This article analyzes the soaring rates of “autism spectrum disorders” reported in the United States and other developed nations over the last twenty to thirty years. The United States Centers for Disease Control (CDC) recently announced new data that one in eighty-eight (1 in 88) children in the United States are diagnosed with autism spectrum disorders, often simplified to autism in press reports.
Autism may be associated with mathematical skills. Autism researcher Simon Baron-Cohen has published studies that autism is more prevalent in the familes of physicists, engineers, and mathematicians. Unusual mathematical skills are reported in a small percentage of cases of “classic autism.” Movies, television, and popular culture such as Rainman (1988), Mercury Rising (1998), and many other works often play up this rare association by presenting autistic characters with extreme mathematical abilities.
It has frequently been suggested that various scientists and mathematicians including the Nobel Prize winning physicist Paul Dirac, the Russian mathematician and Fields Medal refuser Grigori Perelman, and Fields Medal winner Richard Borcherds have had or have Asperger’s Syndrome, now included in the autism spectrum. Vernon L. Smith who won the Nobel Prize for Economics in 2002 has stated that he has Asperger’s Syndrome. Wired Magazine popularized the notion of an association of Asperger’s syndrome and autism with computer technology and math in the article The Geek Syndrome by Steve Silberman.
What is autism?
Prior to 1980, autism referred to a rare severely disabling condition that often resulted in lifelong institutionalization. Autism was first described by the psychiatrist Leo Kanner in 1943. In 1956, Kanner and his colleague Dr. Leon Eisenberg defined autism as:
- a profound lack of affective contact (Author’s Note: language and social skills)
- repetitive, ritualistic behavior, which must be of an elaborate kind.
In practical terms, Kanner’s definition referred to children who experienced an extreme failure to develop normal language and social skills, such as babbling and frequently being unable to carry on even a basic conversation. Similarly the repetetive behavior referred to extremely abnormal behavior such as spinning around for hours at a time.
Kanner’s autism is now often identified as “classic autism.” Many readers are probably familiar with the 1988 movie Rainman starring Tom Cruise and Dustin Hoffman. Rainman present a somewhat romanticized depiction of a relatively high functioning person, Raymond Babbitt played by Hoffman, with classic autism. Hoffman’s character also exhibits unusual mathematical skills, something reported in a small percentage of classic autism cases. Hoffman’s character has been institutionalized most of his adult life and ultimately returns to the institution at the end of the movie.
A Dramatic Increase
Autism Prevalence by Nation
The figure above shows the prevalence of autism at different times and in different nations according to different studies collected and reviewed in the US Centers for Disease Control (CDC) Autism Prevalence Summary Table for 2011. The appendix below gives the data extracted from the CDC document and the GNU Octave script used to generate the figures in this article. The red line in the figure above is a polynomial fit to the worldwide autism prevalence data.
The reported prevalence of autism has increased dramatically, especially since the early 1990s. In the 1960’s and 1970’s, autism as defined by Kanner was extremely rare. There were few studies of the prevalence of the condition. The few studies in the United States and the United Kindgom showed a prevalence of less than one child in one-thousand (1 in 1000). The typical rates were about 3 or 4 children per 10,000 children, less than half of one tenth of one percent. Many people lived their lives without ever encountering a case of autism.
The definition of autism has changed several times over the last forty years. Most significantly, autism was expanded into the “autism spectrum disorders” which includes a range of other, possibly related conditions, notably Asperger’s Syndrome. The definitions are also fairly general like Kanner and Eisenberg’s original definition. They may, therefore, be prone to changing interpretations even if the verbal description does not change. What is profound in Kanner’s original definition, for example? What is elaborate?
The studies reported on the CDC’s web site use a wide range of different definitions to diagnose autism. DSM refers to the Diagnostic and Statistical Manual of Mental Disorders from the American Psychiatric Association. DSM is sort of the Bible of psychiatric diagnoses. Significantly, autism was first recognized as a separate, distinct diagnosis in DSM-III in 1980. The definition of autism in DSM-III was substantially reworded in DSM-III-R in 1987, although it still appears to refer to “classic autism”. In 1994, the DSM expanded autism to a range of disorders, the so-called “autism spectrum disorders”, including Asperberger’s Syndrome. This coincides with the start of the dramatic increase in the prevalence of autism in the United States. The World Health Organization (WHO) ICD-10 manual of metnal disorders, used in other developed nations, also adopted the “autism spectrum disorders” as about the same time.
Diagnostic and Statistical Manual of Mental Disorders IV
The major formal definitions of autism and autism spectrum disorders:
- Kanner
- Kanner (original paper in 1943)
- Kanner and Eisenberg (1956)
- DSM-III (1980)
- DSM-III-R (1987)
- ICD-10 (WHO)
- DSM-IV (1994)
- Autism Spectrum
- Asperger’s Syndrome
- More to come: DSM-V!
The APA is now proposing to change the definition of autism yet again in DSM-V which is slated to come out in the next few years.
Autism by Diagnostic Criteria
Asperger’s Chic: Is Bill Gates Autistic?
