Protocol for an economic evaluation of scalable strategies to improve mental health among perinatal women: non-specialist care delivered via telemedicine vs. specialist care delivered in-person
Bibliographic record
Abstract
BACKGROUND: Perinatal depression affects an estimated 1 in 5 women in North America during the perinatal period, with annualized lifetime costs estimated at $20.6 billion CAD in Canada and over $45.9 billion USD in the US. Access to psychological treatments remains limited for most perinatal women suffering from depression and anxiety. Some barriers to effective care can be addressed through task-sharing to non-specialist providers and through telemedicine platforms. The cost-effectiveness of these strategies compared to traditional specialist and in-person models remains unknown. This protocol describes an economic evaluation of non-specialist providers and telemedicine, in comparison to specialist providers and in-person sessions within the ongoing Scaling Up Maternal Mental healthcare by Increasing access to Treatment (SUMMIT) trial. METHODS: The economic evaluation will be undertaken alongside the SUMMIT trial. SUMMIT is a pragmatic, randomized, non-inferiority trial across five North American study sites (N = 1,226) of the comparable effectiveness of two types of providers (specialist vs. non-specialist) and delivery modes (telemedicine vs. in-person) of a behavioural activation treatment for perinatal depressive and anxiety symptoms. The primary economic evaluation will be a cost-utility analysis. The outcome will be the incremental cost-effectiveness ratio, which will be expressed as the additional cost required to achieve an additional quality-adjusted life-year, as assessed by the EuroQol 5-Dimension 5-Level instrument. A secondary cost-effectiveness analysis will use participants' depressive symptom scores. A micro-costing analysis will be conducted to estimate the resources/costs required to implement and sustain the interventions; healthcare resource utilization will be captured via self-report. Data will be pooled and analysed using uniform price and utility weights to determine cost-utility across all trial sites. Secondary country-specific cost-utility and cost-effectiveness analyses will also be completed. Sensitivity analyses will be conducted, and cost-effectiveness acceptability-curves will be generated, in all instances. DISCUSSION: Results of this study are expected to inform key decisions related to dissemination and scale up of evidence-based psychological interventions in Canada, the US, and possibly worldwide. There is potential impact on real-world practice by informing decision makers of the long-term savings to the larger healthcare setting in services to support perinatal women with common mental health conditions.
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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 | 0.001 | 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".