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PURPOSE: This study aims to assess changes in the receptive and expressive language skills and to determine if the baseline characteristics such as communication, cognitive and motor skills, predict outcomes in preschool children with Autism Spectrum Disorder (ASD) following early intervention. METHODS: We recruited 64 children participating in the Early Start Denver Model (ESDM) early intervention program at an Autism Specific Early Learning and Care Center (ASELCC) in Australia. Baseline characteristics across various developmental domains was measured using the Mullen Scales of Early Learning (MSEL), Vineland Adaptive Behaviour Scales, 2nd Edition (VABS-II), and the ESDM Curriculum Checklist. Linear mixed-effects models were used to examine the effects of the intervention on outcomes. Fixed-effects such as time, groups (verbal and minimally verbal), and time-by-group interactions were assessed whilst adjusting for covariates. Further, multiple linear regression models were used to determine if the baseline characteristics were significant predictors of the outcomes following the early intervention. RESULTS: Among the 64 children who participated in this study, 38 children were verbal, whereas 26 were deemed to have minimal verbal skills. The mean age of the sample was 4.1 years with a significant male predilection (83%) and from a culturally and linguistically diverse (CALD) background (64%). Findings of the linear mixed effects model showed significant within and between group differences in the ESDM subscales, indicating higher magnitude of changes in the verbal group compared to the minimally verbal group. Finally, the multiple linear regression models suggested that baseline MSEL visual reception and expressive language scores were predictive of changes in the ESDM receptive and expressive communication scores. CONCLUSION: Understanding a child's baseline skill levels may provide valuable clues regarding what interventions would work best, or which interventi

(Cell 184, 5916–5931; November 24, 2021) Our paper reported evidence that autism-related dietary preferences mediate autism-microbiome associations. Since publication, we have become aware of an error in our paper that we are now correcting. Specifically, in the code we wrote and used to transform the microbiome count matrices in our variance component analysis, we inadvertently missed a matrix transposition, which affected their centered-log-ratio (clr) transformation and affected variance estimates in Figure 2 and Table S1 (listed in detail below). By missing the matrix transposition, we incorrectly calculated the geometric mean per-taxa rather than per-individual. However, the error does not affect the conclusions of the paper because the per-taxa and per-individual geometric means are similar, and so the resulting clr transformed matrices are similar as well (note that the clr transform should take the quotient of a microbiome/taxa quantity by the geometric mean of microbiome quantities across the sample/individual). To show that this is the case, we compared the correctly (geometric mean calculated per-individual) and incorrectly (geometric mean calculated per-taxa) clr transformed matrices by taking the nth column of both matrices (representing each of 247 individuals’ microbiome data) and calculating the Pearson's correlation coefficient between them. The median Pearson's correlation coefficient ranged from 0.90–0.94 for the common species, rare species, common genes, and rare genes matrices. As the correctly and incorrectly transformed matrices are highly correlated, this error has negligible impact on the variance component analysis results and does not change the overall conclusions of our work. The code error did not affect which microbiome features were identified as being differentially abundant, as the method used for this analysis (ANCOMv2.1) takes un-transformed count data as input. However, the data visualization for this analysis was affected with

Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions (for example, sleep and feeding disorders) and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis (n = 8), sleep disturbances (n = 20) and cognitive function (n = 8) and found that long-chain polyunsaturated fatty acids may causally contribute to sleep disturbances mediated by the FADS gene cluster. We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile (with potential mediation by the microbiome) that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLR gene and two high-confidence ASD genes (ELAVL3 and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.

The novel coronavirus disease (COVID-19) was identified as the cause of an outbreak of respiratory disease in China at the end of 2019. It then spread with enormous rapidity and by mid-March 2020 was declared a world pandemic. Gilles de la Tourette Syndrome (GTS) is a childhood-onset neurodevelopmental disorder with a worldwide prevalence of about 1% of the population. The clinical symptoms include multiple motor and one or more phonic (vocal) tics. Germane to this communication is that 85% of patients with GTS have associated psychiatric co-morbidities, many of which are being exacerbated in the current global health crisis. In addition, several symptoms of GTS may mimic COVID-19, such as a dry cough and sniffing (phonic tics), while other symptoms such as spitting, inappropriate touching of others and "non-obscene socially inappropriate symptoms" can potentially get patients with GTS into trouble with the law. We suggest that a clear explanation of the COVID-19 illness and GTS is important to enable colleagues of various specialities who tend to patients with GTS. It is important to acknowledge at the outset that the information available on the COVID-19 pandemic changes daily, including cases infected, deaths reported, and how various national health systems are planning and or coping or not. It is fair to say that having read the current medical and lay press we conclude that it is not easy to reassure our patients with absolute certainty. However, notwithstanding that, we hope our documentation is of some assistance.

