Pathway from attention-deficit/hyperactivity disorder to suicide/self-harm.

To evaluate the effect of ADHD diagnosis by the age of 10 on the suicide/self-harm risk at age 14 and examine factors that may modify/mediate the association, a longitudinal study based on the nationwide survey consisting of 5,107 children in Australia was used. Self-harm and suicidal risks in children with ADHD at age 14 were the main outcomes; ADHD medication, history of bullied or depression, and other sociodemographic disadvantages, were treated as covariates. The diagnosis of ADHD at age 10 is associated with elevated risks of self-harm, suicidal thoughts, planning, or attempts by age 14, with greater susceptibility in males. The pathway from ADHD symptoms to suicide/self-harm could also be mediated by depression and exposure to bullying assessed at age 12. Note that depression and exposure to bullying might at best contribute to less than 10 % of the total effect of ADHD diagnosis on either the risk of suicide or self-harm. Early gender-tailored intervention and prevention strategies are crucial in clinical practice and health policy.

The relationship between mental health reforms and general population suicide rates in Australia over the past three and a half decades: 1987-2021.

Nearly 3,000 Australians tragically end their lives by suicide each year, underscoring a major national public health challenge with substantial socio-economic ramifications. Australia's National Mental Health Plans (NMHPs) aim to improve mental health and reduce suicide rates. This study investigates their effectiveness by analyzing how age-standardized suicide rates across Australian jurisdictions have fluctuated alongside the implementation of five NMHPs from 1987 to 2021. Findings reveal mixed impacts, with some plans linked to decreases and others associated with increases in suicide rates across different periods and regions. Notably, the recent decline in 2020 requires careful consideration amidst COVID-19 pandemic influences. These insights not only provide valuable evidence for shaping future mental health policies and initiatives but also for future health services research.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Examining the Feasibility of Implementing Digital Mental Health Innovations Into Hospitals to Support Youth in Suicide Crisis: Interview Study With Young People and Health Professionals.

BACKGROUND: Hospitals are insufficiently resourced to appropriately support young people who present with suicidal crises. Digital mental health innovations have the potential to provide cost-effective models of care to address this service gap and improve care experiences for young people. However, little is currently known about whether digital innovations are feasible to integrate into complex hospital settings or how they should be introduced for sustainability. OBJECTIVE: This qualitative study explored the potential benefits, barriers, and collective action required for integrating digital therapeutics for the management of suicidal distress in youth into routine hospital practice. Addressing these knowledge gaps is a critical first step in designing digital innovations and implementation strategies that enable uptake and integration. METHODS: We conducted a series of semistructured interviews with young people who had presented to an Australian hospital for a suicide crisis in the previous 12 months and hospital staff who interacted with these young people. Participants were recruited from the community nationally via social media advertisements on the web. Interviews were conducted individually, and participants were reimbursed for their time. Using the Normalization Process Theory framework, we developed an interview guide to clarify the processes and conditions that influence whether and how an innovation becomes part of routine practice in complex health systems. RESULTS: Analysis of 29 interviews (n=17, 59% young people and n=12, 41% hospital staff) yielded 4 themes that were mapped onto 3 Normalization Process Theory constructs related to coherence building, cognitive participation, and collective action. Overall, digital innovations were seen as a beneficial complement to but not a substitute for in-person clinical services. The timing of delivery was important, with the agreement that digital therapeutics could be provided to patients while they were waiting to be assessed or shortly before discharge. Staff training to increase digital literacy was considered key to implementation, but there were mixed views on the level of staff assistance needed to support young people in engaging with digital innovations. Improving access to technological devices and internet connectivity, increasing staff motivation to facilitate the use of the digital therapeutic, and allowing patients autonomy over the use of the digital therapeutic were identified as other factors critical to integration. CONCLUSIONS: Integrating digital innovations into current models of patient care for young people presenting to hospital in acute suicide crises is challenging because of several existing resource, logistical, and technical barriers. Scoping the appropriateness of new innovations with relevant key stakeholders as early as possible in the development process should be prioritized as the best opportunity to preemptively identify and address barriers to implementation.

Effectiveness of lifestyle interventions for improving the physical health of children and adolescents taking antipsychotic medications: protocol for a systematic review and meta-analysis.

