A Population-Based Longitudinal Community Study of Major Depression and Migraine
Bibliographic record
Abstract
Objective: To examine whether major depressive episodes (MDE) are associated with an increased risk of migraine in the general population and to examine whether migraine is associated with an increase risk of MDE. Background: Population-based cross-sectional studies have consistently reported an association between migraine and depression. However, longitudinal studies about this potentially bidirectional association are inconsistent. Methods: This retrospective cohort study used 12 years of follow-up data from the Canadian National Population Health Survey (15,254 respondents, age >12). Stratified analysis, logistic regression, and proportional hazard modeling were used to quantify the effect of migraine on subsequent MDE status and vice versa. Results: After adjusting for sex, age, and other chronic health conditions, respondents with migraine were 60% more likely (HR 1.6, 95% CI 1.3-1.9) to develop MDE compared to those without migraine. Similarly adjusting for sex and age, respondents with MDE were 40% more likely (HR 1.4, 95% CI 1.0-1.9) to develop migraine compared to those without MDE. However, the latter association disappeared after adjustment for stress and childhood trauma. Conclusions: The current study provides substantial evidence that migraine is associated with the later development of major depressive episodes, but does not provide strong causal evidence of an association in the other direction. Environmental factors such as childhood trauma and stress may shape the expression of this bidirectional relationship, however, the precise underlying mechanisms are not yet known.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".