BACKGROUND AND OBJECTIVES: Watch Me Grow - Electronic (WMG-E) platform is an online resource to enhance the capacity of general practitioners (GPs) to involve parents in developmental surveillance. The aim of this study was to evaluate the acceptability and perceived utility of WMG-E. METHOD(S): Semi-structured interviews were conducted with GPs/paediatricians (n = 6) and parents (n = 6). Focus groups were conducted with child and family health nurses (n = 25). Transcripts were analysed thematically. RESULT(S): Participants indicated that WMG-E could empower clinicians and parents by enhancing health literacy about child developmental issues, but that it could also be disempowering if not used carefully. Clinicians mentioned being strategic at health service and public policy levels. A final theme was that of the need to balance widespread promotion with its targeted use. DISCUSSION: This study established the face validity of WMG-E, and reveals key lessons to inform the ways in which it is promoted and used.

INTRODUCTION: Integrated community healthcare Hubs may offer a 'one stop shop' for service users with complex health and social needs, and more efficiently use service resources. Various policy imperatives exist to implement Hub models of care, however, there is a dearth of research specifically evaluating Hubs targeted at families experiencing adversity. To contribute to building this evidence, we propose to co-design, test and evaluate integrated Hub models of care in two Australian community health services in low socioeconomic areas that serve families experiencing adversity: Wyndham Vale in Victoria and Marrickville in New South Wales. METHODS AND ANALYSIS: This multisite convergent mixed-methods study will run over three phases to (1) develop the initial Hub programme theory through formative research; (2) test and, then, (3) refine the Hub theory using empirical data. Phase 1 involves co-design of each Hub with caregivers, community members and practitioners. Phase 2 uses caregiver and Hub practitioner surveys at baseline, and 6 and 12 months after Hub implementation, and in-depth interviews at 12 months. Two stakeholder groups will be recruited: caregivers (n=100-200 per site) and Hub practitioners (n=20-30 per site). The intervention is a co-located Hub providing health, social, legal and community services with no comparator. The primary outcomes are caregiver-reported: (i) identification of, (ii) interventions received and/or (iii) referrals received for adversity from Hub practitioners. The study also assesses child, caregiver, practitioner and system outcomes including mental health, parenting, quality of life, care experience and service linkages. Primary and secondary outcomes will be assessed by examining change in proportions/means from baseline to 6 months, from 6 to 12 months and from baseline to 12 months. Service linkages will be analysed using social network analysis. Costs of Hub implementation and a health economics analysis of unmet need wil

Financial counselling and income-maximisation services have the potential to reduce financial hardship and its associated burdens on health and wellbeing in High Income Countries. However, referrals to financial counselling services are not systematically integrated into existing health service platforms, thus limiting our ability to identify and link families who might be experiencing financial hardship. Review evidence on this is scarce. The purpose of this study is to review "healthcare-income maximisation" models of care in high-income countries for families of children aged between 0 and 5 years experiencing financial difficulties, and their impacts on family finances and the health and wellbeing of parent(s)/caregiver(s) or children. A systematic review of the MEDLINE, EMBase, PsycInfo, CINAHL, ProQuest, Family & Society Studies Worldwide, Cochrane Library, and Informit Online databases was conducted according to the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) statement. A total of six studies (five unique samples) met inclusion criteria, which reported a total of 11,603 families exposed to a healthcare-income maximisation model. An average annual gain per person of £1661 and £1919 was reported in two studies reporting one Scottish before-after study, whereby health visitors/midwives referred 4805 clients to money advice services. In another UK before-after study, financial counsellors were attached to urban primary healthcare centres and reported an average annual gain per person of £1058. The randomized controlled trial included in the review reported no evidence of impacts on financial or non-financial outcomes, or maternal health outcomes, but did observe small to moderate effects on child health and well-being. Small to moderate benefits were seen in areas relating to child health, preschool education, parenting, child abuse, and early behavioral adjustment. There was a high level of bias in most studies, and insufficient evi

