Estimation of PM_(2.5) mortality burden in China with new exposure estimation and local concentration-response function

摘要

The estimation of PM2.5-related mortality is becoming increasingly important. The accuracy of results is largely dependent on the selection of methods for PM2.5 exposure assessment and Concentration-Response (C-R) function. In this study, PM2.5 observed data from the China National Environmental Monitoring Center, satellite-derived estimation, widely collected geographic and socioeconomic information variables were applied to develop a national satellite-based Land Use Regression model and evaluate PM2.5 exposure concentrations within 2013-2015 with the resolution of 1 km x 1 km. Population weighted concentration declined from 72.52 mu g/m(3) in 2013 to 57.18 mu g/m(3) in 2015. C-R function is another important section of health effect assessment, but most previous studies used the Integrated Exposure Regression (IER) function which may currently underestimate the excess relative risk of exceeding the exposure range in China. A new Shape Constrained Health Impact Function (SCHIF) method, which was developed from a national cohort of 189,793 Chinese men, was adopted to estimate the PM2.5-related premature deaths in China. Results showed that 2.19 million (2013), 1.94 million (2014), 1.65 million (2015) premature deaths were attributed to PM2.5 long-term exposure, different from previous understanding around 1.1-1.7 million. The top three provinces of the highest premature deaths were Henan, Shandong, Sichuan, while the least ones were Tibet, Hainan, Qinghai. The proportions of premature deaths caused by specific diseases were 53.2% for stroke, 20.5% for ischemic heart disease, 16.8% for chronic obstructive pulmonary disease and 9.5% for lung cancer. IER function was also used to calculate PM2.5-related premature deaths with the same exposed level used in SCHIF method, and the comparison of results indicated that IER had made a much lower estimation with less annual amounts around 0.15-0.5 million premature deaths within 2013-2015. (C) 2018 Elsevier Ltd. All rights reserved.