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Abstract
Concentration–response (C–R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C–R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C–R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C–R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C–R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C–R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.
Acknowledgements
The author acknowledges the enormously valuable comments received from six reviewers who were selected by the Editor. These comments prompted substantial expansions of the manuscript, additional references and discussions, and improvements in content and exposition.
Declaration of interest
The employment affiliation of the author is shown on the cover page. Cox Associates is a private firm providing research and consulting services, principally on risk analysis, analytics, operations research, and applied statistics issues, to private and public entities. The work described here was supported by Cox Associates and the American Petroleum Institute. Over the past five years, Cox Associates has received funding from the American Petroleum Institute (API) and the American Chemistry Council (ACC) and their members to analyze causal relations between exposure concentrations and adverse health responses for crystalline silica and PM2.5. The research questions asked, technical methods selected, and conclusions reached are solely those of the author. This paper benefitted from close proof-reading and copy-editing suggestions from API, but these reviews and suggestions were provided for the author’s consideration without constraints that any of them be incorporated. The author has testified before Congress on matters related to data transparency and causation of adverse health effects by air pollutants. He is Editor-in-Chief of Risk Analysis: An International Journal and has contributed to and encouraged discussions of associations vs. causality for C–R functions in that journal. All of the views presented here are solely those of the author and in no way reflect any positions of the journal Risk Analysis or the Society for Risk Analysis, the API, the ACC or their member companies.
Data availability
The full data LA (South Coastal) data set analyzed in this paper can be downloaded from http://cox-associates.com/downloads; it is data set “Sample1” or “LA” in the CAT software at that web site, under the “Excel-to-R” button. The full Boston data is “Sample4” or “Boston”.