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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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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The Results:

1. 8.7% of the entire sample met diagnostic criteria for ADHD. That is, the rate of ADHD, as diagnosed by a structure diagnostic clinical interview, was close to 10% of US teens.

2. While controlling for all other variables, prenatal exposure to tobacco increased the risk of having ADHD by 140%.

3. While controlling for all other variables, current lead levels also increased the risk for having ADHD and this effect was ‘linear’ in relation to lead levels. Specifically, having lead levels in the second tertile (0.09 – 1.2 ug/dl ) increased the risk by 70%. Having lead levels in the third tertile (>1.3 ug/dl) increased the risk by 130%.

4. Other variables associated with an increased risk included: being a boy, being non-Hispanic white, and having attended preschool.

5. The combination of prenatal tobacco exposure and having high lead levels increased the risk for ADHD by 700%.

This study is very compelling given the number of potential explanatory variables that were included in the analysis. The results strongly point towards an association between tobacco exposure before birth and ADHD risk. However, one important explanatory variable was omitted: parental history of ADHD. We know that there is a strong familial risk for ADHD, in that children of parents who have ADHD are a significantly higher risk of having ADHD themselves. It is then possible that those ADHD mothers are more likely to smoke during pregnancy, and that it is the maternal history of ADHD and not the tobacco exposure that explains some of these results. The authors correctly noted this issue but argued that other studies have were able to control for maternal history of ADHD have obtained similar results, making this alternative explanation unlikely.

The reference:
Froehlich, T., Lanphear, B., Auinger, P., Hornung, R., Epstein, J., Braun, J., & Kahn, R. (2009). Association of Tobacco and Lead Exposures With Attention-Deficit/Hyperactivity Disorder PEDIATRICS, 124 (6) DOI: 10.1542/peds.2009-0738

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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 meta-analysis I was reading this week, Dr. Trevor Mazzucchelli compared several types of therapies including Behavioral Activation, Cognitive-Behavioral, and other interpersonal therapies in the treatment of depressed ADULTS. Behavioral Activation therapies are focused on changing behaviors (e.g., getting off the couch!) while Cognitive-Behavior Therapies (CBT) focus on changing both behaviors  and how we “think” about events (e.g., helping us realize some of our thoughts – “no one loves me”  – may be inaccurate). In contrast, Interpersonal therapies are varied, but often focus on the relationship between the therapist and the patient/client as catalyst for change. In Mazzucchelli meta-analysis, Behavioral Activation and Cognitive-Behavioral Therapy were significantly more effective than other therapies. However, the efficacy of Behavioral Activation and Cognitive-Therapy were not different than each other.  This is consistent with previous findings that have suggested that the most effective interventions are “behavioral” in nature and that even within the famous Cognitive-Behavioral Treatment the “effective” component may be the “behavioral” rather than the “cognitive” part.

But what does this have to do with child psychology, after all, this meta-analysis was conducted with adults? My colleague and child depression specialist Maria Kovacs recently developed a new therapy intervention for youth, mostly responding to the poor effectiveness of current treatment interventions for depression in children and adolescents. For example, in 2006 Dr. John R. Weisz, from Harvard University published an extensive meta-analysis of the efficacy of therapies for child  and adolescent depression (see below for the reference). The overall conclusion of that study was that treatment was better than no treatment, but the level of effectiveness was relatively poor. That is, child and adolescent therapy for depression was barely effective. This has prompted the creation of new intervention such as Kovac’s new program.

But the recent Mazzucchelli meta-analysis with adults made me reconsider the Waisz meta-analysis with children. Remember, with adults, Behavioral Activation was more effective than other interventions and equally effective than CBT. So I wonder if the original Waisz study also compared those 3 different modalities. The answer was “no”. Instead, due to the limited diversity of studies, the authors grouped cognitive therapies and compared them to all other therapies. They found that these were not different from each other. What is the problem? That there was no comparison between therapies that use mostly Behavioral Activation (the most effective in adults) and therapies that do not use behavioral activation (the least effective in adults).  So it is possible that treatment for child and adolescent depression is better than we initially thought if we were to examine the effects of Behavioral Activation therapies alone (given the adult findings). Some would argue however that I should not apply the adult findings to children. In most cases this is a valid argument, but in this case there are theoretical and research evidence that would indicate that Behavioral Activation should be even MORE effective with children and adolescents. Specifically, a few studies have suggested that cognitive interventions for depression seems to be more effective as children get older, likely because developmental processes limit the use of cognitive interventions in young children. So it is possible that interventions that focus on behavioral activation, rather than cognitive restructuring, may lead to even better outcomes in the treatment of depression in children and adolescents.

One of my students is currently working on a preliminary review of previous depression treatment studies aiming to identify the level of effectiveness of behavioral therapies for depressed children and adolescents and compare them to cognitive and interpersonal therapies.
Weisz, J., McCarty, C., & Valeri, S. (2006). Effects of Psychotherapy for Depression in Children and Adolescents: A Meta-Analysis. Psychological Bulletin, 132 (1), 132-149 DOI: 10.1037/0033-2909.132.1.132

Mazzucchelli, T., Kane, R., & Rees, C. (2009). Behavioral Activation Treatments for Depression in Adults: A Meta-analysis and Review Clinical Psychology: Science and Practice, 16 (4), 383-411 DOI: 10.1111/j.1468-2850.2009.01178.x

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