Comparative pharmacoeconomic evaluation of the treatment of type 2 diabetes mellitus with insulin degludec and insulin glargine in basal-bolus insulin therapy
Bibliographic record
Abstract
Background. Type 2 diabetes mellitus (T2DM) and its complications are the major causes of long-term disability, significant performance loss, and premature mortality. Poor glycemic control, diabetes-related complications, and insulin therapy are associated with a significant increase in diabetes costs. The high cost of insulin analogues restricts reimbursement of their costs in some countries. Comprehensive clinical and economic assessments of different insulin analogues are the useful tool that may help policy-makers to set priorities in medicine provision within the existing system of healthcare funding.
 Aim — to compare the clinical and economic effectiveness of insulin degludec (100 U/mL) and insulin glargine (100 U/mL) in basal-bolus therapy for T2DM.
 Material and methods. Economic evaluation was based on cost effectiveness analysis (CEA), incremental cost-effectiveness ratio (ICER), cost effectiveness modelling (using 5-year time horizon), sensitivity analysis, and economic feasibility assessment. The clinical effectiveness was evaluated based on changes in the HbA1c level, frequency of severe hypoglycemia, and number of patient-years without complications. Only direct medical costs (costs for insulin therapy and treatment of severe hypoglycemia and diabetes-related complications) were analyzed.
 Results. The mean total direct medical costs amounted to 783,789 RUR/patient for degludec (of these, the cost of insulin therapy accounted for 68%) and 730,805 RUR/patient for glargine (of these, the costs for treatment of severe hypoglycemia and diabetes complications accounted for 62.58%). Degludec has a higher efficacy (additional HbA1c reduction by 0.52%), lower rate of severe hypoglycemia, and larger number of complication-free patient-years. Incremental costs (ICER) for degludec amounted to 101,236 RUR/patient (based on a decrease in the HbA1c level) and 353,224 RUR/patient (based on an increase in the number of complication-free patient-years); these values were 14.3- and 4.1-fold lower than the threshold of willingness-to-pay, respectively. Additional analysis of costs in patients with severe hypoglycemia rates of 3 and >3 events/patient/year demonstrated that the estimated mean total costs for insulin degludec were lower by 5 and 23%, respectively, than those for insulin glargine.
 Conclusion. Our findings demonstrate the clinical and cost benefits of insulin degludec in T2DM patients, which supports its broader use in routine clinical practice for achieving the best glycemic control with minimal adverse effects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".