Concurrent and Longitudinal Predictors of School Non-attendance in Autistic Adolescents

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Adams, D., Gray, K. M., den Houting, J., Paynter, J., Melvin, G., & Simpson, K. (2025). Concurrent and Longitudinal Predictors of School Non-attendance in Autistic Adolescents. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-025-06976-9

Publication date: 14 Aug 2025 Added to AutiHub: 5 Jul 2026 Type: Article Article language: English
Publisher source (DOI) Full text OpenAlex source

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Authors

Publication authors
6
Publication authors identified as autistic
1 / 6 (16.7%)
  1. Dawn Adams
  2. Kylie M. Gray
  3. Jacquiline den Houting *
  1. Jessica Paynter
  2. Glenn Melvin
  3. Kate Simpson

Abstract

Recent research has shown that autistic children are reported to have lower school attendance than non-autistic students. School non-attendance can occur for multiple reasons, including attendance at medical/health appointments and school refusal/emotionally based school avoidance. Providing support to improve autistic children's school attendance requires an understanding of the factors that potentially lead to or influence specific types of school non-attendance. The aim of this study was to identify concurrent and longitudinal school, family, and child factors associated with school non-attendance in autistic children. Parents/caregivers who had previously participated in a 6 year longitudinal study in Australia were invited to complete a follow-up online survey about their child's school attendance. Seventy-seven parents of autistic children aged 11-14 years participated. Over 40% of children had persistent absence (> 10% days) from school. Based on multivariate negative binomial regression models, child anxiety was a significant predictor of days missed for multiple types of school non-attendance. Other factors, including child sensory processing differences, child behavioural and emotional challenges, parent stress, family income, and parent employment, were correlated with specific absence types. Child anxiety was the strongest and most consistent longitudinal predictor, with higher child anxiety significantly predicting more days of school non-attendance 3, 4, and 6 years later. Findings highlight the importance of considering school, child, and family factors specific to different types of school non-attendance to support autistic children. Identifying factors that lead to child anxiety and preventing/reducing child anxiety early is a potentially promising avenue to support attendance.

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Cited bibliography overview

These indicators describe the bibliography cited by this publication. An author name is counted each time it appears in one cited reference, so the same person can be counted more than once. Names not yet linked to an author already present in AutiHub are treated as unknown, not as non-autistic. Last computed: 2 Oct 2026 01:00.

Cited references
57
With a DOI
44
Without a DOI, from raw bibliography text
10
1 / 57 (1.8%) cited references include at least one author identified as autistic.
References with data to complete
5 / 57 (8.8%)
References with detected author names
52 / 57 (91.2%)
Without detected author names
5
Without a structured title
5
Without a stable identifier
10
References with raw author names still to review
5
References with external metadata lookup issues
0
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
208
From DOI or external metadata
208
From validated raw bibliography text
0
Raw names already validated
0
Raw names still to review
10
96 / 208 (46.2%) names are linked to an author already present in AutiHub. 112 / 208 (53.8%) names are not yet linked.
Names linked to a person identified as autistic
2 / 208 (1.0%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 2 / 96 (2.1%). Distinct people identified as autistic: 2 / 173 (1.2%).
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