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The Correlation between Rates of Cancer and Autism: An Exploratory Ecological Investigation

The Correlation between Rates of Cancer and Autism: An Exploratory Ecological Investigation Background Autism is associated with high rates of genomic aberrations, including chromosomal rearrangements and de novo copy-number variations. These observations are reminiscent of cancer, a disease where genomic rearrangements also play a role. We undertook a correlative epidemiological study to explore the possibility that shared risk factors might exist for autism and specific types of cancer. Methodology/Principal Findings To determine if significant correlations exist between the prevalence of autism and the incidence of cancer, we obtained and analyzed state-wide data reported by age and gender throughout the United States. Autism data were obtained from the U.S. Department of Education via the Individuals with Disabilities Education Act (IDEA) (2000–2007, reported annually by age group) and cancer incidence data were obtained from the Centers for Disease Control and Prevention (CDC) (1999–2005). IDEA data were further subdivided depending on the method used to diagnose autism (DSM IV or the Code of Federal Regulations, using strict or expanded criteria). Spearman rank correlations were calculated for all possible pairwise combinations of annual autism rates and the incidence of specific cancers. Following this, Bonferroni's correction was applied to significance values. Two independent methods for determining an overall combined p -value based on dependent correlations were obtained for each set of calculations. High correlations were found between autism rates and the incidence of in situ breast cancer ( p ≤10 −10 , modified inverse chi square, n = 16) using data from states that adhere strictly to the Code of Federal Regulations for diagnosing autism. By contrast, few significant correlations were observed between autism prevalence and the incidence of 23 other female and 22 male cancers. Conclusions These findings suggest that there may be an association between autism and specific forms of cancer. Introduction Autism is a pervasive developmental disorder characterized by severe impairments in social skills, language and communication, as well as behavioral disturbances. There is growing public awareness of autism because rates of this disorder are thought to be rising [1] . The etiology of autism is still unknown and clues as to its cause are urgently needed. Previous studies have reported that children with autism possess a higher number of genetic aberrations, including higher levels of chromosomal rearrangements [2] and copy number variations [3] , [4] , [5] , [6] , [7] . These studies raise the possibility that there may be correlations to cancer, a disease in which chromosomal aberrations are known to play a role. Here, we report a study in which the incidence of cancer is compared to the prevalence of autism. Beginning in 1975, the Individuals with Disabilities Education Act (IDEA) was passed, mandating that states report the number of children who undergo special education, subdivided according to a specified disability. In 1991, autism was added as a separate category by which states must report child count numbers. The IDEA database represents the sole national source of autism prevalence statistics in the U.S. Despite limitations, the IDEA data are the best available for estimates of autism prevalence in the U.S., and recent improvements have been made to this data system. For example, the methods by which states formally diagnose children with autism have been analyzed, and those states adhering to uniform criteria were identified [8] . Cancer statistics, by contrast, are collected with rigor, and the diagnosis is rarely in dispute and methods for determining cancer diagnosis are firmly established. Here, we present an analyses using both cancer and autism databases, incorporating information about state-level differences in autism diagnosis [8] . Results State-Level Correlations of Autism Prevalence with Incidence of All Cancers As depicted in Figure 1 , we present Spearman rank correlations at the state level between autism prevalence (according to age groups and year reported) and cancer incidence (for the specific cancer type or group of cancers by gender and year). All possible combinations of autism and cancer data were correlated to avoid Type 1 bias, and the results were tabulated on a grid depicting the years for which autism or cancer data were reported ( Fig. 1 ). 10.1371/journal.pone.0009372.g001 Figure 1 Spearman rank correlations between annual cancer incidence and autism prevalence. Pairwise correlations were conducted between the annual incidence of adult cancers (all cancers combined) and the prevalence of autism. For each age group, 56 possible pairwise correlations depending on year were determined. For each year that state cancer incidence (from the CDC) and autism prevalence (from the IDEA) were reported, a two-tailed Spearman Rank correlation coefficient was determined. Significance was adjusted using Bonferroni's correction [10] and shaded as indicated to facilitate visual inspection of the results. The CDC consolidates 24 anatomic sites for all female cancers and 22 anatomic sites for all male cancers. Autism prevalence data before the year 2000 were omitted from these analyses because: 1) Data for ages 3–5 are unavailable prior to 2000; and 2) the latest diagnostic criteria for autism, DSM-IV TR, was introduced in 2000. There are 56 different combinations by which autism prevalence (for a specific age group) and cancer incidence may be compared ( Fig. 1 ). Some combinations yield a nominally significant correlation while others do not. Multiple correlations can introduce Type I error (acceptance of a false correlation), a common problem when relationships between two types of biological measurements are extrapolated [9] . Therefore, all the p -values were adjusted using the Bonferroni method of correction, a conservative technique for reducing Type I error. Thus, all calculated p -values were multiplied by 56 to yield an adjusted p -value not to exceed 1 (i.e. the p -value was adjusted to 1 if the Bonferroni correction yielded a value above 1) [10] . Using this approach, Bonferroni-adjusted p -values <0.05 are considered statistically significant (which corresponds to an initial, unadjusted p -value <0.0009). The correlations with autism prevalence conducted in Fig. 1 utilized annual state-level incidence of all cancers according to gender. A pattern of significant correlations emerges from the data between all female cancers and autism, but not between all male cancers and autism. We sought a method for reporting the Spearman rank correlations between annual cancer incidence and autism prevalence as a group, incorporating all Bonferroni-adjusted p -values (both nonsignificant and significant) to produce a combined overall p -value (summarized in Table 1 ). One possibility is to record the percentage of nominally significant correlations (out of 56 correlations conducted per comparison, using the adjusted p -values). Another possibility is to use Fisher's inverse chi-square method [11] , a well-established procedure for combining p -values obtained from independent observations, significant or otherwise. However, each individual p -value comes from observations that are not actually independent from one another, as will be described further in the Discussion . Two methods for combining a group of dependent p -values were used: a modified version of Fisher's chi-square method that takes into account the relationship among the p -values [12] , and an improved Bonferroni procedure that rank p -values from the lowest to the highest values [13] . As shown in Table 1 , the overall Bonferroni-adjusted p -value was nominally significant for correlations between autism prevalence and the incidence of all female but not male cancers. The p -values determined using Fisher's inverse chi-square method are very low, likely because the underlying assumption when using this method is that the p -values come from independent observations. Since this assumption is unlikely, the methods described by Brown or Simes are more appropriate for this analysis, and are reported in the subsequent Tables. 