Cost-effectiveness analysis of a randomized study of depression treatment options in primary care suggests stepped-care treatment may have economic benefits
Bibliographic record
Abstract
BACKGROUND: The stepped-care pathway (SCP) model has previously been found to be clinically effective for depressive disorder in some studies, but not all. Several groups have suggested that a stepped-care approach is the most appropriate in primary care. There is relatively little information, however, regarding which specific stepped-care pathway may be best. This analysis aimed to determine cost-effectiveness of a stepped-care pathway for depression in adults in primary care versus standard care (SC), treatment-as-usual (TAU), and online cognitive behavioural therapy (CBT). METHODS: We conducted a randomized trial with 1400 participants and 12-week follow-up to assess the impact of the four treatment options on health-related quality of life and depression severity. Costs for the groups were calculated on the basis of physician, outpatient, and inpatient services using administrative data. We then calculated the incremental cost-effectiveness ratios using this information. Cost-effectiveness acceptability curves and incremental cost-effectiveness scatterplots were created using Monte Carlo simulation with 10,000 replications. A subgroup analysis was conducted for participants who screened as depressed at baseline. RESULTS: For all participants, TAU was the most expensive followed by CBT, SC, and SCP. QALYs were highest in SCP, followed by SC, CBT, and TAU. In the depressed subgroup, TAU was still the most expensive, followed by SC, SCP, and CBT, while QALYs were still highest in SCP, followed by SC, CBT, and TAU. The cost-effectiveness acceptability curves suggested that SCP had a higher probability for cost-effectiveness than the other three alternatives in all participants. In the depressed subgroup, CBT was associated with the highest probability of cost-effectiveness for a willingness-to-pay cut-off of less than approximately $50,000, while SCP was the highest at a cut-off higher than $50,000. There is considerable uncertainty around the cost-effectiveness estimates. CONCLUSIONS: Our analysis showed that even where there are no clinically significant differences in health outcomes between treatment approaches, there may be economic benefit from implementing the stepped-care model. While more work is required to identify the most clinically effective versions of a stepped-care pathway, our findings suggest that the care pathway may have potential to improve health care system value. TRIAL REGISTRATION: NCT01975207 . The trial was prospectively registered on 4 November 2013.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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".