Residential surrounding greenness is not associated with incident breast cancer in young women in Ontario, Canada
Bibliographic record
Abstract
Objectives Environmental exposures play an important role in the development of breast cancer. The incidence of breast cancer is increasing in young women, and its etiology differs from that of older women. Epidemiological studies have provided mixed evidence about whether proximity to urban greenness reduces the risk of breast cancer, but few studies have evaluated this risk in younger women. Methods We investigated associations between residentially-based measures of greenness and breast cancer among participants of the Ontario Environmental Health Study (OEHS). The OEHS was a case–control study of Ontario women, 18–45 years of age, who provided questionnaire data between 2013 and 2015. The study included 465 cases diagnosed with a pathologically confirmed primary diagnosis of breast cancer, and 242 population-based controls. Residentially-based measures of greenness, the Normalized Difference Vegetation Index (NDVI) and tree coverage percentage, at 100-, 250-, 500-, and 1,000-m buffers, were assigned to the residential histories of the women. Odds ratios and their 95% confidence intervals (CIs) were estimated using logistic regression, adjusted for potential confounders including traffic-related air pollution [nitrogen dioxide (NO2)]. Results We found no evidence that an increase in NDVI or tree coverage were significantly associated with breast cancer. The adjusted odds ratio of breast cancer in relation to an interquartile range increase (IQR) in the NDVI (500-m buffer) was 0.86 (95% CI = 0.59–1.13). Similarly, the odds ratio of breast cancer among those in the highest quartile of tree coverage (500-m buffer) relative to the lowest was 1.11 (95% CI = 0.59–2.07). Risk estimates for both measures of greenness did not vary substantially across different buffer distances. Exposure to NO2 was an important confounder in these associations. Conclusions Our findings do not support the hypothesis that residential greenness reduces the risk of breast cancer among young women, while highlighting the importance of adjusting for air pollution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".