Factors Associated with a Delayed Autism Spectrum Disorder Diagnosis in Children Previously Assessed on Suspicion of Autism

Family-Centric Applied Behavior Analysis Promotes Sustained Treatment Utilization and Attainment of Patient Goals

BACKGROUND/OBJECTIVES

 Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social communication difficulties and restricted repetitive behaviors or interests. Applied behavior analysis (ABA) has been shown to significantly improve outcomes for individuals on the autism spectrum. However, challenges regarding access, cost, and provider shortages remain obstacles to treatment delivery. To this end, parents were trained as parent behavior technicians (pBTs), improving access to ABA, and empowering parents to provide ABA treatment in their own homes. We hypothesized that patients diagnosed with severe ASD would achieve the largest gains in overall success rates toward skill acquisition in comparison to patients diagnosed with mild or moderate ASD. Our secondary hypothesis was that patients with comprehensive treatment plans (>25-40 hours/week) would show greater gains in skill acquisition than those with focused treatment plans (less than or equal to 25 hours/week).  Methods: This longitudinal, retrospective chart review evaluated data from 243 patients aged two to 18 years who received at least three months of ABA within our pBT treatment delivery model. Patients were stratified by utilization of prescribed ABA treatment, age, ASD severity (per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition), and treatment plan type (comprehensive vs. focused). Patient outcomes were assessed by examining success rates in acquiring skills, both overall and in specific focus areas (communication, emotional regulation, executive functioning, and social skills).

RESULTS

Patients receiving treatment within the pBT model demonstrated significant progress in skill acquisition both overall and within specific focus areas, regardless of cohort stratification. Patients with severe ASD showed greater overall skill acquisition gains than those with mild or moderate ASD. In addition, patients with comprehensive treatment plans showed significantly greater gains than those with focused treatment plans.

CONCLUSION

The pBT model achieved both sustained levels of high treatment utilization and progress toward patient goals. Patients showed significant gains in success rates of skill acquisition both overall and in specific focus areas, regardless of their level of treatment utilization. This study reveals that our pBT model of ABA treatment delivery leads to consistent improvements in communication, emotional regulation, executive functioning, and social skills across patients on the autism spectrum, particularly for those with more severe symptoms and those following comprehensive treatment plans.

Clinical, socio-demographic, and parental correlates of early autism traits in a community cohort of toddlers

Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample.

Predictors of age at diagnosis in autism spectrum disorders: the use of multiple regression analyses and a classification tree on a clinical sample

The increasing prevalence of autism spectrum disorders (ASD) has led to worldwide interest in factors influencing the age of ASD diagnosis. Parents or caregivers of 237 ASD children (193 boys, 44 girls) diagnosed using the Autism Diagnostic Observation Schedule (ADOS) completed a simple descriptive questionnaire. The data were analyzed using the variable-centered multiple regression analysis and the person-centered classification tree method. We believed that the concurrent use of these two methods could produce robust results. The mean age at diagnosis was 5.8 ± 2.2 years (median 5.3 years). Younger ages for ASD diagnosis were predicted (using multiple regression analysis) by higher scores in the ADOS social domain, higher scores in ADOS restrictive and repetitive behaviors and interest domain, higher maternal education, and the shared household of parents. Using the classification tree method, the subgroup with the lowest mean age at diagnosis were children, in whom the summation of ADOS communication and social domain scores was ≥ 17, and paternal age at the delivery was ≥ 29 years. In contrast, the subgroup with the oldest mean age at diagnosis included children with summed ADOS communication and social domain scores < 17 and maternal education at the elementary school level. The severity of autism and maternal education played a significant role in both types of data analysis focused on age at diagnosis.

Differential Diagnosis of Autism and Other Neurodevelopmental Disorders

This article discusses the diagnostic criteria for autism spectrum disorder (ASD), as well as other neurodevelopmental disorders that may be confused with or co-occur with ASD. Practitioners involved in diagnostic assessment of ASD must be well versed in the features that differentiate ASD from other conditions and be familiar with how co-occurring conditions may manifest in the context of ASD. ASD symptoms present differently across development, underscoring the need for training about typical developmental expectations for youth. Periodic reevaluations throughout development are also important because support needs for individuals with autism change over time.

Demographic and autism characteristics as predictors of age of autism diagnosis of individuals with autism in Paraguay

LAY ABSTRACT

Autism is a lifelong condition characterized by repetitive behaviors and social communication differences. The reported cases of autism increased globally in the past years. Detecting autism early and providing appropriate supports promptly are crucial for better outcomes. Yet, little research focuses on what factors interplay in the diagnostic process of autistic children in Paraguay. We gathered data from 176 caregivers of autistic children under 18 years in Paraguay. Through a detailed analysis, we found that child's age, child's age at the caregiver's first concerns about their development, and the child's verbal skills are key in predicting the age of autism diagnosis in Paraguay. Educating caregivers and professionals about autism and social communication development can help identify autism early and provide timely support.

