Szyszkowicz Mieczysław, Burr Wesley S
Health Canada, Ottawa, Canada (Population Studies Division).
Int J Occup Med Environ Health. 2016;29(4):613-22. doi: 10.13075/ijomeh.1896.00379.
There are a few accepted and intensively applied statistical methods used to study associations of ambient air pollution with health conditions. Among the most popular methods applied to assess short term air health effects are case-crossover (using events) and time-series methodologies (using counts). A few other techniques for studying counts of events have been proposed, including the Generalized Linear Mixed Models (GLMM). One suggested GLMM technique uses cluster structures based on natural embedded hierarchies: days are nested in the days of a week (dow), which, in turn, are nested in months and months in years (< dow, month, years >).
In this study the authors considered clusters with hierarchical structures in a form of < dow, 14-days, year >, where the 14-days hierarchy determines 7 clusters composed of 2 days (the same days) of a week (2 Mondays, 2 Tuesdays, etc.), in 1 year. In this work the authors proposed hierarchical chained clusters in which 2 days of a week are grouped as follows: (first, second), (second, third), (third, fourth) and so on. Such an approach allows determination of an additional series of the slopes on the clusters (second, third), (fourth, fifth), etc., i.e., estimation of the coefficients for other configurations of air pollutant levels. The authors considered a series of 2 point chained clusters covering a year. In such a construction each cluster has one common data point (day) with another one.
The authors estimated coefficients (slopes) related to the ambient ozone exposure (mortality) and to 3 selected air pollutants (particulate matter, nitrogen dioxide and ozone) combined into index and considered as health risk exposure (emergency department (ED) visits). The generated results were compared to the estimations obtained from the time-series method and the time-stratified case-crossover method applied to the same data.
The proposed statistical method, based on the chained hierarchical clusters (< dow, 14-days, year >), generated results with shorter confidence intervals than the other methods.
有一些被广泛接受并大量应用的统计方法用于研究环境空气污染与健康状况之间的关联。在评估短期空气对健康影响的最常用方法中，有病例交叉法（使用事件）和时间序列法（使用计数）。还提出了一些其他用于研究事件计数的技术，包括广义线性混合模型（GLMM）。一种建议的GLMM技术使用基于自然嵌套层次结构的聚类结构：天嵌套在一周中的日子（dow）中，而一周中的日子又嵌套在月份中，月份嵌套在年份中（<dow, 月, 年>）。
在本研究中，作者考虑了具有<dow, 14天, 年>形式的层次结构的聚类，其中14天的层次结构确定了由一周中2天（相同的日子）组成的7个聚类（2个星期一、2个星期二等），在1年中。在这项工作中，作者提出了层次链式聚类，其中一周中的2天按如下方式分组：（第一，第二）、（第二，第三）、（第三，第四）等等。这种方法允许确定聚类（第二，第三）、（第四，第五）等上的另一系列斜率，即估计空气污染物水平的其他配置的系数。作者考虑了一系列覆盖一年的2点链式聚类。在这样的结构中，每个聚类与另一个聚类有一个共同的数据点（天）。
作者估计了与环境臭氧暴露（死亡率）以及与组合成指数并被视为健康风险暴露（急诊科（ED）就诊）的3种选定空气污染物（颗粒物、二氧化氮和臭氧）相关的系数（斜率）。将生成的结果与应用于相同数据的时间序列法和时间分层病例交叉法获得的估计值进行比较。
基于链式层次聚类（<dow, 14天, 年>）提出的统计方法生成的结果的置信区间比其他方法更短。
