A Systematic Review of Economic Evaluations of Insulin for the Management of Type 2 Diabetes
Bibliographic record
Abstract
Diabetes is a chronic, metabolic disease characterized by hyperglycemia, which occurs as a result of inadequate production or utilization of insulin. Type 2 diabetes (T2D) is the most common type of diabetes with estimates projecting a prevalence of more than 1 billion people living with T2DM by 2050. Hence, it was decided to conduct a systematic literature review of health economic evaluations of insulin, the most common medication used for the treatment of the disease, to inform policy. Pharmacoeconomic analyses, written in English and published after 2016, were considered for inclusion. PubMed/Medline, Global Health, Embase and Health Management Consortium were searched separately between 5 July 2023 and 17 July 2023. Grey literature articles were searched on ISPOR and the Cost-Effectiveness Analysis Registry during the same period. After the exclusion criteria were applied, 21 studies were included. Using the BMJ checklist, a quality appraisal was performed on all included studies. Data extraction was performed manually. Regarding evidence synthesis, data were heterogenous and are presented based on study type. The results showed a variety of treatment combinations being available for the treatment of diabetes, with insulin degludec/DegLira and semaglutide being cost-effective despite their high cost, due to the effectiveness of managing the disease. Research around the cost-effectiveness or cost-utility of insulin has potential to progress further, to ensure informed policy-making in the future.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".