Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records

Cohort profile: the eLIXIR Partnership-a maternity-child data linkage for life course research in South London, UK

PURPOSE

Linked maternity, neonatal and maternal mental health records were created to support research into the early life origins of physical and mental health, in mothers and children. The Early Life Cross Linkage in Research (eLIXIR) Partnership was developed in 2018, generating a repository of real-time, pseudonymised, structured data derived from the electronic health record systems of two acute and one Mental Health Care National Health Service (NHS) Provider in South London. We present early descriptive data for the linkage database and the robust data security and governance structures, and describe the intended expansion of the database from its original development. Additionally, we report details of the accompanying eLIXIR Research Tissue Bank of maternal and neonatal blood samples.

PARTICIPANTS

Descriptive data were generated from the eLIXIR database from 1 October 2018 to 30 June 2019. Over 17 000 electronic patient records were included.

FINDINGS TO DATE

10 207 women accessed antenatal care from the 2 NHS maternity services, with 8405 deliveries (8772 infants). This diverse, inner-city maternity service population was born in over 170 countries with an ethnic profile of 46.1% white, 19.1% black, 7.0% Asian, 4.1% mixed and 4.1% other. Of the 10 207 women, 11.6% had a clinical record in mental health services with 3.0% being treated during their pregnancy. This first data extract included 947 infants treated in the neonatal intensive care unit, of whom 19.1% were postnatal transfers from external healthcare providers.

FUTURE PLANS

Electronic health records provide potentially transformative information for life course research, integrating physical and mental health disorders and outcomes in routine clinical care. The eLIXIR database will grow by ~14 000 new maternity cases annually, in addition to providing child follow-up data. Additional datasets will supplement the current linkage from other local and national resources, including primary care and hospital inpatient data for mothers and their children.

Schizophrenia around the time of pregnancy: leveraging population-based health data and electronic health record data to fill knowledge gaps

BACKGROUND

Research in schizophrenia and pregnancy has traditionally been conducted in small samples. More recently, secondary analysis of routine healthcare data has facilitated access to data on large numbers of women with schizophrenia.

AIMS

To discuss four scientific advances using data from Canada, Denmark and the UK from population-level health registers and clinical data sources.

METHOD

Narrative review of research from these three countries to illustrate key advances in the area of schizophrenia and pregnancy.

RESULTS

Health administrative and clinical data from electronic medical records have been used to identify population-level and clinical cohorts of women with schizophrenia, and follow them longitudinally along with their children. These data have demonstrated that fertility rates in women with schizophrenia have increased over time and have enabled documentation of the course of illness in relation with pregnancy, showing the early postpartum as the time of highest risk. As a result of large sample sizes, we have been able to understand the prevalence of and risk factors for rare outcomes that would be difficult to study in clinical research. Advanced pharmaco-epidemiological methods have been used to address confounding in studies of antipsychotic medications in pregnancy, to provide data about the benefits and risks of treatment for women and their care providers.

CONCLUSIONS

Use of these data has advanced the field of research in schizophrenia and pregnancy. Future developments in use of electronic health records include access to richer data sources and use of modern technical advances such as machine learning and supporting team science.

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.

Understanding increasing rates of psychiatric hospital detentions in England: development and preliminary testing of an explanatory model

BACKGROUND

The steep rise in the rate of psychiatric hospital detentions in England is poorly understood.

AIMS

To identify explanations for the rise in detentions in England since 1983; to test their plausibility and support from evidence; to develop an explanatory model for the rise in detentions.

METHOD

Hypotheses to explain the rise in detentions were identified from previous literature and stakeholder consultation. We explored associations between national indicators for potential explanatory variables and detention rates in an ecological study. Relevant research was scoped and the plausibility of each hypothesis was rated. Finally, a logic model was developed to illustrate likely contributory factors and pathways to the increase in detentions.

RESULTS

Seventeen hypotheses related to social, service, legal and data-quality factors. Hypotheses supported by available evidence were: changes in legal approaches to patients without decision-making capacity but not actively objecting to admission; demographic changes; increasing psychiatric morbidity. Reductions in the availability or quality of community mental health services and changes in police practice may have contributed to the rise in detentions. Hypothesised factors not supported by evidence were: changes in community crisis care, compulsory community treatment and prescribing practice. Evidence was ambiguous or lacking for other explanations, including the impact of austerity measures and reductions in National Health Service in-patient bed numbers.

CONCLUSIONS

Better data are needed about the characteristics and service contexts of those detained. Our logic model highlights likely contributory factors to the rise in detentions in England, priorities for future research and potential policy targets for reducing detentions.

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper

BACKGROUND

Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product.

OBJECTIVE

This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit.

METHODS

We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text.

RESULTS

We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders.

CONCLUSIONS

By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Synthetic cannabinoid use in psychiatric patients and relationship to hospitalisation: A retrospective electronic case register study

INTRODUCTION AND OBJECTIVES

Cannabis use has been associated with psychosis and with poor outcome in patients with mental illness. Synthetic cannabinoids (SCs) have been suggested to pose an even greater risk to mental health, but the effect on clinical outcome has not been directly measured. In this study, we aimed to investigate the demographics and hospitalisation of psychiatric patients who were SC users.

METHODS

We searched the Biomedical Research Centre Clinical Record Interactive Search register for SC users and age- and sex-matched SC non-users who had been psychiatric patients under the South London and Maudsley NHS Trust. We recorded diagnosis, homelessness, cannabis use and the total number of days admitted as an inpatient to secondary and tertiary mental-health services.

RESULTS

We identified 635 SC users and 635 age- and sex-matched SC non-users. SC users were significantly more likely to be homeless (χ²=138.0; p<0.0001) and to use cannabis (χ²=257.3; p<0.0001) than SC non-users. SC users had significantly more inpatient days after their first recorded use of SCs than controls (M (SD)=85.5 (199.7) vs. 25.4 (92.32); p<0.0001). Post hoc tests revealed that SC non-users who used cannabis had fewer inpatient days than SC users (p<0.0001), and that non-users of both SC and cannabis had fewer inpatient days than SC non-using cannabis users (p=0.02).

CONCLUSIONS

SC use may lead to an increase in the number of days spent in hospital in patients with psychiatric illness. This highlights the need for clinicians to ask specifically about SC use.

Clinical factors associated with progression to dementia in people with late-life depression: a cohort study of patients in secondary care

OBJECTIVES

Depression can be a prodromal feature or a risk factor for dementia. We aimed to investigate which clinical factors in patients with late-life depression are associated with a higher risk of developing dementia and a more rapid conversion.

DESIGN

Retrospective cohort study.

SETTING

South London and Maudsley NHS Foundation Trust (SLaM) secondary mental healthcare services.

PARTICIPANTS

The SLaM Clinical Record Interactive Search was used to retrieve anonymised data on 3659 patients aged 65 years or older who had received a diagnosis of depression in mental health services and had been followed up for at least 3 months.

OUTCOME MEASURES

Predictors of development of incident dementia were investigated, including demographic factors, health status rated on the Health of the National Outcome scale for older people (HoNOS65+), depression recurrence and treatments including psychotropic drugs and cognitive behavioural therapy (CBT).

RESULTS

In total, 806 (22.0%) patients developed dementia over a mean follow-up time of 2.7 years. Significant predictors of receiving a dementia diagnosis in fully adjusted models and after accounting for multiple comparisons were older age (adjusted HR=1.04, 95% CI 1.03 to 1.06 per year difference from sample mean) and the HoNOS65+ subscale measuring cognitive problems (HR=4.72, 95% CI 3.67 to 6.06 for scores in the problematic range). Recurrent depressive disorder or past depression (HR=0.65, 95% CI 0.55 to 0.77) and the receipt of CBT (HR=0.73 95% CI 0.61 to 0.87) were associated with a lower dementia risk. Over time, hazards related to age increased and hazards related to cognitive problems decreased.

CONCLUSIONS

In older adults with depression, a higher risk of being subsequently diagnosed with dementia was predicted by higher age, new onset depression, severity of cognitive symptoms and not receiving CBT. Further exploration is needed to determine whether the latter risk factors are responsive to interventions.

