Burden of Treatment Resistant Depression (TRD) in patients with major depressive disorder in Ontario using Institute for Clinical Evaluative Sciences (ICES) databases: Economic burden and healthcare resource utilization
Bibliographic record
Abstract
INTRODUCTION: The burden of treatment-resistant depression (TRD) in Canada requires empirical characterization to better inform clinicians and policy decision-making in mental health. Towards this aim, this study utilized the Institute for Clinical Evaluative Sciences (ICES) databases to quantify the economic burden and resource utilization of Patients with TRD in Ontario. METHODS: TRD, Non-TRD Major Depressive Disorder (Non-TRD MDD) and Non-MDD cohorts were selected from the ICES databases between April 2006-March 2015 and followed-up for at least two years. TRD was defined as a minimum of two treatment failures within one-year of the index MDD diagnosis. Non-TRD and Non-MDD patients were matched with patients with TRD to analyze costs, resource utilization, and demographic information. RESULTS: Out of 277 patients with TRD identified, the average age was 52 years (SD 16) and 53% were female. Compared to Non-TRD, the patients with TRD had more all-cause visits to outpatient (38.2 vs. 24.2) and emergency units (2.7 vs. 2.0) and more depression-related visits to GPs (3.06 vs. 1.63) and psychiatrists (5.88 vs. 1.95) (all p < 0.05). The average two-year cost for TRD patients was $20,998 (CAD). LIMITATIONS: This study included patients with only public plan coverage; therefore, overall TRD population and cash and private claims were not captured. CONCLUSIONS: Patients with TRD exhibit a significantly higher demand on healthcare resources and higher overall payments compared to Non-TRD patients. The findings suggest that there are current challenges in adequately managing this difficult-to-treat patient group and there remains a high unmet need for new therapies.
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 | 0.000 |
| 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".