Evidence of increasing recorded diagnosis of autism spectrum disorders in Wales, UK – an e-cohort study

Estimates place the prevalence of autism spectrum disorders (autism) at around 1% in the population. New services for adult diagnosis have been set up in Wales, UK, at a time of ris-ing awareness of the spectrum of autism experiences, however no studies have examined adult autism prevalence in Wales. In this study we use an anonymised e-cohort comprised of healthcare record data to produce all-age estimates of prevalence and incidence of record-ed autism for the years 2001-2016. We found the overall prevalence rate of autism in healthcare records was 0.51%. The number of new-recorded cases of autism increased from 0.188 per 1000 person-years in 2001 to 0.644 per 1000 person-years in 2016. The estimate of 0.51% prevalence in the population is lower than suggested by population survey and co-hort studies study methodologies, but comparable to other administrative record study es-timates. Rates of new incident diagnoses of autism saw a >150% increase in the years 2008-2016, with a trend towards more diagnoses in those over 35 and an eightfold increase in diagnoses in women from 2000-2016. Our study suggests that while the number of people being diagnosed with autism is increasing, many are still unrecognised by healthcare ser-vices

Neurological and psychiatric disorders among autistic adults: a population healthcare record study

Background Co-occurring psychiatric disorders are common in autism, with previous studies suggesting 54–94% of autistic individuals develop a mental health condition in their lifetime. Most studies have looked at clinically-recruited cohorts, or paediatric cohorts followed into adulthood, with less known about the autistic community at a population level. We therefore studied the prevalence of co-occurring psychiatric and neurological conditions in autistic individuals in a national sample. Methods This retrospective case-control study utilised the SAIL Databank to examine anonymised whole population electronic health record data from 2001 to 2016 in Wales, UK (N = 3.6 million). We investigated the prevalence of co-occurring psychiatric and selected neurological diagnoses in autistic adults' records during the study period using International Classification of Diseases-10 and Read v2 clinical codes compared to general population controls matched for age, sex and deprivation Results All psychiatric conditions examined were more common amongst adults with autism after adjusting for age, sex and deprivation. Prevalence of attention-deficit hyperactivity disorder (7.00%), bipolar disorder (2.50%), obsessive-compulsive disorder (3.02%), psychosis (18.30%) and schizophrenia (5.20%) were markedly elevated in those with autism, with corresponding odds ratios 8.24–10.74 times the general population. Depression (25.90%) and anxiety (22.40%) were also more prevalent, with epilepsy 9.21 times more common in autism. Conclusions We found that a range of psychiatric conditions were more frequently recorded in autistic individuals. We add to understanding of under-reporting and diagnostic overshadowing in autism. With increasing awareness of autism, services should be cognisant of the psychiatric conditions that frequently co-occur in this population

Contacts with primary and secondary healthcare prior to suicide: case–control whole-population-based study using person-level linked routine data in Wales, UK, 2000–2017

Background Longitudinal studies of patterns of healthcare contacts in those who die by suicide to identify those at risk are scarce. Aims To examine type and timing of healthcare contacts in those who die by suicide. Method A population-based electronic case–control study of all who died by suicide in Wales, 2001–2017, linking individuals’ electronic healthcare records from general practices, emergency departments and hospitals. We used conditional logistic regression to calculate odds ratios, adjusted for deprivation. We performed a retrospective continuous longitudinal analysis comparing cases’ and controls’ contacts with health services. Results We matched 5130 cases with 25 650 controls (5 per case). A representative cohort of 1721 cases (8605 controls) were eligible for the fully linked analysis. In the week before their death, 31.4% of cases and 15.6% of controls contacted health services. The last point of contact was most commonly associated with mental health and most often occurred in general practices. In the month before their death, 16.6 and 13.0% of cases had an emergency department contact and a hospital admission respectively, compared with 5.5 and 4.2% of controls. At any week in the year before their death, cases were more likely to contact healthcare services than controls. Self-harm, mental health and substance misuse contacts were strongly linked with suicide risk, more so when they occurred in emergency departments or as emergency admissions. Conclusions Help-seeking occurs in those at risk of suicide and escalates in the weeks before their death. There is an opportunity to identify and intervene through these contacts

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Background: Each year, approximately 800,000 people die by suicide worldwide, accounting for 1-2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person's motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge. Objective: To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services. Methods: Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks. Results: We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks <0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by similar to 0.4. At least 95% of those presenting these factors were identified as suicide cases. Conclusions: Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects

Identification of Suicidal Ideation in the Canadian Community Health Survey—Mental Health Component Using Deep Learning

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be moreattentive to patients at risk for suicide.Methods: Using the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime and last 12 month SI. From 582 possible parameters we produced 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data.Results: For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature. Discussion: Although requiring further study to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior

Creating a Next Generation Phenotype Library : the health data research UK Phenotype Library

Open Access via the OUP AgreementPeer reviewe

The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis

BACKGROUND: Sleep difficulties might be a contributory causal factor in the occurrence of mental health problems. If this is true, improving sleep should benefit psychological health. We aimed to determine whether treating insomnia leads to a reduction in paranoia and hallucinations. METHODS: We did this single-blind, randomised controlled trial (OASIS) at 26 UK universities. University students with insomnia were randomly assigned (1:1) with simple randomisation to receive digital cognitive behavioural therapy (CBT) for insomnia or usual care, and the research team were masked to the treatment. Online assessments took place at weeks 0, 3, 10 (end of therapy), and 22. The primary outcome measures were for insomnia, paranoia, and hallucinatory experiences. We did intention-to-treat analyses. The trial is registered with the ISRCTN registry, number ISRCTN61272251. FINDINGS: Between March 5, 2015, and Feb 17, 2016, we randomly assigned 3755 participants to receive digital CBT for insomnia (n=1891) or usual practice (n=1864). Compared with usual practice, the sleep intervention at 10 weeks reduced insomnia (adjusted difference 4·78, 95% CI 4·29 to 5·26, Cohen's d=1·11; p<0·0001), paranoia (-2·22, -2·98 to -1·45, Cohen's d=0·19; p<0·0001), and hallucinations (-1·58, -1·98 to -1·18, Cohen's d=0·24; p<0·0001). Insomnia was a mediator of change in paranoia and hallucinations. No adverse events were reported. INTERPRETATION: To our knowledge, this is the largest randomised controlled trial of a psychological intervention for a mental health problem. It provides strong evidence that insomnia is a causal factor in the occurrence of psychotic experiences and other mental health problems. Whether the results generalise beyond a student population requires testing. The treatment of disrupted sleep might require a higher priority in mental health provision. FUNDING: Wellcome Trust

Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries

BackgroundThe COVID-19 pandemic is having profound mental health consequences for many people. Concerns have been expressed that at its most extreme, this may manifest itself in increased suicide rates.MethodsWe sourced real-time suicide data from around the world via a systematic internet search and recourse to our networks and the published literature. We used interrupted time series analysis to model the trend in monthly suicides prior to COVID-19 in each country/area-within-country, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic. Countries/areas-within countries contributed data from at least 1 January 2019 to 31 July 2020 and potentially from as far back as 1 January 2016 until as recently as 31 October 2020. We conducted a primary analysis in which we treated 1 April to 31 July 2020 as the COVID-19 period, and two sensitivity analyses in which we varied its start and end dates (for those countries/areas-within-countries with data beyond July 2020).OutcomesWe sourced data from 21 countries (high income [n=16], upper-middle income [n=5]; whole country [n=10], area(s)-within-the-country [n=11]). In general, there does not appear to have been a significant increase in suicides since the pandemic began in the countries for which we had data. In fact, in a number of countries/areas-within-countries there appears to have been a decrease.InterpretationThis is the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. It offers a consistent picture, albeit from high- and upper-middle income countries, of suicide numbers largely remaining unchanged or declining in the early months of the pandemic. We need to remain vigilant and be poised to respond if the situation changes as the longer-term mental health and economic impacts of the pandemic unfold

Association of school absence and exclusion with recorded neurodevelopmental disorders, mental disorders, or self-harm: a nationwide, retrospective, electronic cohort study of children and young people in Wales, UK.

BACKGROUND: Poor attendance at school, whether due to absenteeism or exclusion, leads to multiple social, educational, and lifelong socioeconomic disadvantages. We aimed to measure the association between a broad range of diagnosed neurodevelopmental and mental disorders and recorded self-harm by the age of 24 years and school attendance and exclusion. METHODS: In this nationwide, retrospective, electronic cohort study, we drew a cohort from the Welsh Demographic Service Dataset, which included individuals aged 7-16 years (16 years being the school leaving age in the UK) enrolled in state-funded schools in Wales in the academic years 2012/13-2015/16 (between Sept 1, 2012, and Aug 31, 2016). Using the Adolescent Mental Health Data Platform, we linked attendance and exclusion data to national demographic and primary and secondary health-care datasets. We identified all pupils with a recorded diagnosis of neurodevelopmental disorders (ADHD and autism spectrum disorder [ASD]), learning difficulties, conduct disorder, depression, anxiety, eating disorders, alcohol or drugs misuse, bipolar disorder, schizophrenia, other psychotic disorders, or recorded self-harm (our explanatory variables) before the age of 24 years. Outcomes were school absence and exclusion. Generalised estimating equations with exchangeable correlation structures using binomial distribution with the logit link function were used to calculate odds ratios (OR) for absenteeism and exclusion, adjusting for sex, age, and deprivation. FINDINGS: School attendance, school exclusion, and health-care data were available for 414 637 pupils (201 789 [48·7%] girls and 212 848 [51·3%] boys; mean age 10·5 years [SD 3·8] on Sept 1, 2012; ethnicity data were not available). Individuals with a record of a neurodevelopmental disorder, mental disorder, or self-harm were more likely to be absent or excluded in any school year than were those without a record. Unadjusted ORs for absences ranged from 2·1 (95% CI 2·0-2·2) for those with neurodevelopmental disorders to 6·6 (4·9-8·3) for those with bipolar disorder. Adjusted ORs (aORs) for absences ranged from 2·0 (1·9-2·1) for those with neurodevelopmental disorders to 5·5 (4·2-7·2) for those with bipolar disorder. Unadjusted ORs for exclusion ranged from 1·7 (1·3-2·2) for those with eating disorders to 22·7 (20·8-24·7) for those with a record of drugs misuse. aORs for exclusion ranged from 1·8 (1·5-2·0) for those with learning difficulties to 11·0 (10·0-12·1) for those with a record of drugs misuse. INTERPRETATION: Children and young people up to the age of 24 years with a record of a neurodevelopmental or mental disorder or self-harm before the age of 24 years were more likely to miss school than those without a record. Exclusion or persistent absence are potential indicators of current or future poor mental health that are routinely collected and could be used to target assessment and early intervention. Integrated school-based and health-care strategies to support young peoples' engagement with school life are required. FUNDING: The Medical Research Council, MQ Mental Health Research, and the Economic and Social Research Council. TRANSLATION: For the Welsh translation of the abstract see Supplementary Materials section

Trends in socioeconomic inequalities in incidence of severe mental illness – A population-based linkage study using primary and secondary care routinely collected data between 2000 and 2017

Objective In 2008, the UK entered a period of economic recession followed by sustained austerity measures. We investigate changes in inequalities by area deprivation and urbanicity in incidence of severe mental illness (SMI, including schizophrenia-related disorders and bipolar disorder) between 2000 and 2017. Methods We analysed 4.4 million individuals from primary and secondary care routinely collected datasets (2000–2017) in Wales and estimated the incidence of SMI by deprivation and urbanicity measured by the Welsh Index of Multiple Deprivation (WIMD) and urban/rural indicator respectively. Using linear modelling and joinpoint regression approaches, we examined time trends of the incidence and incidence rate ratios (IRR) of SMI by the WIMD and urban/rural indicator adjusted for available confounders. Results We observed a turning point of time trends of incidence of SMI at 2008/2009 where slope changes of time trends were significantly increasing. IRRs by deprivation/urbanicity remained stable or significantly decreased over the study period except for those with bipolar disorder sourced from secondary care settings, with increasing trend of IRRs (increase in IRR by deprivation after 2010: 1.6 % per year, 95 % CI: 1.0 %–2.2 %; increase in IRR by urbanicity 1.0 % per year, 95 % CI: 0.6 %–1.3 %). Conclusions There was an association between recession/austerity and an increase in the incidence of SMI over time. There were variations in the effects of deprivation/urbanicity on incidence of SMI associated with short- and long-term socioeconomic change. These findings may support targeted interventions and social protection systems to reduce incidence of SMI