<p>Prevalence and Determinants of Long-Term Utilization of Antidepressant Drugs: A Retrospective Cohort Study</p>
Bibliographic record
Abstract
PURPOSE: Antidepressant consumption has risen in recent years, driven by longer treatment duration. The objective of this study was to measure the prevalence of antidepressant long-term and chronic use in the Bologna area, Italy, and to identify their main determinants. MATERIALS AND METHODS: We conducted a retrospective claims-based cohort study by using the Bologna Local Health Authority data. A cohort of 18,307 incident users of antidepressant drugs in 2013 was selected, and subjects were followed for three years. A long-term utilization was defined as having at least one prescription claimed during each year of follow-up, while chronic utilization was defined as claiming at least 180 defined daily doses per year. Factors associated with chronic and long-term use were identified by univariate and multivariate logistic regressions. RESULTS: In our cohort, 5448 (29.8%) and 1817 (9.9%) subjects were dispensed antidepressants for a long-term course and in a chronically way, respectively. Older age, antidepressant polytherapy, polypharmacy, and being prescribed the first antidepressant by a hospital physician were all factors independently associated with chronic and long-term prescriptions of antidepressant drugs. Results were reported separately for men and women. CONCLUSION: Antidepressant long-term and chronic prescriptions are common in the Bologna area. Because longer treatment should be clinically motivated, these results strongly prompt the need to evaluate the actual relevance, as they may indicate potentially inappropriate prescription patterns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".