Antidepressant Utilization in British Columbia From 1996 to 2004: Increasing Prevalence but Not Incidence
Bibliographic record
Abstract
OBJECTIVES: Expenditures on antidepressants in Canada are rapidly increasing; yet few studies have analyzed the characteristics of antidepressant users. This study investigated the prevalence and incidence of antidepressant use in British Columbia over eight years. METHODS: Antidepressant utilization and demographic data were assessed for the population of British Columbia from 1996 to 2004. Prescription claims were identified within the PharmaNet database for serotonin reuptake inhibitors (SSRI), tricyclics, monoamine oxidase inhibitors, bupropion (categorized separately for smoking cessation), and "novel" antidepressants, such as venlafaxine. Incident utilization (dispensed "first" antidepressant after two years without an antidepressant claim) and prevalent utilization were analyzed. All cohort members were required to have continuous registration with British Columbia medical services for at least two years before the first antidepressant claim. RESULTS: Prevalence of antidepressant use doubled, from 34 to 72 users per 1,000 population, between 1996 and 2004. The prevalence of particular classes of antidepressants also changed over time. Prevalence of novel antidepressants and SSRIs increased, although incidence of SSRIs decreased. Prevalent and incident use of bupropion for smoking cessation peaked in 1999 but then declined. Quarterly incident antidepressant use increased in 1998 and 1999 (6.5 and 11.3 users per 1,000) but decreased through 2004 (4.2 users per 1,000). Those aged 20 to 44 years and those aged 45 to 64 years showed the greatest peak in incident antidepressant use. A socioeconomic gradient in prescribing was observed. CONCLUSIONS: Prevalent antidepressant use has increased dramatically since 1996. By contrast, incident use increased from 1998 to 1999 but then decreased through 2004. Many complex factors likely contribute to antidepressant prescribing patterns.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 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".