The impact of using different costing methods on the results of an economic evaluation of cardiac care: microcosting vs gross‐costing approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it