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J Expo Sci Environ Epidemiol. 2015 Jan;25(1):113-8. doi: 10.1038/jes.2014.6. Epub 2014 Feb 5.

A general model to predict individual exposure to solar UV by using ambient irradiance data.

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Institute of Work and Health (IST), University of Lausanne and Geneva, Lausanne, Switzerland.
Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne, Switzerland.
Cancer Epidemiology Unit, University Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
International Prevention Research Institute (IPRI), Lyon, France.
Cancer Research Center of Lyon (CRCL), Lyon, France.


Excessive exposure to solar ultraviolet (UV) is the main cause of skin cancer. Specific prevention should be further developed to target overexposed or highly vulnerable populations. A better characterisation of anatomical UV exposure patterns is however needed for specific prevention. To develop a regression model for predicting the UV exposure ratio (ER, ratio between the anatomical dose and the corresponding ground level dose) for each body site without requiring individual measurements. A 3D numeric model (SimUVEx) was used to compute ER for various body sites and postures. A multiple fractional polynomial regression analysis was performed to identify predictors of ER. The regression model used simulation data and its performance was tested on an independent data set. Two input variables were sufficient to explain ER: the cosine of the maximal daily solar zenith angle and the fraction of the sky visible from the body site. The regression model was in good agreement with the simulated data ER (R(2)=0.988). Relative errors up to +20% and -10% were found in daily doses predictions, whereas an average relative error of only 2.4% (-0.03% to 5.4%) was found in yearly dose predictions. The regression model predicts accurately ER and UV doses on the basis of readily available data such as global UV erythemal irradiance measured at ground surface stations or inferred from satellite information. It renders the development of exposure data on a wide temporal and geographical scale possible and opens broad perspectives for epidemiological studies and skin cancer prevention.

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