The Year in Review, 2021

This year was filled with both challenges and encouraging signs of progress. The world continues to cope with the many hardships associated with the COVID-19 pandemic, which have negatively impacted the community, including scientists who study autism. Families and individuals continue to show individualized and specialized needs, specifically those from racially and ethnically diverse communities, females and girls, and we continue to understand the specific needs of those groups. For example, the close of the year saw the publication of a report by the Lancet Commission, which formally introduces the concept of “profound autism” representing individuals with different support needs. New CDC data released in December also show that autism rates are rising while age at diagnosis is decreasing [1]. While this is not a comprehensive summary of every single autism discovery in 2021, here we summarize many significant autism discoveries and related news of the past year:

Lancet Commission Endorses Use of Term “Profound Autism”

On December 6, The Lancet published an extensive report from a global team of autism researchers and stakeholders. The report, titled “The Lancet Commission on the Future of Care and Clinical Research in Autism,” recognized that effective autism assessment and care require personalized, stepped-care approaches that meet an individual’s needs throughout their lives, and that greater investment is urgently needed to develop and refine practical interventions that can improve the lives of people with autism. The Commission also formally introduced the term “profound autism” to distinguish individuals who have high dependency needs and urged policymakers to focus on the unique needs of this group, which represents approximately 30% of people with autism [2]. The goal of this label is to recognize the uniqueness of these individuals and that their support needs and outcomes are different from those of others. There is also evidence that the underlying biology of those with “profound autism” is different [3-5].

Amy and Jonah Lutz

The term “profound autism” is intended to describe autistic people who are likely to need 24-hour support throughout their lives. The report states that useful categories like “profound autism” can bring attention to the different needs of different people. In fact, the goal of the new term “profound autism” is to equip parents, service providers and the public with the language necessary to ensure that each individual with autism receives the accommodations and interventions they need [2]. These can vary greatly. Some of those diagnosed with autism engage in destructive or self-injurious behavior. Some have intellectual disabilities; others are star students. Some are unable to perform basic tasks like brushing their teeth and getting dressed; others can live fully independent lives. Autism is a disorder in which no two diagnoses look the same, and terms like “profound autism” help distinguish needs.

CDC Reports Autism Prevalence Continues to Rise

The CDC ADDM Network released updated autism prevalence data this year, announcing that one in 44 8-year-old children is diagnosed with autism [1]. This is an increase from the one in 54 number for 8-year-olds reported in March 2020. Using a slightly different but validated methodology from previous years [6], new CDC data confirm that autism prevalence and diagnoses have gone up steadily in the past five years. 

The CDC information makes it clear that we are getting better at diagnosing autism and identifying it earlier, which is encouraging because research has consistently shown the value of early intervention. However, more than 58% of children identified had intellectual disability or borderline intellectual disability. This cohort of children with profound autism warrants more attention from policymakers and service providers, as their needs are dramatically different from those with milder forms of autism. While the prevalence went up, the demographics across race, ethnicity and cognitive ability stayed pretty stable from the last prevalence estimate [1]. This information calls for further understanding of the nature of this rise beyond just diagnostic practices, including alerting pediatricians and supporting further and more expanded studies of gene x environment interactions [7]. One example would be the differential influence of toxic chemicals on cells with genetic mutations associated with autism, which revealed a susceptibility to toxic chemical exposures with cells with autism-related variation [8].

Reaching the Hard to Reach

Those from racially and ethnically diverse backgrounds have long been recognized as being diagnosed later, if at all. There are years and years of CDC data which show that while this trend is improving, it is still problematic in terms of equitable access to services. It also produces another problem that perpetuates the underdiagnosis and lack of access: not enough families from racially and ethnically diverse communities are being studied in research, which means most research findings apply to white communities, not the communities represented in the real world who need help [9]. A few studies this year specifically targeted those from either Hispanic [10] or Black and Hispanic families [11, 12]and found their needs were different or developed tools for their particular culture. However, in a commentary this year, researchers highlighted the need to engage diverse communities at the beginning of the research question, to ensure they have a voice at each step, and to possibly adapt the study question to their particular circumstances [9].  

Unfortunately, not all of the challenges facing underserved communities are the same. For example, those who are minimally verbal and have intellectual disabilities are left out of research for logistical reasons, or, in many cases, the intellectual and verbal abilities of individuals with more profound autism are not reported at all [13]. Those with intellectual disability are usually recognized more often, but there were only four intervention studies published in PubMed in 2021 that specifically included a group of autistic people with intellectual disability.  

Understanding Autism in Females 

While females with ASD have not typically been placed in the “underdiagnosed” category, they certainly are a group that has been underserved by scientific research. Because of the 4:1 difference in prevalence for males to females, autism research studies typically include four times fewer females, which means findings are not generalizable to females [14-17].

In the last year, there have been several studies showing that the challenges faced by autistic females are different from those facing autistic males. For example, a phenomenon called “passing as autistic” (otherwise known as masking) — where someone with autism tries to hide their symptoms to pass in social situations — was found to be elevated in females [18, 19]. Comorbidities like epilepsy have been shown to be higher in females [20], and baseline brain activity in autistic youth is different based on biological sex [16]. While the female brain is clearly different from the male brain, even in autism, the lack of females included in research has also significantly impaired our understanding of brain differences between males and females with ASD for more personalized support [21].

Because of the disparity in diagnosis between males and females, there are very few studies that can examine the effects of sex and gender on diagnosis, making consistent findings across sex/gender almost impossible, but it has been done [22]. What has been learned is that the striatum (and genes controlling striatal development) may play a role in autism symptoms in females. This has not been identified as an area of interest in males [23]. Research also shows that females have a higher burden of variants in the oxytocin receptor gene, which affect them differently than males with ASD [15], and differential links between brain activity and autism features [16], supporting something called the “female protective effect.” This protective effect might be genetic or might occur through the estrogen pathway [24, 25]. Finally, while the entire autism community has a higher than expected rate of gender dysphoria, it seems to affect girls more than boys [26]. Behavioral features are also slightly different, which complicates diagnosis [17]. Together, these results demonstrate that scientific findings, including use of biomarkers for diagnosis, which are seen in males may be different than those seen in females. Scientists need to ensure that enough females are recruited into research studies and better understand the difference between females and males to ensure that scientific findings generalize to care in the community.

