables differing between Finland and Austria. The goal of the present study was to provide another replication with data from Sweden, which is geographically more comparable to Finland than Austria. We also investigated the discussed moderator variables latitude and nightly artificial brightness. There were 48,537 suicides available for analysis. The fraction of suicides during the full-moon quarter in winter did not differ significantly from the expected 25% among premenopausal women (23.3%) and in the full sample (24.7%). The incidence risk ratios for full moon quarter in Poisson regression models were 0.96 (95% CI: 0.90-1.02) for premenopausal women and 1.01 (95% CI: 0.99-1.04) for the full sample. According to Bayes-factor analysis, the evidence supports the null-hypothesis (no association) over the alternative hypothesis (some association). We found similar results when we split the data by latitude and artificial nightly brightness, respectively. In line with the Austrian study, there was no increase of suicides in Sweden among premenopausal women in winter during full-moon. The results from the Finnish study are likely false positive, perhaps resulting from problematic but common research and publication practices, which we discuss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".