Assessing Exposure to Outdoor Lighting and Health Risks
Bibliographic record
Abstract
To the Editor: We read with interest the investigation of Hurley et al.1 into the relation between outdoor light at night (OLAN) and breast cancer risk. Stevens2 proposed a causal relation between artificial light exposure and breast cancer risk, and one possible consequence of this hypothesis is increased risk where outdoor lighting penetrates sleeping environments. Hurley et al. used data from the Defense Meteorological Satellite Program (DMSP) that they described as “the best available satellite imagery data to estimate OLAN.” In fact, more precise data are available and should be used to improve exposure assessment in future studies. Astronaut photographs of Earth provide color images of individual cities at up to 10-m resolution (street level). Calibrated images of the entire Earth are produced by the Visible Infrared Imaging Radiometer Suite Day-Night Band (DNB) at 750-m resolution, corresponding to the neighborhood level. In contrast, the 2.7-km resolution data used by Hurley et al. are far less precise (Figure).FIGURE: Part of San Jose, California, as imaged with 2006 radiance calibrated DMSP (A), DNB 2012 2-month composite (B), and an astronaut photograph with NightPod (ISS034-E-43973) (C). Astronaut photograph found via the cities at night gallery.3Researchers examining relations between OLAN and health effects should no longer use DMSP data, even for retrospective studies. Because street lighting typically changes on a 15- to 30-year time scale, the DNB data from 2012 provide a better indicator of past OLAN exposure than DMSP at most urban locations, for example, among study participants who have remained at the same residence for several years. Higher resolution data will also reduce the apparent correlation between degree of urbanization and light: if OLAN is a true cause of breast cancer (rather than a correlate of another urban parameter), then estimated risk in studies using DNB data should be higher than those using DMSP. Of course, measuring each participant’s light exposure would be ideal compared with using remotely sensed light data, but this is possible only in cross-sectional and prospective studies. An interdisciplinary collaboration with researchers in remote sensing would benefit future epidemiologic studies. For example, the light -emitting diodes replacing traditional street lamps in many cities radiate a large fraction of light in the spectral range 440–500 nm, a critical range for human physiologic response, but unfortunately one to which neither DNB nor DMSP is sensitive.4 Calibration and analysis of astronaut photographs could potentially allow for the determination of associations between specific wavelengths and health risk. Better exposure assessment will lead to more precise evidence about the potential relation between ambient light at night and health effects. ACKNOWLEDGMENTS We thank Helga Kuechly for producing the Figure. Image and data processing by NOAA’s National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency. Astronaut photograph courtesy of the Earth Science and Remote Sensing Unit, NASA Johnson Space Center. Christopher C. M. Kyba Deutsches GeoForschungsZentrum GFZ Telegrafenberg Potsdam, Germany Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin, Germany [email protected] Kristan J. Aronson Queen’s University Kingston, ON Canada
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".