10.1371/journal.pone.0009372.t001 Table 1 Correlations Between the Annual Incidence of All Adult Cancers and Autism Prevalence. Female Cancer Incidence correlated with Autism Prevalence Male Cancer Incidence correlated with Autism Prevalence AGE % p <0.05 † Fisher's P Brown's P Simes' P % p <0.05 † Fisher's P Brown's P Simes' P 3–5 21.4 7.3×10 −8 0.040 0.003 0 1 1 0.047 6–8 14.3 5.8×10 −6 0.136 0.005 0 1 1 0.163 9–11 42.9 1.6×10 −20 6.9×10 −7 0.001 0 1 1 0.126 12–14 12.5 1.6×10 −7 0.051 0.002 0 1 1 0.094 15–17 19.6 6.8×10 −6 0.142 0.002 0 1 1 0.166 18–20 33.9 4.3×10 −11 0.004 0.0005 0 1 1 0.064 3–21 42.9 3.3×10 −22 1×10 −7 0.001 0 1 1 0.110 † % p <0.05 refers the to percentage of correlations out of the total number (56) that reach a nominal significance of p <0.05 (Bonferroni-adjusted). All possible combinations of pairwise correlations were performed between annual cancer incidence (all cancers combined) in females and males and the estimated prevalence of autism (as in Fig. 1 ). Each set of comparisons (autism vs cancer for a specific autism age group) consists of 56 correlations. Four ways of presenting significance are tabulated: 1) the percentage of correlations in which p <0.05 (% p <0.05; Bonferroni-adjusted); 2) an overall p -value using Fisher's inverse chi-square method (Fisher's P) [11] ; 3) a modified inverse chi-square for dependent p -values (Brown's P) [12] ; and 4) a modified Bonferroni procedure to obtain an overall p -value (Simes' P) [13] . Nominally significant values ( p ≤0.01) are bolded. A thorough analysis of each state's approach to diagnosing autism was recently published [8] , thus allowing us to categorize states according to diagnostic method. Autism prevalence data obtained under the IDEA does not depend on DSM-IV-TR criteria (although it may for specific states), but rather, depends on the Code of Federal Regulations (CFR). Seventeen states and the District of Columbia apply a strict wording of the CFR to categorize children as being disabled by autism. The remaining states apply expanded criteria, including DSM-IV-TR or a broader definition to include all autism spectrum disorders. Four subdivisions of states ( Fig. 2 ), were used to derive an overall p -value ( Table 2 ). Significance depends on both effect size and sample size, and by lowering sample size, significance is reduced. Despite this potential drawback to the analysis, nominal significance was still observed between autism prevalence and the incidence of all female cancers combined. 10.1371/journal.pone.0009372.g002 Figure 2 Autism diagnostic criteria used by states. The number and identity (by postal state abbreviation) of states that adhere to strict wording in the CFR (CFR only) or expanded criteria (CFR expanded) to diagnose autism are indicated. The diagnosis of autism by CFR is included within the DSM-IV-TR definition, and those states that use the DSM-IV-TR theme are shown (DSM IV autism). States that expand their criteria to include autism spectrum disorder (ASD) represent the fourth criteria used (DSM IV expanded (ASD)). 10.1371/journal.pone.0009372.t002 Table 2 Correlations Between the Annual Incidence of All Adult Cancers Combined and Autism Prevalence Subdivided by Method of Diagnosis. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR Method for Combining p -values P N P N P N P N P N All Female Cancers Brown 1.0×10 −7 46 0.395 31 0.991 19 1 27 1 16 Simes 0.001 46 0.004 31 0.010 19 0.062 27 0.164 16 All Male Cancers Brown 1 46 1 32 1 19 1 27 1 16 Simes 0.110 46 0.170 32 0.136 19 0.529 27 0.767 16 Pairwise correlations were performed as described in Table 1 using two methods, Brown and Simes [12] , [13] , for combining dependent p -values. Autism prevalence data (ages 3–21) were obtained from groups of states selected on the basis of their criteria for diagnosing autism in all states or states subdivided by 4 groups of criteria ( Fig. 2 ). P represents combined p -values for the Spearman correlations and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S1 ). Correlations between Autism Prevalence and the Incidence of Specific Female and Male Cancers The same types of analyses were applied to 24 specific cancers for females and 22 cancers for males ( Tables 3 , Table 4 Table 5 and Table 6 ). Using Brown's method for combining p -values and the most restrictive diagnostic classification, CFR, significant correlations with autism prevalence was observed with the incidence of only one cancer, breast cancer in situ ( p <10 −10 ; N = 16, Table 3 ). All other correlations between autism prevalence (using the CFR classification) and the other female cancers ( Table 3 ) or male cancers ( Table 5 ) were nonsignificant using Brown's method for combining p -values. Simes' method for combining p -values is less stringent, and other nominally significant correlations emerge using this test ( Tables 4 & 6 ). Uterine cancer (Corpus and Uterus, NOS) displayed significant correlation with autism prevalence regardless of the diagnostic criteria used by state ( Table 4 ). The Spearman rank correlation generally provided similar results when compared to the Pearson product moment coefficient ( Tables S1 , Table S2 , Table S3 , Table S4 and Table S5 ). 10.1371/journal.pone.0009372.t003 Table 3 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 1 44 1 29 1 19 1 25 1 15 Breast, invasive 0.108 46 0.914 30 0.881 19 1 27 1 16 Breast, in situ 1.5×10 −18 46 1 30 1 19 0.861 27 7.5×10 −12 16 Cervix Uteri 1 46 1 30 1 19 1 27 1 16 Colon and Rectum 1 46 1 30 1 19 1 27 1 16 Corpus and Uterus, NOS 2.3×10 −12 46 1 30 1 19 0.638 27 0.990 16 Esophagus 1 34 1 22 1 15 1 20 0.979 13 Hodgkin Lymphoma 1 37 1 24 1 16 1 21 1 14 Kidney and Renal Pelvis 1 46 1 30 1 19 1 27 1 16 Larynx 1 32 1 21 1 14 1 18 1 11 Leukemias 1 46 1 30 1 19 1 27 1 16 Liver and Intrahepatic Bile Duct 1 38 1 24 1 16 1 22 1 14 Lung and Bronchus 1 46 1 30 1 19 1 27 1 16 Melanomas of the Skin 1 46 1 30 1 19 1 27 1 16 Mesothelioma 1 13 1 9 1 6 1 7 1 4 Myeloma 1 43 1 28 1 18 1 25 1 16 Non-Hodgkin Lymphoma 1 46 1 30 1 19 1 27 1 16 Oral Cavity and Pharynx 1 45 1 29 1 19 1 26 1 16 Ovary 1 46 1 30 1 19 1 27 1 16 Pancreas 1 46 1 30 1 19 0.996 27 1 16 Stomach 1 43 1 27 1 18 1 25 1 16 Thyroid 1 46 1 30 1 19 1 27 1 16 Urinary Bladder 0.011 46 1 30 1 19 1 27 1 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S2 ). 