Abstract B011: Associations between county-level air pollutants and early-onset breast cancer incidence rates in the U.S
Bibliographic record
Abstract
Abstract Purpose: The incidence of early-onset breast cancer, diagnosed before age 50 years, has increased significantly in the U.S. and globally in recent decades. While various genetic factors have been implicated in the development of early-onset breast cancer, the role of environmental exposures remains unclear. This study examined associations between ambient air pollution measures and early-onset breast cancer incidence rates in the U.S. Methods: We conducted an ecological analysis of county-level associations between particulate matter <2.5 micrometers (PM2.5) and Nitrogen Dioxide (NO2) exposures (averaged from 2002-2006) and age-adjusted early-onset breast cancer rates (averaged from 2017-2021), accounting for a potential latency period between exposure and diagnosis. The study included 1,422 U.S. counties with available data on pollutants and age-adjusted early-onset breast cancer incidence rates. Generalized additive models were used to estimate associations between pollutants (PM2.5 and NO2 ) and early-onset breast cancer incidence rates, adjusting for county-level prevalence of physical inactivity, college education attainment, poverty, and proportions of Hispanic and non-Hispanic Black populations. Results: After adjusting for confounders, PM2.5 was significantly inversely associated with early-onset breast cancer rates (Tertile 2 vs. Tertile 1: b = -1.66, p=0.01; Tertile 3 vs. Tertile 1: b = -1.85, p=0.01). Similar inverse associations were observed for NO2 (Tertile 2 vs. Tertile 1: b = -1.97, p<0.01); Tertile 3 vs. Tertile 1: b=-2.59, p<0.01). A significant non-linear association between PM2.5 (per 10-unit increase) and early-onset breast cancer rates was detected (estimated degrees of freedom (EDF) = 3.2, p=0.014), with rates increasing up to approximately 10 mcg/m3, then declining at higher concentrations. Conclusion: These findings suggest inverse associations between higher tertiles of PM2.5 and NO2 exposure and early-onset breast cancer rates. However, the observed non-linear relationship between PM2.5 and early-onset breast cancer indicates a possible threshold effect or residual confounding at higher PM2.5 exposure levels. These results underscore the complexity of modeling environmental risk and highlight the need for further research to confirm these findings and investigate underlying biological mechanisms for potential threshold effects. If confirmed, the findings suggest that public health interventions to mitigate air pollution should also target areas with intermediate levels of pollution. Citation Format: Anna L. Fischer, Guoli Zhou, Kelly A. Hirko. Associations between county-level air pollutants and early-onset breast cancer incidence rates in the U.S [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B011.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".