Before I talk about the study I want to provide some background on autism research and twin studies. The question that most behavioral geneticists ask is NOT whether autism is genetic or environmental. There is enough data to show that autism is NOT purely genetic and that autism is NOT purely environmental. The consensus is that autism is a very heterogenous condition that is likely due to multiple genetic and environmental factors.  So real question is what are the relative contributions of the environment, our genes, and other bio-social processes to the development of autism. To this end, behavioral geneticists examine the similarity between monozygotic (MZ) vs. dizygotic (DZ) twins to determine the relative genetic vs. environmental contributions of a specific condition. Specifically, if the correlation within MZ twins in regards to the rate of a disorder is greater than the correlation within DZ twins, then you would assume a significant genetic contribution. Why? MZ twins are genetically identical while DZ are not.  If a disorder has a large genetic contribution, then you would expect those twins that are identical to be more likely to both have the disorder than twins that are not identical. In contrast, in a disorder with little genetic contribution, DZ and MZ twins would be equally likely to share the disorder since the difference in how genetically identical they are would make little difference.

So in this study, the authors examined data from two large twin cohorts from Sweden (N=11,122) and the UK (N= 13,524) who were assessed at age 9 with two different autism scales/interviews. In Sweden the children were assessed with the Autism-Tics, AD/HD, and other Co-morbidities (A-TAC). In the UK, the children were assessed with the Childhood Autism Spectrum Test.

The results:

The graphic below shows the percentage changes in the probability of having a diagnosed ASD by having a father in different age groups.

As you can see, compared to 24-34 year old dads, there was a large increase in the odds of having a child with ASD (almost 100%) for younger dads, a similar increase for dads 35-44, and a very large increase (over 200%) for dads older than 51. However, only the change in fathers >51 in Sweden was statistically significant. The other changes only approached significance, likely because of the low rates of ASD among these cohorts.

As comparison, below you can see the changes in ASD traits for children of fathers in different age groups.

This time, the increase in autism traits for children of young fathers (<25) and older (>50) fathers is statistically significantly when compared to kids whose fathers were 25-34. So this study is consistent with previous research showing an increased risk of ASD among older fathers. However, the study also shows an potential increased risk of ASD for younger fathers as well. There was no effect of maternal age on the risk of ASD.

What about the role of parental age in the relative genetic/environmental contribution to ASD diagnoses?

Below is a graph that presents the correlation within MZ or DZ twins for different paternal age groups.

You will notice that the correlation between the MZ twins is always higher than the correlation between the DZ twins, suggesting some genetic contribution to the disorder. That is, MZ are more likely to BOTH have ASD than DZ twins. However, notice how the difference between the DZ and MZ twins is reduced for the older parents, in both Sweden and the UK. What does this mean? It means that the relative genetic contributions to ASD appear to decrease for older fathers. Now see below the raw correlations for all age groups:

What it is interesting about these data is that the correlation within the MZ increases with the fathers age. For example,  MZ twins of fathers over 40 have an almost 1-to-1 correspondence of the disorder. That is, if one twin had the condition, the other twin almost always had it too. Does this means genetic? Well, at the surface you would think this means genetic, after all both twins are genetically identical and both twins have the disorder. However, remember that MZ were conceived from the same sperm, and in this case, from the same sperm that may be ‘damaged’. So the increase concordance among MZ twins for older dads is not necessarily reflective of a genetic anomaly. In fact, the authors indicated how this effect may be due to the prolonged exposure to environmental toxins among the older fathers leading to sperm mutations. If that hypothesis is correct, it could be the environment, and not the genes, what is responsible for the increase risk in ASD among children of older dads.

The reference:
Lundström, S., Haworth, C., Carlström, E., Gillberg, C., Mill, J., Råstam, M., Hultman, C., Ronald, A., Anckarsäter, H., Plomin, R., Lichtenstein, P., & Reichenberg, A. (2010). Trajectories leading to autism spectrum disorders are affected by paternal age: findings from two nationally representative twin studies Journal of Child Psychology and Psychiatry DOI: 10.1111/j.1469-7610.2010.02223.x

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As simple background, according to the DSM-IV, the basic diagnostic distinction between autism and Asperger’s disorder is absence of clinically significant delays in language, cognitive development, and adaptive functioning in the Asperger’s group. The rest of the diagnostic criteria (impairments in social interactions, restricted repetitive and stereotype patterns of behaviors) between autism and Asperger’s is identical. This makes it difficult to differentiate children with Asperger’s from those with High Functioning Autism (HFA; i.e., those who meet the diagnosis of autism but perform in the average to above average rage in intellectual tests). Therefore, two teens with otherwise identical clinical profiles would be diagnosed differently if they differ on their history of language and cognitive delays. The child with a history of language/cognitive delays would be diagnosed with HFA and the child without a history of language/cognitive delays would be diagnosed with Aspeger’s. I mention this because any discussion about the science of the possible differences between these two categories is limited by the fact that both groups have been selected, by definition, to be different. Thus, the question is not whether these two groups are different – they are different because we have defined them differently. The question is whether these two groups actually represent two distinct typologies that go beyond the distinction of language/cognitive delay vs. no delay.

