Health care resource utilization and costs associated with switching versus augmenting antidepressant monotherapy in second-line treatment of major depressive disorder
Bibliographic record
Abstract
BACKGROUND: While augmenting and switching antidepressant monotherapy are reasonable second-line options for treating major depressive disorder (MDD), understanding the impact of these strategies beyond effectiveness may inform clinical decision-making. We evaluated health care resource utilization (HCRU) and costs of augmenting versus switching antidepressant monotherapy. METHODS: The Merative™ MarketScan® Commercial Database was used to identify adults who initiated first-line antidepressant monotherapy within 60 days of their first observed MDD diagnosis. Patients with ≥2 lines of therapy (LOTs) post-diagnosis were classified as switching or augmenting at the start of second LOT (index date). Rates of all-cause and mental health (MH)-related hospitalizations, emergency department (ED) visits, and outpatient hospital visits (per person-year [PPY]) and health care costs (per person per year [PPPY]) were compared between cohorts using inverse probability of treatment weighting. Rate ratios (RRs) were calculated from Poisson regression models for HCRU; cost differences were calculated from linear regression models. RESULTS: Of 156,703 eligible patients, 133,453 (85 %) switched therapies in their second LOT, and 23,250 (15 %) augmented. Baseline characteristics were similar between weighted cohorts. Rates of hospitalizations, ED visits, and outpatient hospital visits PPY were significantly lower in patients who augmented versus switched (all-cause RRs: 0.80, 0.90, and 0.94, respectively, all P < 0.001; MH-related RRs: 0.81, 0.83, and 0.85, respectively, P < 0.001). Augmenting was associated with significantly lower medical costs than switching ($11,263 vs $11,941 PPPY; mean difference: -$678, P = 0.009). LIMITATIONS: Claims database; generalizability to other insurance types. CONCLUSIONS: Augmenting MDD therapy rather than switching may reduce burdens on the health care system.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".