Greenness, Obesity and Incident Breast Cancer: Evidence from the Canadian National Breast Screening Study
Bibliographic record
Abstract
S07: Of moderators and mediators: Complex relationships between greenness, air pollution, noise, and health behaviors in driving health outcomes, Beatrix Theater, August 27, 2019, 10:30 AM - 12:00 PM Background: Breast cancer is the most commonly diagnosed cancer among Canadian women. Environmental exposures, including air pollution, have been associated with an increased risk of breast cancer. More recently, findings from a multi-centre case-control study in Spain, and the US Nurses Health Study suggest that proximity to greenness may reduce the risk of breast cancer. Both publications highlight the need to better understand the pathways involved. Methods: To address this gap, we investigated associations between residential greenness, obesity and the incidence of breast cancer among 89,247 participants of the Canadian National Breast Screening Study. The original aim of this randomized controlled trial was to investigate whether mammography screening reduced the mortality of breast cancer. Enrollments occurred between 1980 and 1985, and record linkage to national cancer incidence identified approximately 6500 cases of breast cancer through 2005. Estimates of the Normalized Difference Vegetation Index (NDVI) within a 500 m buffer and ambient PM2.5 were linked to the participants’ place at residence at enrollment. Baseline surveys were used to collect information on risk factors for breast cancer, and measured height and weight were obtained used to derive body mass index (BMI). Cox proportional hazards models using attained age as the time scale were fit to provide estimates of the hazard ratios associated with the NDVI and BMI. Findings: Ambient PM2.5 and the NDVI were inversely associated with each other (r= - 0.14). An interquartile range increase in the NDVI was associated with a 4% reduction in the risk of breast cancer (hazard ratio (HR)=0.96, 95% CI=0.92 – 0.99). Analyses of variance indicated that those who were obese (BMI>30) lived in areas with a lower mean NDVI (p<0.05). Conclusions: Our findings provide further support for the hypothesis that proximity to greenness may reduce the risk of breast cancer independently of air pollution, or obesity.
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 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".