Given the historically higher prevalence of white males in autism research studies, many autism diagnostic and outcome instruments have not been specifically validated in people of color or in females. This study will recruit women and individuals from racially and ethnically diverse communities to understand how a measure of treatment outcome, called the BOSCC (Brief Observation of Social Communication Change), can be used more effectively in these communities.
Determining the effectiveness of the BOSCC in females and people of color
Early intervention is vital for children on the autism spectrum but is often only available after a formal diagnosis. Because of the COVID- 19 pandemic, many assessments are now conducted online. This change has sometimes occurred without studying whether modifications made to support online assessments affect the outcomes of the assessments. Researchers at the University of Massachusetts Boston, the University of Washington, Rush University Medical Center, and Michigan State University recently adapted an assessment protocol (the Communication Play Protocol; CPP), to be conducted as an online assessment of ASD (RISE-CPP). ASF’s funding will allow researchers to determine if clinicians can diagnose ASD online using the RISE-CPP protocol as accurately as they can using traditional in-person assessments. An online version has the advantages of possibly reaching a more diverse community and improving opportunities for early intervention.
Quality of Life (QoL) outcome measures have traditionally excluded autistic individuals with minimal verbal ability or cognitive disability. The Patient-Reported Outcomes Measurement Information System (PROMIS®) Autism Battery – Lifespan (PAB-L) is a
recently developed instrument to measure autistic QoL across the lifespan. Although PAB-L has been shown to be an acceptable QoL measure in autism, nonverbal people with cognitive disability were underrepresented among participants in the original validation studies. This grant will expand the research on the PAB-L to examine whether it is appropriate in those with profound autism, and also determine what changes, if any, should be made to effectively measure quality of life in this underserved population.
Genetic testing is recommended for all children with autism. However, many children receive test results that reveal mutations in genes that have not yet been associated with autism. Unfortunately, these variants of uncertain significance can cause confusion and problems for parents seeking clinical diagnoses and support. This study will utilize machine learning to integrate genetic findings with the child’s attainment of key developmental milestones, because often milestone delays are associated with rare genetic disorders. Eventually, this research could lead to a brief, low-cost clinical prediction tool that increases the diagnostic certainty of genetic testing in autism.
Despite awareness that depression is common in autistic people, the mental health of minimally verbal (MV) autistic adults has received inadequate attention. Part of the problem is the lack of valid tools to assess depression in MV autistic adults. This study will investigate the utility and appropriateness of using surveys administered by a caregiver around depression and will gather information about behaviors that caregivers believe reflect low mood or depression. This project addresses a gap in mental health supports for MV autistic adults and will assist clinicians in determining which tools should be used for people with autism who show signs of depression but cannot verbally communicate their feelings.
In the last version of the Diagnostic and Statistical Manual, the different subtypes of autism were folded into one label: autism spectrum disorder. A similar revision is being made around the International Classification of Diseases, the system the WHO uses across the world to describe autism and provide appropriate reimbursements for services and supports. In this version, the ICD-11, a combination of 300 different presentations of autism are described. A diagnosis can be made if 1 feature of social-communication and 1 feature of repetitive behaviors are documented, with an onset of any time in life. This is causing a lot of confusion in the community, because since the presentations are not specific to autism, it is difficult to provide an accurate diagnosis using the ICD-11. On this week’s podcast episode we talk to German psychiatrist Inge Kamp-Becker, MD, who outlines what the changes are, and how misdiagnosis can be made and what those consequences might be. Her summary is linked below.
Abstract
Emerging evidence suggests that the higher prevalence of autism in individuals who are assigned male than assigned female at birth results from both biological factors and identification biases. Autistic individuals who are assigned female at birth (AFAB) and those who are gender diverse experience health disparities and clinical inequity, including late or missed diagnosis and inadequate support. In this Viewpoint, an international panel of clinicians, scientists, and community members with lived experiences of autism reviewed the challenges in identifying autism in individuals who are AFAB and proposed clinical and research directions to promote the health, development, and wellbeing of autistic AFAB individuals. The recognition challenges stem from the interplay between cognitive differences and nuanced or different presentations of autism in some AFAB individuals; expectancy, gender-related, and autism-related biases held by clinicians; and social determinants. We recommend that professional development for clinicians be supported by health-care systems, professional societies, and governing bodies to improve equitable access to assessment and earlier identification of autism in AFAB individuals. Autistic AFAB individuals should receive tailored support in education, identity development, health care, and social and professional sense of belonging
Abstract
Background: Reporting retention data is critical to determining the soundness of a study’s conclusions (internal validity) and broader generalizability (external validity). Although selective attrition can lead to overestimates of effects, biased conclusions, or overly expansive generalizations, retention rates are not reported in many longitudinal studies.
