Identifying Neurodiverse Conditions in Multilingual Children: An Analysis of Referral and Diagnosis Patterns in Wales

ObjectivesThere are many challenges in identifying children with language and communication delays, including autism, from bilingual backgrounds. This study is the first to investigate disparities in autism diagnosis rates and age of diagnosis among multilingual children in the UK. It explores how language background, ethnicity, sex, and deprivation influence diagnostic patterns. MethodsA retrospective cross-sectional study examined population-level data within the Secure Anonymised Information Linkage (SAIL) Databank to analyse trends in autism diagnosis in Wales, UK. Using routinely collected health, education, and Office for National Statistics Census 2011 data, we analysed diagnostic patterns of autism in children (aged ≤ 18 years) over a 20-year period (1999 – 2019). We report the association of language background, ethnicity, area-based relative deprivation level, educational attainment, and sex with autism diagnosis rates and age. ResultsDescriptive statistics were used to compare autism diagnoses between different language backgrounds (mono vs multilingual). Logistic regression models investigated the impact of language background on referral rates and age at diagnosis. Subgroup analyses were conducted to explore variations in diagnostic patterns by sex, ethnicity, and geographical location. Finally, hierarchical linear models were employed to explore whether specific factors, including level of relative deprivation and sex, contribute to disparities in neurodiversity diagnoses amongst multilingual children. Educational outcomes were also analysed whilst assessing the impact of age at diagnosis and language background. Statistical significance was assessed using 95% confidence intervals. ConclusionThese findings will be crucial for identifying systemic barriers in autism diagnosis among multilingual children. By utilising data-driven insights, policymakers can guide improvements in autism screening, promoting equitable access to neurodiversity services. International efforts to improve autism identification and support services for multilingual populations can now be better informed.

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Publication Details

Journal
International Journal for Population Data Science
Published
2025-08-28
DOI
https://doi.org/10.23889/ijpds.v10i4.3269
Primary Topic
Neurobiology of Language and Bilingualism
Type
article
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article

Identifying Neurodiverse Conditions in Multilingual Children: An Analysis of Referral and Diagnosis Patterns in Wales

Richard May, Anna Rawlings, Rebecca Ward, Hywel Turner-Evans
International Journal for Population Data Science
Neurobiology of Language and Bilingualism
article

Identifying Neurodiverse Conditions in Multilingual Children: An Analysis of Referral and Diagnosis Patterns in Wales

Richard May, Anna Rawlings, Rebecca Ward, Hywel Turner-Evans
article en

Abstract

ObjectivesThere are many challenges in identifying children with language and communication delays, including autism, from bilingual backgrounds. This study is the first to investigate disparities in autism diagnosis rates and age of diagnosis among multilingual children in the UK. It explores how language background, ethnicity, sex, and deprivation influence diagnostic patterns. MethodsA retrospective cross-sectional study examined population-level data within the Secure Anonymised Information Linkage (SAIL) Databank to analyse trends in autism diagnosis in Wales, UK. Using routinely collected health, education, and Office for National Statistics Census 2011 data, we analysed diagnostic patterns of autism in children (aged ≤ 18 years) over a 20-year period (1999 – 2019). We report the association of language background, ethnicity, area-based relative deprivation level, educational attainment, and sex with autism diagnosis rates and age. ResultsDescriptive statistics were used to compare autism diagnoses between different language backgrounds (mono vs multilingual). Logistic regression models investigated the impact of language background on referral rates and age at diagnosis. Subgroup analyses were conducted to explore variations in diagnostic patterns by sex, ethnicity, and geographical location. Finally, hierarchical linear models were employed to explore whether specific factors, including level of relative deprivation and sex, contribute to disparities in neurodiversity diagnoses amongst multilingual children. Educational outcomes were also analysed whilst assessing the impact of age at diagnosis and language background. Statistical significance was assessed using 95% confidence intervals. ConclusionThese findings will be crucial for identifying systemic barriers in autism diagnosis among multilingual children. By utilising data-driven insights, policymakers can guide improvements in autism screening, promoting equitable access to neurodiversity services. International efforts to improve autism identification and support services for multilingual populations can now be better informed.

International Journal for Population Data ScienceVol. 10(4)
University of South Wales (GB), Swansea University (GB)
Openalex Percentile: Top 78%
Neurobiology of Language and Bilingualism
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