Pharmacoeconomics of antidepressants in moderate-to-severe depressive disorder in Colombia
Bibliographic record
Abstract
OBJECTIVE: To compare three antidepressant drugs from different classes used in treating moderate-to-severe major depressive disorder (MDD) in Colombian adults. METHODS: Based on expert input, a decision-tree model was adapted for Colombia to analyze data over 6 months from the government-payer perspective. The cost-effectiveness of amitriptyline, fluoxetine, and venlafaxine was determined. The clinical outcome was remission of depression (a score <or=7 on the Hamilton Depression [HAM-D] scale or <or=12 on the Montgomery-Asberg Depression Rating Scale [MADRS]) after 8 weeks of treatment. Clinical data were obtained from the literature and costs from standard Colombian price lists. One-way and multivariate sensitivity analyses tested model robustness. RESULTS: Costs per patient (in 2007 US$) for treatment were: venlafaxine, $1,618; fluoxetine, $1,207; and amitriptyline, $1,068. Overall remission rates were 73.1%, 64.1%, and 71.3%, respectively. Amitriptyline dominated fluoxetine (i.e., it had lower costs and higher outcomes). The incremental cost-effectiveness ratio (ICER) of venlafaxine over amitriptyline was US$ 31,595. The acquisition price of venlafaxine was the model's cost driver, comprising 53.4% of the total cost/patient treated, compared with 18.5% and 24.8% for fluoxetine and amitriptyline, respectively. For the others, hospitalization comprised the major cost (72.1% and 65.2%, respectively). Probabilistic (Monte Carlo) sensitivity analysis confirmed the original findings of the pharmacoeconomic model. CONCLUSIONS: Amitriptyline is cost-effective in comparison to fluoxetine and venlafaxine in Colombia. However, the cost of venlafaxine was estimated for the brand-name product, as generics were not currently available. These cost-effectiveness results can be substantially affected by the presence of generics or drug cost regulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".