BACKGROUND The linearity of concentration-response (C-R) curve between ambient ozone (O3) concentration and mortality has been controversial. The aim of the present analysis was to examine the C-R curve between O3 concentration and mortality with a causal framework approach. METHODS We extracted data of hourly meteorology, hourly O3 concentration and daily non-accidental mortality in Seoul from 2001 to 2009. We divided the dataset into two, odd-number (training set) and even-number years (testing set). Using the training set, we constructed a prediction model from hourly O3 concentration with support vector regression estimating the daily variations of mean O3 concentration caused by sun irradiation, wind speed and direction, controlling temperature, barometric pressure and temporal trend. With this model we predicted variance of daily O3 from the testing set, thus creating an instrumental variable. We analyzed the association between the instrumental variable and daily mortality. We also analyzed the association according to the quartiles of daily mean O3 concentration to examine the linearity of the association. RESULTS The instrumental variable was significantly and negatively associated with daily mortality in the linear model. In the stratified analysis, the negative slope was observed in the lowest quartile and the negative slope of the association diminished as the quartile increased, and the slope became positive over the 3rd quartile (O3 > 23.3 ppb). The interaction between quartiles and instrumental variable was significant (P = 0.0108). CONCLUSION We observed unequal effect of exposure to ambient O3 concentration on mortality according to the different ranges of daily mean O3 concentration with a causal framework approach.

中文翻译：

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韩国首尔的环境臭氧浓度与死亡率之间的因果关系。

背景技术环境臭氧（O3）浓度与死亡率之间的浓度响应（CR）曲线线性关系一直存在争议。本分析的目的是使用因果框架方法研究O3浓度与死亡率之间的CR曲线。方法我们提取了2001年至2009年汉城的每小时气象学，每小时O3浓度和每日非偶然死亡率的数据。我们将数据集分为奇数年（训练集）和偶数年（测试集）两个数据集。使用训练集，我们从每小时的O3浓度构建了一个预测模型，并通过支持向量回归来估计由太阳辐照，风速和风向，控制温度，大气压力和时间趋势引起的平均O3浓度的每日变化。使用此模型，我们可以根据测试集预测每日O3的方差，从而创建一个工具变量。我们分析了仪器变量和每日死亡率之间的关联。我们还根据每日平均O3浓度的四分位数对关联进行了分析，以检验关联的线性。结果在线性模型中，仪器变量与日死亡率显着负相关。在分层分析中，在最低四分位数中观察到负斜率，并且随着四分位数的增加，关联的负斜率减小，并且在第三个四分位数中该斜率变为正数（O3> 23.3 ppb）。四分位数与工具变量之间的交互作用很显着（P = 0.0108）。