Forecasting PM2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM2.5 data from 1998 to 2016: a modeling study

Liao, Wei-Bin; Ju, Ke; Zhou, Qian; Gao, Ya-Min; Pan, Jay

Forecasting PM2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM2.5 data from 1998 to 2016: a modeling study

2020

Liao, Wei-Bin | Ju, Ke | Zhou, Qian | Gao, Ya-Min | Pan, Jay

The serious ambient fine particulate matter (PM₂.₅) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM₂.₅ in China were less considered the regional transport of PM₂.₅ concentration. In this study, we aim to explore the association between lung cancer and PM₂.₅ and then forecast the PM2.5-induced lung cancer morbidity and mortality in China. Ridge regression (RR), partial least squares regression (PLSR), model tree-based (MT) regression, regression tree (RT) approach, and the combined forecasting model (CFM) were alternative forecasting models. The result of the Pearson correlation analysis showed that both local and regional scale PM₂.₅ concentration had a significant association with lung cancer mortality and morbidity and compared with the local lag and regional lag exposure to ambient PM₂.₅; the regional lag effect (0.172~0.235 for mortality; 0.146~0.249 for morbidity) was not stronger than the local lag PM₂.₅ exposure (0.249~0.294 for mortality; 0.215~0.301 for morbidity). The overall forecasting lung cancer morbidity and mortality were 47.63, 47.86, 39.38, and 39.76 per 100,000 population. The spatial distributions of lung cancer morbidity and mortality share a similar spatial pattern in 2015 and 2016, with high lung cancer morbidity and mortality areas mainly located in the central to east coast areas in China. The stakeholders would like to implement a cross-regional PM₂.₅ control strategy for the areas characterized as a high risk of lung cancer.

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