The Pandemic Is Still Causing Problems

Almost two years into the pandemic, scientists are still working to understand the long-term effects on people with autism. Studies focused on increases in challenging behaviors and loneliness in autistic youth and adults [27, 28], and also on understanding the mental health challenges due to prolonged social distancing guidelines, including multiple waves of lockdowns [29-32]. Additionally, studies show that families with autism are disproportionately affected by job losses and food insecurity [33, 34]. And while telehealth-based diagnosis and services are becoming more common as a result of social distancing, families of younger children who need direct behavioral supports remain the least satisfied [35, 36], a trend continuing from 2020 [37]. The challenges associated with the pandemic are not limited to those with a diagnosis and their families. Scientists who dedicate their lives to help those on the spectrum have struggled with some of the same issues that families with autism have [38], including mental health and childcare challenges. This compounds the problem of developing scientific discoveries and delivering them to the community.

New Technologies for Diagnosis and Treatment

Child enrolled in the Duke study watching the video

With the pandemic came the use of remote and virtual technologies, not just to identify and diagnose autism, but also to provide supports and services. As the pandemic continues, researchers are studying what works and what doesn’t, especially in families who say that they found telehealth more accessible and beneficial [35]. Remote assessments have changed the nature of how autism is diagnosed, with scientists emphasizing the need for use of good clinical judgment rather than reliance on singular instruments [39]. Telehealth assessments have meant that diagnosis is now more accessible to those in remote areas who are traditionally underdiagnosed. Another bright spot is that the pandemic has allowed children to be observed remotely in their home environment, which may significantly enhance the ability of clinicians to observe early markers of autism [39, 40]. New technologies that enable videotaping via remote camera — for later review by clinicians — are also gaining traction. Recently, Cognoa received FDA marketing authorization for its new remote videotaping tool, CanvasDx. Duke University also published data a tool that plays different movies and visual scenes on an iPad and allows clinicians to determine the likelihood of an autism diagnosis by examining where the children looked in the scene [41], as past research has shown that children with autism are more likely to look at objects and less likely to look at social stimuli. In both cases, these recordings, together with standard early screening methods, can be analyzed to help facilitate diagnosis. A 2021 review found these mobile digital technologies to be promising in diagnosis [42].

Beyond just supporting diagnosis, mobile technology may be used to improve cognitive and social skills across the lifespan [43]. A recent systematic review indicated that these mobile interventions were particularly helpful in targeting practical skills [43, 44]. They can also be used to predict responses to stressful situations and abnormal sensory arousal [45]. Finally, robots and videogames on devices are showing promise in helping kids with autism develop social skills [46, 47]. While these technologies may have benefits beyond the pandemic and can alleviate some of the burden of traveling to multiple appointments, they will not replace the need for children to be diagnosed and/or receive therapy from trained, in-person clinicians [39, 48].

Intervention Before Diagnosis

A few years ago, scientists in the UK began studying the possibility of promoting skills in parents as a way to mitigate autism symptoms in infants [49]. By working with parents in their home and promoting social and communication skills through activities like reading and play, autism severity scores improved. This year, a group in Australia conducted its own randomized controlled study starting at 9-12 months — before a diagnosis can be made — to provide support to parents and offer video feedback on supporting language and social development in their infants. This study showed that support of infant social and communication skills measured at one year led to a reduction of autism severity scores at 24 months, with these improvements being maintained long after the end of the intervention period [50]. Factors like caregiver interaction and adjusting the environment to promote learning in these toddlers are key ingredients to changing developmental trajectory [51, 52]. New tools are also allowing earlier and earlier detection of markers of ASD, with some evidence that it can be done as early as 12 months of age [53]. These findings represent the potential benefits of decades worth of early detection work and operationalize a methodology for parents to learn to promote social and communication skills in their infants.

However, the need for earlier detection and diagnosis of autism remains a priority within autism research and the autism community. This year, researchers identified changes in the grey matter (cell bodies) and white matter (the neuron branches) in children as young as 12 months of age [54] who go on to be diagnosed with autism. Changes in brain activity, while not a diagnostic marker, can be seen in infants as young as 3 months of age [55] and can prove helpful in diagnosis at 6 months [56]. In addition, some behavioral signs can also trigger preemptive intervention. Groups led by UC Davis demonstrated both declining gaze to faces, which was replicated in two different cohorts [57], and unusual inspection of objects at 9 months, which predicts reduced social engagement at 12 months in those who later develop an autism diagnosis [58]. In addition, vocalizations (or intents to communicate) were lower in children as young as 12 months [59]. Together, while not diagnostic, some of these early markers and signs can facilitate entry into preemptive interventions, which can produce skills in caregivers and infants that change the developmental trajectory. Finally, there is an erroneous perception that parents believe that all of autism is “bad” and needs to “be eliminated.” In fact, when they were specifically asked, parents identified characteristics like love, kindness, humor, humanity and resilience that they value and appreciate in their children [60].

Autism and Aging

There has traditionally been a lack of understanding as to what happens to autistic adults as they enter their golden years. This year, Drexel University utilized Medicaid data to examine the risk of dementia in those with autism and found that those with ASD were 2.6 times more likely to be diagnosed with dementia compared to the general population [61]. This has profound impacts on planning for elderly relatives with ASD and developing interventions that may stunt the development of dementia in this population.

Understanding the Role of Genetics in Autism 

Traditionally, genetic variation association with autism has been bucketed as “rare” mutations and “common” mutations. Rare mutations on genes typically lead to deleterious effects such as seizures or intellectual disability [62]. Sometimes, like in the case of BRCA (breast cancer gene), they can be fatal. Common mutations are seen in lots of people, not just those with autism, but the human body can tolerate many common mutations with no major effects. However, if the genetic variant is found in an autism risk gene, for example, then it can dispose someone to an autism diagnosis [62]. Mutations found in autism risk genes — including those associated with cell adhesion, neuron-glia interactions and synapse formation — are most likely to be common variants involved in autism [3].

This year, sequencing of more than 800 people with an autism diagnosis revealed that 27% had evidence of a rare genetic mutation, mostly in one of the 102 genes identified in 2020 as being relevant for ASD [3, 63]. Presence of a mutation of one of these genes also results in a distinct set of behavioral features early in life that is different from those without a rare mutation [64]. Interestingly, instead of advancing the traditional “rare vs. common variation debate,” scientists this year learned that even in those who have a rare genetic mutation, there is also a high burden of common variation [63]. Scientists found that both rare and common genetic risks contribute to autism susceptibility, and that the dual risks may increase the likelihood of an autism diagnosis [63]. These findings make things complicated for genetic counselors who need to assess all the factors and communicate to families whether or not a particular rare variant is causative. In addition, sequencing technologies are revealing more and more genes that are relevant to ASD but incredibly rare; in fact, they are likely to be part of a multi-factorial cause of individual cases of ASD [65]. Finally, we’ve learned that common variation influences not only core autism symptoms, but also psychiatric comorbidities [66].

