The Camden & Islington Research Database: Using electronic mental health records for research

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Investigating social deprivation and comorbid mental health diagnosis as predictors of treatment access among patients with an opioid use disorder using substance use services: a prospective cohort study

BACKGROUND

Opioid use is a major public health concern across the globe. Opioid use and subsequent access to care is often shaped by co-occurring issues faced by people using opioids, such as deprivation, mental ill-health, and other forms of substance use. We investigated the role of social deprivation and comorbid mental health diagnoses in predicting re-engagement with substance use services or contact with crisis and inpatient services for individuals with opioid use disorder in secondary mental health care in inner-city London.

METHODS

We conducted a prospective cohort study which followed individuals diagnosed with a first episode of opioid use disorder who accessed substance use services between September 2015 and May 2020 for up to 12 months, using anonymised electronic health records. We employed negative binominal regression and Cox proportional survival analyses to assess associations between exposures and outcomes.

RESULTS

Comorbid mental health diagnoses were associated with higher contact rates with crisis/inpatient services among people with opioid use disorder: incidence rate ratios (IRR) and 95% confidence intervals (CI) were 3.91 (1.74-9.14) for non-opioid substance use comorbidity, 8.92 (1.81-64.4) for a single comorbid mental health diagnosis, and 15.9 (5.89-47.5) for multiple comorbid mental health diagnoses. Social deprivation was not associated with contact rates with crisis/inpatient services within this sample. Similar patterns were found with time to first crisis/inpatient contact. Social deprivation and comorbid mental health diagnoses were not associated with re-engagement with substance use services.

CONCLUSION

Comorbid substance and mental health difficulties amongst people with an opioid use disorder led to earlier and more frequent contact with crisis/inpatient mental health services during the first 12 months of follow up. Given the common co-occurrence of mental health and substance use disorders among those who use opioids, a better understanding of their wider needs (such as social, financial and other non-medical concerns) will ensure they are supported in their treatment journeys.

Establishment of a Public Mental Health Database for Research Purposes in the Ferrara Province: Development and Preliminary Evaluation Study

Background

The immediate use of data exported from electronic health records (EHRs) for research is often limited by the necessity to transform data elements into an actual data set.

Objective

This paper describes the methodology for establishing a data set that originated from an EHR registry that included clinical, health service, and sociodemographic information.

Methods

The Extract, Transform, Load process was applied to raw data collected at the Integrated Department of Mental Health and Pathological Addictions in Ferrara, Italy, from 1925 to February 18, 2021, to build the new, anonymized Ferrara-Psychiatry (FEPSY) database. Information collected before the first EHR was implemented (ie, in 1991) was excluded. An unsupervised cluster analysis was performed to identify patient subgroups to support the proof of concept.

Results

The FEPSY database included 3,861,432 records on 46,222 patients. Since 1991, each year, a median of 1404 (IQR 1117.5-1757.7) patients had newly accessed care, and a median of 7300 (IQR 6109.5-9397.5) patients were actively receiving care. Among 38,022 patients with a mental disorder, 2 clusters were identified; the first predominantly included male patients who were aged 25 to 34 years at first presentation and were living with their parents, and the second predominantly included female patients who were aged 35 to 44 years and were living with their own families.

Conclusions

The process for building the FEPSY database proved to be robust and replicable with similar health care data, even when they were not originally conceived for research purposes. The FEPSY database will enable future in-depth analyses regarding the epidemiology and social determinants of mental disorders, access to mental health care, and resource utilization.

Antipsychotic polypharmacy and adverse drug reactions among adults in a London mental health service, 2008-2018

BACKGROUND

Antipsychotic polypharmacy (APP) occurs commonly but it is unclear whether it is associated with an increased risk of adverse drug reactions (ADRs). Electronic health records (EHRs) offer an opportunity to examine APP using real-world data. In this study, we use EHR data to identify periods when patients were prescribed 2 + antipsychotics and compare these with periods of antipsychotic monotherapy. To determine the relationship between APP and subsequent instances of ADRs: QT interval prolongation, hyperprolactinaemia, and increased body weight [body mass index (BMI) ⩾ 25].

