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

Abstract

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.

Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-site Integrative Data Analysis by the Autism Sensory Research Consortium

“…By pooling data from multiple independent research groups and the National Database for Autism Research (NDAR (86)), we compiled a cohort of several thousand autistic children to be analyzed within the methodological framework of integrative data analysis (IDA (87, 88)). The IDA approach has recently gained popularity within autism research, going beyond small sample studies to yield insights about the latent structure of core and associated autism features (54,(89)(90)(91)(92)(93)(94), the psychometric properties of widely-used measures (54,91,(95)(96)(97)(98)(99)(100), and the associations between autism features and other related clinical and demographic variables (92,98,(101)(102)(103). Utilizing modern psychometric techniques such as item response theory (IRT (104, 105)) and bifactor participants as well at the time of data collection.…”

Heterogeneity of Autism Characteristics in Genetic Syndromes: Key Considerations for Assessment and Support

“…Authors have cautioned against interpreting scores on the ADOS-2 and ADI-R when a person has a mental age of 18–24 months [ 35 , 112 ]. Fortunately, researchers have begun to explore how screening tools can be tailored and standardised in samples with ID [ 113 ] and suggested modifications to diagnostic assessment that are more appropriate for those with few and no words [ 114 •, 115 ]. Though this is a step forward, an unintended consequence of focusing on ID broadly is that we are still limited in the ability to conceptualise syndrome-specific profiles of autism.…”

Examining the latent structure and correlates of sensory reactivity in autism: a multi-site integrative data analysis by the autism sensory research consortium

“…By pooling data from multiple independent research groups and the National Database for Autism Research (NDAR [87]), we compiled a cohort of several thousand autistic children to be analyzed within the methodological framework of integrative data analysis (IDA [88,89]). The IDA approach has recently gained popularity within autism research, going beyond small sample studies to yield insights about the latent structure of core and associated autism features [56,[90][91][92][93][94][95], the psychometric properties of widelyused measures [56,92,[96][97][98][99][100][101], and the associations between autism features and other related clinical and demographic variables [93,99,[102][103][104]. However, many of these studies have not explicitly quantified the degree to which effects of interest vary across pooled datasets (i.e., effect size heterogeneity), arguably a major strength of IDA methodology (see [103] for a notable exception).…”