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BACKGROUND: The clinical course of psychotic disorders is highly variable. Typically, researchers have captured different course types using broad pre-defined categories. However, whether these adequately capture symptom trajectories of psychotic disorders has not been fully assessed. Using data from AESOP-10, we sought to identify classes of individuals with specific symptom trajectories over a 10-year follow-up using a data-driven approach. METHOD: AESOP-10 is a follow-up, at 10 years, of 532 incident cases with a first episode of psychosis initially identified in south-east London and Nottingham, UK. Using extensive information on fluctuations in the presence of psychotic symptoms, we fitted growth mixture models to identify latent trajectory classes that accounted for heterogeneity in the patterns of change in psychotic symptoms over time. RESULTS: We had sufficient data on psychotic symptoms during the follow-up on 326 incident patients. A four-class quadratic growth mixture model identified four trajectories of psychotic symptoms: (1) remitting-improving (58.5%); (2) late decline (5.6%); (3) late improvement (5.4%); (4) persistent (30.6%). A persistent trajectory, compared with remitting-improving, was associated with gender (more men), black Caribbean ethnicity, low baseline education and high disadvantage, low premorbid IQ, a baseline diagnosis of non-affective psychosis and long DUP. Numbers were small, but there were indications that those with a late decline trajectory more closely resembled those with a persistent trajectory. CONCLUSION: Our current approach to categorising the course of psychotic disorders may misclassify patients. This may confound efforts to elucidate the predictors of long-term course and related biomarkers.

According to the Adult Psychiatric Morbidity Survey (APMS) 2014, one in six (17%) people in England met the criteria for a common mental disorder (CMD), with most mental disorders frequently present in those with poor physical health (McManus et al, 2016). Data from the same survey using the Alcohol Use Disorders Identification Test (AUDIT) showed that nearly 20% of adults drank at a hazardous level that is potentially harmful to health (AUDIT scores 8+) (Drummond et al, 2016). Alcohol has been found to cause numerous physical health conditions (Wholly Alcohol Attributable Conditions, WAACs; Lopez et al, 2014) and to contribute to the increased risk of a large number of chronic and acute diseases (Partially Alcohol Attributable Conditions, PAACs; PHE, 2020). It has been estimated that one in five patients in UK hospitals use alcohol in a harmful way and one in 10 are dependent on alcohol (Roberts et al, 2019). Alcohol use disorders have been linked with a range of mental health difficulties (PHE, 2016b) with 86% of people using alcohol treatment services having a co-occurring mental health difficulty (PHE, 2017). Multimorbidity (MM) is typically defined as the presence of two or more conditions, and research in this area has investigated how different conditions can occur together. For example, Weich et al (2011) used latent class analysis to analyse the APMS 2007 to describe the extent and nature of psychiatric comorbidity. The results revealed four latent classes of MM with the most common conditions of anxiety and depression dominating three of the multimorbid classes (Co-thymia, Addictions, Highly co-morbid) accounting for 18.4% of the participants. Another study with veterans discovered that most individuals with a mental health problem also had multiple medical and psychiatric diagnoses (Bhalla, Robert, & Rosenheck, 2018). Those with more than one mental disorder are different with more complex healthcare needs and thus treatment need to be tailored accordingly. Treating the conditions in isolation is likely to be ineffective, impractical, or even risky (e.g. medical risk can interact with psychiatric treatment) (Bhalla et al, 2018). Existing research focusing on multimorbidity has typically examined which factors are associated with the number of health problems rather than defining clusters of disease. This provides less specific advice for clinicians to act on, but has identified the important role of mental health in predicting multimorbidity (Read et al., 2017). A recent study examining specific clusters of multimorbidity found that a cluster of mental disorders and substance/alcohol use disorders was associated with the greatest risk of premature mortality (Zhu et al, 2020). There is a recognised dearth of research in understanding the complexity of the links between mental health and alcohol use and how this determines an individual’s future physical health. This project will use the APMS 2014 dataset to identify the multimorbidity clusters of physical non-communicable diseases (NCDs) with common and severe mental disorders (CMDs and SMIs). We know that mental health and behavioural issues are associated with the development of numerous socioeconomically patterned physical health conditions (Kivimäki et al, 2020). Our work will allow us to better understand how the health inequalities relating to alcohol attributable disease are greater for individuals with, compared to without, a mental disorder.

Background and aims: Alcohol use increases throughout adolescence. Emergency department (ED) attendance is an opportunity for alcohol screening and brief intervention (ASBI), which is effective for adults. This trial evaluated the effectiveness and cost-effectiveness of ASBI compared with screening alone (SA) in high-risk adolescents. Design, Setting and Participants: Multi-centre, three-group, single-blind, individually randomized trial with follow-ups after 6 and 12 months in 10 ED settings in England. From October 2014 to May 2015 we screened 3327 adolescents aged 14 to 18 years, of whom 756 (22.7%) scored at least 3 on the Alcohol Use Disorders Identification Test: consumption (AUDIT-C) and consented to participate in this trial. Mean age was 16.1 years; 50.2% were female and 84.9% were white. Interventions: Interventions were personalized feedback and brief advice (PFBA), personalized feedback plus electronic brief intervention (eBI) and SA. Measures: The primary outcome was the weekly alcohol consumed in standard UK units (8 g ethanol) at 12 months post-randomization, derived from extended AUDIT-C. Economic outcomes included quality of life and service use, from perspectives of both the National Health Service and personal social services (NHS&PSS) and society. Findings: At 12 months, mean weekly consumption was 2.99 [95% confidence interval (CI) = 2.38–3.70] standard units for the SA group, 3.56 (95% CI = 2.90, 4.32) for PFBA and 3.18 (95% CI = 2.50, 3.97) for eBI, showing no significant differences. The PFBA group consumed mean 0.57 (−0.36, 1.70) units more than SA; and eBIs consumed 0.19 (−0.71, 1.30) more. Bayes factors suggested lack of effectiveness explained non-significance. From the NHS&PSS perspective, economic analysis showed that PFBA and eBI were not cost-effective compared with SA: PFBA yielded incremental cost-effectiveness ratio of £6213 (−£736 843, £812 884), with the intervention having 54% probability of being cost-effective compared with SA at the £20 000 WTP threshold. Conclusions: In emergency departments in England, neither personalized feedback and brief advice nor personalized feedback plus electronic brief intervention showed evidence of being effective or cost-effective when compared with screening alone in reducing alcohol consumption among adolescents. We thank the participating EDs: St Thomas’ Hospital; King's College Hospital; Ealing Hospital; Croydon University Hospital; Hull Royal Infirmary; Darlington Memorial Hospital; Queen Elizabeth Hospital, Gateshead; North Tees Hospital, Hartlepool; South Tyneside District Hospital, South Shields; and Sunderland Royal Hospital; and all the researchers who successfully delivered the trial in these EDs. We thank the Trial Steering Committee and our software developers at Codeface Ltd for developing the trial management app. and the eBI app. SIPS City. This is a summary of independent research funded by a National Institute for Health Research (NIHR) Programme Grant for Applied Research (RP-PG-0609-10162). 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. C.D., P.D. and J.S. were supported by the NIHR Specialist Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King's College London. C.D. and P.D. were also supported by the NIHR Collaboration for Leadership in Applied Health Research and Care at King's College Hospital NHS Foundation Trust and the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust. T.P. was funded by a NIHR Clinical Doctoral Research Fellowship and the NIHR Yorkshire and the Humber Clinical Research Network.

BACKGROUND: Individuals diagnosed with schizophrenia are often assigned other psychiatric diagnoses during their lives. The significance of changing diagnosis has not been widely studied. AIMS: Our aim was to examine the association between diagnostic change and later outcome. METHODS: Individuals' diagnostic history, clinical and social outcomes were extracted from the AESOP-10 study, a 10-year follow-up of first episode psychosis cases. The association between outcome and different patterns of diagnosis over time were assessed using linear or logistic regression. RESULTS: Individuals always diagnosed with schizophrenia (n = 136) had worse clinical and social outcomes at follow-up than those never diagnosed with schizophrenia (n = 163), being more likely to be symptomatic, unemployed, single, and socially isolated. There was no difference in outcome between individuals always diagnosed with schizophrenia and those changing to a diagnosis of schizophrenia (n = 60), and no difference in outcome between individuals never diagnosed with schizophrenia, and those changing from a diagnosis of schizophrenia (n = 44). CONCLUSIONS: Individuals always and never diagnosed with schizophrenia had different outcomes. In cases of diagnostic instability participants had similar outcomes to those always assigned the diagnosis they changed to irrespective of initial diagnosis.

Additional file 2: Supplementary table 2. The profile of mood states (POMS), Snaith-Hamilton Pleasure Scale (SHAPS), Alcohol Urge Questionnaire (AUQ) and sleep quality at 3, 6 and 12-months post randomisation to placebo or mifepristone.

Additional file 1: Supplementary table 1. The profile of mood states (POMS), Snaith-Hamilton Pleasure Scale (SHAPS), Alcohol Urge Questionnaire (AUQ) and sleep quality at days 7, 14, 21 and 28 post randomisation to placebo or mifepristone.

BACKGROUND: Neuropsychological investigations can help untangle the aetiological and phenomenological heterogeneity of schizophrenia but have scarcely been employed in the context of treatment-resistant (TR) schizophrenia. No population-based study has examined neuropsychological function in the first-episode of TR psychosis. METHODS: We report baseline neuropsychological findings from a longitudinal, population-based study of first-episode psychosis, which followed up cases from index admission to 10 years. At the 10-year follow up patients were classified as treatment responsive or TR after reconstructing their entire case histories. Of 145 cases with neuropsychological data at baseline, 113 were classified as treatment responsive, and 32 as TR at the 10-year follow-up. RESULTS: Compared with 257 community controls, both case groups showed baseline deficits in three composite neuropsychological scores, derived from principal component analysis: verbal intelligence and fluency, visuospatial ability and executive function, and verbal memory and learning (p values⩽0.001). Compared with treatment responders, TR cases showed deficits in verbal intelligence and fluency, both in the extended psychosis sample (t = -2.32; p = 0.022) and in the schizophrenia diagnostic subgroup (t = -2.49; p = 0.017). Similar relative deficits in the TR cases emerged in sub-/sensitivity analyses excluding patients with delayed-onset treatment resistance (p values

© Cambridge University Press 2018Â This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. © Cambridge University Press 2018. Background Neuropsychological investigations can help untangle the aetiological and phenomenological heterogeneity of schizophrenia but have scarcely been employed in the context of treatment-resistant (TR) schizophrenia. No population-based study has examined neuropsychological function in the first-episode of TR psychosis.Methods We report baseline neuropsychological findings from a longitudinal, population-based study of first-episode psychosis, which followed up cases from index admission to 10 years. At the 10-year follow up patients were classified as treatment responsive or TR after reconstructing their entire case histories. Of 145 cases with neuropsychological data at baseline, 113 were classified as treatment responsive, and 32 as TR at the 10-year follow-up.Results Compared with 257 community controls, both case groups showed baseline deficits in three composite neuropsychological scores, derived from principal component analysis: verbal intelligence and fluency, visuospatial ability and executive function, and verbal memory and learning (p valuesâ 0.001). Compared with treatment responders, TR cases showed deficits in verbal intelligence and fluency, both in the extended psychosis sample (t =-2.32; p = 0.022) and in the schizophrenia diagnostic subgroup (t =-2.49; p = 0.017). Similar relative deficits in the TR cases emerged in sub-/sensitivity analyses excluding patients with delayed-onset treatment resistance (p values

Background. A lack of an aetiologically based nosology classification has contributed to instability in psychiatric diagnoses over time. This study aimed to examine the diagnostic stability of psychosis diagnoses using data from an incidence sample of psychosis cases, followed up after 10 years and to examine those baseline variables which were associated with diagnostic change.Method. Data were examined from the ÆSOP and ÆSOP-10 studies, an incidence and follow-up study, respectively, of a population-based cohort of first-episode psychosis cases from two sites. Diagnosis was assigned using ICD-10 and DSM-IV-TR. Diagnostic change was examined using prospective and retrospective consistency. Baseline variables associated with change were examined using logistic regression and likelihood ratio tests.Results. Slightly more (59.6%) cases had the same baseline and lifetime ICD-10 diagnosis compared with DSM-IV-TR (55.3%), but prospective and retrospective consistency was similar. Schizophrenia, psychotic bipolar disorder anddrug-induced psychosis were more prospectively consistent than other diagnoses. A substantial number of cases with other diagnoses at baseline (ICD-10, n = 61; DSM-IV-TR, n = 76) were classified as having schizophrenia at 10 years. Many variables were associated with change to schizophrenia but few with overall change in diagnosis. Conclusions. Diagnoses other than schizophrenia should to be regarded as potentially provisional.