METHODS

We extracted anonymised EHR data. Patients aged 16 + receiving antipsychotic medication at Camden & Islington NHS Foundation Trust between 1 January 2008 and 31 December 2018 were included. Multilevel mixed-effects logistic regression models were used to elucidate the relationship between APP and the subsequent presence of QT interval prolongation, hyperprolactinaemia, and/or increased BMI following a period of APP within 7, 30, or 180 days respectively.

RESULTS

We identified 35 409 observations of antipsychotic prescribing among 13 391 patients. Compared with antipsychotic monotherapy, APP was associated with a subsequent increased risk of hyperprolactinaemia (adjusted odds ratio 2.46; 95% CI 1.87-3.24) and of registering a BMI > 25 (adjusted odds ratio 1.75; 95% CI 1.33-2.31) in the period following the APP prescribing.

CONCLUSIONS

Our observations suggest that APP should be carefully managed with attention to hyperprolactinaemia and obesity.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Suicidality in patients with post-traumatic stress disorder and its association with receipt of specific secondary mental healthcare treatments

BACKGROUND

Post-traumatic stress disorder (PTSD) is a risk factor for suicidality (suicidal ideation, and suicide attempt). This study described the prevalence of suicidality amongst a representative sample of individuals with PTSD and the association between suicidality and receipt of five PTSD treatments.

METHODS

We analysed deidentified data for patients being treated for PTSD at Camden and Islington NHS Foundation Trust between 2009 and 2017 obtained via the Clinical Record Interactive Search tool. We described the sample's sociodemographic and clinical characteristics and used stepwise logistic regression to investigate the association between suicidality and receipt of four, specific PTSD treatments: psychotherapy, antidepressant/antianxiety medication, antipsychotics, benzodiazepines. We used Cox proportional hazards regression to investigate the association between suicidality and hospital/crisis team admission.

RESULTS

Of 745 patients diagnosed with PTSD, 60% received psychotherapy and 66% received psychotropic medication. Those who reported suicidality (6%) were no more likely than those who did not to be prescribed antidepressant/antianxiety medication, but were more likely to receive antipsychotics (AOR = 2.27, 95% CI 1.15 - 4.47), benzodiazepines (AOR 2.28, 95% CI 1.17 - 4.44), psychotherapy (AOR 2.60, 95% CI 1.18 - 5.73) and to be admitted to hospital/crisis team (AOR 2.84, 95% 1.82 - 4.45).

CONCLUSION

In this sample, patients with PTSD and suicidality were more likely to receive psychiatric medication, psychotherapy and psychiatric admission than those who were not suicidal. Overall patients were more likely to receive psychotropic medication than psychotherapy. Adherence to clinical guidelines is important in this population to improve treatment outcomes and reduce the risk of suicide.KEY POINTSNICE guidelines recommend psychological therapy be first line treatment for PTSD, yet we identified that fewer people diagnosed with PTSD received therapy compared to psychotropic medication.Patients with suicidality were more likely to receive antipsychotics and benzodiazepines, yet not antidepressant/antianxiety medication although given that suicidality is characteristic of severe depression, it might be assumed from stepped care models that antidepressant/antianxiety medication be prescribed before antipsychotics.The high proportion of patients prescribed antipsychotics suggests a need for better understanding of psychosis symptoms among trauma-exposed populations.Identifying which combinations of symptoms are associated with suicidal thoughts could help tailor trauma-informed approaches to discussing therapy and medication.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

PURPOSE

NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.

PARTICIPANTS

As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).

FINDINGS TO DATE

The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.

FUTURE PLANS

The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods

BACKGROUND

Identifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.

METHODS

Clinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.

RESULTS

A total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.

CONCLUSIONS

We developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.

