Migraine and Mental Health in a Population-Based Sample of Adolescents
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between migraine and anxiety disorders, mood disorders and perceived mental health in a population-based sample of adolescents. METHODS: The Canadian Community Health Survey (CCHS) is a cross-sectional health survey sampling a nationally representative group of Canadians. In this observational study, data on all 61,375 participants aged 12-19 years from six survey cycles were analyzed. The relationships between self-reported migraine, perceived mental health, and mood/anxiety disorders were modeled using univariate and multivariate logistic regression. The migraine-depression association was also explored in a subset of participants using the Composite International Diagnostic Interview-Short Form (CIDI-SF) depression scale. RESULTS: The odds of migraine were higher among those with mood disorders, with the strongest association in 2011-2 (adjusted odds ratio [aOR]=4.59; 95% confidence interval [CI 95%]=3.44-6.12), and the weakest in 2009-10 (aOR=3.06, CI 95%=2.06-4.55). The migraine-mood disorders association was also significant throughout all cycles, other than 2011-2, when the CIDI-SF depression scale was employed. The odds of migraine were higher among those with anxiety disorders, with the strongest association in 2011-2 (aOR=4.21, CI 95%=3.31-5.35) and the weakest in 2010 (aOR=1.87, CI 95%=1.10-3.37). The inverse association between high perceived mental health and the odds of migraine was observed in all CCHS cycles, with the strongest association in 2011-2 (aOR=0.58, CI 95%=0.48-0.69) and the weakest in 2003-4 (aOR=0.75, CI 95%=0.62-0.91). CONCLUSIONS: This study provides evidence, derived from a large population-based sample of adolescents, for a link between migraine and mood/anxiety disorders.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".