A Family with ADNP Syndrome

Studying Rare Genetic Syndromes Opens the Door to New Therapeutics

The use of induced pluripotent stem cells, or iPSCs, to study the brain on a cellular level has so far been focused on rare genetic diseases associated with autism, like Dup15q syndrome, CNTNAP2 and CDKL5 disorder. However, while the genetic targets may be more specific than in idiopathic autism, there are also converging mechanisms of disrupted connectivity in the brain that make these single gene disorders useful in understanding the neurobiology of ASD [67-71].

In addition to some shared (and some distinct) neurobiology across autism with a known genetic cause, there is overlap on the basic neurobiology level in terms of cortical thickness [72] and G-protein-coupled-receptors across different psychiatric disorders, including autism [73]. Some of these rare genetic syndromes have been responsive to targeted gene therapy, which opens up the door for them to be used in idiopathic autism if proven safe and effective in large groups of people with neurodevelopmental disorders.

Remember Glial Cells? They May Play a Bigger Role Than We Thought.

Photograph of a glial cell

One brain cell type that is experiencing renewed interest in autism is glial cells, particularly with regard to sex differences in ASD. Glial cells are found in the brain, but they do not communicate with each other. Rather, they provide insulation to neurons that do communicate via electrical impulses. Traditionally, because they were not thought to be communication cells, they were not considered critical for study. But recent evidence has shown that there may be different subtypes of autism defined by the upregulation of genes that control glial cells [74]. Gene expression in these microglia may also contribute to differences in brain structure [75]. In addition, the direct study of brain tissue has shown that in certain layers of the cortex, astrocytes — a type of glial cell — are decreased [76]. Taken together, the dysregulation of glial cells may contribute to different cell processes, brain structure, functional changes and psychiatric syndromes associated with autism.

What Can We Do to Improve Outcomes of Those with Autism?

New research shared this year focused on improving outcomes. First, we learned that the presence of a brother or sister not on the autism spectrum improves adaptive behavior across the lifespan for those with an autism diagnosis [77]. On the other hand, parental stress in early life and early adverse events can make outcomes worse [78].

Research continues to show that, especially in the early years, parents and caregivers can play a critical and life-changing role in their child’s development. For young children, Naturalistic Developmental Behavioral Interventions (NDBIs), which are child-led and utilize behavioral principles delivered in the home, are most helpful [79, 80] and now may be delivered via telehealth [81]. One good thing to come out of the pandemic is the availability of remote access to video series, including but not limited to the Autism Navigator, which can help parents identify early signs and deliver these interventions to their young children from home [82]. The literature on the efficacy of these NDBIs grows greater every year.  However, not everyone has access to early interventions or even expert clinicians. To address the disparities seen across the world and across different comorbidities and other individual factors, the Lancet Commission report called for a stepped-care and personalized health model for interventions [2]. This includes provisions not just for individual and family factors, but also for accessibility and cost. These recommendations on how different groups approach care are essential to obtain a more specialized approach to helping families and individuals on the spectrum lead happy, healthy and successful lives. Unfortunately, some promising therapeutics like oxytocin failed to meet the cut of significantly helping those with ASD [83].  Other organ systems besides the brain, including the gastrointestinal system, continue to be investigated to help alleviate co-occurring medical conditions.  Many families turn to things like probiotics to help with issues like constipation and diarrhea, however, new evidence suggests that the microbiome is more influenced by diet than autism itself [84] calling into question the validity of probiotic use for GI problems.

In Memoriam

Sir Michael Rutter

Sadly, the autism community lost three scientists this year who have made enormous contributions to the field and changed the way people think about autism. Sir Michael Rutter, known as the Father of Child Psychiatry, a professor at the Institute of Psychiatry at Kings College London, was one of the most influential psychiatric scientists of the past 50 years. He was one of the first researchers to study autism, publishing a study of autistic twins in 1977. He helped dispel the myth that parenting styles influenced an autism diagnosis and brought scientific rigor to understanding autism. He helped develop the two gold standard tools for diagnosis: the ADI-R and the ADOS. His commitment to helping children and families was not limited to autism, however; he helped families with a number of psychiatric conditions and behavioral issues.

Li-Ching Lee

Li-Ching Lee, who served as the Associate Director for Global Autism at the Wendy Klag Center of Johns Hopkins School of Public Health, was one of the reasons why autism is recognized as a global condition. She focused her research on identifying and helping families with autism across the world, calling it a “human rights issue” when the needs of families in under-resourced countries were ignored. She also worked tirelessly to understand autism in the US, working closely with the CDC to understand who and where people were being diagnosed and how they could be helped. Beyond being an amazing scientist, her fellow students have called her an amazing friend, mentor and teacher who went above and beyond to help her students be successful while helping families.

George C. Wagner

Finally, George C. Wagner of Rutgers University was one of the first behavioral neuroscientists to try to develop a behavioral model of ASD in rodents at a time when scientists were starting to try to understand how to recapitulate the features in model systems. His work helped define how autism should be studied in animals, and how it overlapped or was different than other psychiatric disorders. He based his models on the core features rather than particular behaviors, including delay of skill development, plateauing of skills and possible regression of skills. This helped fundamentally change the field of animal models of ASD. Many of his students (including ASF CSO Alycia Halladay) went on to help families with ASD following training.

All three of these amazing scientists will be remembered not just for their contributions to science, but for their training of early career researchers who continue to make an impact.

The Last Word

Over the last 40 years, autism has moved from a categorial (yes/no) diagnosis to a dimensional diagnosis [85], taking into account the complexity and differences of features across the lifespan. While there may be core features of ASD that are common across the spectrum, people with autism, just like people without autism, are all different and need to be recognized as such [2, 86].  

While this summary captures what happened in 2021, we urge you to read more about how science has changed the way families with autism have been perceived, treated and helped over the past 40 years. The Journal of Autism and Developmental Disorderspublished a series that you can look through here, and Dr. Giacomo Vivanti shared his long-term perspective on the November 14 ASF podcast here: https://asfpodcast.org/archives/1258. In fact, one of the best ways to keep up with changes in autism science is to subscribe to the ASF podcast on Spotify, Apple Podcasts or Google Podcasts.

You can make a difference

These research findings and important discoveries were thanks to the thousands of families and autistic individuals who participated in research studies over the past few years. As you can read from this report, your contributions make an impact. There are other research opportunities and as we continue to live in the pandemic, many more of them are available in your own home with interaction with professionals to support you. You can read more about them here. Finally, there are ways to learn about credible science outside social media, which also includes SpectrumNews and the Autism BrainNet. Just signing up for more information on the Autism BrainNet gets you regular information about what the brains of autistic people look like and how they are different from those without a diagnosis.

