Exposure-lag response of surface net solar radiation on lung cancer incidence: a global time-series analysis

Recently, the impact of solar radiation (RAD) on diseases worldwide has garnered growing attention. However, the association between RAD and lung cancer remains largely unknow and no consensus has been reached. The aim of this study was to investigate the lag exposure-response of RAD on lung cancer and provide robust scientific evidence for updating prevention and treatment strategies of lung cancer.

Data of RAD were obtained from Google Earth Engine, which was post-processed by European Centre for Medium-Range Weather Forecasts (ECMWF). Lung cancer incidence, smoking prevalence and socio-demographic index (SDI) were obtained from Global Burden of Disease (GBD). Spearman's rank correlation tests and linear regression analyses were performed to investigate the relationship between RAD and lung cancer incidence. Additionally, a distributed lag non-linear model (DLNM) was utilized to reveal the lag effects of RAD on lung cancer incidence.

There were 204 countries and territories and selected subnational locations with information recorded in GBD and radiation exposure was calculated in 272 countries and territories. After excluding missing and abnormal data, as well as Kashmir and Western Sahara which were two disputed districts, this study included 186 countries from 1992 to 2019. After adjusted for smoking and SDI, the Spearman's correlation coefficient ranged from -0.630 to -0.581. In the DLNM for lung cancer adjusted for smoking and SDI, the maximum relative risk (RR) was 1.013 [95% confidence interval (CI): 1.011-1.014], at RAD exposure of 12,760,000 with 5.8 lag years, while the minimum RR was 0.973 (95% CI: 0.947-0.992) at RAD exposure of 12,845,000 with 8.0 lag years.

The global rise in lung cancer incidence has been notably associated with low exposure to RAD, whereas the defensive influence of sunlight against lung cancer demonstrated hysteresis. This study shows that properly exposure to sunlight is a possible strategy for lung cancer prevention, which provides scientific support for the formulation of future health strategies. It is also crucial in epidemiological research as it offers a novel pattern for identifying additional potential risk factors for diseases.