Do patterns of past asbestos use and production reflect current geographic variations of cancer risk?: mesothelioma in Ontario and British Columbia, Canada
Bibliographic record
Abstract
PURPOSE: Canada was a major global asbestos producer and consumer. Geographic patterns of Canadian asbestos use and mesothelioma, a highly fatal cancer linked to asbestos exposure, have not been previously reported. This study summarized key trends in mesothelioma incidence by geography and time in two Canadian provinces, Ontario and British Columbia (BC), and explored how past workforce characteristics and geographic trends in asbestos production and use may shape variations in regional rates of mesothelioma. METHODS: We report trends in mesothelioma incidence (1993-2016) for Ontario and British Columbia using population-based incidence data that were age-standardized to the 2011 Canadian population. Historical records of asbestos production and use were analyzed to geo-locate industrial point sources of asbestos in Ontario and BC. The prevalence of occupations in regions with the highest and lowest rates of mesothelioma in Ontario and BC were calculated using labor force statistics from the 1981 Canadian Census. RESULTS: Regional mesothelioma rates varied in both provinces over time; more census divisions in both Ontario and BC registered mesothelioma rates in the highest quintile of incidences during the period 2009 to 2016 than in any prior period examined. Certain occupations such as construction trades workers were more likely to be overrepresented in regions with high mesothelioma rates. CONCLUSION: This work explored how studying asbestos exposure and mesothelioma incidence at small-scale geographies could direct cancer surveillance and research to more targeted areas. Findings indicated that regional variations in mesothelioma could signal important differences in past occupational and potentially environmental exposures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".