Ambient Air Pollution and Cancer Mortality in the Cancer Prevention Study II
Bibliographic record
Abstract
Background: The International Agency for Research on Cancer classified both outdoor air pollution and airborne particulate matter as carcinogenic to humans (Group 1) for lung cancer. There may be associations with cancer at other sites; however, the epidemiological evidence is limited. Objective: The aim of this study was to clarify whether ambient air pollution is associated with specific types of cancer other than lung cancer by examining associations of ambient air pollution with nonlung cancer death in the Cancer Prevention Study II (CPS-II). Methods: Analysis included 623,048 CPS-II participants who were followed for 22 y (1982–2004). Modeled estimates of particulate matter with aerodynamic diameter <2.5µm (PM2.5) (1999–2004), nitrogen dioxide (NO2) (2006), and ozone (O3) (2002–2004) concentrations were linked to the participant residence at enrollment. Cox proportional hazards models were used to estimate associations per each fifth percentile–mean increment with cancer mortality at 29 anatomic sites, adjusted for individual and ecological covariates. Results: We observed 43,320 nonlung cancer deaths. PM2.5 was significantly positively associated with death from cancers of the kidney {adjusted hazard ratio (HR) per 4.4 μg/m3=1.14 [95% confidence interval (CI): 1.03, 1.27]} and bladder [HR=1.13 (95% CI: 1.03, 1.23)]. NO2 was positively associated with colorectal cancer mortality [HR per 6.5 ppb=1.06 (95% CI: 1.02, 1.10). The results were similar in two-pollutant models including PM2.5 and NO2 and in three-pollutant models with O3. We observed no statistically significant positive associations with death from other types of cancer based on results from adjusted models. Conclusions: The results from this large prospective study suggest that ambient air pollution was not associated with death from most nonlung cancers, but associations with kidney, bladder, and colorectal cancer death warrant further investigation. https://doi.org/10.1289/EHP1249
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".