Raising a preschooler with an Autism Spectrum Disorder: the impact on parent and family wellbeing, and the role of the home learning environment

Raising a child with an Autism Spectrum Condition comes with unique gifts and challenges. During the preschool years, parents are depended on to support their child's early intervention, while also adjusting to the Autism diagnosis. The researcher gathered in-depth information about parental wellbeing, parenting, and family life using questionnaire and interview methods. Findings highlighted wellbeing and family difficulties, resilience, and the intensive support provided to children in the family home. These findings encourage health professionals to implement family-centered care, taking into consideration parent and family wellbeing, as well as the needs of the child with an Autism Spectrum Condition

Fatigue, stress and coping in mothers of children with an autism spectrum disorder

Raising a child with an autism spectrum disorder (ASD) can be exhausting, which has the potential to impact on parental health and wellbeing. The current study investigated the influence of maternal fatigue and coping on the relationship between children's problematic behaviours and maternal stress for 65 mothers of young children (aged 2-5 years) with ASDs. Results showed that maternal fatigue but not maladaptive coping mediated the relationship between problematic child behaviours and maternal stress. These findings suggest child behaviour difficulties may contribute to parental fatigue, which in turn may influence use of ineffective coping strategies and increased stress. The significance of fatigue on maternal wellbeing was highlighted as an important area for consideration in families of children with an ASD

Intellectual disabilities and challenging behavior among indigenous Australians

Abstract not available

Family functioning and behaviour problems in children with Autism Spectrum Disorders: the mediating role of parent mental health

Background Parents of young children with Autism Spectrum Disorders (ASDs) are often relied on to help implement therapy with their child, which occurs within a family context. Therefore, it is important to understand and support families where there is a child with an ASD. Although individual parent factors have received substantial research attention, fewer studies have investigated family functioning. This study explored the relationship between child behaviour problems and family functioning in families where there was a preschooler with an ASD. Parent mental health difficulties, including stress, fatigue, and depressive symptoms, were investigated as mediators in this relationship. Method Participants included 97 parents with a preschooler diagnosed with an ASD. Parents completed an online questionnaire reporting on child behaviour problems, their own symptoms of stress, depression and fatigue, and family functioning. Results Path analysis showed that the relationship between child behaviour problems and family functioning was mediated by depressive symptoms, but not stress and fatigue. Conclusions These results highlighted one way that ASDs can impact on the family system, suggesting that when parents are overburdened by behaviour problems, there are implications for the family. The importance of providing clinical interventions and support to strengthen parent mental health and family functioning is discussed

Fatigue, wellbeing and parental self-efficacy in mothers of children with an autism spectrum disorder

Raising a child with an Autism Spectrum Disorder (ASD) presents significant challenges for parents that potentially have a impact on their health and wellbeing. The current study examined the extent to which parents experience fatigue and its relationship to other aspects of wellbeing and parenting. Fifty mothers of children with an ASD aged 2–5 years participated in the study. Compared with mothers of typically developing children, mothers of children with an ASD reported significantly higher fatigue, with overall scores in the moderate range. Factors associated with high levels of fatigue were poor maternal sleep quality, a high need for social support and poor quality of physical activity. Fatigue was also significantly related to other aspects of wellbeing, including stress, anxiety and depression, and lower parenting efficacy and satisfaction. The need for interventions to specifically target parental fatigue and its impact on families affected by ASDs both in the short and long term is clearly indicated

Near-Peer Teaching in Radiation Oncology: a Proof of Principle Study for Learning Treatment Planning

Radiotherapy techniques are expanding in range and complexity; therefore, protecting learning environments where residents nurture treatment planning skills is critical. The evidence base for ‘near-peer’ teaching (NPT), where professionals at a similar career stage assist in each other’s learning, is growing in hospital-based disciplines, but has not been reported in radiation oncology. The feasibility of a resident-led teaching programme for developing treatment planning skills was investigated herein with quality improvement (QI) methodology. Following consultation with attendings (n = 10) and all residents (n = 17) at the two cancer centres in the region, a regular NPT session focused on planning skills was initiated at the largest centre, with video-linking to the second centre. Tutorials were case-based and pitched at the level of qualifying examinations. Plan–Do–Study–Act (PDSA) cycles were designed based on primary and secondary improvement drivers derived by group consensus among residents, with tutorials adopted accordingly. Participation, content, and satisfaction were monitored for 20 months. Six PDSA cycles reformed the tutorial format, leading to logistical and pedagogical benefits including interprofessional contributions and enhanced interactivity. Tutorials occurred on 85% prescribed occasions (n = 45) during the subsequent 18-month follow-up, with 25 distinct tumour sites featured. Resident participation and satisfaction increased, independent of resident seniority. Tutorials were paused for the first 2 months of the SARS-CoV-2 pandemic only. A high-quality and cost-effective regional, trainee-led teaching programme on treatment planning was feasible and cost-effective in this study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13187-022-02150-2

The Lancet Commission on the future of care and clinical research in autism

Affecting about 78 million people worldwide, autism is a condition of global importance because of its prevalence and the degree to which it can affect individuals and families. Autism awareness has grown monumentally in the past 20 years, yet most striking is that much more could be done to improve life outcomes for the highly heterogeneous group of people with autism. Such change will depend on investments in science focused on practical clinical issues, and on social and service systems that acknowledge the potential for change and growth as well as the varied, complex needs of the autistic individuals and their families whose lives could be changed with such an effort. The Lancet Commission on the future of care and clinical research in autism aims to answer the question of what can be done in the next 5 years to address the current needs of autistic individuals and families worldwide. Autism is a neurodevelopmental disorder that typically begins to manifest in early childhood and affects social communication and behaviours throughout the life span. Autism and other neurodevelopmental disorders have seen a tremendous influx of interest from the scientific community in the past 60 years. Substantial progress has been made in many areas of basic and applied science, but the limits of the knowledge and understanding of autism are also very clear. For clinical purposes, reviews and guidelines have proliferated, although the data on which many recommendations are based are typically from short-term interventions that address acquisition of specific skills that are hoped—but not yet known with confidence—to contribute to long-term gains across development. However, large gaps around key questions remain, such as what interventions and support strategies are effective for whom and when, and which interventions lead to changes beyond their proximal outcomes. Underlying these outstanding questions is a deep scarcity of information about what are the active elements or mechanisms, behavioural or neurobiological, for change. These issues are particularly important because autism affects from toddlers to elders and is almost always accompanied by other developmental, behavioural, and mental health difficulties or conditions that have major implications for lifelong outcomes

The Lancet Commission on the future of care and clinical research in autism

Affecting about 78 million people worldwide, autism is a condition of global importance because of its prevalence and the degree to which it can affect individuals and families. Autism awareness has grown monumentally in the past 20 years, yet most striking is that much more could be done to improve life outcomes for the highly heterogeneous group of people with autism. Such change will depend on investments in science focused on practical clinical issues, and on social and service systems that acknowledge the potential for change and growth as well as the varied, complex needs of the autistic individuals and their families whose lives could be changed with such an effort

Erratum: Autism-related dietary preferences mediate autism-gut microbiome associations (Cell (2021) 184(24) (5916–5931.e17), (S0092867421012319), (10.1016/j.cell.2021.10.015))

(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 respect to the x-axes of Figures 3A–3C, 3E, 3F, and S4, which reflect the degree and directionality of differential abundance. In the updated plots, all the significant or near-significant microbiome features have identical directions of effect to the original plots as well as similar magnitudes of effect. We can confirm that the other instances of clr transformation were performed correctly; namely, in generating the dietary PCs, CD4+ T cells, and the PCA plot (Figure 5A). We have updated the following: (1) Figure 2 has been amended with the updated data: • Under age, species_common has changed from 33 to 35, transporter(TCDB)_common from 42 to 36, pathway(MetaCyc)_common from 40 to 39, and food(AES) from 33 to 46.• Under BMI, species, transporter(TCDB)_common has changed from 1 to 4, pathway(MetaCyc)_common from 0 to 1, genes(Microba)_common from 7 to 10, and food(AES) from 12 to 22.• Under ASD, genes(Microba)_rare has changed from 7 to 9.• Under IQ_DQ, species_common has changed from 3 to 5, genes(Microba)_common from 7 to 14, and food(AES) from 3 to 14.• Under Sleep, species_common has changed from 10 to 11, and genes(Microba)_common has changed from 0 to 6.• Under rBSC, species_common has changed from 5 to 6, species_rare from 41 to 40, transporter(TCDB)_common from 3 to 4, and genes(Microba)_common from 49 to 50.• Under dietary_PC1, enzyme(ECL4)_common has changed from 48 to 46, pathway(MetaCyc)_common from 25 to 24, and genes(Microba)_common from 48 to 47.• Under dietary_PC2, species_rare has changed from 1 to 0, genes(Microba)_common from 7 to 8, and genes(Microba)_rare from 3 to 0.• Under dietary_PC3, species_common has changed from 4 to 6, species_rare from 1 to 0, transporter(TCDB)_common from 21 to 17, pathway(MetaCyc)_common from 11 to 10, and genes(Microba)_common from 27 to 28.• Under diet_diversity, species_rare has changed from 20 to 14, transporter(TCDB)_common from 26 to 23, and genes(Microba)_common from 26 to 23.• We have also taken this opportunity to switch the y-axis order for “species_rare” and “enzyme(ECL4)_common” to better separate the taxonomic and functional datasets.(2) Table S1, which contains the raw data presented in Figure 2, has been amended with the updated OREML results.(3) In the main text, the third to fifth paragraphs of the section titled “Negligible variance in ASD diagnostic status is associated with the microbiome compared to age, stool and dietary traits” has been amended: • The age common species b2 estimate and standard error has changed from 33% (SE = 8%) to 35% (SE = 7%).• The p value for the BMI common species analysis has changed from p = 3.5e-2 (not FDR significant) to 1.8e-2 (FDR-significant).• With reference to the age gene-level ORM analyses, the range of standard errors has been changed from 13%–17% to 14%–17%.• The BMI rare genes b2 estimate has changed from 46% to 47%, and the p value has changed from 8.4e-3 to 1.1e-2.• The ASD rare genes b2 estimate has changed from 7% to 9%, and the p value has changed from 0.33 to 0.29.• The IQ-DQ common species b2 estimate has changed from 7% (SE = 13%, p = 0.39) to 5% (SE = 6%, p = 0.20).• The sleep problems common species b2 estimate has changed from 10% to 11%, and the p value has changed from 0.17 to 8.2e-2.• The stool consistency rare species b2 estimate has changed from 41% to 40%, and the p value has changed from 8.7e-6 to 2.8e-5.• The stool consistency rare genes standard error has changed from 20% to 21%, and the p value has changed from 2.5e-5 to 5.8e-5.• We have corrected an error where the dietary PC1 common genes b2 estimate (b2 = 48%, SE = 15%, p = 3.8e-4) was mislabeled as the rare genes analysis. We have also updated the common genes b2 estimate from 48% to 47% and updated the p value from 3.8e-4 to 4.5e-5.(4) Figure S1, which visualizes the diagonals and off-diagonals of the omics relationship matrix (ORM; which, in turn, is based on the centered-log-ratio transformed microbiome matrices) has been amended with the updated OREML results.(5) Figure S2, which draws upon ORMs using rare microbiome features to compare the effects of prior clr transformation versus binary coding as a sensitivity analysis, has been amended with the updated OREML results.(6) Figure S3, which provides a variety of OREML estimates to support Figure 2 (including the impact of estimating b2 with a combination of multiple ORMs and collapsing taxonomic microbiome data into higher levels of hierarchy), has been amended with the updated OREML results.(7) Methods S1, which provides results from extensive sensitivity analyses to support the main results, has also been amended with the updated OREML results. We have also updated the section “Estimating the upper limit of predictivity using non-additive models,” for which we used adaboost as a sensitivity analysis for a method that does not assume additivity. In this analysis, the mean prediction accuracy for ASD changed from 53% (SD = 7%) to 53% (SD = 8%), and the prediction accuracy for age changed from 62% (SD = 7%) to 63% (SD = 9%).(8) Figures 3A–3C, 3E, and 3F, which visualize differentially abundant microbiome features, now have updated x-axes.(9) Figure S4, which supports Figure 3 by providing results from sensitivity analyses for differential abundance, also has updated x-axes.(10) Tables S2.1, S2.3, S2.8, S2.13, and S2.14, which provide data (including x-axis coordinates) for Figures 3A–3C, 3E, and 3F, have been updated.(11) Unrelated to the clr transformation error, we have also updated the heading of the upper plot in Figure 4I to read “Diet ∼ Sensory score” rather than “Taxa ∼ Sensory score.”(12) The accompanying Zenodo code has been updated, and the link has been changed from https://zenodo.org/records/5558047 to https://zenodo.org/records/5558046. The specific code updates can be viewed on the linked GitHub page.These errors have now been corrected in the online version of the paper. We apologize for any inconvenience that this may have caused the readers.[Formula</p