Generation and evaluation of artificial mental health records for Natural Language Processing

A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task. Furthermore, we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task. We found that using this artificial data as training data can lead to classification results that are comparable to the original results. Additionally, using only a small amount of information from the original data to condition the generation of the artificial data is successful, which holds promise for reducing the risk of these artificial data retaining rare information from the original data. This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data.

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Objectives

This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field.

Methods and Results

This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals.

Discussion

Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.

Temporal information extraction from mental health records to identify duration of untreated psychosis

BACKGROUND

Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research. Natural Language Processing (NLP) is a means to addressing this problem by automatically extracting relevant information in a structured form. As a first step, it is important to identify appropriate documents, i.e., those that are likely to include the information of interest. Next, temporal information extraction methods are needed to identify time references for early psychosis symptoms. This NLP challenge requires solving three different tasks: time expression extraction, symptom extraction, and temporal "linking". In this study, we focus on the first step, using two relevant EHR datasets.

RESULTS

We applied a rule-based NLP system for time expression extraction that we had previously adapted to a corpus of mental health EHRs from patients with a diagnosis of schizophrenia (first referrals). We extended this work by applying this NLP system to a larger set of documents and patients, to identify additional texts that would be relevant for our long-term goal, and developed a new corpus from a subset of these new texts (early intervention services). Furthermore, we added normalized value annotations ("2011-05") to the annotated time expressions ("May 2011") in both corpora. The finalized corpora were used for further NLP development and evaluation, with promising results (normalization accuracy 71-86%). To highlight the specificities of our annotation task, we also applied the final adapted NLP system to a different temporally annotated clinical corpus.

CONCLUSIONS

Developing domain-specific methods is crucial to address complex NLP tasks such as symptom onset extraction and retrospective calculation of duration of a preclinical syndrome. To the best of our knowledge, this is the first clinical text resource annotated for temporal entities in the mental health domain.

Community treatment orders and associations with readmission rates and duration of psychiatric hospital admission: a controlled electronic case register study

OBJECTIVES

Limited evidence is available regarding the effect of community treatment orders (CTOs) on mortality and readmission to psychiatric hospital. We compared clinical outcomes between patients placed on CTOs to a control group of patients discharged to voluntary community mental healthcare.

DESIGN AND SETTING

An observational study using deidentified electronic health record data from inpatients receiving mental healthcare in South London using the Clinical Record Interactive Search (CRIS) system. Data from patients discharged between November 2008 and May 2014 from compulsory inpatient treatment under the Mental Health Act were analysed.

PARTICIPANTS

830 participants discharged on a CTO (mean age 40 years; 63% male) and 3659 control participants discharged without a CTO (mean age 42 years; 53% male).

OUTCOME MEASURES

The number of days spent in the community until readmission, the number of days spent in inpatient care in the 2 years prior to and the 2 years following the index admission and mortality.

RESULTS

The mean duration of a CTO was 3.2 years. Patients receiving care from forensic psychiatry services were five times more likely and patients receiving a long-acting injectable antipsychotic were twice as likely to be placed on a CTO. There was a significant association between CTO receipt and readmission in adjusted models (HR: 1.60, 95% CI 1.42 to 1.80, p<0.001). Compared with controls, patients on a CTO spent 17.3 additional days (95% CI 4.0 to 30.6, p=0.011) in a psychiatric hospital in the 2 years following index admission and had a lower mortality rate (HR: 0.66, 95% CI 0.50 to 0.88, p=0.004).

CONCLUSIONS

Many patients spent longer on CTOs than initially anticipated by policymakers. Those on CTOs are readmitted sooner, spend more time in hospital and have a lower mortality rate. These findings merit consideration in future amendments to the UK Mental Health Act.

Recorded poor insight as a predictor of service use outcomes: cohort study of patients with first-episode psychosis in a large mental healthcare database

OBJECTIVES

To investigate recorded poor insight in relation to mental health and service use outcomes in a cohort with first-episode psychosis.

DESIGN

We developed a natural language processing algorithm to ascertain statements of poor or diminished insight and tested this in a cohort of patients with first-episode psychosis.

SETTING

The clinical record text at the South London and Maudsley National Health Service Trust in the UK was used.

PARTICIPANTS

We applied the algorithm to characterise a cohort of 2026 patients with first-episode psychosis attending an early intervention service.

PRIMARY AND SECONDARY OUTCOME MEASURES

Recorded poor insight within 1 month of registration was investigated in relation to (1) incidence of psychiatric hospitalisation, (2) odds of legally enforced hospitalisation, (3) number of days spent as a mental health inpatient and (4) number of different antipsychotic agents prescribed; outcomes were measured over varying follow-up periods from 12 months to 60 months, adjusting for a range of sociodemographic and clinical covariates.

RESULTS

Recorded poor insight, present in 48.9% of the sample, was positively associated with youngest and oldest age groups, unemployment and schizophrenia (compared with bipolar disorder) and was negatively associated with Asian ethnicity, married status, home ownership and recorded cannabis use. It was significantly associated with higher levels of all four outcomes over the succeeding 12 months. Associations with hospitalisation incidence and number of antipsychotics remained independently significant when measured over 60 and 48 months, respectively.

CONCLUSIONS

Recorded poor insight in people with recent onset psychosis predicted higher subsequent inpatient mental healthcare use. Improving insight might benefit patients' course of illness as well as reduce mental health service use.

Ethnic density and other neighbourhood associations for mortality in severe mental illness: a retrospective cohort study with multi-level analysis from an urbanised and ethnically diverse location in the UK

BACKGROUND

Neighbourhood social context might play a role in modifying mortality outcomes in severe mental illness, but has received little attention to date. Therefore, we aimed to assess in an ethnically diverse and urban location the association of neighbourhood-level characteristics and individual-level factors for all-cause, natural-cause, and unnatural-cause mortality in those with severe mental illness.

METHODS

We did a retrospective cohort study using a case-registry from a large secondary mental health-care Trust in an ethnically diverse and urban location in south London, UK. Linked data for deaths and areas of residence were identified from the case-registry. We included all individuals aged 15 years or more at the time of diagnosis for a severe mental illness from Jan 1, 2007, to Dec 31, 2014. We used individual-level information in our analyses, such as gender, marital status, and the presence of current or previous substance use disorders. We assessed neighbourhood or area-level indicators at the Lower Super Output Area level. Association of neighbourhood-level characteristics, which included the interaction between ethnicity and own ethnic density, deprivation, urbanicity, and social fragmentation, alongside individual-level factors for all-cause, natural-cause, and unnatural-cause mortality in those with severe mental illness was assessed.

FINDINGS

A total of 18 201 individuals were included in this cohort for analyses, with a median follow-up of 6·36 years. There were 1767 (9·7%) deaths from all causes, 1417 (7·8%) from natural causes, and 192 (1·1%) from unnatural causes. In the least ethnically dense areas, the adjusted rate ratio (aRR) for all-cause mortality in ethnic minority groups with severe mental illness compared with white British people with severe mental illness were similar (aRR 0·96, 95% CI 0·71-1·29); however in the highest ethnic density areas, ethnic minority groups with severe mental illness had a lower risk of death (aRR 0·52, 95% CI 0·38-0·71; p<0·0001), with similar trends for natural-cause mortality (p=0·071 for statistical interaction). In the cohort with severe mental illness, residency in deprived, urban, and socially fragmented neighbourhoods was not associated with higher mortality rates. Compared with the general population, age-standardised and gender-standardised mortality ratios were elevated in the cohort with severe mental illness across all neighbourhood-level characteristics assessed.

INTERPRETATION

For ethnic minority groups with severe mental illness, residency in areas of higher own-group ethnic density is associated with lower mortality compared to white British groups with severe mental illness.

FUNDING

Health Foundation, National Institute for Health Research, EU Seventh Framework, and National Institute of Mental Health.

Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review

Background

The secondary use of health data is central to biomedical research in the era of data science and precision medicine. National and international initiatives, such as the Global Open Findable, Accessible, Interoperable, and Reusable (GO FAIR) initiative, are supporting this approach in different ways (eg, making the sharing of research data mandatory or improving the legal and ethical frameworks). Preserving patients’ privacy is crucial in this context. De-identification and anonymization are the two most common terms used to refer to the technical approaches that protect privacy and facilitate the secondary use of health data. However, it is difficult to find a consensus on the definitions of the concepts or on the reliability of the techniques used to apply them. A comprehensive review is needed to better understand the domain, its capabilities, its challenges, and the ratio of risk between the data subjects’ privacy on one side, and the benefit of scientific advances on the other.

Objective

This work aims at better understanding how the research community comprehends and defines the concepts of de-identification and anonymization. A rich overview should also provide insights into the use and reliability of the methods. Six aspects will be studied: (1) terminology and definitions, (2) backgrounds and places of work of the researchers, (3) reasons for anonymizing or de-identifying health data, (4) limitations of the techniques, (5) legal and ethical aspects, and (6) recommendations of the researchers.

Methods

Based on a scoping review protocol designed a priori, MEDLINE was searched for publications discussing de-identification or anonymization and published between 2007 and 2017. The search was restricted to MEDLINE to focus on the life sciences community. The screening process was performed by two reviewers independently.

Results

After searching 7972 records that matched at least one search term, 135 publications were screened and 60 full-text articles were included. (1) Terminology: Definitions of the terms de-identification and anonymization were provided in less than half of the articles (29/60, 48%). When both terms were used (41/60, 68%), their meanings divided the authors into two equal groups (19/60, 32%, each) with opposed views. The remaining articles (3/60, 5%) were equivocal. (2) Backgrounds and locations: Research groups were based predominantly in North America (31/60, 52%) and in the European Union (22/60, 37%). The authors came from 19 different domains; computer science (91/248, 36.7%), biomedical informatics (47/248, 19.0%), and medicine (38/248, 15.3%) were the most prevalent ones. (3) Purpose: The main reason declared for applying these techniques is to facilitate biomedical research. (4) Limitations: Progress is made on specific techniques but, overall, limitations remain numerous. (5) Legal and ethical aspects: Differences exist between nations in the definitions, approaches, and legal practices. (6) Recommendations: The combination of organizational, legal, ethical, and technical approaches is necessary to protect health data.

Conclusions

Interest is growing for privacy-enhancing techniques in the life sciences community. This interest crosses scientific boundaries, involving primarily computer science, biomedical informatics, and medicine. The variability observed in the use of the terms de-identification and anonymization emphasizes the need for clearer definitions as well as for better education and dissemination of information on the subject. The same observation applies to the methods. Several legislations, such as the American Health Insurance Portability and Accountability Act (HIPAA) and the European General Data Protection Regulation (GDPR), regulate the domain. Using the definitions they provide could help address the variable use of these two concepts in the research community.

Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust

BACKGROUND

The benefits of indicated primary prevention among individuals at Clinical High Risk for Psychosis (CHR-P) are limited by the difficulty in detecting these individuals. To overcome this problem, a transdiagnostic, clinically based, individualized risk calculator has recently been developed and subjected to a first external validation in 2 different catchment areas of the South London and Maudsley (SLaM) NHS Trust.

METHODS

Second external validation of real world, real-time electronic clinical register-based cohort study. All individuals who received a first ICD-10 index diagnosis of nonorganic and nonpsychotic mental disorder within the Camden and Islington (C&I) NHS Trust between 2009 and 2016 were included. The model previously validated included age, gender, ethnicity, age by gender, and ICD-10 index diagnosis to predict the development of any ICD-10 nonorganic psychosis. The model's performance was measured using Harrell's C-index.

RESULTS

This study included a total of 13702 patients with an average age of 40 (range 16-99), 52% were female, and most were of white ethnicity (64%). There were no CHR-P or child/adolescent services in the C&I Trust. The C&I and SLaM Trust samples also differed significantly in terms of age, gender, ethnicity, and distribution of index diagnosis. Despite these significant differences, the original model retained an acceptable predictive performance (Harrell's C of 0.73), which is comparable to that of CHR-P tools currently recommended for clinical use.

CONCLUSIONS

This risk calculator may pragmatically support an improved transdiagnostic detection of at-risk individuals and psychosis prediction even in NHS Trusts in the United Kingdom where CHR-P services are not provided.

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.

Rates and Associated Factors of Secondary Mental Health Care Utilisation among Ex-Military Personnel in the United States: A Narrative Review

Little is known about ex-serving military personnel who access secondary mental health care. This narrative review focuses on studies that quantitatively measure secondary mental health care utilisation in ex-serving personnel from the United States. The review aimed to identify rates of mental health care utilisation, as well as the factors associated with it. The electronic bibliographic databases OVID Medline, PsycInfo, PsycArticles, and Embase were searched for studies published between January 2001 and September 2018. Papers were retained if they included ex-serving personnel, where the majority of the sample had deployed to the recent conflicts in Iraq or Afghanistan. Fifteen studies were included. Modest rates of secondary mental health care utilisation were found in former military members-for mean percentage prevalence rates, values ranged from 12.5% for at least one psychiatric inpatient episode, to 63.2% for at least one outpatient mental health appointment. Individuals engaged in outpatient care visits most often, most likely because these appointments are the most commonly offered source of support. Post-traumatic stress disorder, particularly re-experiencing symptoms, and comorbid mental health problems were most consistently associated with higher mental health care utilisation. Easily accessible interventions aimed at facilitating higher rates of help seeking in ex-serving personnel are recommended.

Transdiagnostic Individualized Clinically Based Risk Calculator for the Detection of Individuals at Risk and the Prediction of Psychosis: Model Refinement Including Nonlinear Effects of Age

Background: The first rate-limiting step for primary indicated prevention of psychosis is the detection of young people who may be at risk. The ability of specialized clinics to detect individuals at risk for psychosis is limited. A clinically based, individualized, transdiagnostic risk calculator has been developed and externally validated to improve the detection of individuals at risk in secondary mental health care. This calculator employs core sociodemographic and clinical predictors, including age, which is defined in linear terms. Recent evidence has suggested a nonlinear impact of age on the probability of psychosis onset. Aim: To define at a meta-analytical level the function linking age and probability of psychosis onset. To incorporate this function in a refined version of the transdiagnostic risk calculator and to test its prognostic performance, compared to the original specification. Design: Secondary analyses on a previously published meta-analysis and clinical register-based cohort study based on 2008-2015 routine secondary mental health care in South London and Maudsley (SLaM) National Health Service (NHS) Foundation Trust. Participants: All patients receiving a first index diagnosis of non-organic/non-psychotic mental disorder within SLaM NHS Trust in the period 2008-2015. Main outcome measure: Prognostic accuracy (Harrell's C). Results: A total of 91,199 patients receiving a first index diagnosis of non-organic and non-psychotic mental disorder within SLaM NHS Trust were included in the derivation (33,820) or external validation (54,716) datasets. The mean follow-up was 1,588 days. The meta-analytical estimates showed that a second-degree fractional polynomial model with power (-2, -1: age1 = age^-2 and age2 = age^-1) was the best-fitting model (P < 0.001). The refined model that included this function showed an excellent prognostic accuracy in the external validation (Harrell's C = 0.805, 95% CI from 0.790 to 0.819), which was statistically higher than the original model, although of modest magnitude (Harrell's C change = 0.0136, 95% CIs from 0.006 to 0.021, P < 0.001). Conclusions: The use of a refined version of the clinically based, individualized, transdiagnostic risk calculator, which allows for nonlinearity in the association between age and risk of psychosis onset, may offer a modestly improved prognostic performance. This calculator may be particularly useful in young individuals at risk of developing psychosis who access secondary mental health care.

Acute general hospital admissions in people with serious mental illness

BACKGROUND

Serious mental illness (SMI, including schizophrenia, schizoaffective disorder, and bipolar disorder) is associated with worse general health. However, admissions to general hospitals have received little investigation. We sought to delineate frequencies of and causes for non-psychiatric hospital admissions in SMI and compare with the general population in the same area.