10.1371/journal.pone.0009372.t004 Table 4 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 0.795 44 0.613 29 0.273 19 0.911 25 0.982 15 Breast, invasive 0.002 46 0.005 30 0.003 19 0.057 27 0.238 16 Breast, in situ 2.2×10 −5 46 0.006 30 0.032 19 0.002 27 0.001 16 Cervix Uteri 0.064 46 0.843 30 0.141 19 0.198 27 0.015 16 Colon and Rectum 0.174 46 0.740 30 0.535 19 0.404 27 0.633 16 Corpus and Uterus, NOS 0.0004 46 0.015 30 0.032 19 0.004 27 0.010 16 Esophagus 0.006 34 0.193 22 0.407 15 0.036 20 0.003 13 Hodgkin Lymphoma 0.004 37 0.085 24 0.497 16 0.027 21 0.053 14 Kidney and Renal Pelvis 0.719 46 0.460 30 0.817 19 0.968 27 0.928 16 Larynx 0.034 32 0.158 21 0.479 14 0.242 18 0.322 11 Leukemias 0.506 46 0.977 30 0.852 19 0.192 27 0.478 16 Liver and Intrahepatic Bile Duct 0.860 38 0.981 24 0.901 16 0.999 22 0.999 14 Lung and Bronchus 0.102 46 0.087 30 0.063 19 0.966 27 0.981 16 Melanomas of the Skin 0.108 46 0.496 30 0.588 19 0.752 27 0.410 16 Mesothelioma 0.479 13 0.106 9 0.102 6 0.996 7 NA 4 Myeloma 0.768 43 0.682 28 0.923 18 0.997 25 0.976 16 Non-Hodgkin Lymphoma 0.013 46 0.012 30 0.068 19 0.261 27 0.740 16 Oral Cavity and Pharynx 0.016 45 0.070 29 0.290 19 0.100 26 0.301 16 Ovary 0.689 46 0.983 30 0.683 19 0.961 27 0.813 16 Pancreas 0.022 46 0.038 30 0.854 19 0.001 27 0.030 16 Stomach 0.065 43 0.150 27 0.992 18 0.069 25 0.228 16 Thyroid 0.994 46 0.992 30 0.996 19 0.774 27 0.533 16 Urinary Bladder 0.002 46 0.025 30 0.023 19 0.110 27 0.020 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S3 ). 10.1371/journal.pone.0009372.t005 Table 5 Correlations between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 1 45 1 31 1 19 1 27 1 15 Colon and Rectum 1 46 1 31 1 19 1 27 1 16 Esophagus 1 46 1 31 1 19 1 27 1 16 Hodgkin Lymphoma 1 39 1 24 1 17 1 22 1 14 Kaposi Sarcoma 1 14 1 9 1 7 1 7 1 5 Kidney and Renal Pelvis 1 46 1 31 1 19 1 27 1 16 Larynx 1 44 1 28 1 19 1 25 1 16 Leukemias 1 46 1 31 1 19 1 27 1 16 Liver and Intrahepatic Bile Duct 1 44 1 28 1 19 1 25 1 16 Lung and Bronchus 1 46 1 31 1 19 1 27 1 16 Melanomas of the Skin 1 46 1 31 1 19 1 27 1 16 Mesothelioma 1 31 1 20 1 13 1 18 1 11 Myeloma 1 44 1 29 1 19 1 26 1 16 Non-Hodgkin Lymphoma 0.983 46 0.9999 31 1 19 1 27 1 16 Oral Cavity and Pharynx 1 46 1 31 1 19 1 27 1 16 Pancreas 1 46 1 31 1 19 1 27 1 16 Prostate 1 46 1 31 1 19 1 27 1 16 Stomach 1 46 1 30 1 19 0.9944 27 1 16 Testis 1 44 1 30 1 19 1 26 1 15 Thyroid 1 42 1 27 1 18 1 24 1 15 Urinary Bladder 0.002 46 1 31 1 19 1 27 1 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S4 ). 10.1371/journal.pone.0009372.t006 Table 6 Correlations between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 0.175 45 0.381 31 0.210 19 0.832 27 0.803 15 Colon and Rectum 0.807 46 0.996 31 0.875 19 0.484 27 0.970 16 Esophagus 0.111 46 0.277 31 0.022 19 0.694 27 0.578 16 Hodgkin Lymphoma 0.044 39 0.022 24 0.006 17 0.253 22 0.087 14 Kaposi Sarcoma 0.992 14 0.756 9 0.152 7 0.931 7 0.407 5 Kidney and Renal Pelvis 0.374 46 0.414 31 0.710 19 0.878 27 0.967 16 Larynx 0.487 44 0.800 28 0.633 19 0.914 25 0.866 16 Leukemias 0.996 46 0.982 31 0.991 19 0.995 27 0.924 16 Liver and Intrahepatic Bile Duct 0.272 44 0.140 28 0.991 19 0.127 25 0.852 16 Lung and Bronchus 0.984 46 0.992 31 0.994 19 0.939 27 0.824 16 Melanomas of the Skin 0.123 46 0.597 31 0.999 19 0.227 27 0.165 16 Mesothelioma 0.013 31 0.066 20 0.006 13 0.188 18 0.053 11 Myeloma 0.963 44 0.895 29 0.737 19 0.994 26 0.731 16 Non-Hodgkin Lymphoma 0.013 46 0.017 31 0.047 19 0.039 27 0.159 16 Oral Cavity and Pharynx 0.553 46 0.978 31 0.662 19 0.287 27 0.196 16 Pancreas 0.132 46 0.378 31 0.980 19 0.028 27 0.024 16 Prostate 0.381 46 0.848 31 0.395 19 0.999 27 0.809 16 Stomach 0.073 46 0.367 30 0.839 19 0.004 27 0.008 16 Testis 0.222 44 0.310 30 0.189 19 0.899 26 0.553 15 Thyroid 0.939 42 0.985 27 0.990 18 0.404 24 0.429 15 Urinary Bladder 0.003 46 0.043 31 0.030 19 0.018 27 0.006 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S5 ). Discussion This study utilizes information from the IDEA and CDC database that may suggest shared risk factors between autism and specific cancers. Since both the autism and cancer database contain information for up to 50 states and the District of Columbia, the sample number for conducting correlations is high, representing a potentially useful resource for these preliminary ecological analyses. However, the utility of these analyses rests on the quality of the IDEA database. One potential limitation is that of diagnostic substitution [14] , in which cases previously categorized as learning disabled or mental retardation in the 1990s may actually have been cases of autism. Although this may not be a problem in many states [15] , autism as a separate category in the CFR did not occur until 1991. Another issue is that prevalence data before the age of 6 was not reported until the year 2000, probably reflecting continued refinement of the criteria for autism up to the year 2000. Our strategy to minimize this pitfall was to consider autism data only from the year 2000 forward in an effort to limit inaccurate counts due to diagnostic substitution and the changing definition of autism. Perhaps the major criticism of the IDEA database concerns the wide range in the actual prevalence of autism in different states. As much as an eight-fold difference in autism prevalence rates has been reported between states [16] . Some states have been singled out for having unorthodox criteria (Oregon) [17] , exceedingly high rates (Minnesota), a sudden 400% rise in rates from 2001–2002 (Massachusetts) [8] , [18] , or idiosyncratic results (California) [18] . A recent systematic study of the methods that states use to categorize autism does clarify these findings and may be helpful in extracting useful information from the IDEA database [8] . States are free to choose criteria for categorizing children with autism. School administrators and practitioners are not required to use the DSM-IV-TR to classify and to diagnose children, but they must use the diagnostic criteria outlined in the Code of Federal Regulations (CFR). Both the CFR and DSM-IV-TR recognize social interaction and communication as well as restrictive, repetitive, and stereotypical behavior, and thus the basic criteria for diagnosis are highly similar. However, the main difference between CFR and DSM-IV-TR is whether the child is disabled as a result of this diagnosis in order to qualify for special education under the autism category. Accordingly, the IDEA database underestimates autism prevalence, since it uses educational criteria for determining disability; high functioning individuals with autism who do not require special education are not counted [8] . Although states are free to choose their own eligibility criteria for special education services, they must do so as long as it meets or exceeds CFR guidelines. The legal code of every state and the District of Columbia were analyzed, along with inter-state variability [8] . As shown in Fig. 2 , 17 states and the District of Columbia strictly abide by criteria used in the CFR. Interestingly, diagnosis using the CFR theme displayed high inter-rater reliability [8] , and one could consider this category to represent a subset of autism as defined by DSM-IV-TR. The remaining 33 states expanded upon CFR criteria. Since the guidelines used in the CFR fall within those specified by DSM-IV-TR, states that abide by DSM-IV-TR include all those that use CFR plus an additional 13 states ( Fig. 2 ). Autism Spectrum Disorder (ASD) includes other disorders related to autism, including Asperger's Syndrome. These “milder” disorders can account for up to 75% of the cases in some states, thus contributing greatly to the varied prevalence