Sex Differences in Autism Spectrum Disorder based on DSM-5 Criteria: Evidence from Clinician and Teacher Reporting

Hiller, Rachel; Young, Robyn L.; Weber, Nathan

doi:10.1007/s10802-014-9881-x

Cited by 285 publications

(289 citation statements)

References 24 publications

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“…27 The trend of lower sex/gender ratio and dissociation may mean that recent studies have been more successful in identifying higher-functioning females, who may have been missed previously, particularly in clinic-or schoolbased samplings that are susceptible to bias. care-giving 28 The reason why we found equivalent prevalence in both genders may be because of a large sample size used in the study. It is pertinent to note that majority of our subjects who met the diagnostic criteria of autism were mainly within the age of 11-15 years.…”

Section: Resultsmentioning

confidence: 67%

Prevalence and socioeconomic correlates of autism among children attending primary and secondary schools in south east Nigeria

Chinawa¹,

Manyike²,

Aniwada³

et al. 2017

Afr H. Sci.

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Objectives: The objectives of the study were to determine the prevalence and socio-economic determinants of autism among children attending primary and secondary schools in South East, Nigeria. Methods: This was a cross-sectional study that assessed the prevalence and socio-economic pattern of childhood autism among children attending primary and secondary schools in Enugu and Ebonyi states, South East Nigeria. The questionnaire was adapted from American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR, 2000). The study was carried out between June and October, 2014. The schools were selected by listing all the mixed schools in the urban and semi-urban areas by simple random sampling. Results: A total of 721 subjects completed the questionnaire. The age of respondents ranged between 3 and 18 years, with mean age of 12.71 and standard deviation of 3.03 years. Twenty one children fulfilled the criteria for autism giving a prevalence of 2.9%. There is a significant association between age in categories (fishers exact test, p = 0.013) and social class (p=0.033). Conclusion: The prevalence of autism was 2.9%; and the socio-economic characteristics of childhood autism in South East Nigeria are similar to those in other parts of the world.

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Section: Resultsmentioning

confidence: 67%

Prevalence and socioeconomic correlates of autism among children attending primary and secondary schools in south east Nigeria

Chinawa¹,

Manyike²,

Aniwada³

et al. 2017

Afr H. Sci.

View full text Add to dashboard Cite

show abstract

“…Social anxiety and ASD also share many similar aspects of presentation, and it is suggested that some of the avoidance techniques used by autistic individuals to minimise social difficulties may be misinterpreted as demonstrations of social anxiety [68]. Anxiety problems are also more commonly found in females with and without ASD [82,83], although are still highly prevalent amongst males [84]. It has been suggested that anxiety disorders may 'mask' some ASD symptoms in women, as the anxiety may be the most pressing clinical concern and so underlying ASD difficulties remain unidentified [68,85].…”

Section: Anxietymentioning

confidence: 99%

Protective Effect or Missed Diagnosis? Females with Autism Spectrum Disorder

Hull

Mandy

2017

Future Neurol.

