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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The study was conducted by a team of researchers at the University of Cincinnati and published in Archives of Pediatrics. The researchers examined the results of the “ADHD Collaborative” — a initiative in the greater Cincinnati region aimed to improve the adherence of ADHD treatment guidelines by local community pediatricians. The study included 785 children between 7-11 years of age treated by 158 community physicians from 47 separate practices in greater Cincinnati. The children were evaluated when they first started treatment and 3, 6, 9, and 12 months later. The researchers were interested in examining the effectiveness of the treatment in reducing ADHD symptoms and reducing functional impairment (i.e., how the children did at school, home, and with their peers).

The initial results were very promising.

As you can see above, the treatment resulted in a rapid and dramatic decrease in ADHD symptoms as reported by both parents and teacher. This is consistent with most research on the effectiveness of ADHD medications: they are very effective in reducing ADHD symptoms.

On first impression, the picture became less rosy, or better said, outright gloomy, when the authors examined the effects of the treatment on functional impairment. See for example the effect of the treatment on academic difficulties in reading, math, writing, and overall school performance:

The number of children experiencing impairment in reading, math, writing, and overall in school remained virtually identical during the entire year of the study. This suggests that the intervention did not have much of an impact on the kids’ school functioning. The same pattern of results was observed for the kids’ peer, sibling, and family relations.

Does this mean that medication for ADHD is simply ineffective? Not really. What it means is that medication alone may not have a significant impact on the child’s functional domains.  It is possible that medication provides the foundation for additional interventions to work. However, there is a more relevant limitation of this study. If you compare the two graphs above, you will see that the Y axis of the first graph refers to symptoms of ADHD. In contrast, the Y axis of the second graph refers to the % of children showing impairment in those categories. Why is this significant? Because the first graph allows us to observe changes in a continuous scale of symptoms (we would see even a small change in symptoms). However, the graph below does not allow us to see the effect of the medication on the LEVEL of impairment of the children. That is, we only see that the percentage of children who show impairment did not change, but it is still possible that these children improved significantly!- albeit still performing in the impaired range. So although it is disappointing that the treatment alone did not result in a reduction of the percentage of children who had functional impairment, the picture may not be a gloomy as we think.

The reference:
Epstein, J., Langberg, J., Lichtenstein, P., Altaye, M., Brinkman, W., House, K., & Stark, L. (2010). Attention-Deficit/Hyperactivity Disorder Outcomes for Children Treated in Community-Based Pediatric Settings Archives of Pediatrics and Adolescent Medicine, 164 (2), 160-165 DOI: 10.1001/archpediatrics.2009.263

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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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Let me start by explaining that the creation of TDD does NOT deny the existence of classic bipolar disorder in childhood. That is, although extremely rare, bipolar disorder can occur in children and adolescents, and it looks very much like adult bipolar.  Instead, TDD was created to capture a valid syndrome with characteristics and outcomes that are different than those of bipolar disorder. The available scientific data supports the position that the TDD syndrome is NOT simply the manifestation of bipolar disorder in childhood. This means that thousands of children that have been diagnosed with childhood bipolar disorder may not have bipolar and instead have a completely different syndrome now called Temper Dysregulation Disorder with Dysphoria.

So what is TDD?

Here is the proposed criteria for TDD:

A. The disorder is characterized by severe recurrent temper outbursts in response to common stressors.

1.  The temper outbursts are manifest verbally and/or behaviorally, such as in the form of verbal rages, or physical aggression towards people or property.

2.  The reaction is grossly out of proportion in intensity or duration to the situation or provocation.

3.  The responses are inconsistent with developmental level.

B. Frequency: The temper outbursts occur, on average, three or more times per week.

C. Mood between temper outbursts:

1.  Nearly every day, the mood between temper outbursts is persistently negative (irritable, angry, and/or sad).

2.  The negative mood is observable by others (e.g., parents, teachers, peers).

D. Duration: Criteria A-C have been present for at least 12 months.  Throughout that time, the person has never been without the symptoms of Criteria A-C for more than 3 months at a time.

E. The temper outbursts and/or negative mood are present in at least two settings (at home, at school, or with peers) and must be severe in at least in one setting.

