Assessment of Canadian provincial expenditures in depressed patients treated with venlafaxine XR versus SSRIs: the APEX Study
Bibliographic record
Abstract
OBJECTIVE: Empirical studies of antidepressant cost-effectiveness suggest that the use of venlafaxine may be no more costly than selective serotonin reuptake inhibitors (SSRIs) in the treatment of depression. The objectives of this study were to identify patients' characteristics and factors associated with the choice of antidepressant and to assess differences in persistence, healthcare utilization and direct medical costs associated with venlafaxine and SSRIs pharmacotherapy. RESEARCH DESIGN AND METHODS: We examined demographic and clinical characteristics of patients (n = 17 144) who received both a diagnosis of depression and a prescription for venlafaxine or an SSRI between 1996 and 2004 using the Quebec health administrative databases. Logistic regression models were used to identify factors independently associated with the choice of antidepressant. Persistence to treatment and overall direct medical costs during 12 months after initiation of therapy were assessed using Cox proportional hazard and GLM models, respectively. RESULTS: Age, sex, provider specialty, and prior 12-month healthcare utilization significantly influenced initial antidepressant choice. Fewer venlafaxine-treated patients discontinued their initial therapy relative to SSRIs' (persistence to initial treatment: 38.4% vs. 29.4% and 24.4% vs. 15.8% at 6 and 12 months, respectively; p < 0.0001), and they were less likely to require treatment switching. Overall 12-month direct medical costs for SSRI- and venlafaxine-treated patients were Can$2759 and Can$2604, respectively. Patients treated with SSRIs had significantly higher expenditures in a univariate analysis (cost ratio: 1.06 [95% CI: 1.02, 1.10]). However, after controlling for potential confounding factors such as patients' characteristics, prior healthcare utilization, and comorbid conditions in multivariate analyses, the overall expenditures were similar in both groups (cost ratio: 1.03 [95% CI: 0.99, 1.07]). CONCLUSIONS: Direct medical costs were generally similar among patients with depression treated with venlafaxine and SSRIs. In a 'real world' setting, the higher acquisition cost of venlafaxine is offset by savings due to fewer hospitalizations and fewer outpatient medical visits. Differences in treatment persistence may also, in part, explain the observed differences in average direct medical costs between venlafaxine and SSRIs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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".