AIM: This systematic review aimed to identify the most important social, environmental, biological, and/or genetic risk factors for intellectual disability (ID). METHODS: Eligible were published prospective or retrospective comparative studies investigating risk factors for ID in children 4-18 years. Exclusions were single group studies with no comparator without ID and a sample size <100. Electronic databases (Medline, Cochrane Library, EMBASE, PsycInfo, Campbell Collaboration, and CINAHL) were searched for eligible publications from 1980 to 2020. Joanna Briggs Institute critical appraisal instruments, appropriate for study type, were used to assess study quality and risk of bias. Descriptive characteristics and individual study results were presented followed by the synthesis for individual risk factors, also assessed using GRADE. RESULTS: Fifty-eight individual eligible studies were grouped into six exposure topics: sociodemographic; antenatal and perinatal; maternal physical health; maternal mental health; environmental; genetic or biological studies. There were few eligible genetic studies. For half the topics, the certainty of evidence (GRADE) was moderate or high. CONCLUSION: Multiple studies have examined individual potential determinants of ID, but few have investigated holistically to identify those populations most at risk. Our review would indicate that there are vulnerable groups where risk factors we identified, such as low socioeconomic status, minority ethnicity, teenage motherhood, maternal mental illness, and alcohol abuse, may cluster, highlighting a target for preventive strategies. At-risk populations need to be identified and monitored so that interventions can be implemented when appropriate, at preconception, during pregnancy, or after birth. This could reduce the likelihood of ID and provide optimal opportunities for vulnerable infants. SYSTEMATIC REVIEW REGISTRATION: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=120032]

Sensory modulation symptoms form a diagnostic criterion for autism spectrum disorder and are associated with significant daily functional limitations. Utilizing caregiver report on Short Sensory Profile-2 (SSP-2) for 919 autistic children (3-14.11 years), we examined the expression of sensory modulation symptoms by age and sex and investigated the existence of specific sensory modulation subtypes. Sensory modulation symptoms appeared to peak in frequency during middle childhood, particularly in sensory sensitivity and avoidance. Symptoms associated with sensory hypo-reactivity and seeking tended not differ between age cohorts. Males and females demonstrated similar overall sensory modulation profiles, however, females showed elevated symptoms relating to sensory sensitivity. Model-based cluster analysis revealed five interpretable sensory modulation subtypes which related to symptom severity (low, mid-range, high). Subtypes demonstrating mid-range symptom severity differed in focus on sensory hyper-reactivity or seeking symptoms. The findings of this study report for the first time that age-related differences in sensory modulation symptoms may be associated with sensory hyper-reactivity only. The subtyping results also suggest that sensory modulation symptom severity is a reliable means of classifying variance within autistic children, however, consideration of differences in the behavioral strategies employed by individuals to manage sensory modulation symptoms may inform tailored supportive strategies. LAY SUMMARY: We studied sensory symptoms such as over-responding, under-responding and sensation seeking behaviors in 919 autistic children and adolescents. We found that 6-12-year-olds and females displayed the most sensory over-responding symptoms. Autistic children could be grouped into mild, moderate, and severe levels of sensory symptoms, however, children differed in the type of strategies used to cope with their sensory symptoms meaning that they may require

Using a novel trait-based measure, we examined genetic variants associated with obsessive-compulsive (OC) traits and tested whether OC traits and obsessive-compulsive disorder (OCD) shared genetic risk. We conducted a genome-wide association analysis (GWAS) of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of all currently available OCD cases. Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. A locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p = 2.48 × 10−8). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0.0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p’s < 0.01). OC traits were highly, but not significantly, genetically correlated with OCD (rg = 0.71, p = 0.062). We report the first validated genome-wide significant variant for OC traits in PTPRD, downstream of the most significant locus in a previous OCD GWAS. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the feasibility and power of using trait-based approaches in community samples for genetic discovery.