Asperger’s Syndrome is a generally milder condition characterized by poor or seriously impaired social skills and relatively normal language skills. Asperger’s syndrome was first described by the Austrian pediatrician Hans Asperger in 1944. Once obscure, Asperger’s syndrome was added to the so-called autism spectrum disorders. When people like Bill Gates, Paul Dirac, Grigori Perelman, Richard Borcherds, and Vernon Smith are identified as autistic or having a mild form of autism, this usually refers to an imputed diagnosis of Asperger’s syndrome. This is often purely speculative. Of these examples, only the Nobel Prize winning economist Vernon Smith has publicly claimed that he has Asperger’s syndrome.
Nobel Prize Economist Vernon Smith
Asperger’s syndrome in particular has been closely associated in popular culture with scientific, technical, and mathematical skills. How accurate these popular associations are is debatable. Social skills are acquired through practice. Scientists, engineers, mathematicians, and others who spend large amounts of time with machines or symbols instead of people are likely to have poorer social skills than those who spend large amounts of time interacting with other people.
It certainly has been the author’s personal experience that there is a high rate of odd, possibly irrational behavior, what might be characterized as psychological problems, among people who engage in heavily mathematical work. There are folk traditions and some scientific studies like Baron-Cohen’s studies of autism in the families of technical people that support this view.
If real, are these problems Asperger’s syndrome or something else? Mathematical work often involves very high levels of concentration over extended periods of time, meaning months or even years. There is some reason to suspect these high levels of extended concentration can be unhealthy and have adverse effects.
The inclusion of Asperger’s Syndrome (and other syndromes once distinct from Kanner’s autism or wholly new) in the autism spectrum as well as the vague, possibly changing working definition of Asperger’s greatly complicates the interpretation of the prevalence of autism and/or autism spectrum disorders, frequently reported in the popular press as simply “autism”. Since 1990, the prevalence of autism or at least diagnoses of autism spectrum disorders has increased by a factor of about twenty in the United States. Is there an “autism epidemic” or is the increase purely due to the changing definitions and greater awareness of the autism spectrum disorders — or some confusing combination of the two?
An Environmental Cause?
If the increase in the prevalence of autism is real or mostly real, it is probably due to an environmental cause. Although there is evidence that genes play a role in autism, a twenty-fold increase in a genetic disease is implausible unless a mutagenic agent was causing a sharp increase in damaged genes. Autism does not act like a contagious disease. You don’t catch autism from your children. Children don’t catch autism from other children. Thus, a real increase would likely be due to some environmental factor or factors that is either new or has increased in prevalence over the last twenty to thirty years.
There are, of course, many possible candidates for an environmental cause or causes. Many things have increased dramatically in the last twenty years or are entirely new. The one which receives the most attention and bitter controversy with respect to autism is childhood vaccination. Many parents with children diagnosed as autistic report something like the following:
My kid was developing normally, starting to speak, interact. Then, around eighteen months, we took our kid in for a series of vaccine shots, including the MMR (Measles/Mumps/Rubella) shot. My kid seemed to have a reaction to the shots or got sick. My kid was never the same after that. My kid stopped speaking, began to behave strangely. Then my kid was diagnosed as autistic.
In the United States, both the number of recommended vaccine shots and the number of different diseases vaccinated against has increased substantially over the last twenty to thirty years.
CDC 2012 Vaccine Schedule Closeup
Under the Vaccines for Children (VFC) program inaugurated in 1994, the CDC spent $3.9 billion in 2010, over one third of their annual budget, purchasing vaccines for distribution to children in the United States, about one half the total revenues for vaccine sales in the United States. The CDC is heavily invested in childhood vaccination.
Many readers have probably heard of the medical journal The Lancet’s retraction of British physician and researcher Andrew Wakefield’s study of the MMR vaccine as fraudulent as well as Wakefield’s conviction for misconduct by the British General Medical Council. (GMC). Wakefield was publicly pilloried by Bill Gates in a widely cited interview, for example. Various lawsuits and appeals by Wakefield and his co-authors are on going. For example, the conviction of his co-author Professor John Walker Smith by the GMC has, for the moment at least, been overturned.
The relationship between autism and vaccines is unfortunately much more complex than the sound bite media coverage of Wakefield’s woes. The ambiguous and changing definitions of autism and autism spectrum disorders make it extremely difficult, if not impossible, to evaluate the possible relationship between vaccinations and autism, despite the passionate claims of both sides in the dispute.
There are, of course, many other possible causes that have increased dramatically or are entirely new, and several have also been suggested as causing or contributing to the supposed “autism epidemic”. Things that are new and/or have increased dramatically over the last twenty to thirty years include cell phones, the Internet, general computer use, Diet Coke, aspartame (the sweetener used in Diet Coke), Starbucks coffee, anti-depressant drugs such as Prozac, and high fructose corn syrup (HFCS). The reader with a little thought and/or research can probably identify many other possibilities.
One may note, with respect to Wired’s The Geek Syndrome, that most of these possible causes are associated with the modern “geek” lifestyle.
Correlation does not prove causation. Even if A and B are perfectly correlated, a rarity in the real world, A may cause B, B may cause A, A and B may share a common unidentified cause C, or the correlation may be pure coincidence.
Indeed, when there is imperfect measurement, changing definitions, or other problems with data collection as clearly occurs with the autism spectrum disorders, the lack of a correlation does not prove lack of causation. For example, if half the overall increase is due to a real increase and half the increase is due to changing definitions, then the “noise” from the changing definitions could hide the true causal relationship.
The prevalence of autism spectrum disorders could continue to increase even though the use of the causal agent demonstrably declined after an initial increase. The initial rise in autism was real but the continuing rise is due to the changing definitions and greater awareness of the disorders, hopelessly confusing a statistical analysis.
The Limits of Statistics
The autism enigma is an example of the limitations of mathematical modeling and seemingly sophisticated statistical methods in the real world. Even in so-called hard sciences such as physics there can be bitter disputes and controversies over the results of data analyses such as the dispute that occurred with the putative faster than light neutrino measurements at the OPERA experiment just recently. In the OPERA experiment, a complex analysis yielded a seemingly definitive five sigma signal, yet many physicists both within the OPERA collaboration and outside were skeptical. The result did not feel right, not a very scientific criterion. The dramatic result turned out to be due to a systematic bias, an incorrect timing measurement.
When scientists, medical researchers, and others try to apply mathematical modeling or statistical methods to data in economics, finance, marketing, medicine, biology, epidemiology, and other “softer” fields, there are almost always substantial and difficult problems with the selection of data, the definition of terms, various sources of serious and difficult to quantify bias (e.g. the CDC will look really bad if it turns out their vaccination program had anything to do with the rise in autism), and so forth.
Present day widely used mathematical and statistical methods such as regression analysis or the polynomial fit used in this article really can’t address these issues. They assume the data is clean or relatively clean — that it is comparable to the results of flipping a coin in a lab or a fair game of chance with no cheating in a casino, the sort of situations these mathematical and statistical methods were often originally developed to study and model.
In the case of the autism spectrum disorders, the definitions of the many flavors of “autism” are quite general, not specific, and not quantitative. In general terms, Bill Gates reportedly rocking back and forth in some situations is similar to “autistic” repetitive activity. But the frequency and magnitude of this behavior differs dramatically from a child with “classic autism” engaged in rocking back and forth or some other repetitive behavior. The definitions need to be specific, quantitative, and demonstrably repeatable by different psychiatrists in order to fairly compare different people, different times, and different populations.
Conclusion
Remarkably, despite a truly dramatic, twenty-fold, increase in diagnoses of autism spectrum disorders in the last twenty years, we don’t know the cause of this increase or even if it is real. What is needed to resolve this worrisome conundrum is at least:
- Improved, specific, quantitative definitions of the autism spectrum disorders that do not change over time and are not susceptible to changing interpretations.
- An improved, specific, quantitative definition of “classic autism” that can be compared reliably to the old data from the 1960’s, 1970’s, and 1980’s.
- Truly independent, unbiased, disinterested research into the relationship between autism and vaccines, not funded or controlled by the CDC or other interested parties.
- Truly independent, unbiased, disinterested research into the relationship between autism and other possible environmental causes.
Sadly, this is easy to say and very difficult to achieve in the modern world.
References
Simon Baron-Cohen, Patrick Bolton, Sally Wheelwright, Victoria Scahill
Liz Short, Genevieve Mead, and Alex Smith, “Autism occurs more
often in families of physicists, engineers, and mathematicians.” Autism, 1998, 2, 296-301.
Sally Wheelwright and Simon Baron-Cohen, “The link between autism and skills such as engineering, maths, physics, and computing: A reply to Jarrold and Routh” (2001) Autism, 5, 223-227.
Kanner, L. & Eisenberg, L. (1956), Early Infantile Autism 1943-1955, American Journal of Orthopsychiatry 26, pp. 55–65. Reprinted in: Alexander et al., eds. Op. cit. Reprinted in Psychiat. Res. Repts. 1957 (April), American Psychiatric Assn., pp. 55–65.
Resources/Recommended Reading
The Strangest Man: The Hidden Life of Paul Dirac, Mystic of the Atom By Graham Farmelo, Basic Books (2009)
Perfect Rigor: A Genius and the Mathematical Breakthrough of the Century
By Masha Gessen
Houghton Mifflin Harcourt; 2nd Edition edition (November 11, 2009)
Credits
The picture of Vernon Smith is from Wikimedia Commons. It was produced by the US Federal Government and is in the public domain.
The picture of the CDC Recommended Vaccines for 2012 is from the CDC web site and is a work of the US Federal Government and is in the public domain.
© 2012 John F. McGowan
About the Author
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing video compression and speech recognition technologies. He has extensive experience developing software in C, C++, Visual Basic, Mathematica, MATLAB, and many other programming languages. He is probably best known for his AVI Overview, an Internet FAQ (Frequently Asked Questions) on the Microsoft AVI (Audio Video Interleave) file format. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech). He can be reached at jmcgowan11@earthlink.net.
Appendix: Data and Analysis
Original data from Autism_PrevalenceSummaryTable_2011.pdf at the CDC Web Site. This data was acquired by opening the CDC table in Adobe Acrobat Reader 9.5.0, selecting all of the text, copying, and pasting into a buffer in Notepad++ 5.8.7. A few fields in the CDC table were left blank which caused problems in reformatting the data as a tab-delimited file that could be read by GNU Octave. Consequently, these blank fields were replaced with the text LEFT_BLANK in the data below.
Summary of Autism Spectrum Disorder (ASD) Prevalence Studies Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Lotter 1966 England 1964 8 to 10 78,000 Kanner Case enumeration and direct exam 0.45 (0.31-0.62) 84 Brask 1970 Denmark 1962 2 to 14 46,500 Kanner Case enumeration 0.43 (0.26-0.66) NR Treffert 1970 USA 1962-1967 3 to 12 899,750 Kanner Case enumeration 0.07-0.31 (0.0-1.0) NR Wing & Gould 1979 England 1970 0 to 14 35,000 Kanner Case enumeration and direct exam 0.49 (0.29-0.78) 70 Hoshino et al. (1) 1982 Japan 1977 0 to 17 234,039 Kanner Case enumeration and direct exam 0.23 (0.19-0.27) NR Ishii & Takahashi 1983 Japan 1981 6 to 12 35,000 Rutter Case enumeration and direct exam 1.6 (1.2-2.8) NR Bohman et al. 1983 Sweden 1979 0 to 20 69,000 Rutter Case enumeration and direct exam 0.3 (0.2-0.5) NR McCarthy et al. 1984 Ireland 1978 8 to 10 65,000 Kanner Case enumeration and direct exam 0.43 (0.29-0.59) NR Gillberg 1984 Sweden 1980 4 to 18 128,584 DSM-III Case enumeration and direct exam 0.20 (0.13-0.30) 80, 77 Steinhausen et al. 1986 Germany 1982 0 to 14 279,616 Rutter Case enumeration and direct exam 0.19 (0.14-0.24) 44 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Steffenberg & Gillberg 1986 Sweden 1984 <10 78,413 DSM-III Case enumeration and direct exam 0.45 (0.31-0.62) NR Matsuishi et al. 1987 Japan 1983 4 to 12 32,834 DSM-III Case enumeration and direct exam 1.55 (1.16-1.64) NR Burd et al. 1987 USA 1985 2 to 18 180,986 DSM-III Case enumeration and direct exam 0.12 (0.00-0.20) NR Bryson et al. 1988 Canada 1985 6 to 14 20,800 DSM-III Case enumeration and direct exam 1.01 (0.62-1.54) 76 Tanoue et al. 1988 Japan 1977-1985 3 to 7 95,394 DSM-III Case enumeration 1.38 (1.16-1.64) NR Ciadella & Mamelle 1989 France 1986 3 to 9 135,180 DSM-III Case enumeration 0.51 (0.39-0.63) NR Sugiyama & Abe 1989 Japan 1979-1984 2 to 5 12,263 DSM-III Population screen and direct exam 1.3 (0.7-2.1) 38 Ritvo et al. 1989 USA 1984-1988 8 to 12 184,822 DSM-III Case enumeration and direct exam 0.40 (0.31-0.50) NR Gillberg et al. 1991 Sweden 1988 4 to 13 78,106 DSM-III-R Case enumeration and direct exam 0.95 (0.74-1.95) 82, 80 Fombonne & Mazaubrun (1) 1992 France 1985 9 to 13 274,816 ICD-10 Case enumeration and direct exam 0.49 (0.47-0.65) 87 Honda et al. 1996 Japan 1994 1.5 to 6 8,537 ICD-10 Population screen and direct exam 2.11 (1.25-3.33) 50 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Fombonne et al. 1997 France 1992-1993 6 to 16 325,347 ICD-10 Case enumeration and direct exam 0.54 (0.46-0.62) 88 Arivdsson et al. 1997 Sweden 1994 3 to 16 1,941 ICD-10 Population screen and direct exam 3.10 (1.14-6.72) 100 Webb et al. 1997 Wales 1992 3 to 15 73,300 DSM-III-R Case enumeration and direct exam 0.72 (0.54-0.95) NR Sponheim & Skjeldae 1998 Norway 1992 3 to 14 65,688 ICD-10 Case enumeration and direct exam 0.38 (0.25-0.56) 64 Kadesjo et al. 1999 Sweden 1992 6.7 to 7.7 826 ICD-10 Case enumeration and direct exam 6.0 (1.97-14.1) 60 Baird et al. 2000 England 1998 1.5 to 8 16,235 ICD-10 Population screen and direct exam 3.1 (2.29-4.06) 40 Powell et al. 2000 England 1995 1 to 4 29,200 DSM-III-R or DSM-IV Case enumeration 0.96 (0.64-1.39) NR Kielinen et al. 2000 Finland 1996 5 to 18 152,732 DSM-IV Case enumeration 1.22 (1.06-1.41) 50 Magnusson & Saemundsen 2000 Iceland 1997 5 to 14 43,153 ICD-10 Population screen and direct exam 0.86 (0.60-1.18) 49 Chakrabarti & Fombonne 2001 England 1998 2.5 to 6.5 15,500 DSM-IV Population screen and direct exam 1.68 (1.1-2.46) 24 Fombonne et