rates from state to state [19] . An understanding of the different criteria that states use to classify children who qualify for special education under the category of autism greatly clarifies the findings made by previous researchers who have delved into this database. For instance, all the states for which unusual or high prevalence rates were cited (Oregon, Minnesota, Massachusetts, California) are states that have expanded the eligibility criteria for autism beyond CFR. Indeed, states that have expanded their criteria beyond CFR report substantially higher prevalence rates for autism [8] . Therefore, restricting the correlation analyses to those states that adhere strictly to wording used in the CFR would represent the most conservative way to use the IDEA database, even at the cost of reducing the sample size to about a third the number of states. The second least restrictive way is to use data from states that apply DSM-IV-TR to diagnose autism, but not autism spectrum disorder. Data were analyzed using four subdivisions of states according to criteria used to diagnose children for eligibility for special education ( Fig. 2 ). In evaluating significance, the Bonferroni's method for correcting p -values due to multiple comparisons is considered very conservative, because it raises Type II error (rejection of a true correlation) while reducing Type I error [9] . Two methods for calculating an overall p -value based on multiple Bonferroni-adjusted p -values were used. Brown's method [12] , which is a modification of Fisher's original inverse chi-square method [11] , takes all p -values into account and determines if the log transformation of all the values fall within a chi-square distribution. Thus, multiple p -values need to show significance before the overall p -value becomes significant; a single p -value, even if very significant, will not result in an overall significance. A less conservative method, the Simes' procedure [13] , determines if at least one p -value out of a set of p -values is significant. As can be observed from Tables 3 – 6 , a few correlations meet significance using these conditions. When the most restrictive criteria for selecting state-level autism data were used, states abiding by CFR strictly, and Brown's method for combining Bonferroni-adjusted p -values applied, only one correlation was significant: the correlation between autism prevalence and the incidence of in situ breast cancer ( p <10 −10 ; N = 16). When a less conservative statistical method was applied (Simes' procedure), correlations between autism and uterine cancer also emerged as consistently significant. By contrast, the great majority of correlations between specific forms of cancer and autism were negative. Although Type II error may have been increased as a result of these methods, it is appropriate in light of the controversial use of the IDEA database. In conclusion, by using conservative statistical methods and a limited set of autism data from states using a uniform code of diagnosis, nominal statistical significance was observed in a few instances, notably for breast cancer and uterine cancer. In practice, it is not known whether the diagnosis of autism is truly uniform in individual school districts. Consequently, the results should be interpreted with caution, even if the p -values appear to be selective for these cancers and highly significant, as is the case here. Nonetheless, it is of interest that the cumulative exposure to estrogen from endogenous and external sources is an established risk factor for both breast [20] and uterine [21] cancer, the two cancers that appear to be most consistently correlated with autism. Some analyses suggest that mothers are carriers of mutations that predispose children to autism [22] , and there is literature implicating germline mutations in autism [23] , [24] . In this context, we suggest that investigating biomedical mechanisms to account for these epidemiological findings is warranted. Materials and Methods Sources of Data The number of children diagnosed with autism was collected for all states and ages between the years 2000–2007 from the U.S. Department of Education via the Individuals with Disabilities Act (IDEA) ( https://www.ideadata.org ). Six age groups were analyzed: 3–5, 6–8, 9–11, 12–14, 15–17, and 18–20 years; as well as the entire span of ages, 3–21. Autism prevalence separated by gender or before the age of 3 are not available. Annual resident population numbers by age and respective year (2000–2007) were obtained from the U.S. Census Bureau ( http://www.census.gov ), and used as the denominator to calculate the annual prevalence of autism in each state. The age-adjusted annual incidence of specific cancers (standardized to the 2000 U.S. population) for males and females and for all states between the years 1999 and 2005 were obtained from the CDC ( http://apps.nccd.cdc.gov/uscs/ ), the years presently available. Statistical Analyses The Spearman rank correlation coefficients were calculated by comparing the prevalence of autism to the annual incidence of cancer, at the state level, throughout the U.S. This was done for each autism age group and year reported, and for each type of cancer and year reported. Significance was calculated using methods previously described [25] , and adjusted using the Bonferroni correction [9] , [10] . To obtain an overall significance or combined p -value for each set of correlations, three methods were used and compared: Fisher's inverse chi-square method [11] , Brown's method for combining dependent p -values [12] and Simes' procedure [13] . Supporting Information Table S1 Correlations Between the Annual Incidence of All Adult Cancers Combined and Autism Prevalence Subdivided by Method of Diagnosis. Pairwise correlations were performed as described in Table 1 using two methods, Brown and Simes [12] , [13] , for combining dependent p-values. Autism prevalence data (ages 3–21) were obtained from groups of states selected on the basis of their criteria for diagnosing autism in all states or states subdivided by 4 groups of criteria ( Fig. 2 ). P represents combined p-values for the Pearson correlations and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. (0.03 MB DOC) Table S2 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. (0.06 MB DOC) Table S3 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. (0.05 MB DOC) Table S4 Correlations Between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. (0.05 MB DOC) Table S5 Correlations Between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. (0.05 MB DOC) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png PLoS ONE Public Library of Science (PLoS) Journal

The Correlation between Rates of Cancer and Autism: An Exploratory Ecological Investigation

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Public Library of Science (PLoS) Journal
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This is an open-access article distributed under the terms of this Creative Commons License
Subject
Research Article; Cell Biology/Cell Growth and Division; Non-Clinical Medicine/Medical Informatics; Public Health and Epidemiology/Environmental Health; Public Health and Epidemiology/Preventive Medicine
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1932-6203
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1932-6203
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10.1371/journal.pone.0009372