So what would drive the DSM-V to propose the merger between Asperger’s and Autism? In essence, the questions are 1) whether these two conditions represent two different disorders or are simply variations within a larger spectrum, and 2) whether having two categories, as defined today, is clinically useful. If Asperger’s and Autism are simply the same disorder separated by an arbitrary distinction (language/cognitive delays), having two categories would not help us in our understanding or treatment of the conditions, and keeping them as separate categories may be an obstacle for research because it encourages researchers to focus on a domain that may not be relevant or informative. However, if the language/cognitive delay distinction reflects differences between two truly distinct categories, the existence of two categories rather than one should help us make more effective interventions, inform our clinical decisions, or help us better understand the phenomenology of both conditions. Has this been the case?

Let me address the clinical impact of these two conditions from the perception of clinicians (Note: although I am basing these statements on my experience as a clinician interacting at academic/training settings, I admit that this may not represent the experience and practice of all clinicians). I interact weekly with graduate students who are learning how to conduct neuropsychological evaluations for children and adolescents. Often these students have already developed a schema, or prototype, of the child or adolescent with Asperger’s. They would describe such a child as someone who has intense and unusual interests, maybe superior skills in some area such as music or art, rigidity in behaviors and interests, and social and communication ‘deficits’ leading to difficulties interacting and relating to others. The problems begin when we start seeing actual assessment cases. For example, recently a doctoral intern and I sat in supervision to discuss a case of a teenage boy who could be described as having a “perfect” Asperger’s profile, fitting both the student’s schema and the DSM-IV criteria; except for one thing: the client had a documented history of language delays. There was no question about the diagnosis: If the teen had a history of “language delays’ the diagnosis is autism. My student then asked me, so if this is HFA, how does Asperger’s look like? I replied, just like this.

Therefore, in clinical settings, HFA and Aspeger’s disorder look mostly identical, assuming the clinician follows DSM guidelines. But the most important question is whether the current diagnostic difference is clinically useful.  When debating the Autism vs. Asperger’s diagnostic question, I have always asked my students and supervisors whether the diagnostic difference would change anything regarding our approach to the case. This is the most critical question: would our recommendations or conclusions change based on the final diagnosis that we provide (autism vs. Asperger’s)? The answer is usually, if not always, no. Given identical clinical profiles, the recommendation for treatment, school accommodations, parental interventions, and so forth, would be the same for two adolescents who only differ on the presence or absence of language delays in early childhood. The provision of a diagnosis of autism vs. Asperger’s may lead to different political/personal/social consequences, but clinically, the current DSM-IV distinction between these two conditions, and the research that has come out of this distinction, has not informed or improved our clinical practice (e.g., selection of treatment, assessment, prognosis, etc). This is likely one of the main reasons that led the DSM committee to suggest the merger of Asperger’s and Autism.

But why has the DSM-IV distinction failed to improve clinical services or lead to a greater understanding of these conditions? One possibility is that these two conditions are variations of a greater spectrum and that the language/cognitive delay difference is arbitrary (see for example Bennett et al., 2008 for a study showing identical clinical outcomes between HFA and Asperger’s). In such a case, the merger of the two conditions would better reflect the true nature of the conditions as a variations within a single spectrum. However, another possibility is that the DSM-IV criteria is simply wrong. Under that hypothesis, research has failed to find utility for this classification because of an erroneous diagnostic criteria which led to the incorrect classification of people. Some support for this later position was provided by the research team of Fred Volkmar at the Yale University Child Study Center (Klin et al., 2005). They proposed a new diagnostic criteria for Asperger’s disorder that was more inline Asperger’s original 1944 observation of his cases. Under this system HFA and Asperger’s would differ on 3 specific domains:

1. Nature of social impairments: HFA would be characterized by self-isolation and lack of interest while Aspeger’s would be characterized by interest in social relations and ’seeking others’ (social motivation) but in a socially insensitive or atypical manner.

2. Nature of language impairment: HFA would be characterized by delayed, echolalic and stereotyped language while Asperger’s would be characterized by adequate or precocious language but with difficulties in the use of language (pragmatics).

3. In addition, the Asperger’s diagnosis would include one-sided verbosity and the presence of factual, circumscribed interest that interferes with the person’s functioning (e.g., education and social interactions).

Interestingly, some research has shown differences between HFA and Asperger’s when using the Klin criteria above (see for example Mazefsky and Oswald. 2006). Thus, it is possible that the lack of clinical utility of the current DSM-IV diagnostic distinction between HFA and Asperger’s is due to a lack of validity of the DSM-IV criteria rather than the lack of validity of the constructs of HFA and Asperger’s as two distinct syndromes. So why did the DSM-V committee recommend the merger of these two conditions rather than a redefinition of the Asperger’s criteria? It appears that their interpretation of the totality of the data is that there is no sufficient evidence to validate these two conditions as two separate syndromes regardless of diagnostic criteria used, and that the differences observed are better accounted for by differences in language, IQ, and severity, rather than features of the disorder.

