The economics of precision health: preventing air pollution-induced exacerbation in asthma
Bibliographic record
Abstract
The demonstrable value of precision medicine, in the context of common environmental exposures, has scarcely been explored. This study evaluated the cost effectiveness of a preventive personalised intervention to reduce the adverse effect of air pollution in the context of asthma. A decision-analytic model was used to conduct a cost-utility analysis of prevention interventions in case of acute exposure to air pollution in mild asthma. Three different strategies, as follows, were compared: no preventive intervention; precision health strategy based on information from genotype testing, followed with treating high-risk patients; and prescribing additional medication to all mild asthmatics as a preventive intervention. The costs and quality-adjusted life years (QALYs) in the base case and alternative scenarios were obtained through probabilistic analysis. The results showed that the precision prevention intervention (anticipatory intervention for asthmatics, guided by relevant genetic abnormality, in the face of acute air pollution) is a cost-effective strategy compared with no such intervention, with an incremental cost-effectiveness ratio of CAD 49 555 per QALY. Furthermore, this strategy is a dominant strategy compared with an intervention that prescribes medication indiscriminately to all asthmatics. The incorporation of genomic testing to stratify risk of asthmatics to pollution-driven exacerbations, and then tailoring a preventive intervention accordingly, may be cost effective relative to untailored methods. These results lend plausibility to the use of precision medicine for limiting asthma exacerbation in the context of air pollution and, potentially, other exposures.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".