INTRODUCTION: Children and adolescents are increasingly prescribed antipsychotic medications off-label in the treatment of behavioural disorders. While antipsychotic medications are effective in managing behavioural issues, they carry a significant risk of adverse events that compromise ongoing physical health. Of particular concern is the negative impact antipsychotic medications have on cardiometabolic health. Interventions that aim to modify lifestyle habits have the potential to alleviate the adverse effects of antipsychotic medication by enhancing weight management, increasing physical activity, promoting better nutritional practices, improving dietary habits and promoting healthier sleep patterns and sleep hygiene. However, a comprehensive review has not been performed to ascertain the effectiveness of lifestyle interventions for children and adolescents who are at increased risk of antipsychotic-induced compromises to their physical health. METHODS AND ANALYSIS: This systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases will be searched without any year constraints to identify randomised controlled trials that are published in the English language and report a lifestyle intervention compared with usual care with any physical health outcome measure. Trial registers and results repositories will be scoured to identify additional studies. Two reviewers will independently conduct screening, data extraction and quality assessment and compare the results. Quantitative data will be synthesised, where appropriate, through a random-effects meta-analysis model. Otherwise, data will be reported in a qualitative (narrative) synthesis. Heterogeneity will be quantified using the I2 statistic. The Cochrane Risk of Bias 2 tool will be used for risk of bias assessment. The Grading of Recommendations, Assessment, Development and Evaluation system will be used to evaluate the cumulative body of evidence. ETHICS AND DISSEMINATION: Ethics approval is not required. The publication plan will target high-impact, peer-reviewed journals that fall under the scope of Psychiatry and Mental Health. PROSPERO REGISTRATION NUMBER: CRD42022380277.

Attention-deficit hyperactivity disorder in children is related to maternal screen time during early childhood in Taiwan: a national prospective cohort study.

BACKGROUND: It is unclear to familial screen time in early childhood is associated with the subsequent diagnosis of attention-deficit and hyperactivity disorder (ADHD). Our study is to evaluate the association between screen time during early childhood in families and the incidence of ADHD. METHODS: We conducted a population-based birth cohort study by using the Taiwan Birth Cohort Study, which recruited 24 200 mother-child pairs when children were 6 months old. Screen time exposure for children and parents were collected at the age of 18 and 36 months. Whether the child has ever been diagnosed with ADHD was determined at a follow-up interview at age 8. Factors including socioeconomic factors and screen time were analyzed using logistic regression to determine their association with the rate of ADHD. RESULTS: A total of 16 651 term singletons were included in the final analysis. Of them, 382 (2.3%) were diagnosed as having ADHD before the age of 8 years. No significant relationship between children's or fathers' screen time and ADHD was noted. When compared to children whose mothers spent less time on screens, those whose mothers spent more than 3 h a day on screens when the child was 3 years old exhibited a higher incidence of ADHD (adjusted OR [aOR]: 1.31, 95% CI: 1.03-1.66). CONCLUSION: Higher maternal screen time when the child was 3 years old was associated with an increased incidence of ADHD in this population-based study. However, children's screen time did not find related to ADHD. We found that it was the mother's screen time, who typically serves as the primary caregiver in our study participants, not the child's, that mattered. In addition to superficial screen use time, future research is needed to replicate the findings and clarify mechanisms underlying this association.

Machine learning-based prediction for self-harm and suicide attempts in adolescents.

This study aimed to use machine learning (ML) models to predict the risk of self-harm and suicide attempts in adolescents. We conducted secondary analysis of cross-sectional data from the Longitudinal Study of Australian Children dataset. Several key variables at the age of 14-15 years were used to predict self-harm or suicide attempt at 16-17 years. Random forest classification models were used to select the optimal subset of predictors and subsequently make predictions. Among 2809 participants, 296 (10.54%) reported an act of self-harm and 145 (5.16%) reported attempting suicide at least once in the past 12 months. The area under the receiver operating curve was fair for self-harm (0.7397) and suicide attempt (0.7220), which outperformed the prediction strategy solely based on prior suicide or self-harm attempt (AUC: 0.6). The most important factors identified were similar, and included depressed feelings, strengths and difficulties questionnaire scores, perceptions of self, and school- and parent-related factors. The random forest classification algorithm, an ML technique, can effectively select the optimal subset of predictors from hundreds of variables to forecast the risks of suicide and self-harm among adolescents. Further research is needed to validate the utility and scalability of ML techniques in mental health research.

