Risk factor modifications and depression incidence: a 4-year longitudinal Canadian cohort of the Montreal Catchment Area Study
Bibliographic record
Abstract
OBJECTIVE: Few studies have examined the effect of risk factor modifications on depression incidence. This study was to explore psychosocial risk factors for depression and quantify the effect of risk factor modifications on depression incidence in a large-scale, longitudinal population-based study. METHODS: Data were from the Montreal Longitudinal Catchment Area study (N=2433). Multivariate modified Poisson regression was used to estimate relative risk (RR). Population attributable fractions were also used to estimate the potential impact of risk factor modifications on depression incidence. RESULTS: The cumulative incidence rate of major depressive disorder at the 2-year follow-up was 4.8%, and 6.6% at the 4-year follow-up. Being a younger adult, female, widowed, separated or divorced, Caucasian, poor, occasional drinker, having a family history of mental health problems, having less education and living in areas with higher unemployment rates and higher proportions of visible minorities, more cultural community centres and community organisations, were consistently associated with the increased risk of incident major depressive disorder. Although only 5.1% of the disease incidence was potentially attributable to occasional drinking (vs abstainers) at the 2-year follow-up, the attribution of occasional drinking doubled at the 4-year follow-up. A 10% reduction in the prevalence of occasional drinking in this population could potentially prevent half of incident cases. CONCLUSIONS: Modifiable risk factors, both individual and societal, could be the targets for public depression prevention programmes. These programmes should also be gender-specific, as different risk factors have been identified for men and women. Public health preventions at individual levels could focus on the better management of occasional drinking, as it explained around 5%~10% of incident major depressive disorders. Neighbourhood characteristics could also be the target for public prevention programmes. However, this could be very challenging. A cost-effectiveness analysis of a variety of prevention efforts is warranted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".