Effect of trazodone on cognitive decline in people with dementia: Cohort study using UK routinely collected data

OBJECTIVES

Evidence in mouse models has found that the antidepressant trazodone may be protective against neurodegeneration. We therefore aimed to compare cognitive decline of people with dementia taking trazodone with those taking other antidepressants.

METHODS

Three identical naturalistic cohort studies using UK clinical registers. We included all people with dementia assessed during 2008-16 who were recorded taking trazodone, citalopram or mirtazapine for at least 6 weeks. Linear mixed models examined age, time and sex-adjusted Mini-mental state examination (MMSE) change in people with all-cause dementia taking trazodone compared with those taking citalopram and mirtazapine. In secondary analyses, we examined those with non-vascular dementia; mild dementia; and adjusted results for neuropsychiatric symptoms. We combined results from the three study sites using random-effects meta-analysis.

RESULTS

We included 2,199 people with dementia, including 406 taking trazodone, with mean 2.2 years follow-up. There was no difference in adjusted cognitive decline in people with all-cause or non-vascular dementia taking trazodone, citalopram or mirtazapine in any of the three study sites. When data from the three sites were combined in meta-analysis, we found greater mean MMSE decline in people with all-cause dementia taking trazodone compared to those taking citalopram (0·26 points per successive MMSE measurement, 95% CI 0·03-0·49; p = 0·03). Results in sensitivity analyses were consistent with primary analyses.

CONCLUSIONS

There was no evidence of cognitive benefit from trazodone compared to other antidepressants in people with dementia in three naturalistic cohort studies. Despite preclinical evidence, trazodone should not be advocated for cognition in dementia.

Childhood sexual abuse in patients with severe mental Illness: Demographic, clinical and functional correlates

OBJECTIVE

To use data from electronic health records (EHRs) to describe the demographic, clinical and functional correlates of childhood sexual abuse (CSA) in patients with severe mental illness (SMI), and compare their clinical outcomes (admissions and receipt of antipsychotic medications) to those of patients with no recorded history of CSA.

METHODS

We applied a string-matching technique to clinical text records of 7000 patients with SMI (non-organic psychotic disorders or bipolar disorder), identifying 619 (8.8%) patients with a recorded history of CSA. Data were extracted from both free-text and structured fields of patients' EHRs.

RESULTS

Comorbid diagnoses of major depressive disorder, post-traumatic stress disorder and personality disorders were more prevalent in patients with CSA. Positive psychotic symptoms, depressed mood, self-harm, substance use and aggression were also more prevalent in this group, as were problems with relationships and living conditions. The odds of inpatient admissions were higher in patients with CSA than in those without (adjusted OR = 1.95, 95% CI: 1.64-2.33), and they were more likely to have spent more than 10 days per year as inpatients (adjusted OR = 1.32, 95% CI: 1.07-1.62). Patients with CSA were more likely to be prescribed antipsychotic medications (adjusted OR = 2.48, 95% CI: 1.69-3.66) and be given over 75% of the maximum recommended daily dose (adjusted OR = 1.72, 95% CI: 1.44-2.04).

CONCLUSION

Data-driven approaches are a reliable, promising avenue for research on childhood trauma. Clinicians should be trained and skilled at identifying childhood adversity in patients with SMI, and addressing it as part of the care plan.

The side effect profile of Clozapine in real world data of three large mental health hospitals

OBJECTIVE

Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects.

MATERIAL AND METHODS

We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where possible, we compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER).

RESULTS

Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) our chi-square tests show a significant association between most of the ADRs and smoking status and hospital admission, and some in gender, ethnicity and age groups in all trusts hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs.

CONCLUSION

A better understanding of how drugs work in the real world can complement clinical trials.

Risk factors for in-patient admission among adults with intellectual disability and autism: investigation of electronic clinical records

Adults with intellectual disability or autism are at risk of psychiatric admission which carries personal, social and economic costs. We identified 654 adults with intellectual disability or autism in the electronic clinical records of one mental health trust. We investigated the demographic and clinical factors associated with admission and readmission after discharge. Young male patients with intellectual disability, schizophrenia and previous admissions are most at risk of the former, whereas affective and personality disorders predict the latter. Both community intellectual disability services and mental health crisis care must focus on providing effective support for those patients.

Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System

Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person's risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient's care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. We envision increased progress in the decades to come, particularly in externally validating findings across multiple sites and countries, both in terms of clinical evidence and in terms of NLP and machine learning method transferability.

Real-World Clinical Outcomes Two Years After Transition to Psychosis in Individuals at Clinical High Risk: Electronic Health Record Cohort Study

The objective of this study is to describe the 2-year real-world clinical outcomes after transition to psychosis in patients at clinical high-risk. The study used the clinical electronic health record cohort study including all patients receiving a first index primary diagnosis of nonorganic International Classification of Diseases (ICD)-10 psychotic disorder within the early psychosis pathway in the South London and Maudsley (SLaM) National Health Service (NHS) Trust from 2001 to 2017. Outcomes encompassed: cumulative probability (at 3, 6, 12, and 24 months) of receiving a first (1) treatment with antipsychotic, (2) informal admission, (3) compulsory admission, and (4) treatment with clozapine and (5) numbers of days spent in hospital (at 12 and 24 months) in patients transitioning to psychosis from clinical high-risk services (Outreach and Support in south London; OASIS) compared to other first-episode groups. Analyses included logistic and 0-inflated negative binomial regressions. In the study, 1561 patients were included; those who had initially been managed by OASIS and had subsequently transitioned to a first episode of psychosis (n = 130) were more likely to receive antipsychotic medication (at 3, 6, and 24 months; all P < .023), to be admitted informally (at all timepoints, all P < .004) and on a compulsory basis (at all timepoints, all P < .013), and to have spent more time in hospital (all timepoints, all P < .007) than first-episode patients who were already psychotic when seen by the OASIS service (n = 310), or presented to early intervention services (n = 1121). The likelihood of receiving clozapine was similar across all groups (at 12/24 months, all P < .101). Transition to psychosis from a clinical high-risk state is associated with severe real-world clinical outcomes. Prevention of transition to psychosis should remain a core target of future research. The study protocol was registered on www.researchregistry.com; researchregistry5039).

Using de-identified electronic health records to research mental health supported housing services: A feasibility study

BACKGROUND

Mental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical studies of their effectiveness due to the logistic challenges and costs of standard research methods. The Clinical Record Interactive Search (CRIS) tool, developed to de-identify and interrogate routinely recorded electronic health records, may provide an alternative to evaluate supported housing services.

METHODS

The feasibility of using the Camden and Islington NHS Foundation Trust CRIS database to identify a sample of users of mental health supported accommodation services. Two approaches to data interrogation and case identification were compared; using structured fields indicating individual's accommodation status, and iterative development of free text searches of clinical notes referencing supported housing. The data used were recorded over a 10-year-period (01-January-2008 to 31-December-2017).

RESULTS

Both approaches were carried out by one full-time researcher over four weeks (150 hours). Two structured fields indicating accommodation status were found, 2,140 individuals had a value in at least one of the fields representative of supported accommodation. The free text search of clinical notes returned 21,103 records pertaining to 1,105 individuals. A manual review of 10% of the notes indicated an estimated 733 of these individuals had used a supported housing service, a positive predictive value of 66.4%. Over two-thirds of the individuals returned in the free text search (768/1,105, 69.5%) were identified via the structured fields approach. Although the estimated positive predictive value was relatively high, a substantial proportion of the individuals appearing only in the free text search (337/1,105, 30.5%) are likely to be false positives.

CONCLUSIONS

It is feasible and requires minimal resources to use de-identified electronic health record search tools to identify large samples of users of mental health supported housing using structured and free text fields. Further work is needed to establish the availability and completion of variables relevant to specific clinical research questions in order to fully assess the utility of electronic health records in evaluating the effectiveness of these services.

