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Importance: Most evidence to date highlights the importance of genetic influences on the liability to autism and related traits. However, most of these findings are derived from clinically ascertained samples, possibly missing individuals with subtler manifestations, and obtained estimates may not be representative of the population., Objectives: To establish the relative contributions of genetic and environmental factors in liability to autism spectrum disorder (ASD) and a broader autism phenotype in a large population-based twin sample and to ascertain the genetic/environmental relationship between dimensional trait measures and categorical diagnostic constructs of ASD., Design, Setting, and Participants: We used data from the population-based cohort Twins Early Development Study, which included all twin pairs born in England and Wales from January 1, 1994, through December 31, 1996. We performed joint continuous-ordinal liability threshold model fitting using the full information maximum likelihood method to estimate genetic and environmental parameters of covariance. Twin pairs underwent the following assessments: the Childhood Autism Spectrum Test (CAST) (6423 pairs; mean age, 7.9 years), the Development and Well-being Assessment (DAWBA) (359 pairs; mean age, 10.3 years), the Autism Diagnostic Observation Schedule (ADOS) (203 pairs; mean age, 13.2 years), the Autism Diagnostic Interview-Revised (ADI-R) (205 pairs; mean age, 13.2 years), and a best-estimate diagnosis (207 pairs)., Main Outcomes and Measures: Participants underwent screening using a population-based measure of autistic traits (CAST assessment), structured diagnostic assessments (DAWBA, ADI-R, and ADOS), and a best-estimate diagnosis., Results: On all ASD measures, correlations among monozygotic twins (range, 0.77-0.99) were significantly higher than those for dizygotic twins (range, 0.22-0.65), giving heritability estimates of 56% to 95%. The covariance of CAST and ASD diagnostic status (DAWBA, ADOS and best-estimate diagnosis) was largely explained by additive genetic factors (76%-95%). For the ADI-R only, shared environmental influences were significant (30% [95% CI, 8%-47%]) but smaller than genetic influences (56% [95% CI, 37%-82%])., Conclusions and Relevance: The liability to ASD and a more broadly defined high-level autism trait phenotype in this large population-based twin sample derives primarily from additive genetic and, to a lesser extent, nonshared environmental effects. The largely consistent results across different diagnostic tools suggest that the results are generalizable across multiple measures and assessment methods. Genetic factors underpinning individual differences in autismlike traits show considerable overlap with genetic influences on diagnosed ASD.

Objective: To determine the prevalence of autism spectrum disorder (ASD) in Neurofibromatosis Type 1 (NF1)., Methods: Second-phase population-based epidemiologic study using an allcase NF1 registry in a defined UK 4.1 million population area. A total of 109 (52.7%) of 207 responders from the initial screening phase were grouped by using the parent-rated Social Responsiveness Scale (SRS) as significant ASD (SRS≥76; n = 32), moderate ASD (SRS ≥ 60<76; n = 29), or non-ASD (SRS <60, n = 48). Twenty-three cases from the significant ASD group, 16 from moderate ASD, and 8 from non-ASD (total n = 47), invited proportionately by random selection, were seen for detailed confirmatory ascertainment. Assessments on Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Scale-Generic, and verbal IQ were combined by using standard Collaborative Program for Excellence in Autism criteria into an ASD categorization for each case (ASD, broad ASD with partial features, non-ASD). A preplanned weighted analysis was used to derive prevalence estimates for the whole population., Results: Fourteen (29.5%) of 47 showed ASD, 13 (27.7%) broad ASD, and 20 (42.5%) non-ASD. The ASD/broad ASD group showed male predominance (1.7:1.0), but did not differ significantly from the non-ASD group on IQ, age, socioeconomic status, inheritance, physical severity, or education. The population prevalence estimate is 24.9% ASD (95% confidence interval 13.1%-42.1%) and 20.8% broad ASD (95% confidence interval 10.0%-38.1%); a total of 45.7% showing some ASD spectrum phenotype., Conclusions: Findings indicate high prevalence of ASD in NF1, with implications for clinical practice and further research into NF1 as a single-gene model for autism.

