Cost-Effectiveness of Escitalopram versus Citalopram in the Treatment of Severe Depression
Bibliographic record
Abstract
BACKGROUND: Severe depression is associated with an extensive economic burden on both the patient and society. OBJECTIVE: To estimate the cost-effectiveness in Austria of escitalopram compared with citalopram in the management of severe depression (Montgomery-Asberg Depression Rating Scale score > or =30). METHODS: A decision model incorporated treatment paths and associated direct resource use (psychiatric hospitalization, medications, general practitioner and psychiatrist visits, treatment discontinuation, suicide attempts) associated with managing severe depression and the indirect cost of work absenteeism over a 6-month period. Main outcomes were clinical success (remission at 6 mo) and cost (2002 Euros equals approximately 1.25 US) of treatment. The analysis was performed from the Austrian societal and Social Healthcare Insurance System (SHIS) perspectives. Clinical input data were derived from a meta-analysis of 8-week randomized clinical trials. Costs were derived from standard Austrian price lists or from the literature. RESULTS: Six months after the start of treatment, the overall clinical success remission rate was higher for escitalopram (53.7%) than for citalopram (48.7%). From the SHIS perspective, the total expected cost per successfully treated severely depressed patient was 924 (32.1%) lower for escitalopram (2879) compared with citalopram (3803). From the societal perspective, the total expected cost per successfully treated severely depressed patient was 1369 (24.4%) lower for escitalopram (5610) than for citalopram (6979). Sensitivity analyses demonstrated that the model was robust and that, even if citalopram had no acquisition cost, escitalopram remained the dominant strategy for both perspectives. CONCLUSIONS: Treatment with escitalopram was the dominant strategy. These data suggest that escitalopram is a cost-effective antidepressant compared with citalopram in the management of severe depression in Austria.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".