METHODS

Records of 18 380 individuals with SMI aged ⩾20 years in southeast London were linked to hospitalisation data. Age- and gender-standardised admission ratios (SARs) were calculated by primary discharge diagnoses in the 10th edition of the World Health Organization International Classification of Diseases (ICD-10) codes, referencing geographic catchment data.

RESULTS

Commonest discharge diagnosis categories in the SMI cohort were urinary conditions, digestive conditions, unclassified symptoms, neoplasms, and respiratory conditions. SARs were raised for most major categories, except neoplasms for a significantly lower risk. Hospitalisation risks were specifically higher for poisoning and external causes, injury, endocrine/metabolic conditions, haematological, neurological, dermatological, infectious and non-specific ('Z-code') causes. The five commonest specific ICD-10 diagnoses at discharge were 'chronic renal failure' (N18), a non-specific code (Z04), 'dental caries' (K02), 'other disorders of the urinary system' (N39), and 'pain in throat and chest' (R07), all of which were higher than expected (SARs ranging 1.57-6.66).

CONCLUSION

A range of reasons for non-psychiatric hospitalisation in SMI is apparent, with self-harm, self-neglect and/or reduced healthcare access, and medically unexplained symptoms as potential underlying explanations.

First-generation versus second-generation long-acting injectable antipsychotic drugs and time to relapse

BACKGROUND

The development of long-acting injectable formulations (LAIs) of second-generation antipsychotic drugs (SGAs) has been suggested as having advantage over first-generation antipsychotic (FGA) LAIs. In this study, we investigated the hypothesis that there was a longer time to relapse in patients with schizophrenia started on SGA LAI versus FGA LAI.

METHODS

Patients with a diagnosis of schizophrenia or schizoaffective disorder who were started on an SGA LAI while on an inpatient ward were identified through searching of the anonymised historical medical records at the South London and Maudsley NHS Foundation Trust. Patients starting FGA LAIs matched for diagnosis, age and date of hospital admission were identified. Time to readmission, discontinuation of LAI or death were identified. Kaplan-Meier plots were generated for each group, and the difference between groups analysed using log-rank methods.

RESULTS

There were 157 patients identified in each group. There was no difference in time to readmission, medication discontinuation or death in patients on SGA LAI versus FGA LAI.

CONCLUSIONS

We found no evidence of advantage in terms of maintaining response in SGA LAI versus FGA LAI. Prescriber choice should be guided by other factors such as side-effect profile, patient acceptability and price.

Recorded atypical hallucinations in psychotic and affective disorders and associations with non-benzodiazepine hypnotic use: the South London and Maudsley Case Register

OBJECTIVES

Hallucinations are present in many conditions, notably psychosis. Although under-researched, atypical hallucinations, such as tactile, olfactory and gustatory (TOGHs), may arise secondary to hypnotic drug use, particularly non-benzodiazepine hypnotics ('Z drugs'). This retrospective case-control study investigated the frequency of TOGHs and their associations with prior Z drug use in a large mental healthcare database.

METHODS

TOGHs were ascertained in 2014 using a bespoke natural language processing algorithm and were analysed against covariates (including use of Z drugs, demographic factors, diagnosis, disorder severity and other psychotropic medications) ascertained prior to 2014.

RESULTS

In 43 339 patients with International Classification of Diseases, Tenth Edition schizophreniform or affective disorder diagnoses, 324 (0.75%) had any TOGH recorded (0.54% tactile, 0.24% olfactory, 0.06% gustatory hallucinations). TOGHs were associated with male gender, black ethnicity, schizophreniform diagnosis and higher disorder severity on Health of the National Outcome Scales. In fully adjusted models, tactile and olfactory hallucinations remained independently associated with prior mention of Z drugs (ORs 1.86 and 1.60, respectively).

CONCLUSIONS

We successfully developed a natural language processing algorithm to identify instances of TOGHs in the clinical record. TOGHs overall, tactile and olfactory hallucinations were shown to be associated with prior mention of Z drugs. This may have implications for the diagnosis and treatment of patients with comorbid sleep and psychiatric conditions.

Demographic and clinical factors associated with different antidepressant treatments: a retrospective cohort study design in a UK psychiatric healthcare setting

OBJECTIVE

To investigate the demographic and clinical factors associated with antidepressant use for depressive disorder in a psychiatric healthcare setting using a retrospective cohort study design.

SETTING

Data were extracted from a de-identified data resource sourced from the electronic health records of a London mental health service. Relative risk ratios (RRRs) were obtained from multinomial logistic regression analysis to ascertain the probability of receiving common antidepressant treatments relative to sertraline.

PARTICIPANTS

Patients were included if they received mental healthcare and a diagnosis of depression with antidepressant treatment between March and August 2015 and exposures were measured over the preceding 12 months.

RESULTS

Older age was associated with increased use of all antidepressants compared with sertraline, except for negative associations with fluoxetine (RRR 0.98; 95% CI 0.96 to 0.98) and a combination of two selective serotonin reuptake inhibitors (SSRIs) (0.98; 95% CI 0.96 to 0.99), and no significant association with escitalopram. Male gender was associated with increased use of mirtazapine compared with sertraline (2.57; 95% CI 1.85 to 3.57). Previous antidepressant, antipsychotic and mood stabiliser use were associated with newer antidepressant use (ie, selective norepinephrine reuptake inhibitors, mirtazapine or a combination of both), while affective symptoms were associated with reduced use of citalopram (0.58; 95% CI 0.27 to 0.83) and fluoxetine (0.42; 95% CI 0.22 to 0.72) and somatic symptoms were associated with increased use of mirtazapine (1.60; 95% CI 1.00 to 2.75) relative to sertraline. In patients older than 25 years, past benzodiazepine use was associated with a combination of SSRIs (2.97; 95% CI 1.32 to 6.68), mirtazapine (1.94; 95% CI 1.20 to 3.16) and venlafaxine (1.87; 95% CI 1.04 to 3.34), while past suicide attempts were associated with increased use of fluoxetine (2.06; 95% CI 1.10 to 3.87) relative to sertraline.

CONCLUSION

There were several factors associated with different antidepressant receipt in psychiatric healthcare. In patients aged >25, those on fluoxetine were more likely to have past suicide attempt, while past use of antidepressant and non-antidepressant use was also associated with use of new generation antidepressants, potentially reflecting perceived treatment resistance.

Predictors of severe relapse in pregnant women with psychotic or bipolar disorders

Pregnancy in women with severe mental illness is associated with adverse outcomes for mother and infant. There are limited data on prevalence and predictors of relapse in pregnancy. A historical cohort study using anonymised comprehensive electronic health records from secondary mental health care linked with national maternity data was carried out. Women with a history of serious mental illness who were pregnant (2007-2011), and in remission at the start of pregnancy, were studied; severe relapse was defined as admission to acute care or self-harm. Predictors of relapse were analysed using random effects logistic regression to account for repeated measures in women with more than one pregnancy in the study period. In 454 pregnancies (389 women) there were 58 (24%) relapses in women with non-affective psychoses and 25 (12%) in women with affective psychotic or bipolar disorders. Independent predictors of relapse included non-affective psychosis (adjusted OR = 2.03; 95% CI = 1.16-3.54), number of recent admissions (1.37; 1.03-1.84), recent self-harm (2.24; 1.15-4.34), substance use (2.15; 1.13-4.08), smoking (2.52; 1.26-5.02) and non-white ethnicity (black ethnicity: 2.37; 1.23-4.57, mixed/other ethnicity: 2.94; 1.32-6.56). Women on no regular medication throughout first trimester were also at greater risk of relapse in pregnancy (1.99; 1.05-3.75). There was no interaction between severity of illness and medication status as relapse predictors. Therefore, women with non-affective psychosis and higher number of recent acute admissions are at significant risk of severe relapse in pregnancy. Continuation of medication in women with severe mental illness who become pregnant may be protective.

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital

BACKGROUND

Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present.

RESULTS

To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall.

CONCLUSION

We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing

Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches - a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database. Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database. The novelty of the two approaches lies in the malleability of each classifier if a need to refine performance, or meet alternate classification requirements arises. The algorithms can also be adapted to fit infrastructures of other clinical datasets given sufficient clinical recording practice knowledge, without dependency on medical codes or additional data extraction of known risk factors to predict suicidal behaviour.

