Healthcare resource use and cost associated with timing of pharmacological treatment for major depressive disorder in the United States: a real-world study
Bibliographic record
Abstract
Background: Guidelines recommend selective serotonin reuptake inhibitors (SSRI) and serotonin norepinephrine reuptake inhibitors (SNRI) as first-line treatments for major depressive disorder (MDD) and emphasize the importance of early pharmacological treatment as key factors to treatment success.Objectives: To compare the MDD-related healthcare resource utilization (HCRU) and cost among patients (1) with early vs late pharmacological treatment initiation and (2) achieving minimum therapeutic dose (MTD) early vs late.Methods: The MarketScan database (2010–2015) was used. Adults who were newly-treated with SSRI/SNRI within 12 months after the initial MDD diagnosis (index) were included. Patients who initiated SSRI/SNRI within 2 weeks of the index date were defined as early initiators; those who reached MTD within 4 weeks of index date were defined as early MTD achievers. MDD-related HCRU and costs per year after the index date were compared between early and late initiators and between early and late achievers using propensity score matching and generalized linear models.Results: Of the 55,539 patients, 60% were early initiators and 61% were early MTD achievers. The mean number of MDD-related outpatient visits per year were significantly higher for late initiator (6.7 vs 4.2, p < .001) and late MTD achievers (6.5 vs 4.5, p < .001) vs their early counterparts. Mean annual MDD-related outpatient, drug, and total cost were significantly higher for late initiators and MTD achievers vs the early groups.Conclusions: There is an opportunity to improve outcomes by treating MDD patients with SSRI/SNRI within 2 weeks and at or above the MTD within 4 weeks of diagnosis or less.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".