References

1.         Maenner, M.J., et al., Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years – Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. MMWR Surveill Summ, 2021. 70(11): p. 1-16.

2.         Lord, C., et al., The Lancet Commission on the future of care and clinical research in autism. The Lancet, 2021.

3.         Mahjani, B., et al., Prevalence and phenotypic impact of rare potentially damaging variants in autism spectrum disorder. Mol Autism, 2021. 12(1): p. 65.

4.         Havdahl, A., et al., Age of walking and intellectual ability in autism spectrum disorder and other neurodevelopmental disorders: a population-based study. J Child Psychol Psychiatry, 2021. 62(9): p. 1070-1078.

5.         Reardon, A.M., et al., Subtyping Autism Spectrum Disorder Via Joint Modeling of Clinical and Connectomic Profiles. Brain Connect, 2021.

6.         Maenner, M.J., et al., Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children. Am J Epidemiol, 2021. 190(10): p. 2198-2207.

7.         Volk, H.E., et al., Considering Toxic Chemicals in the Etiology of Autism. Pediatrics, 2021.

8.         Modafferi, S., et al., Gene-Environment Interactions in Developmental Neurotoxicity: a Case Study of Synergy between Chlorpyrifos and CHD8 Knockout in Human BrainSpheres. Environ Health Perspect, 2021. 129(7): p. 77001.

9.         Maye, M., et al., Biases, Barriers, and Possible Solutions: Steps Towards Addressing Autism Researchers Under-Engagement with Racially, Ethnically, and Socioeconomically Diverse Communities. J Autism Dev Disord, 2021.

10.       Harris, J.F., et al., Validation of the Developmental Check-In Tool for Low-Literacy Autism Screening.Pediatrics, 2021. 147(1).

11.       Azad, G., et al., The influence of race on parental beliefs and concerns during an autism diagnosis: A mixed-method analysis. Autism, 2021: p. 13623613211044345.

12.       Wagner, S., I.L. Rubin, and J.S. Singh, Underserved and Undermeasured: a Mixed-Method Analysis of Family-Centered Care and Care Coordination for Low-Income Minority Families of Children with Autism Spectrum Disorder. J Racial Ethn Health Disparities, 2021.

13.       Thurm, A., et al., Making Research Possible: Barriers and Solutions For Those With ASD and ID. J Autism Dev Disord, 2021.

14.       Skaletski, E.C., et al., Quality-of-Life Discrepancies Among Autistic Adolescents and Adults: A Rapid Review. Am J Occup Ther, 2021. 75(3).

15.       Lawrence, K.E., et al., Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect. Brain, 2021.

16.       Neuhaus, E., et al., Resting state EEG in youth with ASD: age, sex, and relation to phenotype. J Neurodev Disord, 2021. 13(1): p. 33.

17.       Dillon, E.F., et al., Sex Differences in Autism: Examining Intrinsic and Extrinsic Factors in Children and Adolescents Enrolled in a National ASD Cohort. J Autism Dev Disord, 2021.

18.       Libsack, E.J., et al., A Systematic Review of Passing as Non-autistic in Autism Spectrum Disorder. Clin Child Fam Psychol Rev, 2021. 24(4): p. 783-812.

19.       Cook, J., et al., Camouflaging in autism: A systematic review. Clin Psychol Rev, 2021. 89: p. 102080.

20.       Bougeard, C., et al., Prevalence of Autism Spectrum Disorder and Co-morbidities in Children and Adolescents: A Systematic Literature Review. Front Psychiatry, 2021. 12: p. 744709.

21.       Mo, K., et al., Sex/gender differences in the human autistic brains: A systematic review of 20 years of neuroimaging research. Neuroimage Clin, 2021. 32: p. 102811.

22.       Floris, D.L., et al., Towards robust and replicable sex differences in the intrinsic brain function of autism. Mol Autism, 2021. 12(1): p. 19.

23.       Jack, A., et al., A neurogenetic analysis of female autism. Brain, 2021. 144(6): p. 1911-1926.

24.       Enriquez, K.D., A.R. Gupta, and E.J. Hoffman, Signaling Pathways and Sex Differential Processes in Autism Spectrum Disorder. Front Psychiatry, 2021. 12: p. 716673.

25.       Willsey, H.R., et al., Parallel in vivo analysis of large-effect autism genes implicates cortical neurogenesis and estrogen in risk and resilience. Neuron, 2021. 109(5): p. 788-804 e8.

26.       Brunissen, L., et al., Sex Differences in Gender-Diverse Expressions and Identities among Youth with Autism Spectrum Disorder. Autism Res, 2021. 14(1): p. 143-155.

27.       Hards, E., et al., Loneliness and mental health in children and adolescents with pre-existing mental health problems: A rapid systematic review. Br J Clin Psychol, 2021.

28.       Kalb, L.G., et al., Psychological distress among caregivers raising a child with autism spectrum disorder during the COVID-19 pandemic. Autism Res, 2021. 14(10): p. 2183-2188.

29.       Young, E., et al., Caregiver burnout, gaps in care, and COVID-19: Effects on families of youth with autism and intellectual disability. Can Fam Physician, 2021. 67(7): p. 506-508.

30.       Polonyiova, K., et al., Comparing the impact of the first and second wave of COVID-19 lockdown on Slovak families with typically developing children and children with autism spectrum disorder. Autism, 2021: p. 13623613211051480.

31.       Siracusano, M., et al., Parental Stress and Disability in Offspring: A Snapshot during the COVID-19 Pandemic. Brain Sci, 2021. 11(8).

32.       Lois Mosquera, M., et al., Autistic adults’ personal experiences of navigating a social world prior to and during Covid-19 lockdown in Spain. Res Dev Disabil, 2021. 117: p. 104057.

33.       Karpur, A., et al., Food insecurity in the households of children with autism spectrum disorders and intellectual disabilities in the United States: Analysis of the National Survey of Children’s Health Data 2016-2018. Autism, 2021. 25(8): p. 2400-2411.

34.       Panjwani, A.A., R.L. Bailey, and B.L. Kelleher, COVID-19 and Food-Related Outcomes in Children with Autism Spectrum Disorder: Disparities by Income and Food Security Status. Curr Dev Nutr, 2021. 5(9): p. nzab112.

35.       Bhat, A., Analysis of the SPARK study COVID-19 parent survey: Early impact of the pandemic on access to services, child/parent mental health, and benefits of online services. Autism Res, 2021. 14(11): p. 2454-2470.

36.       Corona, L.L., et al., Utilization of telemedicine to support caregivers of young children with ASD and their Part C service providers: a comparison of intervention outcomes across three models of service delivery. J Neurodev Disord, 2021. 13(1): p. 38.

37.       Jeste, S., et al., Changes in access to educational and healthcare services for individuals with intellectual and developmental disabilities during COVID-19 restrictions. J Intellect Disabil Res, 2020.

38.       Harrop, C., et al., A lost generation? The impact of the COVID-19 pandemic on early career ASD researchers. Autism Res, 2021. 14(6): p. 1078-1087.

39.       Zwaigenbaum, L., et al., Rethinking autism spectrum disorder assessment for children during COVID-19 and beyond. Autism Res, 2021. 14(11): p. 2251-2259.

40.       Delehanty, A.D. and A.M. Wetherby, Rate of Communicative Gestures and Developmental Outcomes in Toddlers With and Without Autism Spectrum Disorder During a Home Observation. Am J Speech Lang Pathol, 2021. 30(2): p. 649-662.

41.       Chang, Z., et al., Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder. JAMA Pediatr, 2021. 175(8): p. 827-836.

42.       Desideri, L., P. Perez-Fuster, and G. Herrera, Information and Communication Technologies to Support Early Screening of Autism Spectrum Disorder: A Systematic Review. Children (Basel), 2021. 8(2).

43.       de Nocker, Y.L. and C.K. Toolan, Using Telehealth to Provide Interventions for Children with ASD: a Systematic Review. Rev J Autism Dev Disord, 2021: p. 1-31.

44.       Leung, P.W.S., et al., Effectiveness of Using Mobile Technology to Improve Cognitive and Social Skills Among Individuals With Autism Spectrum Disorder: Systematic Literature Review. JMIR Ment Health, 2021. 8(9): p. e20892.

45.       Nuske, H.J., et al., Evaluating commercially available wireless cardiovascular monitors for measuring and transmitting real-time physiological responses in children with autism. Autism Res, 2021.

46.       Penev, Y., et al., A Mobile Game Platform for Improving Social Communication in Children with Autism: A Feasibility Study. Appl Clin Inform, 2021. 12(5): p. 1030-1040.

47.       Riches, S., et al., Therapeutic engagement in robot-assisted psychological interventions: A systematic review. Clin Psychol Psychother, 2021.

48.       Nuske, H.J. and D.S. Mandell, Digital health should augment (not replace) autism treatment providers.Autism, 2021. 25(7): p. 1825-1827.

49.       Green, J., et al., Randomised trial of a parent-mediated intervention for infants at high risk for autism: longitudinal outcomes to age 3 years. J Child Psychol Psychiatry, 2017. 58(12): p. 1330-1340.

50.       Whitehouse, A.J.O., et al., Effect of Preemptive Intervention on Developmental Outcomes Among Infants Showing Early Signs of Autism: A Randomized Clinical Trial of Outcomes to Diagnosis. JAMA Pediatr, 2021. 175(11): p. e213298.

51.       Davis, P.H., et al., Caregiver responsiveness as a mechanism to improve social communication in toddlers: Secondary analysis of a randomized controlled trial. Autism Res, 2021.

52.       Grzadzinski, R., et al., Pre-symptomatic intervention for autism spectrum disorder (ASD): defining a research agenda. J Neurodev Disord, 2021. 13(1): p. 49.

53.       Wetherby, A.M., et al., The Early Screening for Autism and Communication Disorders: Field-testing an autism-specific screening tool for children 12 to 36 months of age. Autism, 2021. 25(7): p. 2112-2123.

54.       Godel, M., et al., Altered Gray-White Matter Boundary Contrast in Toddlers at Risk for Autism Relates to Later Diagnosis of Autism Spectrum Disorder. Front Neurosci, 2021. 15: p. 669194.

55.       Tran, X.A., et al., Functional connectivity during language processing in 3-month-old infants at familial risk for autism spectrum disorder. Eur J Neurosci, 2021. 53(5): p. 1621-1637.

56.       Peck, F.C., et al., Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months. J Neurodev Disord, 2021. 13(1): p. 57.

57.       Gangi, D.N., et al., Declining Gaze to Faces in Infants Developing Autism Spectrum Disorder: Evidence From Two Independent Cohorts. Child Dev, 2021. 92(3): p. e285-e295.

58.       Miller, M., et al., Repetitive behavior with objects in infants developing autism predicts diagnosis and later social behavior as early as 9 months. J Abnorm Psychol, 2021. 130(6): p. 665-675.

59.       Plate, S., et al., Infant vocalizing and phenotypic outcomes in autism: Evidence from the first 2 years. Child Dev, 2021.

60.       Cost, K.T., et al., “Best Things”: Parents Describe Their Children with Autism Spectrum Disorder Over Time.J Autism Dev Disord, 2021. 51(12): p. 4560-4574.

61.       Vivanti, G., et al., The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder. Autism Res, 2021. 14(10): p. 2189-2199.

62.       Gaugler, T., et al., Most genetic risk for autism resides with common variation. Nat Genet, 2014. 46(8): p. 881-5.

63.       Klei, L., et al., How rare and common risk variation jointly affect liability for autism spectrum disorder. Mol Autism, 2021. 12(1): p. 66.

64.       Wickstrom, J., et al., Patterns of delay in early gross motor and expressive language milestone attainment in probands with genetic conditions versus idiopathic ASD from SFARI registries. J Child Psychol Psychiatry, 2021. 62(11): p. 1297-1307.

65.       Wilfert, A.B., et al., Recent ultra-rare inherited variants implicate new autism candidate risk genes. Nat Genet, 2021. 53(8): p. 1125-1134.

66.       Rodriguez-Gomez, D.A., et al., A systematic review of common genetic variation and biological pathways in autism spectrum disorder. BMC Neurosci, 2021. 22(1): p. 60.

67.       de Jong, J.O., et al., Cortical overgrowth in a preclinical forebrain organoid model of CNTNAP2-associated autism spectrum disorder. Nat Commun, 2021. 12(1): p. 4087.

68.       Jacot-Descombes, S., et al., Altered synaptic ultrastructure in the prefrontal cortex of Shank3-deficient rats.Mol Autism, 2020. 11(1): p. 89.

69.       Colombo, E., et al., The K63 deubiquitinase CYLD modulates autism-like behaviors and hippocampal plasticity by regulating autophagy and mTOR signaling. Proc Natl Acad Sci U S A, 2021. 118(47).

70.       Victor, A.K., et al., Molecular Changes in Prader-Willi Syndrome Neurons Reveals Clues About Increased Autism Susceptibility. Front Mol Neurosci, 2021. 14: p. 747855.

71.       Vasic, V., et al., Translating the Role of mTOR- and RAS-Associated Signalopathies in Autism Spectrum Disorder: Models, Mechanisms and Treatment. Genes (Basel), 2021. 12(11).

72.       Writing Committee for the Attention-Deficit/Hyperactivity, D., et al., Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders. JAMA Psychiatry, 2021. 78(1): p. 47-63.

73.       Monfared, R.V., et al., Transcriptome Profiling of Dysregulated GPCRs Reveals Overlapping Patterns across Psychiatric Disorders and Age-Disease Interactions. Cells, 2021. 10(11): p. 2967.

74.       Nassir, N., et al., Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells. Hum Genomics, 2021. 15(1): p. 68.

75.       Takanezawa, Y., et al., Microglial ASD-related genes are involved in oligodendrocyte differentiation. Sci Rep, 2021. 11(1): p. 17825.

76.       Falcone, C., et al., Neuronal and glial cell number is altered in a cortical layer-specific manner in autism.Autism, 2021. 25(8): p. 2238-2253.

77.       Rosen, N.E., J.B. McCauley, and C. Lord, Influence of siblings on adaptive behavior trajectories in autism spectrum disorder. Autism, 2021: p. 13623613211024096.

78.       Hollocks, M.J., et al., The association of adverse life events and parental mental health with emotional and behavioral outcomes in young adults with autism spectrum disorder. Autism Res, 2021. 14(8): p. 1724-1735.

79.       Schuck, R.K., et al., Neurodiversity and Autism Intervention: Reconciling Perspectives Through a Naturalistic Developmental Behavioral Intervention Framework. J Autism Dev Disord, 2021.

80.       Waddington, H., et al., The effects of JASPER intervention for children with autism spectrum disorder: A systematic review. Autism, 2021. 25(8): p. 2370-2385.

81.       Dai, Y.G., et al., Development and Acceptability of a New Program for Caregivers of Children with Autism Spectrum Disorder: Online Parent Training in Early Behavioral Intervention. J Autism Dev Disord, 2021. 51(11): p. 4166-4185.

82.       Wainer, A.L., et al., Examining a stepped-care telehealth program for parents of young children with autism: a proof-of-concept trial. Mol Autism, 2021. 12(1): p. 32.

83.       Sikich, L., et al., Intranasal Oxytocin in Children and Adolescents with Autism Spectrum Disorder. N Engl J Med, 2021. 385(16): p. 1462-1473.

84.       Yap, C.X., et al., Autism-related dietary preferences mediate autism-gut microbiome associations. Cell, 2021. 184(24): p. 5916-5931 e17.

85.       Rosen, N.E., C. Lord, and F.R. Volkmar, The Diagnosis of Autism: From Kanner to DSM-III to DSM-5 and Beyond. J Autism Dev Disord, 2021. 51(12): p. 4253-4270.

86.       Lord, C. and S.L. Bishop, Let’s Be Clear That “Autism Spectrum Disorder Symptoms” Are Not Always Related to Autism Spectrum Disorder. Am J Psychiatry, 2021. 178(8): p. 680-682.

CDC: 1 in 68 Children has an Autism Spectrum Disorder

The Centers for Disease Control and Prevention (CDC) today reported that 1 in 68 children is diagnosed with an autism spectrum disorder. This new estimate is roughly 30 percent higher than previous estimates reported in 2012 of 1 in 88 children. The number of children identified with ASD ranged from 1 in 175 children in Alabama to 1 in 45 children in New Jersey.

For the full press release, please visit our website.

Identifying ASD in Community Settings

By Matthew Maenner

Early identification of autism spectrum disorders (ASD) continues to be an important public health objective.  Research has shown that ASD can be reliably identified in children by around 2 years of age, and public health campaigns promote the detection of developmental “early warning signs”  that may indicate ASD.  Despite these efforts, there is a considerable gap between the age ASD is detected in clinical research, and the age at which children are identified as having ASD in typical community settings.  Previous population-based studies have shown that the average age of ASD identification in the community is less than ideal (at 5.7 years), and there is little information about whether these “early warning signs” lead to earlier ASD identification in everyday practice.

Our new study uses data from the CDC Autism and Developmental Disabilities Monitoring (ADDM) Network to answer two questions about how ASD behavioral features are described by community professionals and whether these behaviors are associated with the age of ASD identification. The ADDM Network identified 2,757 8-year-old children that met the surveillance case definition for ASD (based on the DSM-IV-TR criteria) in 2006.

616 combination

First, we examined the frequency and patterns of diagnostic behaviors that lead to a child meeting the diagnostic criteria for ASD (based on the DSM-IV-TR).  There are many different ways to meet the diagnostic criteria for ASD.  For example, there are 616 combinations of the 12 behavioral criteria that fulfill the minimum number (6) and pattern needed for “Autistic Disorder” alone.  Although there was considerable variability between individuals with ASD, boys and girls had similar patterns of documented behaviors, as did black and white children overall.  Among the 2,757 children, the most commonly documented behaviors were impairments in emotional reciprocity (90%), delays in spoken language (89%), and impairments in the ability to hold a conversation (86%).  The least frequently documented behaviors were lack of sharing enjoyment or interests (49%) and lack of spontaneous or pretend play (57%).

Our second question was whether particular ASD behaviors (such as those highlighted by the CDC’s “Learn the Signs” campaign) are actually associated with earlier ASD identification in typical community settings.  We found that the both the total number and types of diagnostic behaviors in a child’s record were strongly associated with the age that they were identified as having ASD.  Children with all 12 behavioral symptoms were diagnosed at a median age of 3.8 years of age, compared to 8.2 years for children with only 7 of the 12 behaviors.  Additionally, children with documented impairments in nonverbal communication, pretend play, inflexible routines, or repetitive motor behaviors tended to have an earlier age at ASD identification than children who did have these features in their records.  Children with impairments in peer relations, conversational ability, or idiosyncratic speech were more likely to be identified as having ASD at a later age.

These findings give us a clearer understanding of how ASD diagnoses are made in the community, and help inform efforts to maximize early identification and intervention among children with ASDs.  It may be more difficult to detect ASD at an early age among children with fewer symptoms, or symptoms that are most apparent at later ages (such as getting along with peers or conversational ability). A recent national telephone survey reported an increase in ASD prevalence among young teenagers, and parents were more likely to describe their later-diagnosed children as having “mild” ASD.  It’s possible that increased awareness and intensified screening for ASD could lead to more individuals being identified at both earlier and later ages. Strategies to improve early ASD identification and interventions could benefit by considering the manner in which individuals may meet ASD criteria.

