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首页> 外文期刊>Dose-response >Temperature, not fine particulate matter (PM2.5), is causally associated with short-term acute daily mortality rates: Results from one hundred United States cities
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Temperature, not fine particulate matter (PM2.5), is causally associated with short-term acute daily mortality rates: Results from one hundred United States cities

机译：温度而不是细颗粒物（PM2.5）与短期急性每日死亡率有因果关系：来自美国一百个城市的结果

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摘要

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, nonparametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger- Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures.
机译：怀疑空气（C）中暴露于细颗粒物（PM2.5）会导致急性（例如，当日或次日）人类死亡率（R）升高。我们使用可公开获得的NMMAPS数据库在美国100个城市中检验了这种因果假设。尽管简单的统计模型中存在显着的，近似线性的统计C-R关联，但仔细分析表明，这不是因果关系。出人意料的是，对先前分析中已广泛考虑的其他变量（通常使用样条曲线或其他平滑器来估计其影响）进行调节，例如一年中的月份和平均每日温度，表明它们会产生强烈的非线性混淆，从而解释了统计数据。该数据集中PM2.5与死亡率之间的相关性。由于这一发现与传统观点不同，我们采用了几种不同的技术对其进行了研究。对潜在因果关系进行的条件独立性检验，非参数分类树分析，贝叶斯模型平均（BMA）和Granger-Sims因果关系检验均未显示出PM2.5浓度对死亡率上升有任何因果影响的证据。尽管存在明显的因果关系，但尽管存在统计学联系，但对于管理和传达与PM2.5暴露相关但不一定由PM2.5暴露引起的不确定健康风险具有潜在的重要意义。

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