A Technique to Re-Assess Epidemiologic Evidence in Light of the Healthy Worker Effect: The Case of Firefighting and Heart Disease
Bibliographic record
Abstract
The healthy worker effect (HWE) is a bias that is believed to have strongly affected the validity of previous cohort mortality studies on the relationship between firefighting and heart disease. There is a strong healthy hired effect (a component of the HWE) among firefighters, owing particularly to the recruitment of nondiabetic candidates. This is shown in previous studies in which the reported standardized mortality ratios for diabetes are much less than unity, generally around 0.3 to 0.5. Because diabetes is known to increase the risk of heart disease, a deficit of diabetes among firefighters is expected to lead to a deficit of heart injury and disease. This would make the cohort mortality studies incapable of detecting any increase in risk of heart injury and disease among firefighters. There is also a strong healthy worker survivor effect (another component of the HWE) among firefighters. In addition, heart disease is a classic example of the HWE because heart disease is chronic and its risk factors can be identified in the selection process. It is believed that (1) a major problem of previous studies on firefighting and heart disease is their failure to recognize the importance of the HWE when interpreting their results, and (2) a technique to re-assess results in light of the HWE is urgently needed. This article addresses the generally accepted principles relating to the HWE, including its definition and sources, and proposes a technique for re-assessing the literature in light of the HWE. The technique was applied to carefully re-assess 23 studies that provided direct evidence for the relationship between firefighting and heart disease. Before the re-assessment, 7 of the 23 studies showed positive evidence and 16 showed no evidence. After the re-assessment, 11 studies showed positive evidence and 12 showed no evidence. Based on the results of the re-assessment of the 23 studies, we concluded that (1) there is strong evidence of an increased risk of death overall from heart disease among firefighters; (2) there is insufficient evidence, even after considering the HWE, that there is an increased risk of death from aortic aneurysm among firefighters; and (3) there is insufficient evidence, even after considering the HWE, for a relationship between firefighting and any heart disease subtype, such as acute myocardial infarction.
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 | 0.402 | 0.688 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".