Appraising risk factors of aggression in children and adolescents at psychiatric inpatient units.

This study examined predictors of aggression and assessed whether different subgroups of children and young people (CYP) display varying risks of aggressive incidents during hospitalization. Data from 10,090 children admitted to the psychiatric inpatient units of Cincinnati Children's Hospital between April 2010 and June 2021 were analysed. Multivariable logistic regression models were used to determine significant predictors associated with aggression, followed by average marginal effects and cluster analyses to rank and establish clusters by the order of predictor importance. About 32.5% reported positive history of an aggressive incident. The mean BRACHA score was doubled compared to those without a prior history. The primary analysis showed that both younger and male CYPs had higher odds of aggressive incidents. We also found that CYP with an African descent, not being able to live with both biological parents, those who reported positive history of psychiatric hospitalisation, and prior externalising behaviours had higher odds of aggressive incidents. These findings have important clinical and public health implications, as they provide valuable knowledge for healthcare professionals to improve prevention strategies for aggression amongst this vulnerable population.

A nationwide study of COVID-19 impact on mental health-related presentations among children and adolescents to primary care practices in Australia.

This study explored the impacts of COVID-19 on the mental health (MH)-related visits to general practices (GPs) among children and young people (CYP) up to 18 years of age in Australia. This study analysed national-level data captured by the NPS MedicineWise program on monthly CYP MH-related visits per 10,000 visits to GPs from January 2014 to September 2021. We considered the pre-COVID-19 period (January 2014-February 2020) and the COVID-19 period (March 2020-September 2021). We used a Bayesian structural time series (BSTS) model to estimate the impact of COVID-19 on MH-related GP visits per 10,000 visits. A total of 103,813 out of 7,690,874 visits to GP (i.e., about 135 per 10,000 visits) were related to MH during study period. The BSTS model showed a significant increase in the overall MH-related visits during COVID-19 period (33%, 95% Credible Interval (Crl) 8.5%-56%), particularly, visits related to depressive disorders (61%, 95% Crl 29%-91%). The greatest increase was observed among females (39%, 95% Crl 12%-64%) and those living in socioeconomically least disadvantaged areas (36%, 95% Crl 1.2-71%). Our findings highlight the need for resources to be directed towards at-risk CYP to improve MH outcomes and reduce health system burden.

Screening for Media Use in the Emergency Department Among Young Australians: Cross-sectional Study.

BACKGROUND: Research on problematic internet use has largely adhered to addiction paradigms, possibly impeding the identification of specific internet behaviors related to psychopathology. This study presents a novel approach to screening for specific problematic internet behaviors by using a new measure, the emergency department media use screener (EDMUS). OBJECTIVE: The purpose of this study was to identify patterns of internet use in young people presenting with mental health concerns to the emergency department (ED), ascertain associations with their mental health, and evaluate whether the EDMUS can be used to predict subsequent ED presentations within 3 months. METHODS: This cross-sectional retrospective study of Australian young people (N=149, aged 11-25 years; female: n=92, 61.7%) sought to use the EDMUS, a 24-item questionnaire, to identify problematic internet behaviors, including accessing or posting prosuicidal or proeating disorder content, cyberbullying, and inappropriate digital content. Data on each person's mental health were extracted from electronic medical records to look for associations with EDMUS responses and ED re-presentation over 3 months. EDMUS items were grouped into clusters for analysis using chi-square tests, binary logistic regression, and path analyses. RESULTS: Sharing suicidal digital content was the most common problematic internet use pattern identified by the EDMUS. However, this did not correlate with having a prior mental health diagnosis or predict readmission. Most participants had families with a concern for their internet use; however, this was less likely in participants with a diagnosis of personality disorder. Diagnoses of personality disorder or posttraumatic stress disorder were independent predictors of readmission (P=.003; P=.048). CONCLUSIONS: Although a history of complex psychopathology increases the likelihood of subsequent ED presentations, its links to internet use-related behaviors are still unclear. The EDMUS has potential for identifying young people who are most vulnerable to problematic internet behaviors and offers the opportunity for early intervention and potential prevention of more entrenched difficulties.

