Seasonal variation in major depressive episode prevalence in Canada
Bibliographic record
Abstract
BACKGROUND: The purpose of this paper is to describe variation, over the months of the year, in major depressive episode (MDE) prevalence. This is an important aspect of the epidemiological description of MDE, and one that has received surprisingly little attention in the literature. Evidence of seasonal variation in MDE prevalence has been weak and contradictory. Most studies have sought to estimate the prevalence of seasonal affective disorder using cut-points applied to scales assessing mood seasonality rather than MDE. This approach does not align with modern classification in which seasonal depression is a diagnostic subtype of major depression rather than a distinct category. Also, some studies may have lacked power to detect seasonal differences. We addressed these limitations by examining the month-specific occurrence of conventionally defined MDE and by pooling data from large epidemiological surveys to enhance precision in the analysis. METHOD: Data from two national survey programmes (the National Population Health Survey and the Canadian Community Health Survey) were used, providing ten datasets collected between 1996 and 2013, together including over 500,000. These studies assessed MDE using a short form version of the Composite International Diagnostic Interview (CIDI) for major depression, with one exception being a 2012 survey that used a non-abbreviated version of the CIDI. The proportion of episodes occurring in each month was evaluated using items from the diagnostic modules and statistical methods addressing complex design features of these trials. Overall month-specific pooled estimates and associated confidence intervals were estimated using random effects meta-analysis and a gradient was assessed using a meta-regression model that included a quadratic term. RESULTS: There was considerable sampling variability when the month-specific proportions were estimated from individual survey datasets. However, across the various datasets, there was sufficient homogeneity to justify the pooling of these estimated proportions, producing large gains in precision. Seasonal variation was clearly evident in the pooled data. The highest proportion of episodes occurred in December, January and February and the lowest proportions occurred in June, July and August. The proportion of respondents reporting MDE in January was 70% higher than August, suggesting an association with implications for health policy. The pattern persisted with stratification for age group, sex and latitude. CONCLUSIONS: Seasonal effects in MDE may have been obscured by small sample sizes in prior studies. In Canada, MDE has clear seasonal variation, yet this is not addressed in the planning of services. These results suggest that availability of depression treatment should be higher in the winter than the summer months.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".