Predictors of Falls and Fractures Leading to Hospitalization in People With Dementia: A Representative Cohort Study

OBJECTIVES

Investigate predictors of falls and fractures leading to hospitalization in a large cohort of people with dementia.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

People with diagnosed dementia between January 2007 and March 2013, aged >65 years, were assembled using data from the Maudsley Biomedical Research Centre Case Register, from 4 boroughs in London serving a population of 1.3 million people.

MEASURES

Falls and/or fractures leading to hospitalization were ascertained from linked national records. Demographic data, cognitive test scores, medications, and symptom and functioning scores from Health of the Nation Outcome Scales (HoNOS65+) were modeled in multivariate survival analyses to identify predictors of falls and fractures.

RESULTS

Of 8036 people with dementia (63.9% female), 2500 (31.1%, incidence rate 125.5 per 1000 person-years) had a fall during a mean follow-up of 2.5 years and 1437 (17.7%, incidence rate 65.5 per 1000 person-years) had a fracture. In multivariable models, significant predictors of falls were increased age, female gender, physical health problems, previous fall or fracture, vascular dementia vs Alzheimer's disease, higher neighborhood deprivation, noncohabiting status, and problems with living conditions. Ethnic minority status was protective of falls (eg, Caribbean/Asian ethnicity). Medications (including psychotropic and antipsychotics), neuropsychiatric symptoms, cognitive (Mini-Mental State Examination scores), or functional problems did not predict hospitalized falls. Predictors of fractures were similar to those predicting falls.

IMPLICATIONS

Over an average of 2.5 years, a third of people with dementia had a fall leading to hospitalization, necessitating action in clinical practice. Clinicians should consider that besides established demographic and physical health-related factors, the risk of hospitalization due to a fall or fractures in dementia is largely determined by environmental and socioeconomic factors. Interestingly, our data suggest that neuropsychiatric symptoms, cognitive status, functioning, or pharmacotherapy were not associated with falls/fractures.

Prevalence and correlates of low-grade systemic inflammation in adult psychiatric inpatients: An electronic health record-based study

Low-grade inflammation is a risk factor for depression, psychosis and other major psychiatric disorders. It is associated with poor response to antidepressant and antipsychotics, and could potentially be a treatment target. However, there is limited data on the prevalence of low-grade inflammation in major psychiatric disorders, and on the characteristics of patients who show evidence of inflammation. We examined the prevalence of low-grade inflammation and associated socio-demographic and clinical factors in acute psychiatric inpatients. An anonymised search of the electronic patient records of Cambridgeshire and Peterborough NHS Foundation Trust was used to identify patients aged 18-65 years who were hospitalised between 2013 and 2016 (inclusive). We excluded patients on antibiotics or oral steroids, or with missing data. Inflammation was defined using serum C-reactive protein (>3 mg/L) or total white cell count (>9.4 × 10⁹/L) as measured within 14 days of admission. Out of all 599 admissions, the prevalence of inflammation (serum CRP >3 mg/L) in the ICD-10 diagnostic groups of psychotic disorders (F20-29), mood disorders (F30-39), neurotic disorders (F40-48) and personality disorders (F60-69) was 32%, 21%, 22% and 42%, respectively. In multivariable analyses, low-grade inflammation was associated with older age, black ethnicity, being single, self-harm, diagnoses of schizophrenia, bipolar disorder, current treatments with antidepressants, benzodiazepines, and with current treatment for medical comorbidities. A notable proportion of acutely unwell psychiatric patients from all ICD-10 major diagnostic groups show evidence of low-grade inflammation, suggesting inflammation may be relevant for all psychiatric disorders.

Using data linkage to electronic patient records to assess the validity of selected mental health diagnoses in English Hospital Episode Statistics (HES)

BACKGROUND

Administrative data can be used to support research, such as in the UK Biobank. Hospital Episode Statistics (HES) are national data for England that include contain ICD-10 diagnoses for inpatient mental healthcare episodes, but the validity of these diagnoses for research purposes has not been assessed.

METHODS

250 peoples' HES records were selected based on a HES recorded inpatient stay at the South London and Maudsley NHS Foundation Trust with a diagnosis of schizophrenia, a wider schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression. A gold-standard research diagnosis was made using Clinical Records Interactive Search pseudonymised electronic patient records using, and the OPCRIT+ algorithm.

RESULTS

Positive predictive value at the level of lifetime psychiatric disorder was 100%, and at the level of lifetime diagnosis in the four categories of schizophrenia, wider schizophrenia spectrum, bipolar or unipolar depression was 73% (68-79). Agreement varied by diagnosis, with schizophrenia having the highest PPV at 90% (80-96). Each person had an average of five psychiatric HES records. An algorithm that looked at the last recorded psychiatric diagnosis led to greatest overall agreement with the research diagnosis.

DISCUSSION

For people who have a HES record from a psychiatric admission with a diagnosis of schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression, HES records appear to be a good indicator of a mental disorder, and can provide a diagnostic category with reasonable certainty. For these diagnoses, HES records can be an effective way of ascertaining psychiatric diagnosis.

Psychological consequences of child trafficking: An historical cohort study of trafficked children in contact with secondary mental health services

Background

Child trafficking is the recruitment and movement of people aged younger than 18 for the purposes of exploitation. Research on the mental health of trafficked children is limited, and little is known about the use of mental health services by this group. This study aimed to investigate the mental health and service use characteristics of trafficked children in contact with mental health services in England.

Methods & findings

The study employed an historical cohort design. Electronic health records of over 250,000 patients were searched to identify trafficked children, and a matched cohort of non-trafficked children was randomly selected. Data were extracted on the socio-demographic and clinical characteristics, abuse history, and trafficking experiences of the trafficked children. Logistic and linear random effects regression models were fitted to compare trafficked and non-trafficked children on their clinical profiles and service use characteristics. Fifty-one trafficked children were identified, 78% were female. The most commonly recorded diagnoses for trafficked children were post-traumatic stress disorder (PTSD) (22%) and affective disorders (22%). Records documented a high prevalence of physical violence (53%) and sexual violence (49%) among trafficked children. Trafficked children had significantly longer duration of contact with mental health services compared to non-trafficked controls (b = 1.66, 95% CI 1.09–2.55, p<0.02). No significant differences were found, however, with regards to pathways into care, prevalence of compulsory psychiatric admission, length of inpatient stays, or changes in global functioning.

Conclusions

Child trafficking is associated with high levels of physical and sexual abuse and longer duration of contact with mental health services. Research is needed on most effective interventions to promote recovery for this vulnerable group.

Weekday and seasonal patterns in psychiatric referrals in three major London A&E departments, 2012-2014

Aims and method To identify temporal and demographic trends in referrals made to psychiatric liaison services. Routine clinical data from 16 105 individual referrals from three central London accident and emergency (A&E) departments to psychiatric liaison services from 2012 to 2014 were obtained and analysed using the Clinical Record Interactive Search (CRIS).

RESULTS

Referrals from A&E to psychiatric liaison services increased 16% over the 3-year study period. There were fewer referrals to psychiatric liaison services in winter months compared with other seasons. There were fewer referrals to psychiatric liaison services over the weekend compared with weekdays (average 15.4 daily weekday referrals v. 13.2 weekend, z = 5.1, P < 0.001), and weekend referrals were slightly less likely to result in admission to psychiatric hospital (11.3% v. 12.8%, respectively, χ2 = 6.33, P = 0.01). Clinical implications Psychiatric staffing in A&E and inpatient psychiatric wards requires planning to meet temporal and regional variations in the pattern of demand. Declaration of interest None.

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

BACKGROUND

Electronic health records (EHRs) are widely used in mental health services. Case registers using EHRs from secondary mental healthcare have the potential to deliver large-scale projects evaluating mental health outcomes in real-world clinical populations.

METHODS

We describe the Camden and Islington NHS Foundation Trust (C&I) Research Database which uses the Clinical Record Interactive Search (CRIS) tool to extract and de-identify routinely collected clinical information from a large UK provider of secondary mental healthcare, and demonstrate its capabilities to answer a clinical research question regarding time to diagnosis and treatment of bipolar disorder.

RESULTS

The C&I Research Database contains records from 108,168 mental health patients, of which 23,538 were receiving active care. The characteristics of the patient population are compared to those of the catchment area, of London, and of England as a whole. The median time to diagnosis of bipolar disorder was 76 days (interquartile range: 17-391) and median time to treatment was 37 days (interquartile range: 5-194). Compulsory admission under the UK Mental Health Act was associated with shorter intervals to diagnosis and treatment. Prior diagnoses of other psychiatric disorders were associated with longer intervals to diagnosis, though prior diagnoses of schizophrenia and related disorders were associated with decreased time to treatment.

CONCLUSIONS

The CRIS tool, developed by the South London and Maudsley NHS Foundation Trust (SLaM) Biomedical Research Centre (BRC), functioned very well at C&I. It is reassuring that data from different organizations deliver similar results, and that applications developed in one Trust can then be successfully deployed in another. The information can be retrieved in a quicker and more efficient fashion than more traditional methods of health research. The findings support the secondary use of EHRs for large-scale mental health research in naturalistic samples and settings investigated across large, diverse geographical areas.

Should Clinicians Split or Lump Psychiatric Symptoms? The Structure of Psychopathology in Two Large Pediatric Clinical Samples from England and Norway

It has been suggested that the structure of psychiatric phenomena can be reduced to a few symptom dimensions. These proposals, mainly based on epidemiological samples, may not apply to clinical populations. We tested the structure of psychiatric symptoms across two pediatric clinical samples from England (N = 8434) and Norway (N = 5866). Confirmatory factor analyses of the parent-reported Strengths and Difficulties Questionnaire (SDQ) evaluated the relative fit of several models, including a first-order model, a second-order model with the widely-established broad symptom dimensions of internalizing-externalizing, and two bi-factor models capturing a general psychopathology factor. Predictive value of the SDQ subscales for psychiatric disorders was examined. A first-order five-factor solution better fit the data. The expected SDQ subscale(s) related best to the corresponding psychiatric diagnosis. In pediatric clinical samples, a granular approach to psychiatric symptoms where several dimensions are considered seems to fit the data better than models based on lumping symptoms into internalizing/externalizing dimensions.

Can risk assessment predict suicide in secondary mental healthcare? Findings from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register

PURPOSE

The predictive value of suicide risk assessment in secondary mental healthcare remains unclear. This study aimed to investigate the extent to which clinical risk assessment ratings can predict suicide among people receiving secondary mental healthcare.

METHODS

Retrospective inception cohort study (n = 13,758) from the South London and Maudsley NHS Foundation Trust (SLaM) (London, UK) linked with national mortality data (n = 81 suicides). Cox regression models assessed survival from the last suicide risk assessment and ROC curves evaluated the performance of risk assessment total scores.

RESULTS

Hopelessness (RR = 2.24, 95% CI 1.05-4.80, p = 0.037) and having a significant loss (RR = 1.91, 95% CI 1.03-3.55, p = 0.041) were significantly associated with suicide in the multivariable Cox regression models. However, screening statistics for the best cut-off point (4-5) of the risk assessment total score were: sensitivity 0.65 (95% CI 0.54-0.76), specificity 0.62 (95% CI 0.62-0.63), positive predictive value 0.01 (95% CI 0.01-0.01) and negative predictive value 0.99 (95% CI 0.99-1.00).

CONCLUSIONS

Although suicide was linked with hopelessness and having a significant loss, risk assessment performed poorly to predict such an uncommon outcome in a large case register of patients receiving secondary mental healthcare.

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials.

Mortality in dementia with Lewy bodies compared with Alzheimer's dementia: a retrospective naturalistic cohort study

OBJECTIVES

To use routine clinical data to investigate survival in dementia with Lewy bodies (DLB) compared with Alzheimer's dementia (AD). DLB is the second most common dementia subtype after AD, accounting for around 7% of dementia diagnoses in secondary care, though studies suggest that it is underdiagnosed by up to 50%. Most previous studies of DLB have been based on select research cohorts, so little is known about the outcome of the disease in routine healthcare settings.

SETTING

Cambridgeshire & Peterborough NHS Foundation Trust, a mental health trust providing secondary mental health care in England.

SAMPLE

251 DLB and 222 AD identified from an anonymised database derived from electronic clinical case records across an 8-year period (2005-2012), with mortality data updated to May 2015.

RESULTS

Raw (uncorrected) median survival was 3.72 years for DLB (95% CI 3.33 to 4.14) and 6.95 years for AD (95% CI 5.78 to 8.12). Controlling for age at diagnosis, comorbidity and antipsychotic prescribing the model predicted median survival for DLB was 3.3 years (95% CI 2.88 to 3.83) for males and 4.0 years (95% CI 3.55 to 5.00) for females, while median survival for AD was 6.7 years (95% CI 5.27 to 8.51) for males and 7.0 years (95% CI 5.92 to 8.73) for females.

CONCLUSION

Survival from first presentation with cognitive impairment was markedly shorter in DLB compared with AD, independent of age, sex, physical comorbidity or antipsychotic prescribing. This finding, in one of the largest clinical cohorts of DLB cases assembled to date, adds to existing evidence for poorer survival for DLB versus AD. There is an urgent need for further research to understand possible mechanisms accounting for this finding.

Only a small proportion of patients with first episode psychosis come via prodromal services: a retrospective survey of a large UK mental health programme

BACKGROUND

Little is known about patients with a first episode of psychosis (FEP) who had first presented to prodromal services with an "at risk mental state" (ARMS) before making the transition to psychosis. We set out to identify the proportion of patients with a FEP who had first presented to prodromal services in the ARMS state, and to compare these FEP patients with FEP patients who did not have prior contact with prodromal services.

METHODS

In this study information on 338 patients aged ≤37 years who presented to mental health services between 2010 and 2012 with a FEP was examined. The data on pathways to care, clinical and socio-demographic characteristics were extracted from the Biomedical Research Council Case Register for the South London and Maudsley NHS Trust.

RESULTS

Over 2 years, 14 (4.1% of n = 338) young adults presented with FEP and had been seen previously by the prodromal services. These ARMS patients were more likely to enter their pathway to psychiatric care via referral from General Practice, be born in the UK and to have had an insidious mode of illness onset than FEP patients without prior contact with the prodromal services.

CONCLUSIONS

In the current pathways to care configuration, prodromal services are likely to prevent only a few at-risk individuals from transitioning to psychosis even if effective preventative treatments become available.

Identification of the delivery of cognitive behavioural therapy for psychosis (CBTp) using a cross-sectional sample from electronic health records and open-text information in a large UK-based mental health case register

OBJECTIVE

Our primary objective was to identify cognitive behavioural therapy (CBT) delivery for people with psychosis (CBTp) using an automated method in a large electronic health record database. We also examined what proportion of service users with a diagnosis of psychosis were recorded as having received CBTp within their episode of care during defined time periods provided by early intervention or promoting recovery community services for people with psychosis, compared with published audits and whether demographic characteristics differentially predicted the receipt of CBTp.

METHODS

Both free text using natural language processing (NLP) techniques and structured methods of identifying CBTp were combined and evaluated for positive predictive value (PPV) and sensitivity. Using inclusion criteria from two published audits, we identified anonymised cross-sectional samples of 2579 and 2308 service users respectively with a case note diagnosis of schizophrenia or psychosis for further analysis.

RESULTS

The method achieved PPV of 95% and sensitivity of 96%. Using the National Audit of Schizophrenia 2 criteria, 34.6% service users were identified as ever having received at least one session and 26.4% at least two sessions of CBTp; these are higher percentages than previously reported by manual audit of a sample from the same trust that returned 20.0%. In the fully adjusted analysis, CBTp receipt was significantly (p<0.05) more likely in younger patients, in white and other when compared with black ethnic groups and patients with a diagnosis of other schizophrenia spectrum and schizoaffective disorder when compared with schizophrenia.

