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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Evidence for and against diagnostic substitution continue to accumulate. For example, last year I commented on a study showing that some adults that were diagnosed with pragmatic language impairment during their childhood actually meet diagnostic criteria for autism. But in an upcoming issue of the Journal of Autism and Developmental Disabilities, researchers from the California Department of Public Health report the findings of an examination in diagnostic coding practices in California during the past 20 years.

Specifically, the authors examined 4 cohorts of children who were born in 1987, 1990, 1994, and 1997, and who had been seen by the California Department of Developmental Services (DDS). The DDS provides services to mostly children with mental retardation, autism, or both. The authors compared the official diagnostic classification in the DDS system with the actual clinical records and clinical impressions of a representative sample of these children to answer 2 main questions: 1) Was there a coding/diagnostic shift from providing diagnoses of “mental retardation only” to  “mental retardation AND autism”? 2) Was there an increase in the number of children classified as autistic but who did not have supporting evidence for the condition?

The results:

1. In 1987 2.3% of the children classified as having MR only also had clinical evidence of autism but were misclassified as having MR only. In addition 7.4% of these children had evidence of other ASD. However, this proportion of misclassification actually increased (although not statistically significant) during subsequent years. This increase is contrary to what the diagnostic substitution hypothesis would suggest.
2. The authors also found that the proportion of children classified with autism but who did not have clinical evidence of the condition was consistent across all cohort years at less than 12%. In addition, the proportion of children classified as having other ASD but who did not have clinical evidence was under 10%.
3. The authors also found that the number of children classified as having autism with co-morbid mental retardation has decreased. But the number of children with the autism classification and no formal MR evaluation has also increased. Thus, it is possible that the apparent net decrease in kids with dual diagnosis is due to a decrease in the evaluation of MR among kids with autism.

Although not conclusively, these data provide support against the diagnostic substitution hypothesis as an explanation of the observed increases in autism in California. However, as correctly discussed by the authors, the results speak mostly to official ASD diagnostic classifications. For example, the results clearly show there has not been a shift in the number of children who were officially “misclassified” as having MR when they actually had MR and autism. Yet, this does not address possible shifts in actual clinical practices. Specifically, the authors examined discrepancies between the official classification and the clinical records/impressions. Although the authors put significant effort in validating these impressions (for example, examining conflicting impressions, and reviewing these impressions when they were not provided by a qualified professional) there is the underlying assumption that the original clinical impressions were correct if provided by a “qualified professional” and in the absence of conflicting data. Thus, shifts in the accuracy of clinical impressions would not have been captured in this analysis.

Grether, J., Rosen, N., Smith, K., & Croen, L. (2009). Investigation of Shifts in Autism Reporting in the California Department of Developmental Services Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-009-0754-z

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The national prevalence of ASD was estimated to be 86 children per 10,000 between the ages of 3 and 18. This translates to approximately 1 case per every 116, which is higher than the often cited 1 per 150 CDC number (See this post about how the original CDC data was gathered). As expected and previously reported, boys, non-Hispanic white children, and poor children, were at higher risk of ASD than girls, Hispanic, African American, and affluent children.

Regarding health care access and financial impact:
1. Children with ASD or other emotional/behavioral problem experienced more delayed or forgone medical care when compared to children with other medical special needs.
2. Children with ASD or other emotional/behavioral problem experienced more difficulty receiving needed referrals when compared to children with other medical special needs.
3. However, when compared to children with other emotional/behavioral problems as well as with children with other medical special needs, families of children with ASDs reported significantly more: a) need for additional income to cover care expenses, b) need for reduction of employment in order to attend to the child’s condition, c) likely to spend over 10 hours per week attending to the child’s condition, d) more likely to spend over 1,000 out of pocket expenses per year in the child’s medical care.

This large national survey study suggests that ASDs results in significantly more financial burden to families than any other health care need including other behavioral, emotional, or medical problems.

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The authors briefly reviewed epidemiological data suggesting that the highest autism rates in the USA are found on Northern and Western States, while the lowest autism rates are found in the deep south (e.g., Alabama). They concluded from these findings that an environmental trigger in these Northern and Western States (such as bad weather) may be a risk factor for the development of autism. To test this hypothesis, the authors examined precipitation rates in various counties within California, Oregon, and Washington and compared these rates to the prevalence of autism in said counties. The authors found that high rate of annual precipitation was associated with high rates of autism cases, even after controlling for variables such as county level income, population size, or access to specialized services. The authors argue that this association may be to a number of factors including 1) higher rates of television viewing in very young children (although no evidence was presented suggesting that young children in rainy counties watch more television than young children in less rainy counties), 2) vitamin D deficiencies due to less sun exposure (although no evidence was presenting suggesting that vitamin D is associated with autism, or that the rates of autism are higher in polar regions given the reduced sun exposure during winter months), 3) environmental triggers associated with playing indoors instead of outdoors, and 4) possible harmful chemicals transported by the rain.

