Training Future Mental Health Professionals in an Evidence-Informed System of Care

High quality mental health services do not reach the youth who need them, leading to efforts to implement effective treatments more broadly. One focus of these efforts concerns training the mental health workforce, of which master’s-level social workers represent a large proportion. However, the curricula of master’s in social work (MSW) programs do not often emphasize evidence-based approaches. One possible solution is Managing and Adapting Practice (MAP; PracticeWise, LLC), a system that allows clinicians to (1) identify clinically indicated evidence-based programs by searching a growing evidence-base of randomized controlled trials (RCTs) and (2) build individualized evidence-informed treatment plans by focusing on common practice elements. MAP may also address the concerns about manual-based programs (e.g., inflexibility). Although some MSW programs have integrated MAP, the benefits of MAP training within MSW education have not yet been evaluated. This project evaluated multiple mechanisms of training in a semester-long MSW-focused MAP course relative to curriculum-as-usual control at a large public university. Participants were advanced MSW students (mean age = 27, SD = 5.8; 92.3% women; 59% white) either enrolled in the MAP course (n = 17) or enrolled in curriculum-as-usual (n = 22). The MAP course was co-taught by an expert MAP trainer and a MAP-trained social worker. Pre- and post-semester, participants completed a battery that included: (1) role-plays with standardized patients that were videotaped and coded using the Therapy Observational Coding System of Child Psychotherapy – Revised Strategies scale; (2) a written task that was subsequently coded to assess participants’ clinical decision-making skills during different phases of a standardized case; and (3) attitudinal factors that may be predictive of future MAP usage, such as attitudes toward evidence-based practice and the acceptability and feasibility of MAP. Results indicate significant uptake of cognitive and behavioral therapeutic strategies in the MAP condition. Overall, participants endorsed positive attitudes toward evidence-based practice broadly and MAP specifically. Findings may be used to inform the development of more effective evidence-informed curriculum for master’s-level clinical programs and future workforce training initiatives. Methodological considerations may inform advances in instrumentation to measure multidimensional training outcome

Therapist Adherence to Cognitive Behavioral Therapy for Anxious Youth Across a Case

The field has developed many evidence-based treatments (EBTs); the integrity of EBTs being delivered, however, has been studied less than rigorously. Because many treatment manuals are developed to be delivered session-by-session, one way to assess treatment adherence, specifically, is across the course of the case: do therapists deliver treatment components in the order prescribed? The goals of this study were to characterize how therapists deviate from prescribed order and how adherence to order relates to child characteristics. Therapy process data were collected from a subsample of children (N = 33, aged 7-15) that received cognitive behavioral therapy (CBT) to address primary symptoms of anxiety. Adherence to CBT was measured by the CBT Adherence Scale for Youth Anxiety (Southam-Gerow & McLeod, 2011). Four methods to assess order were developed. Analyses include descriptive and correlative statistics that characterize the delivery of CBT and the relation between adherence to order and pretreatment characteristics

Hormone replacement therapy in women with cancer and risk of cancer-specific mortality and cardiovascular disease : A protocol for a cohort study from Scotland and Wales.

Funding This work was supported by Cancer Research UK (reference C37316/A29656). The funder had no role in study design, data collection, data analysis, data interpretation or writing the manuscript.Peer reviewedPublisher PD

Long-term oral prednisolone exposure in primary care for bullous pemphigoid: population-based study

Background: Oral prednisolone is the mainstay treatment for bullous pemphigoid, an autoimmune blistering skin disorder affecting older people. Treatment with moderate-to-high doses is often initiated in secondary care, but then continued in primary care. Aim: To describe long-term oral prednisolone prescribing in UK primary care for adults with bullous pemphigoid from 1998 to 2017. Design and setting: A prospective cohort study using routinely collected data from the Clinical Practice Research Datalink, a primary care database containing the healthcare records for over 17 million people in the UK. Method: Oral prednisolone exposure was characterised in terms of the proportion of individuals with incident bullous pemphigoid prescribed oral prednisolone following their diagnosis, and the duration and dose of prednisolone. Results: In total, 2312 (69.6%) of 3322 people with bullous pemphigoid were prescribed oral prednisolone in primary care. The median duration of exposure was 10.6 months (interquartile range [IQR] 3.4–24.0). Of prednisolone users, 71.5% were continuously exposed for >3 months, 39.7% for >1 year, 14.7% for >3 years, 5.0% for >5 years, and 1.7% for >10 years. The median cumulative dose was 2974 mg (IQR 1059–6456). Maximum daily doses were ≥10 mg/day in 74.4% of prednisolone users, ≥20 mg/day in 40.7%, ≥30 mg/day in 18.2%, ≥40 mg/day in 6.6%, ≥50 mg/day in 3.8%, and ≥60 mg/day in 1.9%. Conclusion: A high proportion of people with incident bullous pemphigoid are treated with oral prednisolone in UK primary care. Action is required by primary and second care services to encourage use of steroid-sparing alternatives and, where switching is not possible, ensure prophylactic treatments and proactive monitoring of potential side effects are in place

