Potential Effects of the Choice of Costing Perspective on Cost Estimates
Bibliographic record
Abstract
OBJECTIVE: Because health care resources are constrained, decision-making processes often require clarifying the potential costs and savings associated with different options. This involves calculating a program's costs. The chosen costing perspective defines the costs to be considered and can ultimately influence decisions. Yet reviews of the literature suggest little attention has been paid to the perspective in economic evaluations. This article's purpose is to explore how the costing perspective can affect cost estimates. METHOD: As a vehicle for our discussion, we use service use data for clients enrolled in 6 Ontario early psychosis intervention programs. Governmental and nongovernmental payer costing perspectives are considered. We examine annual costs associated with early psychosis intervention clients enrolled for ≤12 months versus those enrolled for >12 months. This also allows for an assessment of the impact that choice of time horizon can make on the results. RESULTS: The difference in total between group cost for hospital, emergency room, and physicians is $2499; the >12-month group has relatively higher mean costs. When all governmental and nongovernmental costs are considered, there is a mean between-group cost difference of $1272, with lower mean costs for the >12-month group. CONCLUSIONS: Although the Ministry of Health bears a large proportion of costs, other governmental agencies and the private sector can incur a sizeable share. This example demonstrates the potential importance of including other cost perspectives with the hospital sector in analyses as well as the impact of time horizon on cost estimates.
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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 | 0.215 | 0.561 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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; 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".