Characteristics and risk of repeat suicidal ideation and self-harm in patients who present to emergency departments with suicidal ideation or self-harm: A prospective cohort study

BACKGROUND

Characteristics and outcomes of patients presenting to Emergency Departments (EDs) have been under-examined. This paper describes the characteristics and risk of repeat suicidality amongst patients presenting to EDs with (1) suicidal ideation and (2) self-harm, compared to (3) controls in mental health crisis.

METHODS

The Clinical Record Interactive Search tool identified 2211 patients who presented to three London EDs with suicidal ideation or self-harm, and 1108 control patients. All patients received a full psychosocial assessment. Chi-squared tests examined group characteristics. Cox regression models assessed the risk of re-presentation with suicidal ideation or self-harm within one year.

RESULTS

There were a higher proportion of females and individuals under the age of 25 in the self-harm group. Patients presenting with suicidal ideation or self-harm were more likely to be white, live in more deprived areas, and less likely to receive a psychiatric diagnosis within one year compared to controls. Risk of repeat suicidality within one year was 3-4 times higher in those with baseline suicidal ideation (adjusted HR = 3.66, 95% CI 2.44-5.48) or self-harm (HR = 3.53 95% CI 2.47-5.04) compared to controls.

LIMITATIONS

To be included patients needed to have a full psychosocial assessment. Incomplete records meant 21.4% of the sample was excluded. This will have introduced bias which might confound observed associations.

CONCLUSION

Individuals presenting with either suicidal ideation or self-harm have similar risk for re-presentation within one year. Both groups would benefit from personalised risk management plans and active follow-up to reduce the risk of repeat suicidal behaviour.

Mental health-related conversations on social media and crisis episodes: a time-series regression analysis

We aimed to investigate whether daily fluctuations in mental health-relevant Twitter posts are associated with daily fluctuations in mental health crisis episodes. We conducted a primary and replicated time-series analysis of retrospectively collected data from Twitter and two London mental healthcare providers. Daily numbers of ‘crisis episodes’ were defined as incident inpatient, home treatment team and crisis house referrals between 2010 and 2014. Higher volumes of depression and schizophrenia tweets were associated with higher numbers of same-day crisis episodes for both sites. After adjusting for temporal trends, seven-day lagged analyses showed significant positive associations on day 1, changing to negative associations by day 4 and reverting to positive associations by day 7. There was a 15% increase in crisis episodes on days with above-median schizophrenia-related Twitter posts. A temporal association was thus found between Twitter-wide mental health-related social media content and crisis episodes in mental healthcare replicated across two services. Seven-day associations are consistent with both precipitating and longer-term risk associations. Sizes of effects were large enough to have potential local and national relevance and further research is needed to evaluate how services might better anticipate times of higher risk and identify the most vulnerable groups.

Implications for nursing and healthcare research of the general data protection regulation and retrospective reviews of patients' data

BACKGROUND

The European Union's general data protection regulation (GDPR) came into effect in May 2018. It is intended to prevent the unwanted sharing of private data and it has significant implications for healthcare research. A well-established research methodology that GDPR is likely to affect is the retrospective reviewing of patients' data. This has been used widely in healthcare research and commonly involves examining patients' medical records.

AIM

To examine GDPR and its potential effects on the use of patients' data in healthcare research.

DISCUSSION

Previous misuse of patients' data has affected public confidence in healthcare research. GDPR is intended to improve the public's confidence in the handling of their data, but it may negatively impact healthcare research. Researchers who want to review patients' data will need to consider consent issues carefully. GDPR does include exceptions to the rules of consent, but there is uncertainty about this process.

CONCLUSION

If GDPR results in stricter requirements to achieve patients' consent in research, the validity of some studies may be affected. Nurse researchers and organisations may need to consider innovative ways of engaging patients in research.

IMPLICATIONS FOR PRACTICE

Research using patients' data has played an important role in shaping nursing and healthcare policy and practice. Imminent Europe-wide changes prompted by GDPR could affect how patients' data are used in research.