From the DSM-V committee:

Differentiation of autism spectrum disorder from typical development and other “nonspectrum” disorders is done reliably and with validity; while distinctions among disorders have been found to be inconsistent over time, variable across sites and often associated with severity, language level or intelligence rather than features of the disorder.

Update: I just noticed that Dr. Mohammad Ghaziuddin, an accomplished autism and Asperger’s researcher and clinician working at the University of Michigan, just published an opinion piece on the Journal of Autism and Developmental Disorders arguing for a redefinition of Asperger’s rather than its merger with Autism. He argues that the current DSM-IV definition is incorrect and a new updated definition (following the Klin’s criteria outlined above) would be more accurate and clinically useful. He states:

…what is needed is a revision of its criteria taking into account, its quality of social impairment (active but oddrather than aloof and passive); idiosyncratic interests (oftensophisticated and intellectual); communication style (oftenpedantic and verbose); and age of onset/emergence of symptoms (often around 7–8 years). In addition, effortsshould continue to establish its validity not only from autism but also from other conditions.

References:

Klin, A., Pauls, D., Schultz, R., & Volkmar, F. (2005). Three Diagnostic Approaches to Asperger Syndrome: Implications for Research Journal of Autism and Developmental Disorders, 35 (2), 221-234 DOI: 10.1007/s10803-004-2001-y

Bennett, T., Szatmari, P., Bryson, S., Volden, J., Zwaigenbaum, L., Vaccarella, L., et al. (2008). Differentiating Autism and Asperger Syndrome on the Basis of Language Delay or Impairment. Journal of Autism and Developmental Disorders, 38(4), 616-625. doi: 10.1007/s10803-007-0428-7

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1. Prevalence Rates and Home Schooling.

I received a thoughtful email about the impact of home schooling on the CDC prevalence rate and autism research in general, given that many children with ASDs may be home schooled. Here is my response:

Regarding the CDC:
The prevalence was obtained from health records and, in some States, also educational records. States that used educational records had higher prevalence rates, and those records only included public school records. So theoretically, the prevalence would be even higher once home/private school cases are added. While education records may have included some children in private/home schools (many children in home school still receive special education services in some States and would therefore be identified by the CDC teams), many cases are likely being missed.

Interestingly however, the new CDC numbers are in line with the national autism prevalence study published in Pediatrics. This study was not based on educational or health records reviews, but instead it was based on detailed phone screenings of a representative sample of US families. Both of these studies however, would miss some children with ASD that are undiagnosed (and maybe home schooled) due to limited contact with health workers (pediatricians, etc). These children would not have any records showing that they have ASD symptoms and these parents would also respond ‘no’ to the basic phone screening question “have your child ever been diagnosed with an autism spectrum disorder?”

Regarding Research in General:
Fortunately, most research on autism is not conducted via the school systems. Most research is conducted at medical and university centers with families recruited from the community. In my neuropsychology assessment experience, I would say that at least 30% of the ASD kids we see are home schooled, and many of these children are active participants in our research programs. So the news is a bit better for general research, in that it is unlikely that home schooled kids are underrepresented in those studies.

2. Vaccines.

I really dislike writing anything about vaccines, mostly because regardless of how factual I aimed to be, any mention of vaccines is usually followed by a dozen of  ’friendly’ emails. But I’ve received several emails asking how the CDC numbers affect the vaccine theory. The CDC study does not address this issue at all, and the data say little about this theory. However, some reasonable conclusions can be made.

- If the increases in diagnoses among 8 year olds from 2002 to 2006 are due to real increases in true prevalence

and

- If vaccines play a role in the incidence of autism

- Then a 50% increase in the prevalence during the 4 year period should be accompanied by a noticeable change in vaccination practices during key years.

Specifically, the 2002 CDC  study was based on children born in 1994 and the new CDC study was done with children born in 1998.  Thus, given the striking increases in prevalence rates among the 1998 children, you would expect that compared to those born in 1994, children born in 1998 received higher vaccination dosages, received more harmful dosages, or simply were vaccinated at a higher rate. I have some data on vaccination rates:

I took a look at the CDC vaccination rates for MMR for those born in 1994 and 1998 by the time they were 2 years of age. You can take a look at the data here. The National vaccination rate for MMR for those born in 1994 was 90%. For those born in 1998, the vaccination rate was also 90%. For the states included in the CDC autism study, the vaccination rate for those born in 1994 was 90% and for those born in 1998 was also 90%. At the State and National level, there were no changes in vaccination rates for kids born in 1994 and 1998 that could help explain the 50% jump in autism prevalence.

