An Introduction to the Main Types of Economic Evaluations Used for Informing Priority Setting and Resource Allocation in Healthcare: Key Features, Uses, and Limitations
Bibliographic record
Abstract
Economic evidence is increasingly being used for informing health policies. However, the underlining principles of health economic analyses are not always fully understood by non-health economists, and inappropriate types of analyses, as well as inconsistent methodologies, may be being used for informing health policy decisions. In addition, there is a lack of open access information and methodological guidance targeted to public health professionals, particularly those based in low- and middle-income country (LMIC) settings. The objective of this review is to provide a comprehensive and accessible introduction to economic evaluations for public health professionals with a focus on LMIC settings. We cover the main principles underlining the most common types of full economic evaluations used in healthcare decision making in the context of priority setting (namely cost-effectiveness/cost-utility analyses, cost-benefit analyses), and outline their key features, strengths and weaknesses. It is envisioned that this will help those conducting such analyses, as well as stakeholders that need to interpret their output, gain a greater understanding of these methods and help them select/distinguish between the different approaches. In particular, we highlight the need for greater awareness of the methods used to place a monetary value on the health benefits of interventions, and the potential for such estimates to be misinterpreted. Specifically, the economic benefits reported are typically an approximation, summarising the health benefits experienced by a population monetarily in terms of individual preferences or potential productivity gains, rather than actual realisable or fiscal monetary benefits to payers or society.
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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 | 0.039 | 0.160 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".