Given the preliminary/speculative nature of this study, the manuscript was accompanied by a letter from Dr. Noel Weiss from the Department of Epidemiology of the University of Washington that, while arguing that these results may not advance our understanding of the causes of autism, lauds the editorial decision to accept this article for publication. Below I provide some excerpts of Dr. Waiss’ arguments:

First, Dr. Waiss provides a sensible interpretation of the findings:

…there are other possible explanations for the association with precipitation that they have observed. First, the criteria used to diagnose autism, and the completeness with which such diagnoses are identified by state agencies and regional centers, likely vary to a considerable extent across counties. Possibly, the degree of completeness of reporting itself is associated with levels of precipitation. In Oregon and Washington, for example, could it be that state agencies in the western, rainy, relatively urbanized counties have enumerated a greater proportion of children with autism than their counterparts in the eastern, arid, relatively more rural counties?

However, in response to concerns regarding the potential misinterpretation and misuse of the findings by the public, Dr. Waiss states that:

The primary audience for the article of Waldman et al is not the practicing pediatrician, and certainly, it is not a member of the public at large. These individuals cannot take away any practical message from it. Rather, the primary target is an investigator interested in the causes of autism, someone who might be able to test one or more of the etiologic hypotheses that derive from the research of Waldman et al.

I do not agree with Dr. Waiss on this last point. It is no longer the case that most scientific research is read mostly by relevant scientists. The audience of scientific peer reviewed articles has expanded dramatically, mostly due to the internet and the new level of activism and involvement with research by relevant communities (e.g., autism ).

Finally, in regards to the findings, the study does not in any way show or suggest that rain causes autism. It only states that there is an association between rainy counties and autism rates. This may be due to a large number of factors, most of which (e.g., differences in how diagnostic data is collected between counties) have nothing to do with rain.

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The issue of life expectancy and mortality rates in people with autism is a largely understudied and seldom discussed topic. Studies of mortality in people with chronic psychiatric disorders usually show higher mortality rates at all ages than what is expected in the general population. Mortality rates refer to the number of death expected up to a specific age for specific cohorts. For example, what is the expected number of death within a cohort born in year 1973 by the time they are 30?

Only a couple of studies of mortality rates in people with autism have been conducted, one in California and one Denmark. Both studies showed that the mortality rate in people with austim was more than twice as that for the general population. The present study by the Danish team intended to expand on their original findings by examining the mortality rate and causes of death among a cohort of adults with ASDs who are now in their 40s. The sample included 341 adults with various diagnoses including autism (N=118), atypical autism (N=89), childhood disintegrative disorder (N=13), and asperger’s (N=121).

The mean age for the patients was 43.4 with a range of 26 to 60. A total of 26 patients have died by 2006 (7.6%). The expected number of deaths in the general population for a similar cohort was 13.5 (3.8%). Therefore, the mortality rate in the cohort of adults with ASDs was nearly twice of what is expected in the general population. However, this effect was significantly more pronounced among women. The mortality rate for woman with ASD was 4 times higher than what is expected in the general population. Surprisingly, these effects were not moderated (reduced) by IQ, which often reflects functional capacity. That is, within the group with ASDs, the mortality rates was the same for adults regardless of their IQ. The cause of death most commonly reported among the cohort with ASDs was epilepsy.

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The issue of under- or over-representation of a disorder within specific ethnic groups is a complicated one. There are specific disorders that are under-represented within a specific ethnic group because of some protective factor that makes such group less likely to acquire the disorder. For example, the rates of skin cancer in the African-American population are significantly lower than in the European-American population(although this has led to increase mortality rates among African Americans due to reduced screenings leading to late diagnosis). Yet, it is possible that some disorders are under-represented within an ethnic group simply because a systemic clinical bias in diagnosis and referrals. To examine this hypothesis, the authors of this study first examined 712 case records of children referred for ASD assessment in the Netherlands. They found that ethnic minority children (Turkish and Moroccan)were under-represented in this sample of referred kids as compared to Dutch children (2.1% vs. 4.4%). But does this represent a bias or is it simply that Turkish and Moroccan children are less likely to have ASD due to some protective factor? To answer this question, the authors sent 6 clinical vignettes to 82 pediatricians. The vignettes varied in their descriptions of various autism symptoms. Three ethnic background were represented, including 1) European minority (French or English) 2) Non-European minority (Moroccan and Turkish) and 3) Majority (Dutch). However, the ethnicity was independent of the clinical vignette, so that the vignette sent to one pediatrician could describe a Dutch child, while the SAME vignette sent to another pediatrician could describe a Turkish child. The authors found that vignettes describing Dutch (majority) children elicited significantly more references to autism than did vignettes describing European minority or non-European minority children. However, the mean rate of ASD based on an objective scale was equal across all three groups. This suggests that objective assessments may help minimize any potential clinical biases.

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Although most children with autism present very early signs and symptoms and a linear developmental trajectory, a small subset of children present a trajectory characterized by normal development followed by a loss of acquired skills or a failure to use the acquired skills. This pattern has been termed autistic regression. Possible explainations for this phenomenom have varied from a genetic effect on brain restructuring and pruning during the early stages of life, to enterocolitis due to vaccinations, to epilepsy. In this study, the authors explored differences in developmental outcomes for children with and without regressive autism, and the association between regression and enterocolitis and epilepsy. This study examined a population cohort born in the UK in 1990 and 1991. Out of 56,946 children in this cohort, 218 had and ASD diagnosis by age 10. A subset of these children were evaluated via ADOS and ADI and divided into a broad autism (N=105), narrow autism (N=53), and no autism (N=97). The narrow autism group met full criteria for autism based on ICD-10. The broad autism group met clinical consensus for autism but not full ICD-10 criteria. These children were then evaluated for history of epilepsy, gastroinstestinal problems, and developmental regression. 39% of children with narrow autism had a history of regression during development. This compared to 11% of children with broad autism, and 3% of children with no autism. On average this regression occurred around the 25 month of age. There were no differences in IQ or adaptive functioning between those with or without regression. However, those with regression classified in the broad autism group had significantly more symptoms than those without regression also classified in the broad autism group. Regression was not associated with gastroinstestinal symptoms or with epilepsy.

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The journal of Pediatrics just published a population study based on the national Swedish registry, which examined the association between parental psychiatric history and autism. The authors compared the parental psychiatric history of 1,227 of children with autism spectrum disorder and 30,925 typically developing children. Children were identified as having autism spectrum disorder if they were born between 1977 and 2003 and had a diagnosis of ASD recorded in the registry between 1987 and 2003.

Parents of children with autism were 70% more likely than parents of typically developing kids to have a psychiatric diagnosis. When both parents had a psychiatric disorder, the children were 100% more likely to have a diagnosis of autism. Schizophrenia was more common in both parents among children with autism as compared to parents of typically developing kids (90% more likely for mothers and 110% more likely for fathers). In addition, mothers of children with autism were more likely than mothers of typically developing kids to have depression (70%), and personality disorders (70%).

In summary, the study suggests that in Sweden, during the last 30 years, children with a diagnosis of autism were more likely to have parents with psychiatric diagnoses than typically developing children. This could reflect a non-specific, possibly genetic, predisposition in affected families for psychiatric conditions, including autism. It could also reflect that having a child with autism increases stress in the parents possibly leading to psychiatric diagnoses. However, the association noted by the authors was even stronger if the parental diagnosis was provided before the child’s diagnosis. One important consideration, these results were based only on kids who had a history of inpatient treatment. Those with a history of only outpatient treatment were not included. It is possible that the observed link between parental psychiatric history and autism applies only, or mostly, to the most severe cases of autism requiring hospitalization.

One last comment: It’s important to note that the rate of psychiatric conditions among even children with autism were very low. For example, schizophrenia was observed among 0.6% of the mothers of children with autism (compared to 0.2% of the typically developing mothers). 99.4% of the children with autism did not have mothers with schizophrenia. Therefore, the data only suggest that there may be a familial/genetic predisposition that is related to autism among very small subset of children with autism.

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A review of: PALMER, R., BLANCHARD, S., WOOD, R. (2008). Proximity to point sources of environmental mercury release as a predictor of autism prevalence. Health & Place DOI: 10.1016/j.healthplace.2008.02.001

This fascinating, yet bound to be controversial, study hit the news yesterday as it was made available (pre-publication) by the Journal Health & Place. The study is simple, straightforward, elegant, with some powerful findings. In fact, the findings are somewhat daunting given the simplicity of the design. The researchers reviewed the amount of mercury release reported by industrial facilities and power plants in the State of Texas in 1997 from data provided by US Environmental Protection Agency Toxics Release Inventory. They compared these data against autism rates in 1997 and 2002 as measured by schools’ autism classifications provided by the Texas Education Agency. Using a specialized geographical analysis system, the authors were able to locate each source of mercury and calculate the distance between each mercury source and each school. The results:

Industrial release of mercury and distance to industrial sources independently predicted increased rates of autism. The association with industrial release of mercury was not linear, instead the statistical model fit suggested an accelerated risk. This association remained statistically significant after controlling for specific variables such as SES, urbanicity, and race.

Power plant release of mercury and distance to power plant independently predicted increased rates of autism. In this case the association was linear (not accelerated). Again, this association remained statistically significant after controlling for other variables.

It is easy to dismiss these findings as inconsequential because they are ‘correlational’ in nature, or do not really prove anything. Researchers are too often guilty of selective acceptance of research: those studies that fit the consensus are accepted while those that don’t are dismissed for their methodological flaws – even though the studies we accept are equally flawed.

In the spirit of fairness I have to say that these findings are strong. Their methodology and analytical process are not any different from what is commonly seen in social science or epidemiology research. Is it perfect? Far from it. Is it useful or informative? Definitively! The data speak very clearly: In Texas, mercury release from industrial sources and power plants in 1997, and school proximity to these sources, are associated with rates of autism in 2002 as measured by school special education classifications.

Does this mean that mercury causes autism? Not at all. In the last sentence of the previous paragraph you can not replace the words are associated with with the word cause. There is a major difference. The data, albeit strong, have limitations. For example, the most obvious (to me) alternative explanation is that mercury release and proximity to these sources is also associated with another mystery factor that is causing this apparent association and that in fact, mercury release has nothing to do with autism rates in 2002. Let’s hypothesize that these power plants and industrial sources also release another toxin – let’s call this toxin autisimic (this is a made up toxin). These sources release mercury and autisimic at the same rate, so for each pound of mercury released there is a pound of autisimic released. It is possible then that this autisimic toxin directly increases the risk for autism, and this could explain completely the strong (but now obviously inaccurate) association between mercury release and autism.

Does this study show that vaccines cause autism? Absolutely not. I know this question may sound ludicrous to some, but I pose it rhetorically because I am certain that some will make the wide leap and link these findings to the vaccine issue.

There are other problems and limitations with this study, such as how autism rates were calculated (using all children instead of only those born inor after 1997), whether the autism rates are truly climbing and not explained by other factors, whether there are other variables that could be explaining this relation, etc, etc — and yes, this study does not prove or directly indicate that autism is caused by mercury exposure (click here for a much more critical review of this study). But this study is compelling in showing an association between mercury exposure and autism rates, and scientists can not just ignore it under the basis of its imperfect design and inability to make causal links – if that is the case, then only carefully controlled laboratory studies, with poor external validity, should be published and accepted as contributors to our greater scientific knowledge. This is study is far, far, from perfect, and many changes should have been requested prior to publication, but I can say the same of 90% of what is published today.

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The autism rate of 1 in 150 was published by the US Center for Disease Control in 2007 as part of a weekly disease morbidity and mortality surveillance report. The 1 in 150 rate was obtained from a population-based study of 8 year old children conducted in 2002. Specifically, teams in 15 US States reviewed health and educational records of children born in 1994. Trained clinicians classified them as having an autism spectrum disorder if:

1) had a documented previous classification of an ASD (i.e., the child had either an uncontradicted record of an autistic disorder or ASD diagnosis provided by a qualified examiner or documentation of qualification for special education services during 1994–2002 under an autism eligibility category)
or
2) did not have a documented ASD classification but had an evaluation record from an educational or clinical source indicating unusual social behaviors consistent with an ASD.

However, the clinical team conducted an additional detailed analysis of the records to ensure that the accepted DSM-IV criteria for autism and ASD was met prior to classifying each child. Thus, classification was not only based on prior records, but also included a secondary analysis by a clinical team that utilized structured procedures to maximize the validity and reliability of their diagnostic process.

The results:
Overall, the teams reported a rate of 6.6 per 1,000 children as meeting the diagnostic criteria for ASD (1 in 151). Rates by State varied significantly, but this was affected by differences in the way rates were obtained. Some States were able to determine rates based on health records AND educational records, while others could not get access to educational records. As expected, States that had access to educational records had higher prevalence rates than those that only examined health records. On average, States with access to educational and health records reported an autism prevalence rate of 7.2 per 1,000 (1 in 139), while those with only access to health record reported a rate of 5.1 per 1,000 (1 in 196). The male to female ratio significantly varied by State and ranged from 3.4 to 1 in Maryland to 6.5 to 1 in Utah.

Things to keep in mind:
- This report was based ONLY on children born in 1994. Thus it is possible that the rates could not apply to other cohorts.
- The differences in prevalence rates between States with and without access to educational records could suggest that 1) the overall rate is an underestimate because some sites only had access to health records, or 2) that the overall rate is an overestimate because some sites included cases ascertained from educational records which may be less reliable than health records.
- This rate of 1 in 150 does not refer to new cases of autism, or total cases in the population. It only speaks to cases among 8 year old children in 2002.

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