CONCLUSIONS

The methods presented here provided a potential method for evaluating delivery of CBTp on a large scale, providing more scope for routine monitoring, cross-site comparisons and the promotion of equitable access.

Associations of centrally acting ACE inhibitors with cognitive decline and survival in Alzheimer's disease

BACKGROUND

Cognitive improvement has been reported in patients receiving centrally acting angiotensin-converting enzyme inhibitors (C-ACEIs).

AIMS

To compare cognitive decline and survival after diagnosis of Alzheimer's disease between people receiving C-ACEIs, non-centrally acting angiotensin-converting enzyme inhibitors (NC-ACEIs), and neither.

METHOD

Routine Mini-Mental State Examination (MMSE) scores were extracted in 5260 patients receiving acetylcholinesterase inhibitors and analysed against C-/NC-ACEI exposure at the time of Alzheimer's disease diagnosis.

RESULTS

In the 9 months after Alzheimer's disease diagnosis, MMSE scores significantly increased by 0.72 and 0.19 points per year in patients on C-ACEIs and neither respectively, but deteriorated by 0.61 points per year in those on NC-ACEIs. There were no significant group differences in score trajectories from 9 to 36 months and no differences in survival.

CONCLUSIONS

In people with Alzheimer's disease receiving acetylcholinesterase inhibitors, those also taking C-ACEIs had stronger initial improvement in cognitive function, but there was no evidence of longer-lasting influence on dementia progression.

DECLARATION OF INTEREST

R.S. has received research funding from Pfizer, Lundbeck, Roche, Janssen and GlaxoSmithKline.

COPYRIGHT AND USAGE

© The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.

Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes

De-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial. The results show that the integration of the proposed methods can identify Health Information Portability and Accountability Act (HIPAA) defined PHIs with overall F₁-scores of ∼90% and above. Yet, some classes (Profession, Organization) proved again to be challenging given the variability of expressions used to reference given information.

Seclusion and Psychiatric Intensive Care Evaluation Study (SPICES): combined qualitative and quantitative approaches to the uses and outcomes of coercive practices in mental health services

Background

Seclusion (the isolation of a patient in a locked room) and transfer to a psychiatric intensive care unit (PICU; a specialised higher-security ward with higher staffing levels) are two common methods for the management of disturbed patient behaviour within acute psychiatric hospitals. Some hospitals do not have seclusion rooms or easy access to an on-site PICU. It is not known how these differences affect patient management and outcomes.

Objectives

To (1) assess the factors associated with the use of seclusion and PICU care, (2) estimate the consequences of the use of these on subsequent violence and costs (study 1) and (3) describe differences in the management of disturbed patient behaviour related to differential availability (study 2).

Design

The electronic patient record system at one trust was used to compare outcomes for patients who were and were not subject to seclusion or a PICU, controlling for variables, including recent behaviours. A cost-effectiveness analysis was performed (study 1). Nursing staff at eight hospitals with differing access to seclusion and a PICU completed attitudinal measures, a video test on restraint-use timing and an interview about the escalation pathway for the management of disturbed behaviour at their hospital. Analyses examined how results differed by access to PICU and seclusion (study 2).

Participants

Patients on acute wards or PICUs in one NHS trust during the period 2008–13 (study 1) and nursing staff at eight randomly selected hospitals in England, with varying access to seclusion and to a PICU (study 2).

Main outcome measures

Aggression, violence and cost (study 1), and utilisation, speed of use and attitudes to the full range of containment methods (study 2).

Results

Patients subject to seclusion or held in a PICU were more likely than those who were not to be aggressive afterwards, and costs of care were higher, but this was probably because of selection bias. We could not derive satisfactory estimates of the causal effect of either intervention, but it appeared that it would be feasible to do so for seclusion based on an enriched sample of untreated controls (study 1). Hospitals without seclusion rooms used more rapid tranquillisation, nursing of the patient in a side room accompanied by staff and seclusion using an ordinary room (study 2). Staff at hospitals without seclusion rated it as less acceptable and were slower to initiate manual restraint. Hospitals without an on-site PICU used more seclusion, de-escalation and within-eyesight observation.

Limitations

Official record systems may be subject to recording biases and crucial variables may not be recorded (study 1). Interviews were complex, difficult, constrained by the need for standardisation and collected in small numbers at each hospital (study 2).

Conclusions

Closing seclusion rooms and/or restricting PICU access does not appear to reduce the overall levels of containment, as substitution of other methods occurs. Services considering expanding access to seclusion or to a PICU should do so with caution. More evaluative research using stronger designs is required.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Clinical records anonymisation and text extraction (CRATE): an open-source software system

Background

Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information. Patient-identifiable information cannot in general be shared outside clinical care teams without explicit consent, but anonymisation/de-identification allows research uses of clinical data without explicit consent.

Results

This article presents CRATE (Clinical Records Anonymisation and Text Extraction), an open-source software system with separable functions: (1) it anonymises or de-identifies arbitrary relational databases, with sensitivity and precision similar to previous comparable systems; (2) it uses public secure cryptographic methods to map patient identifiers to research identifiers (pseudonyms); (3) it connects relational databases to external tools for natural language processing; (4) it provides a web front end for research and administrative functions; and (5) it supports a specific model through which patients may consent to be contacted about research.

Conclusions

Creation and management of a research database from sensitive clinical records with secure pseudonym generation, full-text indexing, and a consent-to-contact process is possible and practical using entirely free and open-source software.

Contributions of specific causes of death to lost life expectancy in severe mental illness

The life expectancy gap between people with severe mental illness (SMI) and the general population persists and may even be widening. This study aimed to estimate contributions of specific causes of death to the gap. Age of death and primary cause of death were used to estimate life expectancy at birth for people with SMI from a large mental healthcare case register during 2007-2012. Using data for England and Wales in 2010, death rates in the SMI cohort for each primary cause of death category were replaced with gender- and age-specific norms for that cause. Life expectancy in SMI was then re-calculated and, thus, the contribution of that specific cause of death estimated. Natural causes accounted for 79.2% of lost life-years in women with SMI and 78.6% in men. Deaths from circulatory disorders accounted for more life-years lost in women than men (22.0% versus 17.4%, respectively), as did deaths from cancer (8.1% versus 0%), but the contribution from respiratory disorders was lower in women than men (13.7% versus 16.5%). For women, cancer contributed more in those with non-affective than affective disorders, while suicide, respiratory and digestive disorders contributed more in those with affective disorders. In men, respiratory disorders contributed more in non-affective disorders. Other contributions were similar between gender and affective/non-affective groups. Loss of life expectancy in people with SMI is accounted for by a broad range of causes of death, varying by gender and diagnosis. Interventions focused on multiple rather than individual causes of death should be prioritised accordingly.

What proportion of patients with psychosis is willing to take part in research? A mental health electronic case register analysis

OBJECTIVE

The proportion of people with mental health disorders who participate in clinical research studies is much smaller than for those with physical health disorders. It is sometimes assumed that this reflects an unwillingness to volunteer for mental health research studies. We examined this issue in a large sample of patients with psychosis.

DESIGN

Cross-sectional study.

SETTING

Anonymised electronic mental health record data from the South London and Maudsley NHS Foundation Trust (SLaM).

PARTICIPANTS

5787 adults diagnosed with a psychotic disorder.

EXPOSURE

Whether approached prior to 1 September 2014 for consent to be approached about research participation.

MAIN OUTCOME MEASURES

Number of days spent in a psychiatric hospital, whether admitted to hospital compulsorily, and total score on the Health of the Nation Outcome Scale (HoNOS) between 1 September 2014 and 28 February 2015 with patient factors (age, gender, ethnicity, marital status and diagnosis) and treating clinical service as covariates.

RESULTS

1187 patients (20.5% of the total sample) had been approached about research participation. Of those who were approached, 773 (65.1%) agreed to be contacted in future by researchers. Patients who had been approached had 2.3 fewer inpatient days (95% CI -4.4 to -0.3, p=0.03), were less likely to have had a compulsory admission (OR 0.65, 95% CI 0.50 to 0.84, p=0.001) and had a better HoNOS score (β coefficient -0.9, 95% CI -1.5 to -0.4, p=0.001) than those who had not. Among patients who were approached, there was no significant difference in clinical outcomes between those agreed to research contact and those who did not.

CONCLUSIONS

About two-thirds of patients with psychotic disorders were willing to be contacted about participation in research. The patients who were approached had better clinical outcomes than those who were not, suggesting that clinicians were more likely to approach patients who were less unwell.

Analysis of diagnoses extracted from electronic health records in a large mental health case register

The UK government has recently recognised the need to improve mental health services in the country. Electronic health records provide a rich source of patient data which could help policymakers to better understand needs of the service users. The main objective of this study is to unveil statistics of diagnoses recorded in the Case Register of the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in the UK and Europe serving a source population of over 1.2 million people residing in south London. Based on over 500,000 diagnoses recorded in ICD10 codes for a cohort of approximately 200,000 mental health patients, we established frequency rate of each diagnosis (the ratio of the number of patients for whom a diagnosis has ever been recorded to the number of patients in the entire population who have made contact with mental disorders). We also investigated differences in diagnoses prevalence between subgroups of patients stratified by gender and ethnicity. The most common diagnoses in the considered population were (recurrent) depression (ICD10 codes F32-33; 16.4% of patients), reaction to severe stress and adjustment disorders (F43; 7.1%), mental/behavioural disorders due to use of alcohol (F10; 6.9%), and schizophrenia (F20; 5.6%). We also found many diagnoses which were more likely to be recorded in patients of a certain gender or ethnicity. For example, mood (affective) disorders (F31-F39); neurotic, stress-related and somatoform disorders (F40-F48, except F42); and eating disorders (F50) were more likely to be found in records of female patients, while males were more likely to be diagnosed with mental/behavioural disorders due to psychoactive substance use (F10-F19). Furthermore, mental/behavioural disorders due to use of alcohol and opioids were more likely to be recorded in patients of white ethnicity, and disorders due to use of cannabinoids in those of black ethnicity.

Predictors of Mental Health Review Tribunal (MHRT) outcome in a forensic inpatient population: a prospective cohort study

BACKGROUND

Previous studies have investigated factors associated with outcome at Mental Health Review Tribunals (MHRTs) in forensic psychiatric patients; however, dynamic variables such as treatment compliance and substance misuse have scarcely been examined, particularly in UK samples. We aimed to determine whether dynamic factors related to behaviour, cooperation with treatment, and activities on the ward were prospectively associated with outcome at MHRT.

METHODS

At baseline, demographic, clinical, behavioural, and treatment-related factors were ascertained via electronic medical records and census forms completed by the patient's clinical team. Data on MHRTs (i.e., number attended, responsible clinician's recommendation, and outcome) were collected at a 2-year follow-up. Logistic regression analyses were performed to determine factors associated with outcome among those who attended a MHRT within the follow-up period. Of the 135 forensic inpatients examined at baseline, 79 patients (59%) attended a MHRT during the 2-year follow-up period and therefore comprised the study sample. Of these 79 patients included in the current study, 28 (35%) were subsequently discharged.

RESULTS

In univariable analyses, unescorted community leave, responsible clinician's recommendation of discharge, and restricted Mental Health Act section were associated with a greater likelihood of discharge at MHRT; whilst inpatient aggression, a recent episode of acute illness, higher total score on the Historical Clinical Risk - 20 (HCR-20), higher HCR-20 clinical and risk scores, and agitated behaviour were negatively associated with discharge (p < 0.05). In multivariable analyses, HCR-20 clinical scale scores and physical violence independently predicted outcome at tribunal after controlling for other dynamic variables.

CONCLUSION

By identifying dynamic factors associated with discharge at tribunal, the results have important implications for forensic psychiatric patients and their clinical teams. Our findings suggest that by reducing levels of agitated behaviour, verbal aggression, and physical violence on the ward, achieving unescorted community leave, and targeting specific items on the HCR-20 risk assessment tool, patients may be able to improve their changes of discharge at a MHRT.

Admission to acute mental health services after contact with crisis resolution and home treatment teams: an investigation in two large mental health-care providers

BACKGROUND

Crisis resolution and home treatment teams (CRTs) offer an alternative to hospital admission for patients undergoing mental health crises in the UK. Few studies have been done to examine predictors of relapse and readmission after contact with CRTs.

METHODS

We used the Clinical Record Interactive Search to identify all patients receiving care from CRTs in two National Health Service (NHS) mental health trusts in London: Camden and Islington NHS Foundation Trust and South London and Maudsley NHS Foundation Trust. We used Cox regression models to examine rates and predictors of admission to acute mental health services within 1 year of contact with CRTs. Sex, age, ethnicity, marital status, social deprivation, severity of psychopathology, duration of index CRT episode, first contact with services, and diagnosis were extracted and examined as predictors of admission.

FINDINGS

Between Jan 1, 2008, and Aug 31, 2014, 17 666 patients were treated by CRTs-8759 patients in the Camden and Islington trust and 8907 patients in the South London and Maudsley trust. 53·9 patients per 100 person-years (95% CI 52·1-55·8) in Camden and Islington and 51·3 patients per 100 person-years (95% CI 49·6-53·1) in South London and Maudsley were admitted to acute services within 1 year of seeing the CRT. In both cohorts, non-affective psychotic disorders (adjusted hazard ratio [HR] 1·25, 95% CI 1·09-1·44 in Camden and Islington; 1·27, 1·17-1·38 in South London and Maudsley) and age older than 65 years (1·18, 1·01-1·37 in Camden and Islington; 1·32, 1·12-1·56 in South London and Maudsley) were associated with increased risk of admission, whereas first contact with services (0·57, 0·52-0·62 in Camden and Islington; 0·69, 0·63-0·75 in South London and Maudsley), anxiety disorders (0·81, 0·69-0·96 in Camden and Islington; 0·77, 0·67-0·87 in South London and Maudsley), and longer index CRT episodes (adjusted HR per day 0·996, 0·994-0·998 in Camden and Islington; 0·989, 0·987-0·991 in South London and Maudsley) were associated with reduced risk of admission.

INTERPRETATION

Past use of mental health services and a diagnosis of non-affective psychosis, which are markers of severity of mental illness, and older age, which is a marker of chronicity, are all risk factors for future relapse after interactions with CRTs. These findings might help clinicians and policy makers to offer more targeted and cost-effective services to reduce relapse rates.

FUNDING

None.

Predictors of care home and hospital admissions and their costs for older people with Alzheimer's disease: findings from a large London case register

OBJECTIVES

To examine links between clinical and other characteristics of people with Alzheimer's disease living in the community, likelihood of care home or hospital admission, and associated costs.

DESIGN

Observational data extracted from clinical records using natural language processing and Hospital Episode Statistics. Statistical analyses examined effects of cognition, physical health, mental health, sociodemographic factors and living circumstances on risk of admission to care home or hospital over 6 months and associated costs, adjusting for repeated observations.

SETTING

Catchment area for South London and Maudsley National Health Service Foundation Trust, provider for 1.2 million people in Southeast London.

PARTICIPANTS

Every individual with diagnosis of Alzheimer's disease seen and treated by mental health services in the catchment area, with at least one rating of cognition, not resident in care home at time of assessment (n=3075).

INTERVENTIONS

Usual treatment.

MAIN OUTCOME MEASURES

Risk of admission to, and days spent in three settings during 6-month period following routine clinical assessment: care home, mental health inpatient care and general hospital inpatient care.

RESULTS

Predictors of probability of care home or hospital admission and/or associated costs over 6 months include cognition, functional problems, agitation, depression, physical illness, previous hospitalisations, age, gender, ethnicity, living alone and having a partner. Patterns of association differed considerably by destination.

CONCLUSIONS

Most people with dementia prefer to remain in their own homes, and funding bodies see this as cheaper than institutionalisation. Better treatment in the community that reduces health and social care needs of Alzheimer's patients would reduce admission rates. Living alone, poor living circumstances and functional problems all raise admission rates, and so major cuts in social care budgets increase the risk of high-cost admissions which older people do not want. Routinely collected data can be used to reveal local patterns of admission and costs.