Forecasting PM_2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM_2.5 data from 1998 to 2016: a modeling study.

ABSTRACT: The serious ambient fine particulate matter (PM_2.5) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM_2.5 in China were less considered the regional transport of PM_2.5 concentration. In this study, we aim to explore the association between lung cancer and PM_2.5 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 PM_2.5 concentration had a significant association with

SUBMITTER: Liao WB 

PROVIDER: S-EPMC7293676 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature