Impact of ambient fine particulate matter (PM2.5) exposure on the risk of influenza-like-illness: a time-series analysis in Beijing, China.
Impact of ambient fine particulate matter (PM2.5) exposure on the risk of influenza-like-illness: a time-series analysis in Beijing, China.
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DOI：
10.1186/s12940-016-0115-2
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发表时间：
2016-02-11
期刊：
影响因子：
--
通讯作者：
Wang Q
中科院分区：
文献类型：
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作者：
Feng C;Li J;Sun W;Zhang Y;Wang Q
北京的空气污染，特别是PM2.5，近年来越来越受到人们的关注。尽管接触 PM2.5 与许多健康问题有关，但很少有研究量化 PM2.5 对流感样疾病 (ILI) 风险的影响。我们研究的目的是通过广义相加模型来调查北京每日 PM2.5 与流感样疾病风险之间的关联。检索2008年1月1日至2014年12月31日期间每日PM2.5、气象因素和流感样疾病（ILI）计数。使用具有对数链接函数的逆高斯广义加性模型来灵活建模 PM2.5（单日和多日滞后暴露）与 ILI 风险之间的非线性关系，并根据天气条件、季节和年份趋势进行调整。我们还通过在模型中纳入 PM2.5 和流感季节之间的相互作用项，评估了 PM2.5 在流感季节与非流感季节的影响是否有所不同。此外，还进行了按年龄组的分层分析，以研究 PM2.5 的影响在不同年龄组之间有何不同。我们的研究结果表明，在调整了环境每日温度和湿度、月份和年份的影响后，PM2.5 与流感季节（10 月至 4 月）的 ILI 风险之间存在很强的正相关关系（p 值 < 0.001）；而在非流感季节（5 月至 9 月）则没有发现显着关联（p 值 = 0.174）。 PM2.5 的短期延迟效应也被确定为 PM2.5 的 2 天移动平均线（当天到前一天）具有最佳的预测能力。此外，在流感季节，PM2.5 与所有年龄段的 ILI 风险密切相关（p 值 < 0.001），但这种影响在成人（25-59 岁）中最明显，其次是年轻人（15-24 岁）、学童（5-14 岁）和老年人（60 岁以上），PM2.5 对 5 岁以下儿童的影响最不明显（年龄 < 5）。在北京，流感季节期间，环境 PM2.5 浓度与流感样疾病风险显着相关，并且 PM2.5 对中国北京各年龄段的影响不同。
Air pollution in Beijing, especially PM2.5, has received increasing attention in the past years. Although exposure to PM2.5 has been linked to many health issues, few studies have quantified the impact of PM2.5 on the risk of influenza-like illness (ILI). The aim of our study is to investigate the association between daily PM2.5 and ILI risk in Beijing, by means of a generalized additive model. Daily PM2.5, meteorological factors, and influenza-like illness (ILI) counts during January 1, 2008 to December 31, 2014 were retrieved. An inverse Gaussian generalized additive model with log link function was used to flexibly model the nonlinear relationship between the PM2.5 (single- and multiday lagged exposure) and ILI risk, adjusted for the weather conditions, seasonal and year trends. We also assessed if the effect of PM2.5 differs during flu season versus non-flu season by including the interaction term between PM2.5 and flu season in the model. Furthermore, a stratified analysis by age groups was conducted to investigate how the effect of PM2.5 differs across age groups. Our findings suggested a strong positive relationships between PM2.5 and ILI risk at the flu season (October-April) (p-value < 0.001), after adjusting for the effects of ambient daily temperature and humidity, month and year; whereas no significant association was identified at the non-flu season (May-September) (p-value = 0.174). A short term delayed effect of PM2.5 was also identified with 2-day moving average (current day to the previous day) of PM2.5 yielding the best predictive power. Furthermore, PM2.5 was strongly associated with ILI risk across all age groups (p-value < 0.001) at the flu season, but the effect was the most pronounced among adults (age 25–59), followed by young adults (age 15–24), school children (age 5–14) and the elderly (age 60+) and the effect of PM2.5 was the least pronounced for children under 5 years of age (age < 5). Ambient PM2.5 concentrations were significantly associated with ILI risk in Beijing at the flu season and the effect of PM2.5 differed across age groups, in Beijing, China.