Common protocol for validation of the QCOVID algorithm across the four UK nations

Introduction The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm. Methods and analysis We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell’s C, Brier Score, R2 and Royston’s D. Ethics and dissemination Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal

The demand for sports and exercise: Results from an illustrative survey

Funding from the Department of Health policy research programme was used in this study.There is a paucity of empirical evidence on the extent to which price and perceived benefits affect the level of participation in sports and exercise. Using an illustrative sample of 60 adults at Brunel University, West London, we investigate the determinants of demand for sports and exercise. The data were collected through face-to-face interviews that covered indicators of sports and exercise behaviour; money/time price and perceived benefits of participation; and socio- economic/demographic details. Count, linear and probit regression models were fitted as appropriate. Seventy eight per cent of the sample participated in sports and exercise and spent an average of £27 per month and an average of 20 min travelling per occasion of sports and exercise. The demand for sport and exercise was negatively associated with time (travel or access time) and ‘variable’ price and positively correlated with ‘fixed’ price. Demand was price inelastic, except in the case of meeting the UK government’s recommended level of participation, which is time price elastic (elasticity = −2.2). The implications of data from a larger nationally representative sample as well as the role of economic incentives in influencing uptake of sports and exercise are discussed.This article is available through the Brunel Open Access Publishing Fund

Hormone replacement therapy and cancer mortality in women with site specific cancers : A cohort study using linked medical records.

Acknowledgements We would like to acknowledge the support of the eDRIS team (Public Health Scotland) for their involvement in obtaining approvals, provisioning and linking data and the secure analytical platform within the National Safe Haven. We would also like to acknowledge support of SAIL Databank for facilitating access to the dataset from Wales. We acknowledge the contribution of EMIS practices who contribute to the QResearch database and the Chancellor, Masters and Scholars of the University of Oxford for continuing to develop and support the QResearch database. The Hospital Episode Statistics data used in the English portion of this analysis are re438 used by permission from NHS Digital who retain the copyright. We thank the Office for National Statistics (ONS) for providing the mortality data for the English analyses. The ONS bears no responsibility for the analysis or interpretation of the data. The authors would also like to thank the PPI representatives for providing a patient and public perspective on the study design, findings, interpretation of the study and lay summary materials.Peer reviewe

Dynamics & Predictions in the Co-Event Interpretation

Sorkin has introduced a new, observer independent, interpretation of quantum mechanics that can give a successful realist account of the 'quantum microworld' as well as explaining how classicality emerges at the level of observable events for a range of systems including single time 'Copenhagen measurements'. This 'co-event interpretation' presents us with a new ontology, in which a single 'co-event' is real. A new ontology necessitates a review of the dynamical & predictive mechanism of a theory, and in this paper we begin the process by exploring means of expressing the dynamical and predictive content of histories theories in terms of co-events.Comment: 35 pages. Revised after refereein

External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults:national validation cohort study in Scotland

Funding Medical Research Council (MR/R008345/1), National Institute for Health Research Health Technology Assessment Programme, funded through the UK Research and Innovation Industrial Strategy Challenge Fund Health Data Research UK.Background : The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. Methods : We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. Results : Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell’s C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell’s C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. Conclusions : Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively.Publisher PDFPeer reviewe

Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R ]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP] , LLP , Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO] , PLCO , Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R in both sexes in the QResearch validation cohort and 59% of the R in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP and PLCO ), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. Innovate UK (UK Research and Innovation). For the Chinese translation of the abstract see Supplementary Materials section. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.