Validating the International Classification of Functioning, Disability and Health Core Sets for Autism in a Sample of Australian School-Aged Children on the Spectrum

Assessing functioning of children on the autism spectrum is necessary to determine the level of support they require to participate in everyday activities across contexts. The International Classification of Functioning, Disability and Health (ICF) is a comprehensive biopsychosocial framework recommended for classifying health-related functioning in a holistic manner, across the components of body functions, activities and participation, and environmental factors. The ICF Core Sets (ICF-CSs) are sub-sets of relevant codes from the broader framework that provide a basis for developing condition-specific measures. This study combined the ICF-CSs for autism, attention deficit hyperactivity disorder (ADHD) and cerebral palsy (CP) to validate the ICF-CSs for autism in an Australian sample of school-aged children. This cross-sectional study involved caregivers of school-aged children on the spectrum (n = 70) completing an online survey and being visited in their homes by an occupational therapist to complete the proxy-report measure based on the ICF-CSs for autism, ADHD and CP. Absolute and relative frequencies of ratings for each of the codes included in the measure were calculated and reported, along with the number of participants who required clarification to understand the terminology used. Findings indicate that the body functions and activities and participation represented in the ICF-CSs for autism were the most applicable for the sample. However, findings relating to environmental factors were less conclusive. Some codes not currently included in the ICF-CSs for autism may warrant further investigation, and the language used in measures based on the ICF-CSs should be revised to ensure clarity.

Autism spectrum disorder and GABA levels in children with succinic semialdehyde dehydrogenase deficiency

AIM

To elucidate the etiological aspects of autism spectrum disorder (ASD) in succinic semialdehyde dehydrogenase deficiency (SSADHD), related to dysregulation of γ-aminobutyric acid (GABA) and the imbalance of excitatory and inhibitory neurotransmission.

METHOD

In this prospective, international study, individuals with SSADHD underwent neuropsychological assessments, as well as biochemical, neurophysiological, and neuroimaging evaluations.

RESULTS

Of the 29 individuals (17 females) enrolled (median age [IQR] 10 years 5 months [5 years 11 months-18 years 1 month]), 16 were diagnosed with ASD. ASD severity significantly increased with age (r = 0.67, p < 0.001) but was inversely correlated with plasma GABA (r = -0.67, p < 0.001) and γ-hydroxybutyrate levels (r = -0.538, p = 0.004), and resting motor threshold as measured by transcranial magnetic stimulation (r = -0.44, p = 0.03). A discriminative analysis indicated that an age older than 7 years 2 months (p = 0.004) and plasma GABA levels less than 2.47 μM (p = 0.01) are the threshold values beyond which the likelihood of ASD presenting in individuals with SSADHD is increased.

INTERPRETATION

ASD is prevalent but not universal in SSADHD, and it can be predicted by lower levels of plasma GABA and GABA-related metabolites. ASD severity in SSADHD increases with age and the loss of cortical inhibition. These findings add insight into the pathophysiology of ASD and may facilitate its early diagnosis and intervention in individuals with SSADHD.

Reliability and validity of the Autism Screen for Kids and Youth

LAY ABSTRACT

It is important that autistic children be diagnosed as early as possible so their needs can be met and their families can gain important insights into their behavior and interact with them appropriately. However, very few autism screening instruments are appropriate for children who have outgrown early childhood. The Autism Screen for Kids and Youth (ASKY) presents parents of children aged 4-18 years with 30 items that relate to autistic behaviors as defined by the current clinical diagnostic criteria for autism spectrum disorder (DSM-5 ASD). We evaluated the Hebrew instrument's performance on 167 autistic and non-autistic children and adolescents. We found that the ASKY algorithm correctly identified 92% of the autistic individuals as "probable ASD" and correctly identified 72% of the non-autistic individuals as "probable non-ASD," with these classifications showing excellent stability over time. Using total questionnaire score instead of the algorithm improved the ASKY's ability to correctly identify autistic individuals as "probable ASD" and non-autistic individuals as "probable non-ASD" to 93% and 78%, respectively. Overall, the ASKY is a promising instrument for ASD screening of older children.

Socioeconomic factors and autism among 16- to 30-month-old children: Evidence from a national survey of China

We examined the association of socioeconomic status with the diagnosis of autism during 16–30 months of age. Using data from a national survey in China, we included 6049 children (55.6% male) in the final analysis, among which 71 of them were clinically diagnosed with autism. Adjusted for covariates, the odds ratios for having the diagnosis of autism (2.46, 95% confidence interval: [1.32, 4.59]) among children whose mother’s level is “junior middle school or below” were significantly higher than children whose mother’s level is “college or above.” Among children of lower educated mothers, there is a higher risk of being diagnosed with autism at a young age. We recommend more support for families with a low socioeconomic status to early detect, diagnose, and manage autism.

Lay abstract

Does being born in a family of high socioeconomic status mean a higher risk of being diagnosed with autism? The evidence from the Asian area is lacking. This research was conducted among 6049 toddlers who went through an evaluation–diagnose procedure of autism and whose parents were surveyed during the national survey of China, 2016–2017. Parents reported their education levels, occupations, family income, and ethnic background. We recruited the toddlers and parents from kindergartens, communities, and hospitals in five geographically representative areas of China. On average, these toddlers were 23 months of age. We found toddlers whose mothers had less than 9 years of education (junior middle school or below) had 2.46 times the chance to get a diagnosis of autism, compared with toddlers whose mothers had more than 15 years of education (college or above). We also found that 1.17 toddlers could be diagnosed with autism in each 100 Chinese toddlers. These findings have important implications for providing support to families that have low socioeconomic status, especially families with a mother who did not complete 9 years of education. Early detection programs focused on children from low socioeconomic backgrounds should be promoted.

Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts language, communication and social interactions. The current diagnostic process for ASD is based upon a detailed multidisciplinary assessment. Currently no clinical biomarker exists to help in the diagnosis and monitoring of this condition that has a prevalence of approximately 1%. The electroretinogram (ERG), is a clinical test that records the electrical response of the retina to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including ASD. In this study, we have proposed a machine learning based method to detect ASD from control subjects using the ERG waveform. We collected ERG signals from 47 control (CO) and 96 ASD individuals. We analyzed ERG signals both in the time and the spectral domain to gain insight into the statistically significant discriminating features between CO and ASD individuals. We evaluated the machine learning (ML) models using a subject independent cross validation-based approach. Time-domain features were able to detect ASD with a maximum 65% accuracy. The classification accuracy of our best ML model using time-domain and spectral features was 86%, with 98% sensitivity. Our preliminary results indicate that spectral analysis of ERG provides helpful information for the classification of ASD.

A Delayed Diagnosis of Autism Spectrum Disorder in the Setting of Complex Attention Deficit Hyperactivity Disorder

Autism spectrum disorder (ASD) presents a diagnostic challenge due to its highly heterogeneous nature. The most common clinical manifestations include difficulty with social interaction and the presence of repetitive sensory-motor behaviors. Females are more likely to be misdiagnosed or have a delayed diagnosis compared to males. Other factors that contribute to delayed diagnosis include low socioeconomic status and belonging to an ethnic minority. In pediatrics, the goal of ASD screening is to diagnose ASD earlier, with timely referral to early intervention services, so that better long-term neurodevelopmental outcomes can be achieved. Moreover, attention deficit hyperactivity disorder (ADHD) is the most common comorbidity in patients with ASD. While the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-4) prohibited a co-diagnosis of autism and ADHD, the DSM-5 has modified exclusion criteria to allow such. This case describes a minority adolescent male patient who presented initially with complex ADHD, who upon extensive evaluation, was ultimately diagnosed with co-existing autism. This patient’s diagnosis of ASD at the age of 14 in the setting of a pre-existing complex ADHD diagnosis demonstrates how symptoms of inattention or hyperactivity may convolute underlying or newly emerging social interaction difficulties. We highlight how children who are diagnosed with ADHD should be screened or evaluated for autism in the right clinical setting, such as evident persistence of social interaction impairment despite ADHD treatment.

Neuroimaging Techniques as Descriptive and Diagnostic Tools for Infants at Risk for Autism Spectrum Disorder: A Systematic Review

Autism Spectrum Disorder (ASD) has traditionally been evaluated and diagnosed via behavioral assessments. However, increasing research suggests that neuroimaging as early as infancy can reliably identify structural and functional differences between autistic and non-autistic brains. The current review provides a systematic overview of imaging approaches used to identify differences between infants at familial risk and without risk and predictive biomarkers. Two primary themes emerged after reviewing the literature: (1) neuroimaging methods can be used to describe structural and functional differences between infants at risk and infants not at risk for ASD (descriptive), and (2) neuroimaging approaches can be used to predict ASD diagnosis among high-risk infants and developmental outcomes beyond infancy (predicting later diagnosis). Combined, the articles highlighted that several neuroimaging studies have identified a variety of neuroanatomical and neurological differences between infants at high and low risk for ASD, and among those who later receive an ASD diagnosis. Incorporating neuroimaging into ASD evaluations alongside traditional behavioral assessments can provide individuals with earlier diagnosis and earlier access to supportive resources.