Cost‐effectiveness analysis of adding pharmacists to primary care teams to reduce cardiovascular risk in patients with Type 2 diabetes: results from a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Adding pharmacists to primary care teams significantly improved blood pressure control and reduced predicted 10-year cardiovascular risk in patients with Type 2 diabetes. This pre-specified sub-study evaluated the economic implications of this cardiovascular risk reduction strategy. METHODS: One-year outcomes and healthcare utilization data from the trial were used to determine cost-effectiveness from the public payer perspective. Costs were expressed in 2014 Canadian dollars and effectiveness was based on annualized risk of cardiovascular events derived from the UKPDS Risk Engine. RESULTS: The 123 evaluable trial patients included in this analysis had a mean age of 62 ( ± 11) years, 38% were men, and mean diabetes duration was 6 ( ± 7) years. Pharmacists provided 3.0 ( ± 1.9) hours of additional service to each intervention patient, which cost $226 ( ± $1143) per patient. The overall one-year per-patient costs for healthcare utilization were $190 lower in the intervention group compared with usual care [95% confidence interval (CI): -$1040, $668). Intervention patients had a significant 0.3% greater reduction in the annualized risk of a cardiovascular event (95% CI: 0.08%, 0.6%) compared with usual care. In the cost-effectiveness analysis, the intervention dominated usual care in 66% of 10,000 bootstrap replications. At a societal willingness-to-pay of $4000 per 1% reduction in annual cardiovascular risk, the probability that the intervention was cost-effective compared with usual care reached 95%. A sensitivity analysis using multiple imputation to replace missing data produced similar results. CONCLUSIONS: Within a randomized trial, adding pharmacists to primary care teams was a cost-effective strategy for reducing cardiovascular risk in patients with Type 2 diabetes. In most circumstances, this intervention may also be cost saving.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".