12/28/09 UPDATE: Please note that in the paragraphs above I presented a simple logical argument for the vaccines debate. If vaccines played a role in the 1994 to 1998 autism rate change, then there must be a change in vaccination practices between 1994 and 1998-2000. Potential changes may have involved higher vaccination rates, changes in vaccine cocktails or contents, changes in schedules, etc etc. I then provided data for vaccination rates for one single vaccine as an example: MMR. Clearly such data are very limited and does not cover all possible changes that may have taken place during that time.

3. What’s up with Missouri?

Missouri had the highest autism rates of all states assessed (albeit it was a tie with Arizona in many measures), with rates that were often more than twice that of other States. One of my readers asked whether this was due to demographic differences in the target counties in Missouri. For example, is it possible that the data from Missouri came mostly from urban St Louis with a higher proportion of ethnic minorities or lower SES families? The data does not seem to support this theory. The Missouri sample was close to 70% white, and other States with significantly higher % of ethnic minorities in urban settings had significantly lower autism rates (e.g., Colorado – all from metro Denver with only 55% white; Florida – all from Miami with only 23% white; Georgia – all from metro Atlanta with only 38% white). We do not know why the high prevalence of autism in Missouri and Arizona, but it is very unlikely that it is due to demographic differences between these States and the other States included in the study.

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The report is based on the findings by the CDC Autism and Developmental Disabilities Monitoring Network. This network consists of a series of sites across the united states that calculate the rates of autism diagnoses for specific communities. The network first provided autism estimates based on data obtained in 2000 and then 2002. Last week’s report is based on data obtained in 2006. I have previously reviewed how the CDC prevalence rates for autism are obtained, so I will focus this post on highlighting some across state variability and differences between the 2002 and 2006 results.

In sum, the 2006 data came from 11 states (Alabama, Arizona, Colorado, Florida, Georgia, Maryland, Missouri, North Carolina, Pennsylvania, South Carolina, and Wisconsin). Teams at these sites reviewed the records of 8-year-old children living in specific communities. The teams reviewed medical/health and educational records for evidence of a probable autism diagnosis (education records were only monitored in 6 of the 11 states). When probable cases were identified, the records were then reviewed by clinicians to provide a final diagnosis based on DSM-IV criteria. The total number of ASD cases was then compared to the population of 8-year-olds for each target community.

The average ASD estimate across all sites was 9 per 1,000 children (1 in 111 children), but there was significant variability between the states:

Alabama: 1 in 166
Arizona: 1 in 82
Colorado: 1 in 133
Florida: 1 in 238
Georgia: 1 in 98
Maryland: 1 in 108
Missouri: 1 in 82
North Carolina: 1 in 96
Pennsylvania: 1 in 119
South Carolina: 1 in 116
Wisconsin: 1 in 131

Those sites that included a review of educational records had higher prevalence than those that relied only on health records:
Sites that included health and educational records: 1 in 98 children
Sites that included only health records: 1 in 133 children

Prevalence for boys alone:
Alabama: 1 in 110
Arizona: 1 in 53
Colorado: 1 in 87
Florida: 1 in 137
Georgia: 1 in 60
Maryland: 1 in 64
Missouri: 1 in 52
North Carolina: 1 in 59
Pennsylvania: 1 in 89
South Carolina: 1 in 70
Wisconsin: 1 in 79

The picture is much better for girls.

Prevalence for girls alone:
Alabama: 1 in 345
Arizona: 1 in 204
Colorado: 1 in 294
Florida: 1 in 1000
Georgia: 1 in 294
Maryland: 1 in 417
Missouri: 1 in 213
North Carolina: 1 in 294
Pennsylvania: 1 in 303
South Carolina: 1 in 385
Wisconsin: 1 in 435

Increases in ASD diagnoses from 2002 to 2006 among 8-year-old children:

Alabama: 82%
Arizona: 95%
Colorado: 27% (not statistically significant)
Florida: No 2002 data
Georgia: 34%
Maryland: 37%
Missouri: 66%
North Carolina: 60%
Pennsylvania: 58%
South Carolina: 43%
Wisconsin: 46%
AVERAGE: 57% increase.

A few last things to keep in mind:

- The report indicated that increases in prevalence was NOT due to increases in children diagnosed with PPD-NOS. That is, they found increases in the use of pure autism diagnoses too.
- The same diagnostic criteria was used in 2002 and 2006. The changes are NOT due to differences in diagnostic criteria.
- The report was not based on a nationally representative sample.
- Within State variability is so great that it is very likely that fluctuations in prevalence between states are due to methodological differences.
- HOWEVER, significant increases were also observed between sites that did not have changes in methodological procedures between 2002 and 2006.
- Thus, the increases from 2002 to 2006 are unlikely to be due to methodological differences
- There were no major changes from 2000 to 2002, which highlights the significance of the changes in diagnoses from 2002 to 2006.
- The study does not answer the question of “why”. We simply do not know why the prevalence rate of autism increased from 2002 to 2006.
- The new CDC estimates as more in line with a recent nation-wide autism prevalence study published in pediatrics.

