Heterogeneity of exposure and attribution of mesothelioma: Trends and strategies in two American counties
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».