报告题目：《Daily mortality/morbidity and air quality: using multivariate time series with seasonally varying covariances》

报告人：黄国文(汕头大学)

报告时间：2023年9月28日10:30-11:30

报告地点：雁山校区理4-724会议室

报告摘要: We study the associations between daily mortality and short-term variations in the ambient concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in four cities in Canada. Firstly, a novel multivariate time series model within Bayesian framework is proposed for exposure assessment, where the response is a mixture of Gamma and Half-Cauchy distributions and the correlations between pollutants vary seasonally. A case-crossover design and conditional logistic regression model is used to relate exposure to disease data for each city, which then are combined to obtain a global estimate of exposure health effects allowing exposure uncertainty. The results suggest that every 10 ppb increase of O3 is associated with a 3.88% (95% credible interval[CI], 2.5%, 5.18%) increase of all-cause mortality, a 5.04% (2.84%, 7.43%) increase of circulatory mortality, a 7.87% (2.4%, 12.9%) increase of respiratory mortality, a 0.76% (0.19%, 1.35%) increase of all-cause morbidity, a 6.6% (0.58%, 12.7%) increase of respiratory morbidity. Similarly, every 10 ppb increase of NO2 is associated with a 2.13% (0.42%, 3.87%) increase of circulatory morbidity. The health impacts of PM2.5 are not found to be present once other pollutants are accounted for.

报告人简介：黄国文，2010年获南开大学物理学学士学位，2013年获天津大学管理学硕士学位，2016年获英国格拉斯哥大学统计学博士学位，先后在国立清华大学和加拿大多伦多大学做博士后研究，现为汕头大学数学系统计学专业副教授，主要研究空间统计学，对空气污染数据进行统计建模并定量分析其对人类健康的影响。主要论文发表在Statistics in Medicine, Spatial Statistics, Spatial and Spatio-Temporal Epidemiology, Journal of Computational and Graphical Statistics, Environmetrics, Journal of the Royal Statistical Society, Series A (Statistics in Society), Journal of the Royal Statistical Society, Series C (Applied Statistics) 等学术期刊。