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10.1016/j.gloepi.2020.100033 http://scihub22266oqcxt.onion/10.1016/j.gloepi.2020.100033 32905083!7462829!32905083     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32905083&cmd=llinks): Failed to open stream: HTTP request failed! 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Should air pollution health effects assumptions be tested? Fine particulate matter and COVID-19 mortality as an example |pdf=|usr=}}{{32905083}} gab.com Text Should air pollution health effects assumptions be tested Fine particulate matter and COVID-19 mortality as an example JO: Glob Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32905083 #FOAvip 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. Twit Text FOAVip Should air pollution health effects assumptions be tested Fine particulate matter and COVID-19 mortality as an example ????Glob Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32905083 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32905083 #FOAvip English Wikipedia <ref>{{Cite pmid|32905083}}</ref> Should air pollution health effects assumptions be tested? Fine particulate matter and COVID-19 mortality as an example #MMPMID32905083 Cox LA Jr; Popken DA Glob Epidemiol 2020[Nov]; 2 (ä): 100033 PMID32905083show ga 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. äShould air pollution health effects assumptions be tested? Fine partic(epmc).pdf DeepDyve Pubget Overpricing