al. (2) 2001 UK 1999 5 to 15 12,529 DSM-IV Population screen and direct exam 2.61 (1.81-3.70) 44.4 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Bertrand et al. 2001 USA 1998 3 to 10 8,996 DSM-IV Case enumeration and direct exam 4.0 (2.8-5.5) 49 Croen et al. 2001 USA 1987-1999 0 to 21 4,600,000 DSM-III-R or DSM-IV Case enumeration 1.1 (1.06-1.14) NR Yeargin-Allsopp et al. (2) 2003 USA 1996 3 to 10 290,000 DSM-IV Case enumeration 3.4 (3.2-3.6) 62 Gurney et al. (2) 2003 USA 1981-1982, 2001-2002 6 to 17 DSM-IV Case enumeration 4.4 (4.3-4.5) NR Lingam et al. 2003 UK 2000 5 to 14 186,206 ICD-10 Case enumeration 1.5 (1.3-1.7) NR Icasiano et al. 2004 Australia 2002 2 to 17 45,153 DSM-IV Case enumeration 3.9 (3.3-4.5) 47 Lauritsen et al. 2004 Denmark 2001 0 to 9 682,397 ICD-10 Case enumeration 1.2 (1.1-1.3) NR Fombonne et al. 2006 Canada 1987-1998 5 to 21 27,749 DSM-IV Case enumeration 2.16 (1.65-2.78) NR Baird et al. 2006 UK 1990-1991 9 to 10 56,946 ICD-10 Case enumeration, screen, and direct exam 3.89 (3.39-4.43) 56 CDC ADDM Network (1) 2007 USA 2000 8 187,761 DSM-IV Case enumeration and record review 6.7 (6.3-7.0) 36-61 CDC ADDM Network (1) 2007 USA 2002 8 444,050 DSM-IV Case enumeration and record review 6.6 (6.3-6.8) 45 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Oullette-Kuntz et al. 2007 Canada 1996-2004 4 to 9 2,240,537 Special education classification Case enumeration from special education classification 1.2 (1996), 4.3 (2004) NR Wong et al. (1) 2008 Hong Kong 1986-2005 0 to 14 4,247,206 DSM-IV Case enumeration 1.6 NR Williams et al. 2008 Australia 2003-2004 6 to 12 5,459 DSM-IV Questionnaires 1.0 (0.8-1.0) to 4.1 (3.8-4.4) NR Montiel-Nava et al. 2008 Venezuela 2005-2006 3 to 9 254,905 DSM-IV Case enumeration 1.7 (1.3-2.0) NR Baron-Cohen et al. 2009 UK 2003-2004 5 to 9 5,484 Special Education Needs register Case enumeration from survey and direct exam 15.7 (9.9-24.6) NR CDC ADDM Network (1) 2009 USA 2004 8 172,335 DSM-IV Case enumeration and record review 8.0 (7.6-8.4) 44 CDC ADDM Network (1) 2009 USA 2006 8 308,038 DSM-IV Case enumeration and record review 9.0 (8.6-9.3) 41 Al-Farsi et al. 2010 Oman 2009 0 to 14 798,913 DSM-IV Case enumeration 0.1 (0.1-0.2) NR Parner et al. 2011 Denmark 1994-1999 LEFT_BLANK 404,816 DSM-IV Case enumeration 6.9 (6.5-7.2) NR Parner et al. 2011 Western Australia 1994-1999 LEFT_BLANK 152,060 DSM-IV Case enumeration 5.1 (4.7-5.5) NR Chien et al. 2011 Taiwan 1996-2005 0 to 18 372,642 ICD-9 Case enumeration 2.9 NR Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Windham et al. 2011 USA 1994, 1996 0 to 8 82,153 (1994), 80,249 (1996) DSM-IV Case enumeration 4.7 (4.2-5.1) (1994); 4.7 (4.2-5.2) (1996) NR Kim et al. 2011 South Korea 2005-2009 7 to 12 55,266 DSM-IV Case enumeration from survey and direct exam 26.4 (19.1-33.7) 59 Zimmerman et al. 2012 USA 2002, 2006, 2008 8 26,213 (2002); 29,494 (2006); 33,757 (2008) ICD-9 and special education classification Case enumeration 6.5 (2002), 10.2 (2006), 13.0 (2008) NR Kocovska et al. 2012 Faroe Islands 2002, 2009 7-16 (2002), 15-24 (2009) 7122 (2002), 7128 (2009) DSM-IV, ICD-10 Screening and direct exam 5.6 (2002), 9.4 (2009) NR CDC ADDM Network (1) 2012 USA 2008 8 337,093 DSM-IV Case enumeration and record review 11.3 (11.0-11.7) 38 (1) The prevalence reported represents the average. (2) The prevalence study provided overall rate only`
C program reformat.c
to reformat data from CDC web site from one field per line to a
tab-delmited file with one row per autism prevalence study.
#include#include int main(int argc, char ** argv) { char szBuffer[256]; int nFieldCount = 0; printf("reformatting autism data\n"); FILE *fp = fopen("autism_data_working_copy.txt", "r"); if(fp) { FILE * fpOut = fopen("reformat_data.txt", "w"); if(fpOut) { if(fgets(szBuffer, 256, fp) != NULL) // skip first field -- table title { while(fgets(szBuffer, 256, fp) != NULL) // get a line from { nFieldCount++; unsigned int nEnd = (unsigned) strlen(&szBuffer[0]) - 1; if(szBuffer[nEnd] == '\n' || szBuffer[nEnd] == '\r') szBuffer[nEnd] = (char) 0; if(szBuffer[nEnd-1] == '\n' || szBuffer[nEnd-1] == '\r') szBuffer[nEnd-1] = (char) 0; // replace strings with number codes that Octave can handle if(strcmp(szBuffer, "USA") == 0) strcpy(szBuffer, "1"); // use number 1 for USA if(strcmp(szBuffer, "UK") == 0) strcpy(szBuffer, "2"); if(strcmp(szBuffer, "England") == 0) strcpy(szBuffer, "3"); if(strcmp(szBuffer, "Sweden") == 0) strcpy(szBuffer, "4"); if(strcmp(szBuffer, "Canada") == 0) strcpy(szBuffer, "5"); if(strcmp(szBuffer, "Australia") == 0) strcpy(szBuffer, "6"); if(strcmp(szBuffer, "Japan") == 0) strcpy(szBuffer, "7"); if(strcmp(szBuffer, "Germany") == 0) strcpy(szBuffer, "8"); if(strcmp(szBuffer, "France") == 0) strcpy(szBuffer, "9"); if(strcmp(szBuffer, "Ireland") == 0) strcpy(szBuffer, "10"); if(strcmp(szBuffer, "Denmark") == 0) strcpy(szBuffer, "11"); if(strcmp(szBuffer, "South Korea") == 0) strcpy(szBuffer, "12"); // diagnosis criteria if(strcmp(szBuffer, "Kanner") == 0) strcpy(szBuffer, "1"); if(strcmp(szBuffer, "DSM-III") == 0) strcpy(szBuffer, "2"); if(strcmp(szBuffer, "DSM-III-R") == 0) strcpy(szBuffer, "3"); if(strcmp(szBuffer, "ICD-10") == 0) strcpy(szBuffer, "4"); if(strcmp(szBuffer, "DSM-IV") == 0) strcpy(szBuffer, "5"); if(strcmp(szBuffer, "ICD-9") == 0) strcpy(szBuffer, "6"); if (nFieldCount % 10) fprintf(fpOut, "%s\t", szBuffer); else fprintf(fpOut, "%s\n", szBuffer); } } fclose(fpOut); } else { fprintf(stderr, "Unable to open output file\n"); } fclose(fp); } else { fprintf(stderr, "Unable to open data file for input!\n"); } }
Reformated data from the CDC web site reformat_data.txt
Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Lotter 1966 3 1964 8 to 10 78,000 1 Case enumeration and direct exam 0.45 (0.31-0.62) 84 Brask 1970 11 1962 2 to 14 46,500 1 Case enumeration 0.43 (0.26-0.66) NR Treffert 1970 1 1962-1967 3 to 12 899,750 1 Case enumeration 0.07-0.31 (0.0-1.0) NR Wing & Gould 1979 3 1970 0 to 14 35,000 1 Case enumeration and direct exam 0.49 (0.29-0.78) 70 Hoshino et al. (1) 1982 7 1977 0 to 17 234,039 1 Case enumeration and direct exam 0.23 (0.19-0.27) NR Ishii & Takahashi 1983 7 1981 6 to 12 35,000 Rutter Case enumeration and direct exam 1.6 (1.2-2.8) NR Bohman et al. 1983 4 1979 0 to 20 69,000 Rutter Case enumeration and direct exam 0.3 (0.2-0.5) NR McCarthy et al. 1984 10 1978 8 to 10 65,000 1 Case enumeration and direct exam 0.43 (0.29-0.59) NR Gillberg 1984 4 1980 4 to 18 128,584 2 Case enumeration and direct exam 0.20 (0.13-0.30) 80, 77 Steinhausen et al. 1986 8 1982 0 to 14 279,616 Rutter Case enumeration and direct exam 0.19 (0.14-0.24) 44 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Steffenberg & Gillberg 1986 4 1984 <10 78,413 2 Case enumeration and direct exam 0.45 (0.31-0.62) NR Matsuishi et al. 1987 7 1983 4 to 12 32,834 2 Case enumeration and direct exam 1.55 (1.16-1.64) NR Burd et al. 1987 1 1985 2 to 18 180,986 2 Case enumeration and direct exam 0.12 (0.00-0.20) NR Bryson et al. 1988 5 1985 6 to 14 20,800 2 Case enumeration and direct exam 1.01 (0.62-1.54) 76 Tanoue et al. 1988 7 1977-1985 3 to 7 95,394 2 Case enumeration 1.38 (1.16-1.64) NR Ciadella & Mamelle 1989 9 1986 3 to 9 135,180 2 Case enumeration 0.51 (0.39-0.63) NR Sugiyama & Abe 1989 7 1979-1984 2 to 5 12,263 2 Population screen and direct exam 1.3 (0.7-2.1) 38 Ritvo et al. 1989 1 1984-1988 8 to 12 184,822 2 Case enumeration and direct exam 0.40 (0.31-0.50) NR Gillberg et al. 1991 4 1988 4 to 13 78,106 3 Case enumeration and direct exam 0.95 (0.74-1.95) 82, 80 Fombonne & Mazaubrun (1) 1992 9 1985 9 to 13 274,816 4 Case enumeration and direct exam 0.49 (0.47-0.65) 87 Honda et al. 1996 7 1994 1.5 to 6 8,537 4 Population screen and direct exam 2.11 (1.25-3.33) 50 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Fombonne et al. 1997 9 1992-1993 6 to 16 325,347 4 Case enumeration and direct exam 0.54 (0.46-0.62) 88 Arivdsson et al. 1997 4 1994 3 to 16 1,941 4 Population screen and direct exam 3.10 (1.14-6.72) 100 Webb et al. 1997 Wales 1992 3 to 15 73,300 3 Case enumeration and direct exam 0.72 (0.54-0.95) NR Sponheim & Skjeldae 1998 Norway 1992 3 to 14 65,688 4 Case enumeration and direct exam 0.38 (0.25-0.56) 64 Kadesjo et al. 1999 4 1992 6.7 to 7.7 826 4 Case enumeration and direct exam 6.0 (1.97-14.1) 60 Baird et al. 2000 3 1998 1.5 to 8 16,235 4 Population screen and direct exam 3.1 (2.29-4.06) 40 Powell et al. 2000 3 1995 1 to 4 29,200 DSM-III-R or DSM-IV Case enumeration 0.96 (0.64-1.39) NR Kielinen et al. 2000 Finland 1996 5 to 18 152,732 5 Case enumeration 1.22 (1.06-1.41) 50 Magnusson & Saemundsen 2000 Iceland 1997 5 to 14 43,153 4 Population screen and direct exam 0.86 (0.60-1.18) 49 Chakrabarti & Fombonne 2001 3 1998 2.5 to 6.5 15,500 5 Population screen and direct exam 1.68 (1.1-2.46) 24 Fombonne et al. (2) 2001 2 1999 5 to 15 12,529 5 Population screen and direct exam 2.61 (1.81-3.70) 44.4 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Bertrand et al. 2001 1 1998 3 to 10 8,996 5 Case enumeration and direct exam 4.0 (2.8-5.5) 49 Croen et al. 2001 1 1987-1999 0 to 21 4,600,000 DSM-III-R or DSM-IV Case enumeration 1.1 (1.06-1.14) NR Yeargin-Allsopp et al. (2) 2003 1 1996 3 to 10 290,000 5 Case enumeration 3.4 (3.2-3.6) 62 Gurney et al. (2) 2003 1 1981-1982, 2001-2002 6 to 17 LEFT_BLANK 5 Case enumeration 4.4 (4.3-4.5) NR Lingam et al. 2003 2 2000 5 to 14 186,206 4 Case enumeration 1.5 (1.3-1.7) NR Icasiano et al. 2004 6 2002 2 to 17 45,153 5 Case enumeration 3.9 (3.3-4.5) 47 Lauritsen et al. 2004 11 2001 0 to 9 682,397 4 Case enumeration 1.2 (1.1-1.3) NR Fombonne et al. 2006 5 1987-1998 5 to 21 27,749 5 Case enumeration 2.16 (1.65-2.78) NR Baird et al. 2006 2 1990-1991 9 to 10 56,946 4 Case enumeration, screen, and direct exam 3.89 (3.39-4.43) 56 CDC ADDM Network (1) 2007 1 2000 8 187,761 5 Case enumeration and record review 6.7 (6.3-7.0) 36-61 CDC ADDM Network (1) 2007 1 2002 8 444,050 5 Case enumeration and record review 6.6 (6.3-6.8) 45 Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Oullette-Kuntz et al. 2007 5 1996-2004 4 to 9 2,240,537 Special education classification Case enumeration from special education classification 1.2 (1996), 4.3 (2004) NR Wong et al. (1) 2008 Hong Kong 1986-2005 0 to 14 4,247,206 5 Case enumeration 1.6 NR Williams et al. 2008 6 2003-2004 6 to 12 5,459 5 Questionnaires 1.0 (0.8-1.0) to 4.1 (3.8-4.4) NR Montiel-Nava et al. 2008 Venezuela 2005-2006 3 to 9 254,905 5 Case enumeration 1.7 (1.3-2.0) NR Baron-Cohen et al. 2009 2 2003-2004 5 to 9 5,484 Special Education Needs register Case enumeration from survey and direct exam 15.7 (9.9-24.6) NR CDC ADDM Network (1) 2009 1 2004 8 172,335 5 Case enumeration and record review 8.0 (7.6-8.4) 44 CDC ADDM Network (1) 2009 1 2006 8 308,038 5 Case enumeration and record review 9.0 (8.6-9.3) 41 Al-Farsi et al. 2010 Oman 2009 0 to 14 798,913 5 Case enumeration 0.1 (0.1-0.2) NR Parner et al. 2011 11 1994-1999 LEFT_BLANK 404,816 5 Case enumeration 6.9 (6.5-7.2) NR Parner et al. 2011 Western Australia 1994-1999 LEFT_BLANK 152,060 5 Case enumeration 5.1 (4.7-5.5) NR Chien et al. 2011 Taiwan 1996-2005 0 to 18 372,642 6 Case enumeration 2.9 NR Author Year published Country Time period studied Age range studied Number of children in population Criteria used Methodology used ASD prevalence (CI) IQ<70 (%) Windham et al. 2011 1 1994, 1996 0 to 8 82,153 (1994), 80,249 (1996) 5 Case enumeration 4.7 (4.2-5.1) (1994); 4.7 (4.2-5.2) (1996) NR Kim et al. 2011 12 2005-2009 7 to 12 55,266 5 Case enumeration from survey and direct exam 26.4 (19.1-33.7) 59 Zimmerman et al. 2012 1 2002, 2006, 2008 8 26,213 (2002); 29,494 (2006); 33,757 (2008) ICD-9 and special education classification Case enumeration 6.5 (2002), 10.2 (2006), 13.0 (2008) NR Kocovska et al. 2012 Faroe Islands 2002, 2009 7-16 (2002), 15-24 (2009) 7122 (2002), 7128 (2009) DSM-IV, ICD-10 Screening and direct exam 5.6 (2002), 9.4 (2009) NR CDC ADDM Network (1) 2012 1 2008 8 337,093 5 Case enumeration and record review 11.3 (11.0-11.7) 38 (1) The prevalence reported represents the average. (2) The prevalence study provided overall rate only`
GNU Octave script autism_analysis.m
used to process the data and
generate the plots:
autism_data = dlmread('reformat_data.txt', '\t');
dates = real(autism_data(:,4)); % time period studied
country = autism_data(:,3); % country code usa is 1
valid = find(country >= 1);
usa = find(country == 1);
uk = find(country == 2);
eng = find(country == 3);
sweden = find(country == 4);
canada = find(country == 5);
australia = find(country == 6);
japan = find(country == 7);
germany = find(country == 8);
france = find(country == 9);
ireland = find(country == 10);
denmark = find(country == 11);
sk = find(country == 12); % South Korea (most extreme autism rate)
diagnosis = autism_data(:, 7); % diagnostic criteria
kanner = find(diagnosis == 1);
dsm3 = find(diagnosis == 2);
dsm4 = find(diagnosis == 5);
icd10 = find(diagnosis == 4);
prevalence = real(autism_data(:,9));
[p_autism, s] = polyfit(dates(valid), prevalence(valid), 3);
mydates = 1960:2012;
simrate_world = polyval(p_autism, mydates);
figure(1)
h1 = plot(dates, prevalence, 'o', mydates, simrate_world, '-r');
set(h1, 'linewidth', 3);
axis([1960 2012 0.0 30.0]);
ylabel('cases per 1000 children', 'fontsize', 14)
xlabel('Year', 'fontsize', 14)
title('Summary of Autism Spectrum Disorder (ASD) Prevalence Studies', 'fontsize', 14);
legend('DATA', 'FIT', 'location', 'northwest');
print('world_autism.jpg');
p_usa = polyfit(dates(usa), prevalence(usa), 3);
fit_usa = polyval(p_usa, mydates);
figure(2)
h2 = plot(dates(usa), prevalence(usa), 'o', mydates, fit_usa, '-r');
set(h2, 'linewidth', 3);
axis([1960 2012 0.0 10.0]);
ylabel('cases per 1000 children', 'fontsize', 14)
xlabel('Year', 'fontsize', 14)
title('Autism Spectrum Disorder Prevalence (USA)', 'fontsize', 14);
legend('DATA', 'FIT', 'location', 'northwest');
print('usa_autism.jpg');
figure(3)
plot(dates(kanner), prevalence(kanner), 'o');
axis([1960 2012 0.0 10.0]);
ylabel('cases per 1000 children')
xlabel('Year')
title('Autism Spectrum Disorder Prevalence (Kanner)');
figure(4)
plot(dates(dsm3), prevalence(dsm3), 'o');
axis([1960 2012 0.0 10.0]);
ylabel('cases per 1000 children')
xlabel('Year')
title('Autism Spectrum Disorder Prevalence (DSM-III)');
figure(5)
plot(dates(dsm4), prevalence(dsm4), 'o');
axis([1960 2012 0.0 30.0]);
ylabel('cases per 1000 children')
xlabel('Year')
title('Autism Spectrum Disorder Prevalence (DSM-IV)');
figure(6)
plot(dates(icd10), prevalence(icd10), 'o');
axis([1960 2012 0.0 10.0]);
ylabel('cases per 1000 children')
xlabel('Year')
title('Autism Spectrum Disorder Prevalence (ICD-10)');
figure(7)
h7 = plot(dates(usa), prevalence(usa), 'ob',
dates(uk), prevalence(uk), 'or',
dates(sweden), prevalence(sweden), 'ok',
dates(denmark), prevalence(denmark), '*b',
dates(japan), prevalence(japan), '*r',
dates(eng), prevalence(eng), '*k',
dates(france), prevalence(france), '+b',
dates(germany), prevalence(germany), '+r',
dates(canada), prevalence(canada), '+k',
dates(australia), prevalence(australia), 'xb',
dates(ireland), prevalence(ireland), 'xr',
dates(sk), prevalence(sk), 'xk',
mydates, simrate_world, '-r');
set(h7, 'linewidth', 3);
axis([1960 2012 0.0 30.0]);
ylabel('cases per 1000 children', 'fontsize', 14)
xlabel('Year', 'fontsize', 14)
title('Autism Spectrum Disorder Prevalence (By Country)', 'fontsize', 14);
legend('USA', 'UK', 'SWEDEN', 'DENMARK', 'JAPAN', 'ENGLAND', 'FRANCE',
'GERMANY', 'CANADA', 'AUSTRALIA', 'IRELAND', 'SOUTH KOREA', 'FIT',
'location', 'northwest');
print('autism_by_nation.jpg');
figure(8)
h8 = plot(dates(kanner), prevalence(kanner), 'ob',
dates(dsm3), prevalence(dsm3), 'or',
dates(icd10), prevalence(icd10), 'ok',
dates(dsm4), prevalence(dsm4), '*b');
set(h8, 'linewidth', 3);
axis([1960 2012 0.0 30.0]);
ylabel('cases per 1000 children', 'fontsize', 14)
xlabel('Year', 'fontsize', 14)
title('Autism Spectrum Disorder Prevalence (By Diagnostic Criteria)', 'fontsize', 14);
legend('KANNER', 'DSM-III', 'ICD-10', 'DSM-IV', 'location', 'northwest');
print('autism_by_criteria.jpg');