Heterogeneity of exposure and attribution of mesothelioma: Trends and strategies in two American counties
Bibliographic record
Abstract
As mesothelioma risk has begun to decline in the United States, two trends are gaining relative importance. "Legacy" exposures causing this disease are most important in locales having past asbestos industry, shipyards, and/or local distribution of asbestos amphibole-containing material as a result. "Future" exposures are of particular concern in relation to so-called "naturally occurring asbestos" (NOA) areas which include unequivocally asbestiform amphibole. In this paper, Jefferson Parish, Louisiana is used as an example of the first trend, and El Dorado County, California as an example of the second. Available tumor registry, epidemiology, historical and mineralogical data, and lung-retained fibre content are used as indicators of disease and exposure. Jefferson Parish, LA was chosen as the prototype of "legacy" exposures on the basis of historical evidence of asbestos plants with known mesotheliomas in the workforce, known shipyards in the same area, EPA records of distribution of crocidolite-containing scrap to and remediation of over 1400 properties, NIOSH published data on mesothelioma by county, and exposure data including lung-retained fibre analyses in victims, where available. El Dorado, CA was chosen as the prototype of NOA amphibole exposures on the basis of tumor registry data, activity-based EPA sampling data in one area, and lung-retained fibre analyses in area pets, and future risk assessment based on tremolite-specific modelling in Libby, Montana and elsewhere. As expected, the legacy exposure area was high in mesothelioma incidence and mortality. Lung-retained fibre content confirms crocidolite exposures in exposed plant-workers and those exposed to crocidolite-containing scrap, and amosite in shipyard workers. In contrast, to date, cancer registry data in the NOA-amphibole ("future") county does not show a clear increase in incidence or mortality, but grouped county data from the area show a shift in higher incidence rates to the NOA areas and away from California "legacy" (e.g., shipyard) areas from 1988-2005. EPA active sampling has confirmed excess tremolite/ actinolite fibre(s) in air, although there is debate about its nature and the appropriateness of the area sampled. Lung-retained fibre in local pets shows unequivocally elevated asbestiform tremolite/ actinolite in areas thought to be most affected, but numbers are small. Future risk is expected to rise due to a vastly increased population base coupled with exposures potentially created by related construction activities. Although legacy exposures are producing smaller numbers of cases with time, they continue to occur at high rates, and new sources of legacy exposure are being discovered in highly localized "hotspots". Differential exposure sources remain a problem in attribution, but continued remediation seems the best strategy for prevention. In the "future" risk county and surrounding areas, incidence trends are less clear, but again highly localized exposures as opposed to broad areas seem important. Activity-based air sampling; targeted soil samples, and lung-retained fibre analyses may be useful in defining areas of highest future risk and potential prevention.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".