Review of: Sharon M. Friedman et al. eds., Communicating Uncertainty

A review of the book Communicating Uncertainty: Media Coverage of New and Controversial Science (Sharon M. Friedman, Sharon Dunwoody & Carol L. Rogers, eds.; Lawrence Erlbaum Associates 1999). Preface, introduction. ISBN 0-8058-2728-5 [261 pp. $32.50. Paperback, 10 Industrial Avenue, Mahwah, NJ 07430-2262]

Communicating prognostic uncertainty in potential end-of-life contexts: experiences of family members

Background: This article reports on the concept of “communicating prognostic uncertainty” which emerged from a mixed methods survey asking family members to rank their satisfaction in seven domains of hospital end-of-life care. Methods: Open-ended questions were embedded within a previously validated survey asking family members about satisfaction with end-of-life care. The purpose was to understand, in the participants’ own words, the connection between their numerical rankings of satisfaction and the experience of care. Results: Our study found that nearly half of all family members wanted more information about possible outcomes of care, including knowledge that the patient was “sick enough to die”. Prognostic uncertainty was often poorly communicated, if at all. Inappropriate techniques included information being cloaked in confusing euphemisms, providing unwanted false hope, and incongruence between message and the aggressive level of care being provided. In extreme cases, these techniques left a legacy of uncertainty and suspicion. Family members expressed an awareness of both the challenges and benefits of communicating prognostic uncertainty. Most importantly, respondents who acknowledged that they would have resisted (or did) knowing that the patient was sick enough to die also expressed a retrospective understanding that they would have liked, and benefitted, from more prognostic information that death was a possible or probable outcome of the patient’s admission. Family members who reported discussion of prognostic uncertainty also reported high levels of effective communication and satisfaction with care. They also reported long-term benefits of knowing the patient was sick enough to die. Conclusion: While a patient who is sick enough to die may survive to discharge, foretelling with family members in potential end of life contexts facilitates the development of a shared and desired prognostic awareness that the patient is nearing end of life

Optimized reduction of uncertainty in bursty human dynamics

Human dynamics is known to be inhomogeneous and bursty but the detailed understanding of the role of human factors in bursty dynamics is still lacking. In order to investigate their role we devise an agent-based model, where an agent in an uncertain situation tries to reduce the uncertainty by communicating with information providers while having to wait time for responses. Here the waiting time can be considered as cost. We show that the optimal choice of the waiting time under uncertainty gives rise to the bursty dynamics, characterized by the heavy-tailed distribution of optimal waiting time. We find that in all cases the efficiency for communication is relevant to the scaling behavior of the optimal waiting time distribution. On the other hand the cost turns out in some cases to be irrelevant depending on the degree of uncertainty and efficiency.Comment: 4 pages, 1 figur

Graphics for uncertainty

Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data

Communicating Uncertainty in Economic Evaluations:Verifying Optimal Strategies

Background. In cost-effectiveness analysis (CEA), it is common to compare a single, new intervention with 1 or more existing interventions representing current practice ignoring other, unrelated interventions. Sectoral CEAs, in contrast, take a perspective in which the costs and effectiveness of all possible interventions within a certain disease area or health care sector are compared to maximize health in a society given resource constraints. Stochastic league tables (SLT) have been developed to represent uncertainty in sectoral CEAs but have 2 shortcomings: 1) the probabilities reflect inclusion of individual interventions and not strategies and 2) data on robustness are lacking. The authors developed an extension of SLT that addresses these shortcomings. Methods. Analogous to non-probabilistic MAXIMIN decision rules, the uncertainty of the performance of strategies in sectoral CEAs may be judged with respect to worst possible outcomes, in terms of health effects obtainable within a given budget. Therefore, the authors assessed robustness of strategies likely to be optimal by performing optimization separately on all samples and on samples yielding worse than expected health benefits. The approach was tested on 2 examples, 1 with independent and 1 with correlated cost and effect data. Results. The method was applicable to the original SLT example and to a new example and provided clear and easily interpretable results. Identification of interventions with robust performance as well as the best performing strategies was straightforward. Furthermore, the robustness of strategies was assessed with a MAXIMIN decision rule. Conclusion. The SLT extension improves the comprehensibility and extends the usefulness of outcomes of SLT for decision makers. Its use is recommended whenever an SLT approach is considered

Communicating uncertainty in geomagnetic field estimates provided by the BGS to aid directional drilling

Navigating underground when drilling for oil and gas has become more challenging as companies try to hit smaller targets in reservoirs already congested with existing wells. One widely used method is Measurement While Drilling (MWD) using magnetic survey tools to direct the drill head. The provision of accurate geomagnetic field values with verifiable estimates of uncertainty is of utmost importance as the estimates help mitigate the risk of collision or missing a target. The BGS offers three options to the oil industry depending on accuracy required: the basic option is to use estimates of the geomagnetic field from the annually updated BGS Global Geomagnetic Model (BGGM); the second and more accurate option, In-Field Referencing (IFR), includes estimates of the local crustal magnetic field; the third and most accurate option, Interpolation In-Field Referencing (IIFR), includes estimates of the rapidly time-varying magnetic field at the oil field. The estimates of uncertainty in the geomagnetic field values supplied under each of these options are almost as mportant as the values themselves because they are incorporated into company software which derives positional error ellipsoids along the well-path. We describe work done over several years on the derivation and communication of geomagnetic field uncertainty for the oil industry

Don't know, can't know: Embracing deeper uncertainties when analysing risks

This article is available open access through the publisher’s website at the link below. Copyright @ 2011 The Royal Society.Numerous types of uncertainty arise when using formal models in the analysis of risks. Uncertainty is best seen as a relation, allowing a clear separation of the object, source and ‘owner’ of the uncertainty, and we argue that all expressions of uncertainty are constructed from judgements based on possibly inadequate assumptions, and are therefore contingent. We consider a five-level structure for assessing and communicating uncertainties, distinguishing three within-model levels—event, parameter and model uncertainty—and two extra-model levels concerning acknowledged and unknown inadequacies in the modelling process, including possible disagreements about the framing of the problem. We consider the forms of expression of uncertainty within the five levels, providing numerous examples of the way in which inadequacies in understanding are handled, and examining criticisms of the attempts taken by the Intergovernmental Panel on Climate Change to separate the likelihood of events from the confidence in the science. Expressing our confidence in the adequacy of the modelling process requires an assessment of the quality of the underlying evidence, and we draw on a scale that is widely used within evidence-based medicine. We conclude that the contingent nature of risk-modelling needs to be explicitly acknowledged in advice given to policy-makers, and that unconditional expressions of uncertainty remain an aspiration