One of the challenges of developmental psychopathology is to determine whether identifiable pathways to developmental disorders exist in the first months or years of life. Early identification of such disorders poses a similar challenge for clinical services. Using data from a large contemporary birth cohort, we examined whether psychopathology at age seven can be predicted from clinician observation at one year. Two groups of clinical raters observed videos of caregiver-infant interaction. Neither group of raters could reliably identify any precursors of later development of psychopathology in the one-year-old infants in this setting., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Few studies address the daily challenges faced by parents of children diagnosed with autism spectrum disorder. This article reports on a qualitative interview study with 20 parents exploring their experiences, challenges faced, and what has helped them to cope. A thematic analysis of the data identified five core categories: Dealing with challenging behaviour; dealing with judgements from others; lack of support; impact upon the family; coping and the importance of appropriate support. The findings emphasize where the parents themselves believe they still require additional support. It raises key strategies and resources that parents have found helpful.

This is the first UK study to report the reliability, validity, and factor structure of the Social Responsiveness Scale (SRS) in a general population sample. Parents of 500 children (aged 5-8 years) in North East England completed the SRS. Profiles of scores were similar to USA norms, and a single factor structure was identified. Good construct validity and internal consistency were found. Children with identified special needs were found to have significantly higher SRS scores than those without. The findings suggest the SRS performs in similar ways in UK and USA general population samples of children and can be used without modification in the UK., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Background: There are no tested methods for conducting epidemiological studies of autism spectrum disorders (ASDs) in adult general population samples. We tested the validity of the Autism Diagnostic Observation Schedule module-4 (ADOS-4) and the 20-item Autism-Spectrum Quotient (AQ-20)., Method: Randomly sampled adults aged ≥16 years were interviewed throughout England in a general population multi-phase survey. The AQ-20 was self-completed by 7353 adults in phase 1. A random subset completed phase 2, ADOS-4 assessments (n=618); the probability of selection increased with AQ-20 score. In phase 3, informant-based Diagnostic Interview Schedule for Social and Communication Disorders (DISCO) and Autism Diagnostic Interview-Revised (ADI-R) developmental assessments were completed (n=56). Phase 1 and 2 data were presented as vignettes to six experienced clinicians (working in pairs). The probability of respondents having an ASD was compared across the three survey phases., Results: There was moderate agreement between clinical consensus diagnoses and ADOS-4. A range of ADOS-4 caseness thresholds was identified by clinicians: 5+ to 13+ with greatest area under the curve (AUC) at 5+ (0.88). Modelling of the presence of ASD using 56 DISCO assessments suggested an ADOS-4 threshold in the range of 10+ to 13+ with the highest AUC at ADOS 10+ to 11+ (0.93-0.94). At ADOS 10+, the sensitivity was 1 [95% confidence interval (CI) 0.59-1.0] and the specificity 0.86 (95% CI 0.72-0.94). The AQ-20 was only a weak predictor of ADOS-4 cases., Conclusions: Clinically recommended ADOS-4 thresholds are also recommended for community cases: 7+ for subthreshold and 10+ for definite cases. Further work on adult population screening methods is needed.

The setting up of a database of children with autism spectrum disorder (ASD) in the north east of England is described. Best practice has been followed and included involving parents in planning and implementation at all stages, oversight by a multi-agency group, management by a multidisciplinary steering group, and independent administration of the database. From a potential listing of 986 children with ASD aged 3-12 years, the parents of 511 have so far responded (51.8%), although response rate varies considerably by local authority. Data checking has shown the information to be valid and case ascertainment broadly representative. The uses to which the data are being put and the continuing challenges are outlined.

The aim of this study was to determine the prevalence of autistic spectrum disorder (ASD) within a large representative population sample: the Avon Longitudinal Study of Parents and Children (ALSPAC). Cases of ASD were identified from the clinical notes of children in the ALSPAC with a suspected developmental disorder and from the Pupil Level Annual Schools Census (PLASC) for England in 2003. Seventy-one cases of ASD diagnosed after a multidisciplinary assessment were identified from health records. There were an additional 15 cases from PLASC data in which ASD was mentioned as a principal difficulty, thus giving a total of 86 children diagnosed by the age of 11 years. Prevalence of ASD per 10,000 population at 11 years was 51.1 for those with a multi-professional diagnosis, and 61.9 if cases from education were included, made up of 21.6 for childhood autism, 10.8 for atypical autism, 16.6 for Asperger syndrome, and 13.0 for unspecified ASD. The male:female ratio was 6.8:1. Median age at diagnosis ranged from 45 months in childhood autism to 116 months in Asperger syndrome. A comorbid developmental disorder was recorded in 33.8% of cases, including learning disability in 14.7%, epilepsy in 10.3%, and mixed developmental disorder in 4.4%. We conclude that the prevalence of ASD diagnosed at 11 years in a UK representative population-based sample is at least 51.1/10,000.

Context: Prevalence rates of autism-spectrum disorders are uncertain, and speculation that their incidence is increasing continues to cause concern., Objective: To estimate the prevalence of pervasive developmental disorders (PDDs) in a geographically defined population of preschool children., Design, Setting, and Participants: Survey conducted July 1998 to June 1999 in Staffordshire, England. The area's 15 500 children aged 2.5 to 6.5 years were screened for developmental problems. Children with symptoms suggestive of a PDD were intensively assessed by a multidisciplinary team, which conducted standardized diagnostic interviews and administered psychometric tests., Main Outcome Measure: Prevalence estimates for subtypes of PDDs., Results: A total of 97 children (79.4% male) were confirmed to have a PDD. The prevalence of PDDs was estimated to be 62.6 (95% confidence interval, 50.8-76.3) per 10 000 children. Prevalences were 16.8 per 10 000 for autistic disorder and 45.8 per 10 000 for other PDDs. The mean age at diagnosis was 41 months, and 81% were originally referred by health visitors (nurse specialists). Of the 97 children with a PDD, 25.8% had some degree of mental retardation and 9.3% had an associated medical condition., Conclusions: Our results suggest that rates of PDD are higher than previously reported. Methodological limitations in existing epidemiological investigations preclude interpretation of recent high rates as indicative of increased incidence of these disorders although this hypothesis requires further rigorous testing. Attention is nevertheless drawn to the important needs of a substantial minority of preschool children.

Objectives: A population of 16,235 children aged 18 months was screened using the Checklist for Autism in Toddlers (CHAT) to identify childhood autism (CA). Two further screening procedures were conducted at age 3 and 5 years. The population was followed up at age 7 years in order to establish the sensitivity, specificity, and positive predictive value of the instrument., Method: A brief checklist assessing joint attention and pretend play behaviors was administered by primary health care practitioners when the children were 18 months old. Follow-up methods included screening through parents and health practitioners and checking medical and educational records., Results: Nineteen cases of CA were successfully identified by the CHAT at 18 months. At follow-up a total of 50 cases of CA were identified via all surveillance methods. Thus, the CHAT has a sensitivity of 38% and a specificity of 98% for identifying CA. The positive predictive value of the instrument was maximized by concentration on the highest-risk group. Repeated screening 1 month later increased the positive predictive value to 75% for identification of CA but reduced the sensitivity to 20%, although the specificity was close to 100%. The screen also identified cases of pervasive developmental disorder as well as children with language and other developmental disorders., Conclusions: The CHAT can be used to identify cases of autism and related pervasive developmental disorders at 18 months of age. It is emphasized that the CHAT is not a diagnostic instrument but can identify potential cases of autism spectrum disorders for a full diagnostic assessment.