Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Chemosphere. 2018 Dec;212:705-714. doi: 10.1016/j.chemosphere.2018.08.129. Epub 2018 Aug 24.
Daily exposure to ambient particulate matter with aerodynamic diameter <2.5 μm (PM) increases deaths and is an important contributor to burden of disease in population. To better understand the disease burden associated with PM, we examined the effects of PM on daily years of life lost (YLL) in Guangzhou, China.
Using Guangzhou death registry, air pollution and meteorological database, we applied generalized additive models (GAM) to the relationships between YLL and PM. We then adjusted the models for age, gender, seasonality and meteorological variables. We also conducted within-data prediction of YLL while setting 2012-2014 as baseline.
Over 2 million YLLs (800,137 YLLs for females and 1,212,040 YLLs for males) were observed during 2012-2016. YLL was higher for the elderly people. Mean daily average PM concentration was 47.3 μg/m. In model comparisons, the GAM with six meteorological variables (sunshine hours, relative humidity, precipitation, atmospheric pressure, wind speed, evaporation) outperformed the others. The R and total deviance were 0.542 and 53.0%, respectively. Non-linear trends were observed for PM and meteorological variables. Fitted daily YLL increased to the highest level, when PM concentration reached 134.3 μg/m and atmospheric pressure reached 99.4 kPa. Within-data prediction supported the fitted GAM, where low mean absolute percentage errors were observed.
Daily PM exposure has a nonlinear effect on YLL and increased levels of PM may lead to increased YLL. This study highlights the urge to reduce ambient PM pollution in Guangzhou, in order to promote environmental health.
每日暴露于空气动力学直径<2.5μm 的环境颗粒物(PM)中会增加死亡率,是导致人群疾病负担的一个重要因素。为了更好地了解与 PM 相关的疾病负担,我们研究了 PM 对中国广州每日寿命损失年数(YLL)的影响。
利用广州死亡登记处、空气污染和气象数据库,我们应用广义加性模型(GAM)研究了 YLL 与 PM 之间的关系。然后,我们调整了模型以考虑年龄、性别、季节性和气象变量。我们还在设定 2012-2014 年为基线的情况下,进行了 YLL 的内数据预测。
在 2012-2016 年期间,观察到超过 200 万的 YLL(女性 800,137 个 YLL,男性 1,212,040 个 YLL)。YLL 随年龄增长而增加。平均每日 PM 浓度为 47.3μg/m。在模型比较中,包含六个气象变量(日照小时数、相对湿度、降水量、大气压力、风速、蒸发量)的 GAM 表现最佳。R 和总离差分别为 0.542 和 53.0%。PM 和气象变量呈非线性趋势。当 PM 浓度达到 134.3μg/m 且大气压力达到 99.4kPa 时,拟合的每日 YLL 增加到最高水平。内数据预测支持拟合的 GAM,观察到低平均绝对百分比误差。
每日 PM 暴露对 YLL 有非线性影响,较高水平的 PM 可能导致 YLL 增加。本研究强调了减少广州环境 PM 污染的紧迫性,以促进环境健康。