Cost-effectiveness analysis of health tapestry, a complex primary care program for older adults: a post-hoc analysis
Bibliographic record
Abstract
BACKGROUND: We initially reported on the cost-effectiveness of a 6-month randomized controlled implementation trial which evaluated Health TAPESTRY, a primary care program for older adults, at the McMaster Family Health Team (FHT) site and 5 other FHT sites in Ontario, Canada. While there were no statistically significant between-group differences in outcomes at month 6 post randomization, positive outcomes were observed at the McMaster FHT site, which recruited 40% (204/512) of the participants. The objective of this post-hoc study was to determine the cost-effectiveness of Health TAPESTRY based on data from the McMaster FHT site. METHODS: Costs included the cost to implement Health TAPESTRY at McMaster as well as healthcare resource consumed, which were costed using publicly available sources. Health-related-quality-of-life was evaluated with the EQ-5L-5L at baseline and at month 6 post randomization. Quality-adjusted-life-years (QALYs) were calculated under an-area-under the curve approach. Unadjusted and adjusted regression analyses (two independent regression analyses on costs and QALYs, seemingly unrelated regression [SUR], net benefit regression) as well as difference-in-difference and propensity score matching (PSM) methods, were used to deal with the non-randomized nature of the trial. Sampling uncertainty inherent to the trial data was estimated using non-parametric bootstrapping. The return on investment (ROI) associated with Health TAPESTRY was calculated. All costs were reported in 2021 Canadian dollars. RESULTS: With an intervention cost of $293/patient, Health TAPESTRY was the preferred strategy in the unadjusted and adjusted analyses. The results of our bootstrap analyses indicated that Health TAPESTRY was cost-effective compared to usual care at commonly accepted WTP thresholds. For example, if decision makers were willing to pay $50,000 per QALY gained, the probability of Health TAPESTRY to be cost effective compared to usual care varied from 0.72 (unadjusted analysis) to 0.96 (SUR) when using a WTP of $50,000/QALY gained. The DID and ROI analyses indicated that Health Tapestry generated a positive ROI. CONCLUSION: Health TAPESTRY was the preferred strategy when implemented at the McMaster FHT. We caution care in interpreting the results because of the post-hoc nature of the analyses and limited sample size based on one site.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".