Methods: We examined multiple child- and family-level factors potentially associated with retention in a longitudinal study of younger siblings of children with autism spectrum disorder (ASD; n = 304) or typical development (n = 163). The sample was followed from the first year of life to 36 months of age, for up to 7 visits.
Results: Of the 467 infant siblings who were consented and participated in at least one research visit, 397 (85.0%) were retained to study completion at 36 months. Retention rates did not differ by familial risk group (ASD-risk vs. Low-risk), sex, race, ethnicity, age at enrollment, number of children in the family, maternal employment, marital status, or parent concerns about the child at enrollment. A stepwise regression model identified 4 variables that, together, provided the most parsimonious predictive model of study retention: maternal education, maternal age at child’s birth, travel distance to the study site, and diagnostic outcome classification at the final study visit.
Conclusions: The retained and not-retained groups did not differ on most demographic and clinical variables, suggesting few threats to internal and external validity. The significantly higher rate of retention of children diagnosed with ASD (95%) than typically developing children (83%) may, however, present biases when studying recurrence risk. We conclude by describing engagement and tracking methods that can be used to maximize retention in longitudinal studies of children at risk of ASD.
Keywords: attrition; autism; external validity; internal validity; longitudinal study; retention.
A recent publication in the Lancet was dedicated to clinical recommendations to support autistic females at birth. Because more males than females are diagnosed with autism, their needs are often misunderstood, misinterpreted, or just ignored. Researchers, clinicians, scientists, parents and self-advocates from around the world joined together to identify those needs and propose solutions that can be implemented in everyday care. Listen to this week’s podcast episode to learn more, or read the article in its entirety at the link below.
Everyone who has looked for support for autism spectrum disorder is familiar with waitlists. Waitlists for evaluation, diagnosis, intervention, consultations and referrals. These waitlists prevent important opportunities for services and many groups developing technologies, policies, and approaches to reduce the waitlists or work around them. On this week’s podcast, we talk to Dr. Sharief Taraman from Cognoa to hear about their recent study on the scope of the problem on waitlists, what causes them, and how digital therapeutics may help them.
Can biomarkers that measure things like visual social attention be a good proxy for an in person behavioral diagnosis? Why would this be important? This week’s podcast explores two new studies the the Journal of the American Medical Association that show a simple device called EarliPoint can be used to shorten the wait times to receive a diagnostic evaluation. Currently autism can be diagnosed at 18 months but most families do not get into an appointment until 4-5 years of age. That can change. Families were able to easily complete it, it predicted things like not just a diagnosis but behavioral features and cognitive ability. It’s been deployed in 6 speciality centers, been approved by the FDA, and hopefully coming to a clinic near you soon.
Objectives: Autism spectrum disorder (autism) is a heterogeneous condition that poses challenges in describing the needs of individuals with autism and making prognoses about future outcomes. We applied a newly proposed definition of profound autism to surveillance data to estimate the percentage of children with autism who have profound autism and describe their sociodemographic and clinical characteristics.
Methods: We analyzed population-based surveillance data from the Autism and Developmental Disabilities Monitoring Network for 20 135 children aged 8 years with autism during 2000-2016. Children were classified as having profound autism if they were nonverbal, were minimally verbal, or had an intelligence quotient <50.
Results: The percentage of 8-year-old children with profound autism among those with autism was 26.7%. Compared with children with non-profound autism, children with profound autism were more likely to be female, from racial and ethnic minority groups, of low socioeconomic status, born preterm or with low birth weight; have self-injurious behaviors; have seizure disorders; and have lower adaptive scores. In 2016, the prevalence of profound autism was 4.6 per 1000 8-year-olds. The prevalence ratio (PR) of profound autism was higher among non-Hispanic Asian/Native Hawaiian/Other Pacific Islander (PR = 1.55; 95 CI, 1.38-1.73), non-Hispanic Black (PR = 1.76; 95% CI, 1.67-1.86), and Hispanic (PR = 1.50; 95% CI, 0.88-1.26) children than among non-Hispanic White children.
Conclusions: As the population of children with autism continues to change, describing and quantifying the population with profound autism is important for planning. Policies and programs could consider the needs of people with profound autism across the life span to ensure their needs are met.
Keywords: autism; public health; surveillance.