Valuation of health states in the US study to establish disability weights: lessons from the literature
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
The metric of disability-adjusted life years (DALYs) has become the global standard of measuring burden of disease. DALYs are comprised of years of life lost due to premature mortality and years of healthy life lost due to living with disability. In order to calculate the second part of the DALY equation, disease specific disability weights have to be established, i.e. measures for the decline of health associated with these disease states, which vary between zero for perfect health and one for death. Although these disability weights are key for estimating DALYs, there have not been many comprehensive studies with empirical determinations of them. This article describes a systematic review on the state of the art with respect to empirically determining disability weights. Based on this review, a multi-method approach is outlined, which has also been implemented in a US study to measure burden of disease. This approach involves the use of psychometric methodology as well as economic trade-off methods for determining the value of health states. It is conceptualized as a disaggregated approach, where the disability weight of any health state can be calculated if the attributes of this health state are known. The US study received the collaboration of experts from more than 20 institutes of the National Institutes of Health and of the Centers for Disease Control and Prevention. First results will be available by the end of this year.
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.
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 | 0.307 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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