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20186340
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Abstract

Background Autism is associated with high rates of genomic aberrations, including chromosomal rearrangements and de novo copy-number variations. These observations are reminiscent of cancer, a disease where genomic rearrangements also play a role. We undertook a correlative epidemiological study to explore the possibility that shared risk factors might exist for autism and specific types of cancer. Methodology/Principal Findings To determine if significant correlations exist between the prevalence of autism and the incidence of cancer, we obtained and analyzed state-wide data reported by age and gender throughout the United States. Autism data were obtained from the U.S. Department of Education via the Individuals with Disabilities Education Act (IDEA) (2000–2007, reported annually by age group) and cancer incidence data were obtained from the Centers for Disease Control and Prevention (CDC) (1999–2005). IDEA data were further subdivided depending on the method used to diagnose autism (DSM IV or the Code of Federal Regulations, using strict or expanded criteria). Spearman rank correlations were calculated for all possible pairwise combinations of annual autism rates and the incidence of specific cancers. Following this, Bonferroni's correction was applied to significance values. Two independent methods for determining an overall combined p -value based on dependent correlations were obtained for each set of calculations. High correlations were found between autism rates and the incidence of in situ breast cancer ( p ≤10 −10 , modified inverse chi square, n = 16) using data from states that adhere strictly to the Code of Federal Regulations for diagnosing autism. By contrast, few significant correlations were observed between autism prevalence and the incidence of 23 other female and 22 male cancers. Conclusions These findings suggest that there may be an association between autism and specific forms of cancer. Introduction Autism is a pervasive developmental disorder characterized by severe impairments in social skills, language and communication, as well as behavioral disturbances. There is growing public awareness of autism because rates of this disorder are thought to be rising [1] . The etiology of autism is still unknown and clues as to its cause are urgently needed. Previous studies have reported that children with autism possess a higher number of genetic aberrations, including higher levels of chromosomal rearrangements [2] and copy number variations [3] , [4] , [5] , [6] , [7] . These studies raise the possibility that there may be correlations to cancer, a disease in which chromosomal aberrations are known to play a role. Here, we report a study in which the incidence of cancer is compared to the prevalence of autism. Beginning in 1975, the Individuals with Disabilities Education Act (IDEA) was passed, mandating that states report the number of children who undergo special education, subdivided according to a specified disability. In 1991, autism was added as a separate category by which states must report child count numbers. The IDEA database represents the sole national source of autism prevalence statistics in the U.S. Despite limitations, the IDEA data are the best available for estimates of autism prevalence in the U.S., and recent improvements have been made to this data system. For example, the methods by which states formally diagnose children with autism have been analyzed, and those states adhering to uniform criteria were identified [8] . Cancer statistics, by contrast, are collected with rigor, and the diagnosis is rarely in dispute and methods for determining cancer diagnosis are firmly established. Here, we present an analyses using both cancer and autism databases, incorporating information about state-level differences in autism diagnosis [8] . Results State-Level Correlations of Autism Prevalence with Incidence of All Cancers As depicted in Figure 1 , we present Spearman rank correlations at the state level between autism prevalence (according to age groups and year reported) and cancer incidence (for the specific cancer type or group of cancers by gender and year). All possible combinations of autism and cancer data were correlated to avoid Type 1 bias, and the results were tabulated on a grid depicting the years for which autism or cancer data were reported ( Fig. 1 ). 10.1371/journal.pone.0009372.g001 Figure 1 Spearman rank correlations between annual cancer incidence and autism prevalence. Pairwise correlations were conducted between the annual incidence of adult cancers (all cancers combined) and the prevalence of autism. For each age group, 56 possible pairwise correlations depending on year were determined. For each year that state cancer incidence (from the CDC) and autism prevalence (from the IDEA) were reported, a two-tailed Spearman Rank correlation coefficient was determined. Significance was adjusted using Bonferroni's correction [10] and shaded as indicated to facilitate visual inspection of the results. The CDC consolidates 24 anatomic sites for all female cancers and 22 anatomic sites for all male cancers. Autism prevalence data before the year 2000 were omitted from these analyses because: 1) Data for ages 3–5 are unavailable prior to 2000; and 2) the latest diagnostic criteria for autism, DSM-IV TR, was introduced in 2000. There are 56 different combinations by which autism prevalence (for a specific age group) and cancer incidence may be compared ( Fig. 1 ). Some combinations yield a nominally significant correlation while others do not. Multiple correlations can introduce Type I error (acceptance of a false correlation), a common problem when relationships between two types of biological measurements are extrapolated [9] . Therefore, all the p -values were adjusted using the Bonferroni method of correction, a conservative technique for reducing Type I error. Thus, all calculated p -values were multiplied by 56 to yield an adjusted p -value not to exceed 1 (i.e. the p -value was adjusted to 1 if the Bonferroni correction yielded a value above 1) [10] . Using this approach, Bonferroni-adjusted p -values <0.05 are considered statistically significant (which corresponds to an initial, unadjusted p -value <0.0009). The correlations with autism prevalence conducted in Fig. 1 utilized annual state-level incidence of all cancers according to gender. A pattern of significant correlations emerges from the data between all female cancers and autism, but not between all male cancers and autism. We sought a method for reporting the Spearman rank correlations between annual cancer incidence and autism prevalence as a group, incorporating all Bonferroni-adjusted p -values (both nonsignificant and significant) to produce a combined overall p -value (summarized in Table 1 ). One possibility is to record the percentage of nominally significant correlations (out of 56 correlations conducted per comparison, using the adjusted p -values). Another possibility is to use Fisher's inverse chi-square method [11] , a well-established procedure for combining p -values obtained from independent observations, significant or otherwise. However, each individual p -value comes from observations that are not actually independent from one another, as will be described further in the Discussion . Two methods for combining a group of dependent p -values were used: a modified version of Fisher's chi-square method that takes into account the relationship among the p -values [12] , and an improved Bonferroni procedure that rank p -values from the lowest to the highest values [13] . As shown in Table 1 , the overall Bonferroni-adjusted p -value was nominally significant for correlations between autism prevalence and the incidence of all female but not male cancers. The p -values determined using Fisher's inverse chi-square method are very low, likely because the underlying assumption when using this method is that the p -values come from independent observations. Since this assumption is unlikely, the methods described by Brown or Simes are more appropriate for this analysis, and are reported in the subsequent Tables. 