Evidence-based toxicology: A comprehensive framework for causation

University of Colorado Health Science Center, Box B-146, 4200 East 9th Avenue, BRB 723, Denver, CO 80262, USA.
Human &amp Experimental Toxicology (Impact Factor: 1.75). 05/2005; 24(4):161-201. DOI: 10.1191/0960327105ht517oa
Source: PubMed


This paper identifies deficiencies in some current practices of causation and risk evaluation by toxicologists and formulates an evidence-based solution. The practice of toxicology focuses on adverse health events caused by physical or chemical agents. Some relations between agents and events are identified risks, meaning unwanted events known to occur at some frequency. However, other relations that are only possibilities – not known to occur (and may never be realized) – also are sometimes called risks and are even expressed quantitatively. The seemingly slight differences in connotation among various uses of the word ‘risk’ conceal deeply philosophic differences in the epistemology of harm. We label as ‘nomological possibilities’ (not as risks) all predictions of harm that are known not to be physically or logically impossible. Some of these nomological possibilities are known to be causal. We term them ‘epistemic’. Epistemic possibilities are risks. The remaining nomological possibilities are called ‘uncertainties’. Distinguishing risks (epistemic relationships) from among all nomological possibilities requires knowledge of causation. Causality becomes knowable when scientific experiments demonstrate, in a strong, consistent (repeatable), specific, dose-dependent, coherent, temporal and predictive manner that a change in a stimulus determines an asymmetric, directional change in the effect. Many believe that a similar set of characteristics, popularly called the ‘Hill Criteria’, make it possible, if knowledge is robust, to infer causation from only observational (nonexperimental) studies, where allocation of test subjects or items is not under the control of the investigator.

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    • "Uncertainty arises from study limitations regarding internal validity, including exposure assessment, confounding and other potential sources of bias, and external validity or generalization from study populations to the populations for which risk assessments are conducted (Guzelian et al. 2005; Hertz-Picciotto 1995; Lash et al. 2009; Levy 2008; Maldonado 2008; Persad and Cooper 2008). Further, point estimates can be inaccurate because of internal validity issues and since confidence intervals only focus on the potential for random error. "
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    ABSTRACT: Background: There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. Although there is uncertainty associated with the results of most epidemiologic studies, techniques exist to characterize uncertainty that can be applied to improve weight-of-evidence evaluations and risk characterization efforts. Methods: This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytic methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government, and industry convened to discuss uncertainty, exposure assessment, and application of analytic methods to address these challenges. Synthesis: Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. For example, improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure that sufficient information is collected for a thorough uncertainty evaluation. Advanced analytic methods and tools such as directed acyclic graphs (DAGs) and Bayesian statistical techniques can provide important insights and support interpretation of epidemiologic data. Conclusions: The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making.
    Environmental Health Perspectives 07/2014; 122(11). DOI:10.1289/ehp.1308062 · 7.98 Impact Factor
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    • "The translation of evidence-based approaches from medicine to toxicology is already underway, at least at the conceptual level, but this process is only a decade old and still in the formative stage. Guzelian et al. (2005) coined the phrase " evidencebased toxicology " (EBT) and noted its promise in assessing the evidence that specific chemicals cause specific health effects in humans. Around the same time, Hoffmann and Hartung (2005) noted the potential value in translating evidence-based assessments of diagnostic measures in medicine to assessments of test methods in toxicology. "
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    ABSTRACT: The Evidence-based Toxicology Collaboration (EBTC) was established recently to translate evidence-basedapproaches from medicine and health care to toxicology in an organized and sustained effort. The EBTC held a workshop on "Evidence-based Toxicology for the 21st Century: Opportunities and Challenges" in Research Triangle Park, North Carolina, USA on January 24-25, 2012. The presentations largely reflected two EBTC priorities: to apply evidence-based methods to assessing the performance of emerging pathwaybased testing methods consistent with the 2007 National Research Council report on "Toxicity Testing in the 21st Century" as well as to adopt a governance structure and work processes to move that effort forward. The workshop served to clarify evidence-based approaches and to provide food for thought on substantive and administrative activities for the EBTC. Priority activities include conducting pilot studies to demonstrate the value of evidence-based approaches to toxicology, as well as conducting educational outreach on these approaches.
    ALTEX: Alternativen zu Tierexperimenten 01/2013; 30(1):74-104. · 5.47 Impact Factor
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    • "Hypotheses that are not testable do not fall within the realm of science. Likewise, expert opinion should be supported by evidence for rational science-based decision making (Guzelian et al., 2005). Because Hill (1965) and others (Bayne-Jones et al., 1964) articulated their perspectives on causal inference, scientists have further described methods to systematically review and characterize the evidence that might be used to support an inference of causality (Cole, 1997; ECETOC, 2009; Kundi, 2007; Phillips and Goodman, 2004; Rothman 1976; Rothman and Greenland, 2005; Susser, 1986; Weed, 2005). "
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    ABSTRACT: Historically, toxicology has played a significant role in verifying conclusions drawn on the basis of epidemiological findings. Agents that were suggested to have a role in human diseases have been tested in animals to firmly establish a causative link. Bacterial pathogens are perhaps the oldest examples, and tobacco smoke and lung cancer and asbestos and mesothelioma provide two more recent examples. With the advent of toxicity testing guidelines and protocols, toxicology took on a role that was intended to anticipate or predict potential adverse effects in humans, and epidemiology, in many cases, served a role in verifying or negating these toxicological predictions. The coupled role of epidemiology and toxicology in discerning human health effects by environmental agents is obvious, but there is currently no systematic and transparent way to bring the data and analysis of the two disciplines together in a way that provides a unified view on an adverse causal relationship between an agent and a disease. In working to advance the interaction between the fields of toxicology and epidemiology, we propose here a five-step "Epid-Tox" process that would focus on: (1) collection of all relevant studies, (2) assessment of their quality, (3) evaluation of the weight of evidence, (4) assignment of a scalable conclusion, and (5) placement on a causal relationship grid. The causal relationship grid provides a clear view of how epidemiological and toxicological data intersect, permits straightforward conclusions with regard to a causal relationship between agent and effect, and can show how additional data can influence conclusions of causality.
    Toxicological Sciences 05/2011; 122(2):223-34. DOI:10.1093/toxsci/kfr113 · 3.85 Impact Factor
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