Myers, Scott M.

Abstract Aims To compare the prevalence of autism spectrum disorder (ASD) in children with and without epilepsy using clinical diagnoses and chart review with research‐defined criteria and to examine sex ratios, age at ASD onset, and intellectual disability between groups. Method The Olmsted County birth cohort included 30 490 individuals, of whom 257 (0.84%) had epilepsy diagnosed before 19 years of age. The cohort was previously screened for ASD using three case definitions: research‐inclusive (ASD‐R I ), research‐narrow (ASD‐R N ), and clinically diagnosed ASD (ASD‐C). For those with ASD‐R I , we assessed sex, intellectual disability (IQ < 70), and age at ASD onset. Means and proportions are reported when available. Results ASD prevalence was significantly higher in participants with epilepsy across all definitions: ASD‐R I 21.4% versus 3.2%; ASD‐R N 14.0% versus 1.6%; and ASD‐C 7.9% versus 0.7%. Children with both epilepsy and ASD‐R I were more often female (38.2% vs 25.8%), had higher rates of intellectual disability (56.5% vs 15.4%), and met the ASD‐R I criteria at a younger age (mean 7 years 5 months vs 8 years 8 months). Interpretation Co‐occurring ASD and epilepsy are frequently associated with intellectual disability, earlier age at ASD diagnosis, and reduced male predominance.

ABSTRACT: Objective: Determine the risk of autoimmune disease in research-identified cases of autism spectrum disorder (ASD) compared with referents using a longitudinal, population-based birth cohort. Methods: ASD incident cases were identified from a population-based birth cohort of 31,220 individuals. Inclusive ASD definition based on DSM-IV-TR autistic disorder, Asperger syndrome, and pervasive developmental disorder, not otherwise specified, was used to determine ASD cases. For each ASD case, 2 age- and sex-matched referents without ASD were identified. Diagnosis codes assigned between birth and December 2017 were electronically obtained. Individuals were classified as having an autoimmune disorder if they had at least 2 diagnosis codes more than 30 days apart. Cox proportional hazards models were fit to estimate the hazard ratio (HR) between ASD status and autoimmune disorder. Results: Of 1014 ASD cases, 747 (73.7%) were male. Fifty ASD cases and 59 of the 1:2 matched referents were diagnosed with first autoimmune disorder at the median age of 14 and 17.1 years, respectively. ASD cases had increased risk of autoimmune disease compared with matched referents (HR 1.74; 95% confidence interval [CI], 1.21–2.52). The increased risk was statistically significant among male patients (HR 2.01; 95% CI, 1.26–3.21) but not among the smaller number of female subjects (HR 1.38; 95% CI, 0.76–2.50). Conclusion: This study provides evidence from a longitudinal, population-based birth cohort for co-occurrence of ASD and autoimmune disorders. Thus, children with ASD should be monitored for symptoms of autoimmune disease and appropriate workup initiated.

OBJECTIVES We aimed to describe the intellectual ability and ratio of boys to girls with average or higher IQ within autism spectrum disorder (ASD) cases identified in a population-based birth cohort. We hypothesized that research-identified individuals with ASD would be more likely to have average or higher IQ, compared to clinically diagnosed ASD. We also hypothesized the male to female ratio would decrease as the definition of ASD broadened. METHODS ASD incident cases were identified from 31 220 subjects in a population-based birth cohort. Research-defined autism spectrum disorder, inclusive criteria (ASD-RI) was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision, autistic disorder (AD), Asperger Disorder, and pervasive developmental disorder not otherwise specified criteria. Research-defined autism spectrum disorder, narrow criteria (ASD-RN) was a narrower definition based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision AD criteria. Clinical diagnoses of ASD were abstracted from medical and school records. Intellectual ability was based on the last IQ score or on documented diagnoses of intellectual disability if no scores available. Average or higher IQ was defined as IQ ≥86. RESULTS A total of 59.1% of those with ASD-RI (n = 890), 51.2% of those with ASD-RN (n = 453), and 42.8% of those with clinically diagnosed autism spectrum disorder (n = 187) had average or higher IQ. Within the ASD-RI and ASD-RN groups, boys were more likely than girls to have an average or higher IQ (62.0% vs 51.3% [P = .004] and 54.1% vs. 42.5% [P = .03], respectively). CONCLUSION Our data suggest that nearly half of individuals with ASD have average or higher IQ. Boys with ASD are more likely to have average or higher IQ than girls. Patients with ASD and higher IQ remain at risk for not being identified.

