NIHRDataInsights/HRCSData huggingface dataset:
Title: The development of a nature-on-prescription group intervention for people with common mental health conditions
Funder: Medical Research Council

Abstract: Mental health conditions, such as depression, anxiety and stress, are common. There is increasing interest in "social prescriptions" for people with, or at risk of, these conditions. One such offer is a nature-based intervention. Many of these exist and there is tentative evidence of effectiveness. Their content, delivery and approach varies widely. This project will use a structured approach to developing a complex intervention, to design a nature-based intervention for people with, or at risk of, common mental health conditions. In addition to the three core MRC phases of: (a) identifying existing evidence; (b) identifying or developing a theory and (c) modelling process and outcomes; four other phases will also to be incorporated: (d) problem identification and definition; (e) determining of recipients' and providers' needs; (f) examination of current practice and context and (g) intervention design. Three interlinked and iterative activities will inform phases (a) to (f): (i) Evidence syntheses (addressing phases a, b, c, e and f.) (ii) Stakeholder consultation: Initial consultation meetings with three core stakeholder groups: people with common mental health conditions and their families, including those who have previously taken part in nature-based activities, people from primary care, and those involved in delivering nature-based activities (phases b, c, d, f). iii) Formal focus groups with each of the stakeholder groups will allow better understanding of current practice, perceived impacts and the needs of those with mental health conditions, primary care practitioners, and those who deliver nature-based activities (phases c, e, f). A key output will be the detailed programme theory for the intervention. A consultation workshop with stakeholder groups will allow its refinement. We will produce an intervention manual (phase g) showing approaches to activity design; and a protocol for use in a future feasibility study.


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: MENTAL HEALTH
Title: Supervised Treatment in Out-Patients for Schizophrenia (STOPS+)
Funder: Medical Research Council

Abstract: In developing countries but about two third of patients suffering from schizophrenia receive no treatment. This lack of treatment, commonly known as treatment gap arises mainly due to poor treatment adherence, lack of primary care involvement and poor access to the treatment. We previously reported an intervention called Supervised Treatment in Out-Patients for Schizophrenia (STOPS) that led to significantly improved treatment adherence. We now aim to implement and evaluate scaled up version of STOPS, called STOPS+ ; which will consist of following components: treatment for schizophrenia based on WHO mhGAP guidelines, maintaining treatment adherence with the help of a trained family member for dispensing and administering medication, monitoring the availability of essential psychotropic medication and task shifting to PHC Physician and Multipurpose PHC Technician (MT) for treatment and monitoring. The research plan includes three phases. 1. PRE-IMPLEMENTATION PHASE involving community engagement and a qualitative study 2. IMPLEMENTATION OF STOPS+ IN PRIMARY CARE SETTINGS USING A CLUSTER RCT DESIGN 24 PHC centres in district Peshawar, KP will be randomised to STOPS+ or Enhanced Treatment As Usual (ETAU). We will recruit 526 patients (263 in each arm) suffering from Schizophrenia or schizoaffective disorder based on the International Classification of Diseases (ICD)-10 criteria. The outcomes will be: (i) primary clinical outcome-Global Assessment of Functioning (GAF) (ii) primary process outcome - adherence to treatment regimen. 3. EVALUATION OF THE IMPLEMENTATION IN REAL WORLD SETTING will be done using the standardised World Health Organization's Assessment Instrument for Mental Health Systems (WHO-AIMS), economic evaluation, secondary data from the trial and utilisation of PHC, and in depth interview with study participants.


Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL
Health categories: MENTAL HEALTH
Title: Linking electronic health records with passive smartphone activity data to predict outcomes in psychotic disorders
Funder: Medical Research Council

Abstract: It is currently not possible to predict risk of relapse at individual patient level following a first episode of psychosis (FEP). A more refined characterisation of clinical phenotype could enable better prediction of clinical outcomes than current diagnostic classification systems. Electronic health records (EHRs) provide data on psychosis symptom profiles (positive; negative; disorganisation; mania; depression), risk factors and clinical outcomes. However, EHR data are typically only recorded during periods of contact with a clinician. Sensors built into smartphones continuously measure objective markers of activity and social interaction which correlate with changes in behaviour and mental state associated with relapse. Combining EHR and smartphone sensor data could help to better characterise clinical phenotype and predict outcomes in individual patients. During my fellowship, I aim to investigate the relationship between EHR symptom profile data obtained using natural language processing (NLP) and clinical outcomes in people with FEP. I will collect passive smartphone activity and social interaction data in people with FEP and analyse their relationship with EHR symptom profile data. I will then develop and evaluate tools to predict outcomes at individual patient level by combining passive smartphone data and EHR symptom profile data using regression models and supervised machine learning.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: MENTAL HEALTH
Title: Social contagion? : using data science to characterise the distribution and dispersion of health behaviours in adolescence
Funder: Medical Research Council

