A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder–Associated Impairment in Early Childhood

Burrows, Catherine A.; Grzadzinski, Rebecca; Donovan, Kevin; Stallworthy, Isabella C.; Rutsohn, Joshua; John, Tanya St.; Marrus, Natasha; Parish‐Morris, Julia; MacIntyre, Leigh C.; Hampton, Jacqueline; Pandey, Juhi; Shen, Mark D.; Botteron, Kelly N.; Estes, Annette; Dager, Stephen R.; Hazlett, Heather C.; Pruett, John R.; Schultz, Robert T.; Zwaigenbaum, Lonnie; Truong, Kinh; Piven, Joseph; Elison, Jed T.

doi:10.1016/j.biopsych.2022.05.027

Cited by 33 publications

(23 citation statements)

References 40 publications

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“…The study by Burrows et al [29] implies a 1:1 ASD MFOR largely from a genetic source and my data in section 2.4.2 supports this. But the MFOR up to age 18 in my study [1] was 0.75 (3:4), whereas at age 7 it was 0.85.…”

Section: Comorbidities In School Age Girlssupporting

confidence: 59%

“…The major ongoing clinical problem in ASD is poor reciprocal communication and the greater socialization expectations and cultural pressure on girls [25,26,27,28] would tip the diagnostic balance to females as development proceeds. A recent report [29] followed a group of younger siblings of autistic probands from age 6 months to 5 years. By accounting for test bias against females over the observation period they identified a group who had a high degree of ASD features and had a 1:1 gender ratio.…”

Section: Asd and Bpdmentioning

confidence: 99%

“…Gendered differences in early presentation have been hard to tease out [27] probably because early diagnosed girls have behaved like the boys. Focusing on ASD siblings either longitudinally [29] or in cross section with informed carer history [1] appears to be the key to finding a more likely MFOR. My results are shown in table 7 where the MFOR 95% confidence interval includes 1.…”

Section: Asd and Bpdmentioning

confidence: 99%

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Finding the Proportion of Females with Autistic Spectrum Disorder Who Develop Anorexia Nervosa, the True Prevalence of Female ASD and Its Clinical Significance.

McCrossin¹

2022

Preprint

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It appears that up to 80% of females with autistic spectrum disorder (ASD) have not been diagnosed by 18 years of age. This translates to a prevalence of about 5-6%, and if true has serious implications for female mental health. One way of finding the true value is to use Bayes’ Theorem with a comorbid condition as a more easily recognizable flag. An obvious choice is anorexia nervosa (AN), but it transpires that the proportion of women with ASD who develop AN is unknown. This study uses published data in novel ways to provide two methods of estimating a range for this variable, and gives a median value of 8.3% for AN in ASD and with four other methods a median prevalence of 6% for female ASD. The clinical implications for diagnosis and management of ASD and comorbidities are discussed and a solution is provided for the rate of ASD in generalized joint hypermobility.

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Section: Comorbidities In School Age Girlssupporting

confidence: 59%

Section: Asd and Bpdmentioning

confidence: 99%

Section: Asd and Bpdmentioning

confidence: 99%

See 1 more Smart Citation

Finding the Proportion of Females with Autistic Spectrum Disorder Who Develop Anorexia Nervosa, the True Prevalence of Female ASD and Its Clinical Significance.

McCrossin¹

2022

Preprint

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“…In recent years, multiple studies on MI/DIF have been carried out with different ASD symptom measures, including the Childhood Autism Rating Scale (CARS) (Schopler et al, 1988(Schopler et al, , 2010, ADOS (Lord et al, 2000(Lord et al, , 2012, Social Responsiveness Scale, and ADI-R (Constantino, 2005;Constantino and Gruber, 2012). These studies primarily focused on the effects of race/ethnicity, sex/gender and chronological age on scores (ADOS: Harrison et al, 2017;Ronkin et al, 2021;Burrows et al, 2022;Kalb et al, 2022;CARS: Stevanovic et al, 2021;SRS and ADI-R: Frazier and Hardan, 2017), with a few studies also investigating MI across groups with or without ID (Sturm et al, 2017;Dovgan et al, 2019). While these studies provided preliminary evidence that ASD symptom measures should take the impact of cognitive abilities into account, understanding of how cognitive or language abilities influence the measurement of specific ASD symptoms is still limited.…”

