Patient-level micro-simulation model for evaluating the future potential cost–effectiveness of pharmacy-based interventions in the control and management of diabetes-related complications in Canada
Bibliographic record
Abstract
Abstract Background The increased risk of complications among diabetes patients poses a serious threat to population health. Pharmacy-based interventions can decrease the burden of diabetes and its related complications. This study evaluates the cost-effectiveness of pharmacy-based interventions and offers insights on the practicality of their adoption by health practitioners. Methods We developed population-based micro-simulation model using 2,931 patients with diabetes in Canada. We used the risk equations on the UK Prospective Diabetes Study (UKPDS) to estimate the incidence and mortality of four of the most common diabetes-related complications (heart failure, stroke, amputation, and blindness). We extrapolated the potential effects of pharmacy interventions on reducing time-varying risk factors for diabetes complications. Cost was quantified as the annual cost of complications; and, the cost associated with pharmacy-based interventions. The final outcomes were the incremental costs per quality-adjusted life years (QALY) gained. Both deterministic and probabilistic sensitivity analysis were conducted to examine the robustness of the ratio. Result Pharmacy-based interventions could prevent 155 preventable deaths, 159 strokes, 29 cases of blindness, 24 amputations, and 19 heart failures across the lifetime of 2,931 patients. In addition, an estimated 953 QALYs (0.32 per patient) would be gained among the intervention group. Per QALY, the incremental discounted cost is $3,928, suggesting that pharmacy-based interventions are likely cost-effective compared to usual care. At an ICER threshold of $50,000, over 92% of the simulation remains cost-effective. Conclusion Pharmacist-based interventions targeted at addressing the development of diabetes-related complications among Canadian patients have the potential to offer a cost-effective strategy.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".