The impact of using different costing methods on the results of an economic evaluation of cardiac care: microcosting vs gross‐costing approaches
Bibliographic record
Abstract
BACKGROUND: Published guidelines on the conduct of economic evaluations provide little guidance regarding the use and potential bias of the different costing methods. OBJECTIVES: Using microcosting and two gross-costing methods, we (1) compared the cost estimates within and across subjects, and (2) determined the impact on the results of an economic evaluation. METHODS: Microcosting estimates were obtained from the local health region and gross-costing estimates were obtained from two government bodies (one provincial and one national). Total inpatient costs were described for each method. Using an economic evaluation of sirolimus-eluting stents, we compared the incremental cost-utility ratios that resulted from applying each method. RESULTS: Microcosting, Case-Mix-Grouper (CMG) gross-costing, and Refined-Diagnosis-Related grouper (rDRG) gross-costing resulted in 4-year mean cost estimates of $16,684, $16,232, and $10,474, respectively. Using Monte Carlo simulation, the cost per QALY gained was $41,764 (95% CI: $41,182-$42 346), $42,538 (95% CI: $42 167-$42 907), and $36,566 (95% CI: $36,172-$36,960) for microcosting, rDRG-derived and CMG-derived estimates, respectively (P<0.001). CONCLUSIONS: Within subject, the three costing methods produced markedly different cost estimates. The difference in cost-utility values produced by each method is modest but of a magnitude that could influence a decision to fund a new intervention.
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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 | 0.362 | 0.603 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".