Should air pollution health effects assumptions be tested? Fine particulate matter and COVID-19 mortality as an example

Abstract

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.

Effects of air pollution and weather on the initial COVID‐19 outbreaks in United States, Italy, Spain, and China: A comparative study

“…Indeed, the lack of available data of COVID‐19 cases for the initial outbreak weeks in China hampered the assessment of air pollution effects before governmental actions were undertaken (Xu et al., 2020 ). Thus, our results contrasted with those of other studies showing particulate matter and pollutant gases (CO, NO 2 , and O 3 ) as being positively associated with the number of cases and mortality from COVID‐19 in China (Cox & Popken, 2020 ; Zhu et al., 2020 ). Hence, our result should be interpreted carefully, considering the effects of how each country managed the disease.…”

Association between air pollution and COVID-19 mortality and morbidity

“…Last but not least, regression models (as used in this paper) in epidemiological research that involves air pollution are not perfect tools for assessing whether reducing exposure to air pollution will reduce the risk of harm to human [ 27 ]. This role is assigned for the traditional scientific method based on the testing predictive generalizations against data.…”

“…This role is assigned for the traditional scientific method based on the testing predictive generalizations against data. It is worthy to underline that science should be completed (not substituted) by regression models [ 27 ].…”

Studying the Effect of Particulate Matter as SARS-CoV-2 Transmitters

“…However, previous studies have found that air pollution has impact on COVID-19 associated mortality. [27][28][29] This study found a link between the AQI of 77 territories and total COVID-19 cases, but high AQI is not always a predictor for COVID-19 incidence. There are numerous variables that may contribute to SARS-CoV-2 transmission, including strictness of preventive measures, border control, 30 implementation of social distancing, and individual expression of the angiotensin converting enzyme 2 (ACE2) receptor, for which increased expression has been associated with increased risk of COVID-19.…”