Autism Spectrum Disorders in the Stockholm Youth Cohort: Design, Prevalence and Validity

By Matt Carey

The prevalence of autim spectrum disorders in Sweden is estimated at about 1% (1.15%) based on a study just released using the Stockholm Youth Cohort. This prevalence is consistent with current estimates in the U.S. and the U.K., and with a subset of the population from a previous Swedish study. Autism prevalence is relatively flat with age, especially for children born in the 1990’s.

Most autism prevalence data is from Europe and the United States, with the U.S. as the largest source of data. CDC prevalence estimates are reported every two years. CDC estimates use a record-review methodology. While this methodology has its own limitations, including likely underestimating autism prevalence, relying upon administrative registries (such as the California Department of Developmental Services datasets or medical registries) are likely to give an even greater underestimation of autism prevalence. A whole-population approach, such as that used in a recent study of autism prevalence in Korea, should give the most accurate estimates, but are more costly to perform and typically limited to the number of study subjects.

The recent study presents autism prevalence in Sweden, using the Stockholm Youth Cohort (a medical registry). The paper, Autism Spectrum Disorders in the Stockholm Youth Cohort: Design, Prevalence and Validity, is in the journal PLoS ONE, which means the full article is available online.

A previous report, from 2006, gave the prevalence of autistic disorder in Sweden (from data in 2001) at 20.5 per 10,000, with other ASD’s at 32.9 per 10,000. This for a population born from 1977-1994. The prevalence in the youngest population in the study (7-12 years old at time of the study) was 1.23%. Yes, 1.23% for kids born in 1989-1994. That’s a prevalence comparable to the recent CDC estimate for U.S. kids born in 2000.

Back to the present Sweden study. The authors note the potential problem with using medical registries:

Furthermore, Scandinavian studies have frequently ascertained ASD cases via health care registries [8], [9], [10], [11]. This approach may underestimate the prevalence of ASD, since affected children require social and educational interventions more often than health care.

Stockholm county has a very active surveillance program:

All ASD related services, including diagnosis and follow-up health, special educational and social care are provided by services run by, or contracted with the Stockholm County Council and available free of charge. Referrals for diagnostic evaluation of suspected ASD are commonly made by child healthcare centres, whose health- and developmental surveillance program engages 99.8% of all preschool children [15]. Developmental surveillance is performed by specially trained child healthcare centre nurses at regular intervals (1, 2, 6, 10–12, 18, 36, 48 and 60 months of age), with examination by a paediatrician at key ages (2, 6, 10–12 months) and in case of developmental deviation or according to need at other age intervals. Speech abilities and language comprehension are evaluated by nurses at 36 and 48 months, and examination of sight and hearing is made at 48 months.

Interestingly, even with this tight surveillance effort, the median age of diagnosis is 8. Children with intellectual disability were identified as autistic earlier. Girls were identified later.

Where such information was available (n = 148), the median age at diagnosis was 8.0 years for ASD overall (range 1–19, interquartile range [IQR] 8.0). For cases without and with intellectual disability (n = 80 and 68), the corresponding ages were 11.5 (range 4–19, IQR 6.0) and 6.0 (range 1–17, IQR 4.0) years. Girls (n = 48) were older than boys (n = 100) at diagnostic assessment (median age 11.0 as compared to 8.0 years).

The authors were able to test the validity of the diagnoses by checking on a subset of autistic children (starting from a sample of 100 with and 100 without intellectual disability). The researchers were able to check records on 85% of this subset, and 96% of the records checked were consistent with a diagnosis of ASD.

Unlike U.S. CDC prevalence estimates, this study gives prevalence estimates for a range of ages. The graph below (and larger here) is part of Figure 1 from the study:

The prevalence is given for children born between 1983 and 2003, with a peak ASD prevalence of about 1.5% for children born between 1990 and 1997. Lower autism prevalence for younger children is likely due to underdiagnosis: the average age of diagnosis being about 8. The lower prevalence for older autistics is possibly due to “key registers used for case ascertainment only being started in 1997 and 2001, respectively, may have deflated the observed ASD prevalence among older children.”

For those interested in how this ties into the failed notion of thimerosal causing an autism epidemic: Swedish children had low exposures to thimerosal in the 1980’s and it was phased out in 1993. The flat prevalence though the 1990’s speaks strongly against the notion. In fact, the data, especially for autistics without intellectual disability, speaks against a strong rise in autism prevalence, especially during the 1990’s.

The prevalence of ASD without intellectual disability is higher than that with ID throughout the entire age range of the study. The Male:Female ratio (not shown in the graph above) changes with age. It is often about 4-5 to 1, with males predominant. For the older individuals, the ratio decreased from 5.1:1 at age 8 to 1.9:1 at age 18.

What’s most important is that this is only the first report on the autistics in the Stockholm Youth Cohort. The researchers now have a cross section of ages to work with to look at outcomes, risk factors and other studies.

Here is the abstract for the study:

Objective
Reports of rising prevalence of autism spectrum disorders (ASD), along with their profound personal and societal burden, emphasize the need of methodologically sound studies to explore their causes and consequences. We here present the design of a large intergenerational resource for ASD research, along with population-based prevalence estimates of ASD and their diagnostic validity.

Method
The Stockholm Youth Cohort is a record-linkage study comprising all individuals aged 0–17 years, ever resident in Stockholm County in 2001–2007 (N = 589,114). ASD cases (N = 5,100) were identified using a multisource approach, involving registers covering all pathways to ASD diagnosis and care, and categorized according to co-morbid intellectual disability. Prospectively recorded information on potential determinants and consequences of ASD were retrieved from national and regional health and administrative registers. Case ascertainment was validated through case-note review, and cross validation with co-existing cases in a national twin study.

Results
The 2007 year prevalence of ASD in all children and young people was 11.5 per 1,000 (95% confidence interval 11.2–11.8), with a co-morbid intellectual disability recorded in 42.6% (41.0–44.2) of cases. We found 96.0% (92.0–98.4) of reviewed case-notes being consistent with a diagnosis of ASD, and confirmed ASD in 85.2% (66.2–95.8) of affected twins.

Conclusions
Findings from this contemporary study accords with recently reported prevalence estimates from Western countries at around 1%, based on valid case ascertainment. The Stockholm Youth Cohort, in light of the availability of extensive information from Sweden’s registers, constitutes an important resource for ASD research. On-going work, including collection of biological samples, will enrich the study further.