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Autism Spectrum Disorder (ASD) has historically been diagnosed more frequently in males than females. One explanation for this is the 'female protective effect': that there is something inherent in being female that makes girls and women less susceptible to ASD.Another possibility is that ASD is under-diagnosed in girls and women, due to the existence of a 'female autism phenotype', which is not well captured by current, male-biased diagnostic criteria. To evaluate the 'female protective effect' and 'female autism phenotype' hypotheses, this narrative review describes recent developments exploring the genetic underpinning and behavioural expression of ASD in females. We then look at ways to better identify females with ASD who may be missed under the current diagnostic criteria. KeywordsAutism, Autism Spectrum Disorders, Gender Differences, Sex Differences, Genetics, Diagnosis, Person-Environment Fit IntroductionAutism Spectrum Disorder (ASD) is a group of neurodevelopmental conditions characterised by deficits in social communication and reciprocity, and by restricted and repetitive interests and behaviours 1 . ASD is the current term used to integrate the previously separate diagnostic categories of autistic disorder, Asperger's disorder, childhood disintegrative disorder and pervasive developmental disorder -not otherwise specified (aka 'atypical autism' [2]). ASD is a lifelong condition that is present from birth. Although adult diagnosis is possible, symptoms must be reported as present from early developmental stages in order for the 1 In line with preferences from a majority of the autism community [1], we use identity-first language (e.g. 'autistic person') to refer to individuals with an ASD throughout this paper, while recognising that others might prefer person-first language (e.g. 'person with autism'). Gender differences in diagnosisASD affects approximately one to two per cent of the population [3,4], and is much more likely to be diagnosed in males than in females. Gender ratios in diagnosis vary depending on the country, cohort, and characteristics of those included, but average ratios are reported at around four males for every one female [3][4][5]. However, when looking at those individuals with intellectual disability, the male-to-female ratio narrows significantly to around two-toone [6]. In addition, population-based studies, which screen all participants in order to identify cases regardless of whether they have already been diagnosed by local services, suggest that the male-to-female ratio is closer to 3:1 [7,8]. This is in contrast to the average of around 4-5:1 generally reported in studies based on clinically-diagnosed cases [9]. The fact that a lower proportion of females are found in clinical samples than in community-based, epidemiological samples suggests that there is a bias against autistic girls and women coming to clinical attention. This bias may have existed from the very first conceptions of ASD; only 3 of 11 children in Kanner's original cases were female, and no gi...

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“…Several theses have been advanced about why this is the case. See Attwood et al (2006), Attwood (2007), Lai et al (2011Lai et al ( , 2012Lai et al ( , 2015Lai et al ( , 2017, Head et al (2014), Hiller et al (2014), Lehnhardt et al (2016), Parish-Morris et al (2017). 39 For instance, engaging in eye contact is taught as a set of instructions: look at the forehead or nose of the person you are talking to, count to five, look away, count to five, return to look at forehead again, etc.…”

Section: Fakesmentioning

confidence: 99%

Kinds behaving badly: intentional action and interactive kinds

Allen

2018

Synthese

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This paper investigates interactive kinds, a class of kinds suggested by Ian Hacking for which classification generates a feedback loop between the classifiers and what is classified, and argues that human interactive kinds should be distinguished from nonhuman ones. First, I challenge the claim that there is nothing ontologically special about interactive kinds in virtue of their members being classified as such. To do so, I reject Cooper's counterexample to Hacking's thesis that kind descriptions are necessary for intentional action, arguing that classification (although not necessarily linguistic description) is required for intentional action. Having considered ways to characterise the metaphysics of interactive kinds and the semantics of kind terms, I argue that the fact that classification facilitates intentional action makes human interactive kinds ontologically distinctive because of the anomalous nature of the change which the kind-classification brings about. I then briefly examine further distinguishing features of human interactive kinds.Keywords Interactive kinds · Natural kinds · Intentional action · Homeostatic property clusters · Psychiatric kinds · Social ontology Ian Hacking has drawn attention to a group of kinds which appear to have a very interesting feature. In the case of such kinds, there is a feedback loop between classifiers and what they are classifying: kind membership brings about changes in the individual members of the kind or their environment, and these changes bring about changes in the kind classification itself; and then the process starts again. In virtue of being members of a kind, the members of a kind alter what it is to be that kind (Hacking 1986,

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Sex Differences in Autism Spectrum Disorder based on DSM-5 Criteria: Evidence from Clinician and Teacher Reporting

Cited by 285 publications

References 24 publications

Prevalence and socioeconomic correlates of autism among children attending primary and secondary schools in south east Nigeria

Prevalence and socioeconomic correlates of autism among children attending primary and secondary schools in south east Nigeria

Protective Effect or Missed Diagnosis? Females with Autism Spectrum Disorder

Kinds behaving badly: intentional action and interactive kinds

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