Health economic evaluations help inform payers of the best use of scarce health care resources
Bibliographic record
Abstract
OBJECTIVES: The number of new health technologies has risen over the past decade. These new technologies usually are more effective but they also cost more compared to existing ones. In a publicly funded health care system such as Canada, the aim is to maximize the health of the population within the resources available. As a result, it is unavoidable that choices and trade-offs have to be made because there will always be more treatment options than resources will allow (i.e., scarcity of resources) as well as alternative uses for those resources (i.e., opportunity costs). The objective of this paper is to provide an overview of economic evaluations and how these tools can be used to help inform payers of the best use of scarce health care resources. STUDY DESIGN: This descriptive paper includes a summary of key consepts and definitions in economic appraisal and draws upon recently published papers as illustrations. METHODS: Background on the necessity and role of economic evaluations is provided, followed by a description of the approaches for, and types of, economic evaluations. Two illustrative examples are used and some implications for rural, remote and circumpolar communities are discussed. RESULTS: There are 2 main approaches for conducting an economic evaluation (trial- and model-based) and 3 types of evaluations which can be considered to inform payers of the best use of health care resources (cost-effectiveness, cost-utility and cost-benefit analyses). CONCLUSIONS: Techniques of economic evaluation are useful tools and an important input into the decision-making process. Although these techniques have universal application, there are issues specific to rural, remote and circumpolar communities which can affect the results of economic appraisals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".