5-year mental health outcomes for children and adolescents presenting with psychiatric symptoms to general practitioners in England: a retrospective cohort study.

BACKGROUND: Little information is available on the clinical trajectories of children and adolescents who attend general practice (GP) with psychiatric symptoms. We aimed to examine 5-year service use in English primary care for children and adolescents with neurodevelopmental or mental health symptoms or diagnoses. METHODS: In this retrospective cohort study, we used anonymised primary care health records from the Clinical Practice Research Datalink Aurum database (CPRD-Aurum). We identified children and adolescents (aged 3-18 years) presenting to primary care in England between Jan 1, 2000, and May 9, 2016, with a symptom or diagnosis of a mental health, behavioural, or neurodevelopmental condition. Participants were excluded if they had less than 1 year of follow-up. We followed up participants from their index date until either death, transfer out of the practice, or the end of data collection on May 5, 2021, and for trajectory analysis we limited follow-up to 5 years. We used group-based multi-trajectory models to identify clusters with similar trajectories over 5 years of follow-up for three primary outcomes: mental health-related GP contacts, psychotropic medication prescriptions, and specialist mental health-care contact. We did survival analysis to examine the associations between trajectory-group membership and hospital admission for self-harm or death by suicide, as indicators of severe psychiatric distress. FINDINGS: We included 369 340 children and adolescents, of whom 180 863 (49·0%) were girls, 188 438 (51·0%) were boys, 39 (<0·1%) were of indeterminate gender, 290 125 (78·6%) were White, 9161 (2·5%) were South Asian, 10 418 (2·8%) were Black, 8115 (2·2%) were of mixed ethnicity, and 8587 (2·3%) were other ethnicities, and the median age at index presentation was 13·6 years (IQR 8·4-16·7). In the best-fitting, seven-group, group-based multi-trajectory model, over a 5-year period, the largest group (low contact; 207 985 [51·2%]) had low rates of additional service contact or psychotropic prescriptions. The other trajectory groups were moderate, non-pharmacological contact (43 836 [13·0%]); declining contact (25 469 [8·7%]); year-4 escalating contact (18 277 [6·9%]); year-5 escalating contact (18 139; 5·2%); prolonged GP contact (32 147 [8·6%]); and prolonged specialist contact (23 487 [6·5%]). Non-White ethnicity and presentation in earlier study years (eg, 2000-2004) were associated with low-contact group membership. The prolonged specialist-contact group had the highest risk of hospital admission for self-harm (hazard ratio vs low-contact group 2·19 [95% CI 2·03-2·36]) and suicide (2·67 [1·72-4·14]). INTERPRETATION: Most children and adolescents presenting to primary care with psychiatric symptoms or diagnoses have low or declining rates of ongoing contact. If these trajectories reflect symptomatic improvement, these findings provide reassurance for children and adolescents and their caregivers. However, these trajectories might reflect an unmet need for some children and adolescents. FUNDING: National Institute for Health and Care Research and the Wellcome Trust.

Changes to healthcare utilisation and symptoms for common mental health problems over the first 21 months of the COVID-19 pandemic: parallel analyses of electronic health records and survey data in England.

Background: Few studies have investigated the effect of the COVID-19 pandemic on mental health beyond 2020. This study quantifies changes to healthcare utilisation and symptoms for common mental health problems over the pandemic's first 21 months. Methods: Parallel cohort studies using primary care database and survey data for adults (≥16 years) in England from January 2015 to December 2021: 16,551,842 from the Clinical Practice Research Datalink (CPRD) and 40,699 from the UK Household Longitudinal Survey (UKHLS). Interrupted time-series models estimated changes in monthly prevalence of presentations and prescribed medications for anxiety and depression (CPRD); and self-reported psychological distress (UKHLS). The pandemic period was divided into five phases: 1st Wave (April-May 2020); post-1st Wave (June-September 2020); 2nd Wave (October 2020-February 2021); post 2nd Wave (March-May 2021); 3rd Wave (June-December 2021). Findings: Primary care presentations for depression or anxiety dropped during the first wave (4.6 fewer monthly appointments per 1000 patients, 4.4-4.8) and remained lower than expected throughout follow-up. Self-reported psychological distress exceeded expected levels during the first (Prevalence Ratio = 1.378, 95% CI 1.289-1.459) and second waves (PR = 1.285, 1.189-1.377), returning towards expected levels during the third wave (PR = 1.038, 0.929-1.154). Increases in psychological distress and declines in presentations were greater for women. The decrease in primary care presentations for depression and anxiety exceeded that for physical health conditions (rheumatoid arthritis, diabetes, urinary tract infections). Anxiety and depression prescriptions returned to pre-pandemic levels during the second wave due to increased repeat prescriptions. Interpretation: Despite periods of distress during the pandemic, we did not find an enduring effect on common mental health problems. The fall in primary care presentations for anxiety or depression suggests changing healthcare utilisation for mental distress and a potential treatment gap. Funding: National Institute for Health and Care Research (NIHR).

Trends in antipsychotic prescribing to children and adolescents in England: cohort study using 2000-19 primary care data.

BACKGROUND: The prescription of antipsychotics to children and adolescents has been increasing worldwide. We described up-to-date trends in antipsychotic prescribing and identified likely indications in a contemporary English cohort. METHODS: We used a large primary care database, the Clinical Practice Research Datalink (CPRD) Aurum database, and we included all children and adolescents aged 3-18 years in the database and registered in England between Jan 1, 2000, and Dec 31, 2019, excluding those whose gender was recorded as indeterminate. Participants were followed up until the earliest of Dec 31, 2019, June 30 of the year they turned 18 years, their death, when they transferred from the primary care practice, or when the practice left the database. Data were not collected on ethnicity. We recorded antipsychotic prescriptions using the date a prescription was issued. As CPRD prescriptions are not linked to indications, we developed an algorithm to ascertain the most likely indication associated with participants' first antipsychotic prescription using clinical codes. We reported prescribing trends as annual period prevalence and the rate of first antipsychotic prescription, and we used joinpoint regression analysis to identify changes in the outcome trend. We stratified prevalence estimates by age group, gender, and Index of Multiple Deprivation quintiles, we reported frequencies of likely indications associated with incident prescriptions, and we explored clinical preference for typical versus atypical antipsychotics within deprivation quintiles. FINDINGS: Between Jan 1, 2000, and Dec 31, 2019, we included 7 216 791 children and adolescents, of whom 3 480 730 (48·2%) were girls and 3 736 061 (51·8%) were boys, with a mean age at the start of follow-up of 7·3 years (SD 4·9; range 3-18). Median follow-up was 4·1 years (IQR 1·5-8·5). 19 496 (0·3%) individuals received 243 529 antipsychotic prescriptions over follow-up, including 225 710 (92·7%) atypical and 17 819 (7·3%) typical antipsychotic prescriptions. The annual period prevalence of antipsychotic prescriptions rose from 0·057% (95% CI 0·052-0·063%) in 2000 to 0·105% (0·100-0·111%) in 2019. From joinpoint analyses, the period prevalence of all antipsychotic prescriptions increased by an average of 3·3% per year (2·2-4·9%) and the rate of first prescriptions increased by 2·2% per year (1·7-2·7%). The most likely indications of the first identified antipsychotic prescriptions were for autism spectrum disorder (2477 [12·7%]), non-affective psychosis (1669 [8·6%]), anxiety disorders (1466 [7·5%]), ADHD (1391 [7·1%]), depression (1256 [6·4%]), and conduct disorders (1181 [6·1%]). INTERPRETATION: The observed increase in antipsychotic prescriptions over 20 years results from the accumulation of repeated prescriptions to the same individuals combined with an increase in new prescriptions. These findings highlight the need for continued monitoring of trends in antipsychotic use and, although this was not examined in this paper, the findings highlights the need for better information about long-term antipsychotic safety. FUNDING: None.

Risk factors for suicide in prisons: a systematic review and meta-analysis.

BACKGROUND: Rates of suicide among people in prison are elevated compared with people of similar age and sex who are living in the community. Improving assessments and interventions to reduce suicide risk requires updated evidence on risk factors. We aimed to examine risk factors associated with suicide in prisoners. METHODS: We did an updated systematic review and meta-analysis of risk factors for suicide among people in prison. We searched five biblographic databases for articles published between Jan 1, 2006, and Aug 13, 2020, and one database for articles published between Jan 1, 1973, and Aug 13, 2020. Eligible studies reported risk factors in individuals who died by suicide while in prison and in controls from the general prison population. Two reviewers independently extracted data for each study using a standardised form. We calculated random-effects pooled odds ratios (ORs) for the association of suicide with demographical, clinical, criminological, and institutional risk factors, and investigated heterogeneity using subgroup and meta-regression analyses. This systematic review is registered with PROSPERO, CRD42020137979. FINDINGS: We identified 8041 records through our searches, and used 77 eligible studies from 27 countries, including 35 351 suicides, in the main analysis. The strongest clinical factors associated with suicide were suicidal ideation during the current period in prison (OR 15·2, 95% CI 8·5-27·0), a history of attempted suicide (OR 8·2, 4·4-15·3), and current psychiatric diagnosis (OR 6·4, 3·6-11·1). Institutional factors associated with suicide included occupation of a single cell (OR 6·8, 2·3-19·8) and having no social visits (OR 1·9, 1·5-2·4). Criminological factors included remand status (OR 3·6, 3·1-4·1), serving a life sentence (OR 2·4, 1·3-4·6), and being convicted of a violent offence, in particular homicide (OR 3·1, 2·2-4·2). INTERPRETATION: Several modifiable risk factors, such as psychiatric diagnosis, suicidal ideation during the current period in prison, and single-cell occupancy, are associated with suicide among people in prison. Preventive interventions should target these risk factors and include improved access to evidence-based mental health care. Understanding other factors associated with suicide might improve risk stratification and resource allocation in prison services. FUNDING: Wellcome Trust, National Institute for Health Research Applied Research Collaboration Oxford and Thames Valley.

Prediction models for child and adolescent mental health: A systematic review of methodology and reporting in recent research.

Background: There has been a rapid growth in the publication of new prediction models relevant to child and adolescent mental health. However, before their implementation into clinical services, it is necessary to appraise the quality of their methods and reporting. We conducted a systematic review of new prediction models in child and adolescent mental health, and examined their development and validation. Method: We searched five databases for studies developing or validating multivariable prediction models for individuals aged 18 years old or younger from 1 January 2018 to 18 February 2021. Quality of reporting was assessed using the Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis checklist, and quality of methodology using items based on expert guidance and the PROBAST tool. Results: We identified 100 eligible studies: 41 developing a new prediction model, 48 validating an existing model and 11 that included both development and validation. Most publications (k = 75) reported a model discrimination measure, while 26 investigations reported calibration. Of 52 new prediction models, six (12%) were for suicidal outcomes, 18 (35%) for future diagnosis, five (10%) for child maltreatment. Other outcomes included violence, crime, and functional outcomes. Eleven new models (21%) were developed for use in high-risk populations. Of development studies, around a third were sufficiently statistically powered (k = 16%, 31%), while this was lower for validation investigations (k = 12, 25%). In terms of performance, the discrimination (as measured by the C-statistic) for new models ranged from 0.57 for a tool predicting ADHD diagnosis in an external validation sample to 0.99 for a machine learning model predicting foster care permanency. Conclusions: Although some tools have recently been developed for child and adolescent mental health for prognosis and child maltreatment, none can be currently recommended for clinical practice due to a combination of methodological limitations and poor model performance. New work needs to use ensure sufficient sample sizes, representative samples, and testing of model calibration.

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS).

BACKGROUND: Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records. METHODS: In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure). RESULTS: In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61). CONCLUSIONS: It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

The economic impact of violence perpetration in severe mental illness: a retrospective, prevalence-based analysis in England and Wales.

BACKGROUND: Calls for increased funding for mental health services require many lines of evidence in support, including estimates of economic impact. One understudied source of cost is violence perpetrated by individuals with severe mental illness. Estimating this economic impact can inform budget planning across several government sectors and emphasise the importance of violence prevention. Therefore, we aimed to provide a comprehensive estimate of the economic costs of violence perpetrated by people with severe mental illness. METHODS: For this retrospective analysis, we used a prevalence-based modelling approach to estimate the annual economic cost of violent incidents committed by people with severe mental illness in England and Wales during 2015-16. The model was based on secondary data, including the association between violence and severe mental illness, illness prevalence, recidivism, absolute numbers of violent incidents in 2015-16, and costs to society per violent crime, by area of spending. Uncertainty was addressed with probabilistic and deterministic sensitivity analyses that tested the effect of underreporting of domestic violence and distributions of crime types in individuals with severe mental illness. OUTCOMES: The estimated annual economic impact of violence perpetrated by people with severe mental illness was £2·5 (95% CI 1·4-4·5) billion in England and Wales in 2015-16, or 5·3% of the total estimated societal cost of violence. The largest contributors to the cost of violent crime perpetrated by individuals with severe mental illness were the cost of physical and emotional harm to victims (£1·4 [95% CI 0·8-2·5] billion), followed by lost productivity of victims (£348.0 [190·0-628·8] million), while the combined cost to the police and criminal justice system was £561·3 (305·9-1009·2) million and the cost to health services was £136·7 [74·3-246·3] million. The additional cost to secure forensic care was estimated to be £487·7 (302·0-709·1) million. INTERPRETATION: The economic impact of violence perpetrated by individuals with severe mental illness is potentially important. Preventing violence, especially through services for individuals with comorbid substance misuse, and reducing recidivism might lead to cost savings at a governmental and individual level, in addition to the clinical and societal benefits. FUNDING: Wellcome Trust, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, and NIHR Applied Research Collaboration Oxford and Thames Valley.

