Refining a model of collaborative care for people with a diagnosis of bipolar, schizophrenia or other psychoses in England: a qualitative formative evaluation

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: Transcripts will not be shared in their entirety to protect the anonymity of service users and care partners delivering the intervention. However, requests for excerpts of the data will be considered on an individual basis. Please contact the corresponding author.Background Many people diagnosed with schizophrenia, bipolar or other psychoses in England receive the majority of their healthcare from primary care. Primary care practitioners may not be well equipped to meet their needs and there is often poor communication with secondary care. Collaborative care is a promising alternative model but has not been trialled specifically with this service user group in England. Collaborative care for other mental health conditions has not been widely implemented despite evidence of its effectiveness. We carried out a formative evaluation of the PARTNERS model of collaborative care, with the aim of establishing barriers and facilitators to delivery, identifying implementation support requirements and testing the initial programme theory. Methods The PARTNERS intervention was delivered on a small scale in three sites. Qualitative data was collected from primary and secondary care practitioners, service users and family carers, using semi-structured interviews, session recordings and tape-assisted recall. Deductive and inductive thematic analysis was carried out; themes were compared to the programme theory and used to inform an implementation support strategy. Results Key components of the intervention that were not consistently delivered as intended were: interaction with primary care teams, the use of coaching, and supervision. Barriers and facilitators identified were related to service commitment, care partner skills, supervisor understanding and service user motivation. An implementation support strategy was developed, with researcher facilitation of communication and supervision and additional training for practitioners. Some components of the intervention were not experienced as intended; this appeared to reflect difficulties with operationalising the intervention. Analysis of data relating to the intended outcomes of the intervention indicated that the mechanisms proposed in the programme theory had operated as expected. Conclusions Additional implementation support is likely to be required for the PARTNERS model to be delivered; the effectiveness of such support may be affected by practitioner and service user readiness to change. There is also a need to test the programme theory more fully. These issues will be addressed in the process evaluation of our full trial.National Institute for Health Research (NIHR

Patient preference for second- and third-line therapies in type 2 diabetes:a prespecified secondary endpoint of the TriMaster study

Patient preference is very important for medication selection in chronic medical conditions, like type 2 diabetes, where there are many different drugs available. Patient preference balances potential efficacy with potential side effects. As both aspects of drug response can vary markedly between individuals, this decision could be informed by the patient personally experiencing the alternative medications, as occurs in a crossover trial. In the TriMaster (NCT02653209, ISRCTN12039221), randomized double-blind, three-way crossover trial patients received three different second- or third-line once-daily type 2 diabetes glucose-lowering drugs (pioglitazone 30 mg, sitagliptin 100 mg and canagliflozin 100 mg). As part of a prespecified secondary endpoint, we examined patients’ drug preference after they had tried all three drugs. In total, 448 participants were treated with all three drugs which overall showed similar glycemic control (HbA1c on pioglitazone 59.5 sitagliptin 59.9, canagliflozin 60.5 mmol mol−1, P = 0.19). In total, 115 patients (25%) preferred pioglitazone, 158 patients (35%) sitagliptin and 175 patients (38%) canagliflozin. The drug preferred by individual patients was associated with a lower HbA1c (mean: 4.6; 95% CI: 3.9, 5.3) mmol mol−1 lower versus nonpreferred) and fewer side effects (mean: 0.50; 95% CI: 0.35, 0.64) fewer side effects versus nonpreferred). Allocating therapy based on the individually preferred drugs, rather than allocating all patients the overall most preferred drug (canagliflozin), would result in more patients achieving the lowest HbA1c for them (70% versus 30%) and the fewest side effects (67% versus 50%). When precision approaches do not predict a clear optimal therapy for an individual, allowing patients to try potential suitable medications before they choose long-term therapy could be a practical alternative to optimizing treatment for type 2 diabetes

Accounting for risk of non linear portfolios: a novel Fourier approach

The presence of non linear instruments is responsible for the emergence of non Gaussian features in the price changes distribution of realistic portfolios, even for Normally distributed risk factors. This is especially true for the benchmark Delta Gamma Normal model, which in general exhibits exponentially damped power law tails. We show how the knowledge of the model characteristic function leads to Fourier representations for two standard risk measures, the Value at Risk and the Expected Shortfall, and for their sensitivities with respect to the model parameters. We detail the numerical implementation of our formulae and we emphasizes the reliability and efficiency of our results in comparison with Monte Carlo simulation.Comment: 10 pages, 12 figures. Final version accepted for publication on Eur. Phys. J.

A Closed-Form Solution of the Multi-Period Portfolio Choice Problem for a Quadratic Utility Function

In the present paper, we derive a closed-form solution of the multi-period portfolio choice problem for a quadratic utility function with and without a riskless asset. All results are derived under weak conditions on the asset returns. No assumption on the correlation structure between different time points is needed and no assumption on the distribution is imposed. All expressions are presented in terms of the conditional mean vectors and the conditional covariance matrices. If the multivariate process of the asset returns is independent it is shown that in the case without a riskless asset the solution is presented as a sequence of optimal portfolio weights obtained by solving the single-period Markowitz optimization problem. The process dynamics are included only in the shape parameter of the utility function. If a riskless asset is present then the multi-period optimal portfolio weights are proportional to the single-period solutions multiplied by time-varying constants which are depending on the process dynamics. Remarkably, in the case of a portfolio selection with the tangency portfolio the multi-period solution coincides with the sequence of the simple-period solutions. Finally, we compare the suggested strategies with existing multi-period portfolio allocation methods for real data.Comment: 38 pages, 9 figures, 3 tables, changes: VAR(1)-CCC-GARCH(1,1) process dynamics and the analysis of increasing horizon are included in the simulation study, under revision in Annals of Operations Researc

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved

Internet-based medical education: a realist review of what works, for whom and in what circumstances

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