Major Depression Prevalence Increases with Latitude in Canada
Bibliographic record
Abstract
OBJECTIVE: To determine whether there is an association between latitude and annual major depressive episode (MDE) prevalence in Canada. METHODS: Data from 2 national survey programs (the National Population Health Survey and the Canadian Community Health Survey) were used, providing 10 data sets collected between 1996 and 2013, together including 922,260 respondents, of whom 495,739 were assessed for MDE using 1 of 2 versions of the Composite International Diagnostic Interview, a short-form version (8 studies), and a Canadian adaptation of the World Mental Health version (2 studies). Approximate latitude was determined by linkage to postal code data. Data were analyzed using logistic regression and pooled across surveys using individual-level meta-analytic methods. RESULTS: In models including latitude as a continuous variable, a statistically significant association was observed, with prevalence increasing with increasing latitude. This association persisted after adjustment for a set of known risk factors. The latitude gradient was modest in magnitude, a 1% to 2% increase in the prevalence odds of MDE per degree of latitude was observed. Due to sparse data, this gradient cannot be confidently generalized beyond major population centres, which tend to occur at less than 55° latitude in Canada. CONCLUSION: A latitude gradient has not previously been reported. If replicated, the gradient may have implications for the planning of services and generation of aetiological hypotheses. However, this cross-sectional analysis cannot confirm aetiology and could not evaluate the potential contributions of variables such as light exposure, weather patterns, or social determinants.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".