Causal inference in cumulative risk assessment: The roles of directed acyclic graphs. (May 2017)

• Record Type: Journal Article
• Authors: Brewer, L. Elizabeth; Wright, J. Michael; Rice, Glenn; Neas, Lucas; Teuschler, Linda
• Is Part Of: Environment international. Volume 102(2017)
• Page Start: 30
• Page End: 41
• Publication Date: 2017-05
• Subjects: CRA cumulative risk assessment -- DAG directed acyclic graphs -- CAFO concentrated animal feeding operation -- SES socioeconomic status -- MOA mode of action -- AOP adverse outcome pathway -- WOE weight of evidence -- NRC National Research Council -- USEPA U.S. Environmental Protection Agency; Cumulative risk assessment -- Conceptual model -- Directed acyclic graph -- Causal inference -- Confounding -- Causal models; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2016.12.005
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: Cumulative risk assessments (CRAs) address exposures to multiple chemical and nonchemical stressors and often focus on characterization of health risks in vulnerable populations. Evaluating complex exposure-response relationships in CRAs requires the use of formal and rigorous methods for causal inference. Directed acyclic graphs (DAGs) are graphical causal models used to organize and communicate knowledge about the underlying causal structure that generates observable data. Using existing graphical theories for causal inference with DAGs, risk analysts can identify confounders and effect measure modifiers to determine if the available data are both internally valid to obtain unbiased risk estimates and are generalizable to populations of interest. Conditional independencies implied by the structure of a DAG can be used to test assumptions used in a CRA against empirical data in a selected study and can contribute to the evidence evaluations related to specific causal pathways. This can facilitate quantitative use of these data, as well as help identify key research gaps, prioritize data collection activities, and evaluate risk management alternatives. DAGs also enable risk analysts to be explicit about sources of uncertainty and to determine whether a causal effect can be estimated from available data. Using a conceptual model and DAG for a hypothetical community located near a concentrated animal feeding operation (CAFO), we illustrate the advantages of using DAGs for evaluating causality in CRAs. DAGs also can be used in conjunction with weight of evidence (WOE) methodology to improve causal analysis for CRA, which could lead to more effective interventions to reduce population health risks. Highlights: The construction and use of DAGs for causal inference throughout CRA are explained. A conceptual model and corresponding DAG are used to illustrate concepts. Using DAGs for inferences in CRA can increase accuracy in estimations of risk.