Global Estimate of Lung Cancer Mortality Attributable to Residential Radon
Bibliographic record
Abstract
BACKGROUND: Radon is the second most important cause of lung cancer, ranked by the World Health Organization as the fifth leading cause of mortality in 2010. An updated database of national radon exposures for 66 countries allows the global burden of lung cancer mortality attributable to radon to be estimated. OBJECTIVE: Our goal was to estimate the global population attributable burden of lung cancer mortality in 2012 from residential radon. METHODS: Estimates of the population attributable risk (PAR) of lung cancer mortality from radon were determined using the attributable fraction approach, using three models for excess relative risk of lung cancer from radon. RESULTS: The estimates of the median PAR of lung cancer mortality from residential radon in 2012 for the 66 countries having representative national radon surveys were consistent, as 16.5%, 14.4%, and 13.6% for the exposure-age-concentration (EAC) model (BEIR VI), the Hunter model, and the Kreuzer model, respectively. The mean PAR using the EAC model ranged from 4.2% (95% CI: 0.9, 11.7) for Japan, to 29.3% (95% CI: 22.9, 35.7) for Armenia, with a median for the 66 countries of 16.5%. Radon-attributable lung cancer deaths for all 66 countries totaled 226,057 in 2012 and represent a median of 3.0% of total cancer deaths. CONCLUSIONS: Consistent findings between the three models used to estimate excess relative risks of lung cancer from radon, and between the attributable fraction methodology and the life table analysis, confirm that residential radon is responsible for a substantial proportion of lung cancer mortality worldwide. https://doi.org/10.1289/EHP2503.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 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".