Indoor and outdoor artificial light-at-night (ALAN) and cancer risk: A systematic review and meta-analysis of multiple cancer sites and with a critical appraisal of exposure assessment
Bibliographic record
Abstract
Exposure to artificial light-at-night (ALAN) has been linked to cancer risk. Few meta-analyses on this topic have reviewed only breast cancer. This study aimed to systematically review and meta-analyze existing studies on ALAN exposure and cancer incidence, thoroughly evaluating exposure assessment quality. We considered observational studies (cohort, case-control, cross-sectional) on ALAN exposure (indoor and outdoor) and cancer incidence, measured by relative risk, hazard ratio, and odds ratio. We searched six databases, two registries, and Google Scholar from inception until April 17, 2024. Quality of studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools. Random-effects meta-analysis was used to estimate relative risks (RR) and 95 % confidence intervals (CI) for ALAN exposures. We identified 9835 studies and included 28 for qualitative synthesis with 2,508,807 individuals (15 cohort, 13 case-control). Out of the included studies, 20 studies on breast cancer (731,493 individuals) and 2 studies on prostate cancer (53,254 individuals) were used for quantitative synthesis. Higher levels of outdoor ALAN were associated with breast cancer risk (meta-estimate = 1.12, 95 % CI 1.03–1.23 (I 2 = 69 %)). We observed a non-significant positive association between indoor ALAN levels and breast cancer risk (meta-estimate = 1.07, 0.95–1.21, I 2 = 60 %), and no differences by menopausal status. The meta-analysis for prostate cancer suggested a non-statistically significant increased risk for higher levels of outdoor ALAN (meta-estimate = 1.43, 0.75–2.72, I 2 = 90 %). In the qualitative synthesis, we observed positive associations with non-Hodgkin lymphoma and colorectal, pancreatic and thyroid cancer. We found an association between outdoor ALAN and breast cancer risk. However, most studies relied on satellite-images with a very low resolution (1 to 5 km, from the Defense Meteorological Program [DMSP]) and without information on color of light. Future studies with better exposure assessment should focus on investigating other cancer sites. • Exposure to artificial light-at-night (ALAN) has been linked to cancer. • Previous meta-analyses on breast cancer lack thorough exposure quality assessment. • Studies on outdoor ALAN and cancer generally used low-resolution images. • Suggested link between outdoor ALAN and prostate cancer, but evidence is limited. • Future studies should improve exposure assessment and explore other cancers sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".