Background: Schools play a key role in supporting the well-being and resettlement of refugee children, and parental engagement with the school may be a critical factor in the process. Many resettlement countries have policies in place to support refugee parents' engagement with their children's school. However, the impact of these programs lacks systematic evaluation. This study first aimed to validate self-report measures of parental school engagement developed specifically for the refugee context, and second, to identify parent characteristics associated with school engagement, so as to help tailor support to families most in need. Methods and findings The report utilises 2016 baseline data of a cohort study of 233 Arabic-speaking parents (77% response rate) of 10- to 12-year-old schoolchildren from refugee backgrounds across 5 schools in Sydney, Australia. Most participants were born in Iraq (81%) or Syria (11%), and only 25% spoke English well to very well. Participants' mean age was 40 years old, and 83% were female. Confirmatory factor analyses were run on provisional item sets identified from a literature review and separate qualitative study. The findings informed the development of 4 self-report tools assessing parent engagement with the school and school community, school belonging, and quality of the relationship with the schools' bilingual cultural broker. Cronbach alpha and Pearson correlations with an established Teacher-Home Communication subscale demonstrated adequate reliability (α = 0.67 to 0.80) and construct and convergent validity of the measures (p < 0.01), respectively. Parent characteristics were entered into respective least absolute shrinkage and selection operator (LASSO) regression analyses. The degree of parents' psychological distress (as measured by the Kessler10 self-report instrument) and postmigration living difficulties (PLMDs) were each associated with lower school engagement and belonging, whereas less time lived in Australia, lo

Background: The COVID-19 pandemic and the associated economic recession has increased parental psychosocial stress and mental health challenges. This has adversely impacted child development and wellbeing, particularly for children from priority populations (culturally and linguistically diverse (CALD) and rural/regional communities) who are at an already increased risk of health inequality. The increased mental health and psychosocial needs were compounded by the closure of in-person preventive and health promotion programs resulting in health organisations embracing technology and online services. Watch Me Grow- Electronic (WMG-E) – developmental surveillance platform- exemplifies one such service. WMG-E was developed to monitor child development and guide parents towards more detailed assessments when risk is identified. This Randomised Controlled Trial (RCT) aims to expand WMG-E as a digital navigation tool by also incorporating parents’ mental health and psychosocial needs. Children and families needing additional assessments and supports will be electronically directed to relevant resources in the ‘care-as-usual’ group. In contrast, the intervention group will receive continuity of care, with additional in-person assessment and ‘warm hand over’ by a ‘service navigator’ to ensure their needs are met. Methods: Using an RCT we will determine: (1) parental engagement with developmental surveillance; (2) access to services for those with mental health and social care needs; and (3) uptake of service recommendations. Three hundred parents/carers of children aged 6 months to 3 years (recruited from a culturally diverse, or rural/regional site) will be randomly allocated to the ‘care-as-usual’ or ‘intervention’ group. A mixed methods implementation evaluation will be completed, with semi-structured interviews to ascertain the acceptability, feasibility and impact of the WMG-E platform and service navigator. Conclusions: Using WMG-E is expected to: normalise and de-sti

The involvement of young people in the planning of research continues to be rare, particu-larly for young people from culturally and linguistically diverse communities. This paper describes our experience in establishing a Youth Research Advisory Group (YRAG) in South West Sydney (SWS), including barriers and successful strategies. One hundred and fifteen students between school Years 7 and 12 (ages 11–18) took part in at least one of five sessions between 2019 and 2021. In total, we carried out 26 YRAG sessions, with between five and 30 students in each. Sessions focused on mapping the health priorities of the participants and co-developing research project proposals related to their health priorities. Our work with students revealed that their main areas of concern were mental health and stress. This led to material changes in our research strategy, to include “Mental Health” as a new research stream and co-develop new mental health-related projects with the students. Important strategies that enabled our research included maintaining flexibility to work seamlessly with organisational and individual preferences, and ensuring our processes were directed by the schools and—most importantly—the students themselves. Strategies such as maintaining an informal context, responding rapidly to student preference, and regularly renegotiating access enabled us to engage with the students to deepen our understanding of their experiences.