Measuring an individual researcher's impact: new directions and challenges.

Metrics which quantify the impact of a scientist are increasingly incorporated into decisions about how to rate and fund individuals and institutions. Several commonly used metrics, based on journal impact factors and citation counts, have been criticised as they do not reliably predict real-world impact, are highly variable between fields and are vulnerable to gaming. Bibliometrics have been incorporated into systems of research assessment but these may create flawed incentives, failing to reward research that is validated, reproducible and with wider impacts. A recent proposal for a new standardised citation metric based on a composite indicator of 6 measures has led to an online database of 100 000 of the most highly cited scientists in all fields. In this perspective article, we provide an overview and evaluation of this new citation metric as it applies to mental health research. We provide a summary of its findings for psychiatry and psychology, including clustering in certain countries and institutions, and outline some implications for mental health research. We discuss strengths and limitations of this new metric, and how further refinements could align impact metrics more closely with wider goals of scientific research.

Measuring Disturbance of the Endocannabinoid System in Psychosis: A Systematic Review and Meta-analysis.

Importance: The endocannabinoid system (ECS) is a lipid-based endogenous signaling system. Its relevance to psychosis is through the association between cannabis use and the onset and course of illness and through the antipsychotic properties of cannabidiol, a potential ECS enhancer. Objective: To conduct a systematic review and meta-analysis of the blood and cerebrospinal fluid (CSF) measures of the ECS in psychotic disorders. Data Sources: Web of Science and PubMed were searched from inception through June 13, 2018. The articles identified were reviewed, as were citations to previous publications and the reference lists of retrieved articles. Study Selection: Original articles were included that reported blood or CSF measures of ECS activity in patients with psychotic illnesses and in healthy controls. Data Extraction and Synthesis: PRISMA guidelines, independent extraction by multiple observers, and random-effects meta-analysis were used. Heterogeneity was assessed with the I2 index. Sensitivity analyses tested the robustness of the results. Main Outcomes and Measures: The clinical relevance of ECS modifications in psychotic disorders was investigated by (1) a quantitative synthesis of the differences in blood and CSF markers of the ECS between patients and healthy controls, and (2) a qualitative synthesis of the association of these markers with symptom severity, stage of illness, and response to treatment. Results: A total of 18 studies were included. Three individual meta-analyses were performed to identify the differences in ECS markers between people with schizophrenia and healthy controls. Five studies, including 226 patients and 385 controls, reported significantly higher concentrations of anandamide in the CSF of patients than controls (standardized mean difference [SMD], 0.97; 95% CI, 0.67-1.26; P < .001; I2 = 54.8%). In 9 studies, with 344 patients and 411 controls, significantly higher anandamide levels in blood were found in patients, compared with controls (SMD, 0.55; 95% CI, 0.05-1.04; P = .03; I2 = 89.6%). In 3 studies, involving 88 patients and 179 controls, a significantly higher expression of type 1 cannabinoid receptors on peripheral immune cells was reported in patients compared with controls (SMD, 0.57; 95% CI, 0.31-0.84; P < .001; I2 = 0%). Higher ECS tone was found at an early stage of illness in individuals who were antipsychotic naïve or free, and it had an inverse association with symptom severity and was normalized after successful treatment. Moderate to high level of heterogeneity in methods was found between studies. Conclusions and Relevance: Testing clinically relevant markers of the ECS in the blood and CSF of people with psychotic illness appears possible, and these markers provide useful biomarkers for the psychotic disorder; however, not all studies accounted for important variables, such as cannabis use. Trial Registration: PROSPERO identifier: CRD42018099863.