Cox Louis Anthony, Popken Douglas A
Cox Associates LLC, 503 N. Franklin Street, Denver, CO 80218, United States of America.
Glob Epidemiol. 2020 Nov;2:100033. doi: 10.1016/j.gloepi.2020.100033. Epub 2020 Sep 2.
In the first half of 2020, much excitement in news media and some peer reviewed scientific articles was generated by the discovery that fine particulate matter (PM2.5) concentrations and COVID-19 mortality rates are statistically significantly positively associated in some regression models. This article points out that they are non-significantly negatively associated in other regression models, once omitted confounders (such as latitude and longitude) are included. More importantly, positive regression coefficients can and do arise when (generalized) linear regression models are applied to data with strong nonlinearities, including data on PM2.5, population density, and COVID-19 mortality rates, due to model specification errors. In general, statistical modeling accompanied by judgments about causal interpretations of statistical associations and regression coefficients - the current weight-of-evidence (WoE) approach favored in much current regulatory risk analysis for air pollutants - is not a valid basis for determining whether or to what extent risk of harm to human health would be reduced by reducing exposure. The traditional scientific method based on testing predictive generalizations against data remains a more reliable paradigm for risk analysis and risk management.
2020年上半年，新闻媒体以及一些同行评议的科学文章对一项发现表现出极大兴趣，即某些回归模型显示细颗粒物（PM2.5）浓度与新冠死亡率在统计学上存在显著正相关。本文指出，一旦纳入被遗漏的混杂因素（如纬度和经度），在其他回归模型中它们呈非显著负相关。更重要的是，当（广义）线性回归模型应用于具有强非线性的数据时，包括PM2.5、人口密度和新冠死亡率的数据，由于模型设定误差，会出现并确实出现正回归系数。一般来说，当前许多空气污染物监管风险分析中青睐的当前证据权重（WoE）方法，即伴随着对统计关联和回归系数因果解释判断的统计建模，并非确定通过减少暴露是否会降低或在多大程度上降低对人类健康危害风险的有效依据。基于针对数据检验预测性概括的传统科学方法仍然是风险分析和风险管理更可靠的范式。