The COVID-19 pandemic has yielded extensive impacts globally in the year of 2020. Although the mental health of children and adolescents may be particularly susceptible to stressors stemming from the pandemic and anti-contagion policies, most ongoing efforts are geared toward curbing the viral spread. In the current perspective, we have identified four domains of factors corresponding to an ecological framework that may directly or indirectly influence the mental health of children and adolescents during the pandemic. The evidence suggests that anti-contagion policies might trigger cascades that impact the mental health of children and their families through multiple different sectors that used to form a safety net for youths. Additionally, children with neuropsychiatric disorders could experience exacerbated symptoms during the pandemic. Furthermore, the risk of domestic violence has surged during the pandemic, which further compounds the imminent mental health crisis. A mental health pandemic could be inevitable if no proactive prevention strategies were in place. Therefore, we recommend understanding each individual mental health risk pathway via the ecological framework in order to develop integrative prevention and intervention strategies.

Objectives: The identification of subgroups of autism spectrum disorder (ASD) may partially remedy the problems of clinical heterogeneity to facilitate the improvement of clinical management. The current study aims to use machine learning algorithms to analyze microarray data to identify clusters with relatively homogeneous clinical features. Methods: The whole-genome gene expression microarray data were used to predict communication quotient (SCQ) scores against all probes to select differential expression regions (DERs). Gene set enrichment analysis was performed for DERs with a fold-change >2 to identify hub pathways that play a role in the severity of social communication deficits inherent to ASD. We then used two machine learning methods, random forest classification (RF) and support vector machine (SVM), to identify two clusters using DERs. Finally, we evaluated how accurately the clusters predicted language impairment. Results: A total of 191 DERs were initially identified, and 54 of them with a fold-change >2 were selected for the pathway analysis. Cholesterol biosynthesis and metabolisms pathways appear to act as hubs that connect other trait-associated pathways to influence the severity of social communication deficits inherent to ASD. Both RF and SVM algorithms can yield a classification accuracy level >90% when all 191 DERs were analyzed. The ASD subtypes defined by the presence of language impairment, a strong indicator for prognosis, can be predicted by transcriptomic profiles associated with social communication deficits and cholesterol biosynthesis and metabolism. Conclusion: The results suggest that both RF and SVM are acceptable options for machine learning algorithms to identify AD subgroups characterized by clinical homogeneity related to prognosis.

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github. com/unchowdhury/AD-comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Aims: To examine the impact of COVID-19 pandemic on child mental health and socio-emotional and physical well-being (including sleep, diet, exercise, use of electronic media; care giver perceptions of symptoms of child neurodevelopmental disability [NDD] and comorbidities), and care giver mental health and well-being, social support and service use. Methods: An online cross-sectional self-report survey was distributed via disability service providers and support groups. Care givers of children aged 2–17 years with a NDD were invited to respond to questions on child symptom severity and well-being, parent well-being and service access and satisfaction. Results: Overall, 302 care givers (94.7% female) completed the survey. Average child age was 9.7 years and 66.9% were male. Worsening of any child NDD or comorbid mental health symptom was reported by 64.5% of respondents and 76.9% reported child health and well-being was impacted by COVID-19. Children were viewing more television and digital media (81.6%), exercising less (68.0%), experiencing reduced sleep quality (43.6%) and had a poorer diet (32.4%). Almost one fifth (18.8%) of families reported an increase in the dosage of medication administered to their child. Parents reported COVID-19 had impacted their own well-being (76.1%). Over half of respondents were not satisfied with services received during COVID-19 (54.8%) and just 30% reported that telehealth works well for their child. Conclusion: Targeted interventions are required to address worsening child neurodevelopmental disability, mental health symptoms and poor diet, sleep and exercise patterns. Improved access to telehealth services is indicated, as is further research on barriers and enablers of effective telehealth services.

Gilles de la Tourette syndrome (GTS) is a neurodevelopmental disorder characterized by motor and vocal tics with an estimated prevalence of 1% in children and adolescents. GTS has high rates of inheritance with many rare mutations identified. Apart from the role of the neurexin trans-synaptic connexus (NTSC) little has been confirmed regarding the molecular basis of GTS. The NTSC pathway regulates neuronal circuitry development, synaptic connectivity and neurotransmission. In this study we integrate GTS mutations into mitochondrial pathways that also regulate neuronal circuitry development, synaptic connectivity and neurotransmission. Many deleterious mutations in GTS occur in genes with complementary and consecutive roles in mitochondrial dynamics, structure and function (MDSF) pathways. These genes include those involved in mitochondrial transport (NDE1, DISC1, OPA1), mitochondrial fusion (OPA1), fission (ADCY2, DGKB, AMPK/PKA, RCAN1, PKC), mitochondrial metabolic and bio-energetic optimization (IMMP2L, MPV17, MRPL3, MRPL44). This study is the first to develop and describe an integrated mitochondrial pathway in the pathogenesis of GTS. The evidence from this study and our earlier modeling of GTS molecular pathways provides compounding support for a GTS deficit in mitochondrial supply affecting neurotransmission.

Background: The early detection of developmental conditions such as autism is vital to ensure children can access appropriate and timely evidence-based supports, services, and interventions. Children who have undetected developmental conditions early in life are more likely to develop later health, developmental, learning, and behavioral issues, which in turn can have a cumulative effect over the life course. Methods: The current protocol describes a multi-site, cluster randomized control trial comparing a developmental surveillance pathway for autism to usual care, using opportunistic visits to general practitioners (GPs). Units of randomization are GP clinics across two Australian states (New South Wales and Victoria), with thirty clinics within each state, each of which will aim to recruit approximately forty children aged between ~18- and 24-months, for a total of ~2,400 participants. Children will be randomized to two clusters; namely, an autism surveillance pathway (ASP) or surveillance as usual (SaU). The screening process for the ASP arm involves primary and secondary screenings for developmental concerns for autism, using both parent and GP reports and observations. Children in both arms who show signs of developmental concerns for autism will be offered a full developmental assessment by the research team at 24 months of age to determine the efficacy of developmental surveillance in successfully identifying children with autism. Trial Registration: The trial is registered with ANZCTR (ACTRN12619001200178) and reporting of the trial results will be according to recommendations in the CONSORT Statement.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism. Methods: Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals (after quality control) from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European (n = 1,964 European individuals). We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain. Results: The ASD (p = 6.1e−13), sibling (p = 4.9e−3) and unrelated (p = 3.0e−3) groups had significantly higher ASD PGS than UK Biobank controls, whereas this was not the case for height—a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children (r = 0.24, p = 2.1e−3) and parents (r = 0.17, p = 8.0e−7; 4.0% of variance), but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group (r = 0.13, p = 1.9e−3; 1.3% of variance). In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identifie

Psychiatric symptoms are seen in some COVID-19 patients, as direct or indirect sequelae, but it is unclear whether SARS-CoV-2 infection interacts with underlying neuronal or psychiatric susceptibilities. Such interactions might arise from COVID-19 immune responses, from infection of neurons themselves or may reflect social-psychological causes. To clarify this we sought the key gene expression pathways altered in COVID-19 also affected in bipolar disorder, post-traumatic stress disorder (PTSD) and schizophrenia, since this may identify pathways of interaction that could be treatment targets. We performed large scale comparisons of whole transcriptome data and immune factor transcript data in peripheral blood mononuclear cells (PBMC) from COVID-19 patients and patients with psychiatric disorders. We also analysed genome-wide association study (GWAS) data for symptomatic COVID-19 patients, comparing GWAS and whole-genome sequence data from patients with bipolar disorder, PTSD and schizophrenia patients. These studies revealed altered signalling and ontology pathways shared by COVID-19 patients and the three psychiatric disorders. Finally, co-expression and network analyses identified gene clusters common to the conditions. COVID-19 patients had peripheral blood immune system profiles that overlapped with those of patients with psychiatric conditions. From the pathways identified, PTSD profiles were the most highly correlated with COVID-19, perhaps consistent with stress-immune system interactions seen in PTSD. We also revealed common inflammatory pathways that may exacerbate psychiatric disorders, which may support the usage of anti-inflammatory medications in these patients. It also highlights the potential clinical application of multi-level dataset studies in difficult-to-treat psychiatric disorders in this COVID-19 pandemic.

Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design and improper representation of the time-domain signal. Methods: In this study, we focused on designing and evaluating deep convolutional neural network-based classifiers for seizure detection. Signal-to-image conversion methods are proposed to convert time-domain EEG signal to a time-frequency represented image to prepare the input data for classification. We proposed and evaluated three classification methods comprising of five classifiers to determine which is more accurate for seizure detection. Accuracy data were then compared to previous studies of the same dataset. Results: We found our proposed model and signal-to-image conversion method outperformed all previous studies in the most cases. The proposed FT-VGG16 classifier achieved the highest average classification accuracy of 99.21%. In addition, the Shapley Additive exPlanations (SHAP) analysis approach was employed to uncover the feature frequencies in the EEG that contribute most to improved classification accuracy. To the best of our knowledge, this is the first study to compute the contribution of frequency components to target seizure classification; thus allowing the identification of distinct seizure-related EEG frequency components compared to normal EEG measures. Conclusion: Thus our developed deep convolutional neural network models are useful to detect seizures and characteristic frequencies using EEG data collected from the patients and this model could be clinically applicable for the automated seizures detection.

Children with developmental disabilities are experiencing significant challenges to service access due to suspension of in-person assessments during the current COVID-19 pandemic. Telehealth is rapidly becoming the new service delivery model, which presents a unique opportunity for innovation in care that could be beneficial in the post-pandemic period. For example, using a combination of in-home video and telehealth options could form the first step in developmental assessment, allowing children to receive the necessary supports without delay. Recent telehealth funding is welcome but additional Medicare items for joint consultations including general practitioners (GPs), and paediatric, mental health and allied health professionals is critical.Copyright © 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)

Aim: To explore the stability of diagnosis and the relationship between behavioural, adaptive and developmental skills in early to middle childhood in children with autism spectrum disorder (ASD). Methods: Fifty-four children recruited to the study were diagnosed with ASD before 42 months. Outcomes at follow-up after a mean interval of 64 months were measured using the Autism Diagnostic Observation Schedule, Vineland-II adaptive scale and Wechsler Intelligence Scale for Children and parental survey data. Scores before school were compared with follow-up data through descriptive, correlational and multiple regression analyses. Results: ASD was confirmed in all children at follow-up (mean age 10 years). Fifty-eight percent of children were enrolled in a supported educational class or school and 42% were taking a psychotropic medication. Adaptive function improved significantly in 19% of children. Developmental and adaptive behavioural scores before school correlated with cognitive, behaviour and adaptive assessments at follow-up. Conclusion: At follow-up, the diagnosis was confirmed in all children. The children showed gains in their adaptive skills but and many required ongoing educational and behavioural support. Early developmental and adaptive assessments reliably predicted later educational support needs, cognitive and adaptive function and are a useful component of a diagnostic assessment.

Introduction Research highlights the importance of early intervention for children with autism spectrum disorder with better outcomes associated with earlier access to early intensive intervention (EII) programmes. However, there is significant variability in response to EII despite children receiving the same programmes. Methods and analysis A prospective, multisite cohort study using a pre-post design assesses the predictors of early intervention outcomes for children who receive EII through six early intervention services (Autism Specific Early Learning and Care Centres, ASELCCs) across Australia. Child and family characteristics at entry to and exit from ASELCCs are ascertained using measures of autism symptoms (Autism Diagnostic Observation Schedule-2; Social Communication Questionnaire); cognitive, language and developmental skills (Mullen Scale of Early Learning); adaptive function (Vineland Adaptive Behaviour Scale - second Edition); behaviours (Child Behaviour Checklist - 1.5 to 5 years; Restricted Repetitive Behaviour Scale); parental stress (Parent Stress Index-4 Short Form); quality of life (Quality of Life in Autism Scale) and a semistructured family history questionnaire for sociodemographic, family and psychosocial characteristics. Characteristics at entry are used as predictors of outcome at exit following EII approximately 12 months later. The change in score from baseline to exit will be the primary outcome of interest. The mediating role of family and psychosocial factors will also be considered. Ethics approval University of New South Wales Human Research Ethics Committee (HC14267). Dissemination of results Findings will be published in peer-reviewed journals and presented at conferences. A report summarising data and the interpretation of data will be published.Copyright © 2021 BMJ Publishing Group. All rights reserved.

