Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems

Estimating a preference-based index from the Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM): valuation of CORE-6D

Background: The Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders. The objective of this study was to estimate a preference-based index for this population using CORE-6D, a health state classification system derived from CORE-OM consisting of a 5-item emotional component and a physical item, and to demonstrate a novel method for generating states that are not orthogonal. Methods: Rasch analysis was used to identify 11 plausible ‘emotional’ health states from CORE-6D (rather than conventional statistical design that would generate implausible states). By combining these with the 3 response levels of the physical item of CORE-6D, 33 plausible health states can be described, of which 18 were selected for valuation. An interview valuation survey of 220 members of public in South Yorkshire, UK, was undertaken using the time-trade-off method to value the 18 health states; regression analysis was subsequently used to predict values for all possible states described by CORE-6D. Results: A number of multivariate regression models were built to predict values for the 33 plausible health states of CORE-6D, using the Rasch logit value of the emotional health state and the response level of the physical item as independent variables. A cubic model with high predictive value (adjusted R squared 0.990) was finally selected, which can be used to predict utility values for all 927 states described by CORE-6D. Conclusion: The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospective CORE-OM datasets. The new method for generating states may be useful for other instruments with highly correlated dimensions

Patterns of therapist variability: Therapist effects and the contribution of patient severity and risk.

Objective: To investigate the size of therapist effects using multilevel modeling (MLM), to compare the outcomes of therapists identified as above and below average, and to consider how key variables—in particular patient severity and risk and therapist caseload—contribute to therapist variability and outcomes. Method: We used a large practice-based data set comprising patients referred to the U.K.'s National Health Service primary care counseling and psychological therapy services between 2000 and 2008. Patients were included if they had received ≥2 sessions of 1-to-1 therapy (including an assessment), had a planned ending to treatment, and completed the Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM; Barkham et al., 2001; Barkham, Mellor-Clark, Connell, & Cahill, 2006; Evans et al., 2002) at pre- and post-treatment. The study sample comprised 119 therapists and 10,786 patients, whose mean age was 42.1 years (71.5% were female). MLM, including Markov chain Monte Carlo procedures, was used to derive estimates to produce therapist effects and to analyze therapist variability. Results: The model yielded a therapist effect of 6.6% for average patient severity, but it ranged from 1% to 10% as patient non-risk scores increased. Recovery rates for individual therapists ranged from 23.5% to 95.6%, and greater patient severity and greater levels of aggregated patient risk in a therapist's caseload were associated with poorer outcomes. Conclusions: The size of therapist effect was similar to those found elsewhere, but the effect was greater for more severe patients. Differences in patient outcomes between those therapists identified as above or below average were large, and greater therapist risk caseload, rather than non-risk caseload, was associated with poorer patient outcomes

Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of quality adjusted life years in people with common mental health problems.Rasch analysis; health-related quality of life; condition-specific measure; preference-based health; health states; CORE-6D; CORE-OM; mental health; quality-adjusted life years

Training health visitors in cognitive behavioural and person-centred approaches for depression in postnatal women as part of a cluster randomised trial and economic evaluation in primary care: the PoNDER trial

Aim: This paper aims to describe the training preparation for health visitors who took part in the intervention arm of a cluster randomised controlled trial and economic evaluation of training for health visitors – the POstNatal Depression Economic evaluation and Randomised (the PoNDER) trial. A secondary aim is to make available, by electronic links, the training manuals developed for and used for the cognitive behavioural approach (CBA) and the person-centred approach (PCA) training for the health visitors. The paper is of relevance to health visitors, general practitioners, nurse practitioners, midwives, clinical psychologists, mental health nurses, community psychiatric nurses, counsellors, and service commissioners. Background: The trial clinical outcomes have been published, indicating the pragmatic effectiveness of the package of training for health visitors to identify depressive symptoms and provide a psychologically informed intervention. The training was associated with a reduction in depressive symptoms at six months postnatally among intervention group women and some evidence of a benefit for the intervention group for some of the secondary outcomes at 18 months follow-up. Methods: The two experimental interventions examined in the PoNDER trial built upon promising work on the potential for psychological interventions to help women recover from postnatal depression as an alternative to pharmaceutical interventions and to address the limitations of previous research in the area. Findings: The package of health visitor training comprised the development of clinical skills in assessing postnatal women and identifying depressive symptoms, and the delivery of a CBA or a PCA for eligible women. This was the largest trial a health visitor intervention and of postnatal depression ever conducted. We are aware of no other rigorously performed trial that has published details of an extensively tested training programme for the benefit of health-care professionals and clients

Towards personalized allocation of patients to therapists

Objective: Psychotherapy outcomes vary between therapists, but it is unclear how such information can be used for treatment planning or practice development. This proof-of-concept study aimed to develop a data-driven method to match patients to therapists. Method: We analyzed data from N = 4,849 patients who accessed cognitive–behavioral therapy in U.K. primary care services. The main outcome was posttreatment reliable and clinically significant improvement (RCSI) on the Patient Health Questionnaire–9 (PHQ-9) depression measure. Machine-learning analyses were applied in a training sample (N = 2,425 patients treated by 68 therapists in Year 1), including a chi-squared automatic interaction detector (CHAID) algorithm and a random forest (RF) algorithm. The predictive models were cross-validated in a statistically independent test sample (N = 2,424 patients treated by the same therapists in Year 2) and evaluated using odds ratios (ORs) adjusted for baseline depression severity. Results: We identified subgroups of therapists that were differentially effective for highly specific subgroups of patients, yielding 17 classes of patient-to-therapist matches. The overall base rate of RCSI in the sample was 40.4%, but this varied from 10.5% to 69.9% across classes. Cases classed by the prediction algorithms as expected responders in the test sample were ∼60% more likely to attain posttreatment RCSI compared with those classed as nonresponders (adjusted ORs = 1.59, 1.60; p < .001). Conclusions: Machine-learning approaches could help to improve treatment outcomes by enabling the strategic allocation of patients to therapists and therapists to supervisors

Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems