Understanding Harris’ understanding of CEA: is cost effective resource allocation undone?
Bibliographic record
Abstract
Harris has been a vocal critic of CEA and the QALY for over 20 years. In this paper we attempt to summarise and evaluate both Harris’ criticisms of CEA and the alternative procedures he commends to health care decision makers. Harris’ basic position is that all health benefits are indivisible and, unless a strong argument can be made, of equal worth. He argues individuals have a right to treatment that cannot be denied by a decision maker on the basis of their ability to benefit and therefore that life saving treatments dominate life enhancing treatments in all circumstances, regardless of the QALY benefits in both cases. In this paper we review Harris’ arguments against the use of CEA and QALYs and critically appraise his suggestions for alternative approaches to health care resource allocation. We conclude that whilst his work has challenged the proponents of CEA and QALYs to be explicit about the method’s discriminatory characteristics, his arguments are largely based upon the flawed assumptions that lives can be saved, rather than death postponed; and that opportunity cost can be sidestepped by attempting to impose the same outcome for all.
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 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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".