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Record W2039580511 · doi:10.1080/10408440802273156

A Meta-Analysis of Asbestos-Related Cancer Risk That Addresses Fiber Size and Mineral Type

2008· review· en· W2039580511 on OpenAlexaboutno aff
D. Wayne Berman, Kenny S. Crump

Bibliographic record

VenueCritical Reviews in Toxicology · 2008
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsbestosChrysotileMesotheliomaEnvironmental healthAsbestos fibersLung cancerEpidemiologyExposure assessmentAsbestos cementMedicineStatisticsEnvironmental scienceToxicologyPathologyMathematicsBiologyMaterials scienceComposite material

Abstract

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Quantitative estimates of the risk of lung cancer or mesothelioma in humans from asbestos exposure made by the U.S. Environmental Protection Agency (EPA) make use of estimates of potency factors based on phase-contrast microscopy (PCM) and obtained from cohorts exposed to asbestos in different occupational environments. These potency factors exhibit substantial variability. The most likely reasons for this variability appear to be differences among environments in fiber size and mineralogy not accounted for by PCM. In this article, the U.S. Environmental Protection Agency (EPA) models for asbestos-related lung cancer and mesothelioma are expanded to allow the potency of fibers to depend upon their mineralogical types and sizes. This is accomplished by positing exposure metrics composed of nonoverlapping fiber categories and assigning each category its own unique potency. These category-specific potencies are estimated in a meta-analysis that fits the expanded models to potencies for lung cancer (KL's) or mesothelioma (KM's) based on PCM that were calculated for multiple epidemiological studies in our previous paper (Berman and Crump, 2008). Epidemiological study-specific estimates of exposures to fibers in the different fiber size categories of an exposure metric are estimated using distributions for fiber size based on transmission electron microscopy (TEM) obtained from the literature and matched to the individual epidemiological studies. The fraction of total asbestos exposure in a given environment respectively represented by chrysotile and amphibole asbestos is also estimated from information in the literature for that environment. Adequate information was found to allow KL's from 15 epidemiological studies and KM's from 11 studies to be included in the meta-analysis. Since the range of exposure metrics that could be considered was severely restricted by limitations in the published TEM fiber size distributions, it was decided to focus attention on four exposure metrics distinguished by fiber width: "all widths," widths > 0.2 micro m, widths < 0.4 microm, and widths < 0.2 microm, each of which has historical relevance. Each such metric defined by width was composed of four categories of fibers: chrysotile or amphibole asbestos with lengths between 5 microm and 10 microm or longer than 10 microm. Using these metrics three parameters were estimated for lung cancer and, separately, for mesothelioma: KLA, the potency of longer (length > 10 microm) amphibole fibers; rpc, the potency of pure chrysotile (uncontaminated by amphibole) relative to amphibole asbestos; and rps, the potency of shorter fibers (5 microm < length < 10 microm) relative to longer fibers. For mesothelioma, the hypothesis that chrysotile and amphibole asbestos are equally potent (rpc = 1) was strongly rejected by every metric and the hypothesis that (pure) chrysotile is nonpotent for mesothelioma was not rejected by any metric. Best estimates for the relative potency of chrysotile ranged from zero to about 1/200th that of amphibole asbestos (depending on metric). For lung cancer, the hypothesis that chrysotile and amphibole asbestos are equally potent (rpc = 1) was rejected (p < or = .05) by the two metrics based on thin fibers (length < 0.4 microm and < 0.2 microm) but not by the metrics based on thicker fibers. The "all widths" and widths < 0.4 microm metrics provide the best fits to both the lung cancer and mesothelioma data over the other metrics evaluated, although the improvements are only marginal for lung cancer. That these two metrics provide equivalent (for mesothelioma) and nearly equivalent (for lung cancer) fits to the data suggests that the available data sets may not be sufficiently rich (in variation of exposure characteristics) to fully evaluate the effects of fiber width on potency. Compared to the metric with widths > 0.2 microm with both rps and rpc fixed at 1 (which is nominally equivalent to the traditional PCM metric), the "all widths" and widths < 0.4 microm metrics provide substantially better fits for both lung cancer and, especially, mesothelioma. Although the best estimates of the potency of shorter fibers (5 < length < 10 microm) is zero for the "all widths" and widths < 0.4 microm metrics (or a small fraction of that of longer fibers for the widths > 0.2 microm metric for mesothelioma), the hypothesis that these shorter fibers were nonpotent could not be rejected for any of these metrics. Expansion of these metrics to include a category for fibers with lengths < 5 microm did not find any consistent evidence for any potency of these shortest fibers for either lung cancer or mesothelioma. Despite the substantial improvements in fit over that provided by the traditional use of PCM, neither the "all widths" nor the widths < 0.4 microm metrics (or any of the other metrics evaluated) completely resolve the differences in potency factors estimated in different occupational studies. Unresolved in particular is the discrepancy in potency factors for lung cancer from Quebec chrysotile miners and workers at the Charleston, SC, textile mill, which mainly processed chrysotile from Quebec. A leading hypothesis for this discrepancy is limitations in the fiber size distributions available for this analysis. Dement et al. (2007) recently analyzed by TEM archived air samples from the South Carolina plant to determine a detailed distribution of fiber lengths up to lengths of 40 microm and greater. If similar data become available for Quebec, perhaps these two size distributions can be used to eliminate the discrepancy between these two studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.022
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0100.051
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.213
GPT teacher head0.442
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
DomainMethods
GenreReview

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".

Quick stats

Citations285
Published2008
Admission routes1
Has abstractyes

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