No abstract

ObjectivesTo 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.DesignCohort 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).SettingA community-based National Health Service (NHS) cardiac rehabilitation programme serving the Basingstoke and Alton area in Hampshire, UK.ParticipantsAn 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 measuresCardiovascular mortality and all-cause mortality.ResultsA high level of fitness (VO2≥22 mL/kg/min for men, VO2≥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.ConclusionsPrior 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.

This qualitative study using a grounded theory approach, assesses the construction of claims in online news articles and below the line comments in connection with foodbank use in the West Midlands region, UK. The sample includes 146 online news articles and 132 below the line comments, commencing 23 September 2010 until 8 April 2019. Individual foodbank users’ stories are told and these relay discourses of stigma, shame, embarrassment and desperation. In contrast, the below the line comments centre on the undeserving poor. Here, emphasis is on the migrants who are ‘flooding’ the country, and the scroungers who are work-shy.

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.