Publications by authors named "Spiegelhalter D"

The Statistics Expert Group was convened at the request of the Infected Blood Inquiry to provide estimates of the number of infections and deaths from blood-borne infections including hepatitis B virus, human immunodeficiency virus, hepatitis C virus (HCV) and variant Creutzfeldt Jakob disease, as a direct result of contaminated blood and blood products administered in the United Kingdom of Great Britain and Northern Ireland (UK). In the absence of databases of HCV infections and related deaths for all nations of the UK, a statistical model was required to estimate the number of infections and subsequent deaths from HCV acquired from blood transfusions from January 1970 to August 1991. We present this statistical model in detail alongside the results of its application to each of the four nations in the UK.

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Objectives: To help people make decisions about the most effective mitigation measures against SARS-CoV-2 transmission in different scenarios, the likelihoods of transmission by different routes need to be quantified to some degree (however uncertain). These likelihoods need to be communicated in an appropriate way to illustrate the relative importance of different routes in different scenarios, the likely effectiveness of different mitigation measures along those routes, and the level of uncertainty in those estimates. In this study, a pragmatic expert elicitation was undertaken to supply the underlying quantitative values to produce such a communication tool.

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Background: The quality of evidence about the effectiveness of non-pharmaceutical health interventions is often low, but little is known about the effects of communicating indications of evidence quality to the public.

Methods: In two blinded, randomised, controlled, online experiments, US participants (total n = 2140) were shown one of several versions of an infographic illustrating the effectiveness of eye protection in reducing COVID-19 transmission. Their trust in the information, understanding, feelings of effectiveness of eye protection, and the likelihood of them adopting it were measured.

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and offer tips to statisticians communicating through the media, especially in the time of Covid-19.

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Objectives: To develop a new interface for the widely used prognostic breast cancer tool: Predict: Breast Cancer. To facilitate decision-making around post-surgery breast cancer treatments. To derive recommendations for communicating the outputs of prognostic models to patients and their clinicians.

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Background: Mortality from COVID-19 shows a strong relationship with age and pre-existing medical conditions, as does mortality from other causes. We aimed to investigate how specific factors are differentially associated with COVID-19 mortality as compared to mortality from causes other than COVID-19.

Methods: Working on behalf of NHS England, we carried out a cohort study within the OpenSAFELY platform.

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As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information.

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Throughout the COVID-19 pandemic, social and traditional media have disseminated predictions from experts and nonexperts about its expected magnitude. How accurate were the predictions of 'experts'-individuals holding occupations or roles in subject-relevant fields, such as epidemiologists and statisticians-compared with those of the public? We conducted a survey in April 2020 of 140 UK experts and 2,086 UK laypersons; all were asked to make four quantitative predictions about the impact of COVID-19 by 31 Dec 2020. In addition to soliciting point estimates, we asked participants for lower and higher bounds of a range that they felt had a 75% chance of containing the true answer.

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Background: Bisphosphonate drugs can be used to improve the outcomes of women with breast cancer. Whilst many meta-analyses have quantified their potential benefits for patients, attempts at comprehensive quantification of potential adverse effects have been limited. We undertook a meta-analysis with novel methodology to identify and quantify these adverse effects.

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Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

Design: Population based cohort study.

Setting And Participants: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data.

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The interplay between science, risk assessment and risk management has always been complex, and even more so in a world increasingly characterised by rapid technical innovation, new modes of communication, suspicion about authorities and experts, and demands for people to have a say in decisions that are made on their behalf. In this challenging era where scientific advice on food safety has never been in greater demand, risk managers should effectively navigate the interplay between facts and values and be able to rely on robust and fit-for-purpose risk assessments to aid them. The fact that societal resistance is often encountered when scientific advice on food safety operates at a distance from social values and fails to actively engage with citizens, has led to increasing emphasis on the need to advance forms of risk assessment that are more contextual, and socially sound and accountable.

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Uncertainty is inherent to our knowledge about the state of the world yet often not communicated alongside scientific facts and numbers. In the "posttruth" era where facts are increasingly contested, a common assumption is that communicating uncertainty will reduce public trust. However, a lack of systematic research makes it difficult to evaluate such claims.

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Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective.

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Research that is poorly communicated or presented is as potentially damaging as research that is poorly conducted or fraudulent. Recent examples illustrate how the problem often lies with researchers, not press officers or journalists. The quest for publication and 'impact' must not outweigh the importance of accurate representation of science; herein, we suggest steps that researchers, journalists and press officers can take to help ensure this.

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