F.  Chronological age is at least 6 years (or equivalent developmental level).

G. The onset is before age 10 years.

H. In the past year, there has never been a distinct period lasting more than one day during which abnormally elevated or expansive mood was present most of the day for most days, and the abnormally elevated or expansive mood was accompanied by the onset, or worsening, of three of the “B” criteria of mania (i.e., grandiosity or inflated self esteem, decreased need for sleep, pressured speech, flight of ideas, distractibility, increase in goal directed activity, or excessive involvement in activities with a high potential for painful consequences; see pp. XX). Abnormally elevated mood should be differentiated from developmentally appropriate mood elevation, such as occurs in the context of a highly positive event or its anticipation.

I.  The behaviors do not occur exclusively during the course of a Psychotic or Mood Disorder (e.g., Major Depressive Disorder, Dysthymic Disorder, Bipolar Disorder) and are not better accounted for by another mental disorder (e.g., Pervasive Developmental Disorder, post-traumatic stress disorder, separation anxiety disorder). (Note: This diagnosis can co-exist with Oppositional Defiant Disorder, ADHD, Conduct Disorder, and Substance Use Disorders.) The symptoms are not due to the direct physiological effects of a drug of abuse, or to a general medical or neurological condition.

The syndrome captured by section A-C (frequent and intense temper outbursts, happening several times per week in the context of negative emotionality) is the core of the symptoms that has been incorrectly interpreted as indicative of childhood bipolar disorder.  Section H is very interesting. It states that this diagnosis is not appropriate if the person has experienced classic mania (e.g., bnormally elevated or expansive mood), as in such a case the diagnosis of bipolar is likely more accurate.

So why did the DSM-V decide that this syndrome is not simply bipolar disorder of childhood?

1. Lack of continuity to bipolar.

If TDD is simply the expression of bipolar disorder during childhood, then children diagnosed with this condition would eventually develop symptoms of classic bipolar disorder as they reach adulthood. The data do not support this hypothesis. That is, children who display the TDD syndrome in childhood (and are often incorrectly diagnosed as bipolar) are not more likely to develop classic bipolar disorder later in life as their peers (see Brotman et al., 2006; Leibenluft et al, 2006; Stringaris et al, 2009).  Instead, these children are more likely to develop depression, not bipolar!

2. Different Biological Markets.

Youth who are diagnosed with classic bipolar differ significantly from those who have a TDD-like syndrome (see Brotman et al, 2010; Guyer et al, 2007; Rich et al, 2008).  If TDD is simply bipolar, then the biomarkers of TDD should be similar to those of bipolar, but this is not the case.

3. Different Demographic Factors.

If TDD is simply bipolar, then the gender distribution of TDD should be similar to that of bipolar. This does not appear to be the case. Specifically, there is no gender differences in the rate of classic bipolar; male and females are equally likely to develop the condition. However, the TDD-like syndrome is disproportionately observed in boys rather than girls.

4. A need for a new category that would impact treatment and research.

In theory, the presence of TDD will educate clinicians, researchers, and the public that this syndrome is not simply a version of bipolar disorder. This would facilitate research on the causes, features, and treatments for this condition. This has major implications for treatment. For example, the standard treatment for bipolar disorder does NOT seem to work in children that have the TDD syndrome (Dickstein et al, 2009). By explicitly stating that TDD is not bipolar, researchers would be less likely to approach the search for treatments from a “bipolar framework”, which would potentially facilitate the discovery of more effective interventions.

I am actually glad about this change as it will have a clear impact on clinical practice and research that will most likely benefit the children affected with this condition.

References:

Brotman MA, Schmajuk M, Rich BA, Dickstein DP, Guyer AE, Costello EJ, Egger HL, Angold A, Pine DS, & Leibenluft E (2006). Prevalence, clinical correlates, and longitudinal course of severe mood dysregulation in children. Biological psychiatry, 60 (9), 991-7 PMID: 17056393

Dickstein DP, Towbin KE, Van Der Veen JW, Rich BA, Brotman MA, Knopf L, Onelio L, Pine DS, Leibenluft E (2009): Randomized double-blind placebo-controlled trial of lithium in youth with severe mood dysregulation. J Child Adolesc Psychopharm 19: 61-73

Guyer AE, McClure EB, Adler AD, Brotman MA, Rich BA, Kimes AS, Pine DS, Ernst M, Leibenluft E (2007): Specificity of face emotion labeling deficits in childhood psychopathology. Journal of Child Psychiatry and Psychology, 48:863-71

Leibenluft E, Charney DS, Towbin KE, Bhangoo RK, Pine DS (2003): Defining clinical phenotypes of juvenile mania. Am J Psychiatry 160: 430-437

Rich BA, Grimley ME, Schmajuk M, Blair KS, Blair RJR, Leibenluft E (2008): Face emotion labeling deficits in children with bipolar disorder and severe mood dysregulation. Development and Psychopathology 20: 529-546

Stringaris A, Cohen P, Pine DS, Leibenluft E (2009): Adult outcomes of adolescent irritabilty: A 20-year community follow-up. Am J Psychiatry 166: 1048-54

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The current issue of the journal Pediatrics includes a study examined the prevalence and correlates of internalizing symptoms among Children with Special Health Care Needs (CSHCN). CSHCN are children “ who have or are at increased risk for a chronic physical, developmental, behavioral, or emotional condition and who require health and related services of a type or amount beyond those required by children generally.” This includes close to 14% of all US children. The authors examined data obtained from the National Survey of Children with Special Health Care Needs conducted in 2005-2006.  This telephone survey was completed by 40,465 families with CSHCN.  The authors examined 1) whether a parent reported elevated symptoms of depression or anxiety in the target child (i.e., CSHCN) and a number of demographic and health related variables that could be potentially related with a reduction or increase in the odds of experiencing internalizing symptoms. The demographic variables included: the child’s gender, age, race/ethnicity, primary language, poverty status, insurance status, urban vs. rural residence, parents’ education, and parents’ marital status.  The health related variables included: the type of health condition or symptom that qualified the child as CSHCN, frequency of activity limitations due to the condition, number of physician visits, number of school days missed, amount paid for care, hours spent coordinating care, and parental employment changes related to the child’s condition.

The results:

• 32% of all CSHCN experienced internalizing mental health symptoms, but this rate varied significantly by the age of the child
• Only 16% of  3 to 5 year old children experience internalizing symptoms, but this rate increased to almost 39% for children 12 to 17 years of age. There was no difference in the rates of internalizing symptoms for children 12-14 years old as compared to those 15-17 years old.
• After controlling for specific demographic variables (e.g., SES) non-Hispanic black children were 26% less likely to experience internalizing symptoms than non-Hispanic white children.
• Living in a house without both biological parents increased the odds of experiencing internalizing symptoms between 28% and 56%.
• Being uninsured increased the odds of experiencing internalizing symptoms by 51% as compared to having private insurance.
• Having high levels of activity limitations increased the odds of internalizing symptoms by 139%
• More internalizing symptoms were also associated with having more visits to the physician, missing more school days, financial problems, and having a parent having to quit or to cut back on work due to the child’s health problem.

Below is a table with the proportion of children with specific conditions that experienced internalizing problems.

As you can see, the conditions most strongly associated with internalizing symptoms were autism spectrum disorders (70.7% of children with ASD experienced internalizing symptoms), behavior problems (69%), and developmental delays (61%). The conditions least associated with internalizing symptoms were asthma (22%), diabetes (29%), and allergies (29%).

These results highlight how prevalent depressive and anxiety symptoms are among children with special needs. This is particularly worrisome among adolescents. It appears that by age 12, close to 40% of these children are experiencing noticeable levels of anxiety and depression. The results suggest that parents, physicians and other health care providers should be attentive to the possible presence of anxiety and depression among these children, specially during adolescence.

The reference: Ghandour, R., Kogan, M., Blumberg, S., & Perry, D. (2010). Prevalence and Correlates of Internalizing Mental Health Symptoms Among CSHCN PEDIATRICS, 125 (2) DOI: 10.1542/peds.2009-0622

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As you know, the brain has two lobes. The studies show that music engages both hemispheres of the brain — its creativity and emotion engage the right lobe, while rhythm and pitch engage the left. So people who receive musical exposure at a young age develop a bundle of nerves that connects those two halves*

*I should go on, but I must note that the only thing correct in the above quote is that music indeed engages both hemispheres of the brain.