Abstract: The overarching aim of this work is to appraise and develop approaches for examining the distribution and dispersion of health behaviours captured in electronic health records. Self-harm behaviours associated with high-volume healthcare usage (intentional self-injury, substance misuse and violence) in secondary schools will be used as an exemplar. Three phases of work are planned. Phase 1 focuses on developing clinical phenotypes using data science methods to decipher the complexity of electronic health records. Phase 2 will appraise and apply methods for characterising the distribution and dispersion of clinical phenotypes in relation to the wider exposome, using linked health and education data. Phase 3 explores the potential for linked data to be used as a platform for enhancing the efficiency of randomised controlled trials of individually or cluster-randomised (at school or hospital-level) interventions. Data science methods will support the proposed work, in particular, natural language processing will support free text mining (e.g. free text recording of reason for A&E attendance) to enhance the development of clinical phenotypes for self-harm. Machine learning methods (e.g. random forest) will be explored as a means of classifying clinical subtypes and for investigating new approaches to characterising the interplay between the clinical phenome and exposome. Linked health and education data will be investigated as a platform for randomised controlled trials, and for nesting data capture with passive or sensing technology. Collectively the proposed work will establish tools for early phase research using linked health and education data, and provide new knowledge and insight into the dispersion of self-harm health behaviours in schools.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: MENTAL HEALTH
Title: Cognitive Stimulation Therapy (CST) for dementia: International implementation in Brazil, India and Tanzania (CST-International)
Funder: Medical Research Council

Abstract: Despite huge financial costs and impact on quality of life, dementia is largely unrecognised and untreated in low and middle income countries and there is an urgent need for the implementation of simple interventions. Cognitive Stimulation Therapy (CST) is an evidence-based, group non-pharmacological intervention which can significantly improve cognition and quality of life in dementia. It is low cost, does not require specialist equipment and can be provided by community workers. This study aims to create ongoing and sustainable CST implementation programmes in an upper middle (Brazil), lower middle (India) and low income (Tanzania) country. All have a) translated and adapted the CST manual, following a standardised process, b) begun or completed feasibility or pilot work with positive results and c) engaged local stakeholders and gathered initial data on implementation. A secondary objective is to increase awareness and skills in the detection and management of dementia, both for health workers and families. In each country, we propose to: (i) Investigate the likely barriers and facilitators of implementation using meetings and qualitative interviews with stakeholders, including clinicians, policy makers, academics and people with dementia and their families; (ii) Develop an implementation strategy, with generic themes and unique considerations for each setting; (iii) Test this strategy through collecting data on 150 people with dementia (50 in each country) attending 14 sessions of group CST. We will examine feasibility (adherence, attendance, acceptability and attrition), agreed parameters of success (numbers of trained facilitators, numbers of groups run), outcomes (cognition, quality of life, activities of daily living) and costs / affordability of models; (iv) Refine and disseminate implementation strategies, enabling ongoing pathways to practice to include published training manuals, a sustainable CST training programme and changes in policy.


Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL
Health categories: MENTAL HEALTH
Title: Optimising implementation strategies of the scale-up of a primary care psychological intervention: The Friendship Bench
Funder: Medical Research Council

Abstract: A RCT showed that the Friendship Bench (FB) is the first effective psychological treatment programme in primary care to be tested so far in Sub-Saharan Africa. The FB was scaled up to 72 clinics in and around Harare about one year ago. This study aims to evaluate the scaling-up so far and to identify the factors that have influenced the outcome of the implementation of the FB with a view to design an enhanced facilitation intervention to improve the implementation in clinics that have not performed so well. We will use well-known implementation science frameworks to guide our work. We will start with an evaluation of what has been achieved so far using all the data that has been routinely collected during the scale-up effort until now. This will be supplemented with additional data captured through a survey and other qualitative methods. The results will allow us to evaluate RE-AIM outcomes and classify clinics according to their implementation outcomes. Subsequently we will conduct a workshop with the main stakeholders to discuss and agree on the CFIR framework to use for the second phase of exploration. The main aim of the second phase is to identify key or core variables deemed to be key predictors of the successful implementation of the FB. In order to do this we will focus on comparing low and high performing clinics to identify those factors that might explain implementation outcomes. During the second phase, most research will be of a qualitative nature, except for small surveys and the collection of additional economic data. At the end of this phase, we will invite main stakeholders again to review our results and conclusions and to help us identify the components of an enhanced facilitation intervention that we will test in those clinics whose implementation achievements fall into the lowest quartile. We will conduct a hybrid type 3 study to ascertain if implementation outcomes improve four months after this facilitation takes place.


Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL
Health categories: MENTAL HEALTH
Title: Finding psychosis subtypes using machine learning, clinical, genetic and multimodal imaging data
Funder: Medical Research Council

Abstract: It has recently been shown that psychotic illnesses cluster into three neurobiologically distinct 'biotypes' that cut across existing diagnostic boundaries (Clementz et al, 2016, Am J Psych). This clustering was performed on eye movement, structural MRI and raw EEG sensor data, yet schizophrenia is best defined as a disorder of functional connectivity between brain regions, i.e. how they interact with each other. One would therefore predict that using functional connectivity data in clustering analysis would yield much clearer and more biologically-interpretable subgroups (as has recently been shown in major depression, by Drysdale et al, 2017, Nat Med). In my previous work I have used computational models of how the brain generates EEG data to infer the underlying connectivity between brain areas, and shown that subjects with schizophrenia have abnormal synaptic connectivity in prefrontal and sensory cortex - the same locations of abnormalities in the three proposed biotypes. I now have access to two much larger datasets (each of n>1000) containing 'resting state' fMRI and both resting state and task-based EEG data in subjects with schizophrenia, bipolar disorder, their relatives and healthy controls. Both also contain many clinical (e.g. cognitive and symptom) measures and GWAS data (imputed to 2x10^7 SNP per person). I will perform connectivity analyses of the fMRI and EEG studies in these large datasets, and then use the connectivity parameters and clinical data to inform clustering analysis. I will use the first dataset to derive the clusters, and then the second dataset to validate these clusters. I will then analyse the clusters' relationships to clinical and genetic data: in particular, testing whether clinical data accentuates or alters the neurobiological cluster boundaries, and whether any cluster has i) an enriched schizophrenic polygenic risk score, and ii) further enrichment of NMDA receptor-related genetic risk variants.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: MENTAL HEALTH
Title: Towards personalised medicine in psychiatric genetics: the role of cardio-metabolic traits in severe mental illness.
Funder: Medical Research Council

Abstract: The phenotypic association between severe mental illness and cardio-metabolic traits is bidirectional. Recent advances in genome-wide association studies (GWAS) have shown that common genetic loci play an important role in the aetiology of these complex traits. A growing body of evidence suggest that common loci associated with complex traits act through effects on gene regulation, such as epigenetic effects. The aim of the proposed research is to investigate the associations between severe mental illness and cardio-metabolic traits using a prediction modelling approach incorporating phenotypic, and (epi)genetic data. This will be achieved by considering the following aims: 1. Use a prediction modelling approach to examine which cardio-metabolic traits best predict clinical outcomes, prognosis and treatment response in severe mental illness. 2. I will use GWAS and methylation data to examine the shared genetic architecture between severe mental illness and cardio-metabolic traits. Following this I will use Mendelian randomization to examine the direction of effect. 3. I will utilise the information from objective 1 & 2 to combine polygenic risk scores, environmental factors and blood biomarkers for cardio-metabolic traits to predict clinical outcomes, prognosis and treatment response in severe mental illness. In all three objectives I will use data from population based cohorts such as UK Biobank, GS and ELSA, clinical disease ascertained samples and the Psychiatric Genomics Consortium, as well as data from electronic health records both here at Kings and at the University of Bristol. These objectives move forward precision medicine by combining genetic and environmental risk factors and blood biomarkers to detect those at high risk for cardio-metabolic traits early in the course of severe mental illness and would inform the choice of medication or preventive strategies, thereby reducing the burden of cardio-metabolic traits in severe mental illness.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH
Title: Identifying patient-specific brain dynamics markers in Post-Traumatic Stress Disorder and Cognitive Treatment Response.
Funder: Medical Research Council

Abstract: At present, treatment stratification for anxiety disorders remains dependent on psychometric measures and patient classification systems. Effective treatments are available including antidepressants and cognitive behavioural therapy (CBT). However, the underlying neurological processes of these disorders are not well enough understood to define adequate markers for prognosis and treatment response. This fellowship will focus on post-traumatic stress disorder (PTSD). PTSD has clear diagnostic criteria and is associated with alterations in brain electrical activity as measured by electroencephalogram (EEG) such as inter-hemispheric asymmetries (e.g. EEG alpha-rhythm asymmetry) that may relate to CBT response. Although it is not currently leveraged in practice in UK clinics identifying correlations between EEG features and psychological attributes in the context of standard clinical assessment and treatment could lead to novel approaches to clinical screening and interventions. During this fellowship, I will gain knowledge and experience of PTSD diagnosis and treatment from a clinical perspective. I will develop skills in routine and advanced EEG data analysis techniques, including topographic EEG-microstate analysis. This will allow me to elucidate objective indicators in brain electrical activity as measured by EEG for PTSD and CBTs. I will use these indicators to identify dynamic markers for prognosis and treatment response in PTSD. In order to identify patient-specific markers, I will develop large-scale brain network models at different levels of abstraction. These models will combine connectivity structures inferred from resting state EEG with mathematical descriptions of the dynamics of individual brain regions (e.g. phenomenological or neural mass). I will combine results of the modelling framework with known neurophysiological results to classify the data and enable patient-specific prediction of PTSD and treatment success.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH
Title: Firearm access and risk of death by suicide: a population-wide data linkage study
Funder: MQ: Transforming Mental Health

Abstract: Background Death by suicide is a major public mental health concern, especially in Northern Ireland (NI) which has the highest suicide rate in the UK. Restricting access to means is a key principle in effective suicide prevention. In the US there is clear evidence that access to a firearm increases risk of suicide for firearm owners and their families but few studies exist in the with UK. The Police Service of Northern Ireland (PSNI) hold a detailed register of all Firearms Certificate (FAC) holders which can be linked to other administrative data sources to identify those who have access to a firearm, their mental health and suicide risk. Aims This will be the first ever population-wide study to use linked routine administrative data to assess the mental health status and suicide risk of individuals with access to a firearm in NI; specifically: (i) To determine the mental health profile of FAC holders and other household members? (ii) To determine if access to a firearm increases risk of death due to a firearm related suicide? Methods FAC data will be linked to 2011 Census returns, prescribed medication data and death records, facilitated by the Administrative Data Research Centre-Northern Ireland (ADRC-NI) who will provide a de-identified research dataset within the ADRC-NI secure environment. Cox proportional hazards models will determine likelihood of firearm related suicide in individuals with access to a firearm. Due to the rarity of the outcome, Firth’s penalized maximum likelihood bias reduction method for Cox regression will also be implemented.


Research activity codes: 2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: MENTAL HEALTH
Title: Electroconvulsive therapy for treatment-resistant schizophrenia
Funder: Department of Health and Social Care

Abstract: Schizophrenia is a mental illness affecting one in a hundred people. Electroconvulsive therapy (ECT) involves induction of a seizure by administration of an electrical stimulus. It is a treatment with negative connotations and concerns about memory loss1. ECT endures, however, because those connotations are not merited in modern delivery, memory loss is no more than with mild anaesthetics, it is accessible globally, and, the evidence that it does work when other treatments do not. The American Psychiatric Association recommends its use2, the UK’s Royal College of Psychiatrist recommends it for treatment-resistant patients3, and NICE endorse its use in severe depressive illness, mania, catatonia or post-partum psychosis4. The previous high-impact Cochrane review of ECT for schizophrenia6 is outdated. The evidence for ECT’s effects for people whose schizophrenia has been treatment-resistant was not a focus of this first review and guidelines have, understandably, moved past the less useful Cochrane evidence. It is a priority that Cochrane mirrors the needs of those producing guidelines for modern care and not to have an up-to-date review focusing on this inexpensive, accessible and recommended treatment for this highly expensive group of people with treatment resistant schizophrenia is an omission that urgently needs addressed. References: 1. Rose D, Fleischmann P, Wykes T, Leese M, Bindman J. Patients' perspectives on electroconvulsive therapy: systematic review. BMJ2003;326(7403):1363. [PUBMED: 12816822] 2. American Psychiatric Association. The Practice of Electroconvulsive Therapy: Recommendations for Treatment, Training, and Privileging (A Task Force Report of the American Psychiatric Association). Arlington, VA: American Psychiatric Association Publishing, 2008. 3. Scott A. The ECT Handbook: The Third Report of the Royal College of Psychiatrists' Special Committee of ECT. London: RCPsych Publications, 2005. 4. National Institute for Health and Care Excellence. Guidance on the use of electroconvulsive therapy. NICE technology appraisal guidance [TA59]. Published date: April 2003. Available from https://www.nice.org.uk/guidance/ta59. 5. Lloyd K, White J. Democratizing clinical research. Nature2011;474(7351):277-8. 6. Tharyan P, Adams CE. Electroconvulsive therapy for schizophrenia. Cochrane Database of Systematic Reviews 2005, Issue 2. [DOI: 10.1002/14651858.CD000076.pub2; PUBMED: 15846598]


Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL
Health categories: MENTAL HEALTH