The study concludes:

More children than ever before are receiving services for ASDs and are having symptoms of ASDs documented in developmental evaluation records. Even without fully understanding the complex causes of this increase in identified ASD prevalence, the impact on affected children, families, and communities is substantial. Prevalence estimates can be used to plan policy, educational, and intervention services needs for persons with ASDs. In addition to continued evaluation of ASD prevalence changes, major collaborative efforts are needed to improve research into what factors put certain people at risk and how to intervene to help reduce the debilitating symptoms of ASDs. Concerted efforts are essential to address the many needs of affected persons and to provide coordinated support services which improve daily functioning and long-term life outcomes
-

Kogan, M., Blumberg, S., Schieve, L., Boyle, C., Perrin, J., Ghandour, R., Singh, G., Strickland, B., Trevathan, E., & van Dyck, P. (2009). Prevalence of Parent-Reported Diagnosis of Autism Spectrum Disorder Among Children in the US, 2007 PEDIATRICS, 124 (5), 1395-1403 DOI: 10.1542/peds.2009-1522

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The study included 216 mothers of children with autism (age 6 to 17, mean age 11). Eighty eight percent (n=190) of the children were boys, and 21% of the mothers were single parents.  All children were diagnosed based on DSM-IV criteria by a multidisciplinary team. After mothers the enrolled in the study, they were asked to complete a series of questionnaires and a 24-hour activity diary during a typical weekday or weekend day. Most of them (70%) completed the diary on a weekday.

The authors were interested in exploring some specific questions. First, they wanted to know the overall rate of distress among these mothers. To this end, the mothers completed a measure of general psychological health as well as a measure of depression symptoms. The authors were also interested in knowing whether psychological health and depression in the mothers was associated with 1) how much social support they received, 2) the level of behavior problems on the part of the child, 3) how much time the mother spent in care giving activities; and 4) how much time pressure was experienced by the mothers.  All of the variables were measured by a self-report questionnaire except for the total amount of time spent in care giving activities, which was determined based on the 24-hour diary completed by the mothers.

The results:

General Findings:

1. On average, mothers reported spending 6 hours per day caring for their children, and as expected, this was associated with the age of the child; those with older children spent less time than those with younger children. Also as expected, total hours was associated with severity of autism (the more behavior problems the more hours of time care required).

2. Fifty five percent (55%) of mothers scored in the distress range for a general metal health questionnaire, and 48% scored above the healthy cutoff on a depression scale.

What contributed to maternal mental health problems?

General Maternal Mental Health:

1. Surprisingly, severity of behavior problems did not contribute to maternal mental health problems.

2. Social support was associated with lover levels of mental health problems.

3. While controlling for support and behavioral problems, time pressure, but not total time, was associated with higher levels of mental health problems.

Maternal Depressive Symptoms:

1. Social support was also associated with lover levels of depressive symptoms.

2. Severity of autism (e.g., more behavior problems) was associated with higher rates of depressive symptoms.

3. While controlling for support and behavioral problems, time pressure, but not total time, was associated with higher levels of depressive symptoms.

It was not surprising at all that social support was a major protective factor for mothers. The more mothers felt they received social support, the less depressive symptoms they endorsed. It was also not surprising that having more challenging children (with more behavior problems) experience higher symptoms of depression, but it was a bit surprising that this was not the case for other mental health symptoms. What was really surprising is that it was time pressure, but not total time, that contributed to mental health problems including depressive symptoms. That is, it didn’t matter how many hours the mother spent caring for the child – this was not a contributing factor to maternal distress. What was important is how much time crunch the mothers felt. This raises a very interesting issue: it is not uncommon for mothers of children with disabilities, including mothers of children with autism, to have very structured and “packed” days that require the juggling of very busy schedules (coordinating services, schools, playgroups, etc., etc.). The results suggest that it may not be how “packed” the day is that may contribute to stress, but whether the schedules and activities are designed in a way that creates ‘time pressure,’ or a general sense of ‘not having enough time’ on the mother. However, there are a couple of issues that should be noted.  First, as with any correlational study, it is impossible to tell for sure ‘what causes what’. For example, it’s possible that those mothers with more mental health difficulties also ‘perceived’ themselves as having more time pressure whether true or not. In such a case, it is not the increased time pressure that is causing distress, but distress may be causing the ‘perception’ of increased time pressure. It would have been interesting if the authors had done a time pressure analysis of the 24-hour diary. Second, there are a number of variables that could also be at play. For example, the number of siblings in the family was not included in the analysis. It is possible that those mothers that were taking care of more children also felt the most time pressure and thus had higher distress.
Sawyer, M., Bittman, M., La Greca, A., Crettenden, A., Harchak, T., & Martin, J. (2009). Time Demands of Caring for Children with Autism: What are the Implications for Maternal Mental Health? Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-009-0912-3

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Early intervention for toddlers with autism
highly effective, study finds
Significant gains seen in IQ, communication and social interaction

(Seattle, Nov. 30, 2009) – A novel early intervention program for very young children with autism – some as young as 18 months – is effective for improving IQ, language ability, and social interaction, a comprehensive new study has found.

“This is the first controlled study of an intensive early intervention that is appropriate for children with autism who are less than 2½ years of age. Given that the American Academy of Pediatrics recommends that all 18- and 24-month-old children be screened for autism, it is crucial that we can offer parents effective therapies for children in this age range,” said Geraldine Dawson, Ph.D., chief science officer of Autism Speaks and the study’s lead author. “By starting as soon as the toddler is diagnosed, we hope to maximize the positive impact of the intervention.”

The study, published online today in the journal Pediatrics, examined an intervention called the Early Start Denver Model, which combines applied behavioral analysis (ABA) teaching methods with developmental ‘relationship-based’ approaches. This approach was novel because it blended the rigor of ABA with play-based routines that focused on building a relationship with the child. While the youngest children in the study were 18 months old, the intervention is designed to be appropriate for children with autism as young as 12 months of age. Although previous studies have found that early intervention can be helpful for preschool-aged children, interventions for children who are toddlers are just now being tested. Autism is a lifelong neurodevelopmental disorder characterized by repetitive behaviors and impairment in verbal communication and social interaction. It is reported to affect one in 100 children in the United States.

“Infant brains are quite malleable so with this therapy we’re trying to capitalize on the potential of learning that an infant brain has in order to limit autism’s deleterious effects, to help children lead better lives,” said Sally Rogers, a professor of psychiatry and behavioral sciences, a study co-author and a researcher at the UC Davis MIND Institute in Sacramento, Calif. Rogers and Dawson developed the intervention.

The five-year study took place at the University of Washington (UW) in Seattle and was led by Dawson, then a professor of psychology and director of the university’s Autism Center, in partnership with Rogers. It involved therapy for 48 diverse, 18- to 30-month-old children with autism and no other health problems. Milani Smith, who oversees the UW Autism Center’s clinical programs, provided day-to-day oversight.

The children were separated into two groups, one that received 20 hours a week of the intervention – two two-hour sessions five days a week – from UW specialists. They also received five hours a week of parent-delivered therapy. Children in the second group were referred to community-based programs for therapy. Both groups’ progress was monitored by UW researchers. At the beginning of the study there was no substantial difference in functioning between the two groups.

At the conclusion of the study, the IQs of the children in the intervention group had improved by an average of approximately 18 points, compared to a little more than four points in the comparison group. The intervention group also had a nearly 18-point improvement in receptive language (listening and understanding) compared to approximately 10 points in the comparison group. Seven of the children in the intervention group had enough improvement in overall skills to warrant a change in diagnosis from autism to the milder condition known as ‘pervasive developmental disorder not otherwise specified,’ or PDD-NOS. Only one child in the community-based intervention group had an improved diagnosis.

“We believe that the ESDM group made much more progress because it involved carefully structured teaching and a relationship-based approach to learning with many, many learning opportunities embedded in the play,” Rogers said.

“Parental involvement and use of these strategies at home during routine and daily activities are likely important ingredients of the success of the outcomes and their child’s progress. The study strongly affirms the positive outcomes of early intervention and the need for the earliest possible start,” Dawson said.

In this study, the intervention was provided in a toddler’s natural environment (their home) and delivered by trained therapists and parents who received instruction and training as part of the model.

“Parents and therapists both carried out the intervention toward individualized goals for each child, and worked collaboratively to improve how the children were responding socially, playing with toys, and communicating,” said Milani Smith, associate director of the UW Autism Center and a study co-author. “Parents are taught strategies for capturing their children’s attention and promoting communication. By using these strategies throughout the day, the children were offered many opportunities to learn to interact with others.”

Other study authors include Jeffrey Munson, Jamie Winter, Jessica Greenson, and Jennifer Varley, all of UW Autism Center or the department of psychiatry and behavioral sciences, and Amy Donaldson of the department of speech and hearing science, Portland State University, Portland, Ore.

The study was funded by a grant from the National Institute of Mental Health (NIMH). NIMH has also funded a multi-site trial of the Early Start Denver Model which is currently being conducted at the University of Washington, the UC Davis MIND Institute and the University of Michigan.

About Autism Speaks
Autism Speaks is the nation’s largest autism science and advocacy organization, dedicated to funding research into the causes, prevention, treatments and a cure for autism; increasing awareness of autism spectrum disorders; and advocating for the needs of individuals with autism and their families. Autism Speaks funds more than $30 million each year in new autism research, in addition to supporting the Autism Treatment Network, Autism Genetic Resource Exchange, Autism Clinical Trials Network, Autism Tissue Program and a range of other scientific and medical programs. To learn more about Autism Speaks, please visit www.autismspeaks.org.

About UC Davis MIND Institute
The UC Davis MIND (Medical Investigation of Neurodevelopmental Disorders) Institute, in Sacramento, Calif., was founded in 1998 as a unique interdisciplinary research center where parents, community leaders, researchers, clinicians and volunteers work together toward a common goal: researching causes, treatments and eventual preventions and cures for neurodevelopmental disorders. In addition to autism, the MIND Institute has major research efforts in fragile X syndrome, Tourette syndrome, chromosome 22q11.2 deletion syndrome and ADHD. More information about the institute is available on the web at http://www.mindinstitute.org.

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In the latest issue of the journal of the American Academy of Pediatrics there is a new epidemiological examination of the association between prematurity and autism that highlights the point I was making to my students. There are a number of studies that have found a link between prematurity and autism, in that premature babies are at greater risk for developing autism than full term babies. However, other studies have failed to replicate such findings. What could explain such discrepancy? One possibility is that it is not about being premature that increases the risk for autism, but instead such increased risk is due to complications and other factors associated with prematurity. If these variables have not been controlled similarly across studies then you will find studies providing conflicting results due to unknown, or uncontrolled, characteristics of the sample.

In the study published in Pediatrics, a Swedish team of researchers examined a population sample of 1,216 individuals with a autism spectrum diagnosis and over 6,000 non affected peers. These groups were similar in sex distribution, age, and most importantly: birth hospital. This is key, because it theoretically eliminates the possibility that any differences found between the groups are due to differences in the health services received during birth.  The authors first compared the risk for autism spectrum disorders associated with prematurity, and then examined whether specific neonatal complication factors could explain the observed risk.

Main finding:

1. Being born at or before 31 weeks of gestation doubled the risk for developing an autism disorder  when compared to full term infants.
2. Being born between 32 and 36 weeks of gestation increased the risk for an autism disorder by 55%.

However:

After adjusting (controlling) for neonatal complications and related factors, being born prematurely – even before 32 week gestation – DID NOT increase the risk of developing autism. That is, prematurity itself was not associated with autism once we take into account specific complications that are common among premature infants.

So what are the neonatal factors associated with autism risk?

While controlling for all neonatal and related factors:

1. Low weight for gestational age was a risk factor for autism. Specifically, being small for gestational age increased the risk for developing autism by 86%.
2. Having congenital malformations increased the risk by 106%
3. Having intra-cranial bleeding, edema, or seizures increased the risk by 206%
4. Having Hypoglycemia increased the risk for ASD by 120%

The following neonatal factors were NOT associated with an increased risk for autism: Jaundice, respiratory distress, infections, head and neck injuries during delivery, apgar score, being a twin, or being large for gestational age.

The results are consistent with other findings suggesting that “weight for gestational age” and related complications are more informative when estimating the probability of future neurodevelopmental disorders than simply being premature. This may also help explain why many premature babies don’t show any lasting effects, even when they are born severely premature (<32 weeks), while others have significant developmental complications  (autism, ADHD, learning disabilities) even when they are born only a few weeks premature.

Buchmayer, S., Johansson, S., Johansson, A., Hultman, C., Sparen, P., & Cnattingius, S. (2009). Can Association Between Preterm Birth and Autism be Explained by Maternal or Neonatal Morbidity? PEDIATRICS, 124 (5) DOI: 10.1542/peds.2008-3582

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I thought it would be appropriate to start the month with some thoughts about the new Autism prevalence study that was just published in Pediatrics and which hit the news cycle this morning.

In sum, the study found that the prevalence of parent-reported rates of autism was higher than previous estimates. Specifically, they found that 1.1% of all children aged 3 to 17 had autism (1 in 91) as compared to previous estimates of approximately 1 in 150.

Does this suggest an increase in autism rates between 2003 and 2007? Maybe, in that the finding is consistent with the possibility that autism rates are increasing. However, there are a number of issues that must be taken into account, especially the methodological differences between this study and the CDC study that reported prevalence rates for 2002.

For a more detailed explanation of the CDC report from where the 1 in 150 rate comes from read my previous post on the topic: Autism rates in the USA: where did the 1 in 150 number came from?

How was the current study conducted?
The study include an analysis of the 2007 National Survey of Children’s Health (NSCH). The NSCH consists of a random and representative telephone survey of parents of 78,037 children. Each family provided data on a single child, so that if a family had more than one child, only one of the children was selected as the target child for interview purposes. During the interview the parents were asked whether “they had ever been told by a doctor or other health care provider that their child had ‘autism, Asperger’s disorder, pervasive developmental disorder, or other autism spectrum disorder.’ “ If the parent said yes, then they were asked whether the child currently had autism or another ASD.

They found a prevalence of 110 cases of current ASDs per 10,000 children, or 1 in 91 children. As expected rates were 4 times higher in boys than in girls.

There are two key differences between this study and the previous CDC report:

1. Unlike the CDC report, this study was nation-wide and included all US regions. In contrast, the CDC report was based on autism rates observed in only 15 states. Therefore, the current report appears to be more representative of the US population than the previous study.
2. The current study was based on parental reports during a phone survey with no corroboration of the diagnoses endorsed by the parents. That is, we assume that parents are correct when asked… does your child current have Autism or ASD? The authors could not verify the veracity of these parental reports through review of medical or educational records. In contrast, the CDC report was based on a review of medical and educational records, and documented diagnoses were doubled checked by trained clinicians to make sure that there was sufficient evidence for each diagnosis. Therefore, the CDC study was much more conservative in estimating autism rates because diagnoses were obtained from medical and educational records rather than from parental reports.

Clearly, neither the CDC nor the current Pediatric study is near perfect, and they provide only a rough estimation of autism cases in the US. Unfortunately, the very significant differences in methodology between these two studies make it impossible to determine with certainty whether the new rate of 1 in 91 reflects a true increase in autism or is simply a byproduct of different estimation procedures not used in previous studies.

The reference: Michael D. Kogan, PhD,a Stephen J. Blumberg,, PhD,b Laura A. Schieve, PhD,c Coleen A. Boyle, PhD,c, James M. Perrin, MD,, Reem M. Ghandour, DrPH,, Gopal K. Singh, PhD,, Bonnie B. Strickland, PhD,, Edwin Trevathan, MD, MPH,, & Peter C. van Dyck, MD, MPH (2009). Prevalence of Parent-Reported Diagnosis of Autism Spectrum Disorder Among Children in the US, 2007 Pediatrics, 124 (4) : 10.1542/peds.2009-1522

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The authors examined 194 preschool children, including 68 kids with Autism, 57 with non-autism developmental delays, and 69 typically developing. Sleep problems were measured via parents reports but also through the use of actigraphs. These are very sensitive motion sensors that are attached to the child’s leg and are able to reliably identify when the child falls asleep or wakes up during the night. This allows the researcher to determine a number of specific sleep indicators, such as total sleep hours during the day, sleep efficiency (total hours of actual sleep while in bed), sleep latency (how long does it take the child to fall asleep), and wake after sleep duration (total minutes awake after initially falling asleep).

In sum, the authors failed to find support the proposed hypothesis (that sleep problems lead to sleepiness and this is leads to behavioral disruptions) but there were a number very interesting findings regarding discrepancies between parental reports and the actigraphs.

Parental perception of whether his/her child had a sleep problem was not associated with total sleep hours throughout the day/night, efficiency of sleep, or latency of sleep onset as recorded by the motion sensors. Parental report was only associated with ‘wake after sleep’ duration. These findings suggest that parents perception of sleep problem may be reflecting only one aspect of sleep dysregulation, in that parents may be more sensitive (or reactive) to the kids’ waking throughout the night than to other potential problems like reduced total sleep or difficulties falling asleep.

The actigraphs did not fully support the common finding that children with special needs, and specifically kids with autism, have significantly more sleep problems than typically developing kids. That is, the 3 groups of kids (autism, non-autism developmental delays, and typical) did not differ in sleep efficiency, sleep latency, and wake after sleep. Instead, total sleep hours was the only significant difference between these kids.

These findings made me think about the reliability of methods that assess child sleep difficulties based solely on parental report, and the validity of long held beliefs regarding sleep problems when such beliefs respond mostly to parental perceptions of the kids sleep difficulties. The concern is not necessarily that parental reports may be unreliable, but that they may be limited in that they can reflect only one aspect of the potential problems with the sleep cycle. These findings also suggest that clinicians should take a comprehensive approach when interviewing parents about their kids sleep problems.

Goodlin-Jones, B., Tang, K., Liu, J., & Anders, T. (2009). Sleep problems, sleepiness and daytime behavior in preschool-age children Journal of Child Psychology and Psychiatry DOI: 10.1111/j.1469-7610.2009.02110.x

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In order to evaluate the effectiveness of PDAs in autism, the authors taught 3  adolescents with ASD diagnoses to use the PDA to provide self-prompts while completing 3 cooking recipes (hamburger helper, individual sized pizza, and a ham & swiss sandwich). The 3 adolescents were selected because they met a specific inclusion criteria, which included having good fine motor skills (allowing them to manipulate the PDA), having good visual and auditory acuity, and having the cognitive skills necessary to recognize picture prompts. The adolescents were provided with a Cyrano Communication device programmed to provide picture, voice, and video prompts for each step of the cooking process.

The experiment used a multiple probe design that included the following steps for each recipe:

1. Pre-training to learn how to use the PDA
2. Cooking recipe without the PDA
3. Cooking recipe with PDA

The following figure is the results from one of the participants. Notice how the percent correct reached 100 when the PDA was used.

The authors concluded that the devices resulted in a noticeable improvement in performance for all three participants.  The results suggest that PDAs may be very effective in helping persons with ASDs successfully complete tasks, such as those required at educational and/or work setting. However, this study was conducted with 3 individuals only. More research with much larger sample sizes is necessary to help us better understand if, and in what conditions, these devices may  be effective.

The reference: Mechling, L., Gast, D., & Seid, N. (2009). Using a Personal Digital Assistant to Increase Independent Task Completion by Students with Autism Spectrum Disorder Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-009-0761-0

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