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Record W2318565230 · doi:10.1097/ede.0000000000000307

Assessing Exposure to Outdoor Lighting and Health Risks

2015· letter· en· W2318565230 on OpenAlexaboutno aff
Christopher C. M. Kyba, Kristan J. Aronson

Bibliographic record

VenueEpidemiology · 2015
Typeletter
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
Fundersnot available
KeywordsRadianceVisible Infrared Imaging Radiometer SuiteRemote sensingDefense Meteorological Satellite ProgramEnvironmental scienceSatelliteMeteorologyGeography

Abstract

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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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.300
GPT teacher head0.439
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations16
Published2015
Admission routes1
Has abstractyes

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