Solar Ultraviolet Radiation and Breast Cancer Risk: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A protective relationship has been hypothesized between exposure to solar ultraviolet radiation (UVR) and the development of breast cancer. Objective: The objective of this study was to conduct a systematic literature review and meta-analysis of studies examining the association of exposure to solar UVR and breast cancer risk. Methods: We searched Medline, EMBASE, and Web of Science for all studies investigating exposure to solar UVR and breast cancer risk. Separate analyses were performed using estimates of time spent in the sun, and ambient UVR. Associations were estimated using DerSimonian and Laird random-effect models. Heterogeneity was investigated through subgroup analyses and I2 statistics. Results: Fourteen studies were included in the review and 13 in the meta-analysis, with the majority (n=8) conducted in North America. We observed a decreased risk of breast cancer for individuals spending ≥1h/d in the sun during summer months over a lifetime or usual adulthood compared with <1h/d [pooled relative risk (RR)=0.84; 95% CI: 0.77, 0.91]. Spending ≥2h/d in the sun had a similar protective effect as 1 to <2h/d when compared with <1h/d (RR=0.83; 95% CI: 0.75, 0.93 vs. 0.83; 95% CI: 0.78, 0.89). Exposure during adolescence was suggestive of a lower risk of breast cancer than exposure later in life (≥45 years of age) (RR=0.83; 95% CI: 0.71, 0.98 vs. 0.97; 95% CI: 0.85, 1.11). Ambient UVR was not associated with the risk of breast cancer (RR=1.00; 95% CI: 0.93, 1.09). Discussion: To our knowledge, this was the first meta-analysis to estimate the risk of developing breast cancer associated with time spent in the sun. The results suggest that obtaining greater than an hour a day in the sun during the summer months could decrease the risk of developing breast cancer. https://doi.org/10.1289/EHP4861
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".