Further Deliberations on Uncertainty in Risk Assessment

Marks, Harry M.; Coleman, Margaret E.; Michael, Matthew

doi:10.1080/10807030390250886

Cited by 6 publications

(5 citation statements)

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“…Epistemic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. 35 …”

Section: Taxonomies Of Uncertainty: Insights From Other Fieldsmentioning

confidence: 99%

“…Another important distinction is between ''stochastic'' and ''epistemic'' uncertainty. 35,36 Stochastic uncertainty pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals). Epistemic uncertainty, in contrast, reflects limitations in the current ''state of knowledge'' underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision.…”

Section: Varieties Of Uncertainty In Health Carementioning

confidence: 99%

“…Epistemic uncertainty, in contrast, reflects limitations in the current ''state of knowledge'' underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. 35 Morgan and Henrion 37 have enumerated additional sources of scientific uncertainty pertaining to ''empirical quantities'' in risk analysis. These include 1) statistical variation arising from random measurement error; 2) subjective judgment due to systematic measurement error; 3) linguistic imprecision in the representation of quantities; 4) variability occurring naturally in a measured quantity over time or space; 5) inherent randomness and unpredictability arising from the indeterminacy (either real or apparent) of a phenomenon; 6) disagreement in interpretations of scientific evidence; and 7) approximation due to limitations in the capacity of a model to represent real-world systems.…”

Section: Varieties Of Uncertainty In Health Carementioning

confidence: 99%

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Varieties of Uncertainty in Health Care

2011

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Uncertainty is a pervasive and important problem that has attracted increasing attention in health care, given the growing emphasis on evidence-based medicine, shared decision making, and patient-centered care. However, our understanding of this problem is limited, due in part to the absence of a unified, coherent concept of uncertainty. There are multiple meanings and varieties of uncertainty in health care, which are not often distinguished or acknowledged although each may have unique effects or warrant different courses of action. The literature on uncertainty in health care is thus fragmented, and existing insights have been incompletely translated to clinical practice. In this paper we attempt to address this problem by synthesizing diverse theoretical and empirical literature from the fields of communication, decision science, engineering, health services research, and psychology, and developing a new integrative conceptual taxonomy of uncertainty. We propose a three-dimensional taxonomy that characterizes uncertainty in health care according to its fundamental sources, issues, and locus. We show how this new taxonomy facilitates an organized approach to the problem of uncertainty in health care by clarifying its nature and prognosis, and suggesting appropriate strategies for its analysis and management.

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Section: Taxonomies Of Uncertainty: Insights From Other Fieldsmentioning

confidence: 99%

Section: Varieties Of Uncertainty In Health Carementioning

confidence: 99%

Section: Varieties Of Uncertainty In Health Carementioning

confidence: 99%

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Varieties of Uncertainty in Health Care

2011

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“…This type of combination of data and expert knowledge of mechanisms might help establish plausible values of ρ, or some way of grouping strains by virulence. Then, a risk assessment could be conducted with different assumed values of ρ or σ w (Marks et al, 2003).…”

Section: Summary For Risk Assessors and Conclusionmentioning

confidence: 99%

Discerning Strain Effects in Microbial Dose-Response Data

Coleman

Marks

Golden

et al. 2004

Journal of Toxicology and Environmental Health, Part A

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In order to estimate the risk or probability of adverse events in risk assessment, it is necessary to identify the important variables that contribute to the risk and provide descriptions of distributions of these variables for well-defined populations. One component of modeling dose response that can create uncertainty is the inherent genetic variability among pathogenic bacteria. For many microbial risk assessments, the "default" assumption used for dose response does not account for strain or serotype variability in pathogenicity and virulence, other than perhaps, recognizing the existence of avirulent strains. However, an examination of data sets from human clinical trials in which Salmonella spp. and Campylobacter jejuni strains were administered reveals significant strain differences. This article discusses the evidence for strain variability and concludes that more biologically based alternatives are necessary to replace the default assumptions commonly used in microbial risk assessment, specifically regarding strain variability.

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“…EPA, 2011b). The uncertainty inherent in modeled risk estimates (Marks, Coleman, & Matthew, 2003) presents a communication challenge because people have difficulty comprehending uncertainty and typically prefer certain to uncertain information (Han et al, 2009; Han, Moser, & Klein, 2006; Johnson & Slovic, 1995; Longman, Turner, King, & McCaffery, 2012). To address this challenge, cartographers advise incorporating uncertainty into the map (Bostrom, Anselin, & Farris, 2008; MacEachren, 1992; MacEachren et al, 2005).…”

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Testing Map Features Designed to Convey the Uncertainty of Cancer Risk

Severtson

2015

Science Communication

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Barriers to communicating the uncertainty of environmental health risks include preferences for certain information and low numeracy. Map features designed to communicate the magnitude and uncertainty of estimated cancer risk from air pollution were tested among 826 participants to assess how map features influenced judgments of adequacy and the intended communication goals. An uncertain versus certain visual feature was judged as less adequate but met both communication goals and addressed numeracy barriers. Expressing relative risk using words communicated uncertainty and addressed numeracy barriers but was judged as highly inadequate. Risk communication and visual cognition concepts were applied to explain findings.

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Further Deliberations on Uncertainty in Risk Assessment

Cited by 6 publications

References 0 publications

Varieties of Uncertainty in Health Care

Varieties of Uncertainty in Health Care

Discerning Strain Effects in Microbial Dose-Response Data

Testing Map Features Designed to Convey the Uncertainty of Cancer Risk

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