As I mentioned a couple of days ago, during the next few days I’ll be summarizing research presented at the Annual Convention of the Association for Psychological Science. I’ll start today with a non-so-brief report of a study I know very well, mostly because it’s a study I presented yesterday morning.

For the last few years, I’ve been interested in exploring why children of depressed parents ( those at ‘familial-risk’) are more likely than their peers to become depressed. In fact, up to 50% of these children will develop depression by the end of their teen years. If we understand why these kids develop depression, we could create preventive intervention for these children. In our research, we have focused our efforts on examining factors that keep these children from regulating their emotions effectively. For example, we are interested in whether kids at familial risk for depression have biases in their attention and perception  of emotions, which may lead to lower levels of happiness (positive affect) and higher levels of sadness.

In a recent study we just completed, we examined children’s ability to recognize subtle traces of sadness in adult facial expressions. We presented children at familial risk for depression and their low-risk peers (kids whose parents never experience depression) with pictures that varied in emotional intensity from completely neutral (no emotion at all) to 100% emotion (e.g., Sadness). So a child may see a picture that was 40% sad, 10% sad, or 70% sad, etc. After each picture the child was asked to state whether the person on the picture was feeling sad or nothing at all. We wanted to know if children at familial risk for depression could identify sadness at lower levels of intensity (e.g., 20% sadness) than their low risk peers.

What did we find?

• As a group, there were no differences in the ability to identify sadness at different levels of emotional intensity between the at-risk and the low risk children
• However, we found a significant interaction with gender. Specifically, at risk boys (but not girls) were significantly more sensitive to subtle traces of sad cues than their low risk peers. That is, at risk boys were able to recognize sadness in ambiguous faces at much lover levels of sadness intensity than their peers!

What does this mean? We think that oversensitivity to sad expressions may be one of the factors that eventually contribute to these kids getting depressed. Why? Think from the perspective of a child who is an ‘expert at recognizing sadness’…. How would your social world look like? What would happen if during your day-to-day interactions with your parents, teachers, and friends, you could identify very subtle traces of sadness? You would be exposed to significantly more sadness than your peers who are not as good at seeing the sadness in others. What consequences could this bring? One possibility, which is supported by other experiments that we have conducted, is that this over sensitivity to sadness may interact with another feature of these kids brains: they have difficulty attending AWAY from sad stimuli. So now imagine that you are a kid who is not only an expert on indentifying subtle traces of sadness in others but who also have difficulty attending to other things once you see something sad. This could have a major impact on your day to day experience, likely leading to more feelings of sadness and less feelings of happiness: a recipe for the development of depression.

A few final thoughts… some of you may be asking, are these kids simply oversensitive to all emotions? Actually the answer is no. We did not find the same effect for angry faces and others have found the opposite effect for happy faces. That is, there is some evidence that these kids are very good at identifying sad faces but very bad at seeing subtle traces of happiness.

But there is a major question we have not answered… why did we find an effect for at risk boys and not with at risk girls? The honest answer is that we don’t know, but there are some hypotheses we are considering and maybe by next year we could complete the picture.

The reference:

Lopez-Duran, Kuhlman, George, Kovacs (2010). Facial Expression Recognition in Children at Familial Risk for Depression: Evidence for Oversensitivity to Sadness. Presented at the Annual Convention of the Association for Psychological Science. Boston, MA. May, 2010.

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Thank you all for your patience! Nestor.

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In this study, the authors examined the medical records of all residents of British Columbia who were 10 to 18 years of age and who started to use an anti-depressant medication between 1997 and 2005. The British Columbia Ministry of Health keeps detailed records of all medical services provided to British Columbia residents. This allowed the investigators to identify all children who were provided (and filled) a prescription for an anti-depressant medication during those years. The researchers also indentified all suicidal attempts and suicide completions during the same years, and obtained key medical information on these children, such as the diagnosis, past use of anti-depressants, gender, etc. With this information, the researchers examined whether the risk for suicide varied based on the specific medication used after controlling for other factors.

The results:

During the study period, 20,906 children and adolescents started taking an anti-depressant medication. 2% of these children had already attempted suicide before starting the medication.

