To pay or not to pay? A decision and cost-utility analysis of angiotensin-converting-enzyme inhibitor therapy for diabetic nephropathy.
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting-enzyme (ACE) inhibitor therapy can significantly delay the progression of diabetic nephropathy to end-stage renal failure (ESRF). The main obstacle to successful compliance with this therapy is the cost to the patients. The authors performed a cost-utility analysis from the government's perspective to see whether the province or territory should pay for ACE inhibitors for type I diabetic nephropathy on the assumption that cost is a major barrier to compliance with this important therapy. METHODS: A decision analysis tree was created to demonstrate the progression of type I diabetes with macroproteinuria from the point of prescription of ACE inhibitor therapy through to ESRF management, with a 21-year follow-up. Drug compliance, cost of ESRF treatment, utilities and survival data were taken from Canadian sources and used in the cost-utility analysis. One-way and two-way sensitivity analyses were performed to test the robustness of the findings. RESULTS: Compared with a no-payment strategy, provincial payment of ACE inhibitor therapy was found to be highly cost-effective: it resulted in an increase of 0.147 in the number of quality-adjusted life-years (QALYs) and an annual cost savings of $849 per patient. The sensitivity analyses indicated that the cost-effectiveness depends on compliance, effect of benefit and the cost of drug therapy. Changes in the compliance rate from 67% to 51% could result in a swing in cost-effectiveness from a savings of $899 to an expenditure of more than $1 million per additional QALY. A 50% reduction in the cost of ACE inhibitors would result in a cost savings of $299 per additional QALY with compliance rates as low as 58% in the provincial payment strategy. INTERPRETATION: Provincial coverage of ACE inhibitor therapy for type I diabetes with macroproteinuria improves patient outcomes, with a decrease in cost for ESRF services.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".