A summary of the CDC autism prevalence report

by Matt Carey

There has been a great deal of media coverage recently about the new autism prevalence estimate released by the CDC. The CDC provides a good summary page on prevalence data as well as the full report. If those who may see the report as a bit long, here is a bit of a summary of the findings.

The United States Centers for Disease Control (CDC) releases autism prevalence estimates as part of their MMWR (Morbidity and Mortality Weekly Report). They also maintain a page of information on autism. Today the CDC released the latest MMWR on autism: Prevalence of Autism Spectrum Disorders — Autism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008. The one number from it that will be quoted most often is “1 in 88”, the new prevalence estimate.

The researchers working for the CDC use existing records: school, medical or both. So, in one area they may use school records. In another they may use school and medical records. What they don’t do is actually screen individual children and give them tests like the ADOS. This means that if a kid is not flagged somewhere in the records, they won’t find him/her. On the other hand, they don’t just count which kids already have autism diagnoses. They review the records and evaluate them to determine which kids are autistic or not. They cross check, meaning that for some fraction of the kids they use more than one person to check the records and they see how well the various researchers agree.

The CDC works with groups in a subset of states in the U.S.. For this report they used Alabama, Arizona, Arkansas, Colorado, Florida, Maryland, Missouri, New Jersey, North Carolina, Pennsylvania, South Carolina, Utah, West Virginia, and Wisconsin, most of which were used in previous reports.

Overall, the prevalence was 1 in 88 (11.3 per 1,000). This continues the upward trend in prevalence estimates from the CDC. This figure (here for bigger) is from the CDC:

This varied a great deal state-to-state. Alabama had the lowest estimated prevalence at 4.1 per 1,000. Utah the highest at 21.2 per 1,000. Or, there is about a five fold variation in autism prevalence estimates, state-to-state.

Prevalence estimates also varied by race/ethnicity. The report states “the estimated prevalence among non-Hispanic white children (12.0 per 1,000) was significantly greater than that among non-Hispanic black children (10.2 per 1,000) and Hispanic children (7.9 per 1,000). ” The estimate for Hispanic in Alabama was 1.4 per 1,000 and for whites in Utah as 40 per 1,000. More than a 20 fold difference.

This figure (click to enlarge)was interesting in showing two things. First in showing the state-to-state variability in prevalence estimates. The second interesting point to me is the difference between sites with just medical records and those with medical and education records. The sites with health-only records have lower prevalence estimates. i.e. more kids are picked up by their school records.

As with previous CDC reports, a large fraction of the children identified were not classified as autistic previously. This figure (click to enlarge) shows state-by-state and year-by-year what percent were previously unidentified. The figure also shows how many were previously unidentified but where a suspicion of autism was noted. In 2002, as many as 40% in some states were not classified as autistic before their records were reviewed. In general, over time the fraction previously unidentified has gone down. This would be consistent with schools and medical personnel getting better over time with identification of autism.

Many children identified had IQ test scores (or examiner statements) showing “normal” or borderline-normal values. This figure (click to enlarge) shows the percentages in many states with IQ>85, IQ=71–85 and IQ<70 (for children where the IQ data were available).

In Utah, for an extreme example, over 70% of those identified as autistic have IQ scores above 85. The CDC report reads:

When data from these seven sites were combined, 38% of children with ASDs were classified in the range of intellectual disability (i.e., IQ >70 or an examiner’s statement of intellectual disability), 24% in the borderline range (IQ 71–85), and 38% had IQ scores >85 or an examiner’s statement of average or above-average intellectual ability.

I.e. most children were borderline or above. Of course, the other way to read this is most children were borderline or below. Intellectual Disability is roughly defined as IQ below 70, so most children (about 62%) identified as autistic in this report were not intellectually disabled. States with higher prevalence estimates had higher percentages of non-intellectually disabled children.

The prevalence estimates are going up with time.

While ASD prevalence estimates in the overall population increased 23% for the 2-year period 2006–2008, and 78% during the 6-year period 2002–2008, the largest increases over time were noted among Hispanic children and non-Hispanic black children and among children without co-occurring intellectual disability. Better identification in these specific groups explains only part of the overall increase, however, as estimated ASD prevalence increased in all groups when data were stratified by sex, race/ethnicity, and intellectual ability.

The CDC report does have some limitations, and they note two primary limitations:

First, increases in awareness and access to services have improved the ability of the ADDM Network to identify children with ASD over time, and this likely contributes to the increase in estimated prevalence. The proportion of the increase that is attributable to such changes in case ascertainment or attributable to a true increase in prevalence of ASD symptoms cannot be determined. Ongoing monitoring is an important tool to learn why more children are being identified with ASDs and can provide important clues in the search for risk factors.

This study can’t say if there is an increase in the number of autistic children, or if there is, what would be the cause.

Also,

Second, the surveillance areas were not selected to be representative of the United States as a whole, nor were they selected to be representative of the states in which they are located. Limitations regarding population size, surveillance areas, and the consistency of these attributes were considered when analysts evaluated comparisons across multiple time points.

So, these numbers may not represent the United States as a whole.

It is valid to say that while these factors limit the ability of the CDC to define a true autism rate for the United States, the factors that go into these limitations are valid research concerns in themselves. It is very much worthwhile and valuable to ask why there are such variations state-to-state, for example. Answering this could lead to better identification and service provision overall. Likewise, understanding the effects of rising awareness could feed back into more efficient awareness campaigns to, again, help in identifying more autistic children and providing support and services to them.

The CDC concludes:

ASDs continue to be an important public health concern. The findings provided in this report confirm that prevalence estimates of ASD continue to increase in the majority of ADDM Network communities, and ongoing public health surveillance is needed to quantify and understand these changes over time. Further work is needed to evaluate multiple factors affecting ASD prevalence over time. ADDM Network investigators continue to explore these factors in multiple ways, with a focus on understanding disparities in the identification of ASDs among certain subgroups and evaluating temporal changes in the prevalence of ASDs. CDC also is engaged with other federal, state, and private partners in a coordinated response to identify risk factors for ASDs and meet the needs of persons with ASDs and their families. Additional information is available at http://www.cdc.gov/autism.

We need these data. Limitations and all. We need to know what the autism prevalence is, what the makeup is of the autistic population, and where we can do better identifying autisics. Most countries have no autism prevalence information. Other countries have few or even just one study. In my opinion we are fortunate to have the CDC and other researchers focusing on these questions here in the United States.

%d bloggers like this: