Structured expert judgment (SEJ) is a method for obtaining estimates of uncertain quantities from groups of experts in a structured way designed to minimize the pervasive cognitive frailties of unstructured approaches. When the number of quantities required is large, the burden on the groups of experts is heavy, and resource constraints may mean that eliciting all the quantities of interest is impossible. Partial elicitations can be complemented with imputation methods for the remaining, unelicited quantities. In the case where the quantities of interest are conditional probability distributions, the natural relationship between the quantities can be exploited to impute missing probabilities. Here we test the Bayesian intelligence interpolation method and its variations for Bayesian network conditional probability tables, called “InterBeta.” We compare the various outputs of InterBeta on two cases where conditional probability tables were elicited from groups of experts. We show that interpolated values are in good agreement with experts' values and give guidance on how InterBeta could be used to good effect to reduce expert burden in SEJ exercises.

Introduction Food insecurity is associated with increased risk for several health conditions and with poor chronic disease management. Key determinants for household food insecurity are income and food costs. Whereas short-term household incomes are likely to remain static, increased food prices would be a significant driver of food insecurity. Objectives To investigate food price drivers for household food security and its health consequences in the UK under scenarios of Deal and No-deal for Britain’s exit from the European Union. To estimate the 5% and 95% quantiles of the projected price distributions. Design Structured expert judgement elicitation, a well-established method for quantifying uncertainty, using experts. In July 2018, each expert estimated the median, 5% and 95% quantiles of changes in price for 10 food categories under Brexit Deal and No-deal to June 2020 assuming Brexit had taken place on 29 March 2019. These were aggregated based on the accuracy and informativeness of the experts on calibration questions. Participants Ten specialists with expertise in food procurement, retail, agriculture, economics, statistics and household food security. Results When combined in proportions used to calculate Consumer Price Index food basket costs, median food price change for Brexit with a Deal is expected to be +6.1% (90% credible interval −3% to +17%) and with No-deal +22.5% (90% credible interval +1% to +52%). Conclusions The number of households experiencing food insecurity and its severity is likely to increase because of expected sizeable increases in median food prices after Brexit. Higher increases are more likely than lower rises and towards the upper limits, these would entail severe impacts. Research showing a low food budget leads to increasingly poor diet suggests that demand for health services in both the short and longer terms is likely to increase due to the effects of food insecurity on the incidence and management of diet-sensitive conditions.

This paper presents an integrating decision support system (IDSS) for food security in the United Kingdom. In ever-larger dynamic systems, such as the food system, it is increasingly difficult for decision makers (DMs) to effectively account for all the variables within the system that may influence the outcomes of interest under enactments of various candidate policies. Each of the influencing variables is likely, themselves, to be dynamic subsystems with expert domains supported by sophisticated probabilistic models. Recent increases in food poverty in the United Kingdom have raised the questions about the main drivers of food insecurity, how this may be changing over time and how evidence can be used in evaluating policy for decision support. In this context, an IDSS is proposed for household food security to allow DMs to compare several candidate policies which may affect the outcome of food insecurity at the household level.

Objectives: To identify the role of fitness, fitness change, body mass index and other factors in predicting long-term (>5 years) survival in patients with coronary heart disease. Design: Cohort study of patients with coronary heart disease recruited from 1 January 1993 to 31 December 2002, followed up to March 2011 (1 day to 18 years 3 months, mean 10.7 years). Setting: A community-based National Health Service (NHS) cardiac rehabilitation programme serving the Basingstoke and Alton area in Hampshire, UK. Participants: An unselected cohort of NHS patients, 2167 men and 547 women aged 28–88 years, who attended the rehabilitation programme following acute myocardial infarction, an episode of angina or revascularisation, and had a baseline fitness test. Main outcome measures: Cardiovascular mortality and all-cause mortality. Results: A high level of fitness (VO ₂ ≥22 mL/kg/min for men, VO ₂ ≥19 mL/kg/min for women) at completion of the programme was associated with decreased all-cause death, as was a prescription for statins or aspirin, and female gender. Increase in all-cause mortality was associated with higher age and ACE inhibitors prescription. Higher risk of cardiovascular mortality was associated with increasing age, prescriptions for ACE inhibitor, and diagnosis of myocardial infarction or angina as compared with the other diagnoses. Conclusions: Prior fitness and fitness improvement are strong predictors of long-term survival in patients who have experienced a cardiac event or procedure. Some secondary prevention medications make a significant contribution to reducing all-cause mortality and cardiovascular mortality in these patients. This study supports public health messages promoting fitness for life.

Policy-makers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those dealing with ecosystem services. For policy-makers wishing to make evidence-based decisions which will best support pollinator abundance and pollination services, one of the problems faced is how to access the information and evidence they need, and how to combine it to formulate and evaluate candidate policies. This is even more complex when multiple factors provide influence in combination. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees, and other pollinators are well documented, but incomplete. In order to estimate the potential effectiveness of various candidate policy choices, there is an urgent need to quantify the effect of various combinations of factors on the pollination ecosystem service. Using high-quality experimental evidence is the most robust approach, but key aspects of the system may not be amenable to experimentation or may be prohibitive based on cost, time and effort. In such cases, it is possible to obtain the required evidence by using structured expert elicitation, a method for quantitatively characterizing the state of knowledge about an uncertain quantity. Here we report and discuss the outputs of the novel use of a structured expert elicitation, designed to quantify the probability of good pollinator abundance given a variety of weather, disease, and habitat scenarios. Evaluación de la respuesta de la abundancia de polinizadores a las presiones ambientales mediante el uso de elicitación experta estructurada A menudo los legisladores dependen de expertos en diversas áreas de conocimiento para tomar decisiones sobre legislación en entornos complejos, multifacéticos y dinámicos tales como los que tienen que ver con los servicios ecosistémicos. Los legisladores que quieren tomar decisiones basadas en evidencias que respalden mejor los servicios de polinización y la abundancia de polinizadores, se enfrentan al problema de cómo acceder a la información y a las evidencias que necesitan, y de cómo combinar éstas para formular y evaluar futuras leyes. Esto es aún más complejo cuando hay múltiples factores que influyen de manera combinada. Las presiones que afectan a la supervivencia y a la capacidad polinizadora de las abejas de la miel (Apis mellifera), a las abejas silvestres y a otros polinizadores están bien documentadas, pero de manera incompleta. Para estimar la efectividad potencial de varias opciones posibles de legislación, es necesario cuantificar el efecto combinado de varios factores sobre el servicio ecosistémico de polinización. El uso de una evidencia experimental de alta calidad es el enfoque más sólido, pero algunos aspectos clave del sistema podrían no ser susceptibles de experimentación o ser prohibitivos debido al coste, el tiempo y el esfuerzo. En tales casos, es posible obtener la evidencia requerida mediante el uso de la elicitación experta estructurada, un método para caracterizar cuantitativamente el estado del conocimiento sobre una cantidad incierta. En este estudio informamos y discutimos los resultados del uso novedoso de una elicitación experta estructurada, diseñada para cuantificar la probabilidad de una abundancia de polinizadores adecuada teniendo en cuenta una variedad de escenarios climáticos, de enfermedades y de hábitat.