Section: Introductionmentioning

confidence: 99%

Bias in measurement of autism symptoms by spoken language level and non-verbal mental age in minimally verbal children with neurodevelopmental disorders

et al. 2022

Self Cite

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Increasing numbers of children with known genetic conditions and/or intellectual disability are referred for evaluation of autism spectrum disorder (ASD), highlighting the need to refine autism symptom measures to facilitate differential diagnoses in children with cognitive and language impairments. Previous studies have reported decreased specificity of ASD screening and diagnostic measures in children with intellectual disability. However, little is known about how cognitive and language abilities impact the measurement of specific ASD symptoms in this group. We aggregated a large sample of young children (N = 1196; aged 31–119 months) to examine measurement invariance of ASD symptoms among minimally verbal children within the context of the Autism Diagnostic Observation Schedule (ADOS) Module 1. Using confirmatory factor analysis (CFA) and moderated non-linear factor analysis (MNLFA), we examined how discrete behaviors were differentially associated with the latent symptom domains of social communication impairments (SCI) and restricted and repetitive behaviors (RRB) across spoken language levels and non-verbal mental age groupings. While the two-factor structure of SCI and RRB held consistently across language and cognitive levels, only partial invariance was observed for both ASD symptom domains of SCI and RRB. Specifically, four out of the 15 SCI items and one out of the three RRB items examined showed differential item functioning between children with “Few to No Words” and those with “Some Words”; and one SCI item and one RRB item showed differential item functioning across non-verbal mental age groups. Moreover, even after adjusting for the differential item functioning to reduce measurement bias across groups, there were still differences in ASD symptom domain scores across spoken language levels. These findings further underscore the influence of spoken language level on measurement of ASD symptoms and the importance of measuring ASD symptoms within refined spoken language levels, even among those with minimal verbal abilities.

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“…Historically, autism has been viewed as a predominantly male disorder with male to female sex ratios typically reported as 4:1 (Lai, Lombardo, et al, 2015) and ranging as high as 7:1 (Rutherford et al, 2016). Subsequent findings, however, suggest that the sex ratio discrepancy may be smaller than originally thought (Barnard-Brak et al, 2019), and even equal in some samples (see Burrows et al, 2022). In fact, ratios as low as 3:1 have been reported in children (Loomes et al, 2017) and may be even lower among adults (Posserud et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Exclusion of females in autism research: Empirical evidence for a “leaky” recruitment‐to‐research pipeline

D’Mello

Frosch

et al. 2022

Autism Research

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Autism spectrum disorder (ASD) is characterized by challenges in social communication and the presence of repetitive behaviors or restricted interests. Notably, males are four times as likely as females to be diagnosed with autism. Despite efforts to increase representation and characterization of autistic females, research studies consistently enroll small samples of females, or exclude females altogether. Importantly, researchers often rely on standardized measures to confirm diagnosis prior to enrollment in research studies. We retrospectively analyzed the effects of one such measure (Autism Diagnostic Observation Schedule, ADOS) on research inclusion/exclusion rates by sex in autistic adults, all of whom had a preexisting community diagnosis of autism (n = 145, 95 male, 50 female). Using the ADOS as a confirmatory diagnostic measure resulted in the exclusion of autistic females at a rate over 2.5 times higher than that of autistic males. We compared sex ratios in our sample to those in other large, publically available datasets that rely either on community diagnosis (6 datasets, total n = 42,209) or standardized assessments (2 datasets, total n = 214) to determine eligibility of participants for research. Reliance on community diagnosis rather than confirmatory diagnostic assessments resulted in significantly more equal sex ratios. These results provide evidence for a “leaky” recruitment‐to‐research pipeline for females in autism research. Lay Summary Despite efforts to increase the representation of autistic females in research, studies consistently enroll small samples of females or exclude females altogether. We find that despite making up almost 50% of the initially recruited sample based upon self‐report of community diagnosis, autistic females are disproportonately excluded from research participation as a result of commonly used autism diagnostic measures. In our sample, and several other publically available datasets, reliance on community diagnosis resulted in significantly more equal sex ratios.

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A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder–Associated Impairment in Early Childhood

Cited by 33 publications

References 40 publications

Finding the Proportion of Females with Autistic Spectrum Disorder Who Develop Anorexia Nervosa, the True Prevalence of Female ASD and Its Clinical Significance.

Finding the Proportion of Females with Autistic Spectrum Disorder Who Develop Anorexia Nervosa, the True Prevalence of Female ASD and Its Clinical Significance.

Bias in measurement of autism symptoms by spoken language level and non-verbal mental age in minimally verbal children with neurodevelopmental disorders

Exclusion of females in autism research: Empirical evidence for a “leaky” recruitment‐to‐research pipeline

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