10.1371/journal.pone.0009372.t001 Table 1 Correlations Between the Annual Incidence of All Adult Cancers and Autism Prevalence. Female Cancer Incidence correlated with Autism Prevalence Male Cancer Incidence correlated with Autism Prevalence AGE % p <0.05 † Fisher's P Brown's P Simes' P % p <0.05 † Fisher's P Brown's P Simes' P 3–5 21.4 7.3×10 −8 0.040 0.003 0 1 1 0.047 6–8 14.3 5.8×10 −6 0.136 0.005 0 1 1 0.163 9–11 42.9 1.6×10 −20 6.9×10 −7 0.001 0 1 1 0.126 12–14 12.5 1.6×10 −7 0.051 0.002 0 1 1 0.094 15–17 19.6 6.8×10 −6 0.142 0.002 0 1 1 0.166 18–20 33.9 4.3×10 −11 0.004 0.0005 0 1 1 0.064 3–21 42.9 3.3×10 −22 1×10 −7 0.001 0 1 1 0.110 † % p <0.05 refers the to percentage of correlations out of the total number (56) that reach a nominal significance of p <0.05 (Bonferroni-adjusted). All possible combinations of pairwise correlations were performed between annual cancer incidence (all cancers combined) in females and males and the estimated prevalence of autism (as in Fig. 1 ). Each set of comparisons (autism vs cancer for a specific autism age group) consists of 56 correlations. Four ways of presenting significance are tabulated: 1) the percentage of correlations in which p <0.05 (% p <0.05; Bonferroni-adjusted); 2) an overall p -value using Fisher's inverse chi-square method (Fisher's P) [11] ; 3) a modified inverse chi-square for dependent p -values (Brown's P) [12] ; and 4) a modified Bonferroni procedure to obtain an overall p -value (Simes' P) [13] . Nominally significant values ( p ≤0.01) are bolded. A thorough analysis of each state's approach to diagnosing autism was recently published [8] , thus allowing us to categorize states according to diagnostic method. Autism prevalence data obtained under the IDEA does not depend on DSM-IV-TR criteria (although it may for specific states), but rather, depends on the Code of Federal Regulations (CFR). Seventeen states and the District of Columbia apply a strict wording of the CFR to categorize children as being disabled by autism. The remaining states apply expanded criteria, including DSM-IV-TR or a broader definition to include all autism spectrum disorders. Four subdivisions of states ( Fig. 2 ), were used to derive an overall p -value ( Table 2 ). Significance depends on both effect size and sample size, and by lowering sample size, significance is reduced. Despite this potential drawback to the analysis, nominal significance was still observed between autism prevalence and the incidence of all female cancers combined. 10.1371/journal.pone.0009372.g002 Figure 2 Autism diagnostic criteria used by states. The number and identity (by postal state abbreviation) of states that adhere to strict wording in the CFR (CFR only) or expanded criteria (CFR expanded) to diagnose autism are indicated. The diagnosis of autism by CFR is included within the DSM-IV-TR definition, and those states that use the DSM-IV-TR theme are shown (DSM IV autism). States that expand their criteria to include autism spectrum disorder (ASD) represent the fourth criteria used (DSM IV expanded (ASD)). 10.1371/journal.pone.0009372.t002 Table 2 Correlations Between the Annual Incidence of All Adult Cancers Combined and Autism Prevalence Subdivided by Method of Diagnosis. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR Method for Combining p -values P N P N P N P N P N All Female Cancers Brown 1.0×10 −7 46 0.395 31 0.991 19 1 27 1 16 Simes 0.001 46 0.004 31 0.010 19 0.062 27 0.164 16 All Male Cancers Brown 1 46 1 32 1 19 1 27 1 16 Simes 0.110 46 0.170 32 0.136 19 0.529 27 0.767 16 Pairwise correlations were performed as described in Table 1 using two methods, Brown and Simes [12] , [13] , for combining dependent p -values. Autism prevalence data (ages 3–21) were obtained from groups of states selected on the basis of their criteria for diagnosing autism in all states or states subdivided by 4 groups of criteria ( Fig. 2 ). P represents combined p -values for the Spearman correlations and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S1 ). Correlations between Autism Prevalence and the Incidence of Specific Female and Male Cancers The same types of analyses were applied to 24 specific cancers for females and 22 cancers for males ( Tables 3 , Table 4 Table 5 and Table 6 ). Using Brown's method for combining p -values and the most restrictive diagnostic classification, CFR, significant correlations with autism prevalence was observed with the incidence of only one cancer, breast cancer in situ ( p <10 −10 ; N = 16, Table 3 ). All other correlations between autism prevalence (using the CFR classification) and the other female cancers ( Table 3 ) or male cancers ( Table 5 ) were nonsignificant using Brown's method for combining p -values. Simes' method for combining p -values is less stringent, and other nominally significant correlations emerge using this test ( Tables 4 & 6 ). Uterine cancer (Corpus and Uterus, NOS) displayed significant correlation with autism prevalence regardless of the diagnostic criteria used by state ( Table 4 ). The Spearman rank correlation generally provided similar results when compared to the Pearson product moment coefficient ( Tables S1 , Table S2 , Table S3 , Table S4 and Table S5 ). 10.1371/journal.pone.0009372.t003 Table 3 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 1 44 1 29 1 19 1 25 1 15 Breast, invasive 0.108 46 0.914 30 0.881 19 1 27 1 16 Breast, in situ 1.5×10 −18 46 1 30 1 19 0.861 27 7.5×10 −12 16 Cervix Uteri 1 46 1 30 1 19 1 27 1 16 Colon and Rectum 1 46 1 30 1 19 1 27 1 16 Corpus and Uterus, NOS 2.3×10 −12 46 1 30 1 19 0.638 27 0.990 16 Esophagus 1 34 1 22 1 15 1 20 0.979 13 Hodgkin Lymphoma 1 37 1 24 1 16 1 21 1 14 Kidney and Renal Pelvis 1 46 1 30 1 19 1 27 1 16 Larynx 1 32 1 21 1 14 1 18 1 11 Leukemias 1 46 1 30 1 19 1 27 1 16 Liver and Intrahepatic Bile Duct 1 38 1 24 1 16 1 22 1 14 Lung and Bronchus 1 46 1 30 1 19 1 27 1 16 Melanomas of the Skin 1 46 1 30 1 19 1 27 1 16 Mesothelioma 1 13 1 9 1 6 1 7 1 4 Myeloma 1 43 1 28 1 18 1 25 1 16 Non-Hodgkin Lymphoma 1 46 1 30 1 19 1 27 1 16 Oral Cavity and Pharynx 1 45 1 29 1 19 1 26 1 16 Ovary 1 46 1 30 1 19 1 27 1 16 Pancreas 1 46 1 30 1 19 0.996 27 1 16 Stomach 1 43 1 27 1 18 1 25 1 16 Thyroid 1 46 1 30 1 19 1 27 1 16 Urinary Bladder 0.011 46 1 30 1 19 1 27 1 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S2 ). 