International Consensus Statement for the Screening, Diagnosis, and Treatment of Adolescents with Concurrent Attention-Deficit/Hyperactivity Disorder and Substance Use Disorder Abstract. Background: Childhood attention-deficit/hyperactivity disorder (ADHD) is a risk factor for substance misuse and substance use disorder (SUD) in adolescence and (early) adulthood. ADHD and SUD also frequently co-occur in treatment-seeking adolescents, which complicates diagnosis and treatment and is associated with poor treatment outcomes. Research on the effect of treatment of childhood ADHD on the prevention of adolescent SUD is inconclusive, and studies on the diagnosis and treatment of adolescents with ADHD and SUD are scarce. Thus, the available evidence is generally not sufficient to justify robust treatment recommendations. Objective: The aim of the study was to obtain a consensus statement based on a combination of scientific data and clinical experience. Method: A modified Delphi study to reach consensus based upon the combination of scientific data and clinical experience with a multidisciplinary group of 55 experts from 17 countries. The experts were asked to rate a set of statements on the effect of treatment of childhood ADHD on adolescent SUD and on the screening, diagnosis, and treatment of adolescents with comorbid ADHD and SUD. Results: After 3 iterative rounds of rating and adapting 37 statements, consensus was reached on 36 of these statements representing 6 domains: general (n = 4), risk of developing SUD (n = 3), screening and diagnosis (n = 7), psychosocial treatment (n = 5), pharmacological treatment (n = 11), and complementary treatments (n = 7). Routine screening is recommended for ADHD in adolescent patients in substance abuse treatment and for SUD in adolescent patients with ADHD in mental healthcare settings. Long-acting stimulants are recommended as the first-line treatment of ADHD in adolescents with concurrent ADHD and SUD, and pharmacotherapy should pr

INTRODUCTION: Poverty has far-reaching and detrimental effects on children's physical and mental health, across all geographies. Financial advice and income-maximisation services can provide a promising opportunity for shifting the physical and mental health burdens that commonly occur with financial hardship, yet awareness of these services is limited, and referrals are not systematically integrated into existing healthcare service platforms. We aim to map and synthesise evidence on the impact of healthcare-income maximisation models of care for families of children aged 0-5 years in high-income countries on family finances, parent/caregiver(s) or children's health and well-being. METHODS AND ANALYSIS: To be included in the review, studies must be families (expectant mothers or parents/caregivers) of children who are aged between 0 and 5 years, accessing a healthcare service, include a referral from healthcare to an income-maximisation service (ie, financial counselling), and examine impacts on child and family health and well-being. A comprehensive electronic search strategy will be used to identify studies written in English, published from inception to January 2021, and indexed in MEDLINE, EMBase, PsycINFO, CINAHL, Proquest, Family & Society Studies Worldwide, Cochrane Library, and Informit Online. Search strategies will include terms for: families, financial hardship and healthcare, in various combinations. Bibliographies of primary studies and review articles meeting the inclusion criteria will be searched manually to identify further eligible studies, and grey literature will also be searched. Data on objective and self-reported outcomes and study quality will be independently extracted by two review authors; any disagreements will be resolved through a third reviewer. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. ETHICS AND DISSEMINATION: Ethical approval is not required. The results will be disseminate

INTRODUCTION: Research highlights the importance of early intervention for children with autism spectrum disorder with better outcomes associated with earlier access to early intensive intervention (EII) programmes. However, there is significant variability in response to EII despite children receiving the same programmes. METHODS AND ANALYSIS: A prospective, multisite cohort study using a pre-post design assesses the predictors of early intervention outcomes for children who receive EII through six early intervention services (Autism Specific Early Learning and Care Centres, ASELCCs) across Australia. Child and family characteristics at entry to and exit from ASELCCs are ascertained using measures of autism symptoms (Autism Diagnostic Observation Schedule-2; Social Communication Questionnaire); cognitive, language and developmental skills (Mullen Scale of Early Learning); adaptive function (Vineland Adaptive Behaviour Scale-second Edition); behaviours (Child Behaviour Checklist-1.5 to 5 years; Restricted Repetitive Behaviour Scale); parental stress (Parent Stress Index-4 Short Form); quality of life (Quality of Life in Autism Scale) and a semistructured family history questionnaire for sociodemographic, family and psychosocial characteristics. Characteristics at entry are used as predictors of outcome at exit following EII approximately 12 months later. The change in score from baseline to exit will be the primary outcome of interest. The mediating role of family and psychosocial factors will also be considered. ETHICS APPROVAL: University of New South Wales Human Research Ethics Committee (HC14267). DISSEMINATION OF RESULTS: Findings will be published in peer-reviewed journals and presented at conferences. A report summarising data and the interpretation of data will be published.

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources