latest research on autism 2021

EDITORIAL article

Editorial: autism: innovations and future directions in psychological research.

• 1 Body, Eye and Movement Lab, Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
• 2 Centre for Research in Autism and Education (CRAE), UCL Institute of Education, London, United Kingdom
• 3 Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany

Editorial on the Research Topic Autism: Innovations and Future Directions in Psychological Research

Psychological research on autism has a long tradition, covering multiple fields including cognition, perception, clinical research, neuroscience, and social psychology. This Research Topic brings together the latest research in this area, mapping key developments, innovations, and future directions. In this editorial, we will discuss six themes that we have identified across the 22 contributions to this Research Topic: (1) Theories and mechanisms; (2) Characterization of autism; (3) Sensory experiences, perception and movement; (4) Language; (5) Support and interventions; and (6) Methods and technologies. We also provide thoughts on future directions in the field.

Theories and Mechanisms

Recent discussions have focused on the double-empathy theory (e.g., Milton, 2012 ; Bolis et al., 2017 ; but see Georgescu et al., 2020 ), which interprets communication “difficulties” associated with autism as a bidirectional breakdown between two interaction partners. Building on this theory, Crompton et al. conducted an innovative empirical study examining interpersonal rapport as a function of the neurology of interaction partners, and the person rating levels of rapport. When rating rapport after semi-structured conversations, homogeneous dyads of non-autistic people reported highest levels of rapport, followed by homogeneous dyads of autistic people and lastly mixed (autistic/non-autistic) dyads. Interestingly, taking an outside perspective, when rating observed rapport between interaction partners, homogeneous dyads of autistic individuals were rated highest concerning observed rapport, followed by homogeneous dyads of non-autistic individuals and lastly, again, mixed (autistic/non-autistic) dyads, supporting the double empathy theory.

Beyond specific aspects of functioning, Gernert et al. suggest that empirical and theoretical considerations should move toward a more comprehensive outlook on autism. The authors' Generalized Adaptation Account suggests potential connections between findings from genetics, neurobiology, endocrinology, cellular and neuronal connectivity levels. In this framework, aberrations of neurodevelopmental signaling pathways link up to alterations of neuronal connectivity with cascading effects on neuroendocrine dysregulations and impact on circadian functioning. Consequently, chronic distress and hyperactivation of the hypothalamus-pituitary-adrenal (HPA)-axis result in oxytocinergic downregulation linked to social functioning. This unifying account tries to capture both the complexity of presentation of autism and, in particular, its heterogeneity.

Characterization of Autism

Two articles in this Research Topic were concerned with better characterizing different aspects of autism. Li et al. used the Griffiths Mental Development Scales to characterize the cognitive, motor and social profiles of 398 autistic children (18–96 months old) in China. Findings suggested that many children showed an unbalanced profile (e.g., boys scored better than girls on eye-hand coordination, performance and practical reasoning; and differences in motor behavior became more pronounced with age). Significant aspects to take from this study were the characterization of autistic children in different regions of the world and the need to identify a child's strengths and challenges to develop personalized support.

Characterization can also be useful for predicting the future outcomes of autistic children. Forbes et al. predicted adult outcomes using an impressive dataset of participants who had been repeatedly assessed through childhood, adolescence and adulthood. Only verbal and non-verbal IQ, as well as daily living skills, could be confidently predicted from childhood data while prediction of other aspects (e.g., behavior, adult well-being, depression) was more difficult. Importantly, the authors discuss that views on what constitutes good adult outcomes for autistic children can vary. As acknowledged by the authors, this is clearly a challenging and evolving subject where stakeholder involvement is required.

Sensory Experiences, Perception and Movement

Awareness of the significance of sensory experiences and perceptual processing on the lives of autistic individuals has increased in recent years ( Torres and Donnellan, 2015 ; Autistica, 2016 ). In this Research Topic, we featured three perceptual studies that all employed rigorous, well-controlled methods to examine this topic. Mihaylova et al. used detailed psychophysical methods to progress understanding of mid-level visual processing in autistic children and adolescents. Results suggested that atypical global grouping (studied in a contour integration task), may be due to higher stimulus-dependent noise in the autistic group, leading to difficulties rejecting background noise and detecting the target.

The effect of low-mid level perceptual differences on higher level perceptual processes was elegantly shown across two studies by Lebreton et al. Here, the authors demonstrated how the commonly reported autistic preference for local compared to global detail impacted upon implicit (unconscious) and explicit (conscious) memory. This is a fascinating finding requiring replication, but has implications for understanding how perceptual style in both autistic and non-autistic individuals affects later memory recall.

Finally, Silver et al. examined whether the intense interests frequently observed in autistic individuals were related to visual processing changes for objects within that category. Contrary to expectations, there were no differences between autistic and non-autistic individuals in visual search abilities for images associated with intense interests. As such, despite enhanced time spent by autistic individuals gazing at images related to an interest, this did not seem to translate to a direct impact on visual processing ability. Linking back to Lebreton et al. , we wonder whether the degree of local-global bias in the participants may mediate any relationship between visual experience and visual search ability.

In another fascinating study featured in our Research Topic, Parmar et al. conducted qualitative work with a multidisciplinary team of Optometrists, autism researchers and autistic individuals, using focus groups to provide an in-depth understanding of visual sensory issues. As well as providing a rich description of sensory experiences, the researchers highlighted how visual issues had significant negative impacts on personal well-being and daily life, but also some positive aspects (e.g., detecting details that non-autistic individuals may overlook).

Another article in our Research Topic, by Buckle et al. , is the first to highlight Autistic Inertia—a debilitating difficulty of acting on intentions. The article was led by an autistic researcher (based on calls for research on this topic from autistic individuals) and the research highlighted how significant, and potentially common, Autistic Inertia is. Using qualitative methods, the study provided a detailed description of Inertia and the impact of it on autistic people's lives. Two particularly revealing findings were the benefit of other people in helping the individual to overcome being “stuck” and participants wanting to interact with others, but being unable to initiate interaction (which may be interpreted as a lack of social interest).

New approaches in the study of linguistic properties of autism were reported in this Research Topic. Marini et al. combined macrolinguistic (pragmatic, contextual processing) and microlinguistic (word and sentence processing) perspectives of language, which have traditionally been considered independently, showing that morphological and grammatical difficulties were related. Such findings suggest a relationship between difficulties in message planning and organization, which might impact children's grammatical production skills.

New avenues in language research were also highlighted by Sturrock et al. when considering potential gender differences in linguistic studies of autistic people. From a synthesis of previous literature, the authors concluded that there was a very specific profile of language and communication strengths and weaknesses for autistic females without intellectual disability, when compared to both autistic males and non-autistic females. The authors discuss how poorer recognition of autism in females might be influenced by female advantages in aspects of linguistic functioning (but see Lehnhardt et al., 2016 ).

In a further paper, Williams et al. demonstrated a new approach to studying communication differences between autistic and non-autistic people using relevance theory. This account posits that optimal communication is based on shared and mutually recognized relevance of utterances, which might be mismatched between autistic and non-autistic people when communicating due to differences in experiences of the world. This theoretical approach feeds into the discussions of double-empathy theory (see Theories and mechanisms).

Support and Interventions

Leadbitter et al. 's article proposes that early intervention research could and should be aligned with principles derived from autistic self-advocacy and the neurodiversity movement. Engagement with these principles would lead to, for example, intervention research focusing on changing environments (as opposed to changing autistic people), as well as intervention researchers respecting autistic developmental trajectories and priorities for intervention targets.

In line with this approach, Di Renzo et al. examined the interactions between autistic children and their parents during play, finding that parents who were more accepting of their children's autism diagnosis and who were better able to see things from their children's perspective, were more likely to be attuned with their children during play. Such work highlights the central role of parents as partners in supporting autistic children, and the importance of shared understanding between autistic people and their non-autistic communicative partners (see section Theories and Mechanisms).

Two further studies focused on the important role of parents. Papadopoulos et al. considered support and intervention for young disabled people, 41% of whom had a primary diagnosis of autism. The authors concluded that, to ensure that organized physical activities met the needs of young disabled people, there was a need for activities to be enjoyable, for the participation of siblings and parents to be promoted, and for low-income families to be supported to participate. This work again emphasizes that autism interventions can focus on changing the structures around young people, as opposed to changing the young people themselves.

Relatedly, Devenish et al. examined the effects of lower rates of community participation by autistic young people on their caregivers. Devenish et al. found that if caregivers perceived community supportiveness to be low, this predicted caregiver feelings of isolation. Findings were interpreted within a social model of disability, highlighting how autistic people are disabled by barriers in society.

Not all intervention studies featured in this Research Topic found positive effects of interventions (moving away from the publication bias that once dominated published intervention research). Brehm et al. conducted an initial evaluation of a training programme for parents of autistic children without intellectual/language impairments. The purpose of the evaluation was to evaluate how acceptable the training was for parents, and the results were positive with hardly any parents dropping out of the training programme. Yet a variety of primary outcome measures (e.g., quality of life, social communication) did not show significant improvement. Brehm et al. note that these findings can be useful for directing future work on such interventions.

Similarly, Saul and Norbury presented an alternative to Randomized Controlled Trials for research with rare/complex populations. Drawing on a research study with minimally verbal autistic children, the authors tested the efficacy of a parent-mediated app designed to support speech production, via Randomization Tests and Between Case Effect Sizes. As with Brehm et al.'s study, there was no significant effect of the intervention. Yet the research still made an important contribution to the literature; notably demonstrating the importance of robust experimental design and replicable approaches, as well as showing how it is possible to conduct rigorous intervention research with rare or complex samples.

It was also encouraging to see an example of a high-quality case study featured in the article by Courchesne et al. , which critically considered the role of interests and strengths in autism, particularly highlighting that these aspects do not necessarily link with academic potential. Courchesne et al. discussed an autistic teenager, C.A., who had above-average musical and calendar calculation abilities, along with pronounced difficulties in other areas (e.g., receptive and expressive language disorder). This discrepancy was found to lead to anxiety, frustration and some behavioral issues due to pressure to use his relative strengths to learn academic skills. Yet, an intervention package that focused on expectations, anxiety and emotional regulation through psychiatric intervention, parental coaching and psychotherapy, improved well-being and behavior. Courchesne et al. caution that while strengths and interests can lead to emotional well-being they should be seen as independent from adaptive outcomes such as academic achievement.

Methods and Technologies

A key message from studies in this theme is the need to develop and validate more ecologically valid assessments of autistic characteristics. For example, Morrison et al. administered standardized measures of social cognition, social skill, and social motivation to autistic and non-autistic adults, and assessed whether these predicted “real-world” social interaction outcomes (measured using unstructured conversations with unfamiliar social partners). While autistic adults scored lower than their non-autistic peers on the three standardized social tasks and were evaluated less favorably during the unstructured social interaction, the links between performance on the standardized measures and unstructured interaction were minimal. The authors therefore question the utility of traditional measures of social performance in autistic people, calling for more ecologically valid assessments.

In line with this approach, Schaller et al. used mobile eye-tracking glasses during autism diagnostic assessments to record gaze behavior of autistic and non-autistic children and adolescents. The authors focused on the percentage of time spent looking at different areas of interest of the face and body of the interviewer and the surrounding space. Significant group differences were found, with non-autistic participants appearing to process faces and facial expressions in a holistic way focusing on the central-face region, whereas autistic participants tended to avoid this face region. The authors stress that the results are preliminary and in need of replication, but this represents an exciting avenue for further work using an ecologically valid methodology.

Conclusions and Future Directions

Illuminating psychological science on autism from different thematic perspectives has shown several directions we can observe in the field of psychological research. For example: researchers taking a broader perspective, by incorporating previously distinct areas or methods into comprehensive studies; pairing quantitative analysis with qualitative appraisal of experience; putting forward unifying theories spanning different fields; examining an autistic person's strengths and challenges and tailoring more personalized support; developing alternative methods for evaluating interventions in more complex populations; and the implementation of a participatory approach to research. We would like to thank the contributors for their varied and stimulating contributions and hope that this Research Topic stimulates further cutting-edge psychological research that benefits the autistic community.

Author Contributions

EG drafted a first version of the Editorial. EG, LC, and CF-W wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would particularly like to thank the authors and reviewers who contributed to this Research Topic for their valuable commitment to the field during such a trying time caused by the COVID pandemic.

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Keywords: autism, psychological research, cognition, perception, neuroscience, participatory research

Citation: Gowen E, Crane L and Falter-Wagner CM (2022) Editorial: Autism: Innovations and Future Directions in Psychological Research. Front. Psychol. 12:832008. doi: 10.3389/fpsyg.2021.832008

Received: 09 December 2021; Accepted: 22 December 2021; Published: 17 January 2022.

Edited and reviewed by: Irene Ceccato , University of Studies G. d'Annunzio Chieti and Pescara, Italy

Copyright © 2022 Gowen, Crane and Falter-Wagner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Emma Gowen, emma.gowen@manchester.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Autism spectrum disorders are a group of neurodevelopmental disorders that are characterized by impaired social interaction and communication skills, and are often accompanied by other behavioural symptoms such as repetitive or stereotyped behaviour and abnormal sensory processing. Individual symptoms and cognitive functioning vary across the autism spectrum disorders.

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New Research May Change How We Think About the Autism Spectrum

Insar keynote suggests brain differences correlate with cognition—not diagnosis..

Posted May 16, 2022 | Reviewed by Davia Sills

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• Dr. Evdokia Anagnostou presented the results of neuroimaging studies at the International Society for Autism Research 2022 annual meeting.
• Of note, brain differences clustered along dimensions of cognition and hyperactivity, not diagnosis.
• These findings suggest we need to reconsider how we classify neurodivergence.

University of Toronto child neurologist Evdokia Anagnostou dropped a bombshell in her keynote Saturday at the annual meeting of the International Society of Autism Research (INSAR) in Austin, Texas, which may call into question the validity of the autism spectrum disorder (ASD) diagnosis.

What Brain Scans Tell Us About Autism Spectrum Disorder

Anagnostou and her colleagues had set out to use neuroimaging to identify brain differences unique to ASD, as compared to other neurodevelopmental differences like ADHD , OCD , and intellectual disability. And they did find that brain differences clustered into different groups—but not by diagnosis. In fact, brain scans could not distinguish children who had been diagnosed with ASD from those who had been diagnosed with ADHD or OCD.

“Dr. Anagnostou reported data from multiple papers that looked at over 3,500 children,” Dr. Alycia Halladay, Chief Science Officer at the Autism Science Foundation, explained to me. “These studies looked at multiple structural and functional features of the brain—including cortical gyrification (the way the brain folds in the cortex), connectivity of different brain regions, and the thickness of the cortical area—and found no differences based on diagnosis.”

Groupings did emerge, but they were along totally different axes. Added Halladay, “The brains themselves were more similar based on cognitive ability, hyperactivity, and adaptive behavior.” In other words, the brains of mildly affected autistic children looked much more like the brains of kids with ADHD than they did like those of severely autistic children.

Validity of the Autism Spectrum Diagnosis May Be at Stake

If replicated, these findings could have tremendous implications for our current diagnostic framework. During the question and answer period following her talk, Anagnostou described two children who both carried the diagnosis of autism; one was very mildly affected, while the other had such disordered behavior that “even their bus driver knows” he is autistic. “Should these kids have the same diagnosis?” she asked.

Right now, they do—but there has been a growing dissatisfaction among many stakeholders in the autism community with the American Psychiatric Association’s introduction of the all-encompassing ASD diagnosis in the 2013 revision of the Diagnostic and Statistical Manual (DSM-5) to replace more narrowly defined categories, including Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and childhood disintegrative disorder.

In 2021, the Lancet Commission —a group of 32 researchers, clinicians, autistic individuals, and family members—called for the creation of a new label, “profound autism,” that would carve out those autistic individuals who also suffer from cognitive and language impairments and require round-the-clock supervision. “Anagnostou’s data converge nicely with the Lancet Commission’s proposal,” Halladay observed. “They provide biological evidence for a category that was originally defined solely by external criteria.”

At the very least. The real question is whether this work demands an even more radical re-imagining of our classification of neurodevelopmental differences. If, as Anagnostou’s data demonstrates, cognition and hyperactivity are much more correlated with brain difference than variables like social deficit that have been considered core symptoms of autism, then perhaps it’s time to scrap our current model and introduce new diagnoses based on these more salient dimensions. Aligning our diagnostic system with underlying biology is the first step in the development of targeted interventions for some of the most intractable and dangerous behaviors exhibited by the developmentally disabled, such as aggression , elopement, self-injury , and pica (the compulsion to eat inedible objects).

As Anagnostou opened her talk, “Nature doesn’t read the DSM.” But, as our understanding of the brain advances, shouldn’t the DSM reflect these divisions in nature?

Amy S.F. Lutz, Ph.D. , is a historian of medicine at the University of Pennsylvania. She is the author of We Walk: Life with Severe Autism (2020) and Each Day I Like It Better: Autism, ECT, and the Treatment of Our Most Impaired Children (2014) . She is also the Vice-President of the National Council on Severe Autism (NCSA).

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In updated U.S. autism bill, Congress calls for funding boost, expanded scope

The current Autism CARES Act sunsets in late September.

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Lawmakers are ironing out the next major tranche of federal funding for autism research in the United States, hoping to renew critical legislation before it expires on 30 September. The law—known as the Autism Collaboration, Accountability, Research, Education and Support (CARES) Act—has been in effect in some form since 2006 .

The latest slate of updates awaiting floor votes as soon as next week in the House and Senate includes renewing—and possibly expanding—the funding mandated for autism research, training and services. The House bill increases the current amount by $279 million, totaling a more than $2.1 billion investment over the next five years.

The latest Senate version proposes a slightly smaller jump, to $1.95 billion in spending over that same period. (Assuming both versions pass in their respective chambers, a conference committee will then finalize the act’s terms, subject to both House and Senate approval.) Both bills also contain a new provision requiring the National Institutes of Health (NIH) to develop an annual budget outlining how research dollars will be spent.

The funding upgrade proposed in each bill exceeds an inflation adjustment, says Thomas Frazier , professor of psychology at John Carroll University. It’s also notable in light of the sizable cuts facing other research programs , such as the BRAIN Initiative.

“In the context of a fixed or even diminishing federal budget, any increase is important,” says autism researcher M. Daniele Fallin , dean of public health at the Rollins School of Public Health at Emory University.

The revised act draws attention to several understudied areas, such as the dearth of effective communication tools for autistic people who are non- or minimally speaking. Both the House and Senate bills call for a new Autism Intervention Research Network focused on communication needs, a change that has sparked widespread support.

Even a small tweak in the law’s wording can affect research downstream, says Frazier, who also serves on the Interagency Autism Coordinating Committee (IACC), which the Autism CARES Act funds. “When we put language like this into the bill, we do see a shift in the funding agencies’ foci.”

Key among other proposed updates is language that paves the way for more inclusive autism research. The House bill, for example, directs federal agencies to support research that “reflects the entire population of individuals with autism spectrum disorder, including the full range of cognitive, communicative, behavioral, and adaptive functioning, as well as co-occurring conditions and needs for support and services.”

That wording addresses historical patterns of excluding from research autistic people who have intellectual disability or significant support needs. It also represents a “very careful compromise,” says Sam Crane , an independent disability advocacy consultant and a self-advocate member of IACC.

Some groups, including the Profound Autism Alliance and the Autism Science Foundation, had initially pushed for the law to specify “ profound autism ”—a label coined in 2021 to represent those who require round-the-clock care—among its priorities. But some people reject that term, citing, among other reasons, its inconsistent use. Following extended conversations with other organizations and members of Congress, supporters of the “profound autism” language changed course.

That phrasing, though, is not ideal, says Judith Ursitti , co-founder and president of the Profound Autism Alliance. “I’m not going to die on a hill about words,” she says, but the ambiguity could perpetuate the problem of omitting certain subgroups from research.

But incremental progress with this law is the norm, says Zoe Gross , director of advocacy at the Autistic Self Advocacy Network (ASAN). “We can’t call any CARES bill a complete victory,” she says. For example, although ASAN is glad to see the legislation include language surrounding communication needs, the organization did not achieve its ask for the bills to stipulate that half of IACC’s public members be autistic—a jump from the 2019 law’s requirement that at least three autistic people serve. ( One-third of the 21 current public members are autistic.)

Still, both bills manage to satisfy at least some of the requests of the major organizations in this space—many with disparate viewpoints, as  Kim Musheno , executive vice president of public policy at the Autism Society of America, points out. “The hard sunset makes it serious that we really need to all be together on this,” she says.

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tags: Spectrum , Adults with autism , aging , Audio research news , Autism , Funding , Policy

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Autism Spectrum Disorder Articles

At a glance.

Below is a list of recent scientific articles on autism spectrum disorder (ASD) generated from CDC programs and activities.

Key findings and scientific articles

Key findings.

These key findings provide brief summaries of some of CDC's latest ASD research.

Key Findings: ADDM Network Expands Surveillance to Identify Healthcare Needs and Transition Planning for Youth

Five of CDC's ADDM Network sites (Arkansas, Georgia, Maryland, Utah, and Wisconsin) began monitoring autism spectrum disorder (ASD) in 2018 among 16-year-old adolescents who were initially identified as having characteristics of ASD in 2010. (Published: February 25, 2023)

Key Findings: Study Shows Linking Statewide Data for ASD Prevalence is Effective

Linking statewide health and education data is an effective way for states to have actionable local ASD prevalence estimates when resources are limited. (Published: January 18, 2023)

Key Findings: CDC Releases First Estimates of the Number of Adults Living with Autism Spectrum Disorder in the United States

This study fills a gap in data on adults living with ASD in the United States because there is not an existing surveillance system to collect this information. (Published May 10, 2020)

CDC scientific articles

These articles are either from CDC-funded research or have at least one CDC author. These articles are listed by year of publication, with the most recent first.

• Adolescents With Autism Spectrum Disorder: Diagnostic Patterns, Co-occurring Conditions, and Transition Planning. Hughes MM, Shaw KA, Patrick ME, et al. J Adolesc Health. 2023;73(2):271-278.
• Statewide county-level autism spectrum disorder prevalence estimates—seven U.S. states, 2018. Shaw KA, Williams S, Hughes MM, et al. Ann Epidemiol. 2023;79:39-43.
• The Prevalence and Characteristics of Children With Profound Autism, 15 Sites, United States, 2000-2016. Hughes MM, Shaw KA, DiRienzo M, et al. Public Health Rep. 2023;138(6):971-980.
• Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. Maenner MJ, Warren Z, Williams AR, et al. MMWR Surveill Summ. 2023;72(2):1-14. Published 2023 Mar 24. [ Easy-Read Summary ]
• Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. Shaw KA, Bilder DA, McArthur D, et al. MMWR Surveill Summ. 2023;72(1):1-15. Published 2023 Mar 24. [ Easy-Read Summary ]
• Social vulnerability and prevalence of Autism Spectrum Disorder, Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP). Patrick ME, Hughes MM, Ali A, Shaw KA, Maenner MJ. Ann Epidemiol. 2023;83:47-53.e1.
• Individualized Education Programs and Transition Planning for Adolescents With Autism. Hughes MM, Kirby AV, Davis J, et al. Pediatrics. 2023;152(1):e2022060199. [ Watch Video Abstract ]

" There is no epidemic of autism. It's an epidemic of need." ‎

Two authors provide their commentary on CDC's 2023 Community Report in an article published in ST A T News' First Opinion (March 2023).

Read the full article here.

• Toileting Resistance Among Preschool-Age Children With and Without Autism Spectrum Disorder. Wiggins LD, Nadler C, Hepburn S, Rosenberg S, Reynolds A, Zubler J. J Dev Behav Pediatr. 2022;43(4):216-223.
• Defining in Detail and Evaluating Reliability of DSM-5 Criteria for Autism Spectrum Disorder (ASD) Among Children Rice CE, Carpenter LA, Morrier MJ, et al. J Autism Dev Disord. 2022;52(12):5308-5320. [published correction appears in J Autism Dev Disord. 2022 Jan 29;:].
• Reasons for participation in a child development study: Are cases with developmental diagnoses different from controls? Bradley CB, Tapia AL, DiGuiseppi CG, et al. Paediatr Perinat Epidemiol. 2022;36(3):435-445.
• Features that best define the heterogeneity and homogeneity of autism in preschool-age children: A multisite case–control analysis replicated across two independent samples. Wiggins LD, Tian LH, Rubenstein E, et al. Autism Res. 2022;15(3):539-550.
• Progress and Disparities in Early Identification of Autism Spectrum Disorder: Autism and Developmental Disabilities Monitoring Network, 2002–2016. Shaw KA, McArthur D, Hughes MM, et al. J Am Acad Child Adolesc Psychiatry. 2022;61(7):905-914.
• Peri-Pregnancy Cannabis Use and Autism Spectrum Disorder in the Offspring: Findings from the Study to Explore Early Development. DiGuiseppi C, Crume T, Van Dyke J, et al. J Autism Dev Disord. 2022;52(11):5064-5071.
• Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses. Grosse SD, Nichols P, Nyarko K, Maenner M, Danielson ML, Shea L. J Autism Dev Disord. 2022;52(9):4150-4163.
• Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. Shaw KA, Maenner MJ, Bakian AV, et al. MMWR Surveill Summ. 2021;70(10):1-14. Published 2021 Dec 3.
• Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. Maenner MJ, Shaw KA, Bakian AV, et al. MMWR Surveill Summ. 2021;70(11):1-16. Published 2021 Dec 3.
• Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children. Maenner MJ, Graves SJ, Peacock G, Honein MA, Boyle CA, Dietz PM. Am J Epidemiol. 2021;190(10):2198-2207.
• Healthcare Costs of Pediatric Autism Spectrum Disorder in the United States, 2003–2015. Zuvekas SH, Grosse SD, Lavelle TA, Maenner MJ, Dietz P, Ji X. J Autism Dev Disord. 2021;51(8):2950-2958.
• Association between pica and gastrointestinal symptoms in preschoolers with and without autism spectrum disorder: Study to Explore Early Development. Fields VL, Soke GN, Reynolds A, et al. Disabil Health J. 2021;14(3):101052.
• Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood—Four US Sites, 2018–2020. Powell PS, Pazol K, Wiggins LD, et al. MMWR Morb Mortal Wkly Rep. 2021;70(17):605-611. Published 2021 Apr 30.
• Evaluation of sex differences in preschool children with and without autism spectrum disorder enrolled in the study to explore early development. Wiggins LD, Rubenstein E, Windham G, et al. Res Dev Disabil. 2021;112:103897.
• A Distinct Three-Factor Structure of Restricted and Repetitive Behaviors in an Epidemiologically Sound Sample of Preschool-Age Children with Autism Spectrum Disorder. Hiruma L, Pretzel RE, Tapia AL, et al. J Autism Dev Disord. 2021;51(10):3456-3468.
• Spending on Young Children With Autism Spectrum Disorder in Employer-Sponsored Plans, 2011–2017 Grosse SD, Ji X, Nichols P, Zuvekas SH, Rice CE, Yeargin-Allsopp M. Psychiatr Serv. 2021;72(1):16-22. [published correction appears in Psychiatr Serv. 2021 Jan 1;72(1):97].
• A Preliminary Epidemiology Study of Social (Pragmatic) Communication Disorder Relative to Autism Spectrum Disorder and Developmental Disability Without Social Communication Deficits. Ellis Weismer S, Rubenstein E, Wiggins L, Durkin MS. J Autism Dev Disord. 2021;51(8):2686-2696.
• CE: From the CDC: Understanding Autism Spectrum Disorder. Christensen D, Zubler J. Am J Nurs. 2020;120(10):30-37.
• Early Identification of Autism Spectrum Disorder Among Children Aaged 4 Years—Early Autism and Developmental Disability Monitoring Network, Six Sites, United States, 2016. Shaw KA, Maenner MJ, Baio J, et al. MMWR Surveill Summ. 2020;69(3):1-11. Published 2020 Mar 27.
• Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016. Maenner MJ, Shaw KA, Baio J, et al. MMWR Surveill Summ. 2020;69(4):1-12. Published 2020 Mar 27. [published correction appears in MMWR Morb Mortal Wkly Rep. 2020 Apr 24;69(16):503].
• Disparities in Documented Diagnoses of Autism Spectrum Disorder Based on Demographic, Individual, and Service Factors. Wiggins LD, Durkin M, Esler A, et al. Autism Res. 2020;13(3):464-473.

SEED Research

Researchers working on CDC's Study to Explore Early Development (SEED) have published many studies reporting on important findings related to ASD.

For more information on the methods and descriptions of the SEED study sample, SEED publications, and the evaluation of clinical and laboratory methods using SEED data, click the link below.

Featured Article | Summer 2023

Cdc seed study explores prenatal ultrasound use and risk of autism spectrum disorder.

Prenatal ultrasound use and risk of autism spectrum disorder: Findings from the case-control Study to Explore Early Development (SEED). Christensen D, Pazol K, Overwyk KJ, et al. Paediatr Perinat Epidemiol. 2023;37(6):527-535.

Study findings‎

Many additional studies are underway. We will provide summaries of those studies in the future.

All articles

Search CDC Stacks for articles that have been published by CDC authors within the National Center on Birth Defects and Developmental Disabilities from 1990 to present.

Feature articles and an Easy-Read Summary

Easy-Read Summary

Additional resources

Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication and behavioral challenges. CDC is committed to continuing to provide essential data on ASD and develop resources that help identify children with ASD as early as possible.

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The lifeflight legacy: 40 years in 40 photos, july 29, 2024, study sheds new light on autism, but there’s more work to be done.

A target of their investigations is serotonin, a signaling molecule that is well known for its critical roles in regulating mood and which also plays an important role in the development of the brain and nervous system.

Researchers from Columbia and Vanderbilt universities, the University of Illinois Chicago and colleagues across the country are making steady progress in their decades-long quest to understand autism spectrum disorder (ASD), a brain development condition that affects social interaction, communication and behavior.

In a recent study, the researchers measured blood levels of serotonin in women whose children were diagnosed with ASD. Some of the children carried rare genetic variations that strongly contribute to the risk of autism, while others did not.

In their paper, published July 4 in the Journal of Clinical Investigation , the researchers reported that higher serotonin levels were primarily found in women whose children who did not carry the rare variants.

This finding suggests that elevated maternal serotonin levels are associated with autism in a subset of children who have multiple common genetic or environmental factors which likely contribute to risk. Elevated levels are not found as frequently when a single, rare genetic variant explains most of the risk.

The link between autism-associated genetic variations and maternal serotonin levels was first described more than 60 years ago.

But it is a complicated picture that is not fully understood, noted James Sutcliffe , PhD, a pioneer in autism genetics at Vanderbilt University.

The study probed genetic samples from the University of Illinois Chicago (UIC) Autism Center of Excellence and from the UIC and Vanderbilt sites of the Simons Simplex Collection , a repository of samples from 2,600 families of children with ASD maintained by the Simons Foundation Autism Research Initiative.

The study did not have a control group — it did not compare maternal serotonin levels to those from women whose children do not have autism. Another limitation was that serotonin blood levels in the women were measured after their children had been diagnosed with ASD.

Taking measurements throughout pregnancy would provide a more complete picture of how maternal serotonin levels may relate to autism risk, said Jeremy Veenstra-VanderWeele , MD, the Ruane Professor of Psychiatry and director of the Division of Child & Adolescent Psychiatry at Columbia University Irving Medical Center in New York City.

Veenstra-VanderWeele is corresponding author of the paper. Before coming to Columbia in 2014, he directed the Division of Child and Adolescent Psychiatry at Vanderbilt University Medical Center and was medical director of the Treatment and Research Institute for Autism Spectrum Disorders ( TRIAD ) at the Vanderbilt Kennedy Center.

Sutcliffe, who co-authored the paper, is associate professor of Molecular Physiology & Biophysics and of Psychiatry & Behavioral Sciences at Vanderbilt.

Other co-authors are Edwin Cook , MD, also a pioneer in autism genetics who directs the Center for Neurodevelopmental Disorders and the Division of Child and Adolescent Psychiatry at UI Health, and colleagues from New York University and Yale University School of Medicine.

While the true nature of the relationship between serotonin levels and ASD remains elusive, clinical trials are underway at Vanderbilt and elsewhere to evaluate drugs that, by impacting the serotonin system, may relieve irritability or improve social functioning in children with autism.

Genetic studies also have led to the identification of other, possibly related health conditions in children with ASD, including previously undiagnosed cardiac abnormalities and severe epilepsy that occurs during sleep, Sutcliffe said.

The investigators hope that further research may lead to targeted interventions based upon ASD-associated genetic variation or biomarkers. That, Veenstra-Vanderweele said, would be “transformative” for children who are severely affected by autism.

Related Articles

January 28, 2016

Autism study links sensory difficulties, serotonin system.

Vanderbilt researchers have established a link between the neurotransmitter serotonin and certain behaviors of some children with autism spectrum disorder (ASD), a link that may lead to new treatments for ASD.

By VUMC News and Communications

January 9, 2014

Brain-gut connection in autism.

An association between rigid-compulsive behaviors and gastrointestinal symptoms in autism spectrum disorder may point to a common biological pathway that impacts both the brain and the gut.

By Leigh MacMillan

November 16, 2017

Study may point to new treatment approach for asd.

Using sophisticated genome mining and gene manipulation techniques, researchers at Vanderbilt University Medical Center (VUMC) have solved a mystery that could lead to a new treatment approach for autism spectrum disorder (ASD).

By Bill Snyder

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April 16, 2021

Autism develops differently in girls than boys, new research suggests

by University of Virginia

New research has shed light on how autism-spectrum disorder (ASD) manifests in the brains of girls, prompting the scientists to warn that conclusions drawn from studies conducted primarily in boys should not be assumed to hold true for girls.

The researchers discovered that there is a significant difference in the genes and 'genetic burden' that underpin the condition in girls and boys. They also identified specific ways the brains of girls with ASD respond differently to social cues such as facial expressions and gestures than do those of girls without ASD.

"This new study provides us with a roadmap for understanding how to better match current and future evidenced-based interventions to underlying brain and genetic profiles, so that we can get the right treatment to the right individual," said lead investigator Kevin Pelphrey, Ph.D., a top autism expert at the University of Virginia School of Medicine and UVA's Brain Institute. "This advances our understanding of autism broadly by revealing that there may well be different causes for boys vs. girls; this helps us understanding the heterogeneity within and across genders."

Understanding Autism-Spectrum Disorder

The new insights come from a sweeping research project, led by Pelphrey at UVA, that brings together expertise from Yale; Harvard; University of California, Los Angeles; Children's National; University of Colorado, Denver; and Seattle Children's. At UVA, key players included both Pelphrey, of the School of Medicine's Department of Neurology and the Curry School of Education and Human Development, and John D. Van Horn, Ph.D., of the School of Data Science and UVA's Department of Psychology.

The research combined cutting-edge brain imaging with genetic research to better understand ASD's effects in girls. Those effects have remained poorly explored because the condition is four times more common in boys.

Pelphrey and colleagues used functional magnetic-resonance imaging (fMRI) to examine brain activity during social interactions. They found that autistic girls used different sections of their brains than girls who did not have ASD. And, most surprisingly, the difference between girls with and without autism was not the same as the difference in the brain seen when comparing boys with and without autism, revealing different brain mechanisms at play in autism depending on a person's gender.

Likewise, the underlying genetic contributors were quite different, the researchers found. Girls had much larger numbers of rare variants of genes active during the early development of a brain region known as the striatum. This suggests that the effects on the striatum may contribute to ASD risk in girls. (Scientists believe a section of the striatum called the putamen is involved in interpreting both social interaction and language.)

"The convergence of the brain imaging and genetic data provides us with an important new insight into the causes of autism in girls," Pelphrey said. "We hope that by working with our colleagues in UVA's Supporting Transformative Autism Research (STAR), we will be able to leverage our findings to generate new treatment strategies tailored to autistic girls."

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Content Analysis of Abstracts Published in Autism Journals in 2021: The year in Review

Haris memisevic.

Faculty of Educational Sciences, University of Sarajevo, 71000, Sarajevo, Bosnia-Herzegovina

Amina Djipa

Ever since Leo Kanner first described autism in 1943, the research in this field has grown immensely. In 2021 alone, 5837 SCOPUS indexed documents were published with a title that contained the words: “autism”, “autistic”, or “ASD”. The purpose of this study was to examine the most common topics of autism research in 2021 and present a geographical contribution to this research.

We performed a content analysis of 1102 abstracts from the articles published in 11 Autism journals in 2021. The following journals, indexed by the SCOPUS database, were included: Autism, Autism Research, Molecular Autism, Journal of Autism and Developmental Disorders, Research in Autism Spectrum Disorders, Focus on Autism and Other Developmental Disabilities, Education and Training in Autism and Developmental Disabilities, Review Journal of Autism and Developmental Disorders, Advances in Autism, Autism and Developmental Language Impairments , and Autism in Adulthood.

According to the analysis, the main research topics were: mental health, social communication, social skills, quality of life, parenting stress, ADHD, Covid-19, self-efficacy, special education, and theory of mind. In relation to geographic distribution, most studies came from the USA, followed by the UK, Australia, and Canada.

Research topics were aligned with the priorities set by stakeholders in autism, most notably persons with autism themselves and their family members. There is a big gap in research production between developed countries and developing countries.

Introduction

According to the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders , Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and the pattern of stereotypical and repetitive behaviors (American Psychiatric Association, 2013 ). An Austrian-American psychiatrist Leo Kanner was the first scientist who described a condition that we now refer to as ASD in 1943 (Kanner, 1943 ). Ever since that seminal paper describing the case histories of 11 children was published, the interest in autism research has grown immensely. This is not surprising given the high prevalence of the condition. Current estimates show that ASD is a common disorder, with a median prevalence of around 1%, and a median male-to-female ratio of 4.2:1 (Zeidan et al., 2022 ). The rise in the prevalence of autism has been associated with new research and investments in autism research internationally (Pellicano et al., 2014 ). Given this rise in research funds dedicated to autism research, it is of critical importance to determine the research priorities in this field. In a study examining research priorities, stakeholders preferred applied to basic research topics and gave importance to topics such as co-occurring conditions, health and well-being, and lifespan issues (Frazier et al., 2018 ). From the parents’ perspective, the priorities are quite similar, and involve topics such as health and well-being, socialization and social support, community awareness, and understanding of Autism (Clark & Adams, 2020 ). Another topic of interest is the similarities and differences of the impact of autism in different world regions, as it is difficult to draw public attention to this condition in less developed countries (Hahler & Elsabbagh, 2015 ).

Thus, in this article, we examined the most frequent research topics in autism research in 2021 and reviewed from which countries these studies originate. The reference for this research is the SCOPUS database. The SCOPUS is an abstract and indexing database produced by Elsevier and covers abstracts and citations from 1966 to the present (Burnham, 2006 ). The SCOPUS database was selected for this analysis as it has broader coverage in the field of Social Sciences and Humanities than the Web of Science (Memisevic et al., 2019 ).

According to the SCOPUS database, in 2021, 5837 documents were published that in its title contained the words “autism”, autistic” or “ASD”. Most of these documents were scientific articles (5034), with the rest of the documents including books, chapters, and conference papers. As an illustration of this growth in autism research, let us point to the fact that in 2001 there were 558 such documents, and in 2011, there were 2120 documents. More than 100 scientific journals had at least five articles published in 2021 with the terms “autism”, “autistic”, or “ASD” in their titles. Most of the articles were from the fields of medicine, psychology, neuroscience, social sciences, biochemistry, and health professions, but also some less expected fields such as engineering, environmental science, physics, business, and agriculture.

This review aimed to analyze the most prevalent research topics in Autism journals indexed in SCOPUS in the year 2021. We also provided a brief overview of the ten most frequent research topics and additional information on articles dealing with these topics. Lastly, we wanted to examine the main contributing countries to autism research.

The SCOPUS scientific base was used to extract data for this study. We examined all journals indexed by SCOPUS whose titles had the word “autism”. There were 11 such journals: Autism, Autism Research, Molecular Autism, Journal of Autism and Developmental Disorders, Research in Autism Spectrum Disorders, Focus on Autism and Other Developmental Disabilities, Education and Training in Autism and Developmental Disabilities, Review Journal of Autism and Developmental Disorders, Advances in Autism, Autism and Developmental Language Impairments, and Autism in Adulthood.

Procedure and Analysis

The inclusion criteria for this review were that the final version of the article was published in 2021, and that it was a research article including original scientific articles, brief reports, case studies, case reports, and review articles. We did not extract data from Editorials, Commentaries, Letters to Editor, Book Reviews, and Corrections. We extracted the following information for each article: (1) Journal’s name, (2) Title of the article, (3) Country of the corresponding author, and (4) Abstract. Total number of analyzed articles was 1102. From the analysis output, we created two categories. The first is related to research topic (theme). Phrases containing two or more words were extracted, and we manually selected meaningful research topics. The second category was related to the subjects (participants) of the studies. The data were analyzed with R computer program (R Core Team, 2021 ). In addition, we extracted information regarding the country of origin of the corresponding author as a proxy for geographical contribution to autism research.

We first present the number of abstracts retrieved from each of the journals.

As can be seen from Table  1 ., almost 1/3 (68.3%) of all articles were retrieved from the top three journals: Journal of Autism and Developmental Disorders, Autism Research, and Autism .

The number of abstracts of articles retrieved from Autism Journals

The most common research topics in Autism Journals are presented in Table  2 .

The 15 most common research topics in Autism Journals

As can be seen from Table  2 . Mental health was the topic most frequently explored in these articles. Another category that we explored in relation to these abstracts was “participants”.

These data are shown in Table  3 .

Frequency of terms related to the category “participants”

Finally, we examined the corresponding author’s countries to see the geographical contribution to autism research. We only presented data for countries that had 10 or more articles published out of 1102 reviewed articles (Table  4 ).

Corresponding author’s country

By large margin, the USA had the largest share in autism research, followed by the UK, Australia, Canada, and China. There were total of 47 countries that contributed to the entire pool of studies, but the contribution of most of them was rather small. Actually, 32 countries had a contribution of less than 1%. Developing countries were largely underrepresented in the list of contributing countries.

The goal of the present study was to review the most common research topics that were published in Autism journals in 2021. The most frequent topic was mental health . This is not surprising given the challenges that people with ASD are facing with, as well as their families in their everyday lives. The Covid-19 pandemic probably caused an additional incentive for researching this topic. Several factors during the pandemic, such as lockdowns, physical distancing, economic breakdowns, all increase the risk of mental health problems and can even deepen health inequalities (Moreno et al., 2020 ). Given that people with ASD have much higher risk of co-occurring mental health conditions than those without ASD (Rydzewska et al., 2018 ), research interest in mental health deserves to be on the top of priorities in autism research. In line with this, there is a need to create and validate assessment instruments designed specifically for autistic individuals. One such promising instrument is the Assessment of Concerning Behavior which has very good psychometric properties and can be used in future studies (Tarver et al., 2021 ). Mental health was also explored in relation to job prospects of autistic individuals. Thus, mental health issues need to be addressed as they appear to negatively impact job search and maintenance (Martin & Lanovaz, 2021 ). Besides targeting people with autism, research in mental health also dealt with parents of autistic individuals. The research showed that parental mental health could be significantly improved through support services and by strengthening personal relationships (Schiller et al., 2021 ).

A topic that attracted much scientific attention was social communication, which is one of the core features of ASD. When exploring the abstracts containing the phrase “social communication” we discovered that in many abstracts this was not the main topic of the study but just part in which the authors defined and described autism. However, some of the studies dealt with social communication per se. For example, one study explored how social communication is related to early spoken language and how it predicts later language skills (Blume et al., 2021 ). Also, social communication was the subject of neuroanatomical studies. In one such study, authors examined neural synchronization of tempoparietal junction and found that participants with autism showed decreased neural synchrony of that brain region (Quiñones-Camacho et al., 2021 ). Lastly, let us mention an interesting study of yoga, in which authors indicated that creative yoga intervention might be a promising tool for improving social communication in children with ASD (Kaur et al., 2021 ).

Next in frequency was the topic of social skills. Social skills are quite susceptible to various treatment and can be improved. In one intervention study, the authors showed that Mixed Martial Arts intervention benefited social skills (Phung & Goldberg, 2021 ). A review of school-based social skills interventions was conducted by Dean & Chang ( 2021 ). In that review, the authors analyzed 18 intervention studies and concluded that these interventions improved social skills. The authors also pointed to the need for school practitioners to be more aware of school-intervention protocols to improve social skills of children with ASD. Eye-tracking task has been shown to predict social skills intervention outcomes (Raulston et al., 2021 ). Social skills were also examined in relation to another interesting concept, camouflaging. Camouflaging can be defined as behavioral adaptations of people with ASD to mask symptoms in social situations (Corbett et al., 2021 ). Camouflaging is more prevalent in autistic females than in males, which might be related to delays in the provision of support (Wood-Downie et al., 2021 ). Finally, let us mention the potential of modern technologies in improving social skills. One such promising modality for enhancing social skills is the video-enhanced activity schedules using tablet technology (Osos et al., 2021 ).

Quality of life is a topic of universal importance to all stakeholders in the field of autism. Earlier reviews have also shown that quality of life is one of the most important topics in autism research (Memišević & Đorđević, 2020 ). One of the studies reviewed investigated the relationship between sleep quality and quality of life in autistic adults (McLean et al., 2021 ). The authors concluded that interventions that target both sleep quality and stress could improve the quality of life of autistic individuals. The quality of life of caregivers of autistic persons is dependent on behavioral problems in persons with autism. A study by Kurokawa et al., ( 2021 ), indicated a relationship between gastrointestinal symptoms and behavioral problems. Thus, the appropriate assessment and treatment of gastrointestinal symptoms might lead to decreased problematic behavior and improved quality of life.

The next research topic we identified is parenting stress. It is well-established that parents of children with ASD have more stress than parents of typically developing children (Bonis, 2016 ). This review found studies that identified factors associated with higher stress levels. For example, in a study by Raff et al., ( 2021 ), the authors found that parental perceptions about family support, symptom predictability, and treatment beliefs were related to parental stress. Similarly, a study by Kurtz et al., ( 2021 ) pointed to the relationship between a child’s problem behavior and parental stress. The authors highlighted the importance of determining the function of problematic behavior in relation to parental stress. Interestingly, one study found that parental stress was not associated with ASD symptom severity (Voliovitch et al., 2021 ). These authors have also pointed that parental stress should be assessed prior to the diagnostic evaluation of the child.

A frequent co-occurring condition with autism is Attention Deficit Hyperactivity Disorder (ADHD). This topic has also attracted much scientific attention, especially the studies on the prevalence of ADHD in ASD. One of the studies from 2021 was the meta-analysis of the ADHD prevalence in ASD, and the results showed a prevalence rate of 38.5% for pooled current estimate and 40.2% for lifetime prevalence (Rong et al., 2021 ). The prevalence of ADHD symptoms in preschool children with ASD revealed that 27% of children belonged to high ADHD symptom group, and 30% to moderate ADHD symptom group (Hong et al., 2021 ). However, a note of caution is necessary here as that study was referring to ADHD symptoms and not a clinically confirmed ADHD diagnosis.

The next topic was not on the list of autism research priorities before 2020, and that is the topic related to the Covid-19 pandemic. This pandemic has affected all spheres of life, including health, safety, and well-being of individuals and communities (Pfefferbaum & North, 2020 ). Autistic individuals might have an even higher risk of difficulties coping with Covid-19 due to unexpected changes in their routines (Spain et al., 2021 ). Spain et al. stressed that Covid-19 caused major disruption or loss of service provisions to people with ASD. The concern for mental health of persons with ASD was also expressed in an article by Bal et al., ( 2021 ). In that article, the authors found that younger females with personal Covid-19 experience (knowing someone who had Covid-19) reported the greatest negative impacts. According to the authors, one way to cope with this situation is for mental health providers to focus on positive aspects on one’s life, such as fostering hope. Researchers have also investigated the Covid-19 vaccination rate in people with ASD (Weinstein et al., 2021 ). The authors stressed that individuals with ASD are more susceptible to COVID-19 morbidity and should be prioritized for vaccination.

The topic of self-efficacy was ranked on the 8th place. This topic was investigated in relation to job seeking and interview process of autistic individuals. Again, the modern technologies, such as virtual reality, might be very helpful in gaining confidence. For example, virtual interactive training agents were found to increase self-efficacy skills and consequently outcomes for people with ASD (Burke et al., 2021 ). In addition to self-efficacy in autistic people, important consideration needs to be self-efficacy in parents of children with ASD. Findings indicate that parents who are more involved in their child’s therapeutic interventions also report higher levels of self-efficacy (Kurzrok et al., 2021 ).

The next topic on the list was special education. Some of the studies focused on special education eligibility as there seems to be a discrepancy between clinical diagnoses of ASD and the determination of special education eligibility (Stichter et al., 2021 ). Some studies were aimed at special education teachers. In one such study, special education teachers were asked whether they felt prepared to use evidence-based strategies in their work (Hamrick et al., 2021 ). The results of that study indicated a research-to-practice gap as special education teachers reported using many practices that were not identified as evidence-based practices. An interesting review was conducted by Rios & Burke ( 2021 ) regarding the facilitators and barriers to positive special education experiences. In that review, the authors found that facilitators to positive experience were: understanding special education services, parent-school communication, and parent support. On the other hand, barriers to positive special education experience included: limited access to resources, stigma, cultural divergence, and lack of access to special education knowledge.

We end this short overview of topics with theory of mind (TOM), ranked 10th on our list. TOM is the ability to impute mental states to oneself and others, including thoughts, feelings, intentions, and beliefs (Premack & Woodruff, 1978 ). There are some inconsistent results regarding TOM in people with ASD. While some studies reported that people with ASD have difficulties in TOM, (Baron-Cohen, 2000 ; Schneider et al., 2013 ), there are authors who stated that the claim that autistic individuals lack of TOM is not empirically supported and that such claims of autistic persons lack TOM can even be societally harmful (Gernsbacher & Yergeau, 2019 ). In any case, assessing TOM is a very important task in creating intervention programs. Here we will mention two instruments for measuring TOM, one for children and one for adults. The Theory of Mind Inventory-2 for children is a promising measure in assessing TOM. The research indicates that Theory of Mind Inventory − 2 is appropriate for reflecting children’s developmental stages of theory of mind (Lee et al., 2021 ). This in turn, can inform treatment options for improving social cognition. On the other hand, there is a promising attempt at creating instruments for measuring TOM in adults. Hutchins et al., ( 2021 ) created a Theory of Mind Inventory: Self-Report Adult and suggested it could be used as a promising research and clinical tool for the assessment of social cognition in adults.

These were the ten most common research themes published in 2021 that we identified in this review. Due to space constraints, we could not provide more information on other articles and research topics that are equally worthy of scientific attention. We hope this overview of topics will help and encourage authors, especially from developing countries, to conduct these kinds of studies in their own countries.

In relation to the participants of the studies, we can conclude that all groups seem to be well represented, including children, adolescents, and adults. In the future, the differences in the number of studies including children and those including adults will probably be further reduced as the topics related to lifelong supports, transitions, and employment are gaining its momentum.

As for the contributing countries to autism research, it is evident that most corresponding authors come from the USA, UK, Australia, and Canada. Although 47 countries contributed to the pool of 1102 articles published in 2021, only small portion of articles actually came from developing countries. In order to create effective policies, it is important to understand the global burden of autism (Baxter et al., 2015), which is much more severe in developing countries. So, why are there not more autism articles originating from developing countries? Some of the reasons are: poor scientific production, poor preparation of manuscripts, poor access to scientific literature, poor participation in publication-related decision-making processes, and bias of journals (Langer et al., 2004 ). Initiatives such as joint collaboration of authors from developing and developed countries, increased regional representation of scientists from developing countries on editorial boards of autism journals, and special issues of international journals that will have regional focus, are all ways to increase, at least partially, the contribution from developing countries.

Let us mention some of the limitations of the current review. Due to the selection criteria, we did not include many journals that produced articles on autism in this review. Thus, future reviews might be more inclusive and diverse in relation to reviewed journals. Next, in the description of the ten most common research topics, there was some part of subjectivity on the side of authors. We could not reference all published papers on the certain research topic and probably the selection of articles would be different in other authors.

It is quite encouraging that research topics were very much aligned with the priorities set by stakeholders in autism, most notably persons with autism themselves and their family members. We also hope the next review will include more articles from developing countries.

Conflict of interest

Authors report no conflict of interest. All the data are available from the corresponding author upon request.

Ethics Approval

The study was approved by the University of Sarajevo, Faculty of Educational Studies.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Genomics Institute

Researchers to investigate genetic roots of autism, look for new treatments

A $12.3 million award will support research out of uc santa cruz, uc san francisco, and uc berkeley.

August 30, 2024

By  Emily Cerf

A new award from the California Institute for Regenerative Medicine (CIRM) will support a team of UC Santa Cruz researchers in exploring the genetic underpinnings of autism spectrum disorder (ASD) and investigating possible treatments, in collaboration with teams at UC San Francisco and UC Berkeley. 

ASD affects one in 22 children in California, but there is a wide range of variability in how it presents. Many genetic variants have been linked to autism, but research has not clearly defined how and why these variations affect people differently and result in varying severity of autism.  

The SSPsyGene consortium,  an effort to study genes linked to neuropsychiatric conditions  involving UCSC researchers, recently prioritized 120 gene variants associated with ASD. This work will form the basis of the new project, as the researchers will focus on the gene pathways that most commonly lead to ASD. They will investigate the biological processes that cause ASD using cell culture models of brain development derived from pluripotent stem cells. They will use this information to develop laboratory tests that can be used to investigate targets for treatment.

The researchers will use brain organoids, miniature models of brain tissue grown in the lab, to narrow down the most clinically relevant observable signs of ASD in their models. The Braingeneers group have become leaders in automating the cultivation and measuring the properties of brain organoids as part of their  Live Cell Technology Center . This project will be the first time they use their expertise to study neuropsychiatric conditions.

“The Braingeeners group has been working since 2018, and a big part of that has been developing methodologies and platforms for more robust generation and analysis of brain organoids,” said Sofie Salama, Professor of Molecular, Cellular and Developmental Biology who will be an investigator on this project.

“This is a really exciting opportunity where the rubber is hitting the road — we can test how these platforms we’ve developed allow us to study how organoid phenotypes can shed light on the variants that can lead to human diseases.”

CIRM has awarded $12.3 million in total funding  for this project through its pilot ReMIND (Research using Multidisciplinary, Innovative Approaches in Neuro Diseases) Program. The project will be led by UCSF Assistant Professor of Neurology Alex Pollen. $4.1 million of this funding will go to UC Santa Cruz, where research will be spearheaded by scientists in the Braingeneers group, including Salama, Research Scientist Mohammed Mostajo-Radji, Associate Professor of Electrical and Computer Engineering Mircea Teodorescu, and Distinguished Professor of Biomolecular Engineering David Haussler.  

“We are incredibly excited to combine the scalable technologies developed by our Braingeneers group and our Center for Live Cell Genomics with our experience and collaborations within the neuroscience community to address a critical question: how do genetic perturbations in brain development lead to circuit-level phenotypes in autism?” Mostajo-Radji said. “We are deeply grateful to CIRM and the voters of California who supported Proposition 14 in 2020, making this project possible. We are thrilled to take on this challenge alongside our colleagues at UCSF and UC Berkeley.”

“While we understand through our other funded projects that organoids can develop functional properties resembling those of a developing brain, the exact genetic mechanisms of how autism spectrum disorder (ASD) affects developing brains remain unclear,” Teodorescu said. “To fully grasp these mechanisms, in this project we will conduct a large-scale, automated experimental study. This will generate a vast amount of data, requiring advanced AI-powered data analysis.”

The team aims to understand how ASD presents differently across various populations, and what genetic factors might protect certain populations against ASD, by studying these genetic variants in cells from people with diverse ancestral backgrounds. At the end of this project, they will produce a system to classify different types of ASD based on genetic information.  At UC Berkeley, researchers include Associate Professor of Molecular and Cell Biology Helen Bateup; Professor of Chemical and Biomolecular Engineering, Bioengineering, and Neuroscience David Schaffer. At UCSF, researchers include Pollen, Associate Professor of Biochemistry and Biophysics Martin Kampmann, Professor of Neurology Saul Kato, and Professor of Neurology and Pediatrics Elliott Sherr. The successful collaborative grant writing was the result of the efforts of Lise Barbé, the newly hired grants coordinator at  QB3 , where investigator Schaffer is the Executive Director.

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The Gut-Brain Axis Could Accelerate Autism Understanding

“future research on the gut-brain axis may unlock autism diagnosis, potentially guiding symptom management and therapeutic developments.”.

The gut microbiota is believed to play an important role in the development of certain disorders, including autism. We know that many children with autism have digestive problems and microbial imbalances in their guts. The exact connection between autism and the gut microbiome, however, is not yet clear.

To study this emerging issue, our team at the Simons Foundation analyzed 25 datasets collected across 18 studies that involved 600 children with autism. We created and ran an algorithm to identify differences between kids with autism and their neurotypical counterparts. We then correlated the microbiome against so-called cytokines, which are immune markers, to see how the gut microbiome is linked to individuals’ immunity and inflammation. 1

This is important because many chronic disorders, such as inflammatory bowel disease and diabetes, demonstrate components of immune dysfunction. Identifying the immune-microbe link in autism not only creates new opportunities for managing autism symptoms, but it also introduces ideas regarding how the microbiome could be manipulated to regulate immune response.

Gut Microbiota May Signal Autism

When we looked at the microbes predicted to be associated with autism and compared them with microbiota likely associated with neurotypical controls, we observed a strong difference between the two groups. Further investigation suggested that we could distinguish accurately between individuals with autism and neurotypical controls just by using their measured gut microbiota. This means that future research might unlock autism diagnoses through gut microbiome analysis, potentially guiding symptom management and therapeutic developments.

[ 10 Foods (and Supplements and Vitamins!) to Boost Your ADHD Brain ]

When we compared the gut-brain axis — the gut microbiota and human brain pathways, or the enzymes responsible for the breakdown of products in the brain — we observed a strong overlap between pathways detected in the gut and those detected in the brain. This suggests that diet affects gut diversity and brain function, from thinking to mood.

GI and Sleep Problems

Many children with autism have gastrointestinal (GI) and sleep problems . In one study, researchers found that the chances for behavioral problems substantially increase when children have GI and sleep problems. 2

Parents should track foods eaten, bowel movements, and sleep patterns to establish connections between these factors and their child’s symptoms. Preventing or treating GI symptoms may provide children with some relief and help them sleep through the night.

Research into the gut-brain axis, and what this correlation can tell us about certain conditions like autism and depression , is still new. In the near future, it is possible that knowledge of gut bacteria will help determine predictive biomarkers of disease, with implications for new therapeutics. What we know about the gut-brain axis will look very different 5 to 10 years from now.

Autism and Gastrointestinal Issues: Next Steps

• Download: Autism Evaluation Checklist
• Read: The ADHD Diet Plan — Healthy Foods and Supplements for Kids & Adults 
• Read: Autism vs. ADHD — A Parent’s Guide to Tricky Diagnoses

Jamie Morton, Ph.D., is an independent consultant who has previously worked for the National Institute of Child Health and Human Development .

SUPPORT ADDITUDE Thank you for reading ADDitude. To support our mission of providing ADHD education and support, please consider subscribing . Your readership and support help make our content and outreach possible. Thank you.

View Article Sources

1 Morton, James T., Dong-Min Jin, Robert H. Mills, Yan Shao, Gibraan Rahman, Daniel McDonald, Qiyun Zhu, et al. (2023). Multi-Level Analysis of the Gut–Brain Axis Shows Autism Spectrum Disorder-Associated Molecular and Microbial Profiles. Nature Neuroscience 26 (7): 1208–17. 10.1177/1087054718816169

2 Leader, G., Barrett, A., Ferrari, C., Casburn, M., Maher, L., Naughton, K., Arndt, S., Mannion, A. (2021). Quality of Life, Gastrointestinal Symptoms, Sleep Problems, Social Support, and Social Functioning in Adults with Autism Spectrum Disorder. Res Dev Disabil. ;112:103915. 10.1016/j.ridd.2021.103915

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9 Conditions Often Diagnosed with ADHD

Risk and Influencing Factors for School Absenteeism among Students on the Autism Spectrum—A Systematic Review

• Review Paper
• Open access
• Published: 05 September 2024

Cite this article

You have full access to this open access article

• Isabella Sasso   ORCID: orcid.org/0009-0007-0726-0939 1 &
• Teresa Sansour 1  

School plays an important role in the development of a child. The impact of school absenteeism extends beyond academic achievement, affecting one's ability to participate in life successfully. In particular, children with difficulties in communication and interaction are at risk of developing school absences. This systematic review therefore focused on school absenteeism among children on the autism spectrum and examined the risk and influencing factors contributing to school absences. Eighteen studies were included, thirteen of which used a quantitative design, two of which were mixed-method studies, and three of which had a qualitative design. Different studies had varying definitions of school absenteeism and employed diverse study designs, prompting the need for a narrative synthesis. We evaluated the data regarding the factors of individual, parental, and school based on the KiTes bioecological systems framework for school attendance and absence by Melvin et al. (2019). We identified the majority of risks and influences in relation to the school factor and identified interacting factors contributing to school absenteeism in all factors. We recognised research gaps and provided guidance for further research.

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Avoid common mistakes on your manuscript.

Introduction

When children and youth attend school, they have access to education, a right proclaimed in Article 28 of the United Nations Convention on the Rights of the Child (The United Nations, 1989 ). School education fundamentally contributes to the cognitive, social, and emotional development of children and young people while simultaneously fulfilling essential social tasks (Pellegrini, 2007 ). Accordingly, absences from school have enormous consequences not only for educational success but also for emotional and social development and successful participation in life. School absenteeism increases the risk of all forms of mental illness (Lenzen et al., 2013 ; Melvin et al., 2019 ). Absences can cause distress in families (Gallé-Tessonneau & Heyne, 2020 ) and challenge professionals and resources (Wilson et al., 2008 ; Finning et al., 2019 ).

Defining School Absenteeism

In international research, scholars utilise a range of terms and criteria to assess school absenteeism. Consequently, it's imperative to interpret and compare research findings within this context. For the scope of this review, we employ the term 'school absenteeism' as an overarching concept. Absenteeism, broadly defined, refers to a student's absence from school for any reason, encompassing various forms of non-attendance (Kearney, 2016 ).

A distinction exists between problematic and non-problematic absences. Non-problematic absences may result from factors such as illness, bereavement, or other causes. However, even initially non-problematic or technically excused absences can transition into problematic ones if more than 10% of lessons are missed (Heyne et al., 2019 ; Lenzen et al., 2013 ), or if the child's development is compromised by the absence, leading to decreased grades or challenges in reintegrating into the academic environment (Kearney, 2016 ). Hence, Kearney ( 2003 ) defines non-problematic absenteeism as short- or long-term absences mutually agreed upon by parents and the school, with the possibility of compensatory measures. Additional terms for distinguishing between problematic and non-problematic absences include unexcused/excused, unauthorised/authorised (Gentle-Genitty et al., 2015 ), and illegitimate/legitimate (Kearney, 2003 ).

Heyne et al. ( 2019 ) differentiates four types of problematic absenteeism:

School refusal is defined as non-attendance at school due to emotional stress related to school attendance, where the parents are informed of absences and make reasonable efforts to ensure the child's attendance at school.

School withdrawal is defined as non-attendance with the knowledge of the parents or withholding by the parents.

Truancy includes absence without permission from the school and the parents. In addition, there are efforts to hide truancy from parents.

In the case of school exclusion , the school initiates the absence, for example, as a disciplinary action.

Kearney et al. ( 2019 ) intend to categorise heterogeneous concepts and provide general descriptions of common terms. School refusal must be distinguished from school refusal behaviour . While school refusal involves absence from school, school refusal behaviour is a broader term for various behaviour patterns based on the goal of avoiding school, whether anxiety-related or not (Kearney, 2016 ). School avoidance refers to an absence based on anxiety related to school. Most of these terms refer to an absence initiated by the individual, while school exclusion is initiated by the school, and school withdrawal is parent-initiated (Kearney et al., 2019 ).

Risk and Influencing Factors for School Absenteeism

To identify factors increasing the likelihood of experiencing school absenteeism, various system levels must be considered (Kearney, 2008 ). Melvin et al. ( 2019 ) propose a multilevel approach that applies Bronfenbrenner’s bio-ecological model to the factors associated with school absenteeism.

At the micro- and meso-system levels, factors such as the individual, parental/family, and school levels have been demonstrated to be associated with school attendance. Knowledge of these factors and their interactions can contribute to an understanding of school absenteeism (Melvin et al., 2019 ) (Fig.  1 ).

The KiTeS bioecological systems framework for school attendance and absence (Melvin et al., 2019 )

To categorise different types of absenteeism according to their initiation and relationship to the individual, the school, and the parental level, we constructed Fig.  2 . The categorisation is based on comprehensive research regarding different types of school absenteeism and their relation (Heyne et al., 2019 ; Kearney, 2008 ; Reissner et al., 2019 ; Tonge & Silverman, 2019 ).

Categorisation of absenteeism types based on individual, school, and parental levels

Defining Autism

The World Health Organisation (WHO, 2023 ) categorises “Autism Spectrum Disorder” (ASD) as a neurodevelopmental disorder characterised by “persistent deficits in the ability to initiate and to sustain reciprocal social interaction and social communication”. Another criterion implies “a range of restricted, repetitive, and inflexible patterns of behaviour, interests or activities that are clearly atypical or excessive for the individual’s age and sociocultural context”. The onset is typically in early childhood, but symptoms may manifest when social demands increase. Since there is a surge in social demands at school age, coping with developmental tasks becomes even more difficult, and special support is often needed (Kamp-Becker & Bölte, 2021 ). Overall, transitions from different developmental and life phases are important, as these are associated with a rise in vulnerability. As the term ‘spectrum’ suggests, symptoms and therefore needs for support vary among autistic individuals (Kamp-Becker & Bölte, 2021 ). Since autism is a lifelong condition, most individuals need support and services throughout their lifetime. Most services (such as therapy and social skills training) are used in early and middle childhood (Song et al., 2022 ). The US Center for Disease Control and Prevention (CDC, 2023 ) estimated that one in 36 children is diagnosed with ASD. ASD was 3,8 times more prevalent among boys than among girls (approximately 4% of boys and 1% of girls). In total, 37.9% of the autistic individuals had an intellectual disability (IQ < 70), 23.5% had an IQ between 71 and 85, and 38.6% had an IQ > 85. However, it should be noted that the prevalence varies between countries and studies. Autism is also often associated with psychiatric conditions. It is estimated that 70–72% of autistic youth have at least one psychiatric condition. Some of the most common disorders are anxiety, depression and attention deficit hyperactivity disorder (ADHD; Rosen et al., 2018 ).

School Absenteeism among Autistic Individuals

According to the Department for Education ( 2019 ) autistic students in England explore higher rates of school absences than non-autistic students with or without special educational needs. When educational needs align with challenges in social skills and communication, absenteeism rates tend to increase. On the other hand, schools comprise a variety of communication and interaction situations (Ashburner et al., 2010 ). Autistic students often find social interactions stressful, thereby facing an increased risk of limited participation and social exclusion (Roberts & Simpson, 2016 ). These circumstances elevate the risk of psychiatric conditions (Hebron & Humphrey, 2014 ). These co-occurring psychiatric conditions heighten the risk of school absenteeism (Finning et al., 2019 ).

To date no systematic review has investigated the risk factors of school absenteeism among autistic students. The aim of this study is to systematically review studies regarding the risk and influence factors for school absenteeism in autistic students. In particular, individual, educational, and parental factors of the micro- and mesosystems are considered. Another aim is to identify research gaps for further investigations.

The primary research questions for this systematic review include:

What types of school absenteeism have been identified in prior studies for autistic students?

What individual, school, and parental factors contribute to school absenteeism among autistic students?

Review Methods

The protocol for this systematic review has been registered online at PROSPERO, an international register for systematic reviews (Registration number: CRD42022343467). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards has been followed for all stages of this systematic review (Page et al., 2021 ). The following electronic databases covering all relevant disciplines have been searched for journal articles: ERIC (Ped), Web of Science and Scopus (Psych), and PubMed (Med) on 26th June 2022. Prior to this search, a preliminary search was performed and an updated search was carried out on 30th November 2023. The Cochrane and PROSPERO databases have been searched to confirm that there was no other existing or registered systematic review about the current topic. The search strategy included terms regarding autism and school absenteeism (see Table  1 ).

Inclusion Criteria

The studies included in this review have been selected based on the following predetermined inclusion criteria: (a) they focused on school-aged individuals with a formal diagnosis of autism; (b) they focused on individual, family or school factors having an influence on any form of school absenteeism; and (c) they were published in German or English. No restrictions were applied regarding the publication period of the included articles.

Exclusion Criteria

Studies were excluded from the review based on one or more of the following criteria: (a) they were published in languages other than English or German; (b) they were not empirical studies; (c) they focused on non-autism samples or mixed etiology groups and the data for autistic individuals were not reported separately; and (d) they did not scrutinise school absenteeism and influencing factors. This review did not include grey literature, but the search was not restricted to peer-reviewed articles.

Study Selection

Electronic searches identified 322 records. Following the removal of duplicates, each reviewer independently assessed 148 articles based on the title and abstract; each reviewer was blinded to the other’s ratings. Disagreements were solved by discussion.

After screening the full texts, 18 studies that met the inclusion criteria were identified and included in the current review.

Quality Assessment

The quality of each included study was assessed by both reviewers independently by using the Mixed Method Appraisal Tool (MMAT) described by Hong et al. ( 2018 ). Disagreements were resolved by both authors discussing the information presented. The MMAT is an appraisal tool for systematic reviews that include quantitative, qualitative and mixed methods studies. Hong et al. ( 2018 ) developed the tool based on a literature review of critical appraisal tools. By using this tool, the study quality was categorised as good, moderate or low. Sixteen studies were rated as ‘good’, and two studies were rated as ‘moderate’. One of these studies has limitations in its quality since the actual research question was not answered (Ochi et al., 2020 ). However, the authors identified these limitations. The other study did not formulate any explicit research questions (Kurita, 1991 ). No studies were excluded due to low quality.

Data Extraction

The first author (I.S.) extracted the data according to predefined criteria. The second author (T.S.) controlled the integrity and verified the accuracy of all the extracted data.

Data were extracted and coded for each study that met the inclusion criteria. The following descriptive data were extracted: study details, information about the sample, the definition of school absenteeism, the criteria for school absenteeism (e.g., 10% absence of school days), the data collection tool for school absenteeism, the risk and influencing factors, the absence rate, the intervention, and the effect of the intervention.

A narrative synthesis was provided due to the heterogeneity of the studies, especially regarding the terminology and measurement of school absenteeism as well as the criteria of different forms of school absenteeism.

Figure  3 shows the evaluation and screening process used to select the 18 studies included in this systematic review. Table 1 provides the details for each included study.

PRISMA 2020 flow diagram

Three studies were from the same research group (Bitsika et al., 2020 , 2021 , 2022 ). All three address the topic of bullying. Bitsika et al. ( 2022 ) studied a subsample of a larger cohort from Bitsika and Sharpley ( 2016 ). Bitsika et al. ( 2021 ) used a sample from Bitsika et al. ( 2020 ). Each article has a different research question.

Munkhaugen et al. ( 2019 ) based their research on a subsample from the first study (Munkhaugen et al., 2017 ).

Study Characteristics

All studies except Kurita ( 1991 ) are very recent (2017–2023). These studies were conducted in Australia (Adams, 2021 ; Bitsika et al., 2020 , 2021 , 2022 ), the United Kingdom (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ; Totsika et al., 2020 ; Truman et al., 2021 ), Sweden (Anderson, 2020 ), Japan (Kurita, 1991 ; Ochi et al., 2020 ), Norway (Munkhaugen et al., 2017 , 2019 ), the United States (Mattson et al., 2022 ; McClemont et al., 2021 ) and Denmark (Lassen et al., 2022 ).

Three of the 18 studies used qualitative designs, two used mixed method designs, and 13 used quantitative designs. Eleven studies employed cross-sectional designs, while there was also an evaluative case study that aimed to identify the impact of an intervention, a retrospective chart review study, a longitudinal study based on retrospective school datasets, a brief report of an observational study, a qualitative study with a multi-informant approach, a qualitative study that is based on case reports and a qualitative study consisting of an interpretative phenomenological approach.

The sample size ranged from N = 1799 in a quantitative cross-sectional study to N = 3 in qualitative case reports. The total sample included 3304 autistic students. The ages ranged between 3 and 21 years. Two studies examined absenteeism in preschoolers with autism. The sample was predominantly male except for the only qualitative study that explicitly focused on girls with autism (O’Hagan et al., 2022 ). Fourteen studies based their results on parental reports, and four studies considered additional school staff or other professionals involved. One qualitative study collected data by interviewing the autistic young people and one used a multi-informant approach by interviewing parents, professionals and school staff. Two studies focused on data bases: one used clinical data (Ochi et al., 2020 ) and the other used school datasets (Mattson et al., 2022 ). All studies collected data regarding mainstream schools. Six studies also collected data in a special school setting. The detailed information for each included study is summarised in Table  1 .

Types of School Absenteeism

In the included studies, school refusal was the most commonly used term for absenteeism. In total, 11 studies referred to this term (Adams, 2021 ; Bitsika et al., 2020 , 2021 , 2022 ; Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ; Ochi et al., 2020 ; Preece & Howley, 2018 ; Totsika et al., 2020 ). The studies are based on the definition in which school refusal occurs due to emotional distress with knowledge of the parents (Heyne et al., 2019 ; Kearney, 2008 ). Totsika et al. ( 2020 ) referred to school withdrawal, truancy, school exclusion, and nonproblematic absence, as these are all categories included in the data collection tool they used (the School Non-Attendance Checklist [SNACK] by Heyne et al., 2019 ). Adams ( 2021 ) also used the SNACK and referred to the types defined by Heyne et al. ( 2019 ) but also described the difference between emerging and established school refusal . Furthermore, the author investigated full- and half-day absences. Bitsika et al., ( 2020 , 2021 , 2022 ) also followed the definition of school refusal established by Heyne et al. ( 2019 ). They argued that school refusal is often associated with absence from school, but it is not necessarily defined by absence; therefore, they used the term emerging school refusal (Bitsika et al., 2020 ).

Munkhaugen et al., ( 2017 , 2019 ) chose school refusal behaviour as the object of research. They referred to Kearney ( 2008 ) who defined school refusal behaviour as ‘child-motivated refusal to attend school and/or difficulties remaining in class’ (Munkhaugen et al., 2017 ).

Kurita ( 1991 ) operationalised school refusal according to Berg et al.’s ( 1969 ) definition as absence from school due to reluctance to attend with the knowledge of the parents, while no antisocial disorders occur with this absence.

O’Hagan et al. ( 2022 ) used the phrase ‘emotionally based’ school avoidance and referred to Munkhaugen et al. ( 2017 ). Hence, it can be assumed that O’Hagan et al. ( 2022 ) used school avoidance as a synonym for school refusal behaviour . Gray et al. ( 2023 ) also used the term school avoidance as a synonym for school refusal .

Truman et al. ( 2021 ) did not directly focus on school absenteeism. They evaluate school experiences in the context of extreme demand avoidance behaviour. One aspect relating to this group of autistic children is school exclusion due to challenging behaviour. They included both formal and informal exclusions. In contrast, Gray et al. ( 2023 ) and Martin-Denham ( 2022 ) had an explicit focus on school exclusion. Martin-Denham ( 2022 ) referred to the Education Act and the European Court, which stated that a decision to exclude has to be lawful, rational, proportionate and fair. A differentiation was made between a fixed period exclusion, where a student was excluded from school for a set period, and a permanent exclusion, when a student did not return to school. Gray et al. ( 2023 ) also referred to this differentiation between fixed-term and permanent exclusion.

Two other included studies addressed the differentiation between unexcused and excused absences (Mattson et al., 2022 ), school absences and nonproblematic absences, respectively (Anderson, 2020 ) (Table  2 ).

Criteria and Frequency of School Absenteeism

Criteria for school absenteeism.

The included studies used different criteria to operationalise absenteeism. Two studies used the criterion of 10% absence from school days (O’Hagan et al., 2022 ; Totsika et al., 2020 ). Adams ( 2021 ) also used this criterion but to discuss ‘persistent’ absence. Munkhaugen et al., ( 2017 , 2019 ) relied on the criteria described by Kearney and Silverman ( 1996 ), who differentiated between ‘self-corrective’ for < 2 weeks, ‘acute’ absence for 2–52 weeks, and ‘chronic’ absence for > 53 weeks. However, they did not provide information about how often the behaviour occurred during the period. Ochi et al. ( 2020 ) used more than 30 days per year as a criterion. Gray et al. ( 2023 ) utilised a broad definition of school exclusion “to ensure it captured the full range of experiences of autistic pupils who had persistent, problematic attendance and experience of leaving a mainstream setting due to unmet needs”. Martin-Denham ( 2022 ) refers to school exclusion as a legal term. Five studies did not explicitly determine a criterion for absence in terms of a number. Rather, they explained it with descriptions such as ‘prolonged’ (Preece & Howley, 2018 ). For the remaining five studies, it was not necessary to determine the criterion thematically or because of the study design.

Frequency of Absences

Despite the different study designs and terms, the results regarding the frequencies of absences are considered in the following.

Adams ( 2021 ) reported the highest rate of absenteeism: 72.6% of autistic children had shown ‘persistent absence’, defined as a 10% absence within the 20-day survey period. The average absenteeism rate in the study was 6.3 full days and 3.8 half days. In addition, 5.7% of the autistic students were absent for 4 weeks; all of them (partly among other reasons) did so due to school refusal.

Totsika et al. ( 2020 ) reported that 43% of autistic children showed persistent absence during a 23-day period. The average absence rate was 5 days. The median number of days missed was 2. Moreover, 64% of the autistic children missed at least 1 day, and 7% did not attend school on any of the 23 days. Similarly, Munkhaugen et al. ( 2017 ) reported that 42.6% of autistic students exhibited school refusal behaviour. Bitsika et al. ( 2020 ) reported that 56.1% of autistic boys who reported being bullied experienced emerging school refusal, but as seen in the definition stated above, this is not a clear indication of actual absence from school.

Kurita ( 1991 ) reported the lowest frequencies: 23.7% of autistic students experienced school refusal (as defined above). In addition, 28.1% were reported to have shown an unwillingness to go to school that did not result in absenteeism. According to the data provided by parents of autistic children, 35% indicated that their child had already refused to go to school (McClemont et al., 2021 ). Regarding school exclusion, Truman et al. ( 2021 ) reported that 50% of autistic children were informally excluded from school.

Anderson ( 2020 ) reported the frequency of absences between different school types. The rates of absences for reasons other than illness (unexcused absence) did not significantly differ between primary (51.3%) and secondary (57.6%) schools. In primary schools for students with learning disabilities absences due to illness (excused absences) were the main cause (83.8%) among autistic students. The rate of absenteeism for reasons other than illness (unexcused absence) increased in secondary schools for students with learning disabilities (36.3%). In elementary schools, the median percentage of school day absences was 9.1% in the study by Mattson et al. ( 2022 ). On 39.1% of all missed school days analysed, students had excused absences, while 60.9% of absences were unexcused. Lassen et al. ( 2022 ) reported more absences among autistic children than among the control group.

Data Collection Tool for School Absenteeism

As stated above, two studies used the SNACK conducted by Heyne et al. ( 2019 ). Adams ( 2021 ) modified the SNACK by also asking about half-day absences.

The majority of studies used nonvalidated scales. Six studies used self-constructed questionnaires (Anderson, 2020 ; Bitsika et al., 2020 , 2021 , 2022 ; Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ; Truman et al., 2021 ). Lassen et al. ( 2022 ) asked about frequency via a 5-point Likert scale with descriptive ratings (never, rarely, sometimes, often, very often).

Qualitative studies (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ) as well as a mixed-method study (Preece & Howley, 2018 ) have used interviews for data collection.

Two studies used existing datasets. One of them used clinical data (Ochi et al., 2020 ), whereas the other used school datasets (Mattson et al., 2022 ).

Risk and Influencing Factors

According to the Kids and Teens at School Framework (KiTeS) by Melvin et al. ( 2019 ), the extracted risk and influencing factors for school absenteeism among autistic students were divided into individual, school and parental factors.

Individual Factors

Age, gender, diagnosis, intellectual level and psychiatric conditions were identified as factors at the individual level.

Mattson et al. ( 2022 ) reported that age was weakly and negatively correlated with the median percentage of days absent. They demonstrated that younger participants exhibited more frequent absences on average than older students. Another study reported that the mean age at the onset of school refusal was 12.6 ± 2.2 years in autistic students, which was significantly younger than in those without autism (13.8 ± 2.1 years; Ochi et al., 2020 ). In contrast, Totsika et al. ( 2020 ) reported slightly increased rates of not attending school with increasing age. Among children who missed any school days, refusal was more likely among older children.

Anderson ( 2020 ) revealed a gender difference in the disadvantage of girls on the autism spectrum. They exhibited higher rates of absence (54.6%) for reasons other than illness than autistic boys (43.9%). Compared with boys, girls exhibited significantly more short absences for reasons other than illness. For continuous periods of absence longer than four weeks, there was no significant difference between boys and girls.

In the study of O’Hagan et al. ( 2022 ), two mothers of autistic children with school avoidance indicated that feeling different from others without an explanation of a diagnosis led to low confidence and self-esteem. Families and professionals, in the study of Preece and Howley ( 2018 ), identified a late diagnosis as contributing to non-attendance since the special needs of autistic students were therefore not recognised and addressed. The findings of Martin-Denham ( 2022 ) indicate “barriers to gaining prompt assessment and identification of special educational needs and disability (SEND)”. However, students who already have a diagnosis may struggle with feeling different (Martin-Denham, 2022 ) and having a “desire to fit in” (Gray et al., 2023 ).

Intellectual Level

While most of the related studies have focused on autistic students without intellectual disabilities, Kurita ( 1991 ) found that autistic students who experienced school refusal tended to be more intelligent than those who did not. The intellectual level was significantly greater for autistic children who refused school than for those who did not.

Psychiatric Conditions

Truman et al. ( 2021 ) focused on a group of autistic children with extreme demand avoidance behaviour. These children showed more specific behavioural difficulties. They were able to mask their difficulties at school and then experienced a meltdown after. Fifty percent of parents informally excluded their children from school so that they could be home-educated, reducing their anxiety and stress.

On the other hand, the parents in the study of Gray et al. ( 2023 ) reported that they did not notice the anxiety of their children because they could not communicate their feelings. Anxiety was also demonstrated by aggression, which in turn led to school exclusions and, in some cases, led to symptoms of depression, including self-harm and suicide attempts.

Adams ( 2021 ) reported a 3% increased risk for half-day absences when the child experienced anxiety.

Munkhaugen et al. ( 2019 ) showed that autistic students with school refusal behaviour were more socially impaired than those without such behaviour. Nonetheless, low social motivation had the strongest association with school refusal behaviour. Parents commented that negative thoughts about relationships with peers and teachers, as well as about school subjects, were frequent reasons for their children’s school refusal behaviour. Lassen et al. ( 2022 ) reported that school absence is accompanied by internalising symptoms such as anxiety. This association was even stronger than that with autistic or externalising symptoms and was not unique to the autism group.

School Factors

School factors had the greatest influence on school absenteeism. Five studies focused their research on bullying. Other factors related to the school setting are the school type, the school environment and negative experiences.

The significance of school factors can even be seen in the oldest study. Two-thirds of parents of autistic children who refused school indicated that school refusal behaviour was a precipitating factor. The majority were school-related, with “teasing by schoolmates” being the most common factor (Kurita, 1991 ).

McClemont et al. ( 2021 ) reported that autistic children with ADHD were more likely to refuse school due to bullying (68%) than autistic children without ADHD (28%) or no diagnosis (18%). In this study, an autism diagnosis or another diagnosis did not impact the frequency of school refusal due to bullying compared to children with no diagnosis. In contrast, Ochi et al. ( 2020 ) reported that bullying was significantly associated with school refusal in autistic boys and girls. In the sample of Bitsika et al. ( 2020 ), which consisted only of autistic boys, “being bullied explained more of the variance in emerging school refusal than did age, ASD-related difficulties (judged by their mothers), and self-reported anxiety and depression”. Eighty-five percent of the surveyed boys reported that they had been bullied at school, and 56% of them asked their parents if they could stay at home as a result of the bullying. Bitsika et al. ( 2021 ) identified in another sample from a previous study (Bitsika & Sharpley, 2016 ) the most common bullying experiences: being called mean names or being sworn at (experienced by 75.9% of the sample); being joked about or laughed at (67.2%); being hit, pushed, or kicked (63.8%); having had something taken from them (55.1%); being “ganged up on” (56.9%); and having been reported to teachers when they had not done things that were reportable (51.3%). A participant of Gray et al. ( 2023 ) talked about being bullied because he “didn’t know what they were going on about”.

McClemont et al. ( 2021 ) described another aspect of bullying: autistic youth with a behaviour support plan (BSP) were more likely to refuse school due to bullying than were those without. Anderson ( 2020 ) cited bullying as a factor that had a limited influence. The remaining factors outlined below exerted a more pronounced influence on absences.

School Type

Totsika et al. ( 2020 ) highlighted the significance of school type. The risk for persistent non-attendance increased by 104% when the autistic child attended a mainstream school, by 100% for total days absent, and by 79% for total number of days missed. Additionally, school exclusions were slightly more frequent in mainstream schools. Anderson ( 2020 ) also revealed a significant difference between school type and absence from school. School absence due to illness was the main cause of absence in primary schools for students with learning disabilities (83.8%), but the rate of absenteeism for reasons other than illness increased when students attended secondary schools for students with learning disabilities (36.3%). The results indicate that the rate of school absenteeism among autistic students in primary school is relatively high and increases when pupils start secondary school. Gray et al. ( 2023 ) revealed that the amount of support in schools varied “depending on knowledge, willingness to accommodate needs and carrying out advice and implementing statutory guidelines”.

In a study by Martin-Denham ( 2022 ), caregivers noted a lack of knowledge, skills, understanding, and funding in mainstream secondary schools. All participants noted that barriers to mainstream education occurred because the school staff was not adequately trained in supporting children with SEND. Similar factors regarding the school staff were mentioned by participants in the study by Gray et al. ( 2023 ): lack of understanding of autism, negative attitudes and problematic responses and interactions. Additional factors included a lack of flexibility regarding rules and homework on the one hand and unstructured times on the other hand. Not knowing the needs and not understanding the reactions of the autistic children led to school exclusions or exclusions from school events.

Anderson ( 2020 ) asserted that a lack of autism competence among school staff was the most common reason for children’s school absence.

School Environment

The second most common reason in the study by Anderson ( 2020 ) was the lack of adaptation of the school environment (24.3%), followed by a lack of support in learning (23.5%) and social situations (23.8%). Factors identified by Preece and Howley ( 2018 ) regarding the school environment include a lack of understanding and appropriate support, the size of the school, and the number of students because of sensory issues such as noise. Gray et al. ( 2023 ) also listed the sensory issues of participants. The number of people, large classrooms with bright light and unstructured times led to feelings of overwhelm or sensory overload. Contrary to reports recommending the use of safe spaces to support emotional regulation, Martin-Denham ( 2022 ) noted that schools were unable to implement them due to a lack of space. A different perspective regarding the learning environment was found in the study by Gray et al. ( 2023 ). Many young people described meltdowns when doing homework because of their need for “straight separation between school and home”.

Negative Experiences

The factors described above led in the study by Gray et al. ( 2023 ) to a feeling of being treated unfairly, “which made me just feel stressed and I just refused to engage [in school]”. The results of a parent and teacher questionnaire survey of autistic students by Munkhaugen et al. ( 2017 ) suggested avoiding specific subjects, conflicts with peers or teachers, and insufficient information concerning the subjects or activities in school as possible reasons for school refusal behaviour. In the study of Truman et al. ( 2021 ), parents described negative school experiences to be at least partly caused by a lack of understanding of autism. Some of these parents considered the reason for the misunderstanding in their child’s ability to mask their difficulties. The parents also indicated that ‘masking’ may be the reason why their children’s special needs were not adequately addressed. Others reported that home-education can reduce the anxiety of their autistic children: “All the stress of having to deal with the situations gone. Can now concentrate on learning and living” (Truman et al., 2021 ). A mother in the study by Martin-Denham ( 2022 ) described that anxiety due to a focus on negative aspects in school led to a desire to die: “You can see the anxiety, and when your son says he wants to die that is hard to listen to. So, every day he would come home with this planner and […] there would be no positives, […]. So, he felt down all the time”.

Parental Factors

Parental factors that exert an influence are parental unemployment and illness. In addition, demographic characteristics are closely linked to parental factors, such as living in a two-parent household or having educational qualifications. In most studies, demographic characteristics had no influence on school absenteeism (Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ). However, Totsika et al. ( 2020 ) reported an association between school exclusions and not living in a two-parent household: the risk increased by 37% to 75%.

Parental Unemployment

The risk of non-attendance increased by 52% to 78% if parents were unemployed. Fifty-two percent had persistent absence, 57% had total days missed, and 78% had days absent (Totsika et al., 2020 ). Adams ( 2021 ) even reported an increase of 85% when parents reported not having paid employment. On the other hand, parents in two studies (Gray et al., 2023 ; Martin-Denham, 2022 ) reported “having to give up” their jobs as a result of school absenteeism.

Illness of family members

In the study by Munkhaugen et al. ( 2017 ), illness of other family members was the only sociodemographic factor that showed a significant association with school refusal behaviour in autistic students. Similarly, Adams ( 2021 ) reported that the risk of school refusal increased by 20% as parental depression scores increased.

Supporting Factors

Mainly, four included studies (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ) identified several aspects to support re-engagement. The most mentioned aspects were the quality of interactions between teachers and autistic students, as well as between parents and teachers. The development of a flexible learning approach was identified as supporting, as well as incorporating the voice of the young person into their support plan and the opportunity to ask questions (Martin-Denham, 2022 ; O’Hagan et al., 2022 ). Autistic students valued a flexible and structured approach in support, as well as the opportunity to control their own learning and feel respected and listened to (Gray et al., 2023 ). Additionally, regarding the overall school environment, smaller group sizes and structures in the classroom and learning were mentioned, as well as being part of the school community and relationships with peers (Gray et al., 2023 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ). The consistent and effective collaboration and communication between parents and teachers were affirmed by both parents and teachers (Gray et al., 2023 ; Martin-Denham, 2022 ). Parents also claim that an earlier diagnosis contributes to school engagement (Martin-Denham, 2022 ; O’Hagan et al., 2022 ).

The risk and influencing factors of school absenteeism among students on the autism spectrum were systematically reviewed across 18 studies. Nine studies solely included parents as participants, one study additionally included teachers, and one other study additionally included autistic children. Three studies based their data on the children, parents and professional staff. Two studies used existing records.

The most common term for school absenteeism was school refusal, while studies have used different criteria for determining absences. Based on the frequencies of school absenteeism shown among different study designs, it is clear that this is a serious phenomenon that occurs among autistic students internationally. Several identified factors also showed similarities and complementarities within the included studies.

The school level predominantly exhibited the most significant factors influencing absenteeism. Bullying was the most frequently studied influence and the factor with the greatest impact.

Other factors at the school level that significantly influenced absenteeism included school type, school environment, and negative experiences. In five studies, bullying was found to be a risk factor for school absenteeism (McClemont et al., 2021 ; Ochi et al., 2020 ; Bitsika et al., 2020 , 2021 , 2022 ). All studies revealed significant associations between bullying and absenteeism. Being bullied can also lead to anxiety and depression up to suicidal attempts or ideation (Martin-Denham, 2022 ). Autistic students with school refusal had higher scores for major depression, general anxiety, and separation anxiety, as well as significantly greater levels of somatic symptoms and sleeping difficulties (Bitsika et al., 2022 ). Conversely, individual factors, including externalizing symptoms and reduced social motivation, also increase the risk for bullying (Karande, 2018 ). However, other studies that examined bullying, among other factors, found that these factors had greater influences on absences (Anderson, 2020 ; Gray et al., 2023 ). The influence of school type was mostly related to a lack of knowledge of school staff in mainstream schools (Anderson, 2020 ; Gray et al., 2023 ; Martin-Denham, 2022 ). This factor was accompanied by a lack of adapting to the school environment due to a lack of resources or support (Anderson, 2020 ; Gray et al., 2023 ; Martin-Denham, 2022 ; Preece & Howley, 2018 ). A lack of support and understanding in schools increases the risk of anxiety and depression (Martin-Denham, 2022 ). The results may suggest that these conditions are contributing factors because individual needs are not addressed. Therefore, it is necessary to explore in more detail what leads to bullying and which interactional processes take place. Similarly, more research focusing on interactive processes in schools is needed.

At the individual level, age, gender, diagnosis, intellectual level and psychiatric conditions were identified. Three studies reported on the influence of age (Mattson et al., 2022 ; Ochi et al., 2020 ; Totsika et al., 2020 ). Autistic students were younger at the onset of school absences, and the frequency of absences increased with age. These results are consistent with the increasing social demands at school age (Kamp-Becker & Bölte, 2021 ). However, there is a need for further investigation of the influence of age. Mediating variables associated with age must also be considered. Anderson ( 2020 ) showed that girls had greater rates of short absences than boys. There was no other statement regarding gender since most participants were boys. Recent surveys continue to show that autism is four times more prevalent in boys than in girls (CDC, 2020 ). Nevertheless, there is clearly a lack of research regarding autism in female students. Three studies showed that feeling different from others contributed to school absences (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ). This shows the need for education on neurodiversity in schools (Honeybourne, 2018 ). Since Kurita ( 1991 ) reported that autistic students who experienced school refusal tended to be more intelligent, studies have focused on autistic students without intellectual disability. Nevertheless, more current research on the school attendance of autistic children with intellectual disabilities would be desirable. Five studies acknowledged the contribution of psychiatric conditions to school absences (Adams, 2021 ; Gray et al., 2023 ; Lassen et al., 2022 ; Munkhaugen et al., 2019 ; Truman et al., 2021 ). All five studies found anxiety to be a contributing factor. Lassen et al. ( 2022 ) even found a stronger association with internalising symptoms such as anxiety than with externalising or autistic symptoms. As anxiety is one of the most common psychiatric conditions in autistic individuals, it can influence school outcomes (for more information, see the review of Adams et al., 2019 ). The influence of other psychiatric disorders should be investigated in more detail in further research.

The parental factors that influence school absenteeism are parental unemployment and illness.

Two studies (Adams, 2021 ; Totsika et al., 2020 ) revealed an increased risk when parents were unemployed.

On the other hand, parents reported having to quit their jobs due to the school absenteeism of their children (Gray et al., 2023 ; Martin-Denham, 2022 ). Due to the effects on the socioeconomic status of a family, a further link between these two aspects should be explored in further research.

The effect of the illness of a family member was also reported by two studies. Munkhaugen et al. ( 2017 ) found this to be the only sociodemographic factor with a significant association. However, Adams ( 2021 ) reported an increased risk when parents had high depression scores. Mental health issues in parents due to stress and guilt were also shown to result from school absenteeism in two studies (Gray et al., 2023 ; Martin-Denham, 2022 ). Parents mentioned that they also need support in regard to school absenteeism in their autistic children (Martin-Denham, 2022 ). Families benefit from organisations that support the family as well as the school (Martin-Denham, 2022 ). In particular, children with special needs are dependent on the support of their parents or other caregivers (Romero & Lee, 2007 ), emphasising the need to support them in dealing with their children and school. Research that involves further system levels is needed. Support for parents can also be provided through interaction between parents and schools. Successful collaboration between parents and schools has a positive impact on the school experience of autistic students (Lilley, 2019 ) (Fig.  4 ).

Interactions between factors

Missing school is claimed to be going hand in hand with missing important developmental steps for life in society (Pellegrini, 2007 ). However, as seen in the results, for children on the autism spectrum, there are also risks for development and mental health in schools, which need to be fixed to enable the development of autistic children in schools. Parents affirmed home-education as a possibility for their autistic children to “concentrate on learning and living” (Truman et al., 2021 ). The advantages and disadvantages of home-education could be further investigated, as school is an important area in the lives of children and adolescents. The results show that the interaction between parental and individual factors is necessary but has not been adequately investigated. None of the included studies investigated parental withholding. Nevertheless, this sensitive and complex phenomenon should be examined in future research.

The results show that the school situation of autistic children should be investigated further. There is a lack of longitudinal studies regarding the education and school situation of autistic students, as well as a lack of validated scales for data collection. Nevertheless, the actuality of the included studies indicates that there is a growing research base on this topic.

Limitations

The results of this systematic review must be classified within its limitations. Given the overall scarcity of research in this area, a research question was developed that yielded the broadest possible results while allowing us to draw consistent conclusions. Therefore, studies with different terms of school absenteeism and different study designs were included. In addition, school absenteeism must be viewed in the context of school and health care systems in each country. This diversity makes comparability difficult and was carried out by the researchers on the basis of a narrative synthesis. The synthesis might reflect the researcher’s interpretation of the data. Given the heterogeneity, it is possible that other studies included risk and influencing factors that were not identified. The specific influences of the COVID-19 pandemic were not considered. Due to limited resources, this review was conducted by only two researchers, which may have introduced limitations in the search strategy. Finally, only studies published in English or German were considered.

This systematic review provides a comprehensive summary of mainly recently published studies on the factors influencing school absenteeism among autistic students. Eighteen studies were included, each with a different research focus and study design. Taken together, the results provide a picture of the different influences at the individual, school and parental levels. Future research should incorporate other system levels as well as self-reports of autistic students and validated scales to draw conclusions for the inclusion of neurodivergent students.

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Sasso, I., Sansour, T. Risk and Influencing Factors for School Absenteeism among Students on the Autism Spectrum—A Systematic Review. Rev J Autism Dev Disord (2024). https://doi.org/10.1007/s40489-024-00474-x

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Study Debunks Theory Linking Autism to Changes in Brain's Amygdala

Key Takeaways

Autism likely isn’t caused by faulty connections to the amygdala, as theorized

Brain scans showed similar variation in connectivity in the brain region between people with and without autism

Researchers urged more study to further understand brain differences in people with autism

WEDNESDAY, Sept. 4, 2024 (HealthDay News) -- A new brain imaging study has concluded that autism likely isn’t caused by faulty connections to the amygdala.

A prevailing hypothesis of autism spectrum disorder has held that people with the condition have poorer neural connections in certain brain regions, including the amygdala.

However, researchers found no evidence that people with autism had amygdala connections that differed substantially to those found in people without autism.

The amygdala is a small, almond-shaped structure in the brain that plays a key role in processing emotions and social cues, researchers explained.

For the study, researchers analyzed high-quality MRI brain scans from 488 people, including 212 with autism.

The team focused on the neural connections emanating from the amygdala when the participants were not actively engaged in any tasks.

Average variation in connectivity to the amygdala was similar in people with and without autism, results showed.

The results were the same when researchers looked at specific subregions of the amygdala, researchers added.

The new study was published in the American Journal of Psychiatry .

“It is important to note that we do not conclude that amygdala [connectivity] is generally typical in autism. Instead, we conclude that the evidence for atypical [connectivity] of the amygdala in autism is weak at best, and unreliable,” concluded the researchers led by Dorit Kliemann , an assistant professor of psychological and brain sciences with the University of Iowa.

The researchers said in a news release from the American Psychiatric Association that more brain scan research should be done to further understand the differences in people with and without autism, calling it “an investment worth prioritizing if we are to better understand and delineate the neurobiological substrates of autism.”

More information

The University of California-Davis has more on the amygdala and autism .

SOURCE: American Psychiatric Association, news release, Aug. 29, 2024

What This Means For You

There remains no clear evidence what brain processes are involved in autism.

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