The Healthy Worker Effect in Asthma
Bibliographic record
Abstract
Despite the increasing attention to the relationship between asthma and work exposures, occupational asthma remains underrecognized and its population burden underestimated. This may be due, in part, to the fact that traditional approaches to studying asthma in populations cannot adequately take into account the healthy worker effect (HWE). The HWE is the potential bias caused by the phenomenon that sicker individuals may choose work environments in which exposures are low; they may be excluded from being hired; or once hired, they may seek transfer to less exposed jobs or leave work. This article demonstrates that population- and workplace-based asthma studies are particularly subject to HWE bias, which leads to underestimates of relative risks. Our objective is to describe the HWE as it relates to asthma research, and to discuss the significance of taking HWE bias into account in designing and interpreting asthma studies. We also discuss the importance of understanding HWE bias for public health practitioners and for clinicians. Finally, we emphasize the timeliness of this review in light of the many longitudinal "child to young adult" asthma cohort studies currently underway. These prospective studies will soon provide an ideal opportunity to examine the impact of early workplace environments on asthma in young adults. We urge occupational and childhood asthma epidemiologists collaborate to ensure that this opportunity is not lost.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".