Evaluating the Association between Artificial Light-at-Night Exposure and Breast and Prostate Cancer Risk in Spain (MCC-Spain Study)
Bibliographic record
Abstract
BACKGROUND: Night shift work, exposure to light at night (ALAN) and circadian disruption may increase the risk of hormone-dependent cancers. OBJECTIVES: We evaluated the association of exposure to ALAN during sleeping time with breast and prostate cancer in a population based multicase-control study (MCC-Spain), among subjects who had never worked at night. We evaluated chronotype, a characteristic that may relate to adaptation to light at night. METHODS: We enrolled 1,219 breast cancer cases, 1,385 female controls, 623 prostate cancer cases, and 879 male controls from 11 Spanish regions in 2008-2013. Indoor ALAN information was obtained through questionnaires. Outdoor ALAN was analyzed using images from the International Space Station (ISS) available for Barcelona and Madrid for 2012-2013, including data of remotely sensed upward light intensity and blue light spectrum information for each geocoded longest residence of each MCC-Spain subject. RESULTS: Among Barcelona and Madrid participants with information on both indoor and outdoor ALAN, exposure to outdoor ALAN in the blue light spectrum was associated with breast cancer [adjusted odds ratio (OR) for highest vs. lowest tertile, OR=1.47; 95% CI: 1.00, 2.17] and prostate cancer (OR=2.05; 95% CI: 1.38, 3.03). In contrast, those exposed to the highest versus lowest intensity of outdoor ALAN were more likely to be controls than cases, particularly for prostate cancer. Compared with those who reported sleeping in total darkness, men who slept in "quite illuminated" bedrooms had a higher risk of prostate cancer (OR=2.79; 95% CI: 1.55, 5.04), whereas women had a slightly lower risk of breast cancer (OR=0.77; 95% CI: 0.39, 1.51). CONCLUSION: Both prostate and breast cancer were associated with high estimated exposure to outdoor ALAN in the blue-enriched light spectrum. https://doi.org/10.1289/EHP1837.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".