American Academy of Pediatrics (AAP) guidelines for children with Down syndrome (DS) include assessment for celiac disease (CD), although data to support this recommendation have been inconsistent. We determined the incidence of CD among children with DS in a population-based birth cohort of children born from 1976 to 2000 in Olmsted County, Minnesota. Individuals with karyotype-confirmed DS and CD (using diagnosis codes, positive serology, and duodenal biopsies) were identified. The incidence of CD in DS was compared with the published incidence of CD for Olmsted County residents (17.4 [95% confidence interval = 15.2-19.6] per 100 000 person-years). Among 45 individuals with DS from the birth cohort, 3 (6.7%) were identified with positive celiac serology and confirmatory biopsies at ages 9, 12, and 23 years, for an incidence of 325 per 100 000 person-years. Thus, individuals with DS have more than 18 times the incidence rate of CD compared with the general population, supporting the AAP guidelines.

We consider the problem of model‐based clustering in the presence of many correlated, mixed continuous, and discrete variables, some of which may have missing values. Discrete variables are treated with a latent continuous variable approach, and the Dirichlet process is used to construct a mixture model with an unknown number of components. Variable selection is also performed to identify the variables that are most influential for determining cluster membership. The work is motivated by the need to cluster patients thought to potentially have autism spectrum disorder on the basis of many cognitive and/or behavioral test scores. There are a modest number of patients (486) in the data set along with many (55) test score variables (many of which are discrete valued and/or missing). The goal of the work is to (1) cluster these patients into similar groups to help identify those with similar clinical presentation and (2) identify a sparse subset of tests that inform the clusters in order to eliminate unnecessary testing. The proposed approach compares very favorably with other methods via simulation of problems of this type. The results of the autism spectrum disorder analysis suggested 3 clusters to be most likely, while only 4 test scores had high (>0.5) posterior probability of being informative. This will result in much more efficient and informative testing. The need to cluster observations on the basis of many correlated, continuous/discrete variables with missing values is a common problem in the health sciences as well as in many other disciplines.

A large cohort consisting of all children born to mothers from community provides ‘natural’ selection into different exposures and is a powerful resource for epidemiological research. A large population-based birth cohort with detailed systematic information already recorded, as part of longitudinal medical care, historical and current school data, detailed birth certificate data and all three resources available for every member of the birth cohort, are extremely rare. Our population-based birth cohort consists of all children born between 1976 and 2000 to mothers residing in Olmsted County, Minnesota, at the time of child's birth (N=39 890). In this paper, we provide a comprehensive report of the method describing the identification, the characteristics and longitudinal follow-up of each child (and family members) from the birth cohort, wealth of complementary resources of data and study measures and designs (retrospective, combined retrospective/prospective). In the last decade or so, we obtained scientific and clinically needed answers for incidence rates, potential risk/protective factors, treatment, comorbidities, outcomes, cost/usage and potential biases (that are always assessed and clinically interpreted) of many developmental learning and behavioural disorders (DLBDs) including learning and attention-deficit/hyperactivity disorders, intellectual disability, speech-language impairment and autism spectrum disorder. Many current and future questions related to DLBDs are remaining to be answered. The Olmsted County Birth Cohort (OCBC) is an example of a comprehensive, contemporary epidemiological research model for the development of similar research infrastructures, and its current and future results are important for replication and comparison with other population-based retrospective and prospective birth cohort studies.