Economic analysis of insulin initiation by pharmacists in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Conducted in Alberta, the RxING study examined the effect of a community pharmacist prescribing intervention on glycemic control in patients with uncontrolled type 2 diabetes mellitus (T2DM) using insulin glargine. The objective of this study was to assess the cost-effectiveness of pharmacists' prescribing of insulin glargine as an early intervention in uncontrolled patients with T2DM vs usual clinical practice. METHODS: The IMS CORE diabetes Markov model was used to project long-term clinical outcomes, costs and cost-effectiveness of interventions. The efficacy of insulin glargine, in terms of hemoglobin A1c reduction and hypoglycemia rates, was obtained from the RxING study. Health utility and cost data were found in Canadian publications. The base-case analyses examined the economic and clinical effects of having pharmacists initiate insulin therapy in patients with uncontrolled T2DM in comparison to a physician initiate it up to 3 years later. RESULTS: Insulin initiation by pharmacists with uncontrolled T2DM patients is cost-effective. Having pharmacists prescribe insulin 1 year earlier than usual clinical practice resulted in an incremental cost savings of $805 (CDN$) and a gain of 0.048 QALYs per patient. Pharmacists prescribing insulin 2 years earlier resulted in an incremental cost savings of $624 (CDN$) per year and a gain of 0.075 quality-adjusted life-years (QALYs). Prescribing 3 years earlier allowed for a minor increase of $26 and a gain of 0.086 QALYs. CONCLUSION: Earlier initiation of insulin by pharmacists, in uncontrolled T2DM patients, resulted in cost savings and delays in the development of diabetes-related complications, leading to an improved quality of life and increased survival rates.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".