Cognitive neuropsychiatric analysis of an additional large Capgras delusion case series

INTRODUCTION

Although important to cognitive neuropsychiatry and theories of delusions, Capgras delusion has largely been reported in single case studies. Bell et al. [2017. Uncovering Capgras delusion using a large scale medical records database. British Journal of Psychiatry Open, 3(4), 179-185] previously deployed computational and clinical case identification on a large-scale medical records database to report a case series of 84 individuals with Capgras delusion. We replicated this approach on a new database from a different mental health service provider while additionally examining instances of violence, given previous claims that Capgras is a forensic risk.

METHODS

We identified 34 additional cases of Capgras. Delusion phenomenology, clinical characteristics, and presence of lesions detected by neuroimaging were extracted.

RESULTS

Although most cases involved misidentification of family members or partners, a notable minority (20.6%) included the misidentification of others. Capgras typically did not present as a monothematic delusion. Few cases had identifiable lesions with no evidence of right-hemisphere bias. There was no evidence of physical violence associated with Capgras.

CONCLUSIONS

Findings closely replicate Bell et al. (2017). The majority of Capgras delusion phenomenology conforms to the "dual route" model although a significant minority of cases cannot be explained by this framework.

Ethnic differences in cognition and age in people diagnosed with dementia: A study of electronic health records in two large mental healthcare providers

OBJECTIVES

Qualitative studies suggest that people from UK minority ethnic groups with dementia access health services later in the illness than white UK-born elders, but there are no large quantitative studies investigating this. We aimed to investigate interethnic differences in cognitive scores and age at dementia diagnosis.

METHODS

We used the Clinical Record Interactive Search (CRIS) applied to the electronic health records of two London mental health trusts to identify patients diagnosed with dementia between 2008 and 2016. We meta-analysed mean Mini Mental State Examination (MMSE) and mean age at the time of diagnosis across trusts for the most common ethnic groups, and used linear regression models to test these associations before and after adjustment for age, sex, index of multiple deprivation, and marital status. We also compared percentage of referrals for each ethnic group with catchment census distributions.

RESULTS

Compared with white patients (N = 9380), unadjusted mean MMSE scores were lower in Asian (-1.25; 95% CI -1.79, -0.71; N = 642) and black patients (-1.82, 95% CI -2.13, -1.52; N = 2008) as was mean age at diagnosis (Asian patients: -4.27 (-4.92, -3.61); black patients -3.70 (-4.13, -3.27) years). These differences persisted after adjustment. In general, ethnic group distributions in referrals did not differ substantially from those expected in the catchments.

CONCLUSIONS

People from black and Asian groups were younger at dementia diagnosis and had lower MMSE scores than white referrals.

Dementia severity at death: a register-based cohort study

BACKGROUND

One third of older people are estimated to die with dementia, which is a principal cause of death in developed countries. While it is assumed that people die with severe dementia this is not based on evidence.

METHODS

Cohort study using a large secondary mental healthcare database in North London, UK. We included people aged over 65 years, diagnosed with dementia between 2008 and 2016, who subsequently died. We estimated dementia severity using mini-mental state examination (MMSE) scores, adjusting for the time between last score and death using the average annual MMSE decline in the cohort (1.5 points/year). We explored the association of sociodemographic and clinical factors, including medication use, with estimated MMSE score at death using linear regression.

RESULTS

In 1400 people dying with dementia, mean estimated MMSE at death was 15.3 (standard deviation 7.0). Of the cohort, 22.2% (95% confidence interval 20.1, 24.5) died with mild dementia; 50.4% (47.8, 53.0) moderate; and 27.4% (25.1, 29.8) with severe dementia. In fully adjusted models, more severe dementia at death was observed in women, Black, Asian and other ethnic minorities, agitated individuals, and those taking antipsychotic medication.

CONCLUSIONS

Only one quarter of people who die with dementia are at the severe stage of the illness. This finding informs clinical and public understanding of dementia prognosis. Provision of end-of-life services should account for this and healthcare professionals should be aware of high rates of mild and moderate dementia at end of life and consider how this affects clinical decision-making.