Since then, the effect of Mozart on intelligence was discredited. In fact, a comprehensive meta-analysis (a statistical reviews of previous studies on the topic) concluded that listening to Mozart actually had no effect on intelligence.

Yet, something very positive came out of these studies. Soon after, a series of studies showed that Mozart improves performance in some people because of its calming effects. That is, listening to mozart reduces stress in many people, and for those who are anxious, such reduction in stress would lead to better performance (e.g., whether a test or a sporting event). Other studies also showed that playing Mozart to at risk infants (premature or those with severe medical complications) resulted in better medical outcomes, such as fewer hospitalization days and more rapid weight gain.  Yet, researchers have not been able to identify the actual mechanisms that explain why premature babies react this way to Mozart.

In the last issue of the journal Pediatrics, there was a very small yet fascinating study on the effects of Mozart on premature babies. A team in Tel Avid was interested in examining whether changes in metabolic efficiency could explain the better outcomes observed among premature babies exposed to Mozart. In the study, the authors examined 20 preterm infants with a mean gestational age at birth of 29 weeks (range 26-35) and who were otherwise medically stable. At the time of the study, the infants were at a chronological gestational age of 30 to 37 weeks.  The methodology involved a “randomized cross-over design”. This means all babies where tested in both conditions during 2 consecutive days at the same time of the day. Some babies listened to Mozart during day 1 and underwent the no music condition during day 2, while other babies experienced the no music condition during day 1 and listened to Mozart during day 2.

The results:

The authors found that within 10 minutes of the start of the music the infants experienced an average of a 10-13% reduction in their “Resting Energy Expenditure” (REE). REE is often considered a measure of the amount of calories required to function during a specific time period during resting conditions. How could this contribute to our understanding of the Mozart effect on premature babies? If a baby reduces his/her REE, the baby then requires LESS calories to function. Imagine for a second that you require 2000 calories to function during the day. If you eat a 2,000 calorie diet, you would theoretically maintain your weight. Now imagine that you reduce your REE so now you only require 1,500 calories to function, yet you continue to eat the 2,000 calories (I think we call this aging!). What would happen? A similar process may be at play with these infants. It is possible that exposing the infants to Mozart reduces their REE and this results in a higher ratio of ‘consumed calories’ to ‘calories used’, and thus more rapid weight gain and better medical outcomes.

Although this is a very compelling study, the authors warned that more research is necessary with larger samples. Yet, these findings, combined to previous findings showing improved medical outcomes among at-risk infants exposed to music, makes you wonder whether neonatal intensive care units should consider music exposure as standard practice for at risk infants.

The Reference:

Lubetzky, R., Mimouni, F., Dollberg, S., Reifen, R., Ashbel, G., & Mandel, D. (2009). Effect of Music by Mozart on Energy Expenditure in Growing Preterm Infants PEDIATRICS, 125 (1) DOI: 10.1542/peds.2009-0990

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How do we improve early interventions for ADHD?

The authors of this paper present a basic framework that should guide the development of new treatments:

1. That the development of treatment involves the identification of, and targeting, the underlying causes of the condition (rather than only addressing symptoms – see below for more about this)

2. That ’causes’ are framed within a developmental process. That is, rather than seeing causes as fixed events (e.g., a specific physiological anomaly), causes for developmental disorders are indeed ‘developmental processes’ (e.g., anomalies in the development of specific physiological process for a specific developmental period).

3. That treating these processes early can alter the developmental trajectory of this condition and thus prevent the full emergence of the disorder.

Regarding the last point the authors go on to explain how we need to reconceptualize the definition of disorder, or disorder-onset. Traditionally, most diagnostic criteria of psychiatric disorders require that the condition produce functional impairment. Thus, the symptoms must be severe enough to cause actual dysfunction in the person’s personal, occupational, or educational life. Only if the symptoms produce impairment you “have” the disorder. Although there are many valid theoretical arguments for the need of the ‘impairment’ requirement in the current diagnostic definition of most psychiatric disorders, this criteria has a political rather than empirical foundation. That is, the wide application of the ‘impairment’ criteria to most conditions is not consistent with our understanding of the development of many psychiatric condition. Specifically,  in many cases, the syndrome is likely present before there are significant symptoms and consequently before there is noticeable impairment.

But what does this have to do with treatment? How can a change in our conceptualization of disorders improve the prevention of these conditions?

Imagine for a second that oncologists decided that you have to show symptoms that are so apparent that you can actually describe them (e.g., I feel a mass on my back) and that these symptoms have to cause impairment (e.g., it hurts so much I can’t go to work) BEFORE they can provide a diagnosis of cancer, offer treatment, and be reimbursed by insurance companies. We would all think this would be crazy because we know that the effectiveness of cancer treatment increases if you can treat the condition at the earliest possible stage. I know this is an extreme example, but it highlights the current limitations of psychiatry. Our practice of providing diagnoses and interventions once symptoms are observable and producing functional impairment is greatly limiting the effectiveness of current therapeutic approaches. Furthermore, such conceptualization has resulted in a view of disorders that is static  – you have it when it causes impairment — rather than dynamic — disorders have a developmental trajectory and only the end of the trajectory may cause impairment. Today we mostly diagnose and treat the end of the trajectory, when it’s likely too late for too many.

The reference:

Edmund J.S. Sonuga-Barke, & Jeffrey M. Halperin (2010). Developmental phenotypes and causal pathways in attention deficit/hyperactivity disorder: potential targets for early intervention? Journal of Child Psychology and Psychiatry

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In order to address these issues, a team of scientists at the RAND Corporation conducted an interesting study that was just published on the prestigious journal “Pediatrics”. The sample for the study included over 1,500 military children (11 to 17 years of age) and non-deployed parent/caregiver. These children and their parents/caregivers participated in an extensive phone interview that covered a number of domains, such as academics and behavior problems, anxiety, peer functioning, parental mental health, family functioning, etc. The investigators were interested primarily in two questions: How the military children compares to the national average in their levels of emotional and behavioral difficulties; and 2) what factors contribute to more or less difficulties during parental deployment and reintegration.

The results:

Below you can see a graph comparing military kids (dark bars) and the national average in mean behavioral and emotional difficulties for all age groups.

Children of military families endorsed significantly more emotional and behavioral problems than then non-military peers at all age groups. For example, when looking at only anxiety, 30% of the military children endorsed clinical levels of anxiety. In contrast, the rates of anxiety problems in these age groups among the general US population is closer to 10%.

Predictors of difficulties during deployment:

The authors found a number of factors that were related to more difficulties adjusting to parental deployment. These included:

1. Older kids had more difficulties than younger kids.

2. Girls had more difficulty than boys.

3. Those living in military housing had less difficulties than those renting non-military homes/apartments.

4. Parental emotional distress (on the part of the non-deployed parent) was also associated with more difficulties adjusting to deployment on the part of the child.

5. Length of deployment (longer deployment) was also associated with more adjustment difficulties.

Predictors of difficulties during reintegration:

The authors also examined what factors contributed to difficulties adjusting to parental reintegration after deployment.

1. Older kids had more difficulties than younger kids.

2. Girls had more difficulties than boys.

3. Length of deployment (longer deployment) was also associated with more adjustment difficulties.

In sum, the study provides some compelling evidence that children of military families experience significantly more emotional and behavioral difficulties than their non-military peers. This also provides evidence for the need to create more effective programs that address the needs of these families before, during, and after parental deployment. The study also provides some insight on how to improve current programs. For example, more emphasis on older children may be needed as these seem to have the most difficulty. Also, why are families living in military housing better adjusted to deployment than families renting their own place? This could be do to increased access to key resources among those living in military housing. However, it may also be due to other factors that contribute to the decision -or need- to live in military housing vs. non-military apartments, etc.
Chandra, A., Lara-Cinisomo, S., Jaycox, L., Tanielian, T., Burns, R., Ruder, T., & Han, B. (2009). Children on the Homefront: The Experience of Children From Military Families PEDIATRICS, 125 (1), 16-25 DOI: 10.1542/peds.2009-1180

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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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