The medications were divided into 5 groups:

1. SSRI, including Citalopram (celexa), Fluoxetine (Prozac), Fluvoxamine (Luvox), Paroxetine (Paxil), Sertaline (Zoloft)

2. SNRIs, including Venlafaxine (Effexor)

3. Triclyclic Antidepressants, including Amitriptyline, Amoxapine, Clomipramine, Desipramine, Doxepin, Imipramine, Maprotiline, Nortiptyline, Protriptyline, and trimipranime.

4 Atypical New Agents, including Mirtazapine, Nefazodone, and Trazodone

5. MAOIs, including Moclobemide, Phenelzine, and Tranyclypromine

During the 12 months after the children/adolescents started using the medication, the researchers identified 268 children who attempted suicide, including 3 who completed suicide. That is, only 1.2% of those children who started the medication had a suicide attempt during the first 12 months. This rate was actually surprisingly low given that 2% of these kids had already attempted suicide before starting treatment. However, if we consider only those kids who had not been previously on anti-depressant medication, the rate increased to 2.5%.

Was one medication safer or riskier than others? Apparently the answer is no. The researchers did not identify any difference in suicide risk among all the medications. The rates of suicide attempts were comparable across all medications. One exception was MAOI’s. No suicide attempts were observed among children taking MAOI’s. However, likely due to MAOIs’ other significant risks, only 37 children (0.17% of the sample) were prescribed MAOI’s, which prevented a proper statistical examination of risk for suicide when taking MAOIs.

But what does this have to do with my initial questions? This helps narrow the answer as to why there is an increase risk of suicide when taking anti-depressant medications. Some have proposed that the increased risk was due to the use of a specific type of medication (e.g., SSRIs such as Prozac) because of some unknown effect on brain functioning. But these data suggest that all anti-depressant medication, not only SSRIs, have lead to an equal increase in the risk for suicide. It appears that this risk is not unique to any type or brand of medication. Some have suggested that the increase in suicide risk is due to the therapeutic effect of the drug. Specifically, as patients begin to feel better, there is an increase in behavioral activity and self-confidence. In theory, this could increase the risk of suicide among those who were so depressed that they lacked motivation even to attempt suicide (either because of a lack of energy/motivation or the belief that even suicide wouldn’t help). But under this hypothesis, there also should be an increase in the risk for suicide after adolescents engage in psychotherapy. Unfortunately, there are significantly fewer (and much smaller) studies of psychotherapy with children and adolescents than of anti-depressant medications, which has prevented researchers from examining this question in an adequately large sample. So, the question remains as to whether the increased risk for suicide is an unfortunate byproduct of the therapeutic effect of effect interventions and not just of antidepressant drugs.
Schneeweiss, S., Patrick, A., Solomon, D., Dormuth, C., Miller, M., Mehta, J., Lee, J., & Wang, P. (2010). Comparative Safety of Antidepressant Agents for Children and Adolescents Regarding Suicidal Acts PEDIATRICS, 125 (5), 876-888 DOI: 10.1542/peds.2009-2317

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The last issue of the Journal of Autism and Developmental Disorders included one of the largest examinations of the association between maternal infection during pregnancy and risk for autism. The study was conducted in the Denmark where researchers examined the maternal infection rates during pregnancy and autism diagnoses for all children born in Denmark between 1980 and 2005. ASD diagnosis was calculated by examining the Danish Psychiatric Central Registry, which includes information of all children who received outpatient or inpatient treatment with a diagnosis of ASD during those years. The researchers also examined the Danish National Hospital Registry, which includes information about all hospital admissions in the entire country during that time.

The researchers wanted to know whether children whose mothers had an infection during pregnancy were more likely than their peers to develop an autism spectrum disorder. In addition, they explored whether the nature of the infection (viral vs. bacterial), or the trimester during which the infection occurred, affected the risk of developing an ASD for the child.

The results:

1. Overall, there was no relation between maternal infection during the entire pregnancy and ASD risk. However, there was a relation between infection at specific trimesters of the pregnancy and ASD risk:
2. Viral infection during the 1st trimester of the pregnancy significantly increased the child’s risk of developing ASD. Specifically, children whose mother had a viral infection during the first trimester were about 200% more likely than their peers to develop an autism disorder.
3. Bacterial infection during the 2nd trimester also increased the child’s risk of developing ASD but at a much smaller rate. Specifically, children whose mother had a bacterial infection during the second trimester were about 42% more likely than their peers to develop an ASD.

The authors indicated that this effect is likely due to exposure to the influenza virus. Specifically, in 50% of the children who had ASD and whose mothers had a viral infection during the first trimester, the virus was influenza.  Further, 4% of children whose mothers were admitted to the hospital during the first trimester due to the influenza virus developed autism. In contrast, only 0.6% of the children born during the period examined developed ASD. The rate of ASD among those whose mothers were admitted to the hospital during the first trimester due to the influenza virus was 6 times higher than in the general population.

Why is this the case? Researchers don’t know for sure but a few theories have been proposed. One theory is that exposure of the fetus to the influenza virus increases the risk for developmental disorders. However, others believe that it is not the virus itself, but the maternal immune response to the virus that is harmful to the developing fetus.

UPDATE: Here is one additional piece of information that helps puts these findings into perspective. ONLY 1.5% of all ASD cases were children whose mother had infections during pregnancy. That is, even though infection during the 1st trimester significantly increased the risk of ASD, 98.5% of all ASD cases could be considered to be completely unrelated to maternal infection during pregnancy.

The reference: Atladóttir, H., Thorsen, P., Østergaard, L., Schendel, D., Lemcke, S., Abdallah, M., & Parner, E. (2010). Maternal Infection Requiring Hospitalization During Pregnancy and Autism Spectrum Disorders Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-010-1006-y

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In this study a Dutch team used a modified version of a game called the Iowa Gambling Task. In this task, people are asked to choose a card from 4 decks.  Each card has a reward and a penalty. In some decks, the reward is large but the penalty also tends to be high. In other decks the reward is low, but the penalty is also low and often non-existing. In the long run, you earn more money by selecting cards from the low reward decks. Most people realize this quickly and begin to draw cards from the low reward decks more often than from the more risky decks.

In this study the authors examined the performance to a modified version of this task among 18 children with oppositional defiant disorder and 24 non-affected peers. The modified task included 3 conditions decks):

Choice 1 was the advantageous choice.  Cards from this choice included small rewards every trial and small penalties every 3^rd trial. In the long run, you make money if you select this choice often.

Choice 2 was the seductive choice. Cards from this choice included large rewards in every trial but very large penalties every 3^rd trial. In the long run, you lose money if you select this choice often.

Choice 3 was the disadvantageous choice. Cards from this choice included small rewards in every trial but very large penalties every 3^rd trial.

Furthermore, as the game progress either the magnitude or the frequency of the penalties increased linearly. That is, in the magnitude condition, the size of the penalties increased. In the frequency condition, the penalty was provided progressively more often.

Below you can see the performance of the non-affected peers in the magnitude condition.

As you can see, by trial 18 the kids have increased the selection of the advantageous choice and have decreased the selection of the other two choices. Now see below the performance of kids with ODD in the magnitude condition.

Similarly to their non-affected peers, kids with ODD showed a decrease in preference for the disadvantageous choice by the 18^th trial. However, they continued to select the seductive (but eventually disadvantageous) and the advantageous conditions equally. Furthermore, the ODD kids had a significantly greater preference for the seductive and disadvantageous condition when compared to their peers.

It is very interesting that the size of the penalties increased equally in the seductive and the disadvantageous conditions, yet the ODD kids seemed to prefer the advantageous and the seductive position equally. What does this mean? That in the face of increasing penalties, the child’s behavior doesn’t change if the reward is high!!! It is only in conditions of low reward, that increasing the magnitude (severity) of penalties led to a change in behavior. Here, by ‘reward’ I’m taking about the child’s gain obtained from their actions. So for example, a child that receives significant pleasure from engaging in one activity would not respond to increasing the intensity of penalties for engaging in such activities.

Now, see the performance of both groups of children in the frequency condition. Remember, in this condition the size of the penalty does not increase. Instead, the frequency of the penalty is what increases.

The results of this condition were actually the opposite! The ODD kids showed a greater preference for the advantageous condition as the frequency of penalties increased than their non-affected peers! In this case the size of the reward did not make a difference. Facing more frequent (as opposed to larger) penalties, the ODD kids rapidly desisted from picking the “high reward – high penalty” choice and instead increased their preference for the low reward-low penalty choice.

In sum, the results from the magnitude condition were a bit surprising. The fact that ODD children showed a decrease in preference for the disadvantageous choice shows that they in fact respond to negative consequences. However, the fact that they didn’t decrease the selection of the seductive choice, suggests that they do not respond to increasing the intensity of consequences if the benefits of their actions are high. On the other hand, the results from the frequency condition were very surprising. It is often believed that children with OOD have difficulty responding to consequences, even severe consequences. Yet these results suggest that they are as responsive to consequences as their non-affected peers if the consequences increase in frequency rather than intensity! Although generalizing these results to real life behavior management is not a simple process, this is the type of research that could have a major impact on the development of new interventions for children with conduct problems.

The reference:
Luman, M., Sergeant, J., Knol, D., & Oosterlaan, J. (2010). Impaired Decision Making in Oppositional Defiant Disorder Related to Altered Psychophysiological Responses to Reinforcement Biological Psychiatry DOI: 10.1016/j.biopsych.2009.12.037

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One theory about autism suggests that children with autism have abnormalities within their intestinal system.  This ‘leaky gut’ theory is based on the belief that the abdominal barrier in these children is defective and allows specific substances and toxins to leak into the child’s bloodstream. This has led to the development of a large industry that offers assistance to families in a variety of forms, from helping them create gluten free diets to providing supplements that help breakdown the proposed toxins. Some of these practices (i.e. gluten free) have been studied scientifically and unfortunately have been shown not to be as effective as their proponents claim. Other practices, such as enzyme supplementation, simply have not been properly examined.

The study examined the effects of Peptizyde, a dietary supplement, on the core symptoms of autism among 43 properly diagnosed children. This study was simple and elegant, and used a double blind randomized placebo controlled cross over methodology. In brief, this is how it worked: about 40 kids with autism were randomly assigned to either receive the enzyme supplement for 3 months or receive a placebo for 3 months. After the 3 months, the groups switched so that those receiving the placebo would now receive the supplement and those first receiving the supplement would now receive the placebo. The parents, teachers, and most of the study staff did not know who was receiving the placebo and who was receiving the supplement. Under this method all children received both the treatment and the placebo in 2 controlled sequences.

The authors then compared parental and teacher reports of symptoms during the time the children were taking the supplement as compared to the time the children were taking the placebo (Remember that the parents didn’t know when their kids were taking the real supplement). If the supplement has an effect on autism symptoms or functioning, it is sensible to expect that parents would correctly identify changes when their children were taking the supplement but not when the children were taking the placebo.

The results:

No significant differences between the placebo and the supplement condition were noted on:

· Core symptoms as reported by parents

· Gastroinstestinal Symptoms

· Sleep Quality

· Social Engagement as reported by the therapist

· Vocabulary

· Sentence length

The only statistically significant effect was an increase in food variety during the supplement condition. That is, during the time the children were taking the supplement their parents rated them as having statistically significantly more food variety than during the time they were taking the placebo. However, the authors did not believe that this statistical finding was clinically significant because their mean scores of the two groups, 4.06 (placebo) and 4.42 (supplement), was small and clustered around the score 4, which means “no change”. I’m not sure I agree with that last conclusion because determining “clinical significance” is a complex process that is based on whether the difference in scores translate to meaningful differences in the day to day life of these children. In this case for example, clinical significance would mean that the difference between such scores, even if small, results in real changes in the child’s diet, such as wanting to eat 3 more variety of vegetables, etc. That question of ‘clinical significance’ can not be easily answered by simply examining the numeric difference of the scores and their location in the scale (near the ‘no change’ mark).

So unfortunately this study failed to provide evidence that enzyme supplementation is effective in the treatment of children with autism. I must mention however, that the study was conducted with a very small sample (less than 20 participants per group by the end of the study) which certainly impacted the researchers’ ability to find statistically significant differences between the groups. It would be reasonable for someone to argue that a larger study should be conducted before we can form a final conclusion regarding the effectiveness of this experimental intervention.

The reference:
Munasinghe, S., Oliff, C., Finn, J., & Wray, J. (2010). Digestive Enzyme Supplementation for Autism Spectrum Disorders: A Double-Blind Randomized Controlled Trial Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-010-0974-2

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One argument against such criticism of mental health providers is that today’s medical doctors also routinely engage in practices that have little scientific support, such as when using off-label medications. For example, when a psychiatrist prescribes a child an anti-psychotic drug that has only been approved by the FDA for use with adults, she is making a clinical decision that is not supported by scientific evidence (assuming that lack of FDA approval means limited scientific support for the drug’s efficacy and safety). However, there is a major difference between a physician who uses an off-label medication for children and a therapist that refuses to change their therapeutic modality despite evidence that what they do doesn’t work, or that another modality may be more effective. The former usually engages in a non-scientifically supported practice because of the lack of alternatives, while the latter engages in non-supported practices despite scientific evidence that contraindicate such practice. For example, a psychiatrist may use an adult medication with a child if the child does not respond to child-approved medications. In this case, the psychiatrist is not purposely ignoring science. Instead, she is using her clinical judgment to provide the best available alternative. In contrast, a psychologist may use a therapeutic modality that has no empirical support not because there are no alternatives, but because that is the only modality that she knows, or because she is ‘married’ to an ideology that makes her believe that what she does works best.   Under this view, psychologists and other clinicians are susceptible to non-scientific pressures (tradition, limited training, ideology) that guide their clinical decidions, while physicians are simply pragmatics who practice outside the scope of scientific evidence only when no alternatives exist. But, is this true?

This past weekend I was reading Pediatrics, the journal of the American Academy of Pediatrics, and encountered an article suggesting that psychiatrist may also be susceptible to non-scientific pressures in their practices. In the article, a group of researchers from Harvard University examined physicians’ use of off-label medications when such medications enter the market. Specifically, the authors examined the use of Ziprasidone (Geodon) with children after Ziprasidone, an atypical anti-psychotic, was approved for use with adults in the early 2000s. During the same years, other atypical antipsychotics, such as risperidon, were already FDA approved for use with children. This allowed the authors of the study to evaluate how often, and under what conditions, physicians would use Ziprasidone with children despite having another similar medication already approved for the use of children.

Before I discuss the results of the study, let me review what are usually considered “acceptable” reasons for the use of off-label medications. One reason is when no empirically supported alternative exist. For example, psychiatrists may use an adult antipsychotic with children if there is no child antipsychotic available. The off-label use of a medication would also be acceptable if the alternative medication is not working. For example, if a physician prescribes an approved medication and this medication does not result in symptom reduction, the physician may then try an off-label medication. Finally, if a physician prescribes an approved medication and this medication results in severe side effects in the child, then the physician may move on and try an off label drug with the hope that this medication provides relief without the side effects.

In the Pediatrics study, the authors examined the prescription practices in Michigan during the early 2000’s by reviewing the Medicaid data for the year following the approval of ziprasidone for adults (2001), which also represents the first time the drug was available for of-label use with children.  The authors identified 292 persons under 21 who were prescribed ziprasidone during that year.  Close to 60% of these individuals were under the age of 18. Here are some interesting results:

What diagnoses were provided to these individuals?

36% were provided a diagnosis of a psychotic disorder
32% were provided a diagnosis of another mental health disorder
32% were not provided a mental health disorder diagnosis at all

How many kids were prescribed a different antipsychotic before being prescribed ziprasidone?

33% were not prescribed any antipsychotic before ziprasidone.
54% were prescribed only 1 antipsychotic before ziprasidone.
Only 12 % were prescribed 2 or more antipsychotics before ziprasidone.

Who provided the prescription?

36% were general practitioners
36% were psychiatrists
13% were emergency room physicians

A couple of things I found surprising. First, we know that the use of ziprasidone in this sample was not simply due to the lack of alternative because other antipsychotics were already approved for children during this time. That is, physicians used ziprasidone despite the availability of similar medications that had been approved by the FDA for use with children. So the next question is whether ziprasidone was used after the child did not respond to FDA-approved medications, otherwise known as treatment resistance, which is defined as failure with at least 2 previous medications. It seems that only 12% of the sample met treatment resistance criteria. That is, only 12% tried at least 2 other medications before trying ziprasidone. For the other 88%, ziprasidone was prescribed before properly ‘trying’ at least 2 other medications, and for 33% of these patients, ziprasidone was their first medication (being prescribed before trying any child-approved medication!).

Is it possible that ziprasidone was prescribed because it was perceived to be even safer than child-approved medications? The authors indicated that this was not likely the case because at that time there were no published studies on the safety of ziprasidone with children, and there was evidence of dangerous cardiac side effects with adults, which should have been a red flag for these physycians. Therefore, these physicians used ziprasidone with no knowledge as to whether this medication was safe for kids (we now know that ziprasidone is safe as it eventually got FDA approval for children in 2009).

So what explains the rapid and seemingly careless adoption of ziprasidone as a drug of choice for the treatment of psychosis in children before there was any evidence of the drug efficacy or safety with that population?

The authors argued that this early adoption was simply due to marketing. Ziprasidone was the new kid in the block. The manufacturer spent 2.3 million in early marketing before the drug was available and $47 million in advertisement directly to practitioners the year the drug became available. It seems the ad campaign worked.

What this suggests is that physicians do engage in non-evidence based practices but not necessarily because of a lack of alternatives. Instead, just as psychologists, physicians are also susceptible to non-scientific pressures that impact their practices to the detriment of their patients. Psychologists are susceptible to tradition and rigid ideological beliefs. Physicians are susceptible to shinny pens and coffee mugs with flashy ziprasidone logos.

Penfold RB, Kelleher KJ, Wang W, Strange B, & Pajer K (2010). Pediatric uptake of a newly available antipsychotic medication. Pediatrics, 125 (3), 475-82 PMID: 20142282

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But do they work?

The journal Archives of Pediatrics and Adolescent Medicine just published a study conducted by a team of researchers at the University of California at Riverside. The study included 95 babies/toddlers between the ages of 12 and 25 months. These children were randomly assigned to a Baby Wordsworth DVD condition or to a no DVD group. Parents of the children in the DVD group were asked to use the Baby Wordworth DVD as they would use any other media at home. The no DVD group simply completed a series of laboratory tasks but were not provided with a dvd to watch at home.  The study lasted for 6 weeks. Before and after the 6 weeks, the children went through a battery of tests and the parents completed a series of scales designed to measure the baby’s language and cognitive skills.

The results:

1.      By the end of the 6 weeks,  there was no difference between those infants who were repeatedly exposed to the dvd and those who were not exposed to the dvd in regards to their general language and cognitive abilities.

The above finding is not really surprising as it would be unrealistic to expect that the dvd would have a major impact on the children’s cognitive or language function in just 6 weeks. So a better question would be: Does the dvd help children learn those 30 words?

2.      By the end of the 6 weeks, those infants who watched the dvd during this time were not more likely than those who didn’t watch the DVD to say the words, recognize the words, or identify the words using pictures of the objects.

The findings suggest that the educational DVD does not facilitate the learning of these words by infants when exposed to the dvd in a naturalistic setting for 6 weeks. So far, I had not been surprised by these results, but I was a bit surprised by the following:

3.      Those infants exposed to the dvd at an earlier age (closer to 12 months) had lower overall language scores at the end of the 6 weeks than those exposed to the dvd at a later age (closer to 24 months) or those not exposed at all.

This seems to suggest that early exposure to the dvd can actually negatively impact language development. Although a couple of previous studies have found similar effects, this study is critical because it helps us answer one key question: does early exposure to the dvd affect language development or do children with language delays simply tend to watch more tv/dvds? For example, it is possible that parents of children who have more language delays buy the educational DVDs in order to help their children. If this is the case, there would be an association between watching dvds and language delays, but it would not be the DVD that contributed to the language delay. But this study suggests that this may not be the case because the participants in this study were randomly assigned to the DVD or the no DVD group. That is, in this study, the use of the DVD is unlikely to be due to parental concerns about the children’s language development.

So what can explain the possible detrimental effects of watching these educational dvds at an early age? The authors mentioned a couple of possibilities. It is possible that having the dvd as a tool kept the parents from engaging verbally with the infants leading to a delay in language development. It is also possible that the dvd lacks one major component of the language learning process: The Authors explain:

Regarding word learning specifically, a large body of language acquisition research suggests infants are more likely to learn words for novel objects if a speaker is looking at an object rather than attending elsewhere or looking directly at the child.18 Thus, learning words from a television screen requires children to be paying attention to the screen and also to be aware of the relevant referent object to which the on-screen labeler is referring. In the case of the DVD used in this study, the onscreen character looked directly at the children and signed the name for the object while a voice-over spoke the label. This scenario is very different from the optimal word learning scenario for children younger than 2 years.

Regardless of the reason, the results of this and previous studies seem to suggest that exposing young infants to television, even when such media was specifically designed as an educational tool for babies, may be associated with a delay in language development.
The reference: Richert, R., Robb, M., Fender, J., & Wartella, E. (2010). Word Learning From Baby Videos Archives of Pediatrics and Adolescent Medicine DOI: 10.1001/archpediatrics.2010.24

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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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