Using routine clinical and administrative data to produce a dataset of attendances at Emergency Departments following self-harm

Cohort profile: The Social media, smartphone use and Self-harm in Young People (3S-YP) study-A prospective, observational cohort study of young people in contact with mental health services

OBJECTIVES

The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the mechanisms underpinning associations between social media and smartphone use and self-harm in a clinical youth sample. We present here a comprehensive description of the cohort from baseline data and an overview of data available from baseline and follow-up assessments.

METHODS

Young people aged 13-25 years were recruited from a mental health trust in England and followed up for 6 months. Self-report data was collected at baseline and monthly during follow-up and linked with electronic health records (EHR) and user-generated data.

FINDINGS

A total of 362 young people enrolled and provided baseline questionnaire data. Most participants had a history of self-harm according to clinical (n = 295, 81.5%) and broader definitions (n = 296, 81.8%). At baseline, there were high levels of current moderate/severe anxiety (n = 244; 67.4%), depression (n = 255; 70.4%) and sleep disturbance (n = 171; 47.2%). Over half used social media and smartphones after midnight on weekdays (n = 197, 54.4%; n = 215, 59.4%) and weekends (n = 241, 66.6%; n = 263, 72.7%), and half met the cut-off for problematic smartphone use (n = 177; 48.9%). Of the cohort, we have questionnaire data at month 6 from 230 (63.5%), EHR data from 345 (95.3%), social media data from 110 (30.4%) and smartphone data from 48 (13.3%).

CONCLUSION

The 3S-YP study is the first prospective study with a clinical youth sample, for whom to investigate the impact of digital technology on youth mental health using novel data linkages. Baseline findings indicate self-harm, anxiety, depression, sleep disturbance and digital technology overuse are prevalent among clinical youth. Future analyses will explore associations between outcomes and exposures over time and compare self-report with user-generated data in this cohort.

Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

OBJECTIVES

To assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).

SETTING

The Clinical Records Interactive Search system allows detailed research based on deidentified EHRs from the South London and Maudsley NHS Foundation Trust, a large south London Mental Health Trust providing secondary and tertiary mental healthcare.

PARTICIPANTS AND METHODS

We developed a gazetteer of online activity terms and annotation guidelines, from 5480 clinical notes (200 adolescents, aged 11-17 years) receiving specialist mental healthcare. The preprocessing and manual curation steps of this real-world data set allowed development of a rule-based NLP application to automate identification of online activity (internet, social media, online gaming) mentions in EHRs. The context of each mention was also recorded manually as: supportive, detrimental or neutral in a subset of data for additional analysis.

RESULTS

The NLP application performed with good precision (0.97) and recall (0.94) for identification of online activity mentions. Preliminary analyses found 34% of online activity mentions were considered to have been documented within a supportive context for the young person, 38% detrimental and 28% neutral.

CONCLUSION

Our results provide an important example of a rule-based NLP methodology to accurately identify online activity recording in EHRs, enabling researchers to now investigate associations with a range of adolescent mental health outcomes.

Observational prospective study of social media, smartphone use and self-harm in a clinical sample of young people: study protocol

INTRODUCTION

Young people are the most frequent users of social media and smartphones and there has been an increasing speculation about the potential negative impacts of their use on mental health. This has coincided with a sharp increase in the levels of self-harm in young people. To date, studies researching this potential association are predominantly cross-sectional and reliant on self-report data, which precludes the ability to objectively analyse behaviour over time. This study is one of the first attempts to explore temporal patterns of real-world usage prior to self-harm, to identify whether there are usage patterns associated with an increased risk.

METHODS AND ANALYSIS

To study the mechanisms by which social media and smartphone use underpin self-harm in a clinical sample of young people, the Social media, Smartphone use and Self-harm in Young People (3S-YP) study uses a prospective, observational study design. Up to 600 young people aged 13-25 years old from secondary mental health services will be recruited and followed for up to 6 months. Primary analysis will compare real-world data in the 7 days leading up to a participant or clinician recorded self-harm episode, to categorise patterns of problematic usage. Secondary analyses will explore potential mediating effects of anxiety, depression, sleep disturbance, loneliness and bullying.

ETHICS AND DISSEMINATION

This study was approved by the National Research Ethics Service, London - Riverside, as well as by the Joint Research and Development Office of the Institute of Psychiatry, Psychology and Neuroscience and South London and Maudsley NHS Foundation Trust (SLaM), and the SLaM Clinical Research Interactive Search (CRIS) Oversight Committee. The findings from this study will be disseminated through peer-reviewed scientific journals, conferences, websites, social media and stakeholder engagement activities.

TRIAL REGISTRATION NUMBER

NCT04601220.

A scoping review of ethics considerations in clinical natural language processing

Objectives

To review through an ethics lens the state of research in clinical natural language processing (NLP) for the study of bias and fairness, and to identify gaps in research.

Methods

We queried PubMed and Google Scholar for articles published between 2015 and 2021 concerning clinical NLP, bias, and fairness. We analyzed articles using a framework that combines the machine learning (ML) development process (ie, design, data, algorithm, and critique) and bioethical concepts of beneficence, nonmaleficence, autonomy, justice, as well as explicability. Our approach further differentiated between biases of clinical text (eg, systemic or personal biases in clinical documentation towards patients) and biases in NLP applications.

Results

Out of 1162 articles screened, 22 met criteria for full text review. We categorized articles based on the design (N = 2), data (N = 12), algorithm (N = 14), and critique (N = 17) phases of the ML development process.

Discussion

Clinical NLP can be used to study bias in applications reliant on clinical text data as well as explore biases in the healthcare setting. We identify 3 areas of active research that require unique ethical considerations about the potential for clinical NLP to address and/or perpetuate bias: (1) selecting metrics that interrogate bias in models; (2) opportunities and risks of identifying sensitive patient attributes; and (3) best practices in reconciling individual autonomy, leveraging patient data, and inferring and manipulating sensitive information of subgroups. Finally, we address the limitations of current ethical frameworks to fully address concerns of justice. Clinical NLP is a rapidly advancing field, and assessing current approaches against ethical considerations can help the discipline use clinical NLP to explore both healthcare biases and equitable NLP applications.

Autism spectrum disorders as a risk factor for adolescent self-harm: a retrospective cohort study of 113,286 young people in the UK

BACKGROUND

Individuals with autism spectrum disorder (ASD) are at particularly high risk of suicide and suicide attempts. Presentation to a hospital with self-harm is one of the strongest risk factors for later suicide. We describe the use of a novel data linkage between routinely collected education data and child and adolescent mental health data to examine whether adolescents with ASD are at higher risk than the general population of presenting to emergency care with self-harm.

METHODS

A retrospective cohort study was conducted on the population aged 11-17 resident in four South London boroughs between January 2009 and March 2013, attending state secondary schools, identified in the National Pupil Database (NPD). Exposure data on ASD status were derived from the NPD. We used Cox regression to model time to first self-harm presentation to the Emergency Department (ED).

RESULTS

One thousand twenty adolescents presented to the ED with self-harm, and 763 matched to the NPD. The sample for analysis included 113,286 adolescents (2.2% with ASD). For boys only, there was an increased risk of self-harm associated with ASD (adjusted hazard ratio 2·79, 95% CI 1·40-5·57, P<0·01). Several other factors including school absence, exclusion from school and having been in foster care were also associated with a higher risk of self-harm.

CONCLUSIONS

This study provides evidence that ASD in boys, and other educational, social and clinical factors, are risk factors for emergency presentation with self-harm in adolescents. These findings are an important step in developing early recognition and prevention programmes.

Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach

Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.

Developing a Natural Language Processing tool to identify perinatal self-harm in electronic healthcare records

BACKGROUND

Self-harm occurring within pregnancy and the postnatal year ("perinatal self-harm") is a clinically important yet under-researched topic. Current research likely under-estimates prevalence due to methodological limitations. Electronic healthcare records (EHRs) provide a source of clinically rich data on perinatal self-harm.

AIMS

(1) To create a Natural Language Processing (NLP) tool that can, with acceptable precision and recall, identify mentions of acts of perinatal self-harm within EHRs. (2) To use this tool to identify service-users who have self-harmed perinatally, based on their EHRs.

METHODS

We used the Clinical Record Interactive Search system to extract de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust. We developed a tool that applied several layers of linguistic processing based on the spaCy NLP library for Python. We evaluated mention-level performance in the following domains: span, status, temporality and polarity. Evaluation was done against a manually coded reference standard. Mention-level performance was reported as precision, recall, F-score and Cohen's kappa for each domain. Performance was also assessed at 'service-user' level and explored whether a heuristic rule improved this. We report per-class statistics for service-user performance, as well as likelihood ratios and post-test probabilities.

RESULTS

Mention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality >0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance with heuristic: F-score, precision, recall of minority class 0.69, macro-averaged F-score 0.81, positive LR 9.4 (4.8-19), post-test probability 69.0% (53-82%). Considering the task difficulty, the tool performs well, although temporality was the attribute with the lowest level of annotator agreement.

CONCLUSIONS

It is feasible to develop an NLP tool that identifies, with acceptable validity, mentions of perinatal self-harm within EHRs, although with limitations regarding temporality. Using a heuristic rule, it can also function at a service-user-level.

Variation in rates of self-harm hospital admission and re-admission by ethnicity in London: a population cohort study

PURPOSE

To compare sex-specific rates of hospital admission and repeat admission following self-harm between ethnic groups in London and test whether differences persist after adjustment for socio-economic deprivation.

METHODS

A population-based cohort of all individuals aged over 11 admitted to a general hospital for physical health treatment following self-harm between 2008 and 2018, using administrative Hospital Episode Statistics for all people living in Greater London.

RESULTS

There were 59,510 individuals admitted to the hospital following self-harm in the 10 year study period, ethnicity data were available for 94% of individuals. The highest rates of self-harm admission and readmission were found in the White Irish group. Rates of admission and readmission were lower in Black and Asian people compared to White people for both sexes at all ages and in all more specific Black and Asian ethnic groups compared to White British. These differences increased with adjustment for socio-economic deprivation. People of Mixed ethnicity had higher rates of readmission. Rates were highest in the 25-49 age group for Black and Mixed ethnicity men, but in under-25 s for all other groups. There were substantial differences in rates within the broader ethnic categories, especially for the Black and White groups.

CONCLUSION

In contrast to earlier UK studies, self-harm rates were not higher in Black or South Asian women, with lower self-harm admission rates seen in almost all ethnic minority groups. Differences in rates by ethnicity were not explained by socio-economic deprivation. Aggregating ethnicity into broad categories masks important differences in self-harm rates between groups.

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

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

Differences in hospital admissions practices following self-harm and their influence on population-level comparisons of self-harm rates in South London: an observational study

OBJECTIVES

To compare the proportions of emergency department (ED) attendances following self-harm that result in admission between hospitals, examine whether differences are explained by severity of harm and examine the impact on spatial variation in self-harm rates of using ED attendance data versus admissions data.

SETTING

A dataset of ED attendances and admissions with self-harm to four hospitals in South East London, 2009-2016 was created using linked electronic patient record data and administrative Hospital Episode Statistics.

DESIGN

Proportions admitted following ED attendance and length of stay were compared. Variation and spatial patterning of age and sex standardised, spatially smoothed, self-harm rates by small area using attendance and admission data were compared and the association with distance travelled to hospital tested.

RESULTS

There were 20 750 ED attendances with self-harm, 7614 (37%) resulted in admission. Proportion admitted varied substantially between hospitals with a risk ratio of 2.45 (95% CI 2.30 to 2.61) comparing most and least likely to admit. This was not altered by adjustment for patient demographics, deprivation and type of self-harm. Hospitals which admitted more had a higher proportion of admissions lasting less than 24 hours (54% of all admissions at highest admitting hospital vs 35% at lowest). A previously demonstrated pattern of lower rates of self-harm admission closer to the city centre was reduced when ED attendance rates were used to represent self-harm. This was not altered when distance travelled to hospital was adjusted for.

CONCLUSIONS

Hospitals vary substantially in likelihood of admission after ED presentation with self-harm and this is likely due to the differences in hospital practices rather than in the patient population or severity of self-harm seen. Public health policy that directs resources based on self-harm admissions data could exacerbate existing health inequalities in inner-city areas where these data may underestimate rates relative to other areas.

The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Electronic health records (EHRs) have emerged among health information technology as "meaningful use" to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records' use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE (PubMed), 10/02/2012-10/02/2017, core clinical/academic journals, MEDLINE full text, English only, human species and evaluated the articles that were germane to our research objective. Each article was analyzed by multiple reviewers. Group members recognized common facilitators and barriers associated with EHRs effect on population health. A final list of articles was selected by the group after three consensus meetings (n = 55). Among a total of 26 factors identified, 63% (147/232) of those were facilitators and 37% (85/232) barriers. About 70% of the facilitators consisted of productivity/efficiency in EHRs occurring 33 times, increased quality and data management each occurring 19 times, surveillance occurring 17 times, and preventative care occurring 15 times. About 70% of the barriers consisted of missing data occurring 24 times, no standards (interoperability) occurring 13 times, productivity loss occurring 12 times, and technology too complex occurring 10 times. The analysis identified more facilitators than barriers to the use of the EHR to support public health. Wider adoption of the EHR and more comprehensive standards for interoperability will only enhance the ability for the EHR to support this important area of surveillance and disease prevention. This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.

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.

Compassion in emergency departments. Part 2: barriers to the provision of compassionate care

In the second part of this three-part series, David Hunter and colleagues discuss the barriers to the provision of compassionate care in emergency departments (EDs). Part one reported findings from doctoral-level research exploring nursing students' experiences of compassionate care in EDs. Many of the findings related to what the students considered as barriers to the provision of compassionate care in this clinical environment. Six barriers to compassionate care were identified and this article considers them in detail.

Routine hospital data - is it good enough for trials? An example using England's Hospital Episode Statistics in the SHIFT trial of Family Therapy vs. Treatment as Usual in adolescents following self-harm

BACKGROUND

Use of routine data sources within clinical research is increasing and is endorsed by the National Institute for Health Research to increase trial efficiencies; however there is limited evidence for its use in clinical trials, especially in relation to self-harm. One source of routine data, Hospital Episode Statistics, is collated and distributed by NHS Digital and contains details of admissions, outpatient, and Accident and Emergency attendances provided periodically by English National Health Service hospitals. We explored the reliability and accuracy of Hospital Episode Statistics, compared to data collected directly from hospital records, to assess whether it would provide complete, accurate, and reliable means of acquiring hospital attendances for self-harm - the primary outcome for the SHIFT (Self-Harm Intervention: Family Therapy) trial evaluating Family Therapy for adolescents following self-harm.

METHODS

Participant identifiers were linked to Hospital Episode Statistics Accident and Emergency, and Admissions data, and episodes combined to describe participants' complete hospital attendance. Attendance data were initially compared to data previously gathered by trial researchers from pre-identified hospitals. Final comparison was conducted of subsequent attendances collected through Hospital Episode Statistics and researcher follow-up. Consideration was given to linkage rates; number and proportion of attendances retrieved; reliability of Accident and Emergency, and Admissions data; percentage of self-harm episodes recorded and coded appropriately; and percentage of required data items retrieved.

RESULTS

Participants were first linked to Hospital Episode Statistics with an acceptable match rate of 95%, identifying a total of 341 complete hospital attendances, compared to 139 reported by the researchers at the time. More than double the proportion of Hospital Episode Statistics Accident and Emergency episodes could not be classified in relation to self-harm (75%) compared to 34.9% of admitted episodes, and of overall attendances, 18% were classified as self-harm related and 20% not related, while ambiguity or insufficient information meant 62% were unclassified. Of 39 self-harm-related attendances reported by the researchers, Hospital Episode Statistics identified 24 (62%) as self-harm related while 15 (38%) were unclassified. Based on final data received, 1490 complete hospital attendances were identified and comparison to researcher follow-up found Hospital Episode Statistics underestimated the number of self-harm attendances by 37.2% (95% confidence interval 32.6%-41.9%).

CONCLUSION

Advantages of routine data collection via NHS Digital included the acquisition of more comprehensive and timely trial outcome data, identifying more than double the number of hospital attendances than researchers. Disadvantages included ambiguity in the classification of self-harm relatedness. Our resulting primary outcome data collection strategy used routine data to identify hospital attendances supplemented by targeted researcher data collection for attendances requiring further self-harm classification.

Integrating electronic healthcare records of armed forces personnel: Developing a framework for evaluating health outcomes in England, Scotland and Wales

•

A framework which integration national Electronic Healthcare Record datasets from England, Scotland and Wales is proposed.

•

Variable similarity is used to develop a schema which allows for variables to be linked and combined across the nations.

•

Evaluation of integration shows that it is possibly to perform data linkage across the nations.

Background

Electronic Healthcare Records (EHRs) are created to capture summaries of care and contact made to healthcare services. EHRs offer a means to analyse admissions to hospitals for epidemiological research. In the United Kingdom (UK), England, Scotland and Wales maintain separate data stores, which are administered and managed exclusively by devolved Government. This independence results in harmonisation challenges, not least lack of uniformity, making it difficult to evaluate care, diagnoses and treatment across the UK. To overcome this lack of uniformity, it is important to develop methods to integrate EHRs to provide a multi-nation dataset of health.

Objective

To develop and describe a method which integrates the EHRs of Armed Forces personnel in England, Scotland and Wales based on variable commonality to produce a multi-nation dataset of secondary health care.

Methods

An Armed Forces cohort was used to extract and integrate three EHR datasets, using commonality as the linkage point. This was achieved by evaluating and combining variables which shared the same characteristics. EHRs representing Accident and Emergency (A&E), Admitted Patient Care (APC) and Outpatient care were combined to create a patient-level history spanning three nations. Patient-level EHRs were examined to ascertain admission differences, common diagnoses and record completeness.

Results

A total of 6,336 Armed Forces personnel were matched, of which 5,460 personnel had 7,510 A&E visits, 9,316 APC episodes and 45,005 Outpatient appointments. We observed full completeness for diagnoses in APC, whereas Outpatient admissions were sparsely coded; with 88% of diagnoses coded as “Unknown/unspecified cause of morbidity”. In addition, A&E records were sporadically coded; we found five coding systems for identifying reason for admission.

Conclusion

At present, EHRs are designed to monitor the cost of treatment, enable administrative oversight, and are not currently suited to epidemiological research. However, only small changes may be needed to take advantage of what should be a highly cost-effective means of delivering important research for the benefit of the NHS.

Management by geographical area or management specialised by disorder? A mixed-methods evaluation of the effects of an organisational intervention on secondary mental health care for common mental disorder

Background

In 2010, South London and Maudsley NHS Foundation Trust (SLaM) established a programme replacing the borough directorates responsible for adult mental health services with three Clinical Academic Groups (CAGs), each of which took on a subset of adult services straddling all four boroughs. Care pathways were also introduced. We studied the Mood Anxiety and Personality CAG, which took on assessment and treatment teams and psychotherapy services.

Objectives

We aimed (1) to understand the CAG programme using realistic evaluation and (2) to assess whether or not it led to changes in activity and health-care quality.

Methods

Qualitative analysis was based on interviews and project documents. Quantitative analyses were based on electronic patient records and compared care in community mental health teams (CMHTs) and psychotherapy teams before and after CAG implementation. Analyses of activity covered caseload, counts of new episodes, episode length and number of contacts per episode. We also looked at CMHT costs. Analyses of effectiveness covered processes (pharmacological and psychological treatment of depression in CMHTs) and outcomes (effect on the Health of the Nation Outcome Scales total score or the Clinical Outcomes in Routine Evaluation 10-item version total score). Analyses of safety examined the rates of self-harm among current or recent CMHT patients. Patient centredness was represented by waiting time.

Results

The first core component of SLaM’s CAG programme was the CAG restructuring itself. The second was the promotion of care pathways; interpreted as ‘high level pathways’, these schematised processes of referral, assessment, treatment, reassessment and discharge, but abstracted from the details of treatment. The three mechanisms of the CAG restructuring were increasing oversight, making teams fit the template of team types defined for each CAG (‘CAG compliance’) and changing financial accounts by grouping services in new ways; these mechanisms resulted in further reconfigurations. The use of high-level pathways supported service redesign and performance management. In CMHTs and psychotherapy teams activity tended to decrease, but this was probably not because of the CAG programme. CMHT costs were largely unchanged. There was no evidence that the CAG programme altered effectiveness or safety. Effects on waiting times varied but these were reduced in some cases. Overall, therefore, the CAG programme appeared to have had few effects on quality. We attributed this to the limited effect of the programme on individual treatment.