10.1371/journal.pone.0009372.t004 Table 4 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 0.795 44 0.613 29 0.273 19 0.911 25 0.982 15 Breast, invasive 0.002 46 0.005 30 0.003 19 0.057 27 0.238 16 Breast, in situ 2.2×10 −5 46 0.006 30 0.032 19 0.002 27 0.001 16 Cervix Uteri 0.064 46 0.843 30 0.141 19 0.198 27 0.015 16 Colon and Rectum 0.174 46 0.740 30 0.535 19 0.404 27 0.633 16 Corpus and Uterus, NOS 0.0004 46 0.015 30 0.032 19 0.004 27 0.010 16 Esophagus 0.006 34 0.193 22 0.407 15 0.036 20 0.003 13 Hodgkin Lymphoma 0.004 37 0.085 24 0.497 16 0.027 21 0.053 14 Kidney and Renal Pelvis 0.719 46 0.460 30 0.817 19 0.968 27 0.928 16 Larynx 0.034 32 0.158 21 0.479 14 0.242 18 0.322 11 Leukemias 0.506 46 0.977 30 0.852 19 0.192 27 0.478 16 Liver and Intrahepatic Bile Duct 0.860 38 0.981 24 0.901 16 0.999 22 0.999 14 Lung and Bronchus 0.102 46 0.087 30 0.063 19 0.966 27 0.981 16 Melanomas of the Skin 0.108 46 0.496 30 0.588 19 0.752 27 0.410 16 Mesothelioma 0.479 13 0.106 9 0.102 6 0.996 7 NA 4 Myeloma 0.768 43 0.682 28 0.923 18 0.997 25 0.976 16 Non-Hodgkin Lymphoma 0.013 46 0.012 30 0.068 19 0.261 27 0.740 16 Oral Cavity and Pharynx 0.016 45 0.070 29 0.290 19 0.100 26 0.301 16 Ovary 0.689 46 0.983 30 0.683 19 0.961 27 0.813 16 Pancreas 0.022 46 0.038 30 0.854 19 0.001 27 0.030 16 Stomach 0.065 43 0.150 27 0.992 18 0.069 25 0.228 16 Thyroid 0.994 46 0.992 30 0.996 19 0.774 27 0.533 16 Urinary Bladder 0.002 46 0.025 30 0.023 19 0.110 27 0.020 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S3 ). 10.1371/journal.pone.0009372.t005 Table 5 Correlations between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 1 45 1 31 1 19 1 27 1 15 Colon and Rectum 1 46 1 31 1 19 1 27 1 16 Esophagus 1 46 1 31 1 19 1 27 1 16 Hodgkin Lymphoma 1 39 1 24 1 17 1 22 1 14 Kaposi Sarcoma 1 14 1 9 1 7 1 7 1 5 Kidney and Renal Pelvis 1 46 1 31 1 19 1 27 1 16 Larynx 1 44 1 28 1 19 1 25 1 16 Leukemias 1 46 1 31 1 19 1 27 1 16 Liver and Intrahepatic Bile Duct 1 44 1 28 1 19 1 25 1 16 Lung and Bronchus 1 46 1 31 1 19 1 27 1 16 Melanomas of the Skin 1 46 1 31 1 19 1 27 1 16 Mesothelioma 1 31 1 20 1 13 1 18 1 11 Myeloma 1 44 1 29 1 19 1 26 1 16 Non-Hodgkin Lymphoma 0.983 46 0.9999 31 1 19 1 27 1 16 Oral Cavity and Pharynx 1 46 1 31 1 19 1 27 1 16 Pancreas 1 46 1 31 1 19 1 27 1 16 Prostate 1 46 1 31 1 19 1 27 1 16 Stomach 1 46 1 30 1 19 0.9944 27 1 16 Testis 1 44 1 30 1 19 1 26 1 15 Thyroid 1 42 1 27 1 18 1 24 1 15 Urinary Bladder 0.002 46 1 31 1 19 1 27 1 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S4 ). 10.1371/journal.pone.0009372.t006 Table 6 Correlations between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. ALL Expanded Criteria (CFR) Expanded Criteria (DSM-IV) Autism (DSM-IV) CFR P N P N P N P N P N Brain and Other Nervous System 0.175 45 0.381 31 0.210 19 0.832 27 0.803 15 Colon and Rectum 0.807 46 0.996 31 0.875 19 0.484 27 0.970 16 Esophagus 0.111 46 0.277 31 0.022 19 0.694 27 0.578 16 Hodgkin Lymphoma 0.044 39 0.022 24 0.006 17 0.253 22 0.087 14 Kaposi Sarcoma 0.992 14 0.756 9 0.152 7 0.931 7 0.407 5 Kidney and Renal Pelvis 0.374 46 0.414 31 0.710 19 0.878 27 0.967 16 Larynx 0.487 44 0.800 28 0.633 19 0.914 25 0.866 16 Leukemias 0.996 46 0.982 31 0.991 19 0.995 27 0.924 16 Liver and Intrahepatic Bile Duct 0.272 44 0.140 28 0.991 19 0.127 25 0.852 16 Lung and Bronchus 0.984 46 0.992 31 0.994 19 0.939 27 0.824 16 Melanomas of the Skin 0.123 46 0.597 31 0.999 19 0.227 27 0.165 16 Mesothelioma 0.013 31 0.066 20 0.006 13 0.188 18 0.053 11 Myeloma 0.963 44 0.895 29 0.737 19 0.994 26 0.731 16 Non-Hodgkin Lymphoma 0.013 46 0.017 31 0.047 19 0.039 27 0.159 16 Oral Cavity and Pharynx 0.553 46 0.978 31 0.662 19 0.287 27 0.196 16 Pancreas 0.132 46 0.378 31 0.980 19 0.028 27 0.024 16 Prostate 0.381 46 0.848 31 0.395 19 0.999 27 0.809 16 Stomach 0.073 46 0.367 30 0.839 19 0.004 27 0.008 16 Testis 0.222 44 0.310 30 0.189 19 0.899 26 0.553 15 Thyroid 0.939 42 0.985 27 0.990 18 0.404 24 0.429 15 Urinary Bladder 0.003 46 0.043 31 0.030 19 0.018 27 0.006 16 Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p -values for Spearman correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Data are similar when the Pearson Correlation Coefficient is used ( Table S5 ). Discussion This study utilizes information from the IDEA and CDC database that may suggest shared risk factors between autism and specific cancers. Since both the autism and cancer database contain information for up to 50 states and the District of Columbia, the sample number for conducting correlations is high, representing a potentially useful resource for these preliminary ecological analyses. However, the utility of these analyses rests on the quality of the IDEA database. One potential limitation is that of diagnostic substitution [14] , in which cases previously categorized as learning disabled or mental retardation in the 1990s may actually have been cases of autism. Although this may not be a problem in many states [15] , autism as a separate category in the CFR did not occur until 1991. Another issue is that prevalence data before the age of 6 was not reported until the year 2000, probably reflecting continued refinement of the criteria for autism up to the year 2000. Our strategy to minimize this pitfall was to consider autism data only from the year 2000 forward in an effort to limit inaccurate counts due to diagnostic substitution and the changing definition of autism. Perhaps the major criticism of the IDEA database concerns the wide range in the actual prevalence of autism in different states. As much as an eight-fold difference in autism prevalence rates has been reported between states [16] . Some states have been singled out for having unorthodox criteria (Oregon) [17] , exceedingly high rates (Minnesota), a sudden 400% rise in rates from 2001–2002 (Massachusetts) [8] , [18] , or idiosyncratic results (California) [18] . A recent systematic study of the methods that states use to categorize autism does clarify these findings and may be helpful in extracting useful information from the IDEA database [8] . States are free to choose criteria for categorizing children with autism. School administrators and practitioners are not required to use the DSM-IV-TR to classify and to diagnose children, but they must use the diagnostic criteria outlined in the Code of Federal Regulations (CFR). Both the CFR and DSM-IV-TR recognize social interaction and communication as well as restrictive, repetitive, and stereotypical behavior, and thus the basic criteria for diagnosis are highly similar. However, the main difference between CFR and DSM-IV-TR is whether the child is disabled as a result of this diagnosis in order to qualify for special education under the autism category. Accordingly, the IDEA database underestimates autism prevalence, since it uses educational criteria for determining disability; high functioning individuals with autism who do not require special education are not counted [8] . Although states are free to choose their own eligibility criteria for special education services, they must do so as long as it meets or exceeds CFR guidelines. The legal code of every state and the District of Columbia were analyzed, along with inter-state variability [8] . As shown in Fig. 2 , 17 states and the District of Columbia strictly abide by criteria used in the CFR. Interestingly, diagnosis using the CFR theme displayed high inter-rater reliability [8] , and one could consider this category to represent a subset of autism as defined by DSM-IV-TR. The remaining 33 states expanded upon CFR criteria. Since the guidelines used in the CFR fall within those specified by DSM-IV-TR, states that abide by DSM-IV-TR include all those that use CFR plus an additional 13 states ( Fig. 2 ). Autism Spectrum Disorder (ASD) includes other disorders related to autism, including Asperger's Syndrome. These “milder” disorders can account for up to 75% of the cases in some states, thus contributing greatly to the varied prevalence rates from state to state [19] . An understanding of the different criteria that states use to classify children who qualify for special education under the category of autism greatly clarifies the findings made by previous researchers who have delved into this database. For instance, all the states for which unusual or high prevalence rates were cited (Oregon, Minnesota, Massachusetts, California) are states that have expanded the eligibility criteria for autism beyond CFR. Indeed, states that have expanded their criteria beyond CFR report substantially higher prevalence rates for autism [8] . Therefore, restricting the correlation analyses to those states that adhere strictly to wording used in the CFR would represent the most conservative way to use the IDEA database, even at the cost of reducing the sample size to about a third the number of states. The second least restrictive way is to use data from states that apply DSM-IV-TR to diagnose autism, but not autism spectrum disorder. Data were analyzed using four subdivisions of states according to criteria used to diagnose children for eligibility for special education ( Fig. 2 ). In evaluating significance, the Bonferroni's method for correcting p -values due to multiple comparisons is considered very conservative, because it raises Type II error (rejection of a true correlation) while reducing Type I error [9] . Two methods for calculating an overall p -value based on multiple Bonferroni-adjusted p -values were used. Brown's method [12] , which is a modification of Fisher's original inverse chi-square method [11] , takes all p -values into account and determines if the log transformation of all the values fall within a chi-square distribution. Thus, multiple p -values need to show significance before the overall p -value becomes significant; a single p -value, even if very significant, will not result in an overall significance. A less conservative method, the Simes' procedure [13] , determines if at least one p -value out of a set of p -values is significant. As can be observed from Tables 3 – 6 , a few correlations meet significance using these conditions. When the most restrictive criteria for selecting state-level autism data were used, states abiding by CFR strictly, and Brown's method for combining Bonferroni-adjusted p -values applied, only one correlation was significant: the correlation between autism prevalence and the incidence of in situ breast cancer ( p <10 −10 ; N = 16). When a less conservative statistical method was applied (Simes' procedure), correlations between autism and uterine cancer also emerged as consistently significant. By contrast, the great majority of correlations between specific forms of cancer and autism were negative. Although Type II error may have been increased as a result of these methods, it is appropriate in light of the controversial use of the IDEA database. In conclusion, by using conservative statistical methods and a limited set of autism data from states using a uniform code of diagnosis, nominal statistical significance was observed in a few instances, notably for breast cancer and uterine cancer. In practice, it is not known whether the diagnosis of autism is truly uniform in individual school districts. Consequently, the results should be interpreted with caution, even if the p -values appear to be selective for these cancers and highly significant, as is the case here. Nonetheless, it is of interest that the cumulative exposure to estrogen from endogenous and external sources is an established risk factor for both breast [20] and uterine [21] cancer, the two cancers that appear to be most consistently correlated with autism. Some analyses suggest that mothers are carriers of mutations that predispose children to autism [22] , and there is literature implicating germline mutations in autism [23] , [24] . In this context, we suggest that investigating biomedical mechanisms to account for these epidemiological findings is warranted. Materials and Methods Sources of Data The number of children diagnosed with autism was collected for all states and ages between the years 2000–2007 from the U.S. Department of Education via the Individuals with Disabilities Act (IDEA) ( https://www.ideadata.org ). Six age groups were analyzed: 3–5, 6–8, 9–11, 12–14, 15–17, and 18–20 years; as well as the entire span of ages, 3–21. Autism prevalence separated by gender or before the age of 3 are not available. Annual resident population numbers by age and respective year (2000–2007) were obtained from the U.S. Census Bureau ( http://www.census.gov ), and used as the denominator to calculate the annual prevalence of autism in each state. The age-adjusted annual incidence of specific cancers (standardized to the 2000 U.S. population) for males and females and for all states between the years 1999 and 2005 were obtained from the CDC ( http://apps.nccd.cdc.gov/uscs/ ), the years presently available. Statistical Analyses The Spearman rank correlation coefficients were calculated by comparing the prevalence of autism to the annual incidence of cancer, at the state level, throughout the U.S. This was done for each autism age group and year reported, and for each type of cancer and year reported. Significance was calculated using methods previously described [25] , and adjusted using the Bonferroni correction [9] , [10] . To obtain an overall significance or combined p -value for each set of correlations, three methods were used and compared: Fisher's inverse chi-square method [11] , Brown's method for combining dependent p -values [12] and Simes' procedure [13] . Supporting Information Table S1 Correlations Between the Annual Incidence of All Adult Cancers Combined and Autism Prevalence Subdivided by Method of Diagnosis. Pairwise correlations were performed as described in Table 1 using two methods, Brown and Simes [12] , [13] , for combining dependent p-values. Autism prevalence data (ages 3–21) were obtained from groups of states selected on the basis of their criteria for diagnosing autism in all states or states subdivided by 4 groups of criteria ( Fig. 2 ). P represents combined p-values for the Pearson correlations and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. (0.03 MB DOC) Table S2 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. (0.06 MB DOC) Table S3 Correlations Between the Annual Incidence of Specific Female Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. (0.05 MB DOC) Table S4 Correlations Between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Brown's P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Brown's method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. (0.05 MB DOC) Table S5 Correlations Between the Annual Incidence of Specific Male Adult Cancers and Autism Prevalence Subdivided by Method of Diagnosis, using Simes' P-value Method. Pairwise correlations were performed, as described in Table 1 , between state-level annual incidence for specific male cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism ( Fig. 2 ). P represents combined p-values for Pearson correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. (0.05 MB DOC)

Journal

PLoS ONE – Public Library of Science (PLoS) Journal

Published: Feb 23, 2010

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