Prevention of long term social disability amongst young people with emerging signs of severe mental illness – estimating the value of further research and what data to collect in a future definitive study (Value of information analysis)
Type of research:
Value of information analysis (Health Economics)
Background & Scientific Rationale:
The estimation of cost-effectiveness, within a health technology assessment, is an iterative process (Ramsey et al, 2001). Moreover, randomised trials to compare different treatment options are expensive, as they require many patients and seek to collect a large amount of data. Feasibility studies are often thereby undertaken, where one of the components is to monitor levels of resource use (to estimate costs) and quality of life (to estimate benefits), where this will inform how cost and benefit data can be estimated in a future larger study. Recently, such a feasibility study has started. We wish to conduct value of information analysis to estimate the value of undertaking further research. The results of the value of information analysis can then be used to inform the decision as to what data to collect in any subsequent more definitive study, including what parameters to focus on and the optimal sample size of that study. It is now widely recognised that most socially disabling chronic and severe mental health problems begin in adolescence with 75% of all severe and chronic mental illnesses emerging between the ages of 15 and 25 (Kessler, et al., 2005; Kim-Cohen, et al., 2003). Despite poor outcomes, associated disorder costs and social decline, young people with complex needs frequently do not access treatment and fewer than 25% of young people and their families who have such needs get access to specialist mental health services (DoH, 2008). The economic costs of not addressing this disability are very large (Mangalore & Knapp 2007). Several recent reports have highlighted that there is a major gap in identifying and managing the mental health problems of young people with severe and complex mental health problems and particularly those at risk of social disability (DoH, 2008; Singh et al, 2010). Current evidence for effective interventions to address social disability amongst young people in the early course of severe mental illness is very limited (see Fowler, et al., 2010 review). We consider that better outcomes on social disability and hopelessness can be obtained from a more targeted intervention specifically focussed on improving social disability amongst those who have low functioning. We have developed a multisystemic form of CBT which targets social disability and have published a successful MRC trial platform study carried out amongst a group of young people who had established chronic and severe social disability problems some years after first episode psychosis (Fowler, et al., 2009).
To estimate the value of further research and what data to collect in a future definitive study focusing on the prevention of long term social disability amongst young people with emerging signs of severe mental illness.
Levels of resource use and quality of life will be monitored, in order to estimate the costs and benefits associated with the treatment options which are being evaluated. The main measure of outcome in the economic analysis will be the EQ-5D (Brooks et al, 2006) (a generic measure of quality of life) which will be assessed at pre- and post-intervention. Value of information analysis will subsequently be undertaken, where the relevant statistics are the expected value of perfect information (EVPI), expected value of perfect parameter information (EVPPI), expected value of sample information (EVSI) and expected net gain of sampling (ENGS).
Expected Output of Research / Impact and added value:
By using the data from a current pilot study to estimate the value of further research we will be able to estimate the value of further research. i.e. the expected value of a future trial, after taking account of the costs of running the trial. Thus, we will be able to assess whether a future trial is likely to be worthwhile. Additionally, we will be able to make recommendations as to what data to collect in any subsequent more definitive study (what parameters to focus on) and the optimal sample size of that study.
For further information on this project, contact Dr Garry Barton, Reader in Health Economics, Health Economics Group, Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia