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Background: A self-harm episode is a major risk factor for repeat self-harm. Existing tools to assess and predict repeat self-harm have major methodological limitations, and few are externally validated., Objective: To develop and validate a risk assessment model of repeat self-harm up to 6 months after an episode of non-fatal self-harm that resulted in an emergency visit to hospital or specialised care., Methods: Using Swedish national registers, we identified 53 172 people aged≥10 years who self-harmed during 2008-2012. We allocated 37 523 individuals to development (2820 or 7.5% repeat self-harm incidents within 6 months) and 15 649 to geographic validation (1373 repeat episodes) samples, based on region of residence. In a temporal validation of people who self-harmed during 2018-2019, we identified 25 036 individuals (2886 repeat episodes). We fitted a multivariable accelerated failure time model to predict risk of repeat self-harm., Findings: In the external validations (n=40 685), rates of repeat self-harm were 8.8%-11.5% over 6 months. The final model retained 17 factors. Calibration and discrimination were similar in both validation samples, with observed-to-expected ratio=1.15 (95% CI=1.09 to 1.21) and c-statistic=0.72 (95% CI=0.70 to 0.73) in the geographical validation. At 6 months and a 10% risk cut-off, sensitivity was 51.5% (95% CI=48.8% to 54.2%) and specificity was 80.7% (95% CI=80.1% to 81.4%) in geographic validation; corresponding values were 56.9% (95% CI=55.1% to 58.7%) and 76.0% (95% CI=75.5% to 76.6%) in temporal validation. Discrimination was slightly worse at the 1-month prediction horizon (c-statistics of 0.66-0.68)., Conclusions: Using mostly routinely collected data, simple risk assessment models and tools can provide acceptable levels of accuracy for repeat of self-harm., Clinical Implications: This risk model (OXford SElf-harm repeat tool) may assist clinical decision-making., Competing Interests: Competing interests: HL reports receiving grants from Shire Pharmaceuticals; personal fees from and serving as a speaker for Medice, Shire/Takeda Pharmaceuticals and Evolan Pharma AB; all outside the submitted work. HL is editor-in-chief of JCPP Advances., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Aim: To describe the pattern of the prevalence of mental health problems during the first year of the COVID-19 pandemic and examine the impact of containment measures on these trends., Methods: We identified articles published until 30 August 2021 that reported the prevalence of mental health problems in the general population at two or more time points. A crowd of 114 reviewers extracted data on prevalence, study and participant characteristics. We collected information on the number of days since the first SARS-CoV-2 infection in the study country, the stringency of containment measures and the number of cases and deaths. We synthesised changes in prevalence during the pandemic using a random-effects model. We used dose-response meta-analysis to evaluate the trajectory of the changes in mental health problems., Results: We included 41 studies for 7 mental health conditions. The average odds of symptoms increased during the pandemic (mean OR ranging from 1.23 to 2.08). Heterogeneity was very large and could not be explained by differences in participants or study characteristics. Average odds of psychological distress, depression and anxiety increased during the first 2 months of the pandemic, with increased stringency of the measures, reported infections and deaths. The confidence in the evidence was low to very low., Conclusions: We observed an initial increase in the average risk of psychological distress, depression-related and anxiety-related problems during the first 2 months of the pandemic. However, large heterogeneity suggests that different populations had different responses to the challenges imposed by the pandemic., Competing Interests: Competing interests: AC has received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation, Lundbeck and Angelini Pharma. VP received consulting fees from Google Modern Health. CK is a cofounder of Wida, a digital mental health platform. TAF reports personal fees from Boehringer-Ingelheim, Daiichi Sankyo, DT Axis, Kyoto University Original, Shionogi, SONY and UpToDate, and a grant from DT Axis and Shionogi, outside the submitted work; In addition, TAF has a patent 7448125, and a pending patent 2022-082495, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. SL received consulting fees from Alkermes, Angelini, Karuna, Kynexis, Lundbeck, Neurotorium, Novo Nordisk, Otsuka, Roche, ROVI, TEVA and honoraria for lectures and presentations from Angelini, Apsen, Eisai, Ekademia, Gedeon Richter, Janssen, Lundbeck, Medichem, Medscape, Merck, Mitshubishi, Recordati, ROVI, Sanofi Aventis. SNV receives royalties from UpToDate Inc for authorship of materials on depression and pregnancy. From The MHCOVID Crowd Investigators: EO has received consulting fees Angelini Pharma. AS received 30 days of complimentary access from Elsevier to Science Direct, Scopus, Reaxys and Geofacets after reviewing a manuscript for CHEST., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: New National Institute for Health and Care Excellence (NICE) guidance endorses the prescription of statins in larger population groups for the prevention of cardiovascular and cerebrovascular morbidity and mortality, especially in people with severe mental illness. However, the evidence base for their safety and risk/benefit balance in depression is not established., Objectives: This study aims to assess the real-world mortality and adverse events of statins in depressive disorders., Methods: Population-based, nationwide (England), between-subject, cohort study. We used electronic health records (QResearch database) of people aged 18-100 years with first-episode depression, registered with English primary care practices over January 1998-August 2020 for 12(+) months, divided into statin users versus non-users.Primary safety outcomes included all-cause mortality and any adverse event measured at 2, 6 and 12 months. Multivariable logistic regression was employed to control for several potential confounders and calculate adjusted ORs (aORs) with 99% CIs., Findings: From over 1 050 105 patients with depression (42.64% males, mean age 43.23±18.32 years), 21 384 (2.04%) died, while 707 111 (67.34%) experienced at least one adverse event during the 12-month follow-up. Statin use was associated with lower mortality over 12 months (range aOR 2-12months 0.66-0.67, range 99% CI 0.60 to 0.73) and with lower adverse events over 6 months (range aOR 2-6months 0.90-0.96, range 99% CI 0.91 to 0.99), but not at 1 year (aOR 12months 0.99, 99% CI 0.96 to 1.03). No association with any other individual outcome measure (ie, any other neuropsychiatric symptoms) was identified., Conclusions: We found no evidence that statin use among people with depression increases mortality or other adverse events., Clinical Implications: Our findings support the safety of updated NICE guidelines for prescribing statins in people with depressive disorders., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare the following: RDG is supported by the Wellcome Trust (award 102176/Z/13/Z, grant 216452/Z/19/Z, title 'The effects of anti-inflammatory drugs on emotional and reward processing'). FDC is supported by the National Institute for Health Research (NIHR) Research Professorship to AC (grant RP-2017-08-ST2-006) and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005). EGO is supported by the NIHR Research Professorship to AC (grant RP-2017-08-ST2-006). PC is supported by the Medical Research Council (MRC) (grant MR/S003037/1, title 'Clinical Psychopharmacology of Depression'). CJH is supported by the NIHR Oxford Health Biomedical Research Centre. SF is supported by the NIHR Oxford Health Biomedical Research Centre. AC is supported by the NIHR Oxford Cognitive Health Clinical Research Facility, by an NIHR Research Professorship (grant RP-2017-08-ST2-006), by the NIHR Oxford and Thames Valley Applied Research Collaboration, by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005) and by Wellcome Trust (GALENOS Project). CJH has received consultancy fees from P1vital, Lundbeck, Servier, UCB, Zogenix, J&J and Syndesi outside of the current work. AC has received research and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO, Lundbeck and Angelini Pharma outside of the current work. The other authors declare that they have no conflict of interest., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

The number of prediction models for suicide-related outcomes has grown substantially in recent years. These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine approach to the prevention of suicidal behaviour. However, there are contrasting views as to whether prediction models have potential to inform and improve assessment of suicide risk. In this perspective, we discuss common misconceptions that characterise criticisms of suicide risk prediction research. First, we discuss the limitations of a classification approach to risk assessment (eg, categorising individuals as low-risk vs high-risk), and highlight the benefits of probability estimation. Second, we argue that the preoccupation with classification measures (such as positive predictive value) when assessing a model's predictive performance is inappropriate, and discuss the importance of clinical context in determining the most appropriate risk threshold for a given model. Third, we highlight that adequate discriminative ability for a prediction model depends on the clinical area, and emphasise the importance of calibration, which is almost entirely overlooked in the suicide risk prediction literature. Finally, we point out that conclusions about the clinical utility and health-economic value of suicide prediction models should be based on appropriate measures (such as net benefit and decision-analytic modelling), and highlight the role of impact assessment studies. We conclude that the discussion around using suicide prediction models and risk assessment tools requires more nuance and statistical expertise, and that guidelines and suicide prevention strategies should be informed by the new and higher quality evidence in the field., Competing Interests: Competing interests: SF is part of the team that developed OxSATS, OxMIS, OxMIV and other OxRisk calculators. AS has no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Although there is robust evidence for several factors which may precipitate self-harm, the contributions of different physical injuries are largely unknown., Objective: To examine whether specific physical injuries are associated with risks of self-harm in people with psychiatric disorders., Methods: By using population and secondary care registers, we identified all people born in Finland (1955-2000) and Sweden (1948-1993) with schizophrenia-spectrum disorder (n=136 182), bipolar disorder (n=68 437) or depression (n=461 071). Falls, transport-related injury, traumatic brain injury and injury from interpersonal assault were identified within these subsamples. We used conditional logistic regression models adjusted for age and calendar month to compare self-harm risk in the week after each injury to earlier weekly control periods, which allowed us to account for unmeasured confounders, including genetics and early environments., Findings: A total of 249 210 individuals had been diagnosed with a psychiatric disorder and a physical injury during the follow-up. The absolute risk of self-harm after a physical injury ranged between transport-related injury and injury from interpersonal assault (averaging 17.4-37.0 events per 10 000 person-weeks). Risk of self-harm increased by a factor of two to three (adjusted OR: 2.0-2.9) in the week following a physical injury, as compared with earlier, unexposed periods for the same individuals., Conclusions: Physical injuries are important proximal risk factors for self-harm in people with psychiatric disorders., Clinical Implications: Mechanisms underlying the associations could provide treatment targets. When treating patients with psychiatric illnesses, emergency and trauma medical services should actively work in liaison with psychiatric services to implement self-harm prevention strategies., Competing Interests: Competing interests: HL reports receiving grants from Shire Pharmaceuticals; personal fees from serving as a speaker for Medice, Shire/Takeda Pharmaceuticals and Evolan Pharma AB; and sponsorship for a conference on attention-deficit/hyperactivity disorder from Shire/Takeda Pharmaceuticals and Evolan Pharma AB, all outside the submitted work. KH is a member of the National Suicide Prevention Strategy for England Advisory Group. The remaining authors declare that they have no conflicts of interest., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Violence perpetration is a key outcome to prevent for an important subgroup of individuals presenting to mental health services, including early intervention in psychosis (EIP) services. Needs and risks are typically assessed without structured methods, which could facilitate consistency and accuracy. Prediction tools, such as OxMIV (Oxford Mental Illness and Violence tool), could provide a structured risk stratification approach, but require external validation in clinical settings., Objectives: We aimed to validate and update OxMIV in first-episode psychosis and consider its benefit as a complement to clinical assessment., Methods: A retrospective cohort of individuals assessed in two UK EIP services was included. Electronic health records were used to extract predictors and risk judgements made by assessing clinicians. Outcome data involved police and healthcare records for violence perpetration in the 12 months post-assessment., Findings: Of 1145 individuals presenting to EIP services, 131 (11%) perpetrated violence during the 12 month follow-up. OxMIV showed good discrimination (area under the curve 0.75, 95% CI 0.71 to 0.80). Calibration-in-the-large was also good after updating the model constant. Using a 10% cut-off, sensitivity was 71% (95% CI 63% to 80%), specificity 66% (63% to 69%), positive predictive value 22% (19% to 24%) and negative predictive value 95% (93% to 96%). In contrast, clinical judgement sensitivity was 40% and specificity 89%. Decision curve analysis showed net benefit of OxMIV over comparison approaches., Conclusions: OxMIV performed well in this real-world validation, with improved sensitivity compared with unstructured assessments., Clinical Implications: Structured tools to assess violence risk, such as OxMIV, have potential in first-episode psychosis to support a stratified approach to allocating non-harmful interventions to individuals who may benefit from the largest absolute risk reduction., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes., Objective: We developed and validated a predictive model for suicide following self-harm., Methods: We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work., Findings: An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS)., Conclusions: OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility., Clinical Implications: Using a clinical prediction score may assist clinical decision-making and resource allocation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Bipolar disorder (BD) is associated with increased mortality, but evidence on cause-specific mortality is limited., Objective: To investigate cause-specific premature excess mortality in BD., Methods: Finnish nationwide cohort study of individuals with and without a diagnosis of BD who were aged 15-64 years during 2004-2018. Standardised mortality ratios (SMRs) with 95% CIs were calculated for BD using the mortality rates in the Finnish general population without BD as weights. Causes of death were defined by the International Classification of Diseases, 10th revision codes., Findings: Of the included 47 018 individuals with BD, 3300 (7%) died during follow-up. Individuals with BD had sixfold higher mortality due to external causes (SMR: 6.01, 95% CI: 5.68, 6.34) and twofold higher mortality due to somatic causes (SMR: 2.06, 95% CI: 1.97, 2.15). Of the deaths due to external causes, 83% (1061/1273) were excess deaths, whereas 51% (1043/2027) of the deaths due to somatic causes were excess. About twice the number of potential years of life were lost in excess due to external causes than due to somatic causes. Alcohol-related causes contributed more to excess mortality than deaths due to cardiovascular disease., Conclusion: External causes of death contributed more to the mortality gap than somatic causes after controlling for age-specific background general population mortality., Clinical Implication: A balanced consideration between therapeutic response, different treatment options and risk of cause-specific mortality is needed to prevent premature mortality in BD and to reduce the mortality gap., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. Published by BMJ.)