Conclusions

SLaM’s CAG programme had clear effects on service reconfiguration at team level, with high-level pathways changing the ways that managers conceptualised their work. However, our quantitative work indicated no clear effects on quality. Thinking about how to use care pathways in ways that complement ‘high-level’ pathways by supporting the delivery of evidence-based treatments is a strategy that could help SLaM and other providers. Future research should look at the genesis of organisational change and how this is altered through implementation; it should also look at the effectiveness of care pathways in mental health services.

Funding

The research was supported by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and was performed using infrastructure provided by the NIHR South London and Maudsley and Institute of Psychiatry Biomedical Research Centre.

Rates of self-harm presenting to general hospitals: a comparison of data from the Multicentre Study of Self-Harm in England and Hospital Episode Statistics

OBJECTIVE

Rates of hospital presentation for self-harm in England were compared using different national and local data sources.

DESIGN

The study was descriptive and compared bespoke data collection methods for recording self-harm presentations to hospital with routinely collected hospital data.

SETTING

Local area data on self-harm from the 3 centres of the Multicentre Study of Self-harm in England (Oxford, Manchester and Derby) were used along with national and local routinely collected data on self-harm admissions and emergency department attendances from Hospital Episode Statistics (HES).

PRIMARY OUTCOME

Rate ratios were calculated to compare rates of self-harm generated using different data sources nationally and locally (between 2010 and 2012) and rates of hospital presentations for self-harm were plotted over time (between 2003 and 2012), based on different data sources.

RESULTS

The total number of self-harm episodes between 2010 and 2012 was 13,547 based on Multicentre Study data, 9600 based on HES emergency department data and 8096 based on HES admission data. Nationally, routine HES data underestimated overall rates of self-harm by approximately 60% compared with rates based on Multicentre Study data (rate ratio for HES emergency department data, 0.41 (95% CI 0.35 to 0.49); rate ratio for HES admission data, 0.42 (95% CI 0.36 to 0.49)). Direct local area comparisons confirmed an overall underascertainment in the HES data, although the difference varied between centres. There was a general increase in self-harm over time according to HES data which contrasted with a fall and then a rise in the Multicentre Study data.

CONCLUSIONS

There was a consistent underestimation of presentations for self-harm recorded by HES emergency department data, and fluctuations in year-on-year figures. HES admission data appeared more reliable but missed non-admitted episodes. Routinely collected data may miss important trends in self-harm and cannot be used in isolation as the basis for a robust national indicator of self-harm.

The prevalence and correlates of self-harm in pregnant women with psychotic disorder and bipolar disorder

Women with severe mental illness are at increased risk of suicide in the perinatal period, and these suicides are often preceded by self-harm, but little is known about self-harm and its correlates in this population. This study aimed to investigate the prevalence of suicidal ideation and self-harm, and its correlates, in women with psychotic disorders and bipolar disorder during pregnancy. Historical cohort study using de-identified secondary mental healthcare records linked with national maternity data. Women pregnant from 2007 to 2011, with ICD-10 diagnoses of schizophrenia and related disorders, bipolar disorder or other affective psychoses were identified. Data were extracted from structured fields, natural language processing applications and free text. Logistic regression was used to examine the correlates of self-harm in pregnancy. Of 420 women, 103 (24.5 %) had a record of suicidal ideation during the first index pregnancy, with self-harm recorded in 33 (7.9 %). Self-harm was independently associated with younger age (adjusted odds ratio (aOR) 0.91, 95 % CI 0.85-0.98), self-harm in the previous 2 years (aOR 2.55; 1.05-6.50) and smoking (aOR 3.64; 1.30-10.19). A higher prevalence of self-harm was observed in women with non-affective psychosis, those who discontinued or switched medication and in women on no medication at the start of pregnancy, but these findings were not statistically significant in multivariable analyses. Suicidal thoughts and self-harm occur in a significant proportion of pregnant women with severe mental illness, particularly younger women and those with a history of self-harm; these women need particularly close monitoring for suicidality.