COVID-19 and Pediatric Mental Health Hospitalizations.

OBJECTIVES: To analyze Australian national data to examine the impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health-related hospital presentations among children and adolescents during the pandemic period with restrictions, and the period after the restrictions eased. METHODS: We analyzed the monthly mental health-related inpatient admissions and emergency department (ED) attendances data from 6 large pediatric hospitals across Australia, using the Bayesian structural time series models. The COVID-19 restriction period was from March 2020 to December 2021 and the COVID-19 restriction-eased period from January to June 2022. RESULTS: A total of 130 801 mental health-related hospital admissions (54 907) and ED attendances (75 894) were analyzed. During the COVID-19 restriction period, there was a significant increase in inpatient admissions related to deliberate self-harm behaviors (82%, 95% credible interval [CrI], 7%-160%) and ED attendances related to overall mental health disorders (15%, 95% CrI, 1.1%-30%) and eating disorders (76%, 95% CrI, 36%-115%). The increase was higher among females and those living in the least socioeconomically disadvantaged areas, suggesting a widening gap between mental health-related presentations by sex and socioeconomic status. After the restrictions eased, there were slight declines in mental health-related hospital presentations; however, the numbers remained higher than the pre-COVID-19 levels. CONCLUSIONS: The increase in mental health-related hospital presentations during the COVID-19 period calls for additional support for pediatric mental health care, particularly for eating disorders and deliberate self-harm among female adolescents. It is important to monitor pediatric mental health service use as we enter "COVID-19 normal" period.

Genetic Pathways Associated With Sleep Problems in Children With Autism Spectrum Disorder.

Aims: Children on the autism spectrum are more likely to have sleep problems than non-autistic children. Sleep disturbance may exacerbate emotional and behavioral problems of children on the autism spectrum. A better understanding of the biological mechanisms underlying sleep disturbance provide clues to better management for this co-morbid condition in autism. The goal of the current study is to identify genetic variants associated with sleep disturbance and melatonin levels in autistic children. Methods: A total of 969 children on the autism spectrum were genotyped using the Global Screening Array v1 or Global Screening Array v2. Sleep problems were assessed using the Children's Sleep Habits Questionnaire (CSHQ). Melatonin levels were measured using the urine samples of 219 probands. The relationship between the melatonin level and CSHQ score was examined using the general linear model. The genetic variants associated with the CSHQ score and melatonin level as two separate quantitative traits were determined using genomewide association studies. Results: The data indicates that urine melatonin levels were positively associated with CSHQ scores, suggesting that autistic children with a poorer sleep qualiy could has higher melatonin level. Furthermore, genetic assocication studies suggest that genetic pathways involved in pro-inflammatory responses might be involved in sleep disturbance, while genetic pathways involved in catecholamine-secreting PC12 cells and Schwann cells could be associated with melatonin levels. Conclusions: Taken together, our findings indicate that sleep disturbance and melatonin metabolism could be attributable to distinct biological mechanisms in autistic children since they might not share genetic contributors.

Clinical and behavioral attributes leading to sleep disorders in children on the autism spectrum.

Sleep disorders are a common comorbid condition in children diagnosed with autism spectrum disorder ("autism"). However, the relationship between the clinical features of autism and sleep disorders remains unclear. A better understanding of the inherent autism-related characteristics linked to comorbid sleep disorders would improve comprehensive assessment and management. This study examined the relationship between sociodemographics, autism symptoms, sleep problems, cognitive status, behavioral attributes, and sensory profiles. Using data from 1268 participants who took part in the Australian Autism Biobank, sleep-related measurements using the Child Sleep Habits Questionnaire (CSHQ) were compared between autistic children aged 2 to 17 (N = 969), their siblings (N = 188), and unrelated children without an autism diagnosis (N = 111). The known relationship between sleep problems and autism was further explored by including scores from the Autism Diagnostic Observation Schedule-2, Mullen Scales of Early Learning, Vineland Adaptive Behavioral Scale-II and the Short Sensory Profile-2; which were included in analyses for autistic participants who had a completed CSHQ. Multiple regression models were used to identify clinical/behavioral variables associated with CSHQ subscales. The autism group had a significantly higher total CSHQ score than the sibling and comparison groups (p < 0.001), indicating worse sleep quality. Within the autism group, lower adaptive behaviors (i.e., VABS-II) and sensory issues (i.e., SSP-2 subclass scores) were positively associated with the severity of sleep problems (i.e., the CSHQ subclass scores) (p < 0.001). The significant functional impact of poor sleep on autistic children warrants an assessment of sleep as a critical part of a holistic approach to supporting autistic children and their families. LAY SUMMARY: Autistic children generally have co-occurring conditions. Sleep disorders impact approximately 50%-80% of autistic children. The impact on the quality of life for both the children and their families can be significant. This study compares sleep problems in autistic children and adolescents with their siblings and children without a diagnosis of autism, and investigates the relationship between specific autistic traits, daily life behaviors and sleep problems. The findings highlight the importance of a holistic assessment for autistic children and matching appropriate sleep intervention and supports where indicated.

Exploring polygenic contributors to subgroups of comorbid conditions in autism spectrum disorder.

Individuals with autism spectrum disorder (ASD) have heterogeneous comorbid conditions. This study examined whether comorbid conditions in ASD are associated with polygenic risk scores (PRS) of ASD or PRS of comorbid conditions in non-ASD specific populations. Genome-wide single nucleotide polymorphism (SNP) data were obtained from 1386 patients with ASD from the Autism Genetic Resource Exchange (AGRE) study. After excluding individuals with missing clinical information concerning comorbid conditions, a total of 707 patients were included in the study. A total of 18 subgroups of comorbid conditions ('topics') were identified using a machine learning algorithm, topic modeling. PRS for ASD were computed using a genome-wide association meta-analysis of 18,381 cases and 27,969 controls. From these 18 topics, Topic 6 (over-represented by allergies) (p = 1.72 × 10-3) and Topic 17 (over-represented by sensory processing issues such as low pain tolerance) (p = 0.037) were associated with PRS of ASD. The associations between these two topics and the multi-locus contributors to their corresponding comorbid conditions based on non-ASD specific populations were further explored. The results suggest that these two topics were not associated with the PRS of allergies and chronic pain disorder, respectively. Note that characteristics of the present AGRE sample and those samples used in the original GWAS for ASD, allergies, and chronic pain disorder, may differ due to significant clinical heterogeneity that exists in the ASD population. Additionally, the AGRE sample may be underpowered and therefore insensitive to weak PRS associations due to a relatively small sample size. Findings imply that susceptibility genes of ASD may contribute more to the occurrence of allergies and sensory processing issues in individuals with ASD, compared with the susceptibility genes for their corresponding phenotypes in non-ASD individuals. Since these comorbid conditions (i.e., allergies and pain sensory issues) may not be attributable to the corresponding comorbidity-specific biological factors in non-ASD individuals, clinical management for these comorbid conditions may still depend on treatments for core symptoms of ASD.

Co-Design of a Neurodevelopment Assessment Scale: A Study Protocol.

Neurodevelopmental disorders are a heterogeneous group of conditions with overlapping symptomatology and fluctuating developmental trajectories that transcend current diagnostic categorisation. There is a need for validated screening instruments which dimensionally assess symptomatology from a holistic, transdiagnostic perspective. The primary aim is to co-design a Neurodevelopment Assessment Scale (NAS), a user-friendly transdiagnostic assessment inventory that systematically screens for all signs and symptoms commonly encountered in neurodevelopmental disorders. Our first objective is to undertake development of this tool, utilising co-design principles in partnership with stakeholders, including both those with lived experience of neurodevelopmental disorders and service providers. Our second objective is to evaluate the face validity, as well as the perceived utility, user-friendliness, suitability, and acceptability (i.e., 'social validity'), of the NAS from the perspective of parents/caregivers and adults with neurodevelopmental disorders, clinicians, and service providers. Our third objective is to ascertain the psychometric properties of the NAS, including content validity and convergent validity. The NAS will provide an efficient transdiagnostic tool for evaluating all relevant signs, symptoms, and the dimensional constructs that underpin neurodevelopmental presentations. It is anticipated that this will maximise outcomes by enabling the delivery of personalised care tailored to an individual's unique profile in a holistic and efficient manner.