Cost-Utility of Dialysis in Canada: Hemodialysis, Peritoneal Dialysis, and Nondialysis Treatment of Kidney Failure
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: The kidney failure population is growing, necessitating the expansion of dialysis programs. These programs are costly and require a substantial amount of health care resources. Tools that accurately forecast resource use can aid efficient allocation. The objective of this study is to describe the development of an economic simulation model that incorporates treatment history and detailed modality transitions for patients with kidney disease using real-world data to estimate associated costs, utility, and survival by initiating modality. STUDY DESIGN: Cost-utility model with microsimulation. SETTING & POPULATION: Adult incident maintenance dialysis patients in Canada who initiated facility-based hemodialysis (HD) or home peritoneal dialysis (PD) between 2004 and 2013. INTERVENTION: HD and PD. OUTCOMES: Costs (related to dialysis, transplantation, infections, and hospitalizations), survival, utility, and dialysis modality mix over time. MODEL PERSPECTIVE & TIMEFRAME: The model took the perspective of the health care payer. Patients were followed up for 10 years from initiation of dialysis. Our cost-utility analysis compared the intervention with receiving no treatment. RESULTS: During a 10-year time horizon, the cost-utility ratio for all patients initiating dialysis was $103,779 per quality-adjusted life-year (QALY) in comparison to no treatment. Patients who initiated with facility-based HD were treated at a cost-utility ratio of $104,880/QALY and patients who initiated with home PD were treated at a cost-utility ratio of $83,762/QALY. During this time horizon, the total mean cost and QALYs per patient were estimated at $350,774 ± $204,704 and 3.38 ± 2.05) QALYs respectively. LIMITATIONS: The results do not include costs from the societal perspective. Rare patient trajectories were unable to be assessed. CONCLUSIONS: This model demonstrates that patients who initiated dialysis with PD were treated more cost-effectively than those who initiated with HD during a 10-year time horizon.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".