Abstract
Chapter 2 suggested that dynamic simulation models, Bayesian networks, and causal analysis can add value to statistical regression modeling for understanding causal exposure concentration-response (C-R) relationships well enough to predict how changes in exposure would affect health risks—a task that typically requires causal insights that regression modeling alone cannot deliver (Pearl 2009). Chapters 3, 4, 5, and 6 have discussed dynamic simulation models. This part of the book turns to Bayesian networks and causal analysis (Chaps. 9, 10, and 11). First, however, this chapter and Chap. 8 examine some ways in which regression has been misapplied in public health risk analysis, both to motivate the need for other methods and to explain why regression alone is not an adequate substitute for quantitative risk assessment (QRA), with its explicit emphasis on preventable causes of disease and the quantitative causal relationships between reductions in exposures and resulting reductions in health risks. Part 3 will apply these lessons specifically to air pollution and public health, with greatest emphasis on National Ambient Aie Quality Standards (NAAQS) for fine particulate matter (PM2.5). This chapter previews some of the issues developed in Part 3 by considering public health risks from a much more local form of air pollution: emissions from factory farms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Biggs D, de Ville B, Suen E. A method of choosing multiway partitions for classification and decision trees. J Appl Stat. 1991;18(1):49–62.
Brodkin CA, Balmes JR, Redlich CA, Cullen MR. Residential proximity to naturally occurring asbestos: health risk or ecologic fallacy? Am J Respir Crit Care Med. 2006;173(5):573.
Chen W-D. Testing for spurious regression in a panel data model with the individual number and time length growing. J Appl Stat. 2006;33(8):759–72.
Christenfeld NJ, Sloan RP, Carroll D, Greenland S. Risk factors, confounding, and the illusion of statistical control. Psychosom Med. 2004;66(6):868–75.
Cox LA Jr. Some limitations of a proposed linear model for antimicrobial risk management. Risk Anal. 2005;25(6):1327–32.
Cui Y, Zhang ZF, Froines J, Zhao J, Wang H, Yu SZ, Detels R. Air pollution and case fatality of SARS in the People’s Republic of China: an ecologic study. Environ Health. 2003;2(1):15.
Ellis B, Wong WH. Learning causal Bayesian Network structures from experimental data. J Am Stat Assoc. 2008;103(482):778–89.
Fewell Z, Davey Smith G, Sterne JA. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol. 2007;166(6):646–55.
Greenland S. Ecologic versus individual-level sources of bias in ecologic estimates of contextual health effects. Int J Epidemiol. 2001;30(6):1343–50.
Greenland S, Brumback B. An overview of relations among causal modeling methods. Int J Epidemiol. 2002;31(5):1030–7.
Jacobs RL, Leamer EE, Ward MP. Difficulties with testing for causation. Econ Inq. 1979;17(3):401–13.
Kwok RK, Yankaskas BC. The use of census data for determining race and education as SES indicators: a validation study. Ann Epidemiol. 2001;11(3):171–7.
Lucas R. Econometric policy evaluation: a critique. Carnegie-Rochester Conf Ser Public Policy. 1976;1:19–46.
Pearl J. Causal inference in statistics: an overview. Stat Surveys. 2009;3:96–146.
Selvin S, Merrill D, Wong L, Sacks ST. Ecologic regression analysis and the study of the influence of air quality on mortality. Environ Health Perspect. 1984;54:333–40.
Shih J-S, Burtraw D, Palmer K, Siikamäki J (2006) Air emissions of ammonia and methane from livestock operations: valuation and policy options. Resources for the future. Discussion paper. Washington, D.C. http://www.rff.org/Documents/RFF-DP-06-11.pdf
Shipley B. Cause and correlation in biology. a user’s guide to path analysis, structural equations and causal inference. New York: Cambridge University Press; 2000.
Sneeringer S. Does animal feeding operation pollution hurt public health? A national longitudinal study of health externalities identified by geographic shifts in livestock production. Am J Agric Econ. 2009;91(1):124–37.
Wakefield J. Multi-level modelling, the ecologic fallacy, and hybrid study designs. Int J Epidemiol. 2009;38(2):330–6.
Author information
Authors and Affiliations
Appendix: Data Sources
Appendix: Data Sources
Main data page | |
Violent crime per 100,000, 2004 | |
Unemployment | |
Infant mortality 2003 | |
Infant mortality rates 2004, 2005 | |
Divorce | |
Abortions | |
Income 2005 | |
% Black, Hispanic, White | |
Poverty 2005 | |
Education 2005 | |
Livestock ($M) | http://www.ors2.state.sc.us/abstract/chapter1/staterank12.php |
Pigs&Hogs 2005–2007 (number, production) | |
Cattle&Calves 2006–2008 (number, production) | |
Broilers&Turkeys 2005–2007 (production) | |
Farm acreage | |
Death rates | |
Causes of death | |
Manufacturing employment % 2005 | |
Violent crime 2005 | |
Teacher salaries 2004 | |
Marriage rates |
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cox Jr., L.A. (2021). Why Not Replace Quantitative Risk Assessment Models with Regression Models?. In: Quantitative Risk Analysis of Air Pollution Health Effects. International Series in Operations Research & Management Science, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-030-57358-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-57358-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57357-7
Online ISBN: 978-3-030-57358-4
eBook Packages: Business and ManagementBusiness and Management (R0)