Assessing Exposure to Outdoor Lighting and Health Risks
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».