The cost-effectiveness of insulin analogs and regular insulin for diabetes control: a case study in Iran
Bibliographic record
Abstract
PURPOSE: For years, traditional techniques have been used for diabetes treatment. There are two major types of insulin: insulin analogs and regular insulin. Insulin analogs are similar to regular insulin and lead to changes in pharmacokinetic and pharmacodynamic properties. The purpose of the present research was to determine the cost-effectiveness of insulin analogs versus regular insulin for diabetes control in Yazd Diabetes Center in 2017. DESIGN/METHODOLOGY/APPROACH: In this descriptive-analytical research, the cost-effectiveness index was used to compare insulin analogs and regular insulin (pen/vial) for treatment of diabetes. Data were analyzed in the TreeAge Software and a decision tree was constructed. A 10% discount rate was used for ICER sensitivity analysis. Cost-effectiveness was examined from a provider's perspective. FINDINGS: QALY was calculated to be 0.2 for diabetic patients using insulin analogs and 0.05 for those using regular insulin. The average cost was $3.228 for analog users and $1.826 for regular insulin users. An ICER of $0.093506/QALY was obtained. The present findings suggest that insulin analogs are more cost-effective than regular insulin. ORIGINALITY/VALUE: This study was conducted using a cost-effectiveness analysis to evaluate insulin analogs versus regular insulin in controlling diabetes. The results of study are helpful to the government to allocate more resources to apply the cost-effective method of the treatment and to protect patients with diabetes from the high cost of treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".