Use of the child health utility and strengths and difficulties outcome measures in economic evaluations of school-based interventions: data from a cluster-randomised controlled trial in Northern Ireland

Background There is growing evidence linking early social and emotional wellbeing to later academic performance and various health outcomes including mental health. An economic evaluation was designed alongside the Roots of Empathy cluster-randomised trial evaluation, which is a school-based intervention for improving pupils’ social and emotional wellbeing. Exploration of the relevance of the Strengths and Diffi culties Questionnaire (SDQ) and Child Health Utility 9D (CHU9D) in school-based health economic evaluations is warranted. The SDQ is a behavioural screening questionnaire for 4–17-year-old children, consisting of a total diffi culties score, and also prosocial behaviour,
which aims to identify positive aspects of behaviour. The CHU9D is a generic preference-based health-related quality of life instrument for 7–17-year-old children.

The Cluster Effect

No abstract available

A cluster randomised controlled trial evaluation and cost-effectiveness analysis of the Roots of Empathy schools-based programme for improving social and emotional wellbeing outcomes among 8-9 year olds in Northern Ireland

A substantial body of evidence suggest that well designed school based prevention programmes can be effective in improving a variety of social, health and academic outcomes for children and young people. This poster presents the methodology for evaluating the Roots of Empathy (ROE) programme. ROE is a universal programme delivered on a whole-class basis for one academic year. It consists of 27 lessons that run over a school year and is based around a monthly classroom visit by an infant and parent, typically recruited from the local community, whom the class 'adopts' at the start of the school year. The evaluation aims to evaluate the immediate and longer term impact of ROE on social and emotional wellbeing outcomes among 8-9 year old pupils, as well as evaluate the cost-effectiveness of the programme.

Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms

Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X) and g(Y), E[f(X)g(Y)]=E[f(X)] *times* E[g(Y)], where E[.] denotes interval-valued expectation and *times* denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties.

Algorithms for Hidden Markov Models With Imprecisely Specified Parameters

Hidden Markov models (HMMs) are widely used models for sequential data. As with other probabilistic graphical models, they require the specification of precise probability values, which can be too restrictive for some domains, especially when data are scarce or costly to acquire. We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions. Our models have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. Efficient inference algorithms are developed to address standard HMM usage such as the computation of likelihoods and most probable explanations. Experiments with real data show that the use of imprecise probabilities leads to more reliable inferences without compromising efficiency.