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Background Rates of suicide attempts and deaths are highest on Mondays and these occur more frequently in the morning or early afternoon, suggesting weekly temporal and diurnal variation in suicidal behaviour. It is unknown whether there are similar time trends on social media, of posts relevant to suicide. We aimed to determine temporal and diurnal variation in posting patterns on the Reddit forum SuicideWatch, an online community for individuals who might be at risk of, or who know someone at risk of suicide. Methods We used time series analysis to compare date and time stamps of 90,518 SuicideWatch posts from 1st December 2008 to 31st August 2015 to (i) 6,616,431 posts on the most commonly subscribed general subreddit, AskReddit and (ii) 66,934 of these AskReddit posts, which were posted by the SuicideWatch authors. Results Mondays showed the highest proportion of posts on SuicideWatch. Clear diurnal variation was observed, with a peak in the early morning (2:00–5:00 h), and a subsequent decrease to a trough in late morning/early afternoon (11:00–14:00 h). Conversely, the highest volume of posts in the control data was between 20:00–23:00 h. Conclusions Posts on SuicideWatch occurred most frequently on Mondays: the day most associated with suicide risk. The early morning peak in SuicideWatch posts precedes the time of day during which suicide attempts and deaths most commonly occur. Further research of these weekly and diurnal rhythms should help target populations with support and suicide prevention interventions when needed most.

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.

ObjectiveSuicide attempts requiring hospitalization are known to be common in patients who are diagnosed with eating disorders. Attempting suicide is a major indicator for those at risk of completed suicide. Both the specific eating disorder diagnosis and the influence of psychiatric comorbidities on suicide attempts requiring hospitalization were investigated, with demographic and socioeconomic variables as confounders, over a 10‐year observation period from January 2007 to March 2017.MethodsAnonymized health‐record data from the South London and Maudsley NHS Foundation Trust (SLaM) were retrieved through the Clinical Record Interactive Search (CRIS) data resource; this is linked to national Hospital Episode Statistics (HES) data. These data include all diagnoses for inpatient admissions. Hazard ratios, with 95% confidence intervals (CIs), were calculated from cox regression analyses and the effects of a number of confounders were estimated by performing multivariable analyses.ResultsIn total, 4,895 patients were diagnosed with anorexia nervosa (AN), bulimia nervosa (BN), or eating disorder otherwise not specified (EDNOS). Of these, 331 (6.7%) had attempted suicide requiring hospitalization and 21 (0.04%) completed suicide. The eating disorder category associated with the highest risk of a suicide attempt was AN (HR: 1.43, 95%CI: 1.08–1.89, p = .01). The risk was significantly increased further if the patient had a comorbid diagnosis of personality disorder, depression, bipolar affective disorder, and substance misuse.DiscussionSuicide attempts requiring hospitalization have a high incidence rate among patients with eating disorders, and the risk is significantly increased in AN. Comorbid psychiatric illness and suicidal ideation should be carefully assessed in all eating disorder patients.

Summary We describe the incidence of suicidality (2007–2017) in people with depression treated by secondary mental healthcare services at South London and Maudsley NHS Trust (n = 26 412). We estimated yearly incidence of ‘suicidal ideation’ and ‘high risk of suicide’ from structured and free-text fields of the Clinical Record Interactive Search system. The incidence of suicidal ideation increased from 0.6 (2007) to 1 cases (2017) per 1000 population. The incidence of high risk of suicide, based on risk forms, varied between 0.06 and 0.50 cases per 1000 adult population (2008–2017). Electronic health records provide the opportunity to examine suicidality on a large scale, but the impact of service-related changes in the use of structured risk assessment should be considered.

Risk of suicidal behaviour (SB) in schizophrenia spectrum disorders (SSD) is a major concern, particularly in early stages of the illness, when suicide accounts for a high number of premature deaths. Although some risk factors for SB in SSD are well understood, the extent to which personality traits may affect this risk remains unclear, which may have implications for prevention. We conducted a systematic review of previous studies indexed in MEDLINE, PsycINFO and Embase examining the relationship between personality traits and SB in samples of patients with SSD. Seven studies fulfilled predetermined selection criteria. Harm avoidance, passive-dependent, schizoid and schizotypal personality traits increased the risk of SB, while self-directedness, cooperativeness, excluding persistence and self-transcendence acted as protective factors. Although only seven studies were retrieved from three major databases after applying predetermined selection criteria, we found some evidence to support that personality issues may contribute to SB in patients with SSD. Personality traits may therefore become part of routine suicide risk assessment and interventions targeting these personality-related factors may contribute to prevention of SB in SSD.

Background Prevalence of self-harm in the UK was reported as 6.4% in 2014. Despite sparse evidence for effectiveness, guidelines recommend harm minimisation; a strategy in which people who self-harm are supported to do so safely. Aims To determine the prevalence, sociodemographic and clinical characteristics of those who self-harm and practise harm minimisation within a London mental health trust. Method We included electronic health records for patients treated by South London and Maudsley NHS Trust. Using an iterative search strategy, we identified patients who practise harm minimisation, then classified the approaches using a content analysis. We compared the sociodemographic characteristics with that of a control group of patients who self-harm and do not use harm minimisation. Results In total 22 736 patients reported self-harm, of these 693 (3%) had records reporting the use of harm-minimisation techniques. We coded the approaches into categories: (a) ‘substitution’ (>50% of those using harm minimisation), such as using rubber bands or using ice; (b) ‘simulation’ (9%) such as using red pens; (c) ‘defer or avoid’ (7%) such as an alternative self-injury location; (d) ‘damage limitation’ (9%) such as using antiseptic techniques; the remainder were unclassifiable (24%). The majority of people using harm minimisation described it as helpful (>90%). Those practising harm minimisation were younger, female, of White ethnicity, had previous admissions and were less likely to have self-harmed with suicidal intent. Conclusions A small minority of patients who self-harm report using harm minimisation, primarily substitution techniques, and the large majority find harm minimisation helpful. More research is required to determine the acceptability and effectiveness of harm-minimisation techniques and update national clinical guidelines.

Self-harm and suicidal ideation in children and adolescents are common and are risk factors for completed suicide. Social exclusion, which can take many forms, increases the risk of self-harm and suicidal ideation. One important marker of social exclusion in young people is school absenteeism. Whether school absenteeism is associated with these adverse outcomes, and if so to what extent, remains unclear. To determine the association between school absenteeism and both self-harm (including completed suicide) and suicidal ideation in children and adolescents, we conducted a systematic review of observational studies. We conducted meta-analysis and report a narrative synthesis where this was not possible. Meta-analysis of cross-sectional studies showed that school absenteeism was associated with an increased risk of self-harm [pooled adjusted odds ratio (aOR) 1.37, 95% confidence interval 1.20–1.57, P = 0.01] and of suicidal ideation (pooled aOR 1.20, 95% CI 1.02–1.42, P = 0.03). A small number of studies showed that school absenteeism had a longitudinal association with both adverse outcomes. Heterogeneity in the exposure and outcome variables, study design and reporting was prominent and limited the extent to which it was appropriate to pool results. School absenteeism was associated with both self-harm and suicidal ideation in young people, but this evidence was derived from a small number of cross-sectional studies. Further research into the mechanisms of this association could help to inform self-harm and suicide prevention strategies at clinical, school and population levels.

Background Suicide is a serious public health issue, accounting for 1.4% of all deaths worldwide. Current risk assessment tools are reported as performing little better than chance in predicting suicide. New methods for studying dynamic features in electronic health records (EHRs) are being increasingly explored. One avenue of research involves using sentiment analysis to examine clinicians’ subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora. Objective This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment. Methods The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score. Results The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful. Conclusions Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non–suicide-related EHR texts.

In the global effort to prevent death by suicide, many academic medical institutions are implementing natural language processing (NLP) approaches to detect suicidality from unstructured clinical text in electronic health records (EHRs), with the hope of targeting timely, preventative interventions to individuals most at risk of suicide. Despite the international need, the development of these NLP approaches in EHRs has been largely local and not shared across healthcare systems. In this study, we developed a process to share NLP approaches that were individually developed at King’s College London (KCL), UK and Weill Cornell Medicine (WCM), US - two academic medical centers based in different countries with vastly different healthcare systems. After a successful technical porting of the NLP approaches, our quantitative evaluation determined that independently developed NLP approaches can detect suicidality at another healthcare organization with a different EHR system, clinical documentation processes, and culture, yet do not achieve the same level of success as at the institution where the NLP algorithm was developed (KCL approach: F1-score 0.85 vs. 0.68, WCM approach: F1-score 0.87 vs. 0.72). Shared use of these NLP approaches is a critical step forward towards improving data-driven algorithms for early suicide risk identification and timely prevention.

Suicide is a major public health problem worldwide and continues to be one of the main causes of death. Implementing surveillance strategies for suicidal thoughts and behaviours would make it possible to identify individuals at high risk of ending their lives by suicide. While a universal screening would be controversial, the increasing use of the 9-item version of the Patient Health Questionnaire (PHQ-9) in different healthcare settings, such as primary care or hospital emergency departments, offers an opportunity for testing its performance for suicide surveillance. Beyond being a screening of depression, the PHQ-9 has shown merit as a marker of suicidal thinking, thoughts of self-harm, and suicide. Implementing systematic surveillance strategies for suicide in different healthcare settings including data from the PHQ-9 might be an effective way to improve case detection. This could help to enhance the identification of highest risk population groups and, consequently, to avoid potentially preventable suicides.

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.

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.

Aims1.To generate a Natural Language Processing (NLP) application that can identify mentions of perinatal self-harm among electronic healthcare records (EHRs)2.To use this application to estimate the prevalence of perinatal self-harm within a data-linkage cohort of women accessing secondary mental healthcare during the perinatal period.MethodData source: the Clinical Record Interactive Search system. This is a database of de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust (SLaM). CRIS has pre-existing ethical approval via the Oxfordshire Research Ethics Committee C (ref 18/SC/0372) and this project was approved by the CRIS Oversight Committee (16-069). After developing a list of synonyms for self-harm and piloting coding rules, a gold standard dataset of EHRs was manually coded using Extensible Human Oracle Suite of Tools (eHOST) software. An NLP application to detect perinatal self-harm was then developed using several layers of linguistic processing based on the spaCy NLP library for Python. Evaluation of mention-level performance was done according to the attributes of mentions the application was designed to identify (span, status, temporality and polarity), by comparing application performance against the gold standard dataset. Performance was described as precision, recall, F-score and Cohen's kappa. Most service-users had more than one EHR in their period of perinatal service use. Performance was therefore also measured at “service-user level” with additional performance metrics of likelihood ratios and post-test probabilities. Linkage with the Hospital Episode Statistics datacase allowed creation of a cohort of women who accessed SLaM during the perinatal period. By deploying the application on the EHRs of the women in the cohort, we were able to estimate the prevalence of perinatal self-harm.ResultMention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality all >0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance: F-score, precision, recall all 0.69, overall F-score 0.81, positive likelihood ratio 9.4 (4.8–19), post-test probability 68.9% (95%CI 53–82).Cohort prevalence of self-harm in pregnancy was 15.3% (95% CI 14.3–16.3); self-harm in the postnatal year was 19.7% (95% CI 18.6–20.8). Only a very small proportion of women self-harmed in both pregnancy and the postnatal year (3.9%, 95% CI 3.3–4.4).ConclusionNLP can be used to identify perinatal self-harm within EHRs. The hardest attribute to classify was temporality. This is in line with the wider literature indicating temporality as a notoriously difficult problem in NLP. As a result, the application probably over-estimates prevalence, to a degree. However, overall performance, given the difficulty of the task, is good.Bearing in mind the limitations, our findings suggest that self-harm is likely to be relatively common in women accessing secondary mental healthcare during the perinatal period.Funding: KA is funded by a National Institute for Health Research Doctoral Research Fellowship (NIHR-DRF-2016-09-042). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. RD is funded by a Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences which also party funds AB. AB's work was also part supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities, as well as the Maudsley Charity.Acknowledgements: Professor Louise M Howard, who originally suggested using NLP to identify perinatal self-harm in EHRs. Professor Howard is the primary supervisor of KA's Fellowship.

Introduction: Caregivers for people with dementia face a number of challenges such as changing family relationships, social isolation, or financial difficulties. Internet usage and social media are increasingly being recognised as resources to increase support and general public health. Objective: Using automated analysis, the aim of this study was to explore (i) the age and sex of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) which subreddits authors are posting to, (iv) the types of messages posted, and (v) the content of these posts. Methods: We analysed Reddit posts concerning dementia diagnoses and used a previously developed text analysis pipeline to determine attributes of the posts and their authors. The posts were further examined through manual annotation of the diagnosis provided and the person affected. Lastly, we investigated the communities posters engage with and assessed the contents of the posts with an automated topic gathering/clustering technique. Results: Five hundred and thirty-five Reddit posts were identified as relevant and further processed. The majority of posters in our dataset are females and predominantly close relatives, such as parents and grandparents, are mentioned. The communities frequented and topics gathered reflect not only the person’s diagnosis but also potential outcomes, for example hardships experienced by the caregiver or the requirement for legal support. Conclusions: This work demonstrates the value of social media data as a resource for in-depth examination of caregivers’ experience after a dementia diagnosis. It is important to study groups actively posting online, both in topic-specific and general communities, as they are most likely to benefit from novel internet-based support systems or interventions.

Background: Despite the strong links between mental disorder, self-harm and suicide, there is little prospective data on risk factors for later suicide in women who experience perinatal mental disorders. We aimed to examine later suicide in a cohort of women in contact with a mental healthcare provider perinatally and whether self-harm increased suicide risk. Methods: Via data-linkage, de-identified service-user electronic healthcare records, national hospital episode statistics and mortality data generated a clinical cohort of women who gave birth in hospital and were in contact with a mental healthcare provider in London, UK, perinatally. Using Natural Language Processing and structured field extraction, we identified clinical, socio-demographic characteristics, self-harm, and suicide. Outcomes: Among 5204 women, crude rate of suicide 9·69/100 000 person-years (95% CI 3·64 - 25·82) within one year of delivery; 27·56 (15·26 - 49·77) within two, and 55·12 (36·29 - 83·71) during total follow up time (39,912·9 person-years). Characteristics of women who did and did not die by suicide were similar apart from illness severity indicators, which were more common in women who died by suicide. In a Cox regression model, lifetime self-harm was not associated with later suicide. Suicides occurred most frequently in the second year after delivery, most commonly by violent means. Suicides beyond two years were most commonly by poisoning. Interpretation: This study provides support for the extension of perinatal mental healthcare to two years post-delivery, particularly for women with severe mental disorders. Funding : The National Institute for Health Research, King's College London Biomedical Research Council, Health Foundation, Academy of Medical Sciences. Declaration of Interests: None. Ethics Approval Statement: CRIS has pre-existing ethical approval via the Oxfordshire Research Ethics Committee C (ref 18/SC/0372). The use of HES data is included in the CRIS REC approval and the BRC also have approval through the Health Research Authority’s support under Section 251 of the NHS Act 2006 (Ref: ECC 3- 04(f)/2011). Linkage between CRIS and ONS Mortality data is approved via the Health Research Authority’s support under Section 251 of the NHS Act 2006. Individual CRIS projects require ethical approval from the CRIS Oversight Committee (approval 16-069), which is chaired by a service-user and member of the SLAM BRC Stakeholder Participation theme.

Objective To perform a systematic review and meta-analysis on research on prevalence and correlates of self-harm in pregnancy and the postpartum year ("perinatal self-harm"). Data sources Six databases (EMBASE, MEDLINE, PsycINFO, Maternity and Infant Care Database, CINAHL, Cochrane Controlled Register of Trials) were searched from inception through October 31, 2018. Study selection Inclusion criteria were (1) peer-reviewed articles with (2) data available for estimating prevalence and correlates. Exclusion criteria were (1) studies of women seeking abortion and (2) letters, editorials, or case reports/series. Data extraction Two reviewers independently screened all articles, extracted data, and appraised quality. Results Of 3,913 articles screened, 39 (reporting 19,191,431 pregnancies) were included. Prevalence ranges were as follows: self-harm during pregnancy (14 studies): 0%-2.39% (median = 0.0004%; interquartile range [IQR], 0.0002%-0.18%); self-harm during postpartum year (10 studies): 0%-2.41% (median = 0.17%; IQR, 0.04%-1.05%); self-harm during pregnancy in women with serious mental illness (SMI) (6 studies): 0%-23.78% (median = 2.16%; IQR, 0.26%-7.9%); self-harm during postpartum year in women with SMI (7 studies): 0%-21.9% (median = 7.97%; IQR, 0%-18%). Key correlates of self-harm during pregnancy and the postpartum year include mental disorder, substance misuse, younger age, being unmarried, and obstetric and neonatal complications. Additionally, a history of self-harm and fetal/infant loss were associated with postpartum self-harm. There were limited data on correlates of perinatal self-harm in women with SMI. Conclusions Perinatal self-harm appears to be rare but is associated with adverse obstetric and neonatal outcomes. However, it is common in women with SMI, though there is limited evidence regarding correlates and outcomes in this population. More research into the prevalence, correlates, and outcomes of perinatal self-harm, particularly in women with SMI, is needed.

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. Electronic supplementary material The online version of this article (10.1007/s00127-018-1536-8) contains supplementary material, which is available to authorized users.

Aims and methodTo assess the compliance of contemporary online media output with guidelines for the responsible reporting of suicidal acts. A search engine was used to identify online media reports of suicide from UK sources over the course of 1 month. Each article was assessed against guidelines for the responsible reporting of suicide produced by the Samaritans, a UK mental health charity.ResultsWe identified 229 articles, of which 199 failed to comply with at least one of the Samaritans' guidelines. Failure to mention support sources, excessive detail about the method used and undue speculation about the trigger for suicide were the most commonly breached guidelines. Significant differences were found between the quality of local and national media sources, with local media sources being broadly more compliant with guidelines.Clinical implicationsThis study highlights the urgent need for the implementation of responsible reporting guidelines in online media articles as a component of suicide prevention efforts.

Background: The role of insight dimensions – illness recognition (IR), symptoms relabelling (SR), treatment compliance (TC) - in suicide risk in first-episode psychosis (FEP) remains unclear. Method: The AESOP (n=181) and GAP (n=112) FEP cohorts were followed-up over 10- and 5 years. Survival analysis modelled time to first suicidal event in relation to baseline scores on the Schedule for the Assessment of Insight, whilst adjusting for demographic, clinical, psychopathological and neuropsychological variables., This work represents independent research which was supported by the UK Medical Research Council (grant number G0500817) and partly-funded by the UK Department of Health via the National Institute for Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health award to the South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry, Psychology and Neuroscience at Kings College London. JDLM was funded by the British Medical Association via the Margaret Temple Research Award for Schizophrenia. PBJ received funding support from the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care (CLAHRC), East of England. RMM was supported by the Medical Research Council and Lord Leverhulme Trust. CM is funded by grants from the Medical Research Council (grant number MR/P025927/1) and EU (grant number ERC Ref: 648837 REACH). RD is funded by a Clinician Scientist Fellowship awarded by the Academy of Medical Sciences in partnership with The Health Foundation. ASD is supported by National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London.

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity formental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer fromlow positive predictive values.More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice, This manuscript was written as a result of a workshop that was held at the Institute of Psychiatry, Psychology and Neuroscience, King’s College London, financially supported by the European Science Foundation (ESF) Research Networking Programme Evaluating Information Access Systems: http://elias-network.eu/. SV is supported by the Swedish Research Council (2015-00359) and the Marie Skłodowska Curie Actions, Cofund, Project INCA 600398. EB-G is partially supported by grants from Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073); American Foundation for Suicide Prevention (AFSP) (LSRG- 1-005-16). NW is supported by the UCLH NIHR Biomedical Research Centre. DN is supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, with an Alan Turing Institute Fellowship (TU/A/000006). RP has received support from a Medical Research Council (MRC) Health Data Research UK Fellowship (MR/S003118/1) and a Starter Grant for Clinical Lecturers (SGL015/1020) supported by the Academy of Medical Sciences, The Wellcome Trust, MRC, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK. DL is supported by the UK Medical Research Council under grant MR/N028244/2 and the King’s Centre for Military Health Research. JD is supported by a Medical Research Council (MRC) Clinical Research Training Fellowship (MR/L017105/1). RD is funded by a Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences

ObjectiveClinician narrative style in electronic health records (EHR) has rarely been investigated. Clinicians sometimes record brief quotations from patients, possibly more frequently when higher risk is perceived. We investigated whether the frequency of quoted phrases in an EHR was higher in time periods closer to a suicide attempt.DesignA case–crossover study was conducted in a large mental health records database. A natural language processing tool was developed using regular expression matching to identify text occurring within quotation marks in the EHR.SettingElectronic records from a large mental healthcare provider serving a geographic catchment of 1.3 million residents in South London were linked with hospitalisation data.Participants1503 individuals were identified as having a hospitalised suicide attempt from 1 April 2006 to 31 March 2017 with at least one document in both the case period (1–30 days prior to admission) and the control period (61–90 days prior to admission).Outcome measuresThe number of quoted phrases in the control as compared with the case period.ResultsBoth attended (OR 1.05, 95% CI 1.02 to 1.08) and non-attended (OR 1.15, 95% CI 1.04 to 1.26) clinical appointments were independently higher in the case compared with control period, while there was no difference in mental healthcare hospitalisation (OR 0.99, 95% CI 0.98 to 1.01). In addition, there was no difference in the levels of quoted text between the comparison time periods (OR 1.09, 95% CI 0.91 to 1.30).ConclusionsThis study successfully developed an algorithm to identify quoted speech in text fields from routine mental healthcare records. Contrary to the hypothesis, no association between this exposure and proximity to a suicide attempt was found; however, further evaluation is warranted on the way in which clinician-perceived risk might be feasibly characterised from clinical text.

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

For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.

The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of 'in the moment' daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

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.

The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients’ own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of ‘in the moment’ daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

OBJECTIVE: This study estimated the prevalence and correlates of self-harm among adult victims of human trafficking who accessed secondary mental health services, and it estimated the responses of mental health services to these individuals. METHODS: A clinical records database was searched for self-harm, sociodemographic, clinical, and service use characteristics among trafficked adults who accessed secondary mental health services in South London (2006-2012). Logistic regression models compared trafficked patients (N=84) and a matched cohort of nontrafficked patients (N=287). RESULTS: Among trafficked patients, 33% had engaged in self-harm prior to care and 25% in self-harm during care. After engaging in self-harm, trafficked patients were more likely than nontrafficked patients to be admitted as a psychiatric inpatient (adjusted odds ratio [AOR]=2.81) but less likely to visit an emergency department (AOR=.47). CONCLUSIONS: Self-harm is prevalent among trafficked adults accessing secondary mental health services, and mental health professionals have a crucial role to play in supporting survivors.

Mental Health Records (MHRs) contain free- text documentation about patients’ suicide and suicidality. In this paper, we address the prob- lem of determining whether grammatic vari- ants (inflections) of the word “suicide” are af- firmed or negated. To achieve this, we pop- ulate and annotate a dataset with over 6,000 sentences originating from a large repository of MHRs. The resulting dataset has high Inter- Annotator Agreement ( κ 0.93). Furthermore, we develop and propose a negation detection method that leverages syntactic features of text 1 . Using parse trees, we build a set of ba- sic rules that rely on minimum domain knowl- edge and render the problem as binary clas- sification (affirmed vs. negated). Since the overall goal is to identify patients who are ex- pected to be at high risk of suicide, we focus on the evaluation of positive (affirmed) cases as determined by our classifier. Our negation detection approach yields a recall (sensitivity) value of 94.6% for the positive cases and an overall accuracy value of 91.9%. We believe that our approach can be integrated with other clinical Natural Language Processing tools in order to further advance information extrac- tion capabilities

Purpose: The South London and Maudsley National Health Service (NHS) Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register and itsClinical Record Interactive Search (CRIS) application were developed in 2008, generating a research repository of real-time, anonymised, structured andopen-text data derived from the electronic health record system used by SLaM, a large mental healthcare provider in southeast London. In this paper, we updatethis register’s descriptive data, and describe the substantial expansion and extension of the data resource since its original development.Participants: Descriptive data were generated from the SLaM BRC Case Register on 31 December 2014. Currently, there are over 250 000 patient records accessed through CRIS.Findings to date: Since 2008, the most significant developments in the SLaM BRC Case Register have been the introduction of natural language processing toextract structured data from open-text fields, linkages to external sources of data, and the addition of a parallel relational database (Structured QueryLanguage) output. Natural language processing applications to date have brought in new and hitherto inaccessible data on cognitive function, education,social care receipt, smoking, diagnostic statements and pharmacotherapy. In addition, through external data linkages, large volumes of supplementary information have been accessed on mortality, hospital attendancesand cancer registrations.Future plans: Coupled with robust data security and governance structures, electronic health records provide potentially transformative information onmental disorders and outcomes in routine clinical care. The SLaM BRC Case Register continues to grow as a database, with approximately 20 000 new cases added each year, in addition to extension of follow-up for existing cases. Data linkages and natural language processing present important opportunities toenhance this type of research resource further, achieving both volume and depth of data. However, research projects still need to be carefully tailored, so that they take into account the nature and quality of the source information.

Background Suicide has been shown to represent the major single cause of premature death among patients with schizophrenia spectrum disorders. Insight has been proposed to increase such risk. However, this subject has not been sufficiently investigated, and inconclusive results have been reported. Objective The objective of this study is to systematically examine the role of insight in the risk of suicide attempts and completed suicide among patients with schizophrenia and related disorders. Method Articles assessing insight and suicidality in patients with schizophrenia spectrum disorders published between 1977 and 2010 were reviewed. A MEDLINE search strategy was used to identify studies using keywords. Application of meta-analytic techniques to selected studies was not possible because of important methodological differences between them. Results Fifteen studies met predetermined selection criteria. Ten failed to demonstrate a positive association between insight and risk for suicide. Discussion There is little evidence to support the suggestion that insight may represent a risk factor for suicide in patients with schizophrenia. If there is an association between such risk and insight, it appears to be mediated by other variables such as depression and, above all, hopelessness. Further studies with larger samples and longer follow-up periods in naturalistic conditions, in which insight should be evaluated from a multidimensional approach, are required to analyze this issue in depth, given the crucial implications that it may have on the development of a model for suicide prevention in schizophrenia.

BackgroundThe excess mortality following first-contact psychosis is well recognized. However, the causes of death in a complete incidence cohort and mortality patterns over time compared with the general population are unknown.MethodAll 2723 patients who presented for the first time with psychosis in three defined catchment areas of the UK in London (1965–2004, n=2056), Nottingham (1997–1999, n=203) and Dumfries and Galloway (1979–1998, n=464) were traced after a mean of 11.5 years follow-up and death certificates were obtained. Data analysis was by indirect standardization.ResultsThe overall standardized mortality ratio (SMR) for first-contact psychosis was 184 [95% confidence interval (CI) 167–202]. Most deaths (84.2%, 374/444) were from natural causes, although suicide had the highest SMR (1165, 95% CI 873–1524). Diseases of the respiratory system and infectious diseases had the highest SMR of the natural causes of death (232, 95% CI 183–291). The risk of death from diseases of the circulatory system was also elevated compared with the general population (SMR 139, 95% CI 117–164) whereas there was no such difference for neoplasms (SMR 111, 95% CI 86–141). There was strong evidence that the mortality gap compared with the general population for all causes of death (pp=0.01) increased over the four decades of the study. There was weak evidence that cardiovascular deaths may be increasing relative to the general population (p=0.07).ConclusionsPeople with first-contact psychosis have an overall mortality risk that is nearly double that of the general population. Most excess deaths are from natural causes. The widening of the mortality gap over the last four decades should be of concern to all clinicians involved in delivering healthcare.

Background: There is concern about the level of satisfaction with mental healthcare among minority ethnic patients in the UK, particularly as black patients have more compulsory admissions to hospital. Aims: To determine and compare levels of satisfaction with mental healthcare between patients from different ethnic groups in a three-centre study of first-onset psychosis. Method: Data were collected from 216 patients with first-episode psychosis and 101 caregivers from South London, Nottingham and Bristol, using the Acute Services Study Questionnaire (Patient and Relative Version) and measures of sociodemographic variables and insight. Results: No differences were found between ethnic groups in most domains of satisfaction tested individually, including items relating to treatment by ward staff and number of domains rated as satisfactory. However, logistic regression modelling (adjusting for age, gender, social class, diagnostic category and compulsion) showed that black Caribbean patients did not believe that they were receiving the right treatment and were less satisfied with medication than white patients. Black African patients were less satisfied with non-pharmacological treatments than white patients. These findings were not explained by lack of insight or compulsory treatment. Conclusions: The study found that black patients were less satisfied with specific aspects of treatment, particularly medication, but were equally satisfied with nursing and social care. Understanding the reasons behind this may improve the acceptability of psychiatric care to black minority ethnic groups.

Background: Puerperal sepsis in developing countries is a major health problem. Antenatal and labour related risk factors play a huge role in causing puerperal sepsis, many of which are modifiable. The aim of the present study is to study the risk factors, clinical features and outcomes of puerperal sepsis in the mother and determine the causative microorganisms involved.Methods: A prospective observational study was conducted at the department of Obstetrics and Gynaecology, Assam Medical college and Hospital from 1st July 2016 to 30th June 2017.Peurperal sepsis cases were identified based on the WHO clinical definition. A total of 88 patients were identified. Information regarding occupation, antenatal care, delivery related events and outcome were recorded on a predesigned proforma. Mothers were followed up till discharge from the hospital.Results: Out of 88 clinically diagnosed patients with puerperal sepsis, common risk factors associated with mortality were young age(p

Cervical ectopic pregnancy is implantation of an embryo into the cervical mucosa below the level of the internal os. Cervical pregnancy is an infrequent form of ectopic gestation both in naturally conceived pregnancies and even after assisted reproductive technology (ART). It accounts for less than 1% of all ectopic pregnancies. The diagnosis can be missed as a case of incomplete abortion unless an experienced sonologist performs a transvaginal ultrasound. Management options vary and depend on the gestational age at diagnosis, general condition of the patient and woman’s desire to maintain fertility. Medical treatment with methotrexate is the therapy of choice in early gestation with hemodynamically stable patient. Surgical intervention is indicated in late gestation, unstable cases or failed medical treatment. At times hysterectomy is elected as the final path when bleeding does not subside. We present an unique case of twin cervical ectopic pregnancy managed initially conservatively later surgical management was undertaken in view of bleeding.

Background. The high risk of suicide in bipolar disorder is well recognized, but may have been overestimated. There is conflicting evidence about deaths from other causes and little known about risk factors for suicide. We aimed to estimate suicide and mortality rates in a cohort of bipolar patients and to identify risk factors for suicide.Method. All patients who presented for the first time with a DSM-IV diagnosis of bipolar I disorder in a defined area of southeast London over a 35-year period (1965–1999) were identified. Mortality rates were compared with those of the 1991 England and Wales population, indirectly standardized for age and gender. Univariate and multivariate analyses were used to test potential risk factors for suicide.Results. Of the 239 patients in the cohort, 235 (98·3%) were traced. Forty-two died during the 4422 person-years of follow-up, eight from suicide. The standardized mortality ratio (SMR) for suicide was 9·77 [95% confidence interval (CI) 4·22–19·24], which, although significantly elevated compared to the general population, represented a lower case fatality than expected from previous literature. Deaths from all other causes were not excessive for the age groups studied in this cohort. Alcohol abuse [hazard ratio (HR) 6·81, 95% CI 1·69–27·36, p=0·007] and deterioration from pre-morbid level of functioning up to a year after onset (HR 5·20, 95% CI 1·24–21·89, p=0·024) were associated with increased risk of suicide.Conclusions. Suicide is significantly increased in unselected bipolar patients but actual case fatality is not as high as previously claimed. A history of alcohol abuse and deterioration in function predict suicide in bipolar disorder.

Background: Self-harm is a significant public health concern in the UK. This is reflected in the recent addition to the English Public Health Outcomes Framework of rates of attendance at Emergency Departments (EDs) following self-harm. However there is currently no source of data to measure this outcome. Routinely available data for inpatient admissions following self-harm miss the majority of cases presenting to services. We aimed to investigate (i) if a dataset of ED presentations could be produced using a combination of routinely collected clinical and administrative data and (ii) to validate this dataset against another one produced using methods similar to those used in previous studies. Methods: Using the Clinical Record Interactive Search system, the electronic health records (EHRs) used in four EDs were linked to Hospital Episode Statistics to create a dataset of attendances following self-harm. This dataset was compared with an audit dataset of ED attendances created by manual searching of ED records. The proportion of total cases detected by each dataset was compared. Results: There were 1932 attendances detected by the EHR dataset and 1906 by the audit. The EHR and audit datasets detected 77 % and 76 % of all attendances respectively and both detected 82 % of individual patients. There were no differences in terms of age, sex, ethnicity or marital status between those detected and those missed using the EHR method. Both datasets revealed more than double the number of self-harm incidents than could be identified from inpatient admission records. Conclusions: It was possible to use routinely collected EHR data to create a dataset of attendances at EDs following self-harm. The dataset detected the same proportion of attendances and individuals as the audit dataset, proved more comprehensive than the use of inpatient admission records, and did not show a systematic bias in those cases it missed.

Background: Excess mortality in severe mental illnesses (SMI) (schizophrenia-spectrum and bipolar disorders) is well described. Little is known about this inequality in ethnic minorities. The aim of this study was to assess mortality in SMI by ethnicity (White British, Black African, Black Caribbean, South Asian, Irish) and assess social and clinical risk factors for excess mortality.

AIM: To provide a systematic update of the evidence concerning the relationship between economic recession and suicide.METHODS: A keyword search of Ovid Medline, Embase, Embase Classic, PsycINFO and PsycARTICLES was performed to identify studies that had investigated the association between economic recession and suicide.RESULTS: Thirty-eight studies met predetermined selection criteria and 31 of them found a positive association between economic recession and increased suicide rates. Two studies reported a negative association, two articles failed to find such an association, and three studies were inconclusive.CONCLUSION: Economic recession periods appear to increase overall suicide rates, although further research is warranted in this area, particularly in low income countries.

Context: The long-term risk of suicide after a first episode of psychosis is unknown because previous studies often have been based on prevalence cohorts, been biased to more severely ill hospitalized patients, extrapolated from a short follow-up time, and have made a distinction between schizophrenia and other psychoses. Objective: To determine the epidemiology of suicide in a clinically representative cohort of patients experiencing their first episode of psychosis. Design: Retrospective inception cohort. Setting: Geographic catchment areas in London, England (between January 1, 1965, and December 31, 2004; n=2056); Nottingham, England (between September 1, 1997, and August 31, 1999; n=203); and Dumfries and Galloway, Scotland (between January 1, 1979, and December 31, 1998; n=464). Participants: All 2723 patients who presented for the first time to secondary care services with psychosis in the 3 defined catchment areas were traced after a mean follow-up period of 11.5 years. Main Outcome Measure: Deaths by suicide and open verdicts according to the International Classification of Diseases (seventh through tenth editions). Results: The case fatality from suicide was considerably lower than expected from previous studies (1.9% [53/2723]); the proportionate mortality was 11.9% (53/444). Although the rate of suicide was highest in the first year after presentation, risk persisted late into follow-up, with a median time to suicide of 5.6 years. Suicide occurred approximately 12 times more than expected from the general population of England and Wales (standardized mortality ratio, 11.65; 95% confidence interval, 8.73-15.24), and 49 of the 53 suicides were excess deaths. Even a decade after first presentation-a time when there may be less intense clinical monitoring of risk-suicide risk remained almost 4 times higher than in the general population (standardized mortality ratio, 3.92; 95% confidence interval, 2.22-6.89). Conclusions: The highest risk of suicide after a psychotic episode occurs soon after presentation, yet physicians should still be vigilant in assessing risk a decade or longer after first contact. The widely held view that 10% to 15% die of suicide is misleading because it refers to proportionate mortality, not lifetime risk. Nevertheless, there is a substantial increase in risk of suicide compared with the general population.

Background: Much remains unknown about whether there are early risk factors for suicide in psychosis. Aim: The aim of the study was to determine whether there are any identifiable early symptom clusters, aetiological factors or illness course markers for suicide in first episode psychosis. Method: A total of 2132 patients with first episode psychosis presenting to secondary care services in London (1965-2004; n = 1474), Nottingham (1997-1999; n = 195) and Dumfries and Galloway (1979-1998; n = 463) were traced after up to 40 years (mean 13 years) following first presentation. Risk factors were identified from the Operational Checklist for Psychotic Disorders rated for the first year following presentation. Results: Overall, there were 51 suicides and 373 deaths from other causes. Male gender (RR 2.84, 95% Cl 1.20-6.69, p = 0.02) and a cumulative threshold effect of symptoms early in the illness (RR 6.81,95% Cl 2.33-19.85, p

Background: The long term risk of suicide following first onset of psychosis is unknown, because previous studies have (1) been based on prevalence cohorts, (2) been biased to more severely ill, hospitalised patients, (3) extrapolated from short follow–up times and (4) made a distinction between schizophrenia and other psychoses. Previous research has identified risk factors in the period leading up to suicide in psychotic illness, but little is known about whether factors identifiable early in the course of illness might be markers for later suicide. Objectives: (i) To determine the epidemiology of suicide and (ii) to investigate potential early risk factors for suicide in a clinically representative, retrospective inception cohort of n=2,723 first onset psychosis patients. Method: All 2,723 patients who presented for the first time to secondary care services with psychosis in three defined geographical catchment areas in London (1965-2004; n=2056), Nottingham (1997-1999; n=203) and Dumfries and Galloway (1979-1998; n=464) were traced after a mean follow-up period of 11.5 years and death certificates were obtained to identify deaths by suicide and open verdicts according to ICD-7-10. Potential early risk factors for suicide were identified from the Operational Checklist for Psychotic Disorders rated for the first year following presentation. Results: Overall there were 53 suicides and 391 deaths from other causes. Case fatality from suicide was considerably lower than expected from previous studies: 1.9% (53/2723); proportionate mortality was 11.9% (53/444). Although the rate of suicide was highest in the first year after presentation, risk persisted late into follow-up, with median time to suicide being 5.6 years. Suicide occurred approximately 12 times more than expected from the general population of England and Wales (SMR 11.65; 95%CI 8.7315.24), and 49 of the 53 suicides were excess deaths. Even a decade after first presentation, suicide risk remained almost 4 times higher than in the general population (SMR 3.92; 95%CI 2.22-6.89): a time when there may be less intense clinical monitoring of risk. Male gender (RR 2.84, 95% CI 1.206.69, p=0.02) and a cumulative threshold effect of symptoms early in the illness (RR 6.81, 95% CI 2.33-19.85, p

Background The long-term risk of suicide after a first episode of psychosis is unknown, because previous studies have often been based on prevalence cohorts, have been biased to more severely ill, hospitalised patients, have extrapolated from a short follow-up time, and have made a distinction between schizophrenia and other psychoses. The aim of this study was to determine the epidemiology of suicide in a clinically representative, retrospective inception cohort of patients with a first episode of psychosis. Methods All 2723 patients who presented for the first time to secondary care services with psychosis in three defined geographical catchment areas in London (1965–2004, n=2056), Nottingham (1997–1999, n=203), and Dumfries and Galloway (1979–1998, n=464) were traced after a mean follow-up of 11·5 years. The main outcome measure was number of deaths by suicide and open verdicts according to International Classification of Diseases, editions 7–10. Findings Case fatality from suicide was considerably lower than expected from previous studies: 1·9% (53/2723); proportionate mortality was 11·9% (53/444). Although the rate of suicide was highest in the first year after presentation, risk persisted late into follow-up, with median time to suicide being 5·6 years. Suicide occurred nearly 12 times more than expected from the general population of England and Wales (standardised mortality ratio 11·65, 95%CI 8·73–15·24), and 49 of the 53 suicides were excess deaths. Even a decade after first presentation, suicide risk remained almost four times higher than in the general population (3·92, 95%CI 2·22–6.89), a time when there may be less intense clinical monitoring of risk. Interpretation The highest risk of suicide after a psychotic episode occurs soon after presentation, yet clinicians should still be vigilant in assessing risk a decade or more after first contact. The widely held view that 10–15% die from suicide is misleading because it refers to proportionate mortality rather than lifetime risk. Nonetheless, after a first espisode of psychosis, risk of suicide is substantially increased compared with that in the general population. Funding UK Medical Research Council.

Background: Doctors' hands are a common source of bacterial contamination. Often, these organisms are found to be virulent species with multidrug-resistance patterns. These are the sources of nosocomial infections in many patients. Aims: The present study was undertaken to find out the prevalence of bacterial contamination in the hands of doctors in the Medicine and Dermatology wards of a tertiary care hospital. Methods: The hands of 44 doctors were swabbed and cultured at entry to ward and at exit. Then, tap water and alcohol swab wash techniques were used and further swabs were done at each step. Thus, each doctor was sampled four-times for the study. The antibiotic-sensitivity pattern of the organisms was determined by the disc-diffusion method. Results: There was a significant contamination of the doctors' hands at entry (59.1%) and at exit (90.9%). Overall, Staphylococcus was the predominant organism (59% at entry and 85% at exit); coagulase-negative ones were more prevalent at entry (32%) and coagulase-positive ones were more prevalent at exit (54%). There was no difference in the hand contamination rates of junior and senior doctors. Also, the contamination rates were similar in the Medicine and Dermatology wards. Among the Gram negative organisms, Escherichia coli (4.5%), Pseudomonas (4.5%), Enterococci (13.6%) and Klebsiella (9%) were the main ones isolated. Gram negative organisms were significantly more prevalent at exit (P = 0.009) compared with their numbers at entry. Hand washing techniques reduced the contamination rates significantly, 76% with tap water wash and further 16.5% with alcohol swab. The removal rate for both groups of organisms was similar. Also, coagulase-positive and -negative Staphylococci showed equal rates of removal with hand washing (P = 0.9793). The organisms were found to be resistant to most of the commonly used antibiotics; the beta-lactam group was especially largely resistant both for Gram positive and Gram negative bacteria. Both cheaper ones like cloxacillin (50-100%) and very costly ones like cefepime (100%) were equally vulnerable to resistance. Even newer antibiotics like linezolid and vancomycin showed a significant resistance to Staphylococcus. In Gram negative organisms, drugs like ceftazidime and gentamicin showed 100% resistance. Conclusion: This study shows the high level of contamination of doctors' hands. It emphasizes the need for proper hygienic measures in day to day practice in hospitals to reduce the level of nosocomial infections. Also, it shows that most of the commonly used antibiotics will be ineffective in nosocomial infections.