Illustrating the impact of including future costs in economic evaluations: an application to end‐stage renal disease care
Bibliographic record
Abstract
There are strong theoretical arguments for including future costs for related and unrelated medical care and non-medical expenditures within economic evaluations. Nevertheless, there is limited data on how inclusion of such costs affects the cost effectiveness of medical interventions in practice. For a low-cost intervention that improves survival in end-stage renal disease (ESRD) patients, we sought to determine how the inclusion of future costs for related medical care (i.e. dialysis and transplantation) and for unrelated medical care and non-medical expenditure would affect the magnitude of the cost per QALY ratio. We performed a cost-utility analysis comparing hemodialysis using a synthetic dialyser (the current treatment of choice in Canada) with the historical gold-standard treatment (use of a cellulose dialyser). We contrasted the results of the analysis including and excluding various measures of future costs. While the inclusion of future costs for unrelated medical care and non-medical expenditures had a significant impact on the cost per QALY ratio, the size of the cost per QALY ratio was most sensitive to inclusion of future costs for related medical care. Our analysis shows that even relatively inexpensive interventions that extend survival of dialysis patients may not be cost-effective since, by extending survival, the extra outpatient dialysis costs that are incurred are large. Inclusion of such costs (which, in and of itself, is methodologically correct) in economic evaluations in this area may mitigate against the acceptance of interventions that are relatively inexpensive themselves but which improve patient survival.
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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 | 0.079 | 0.254 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".