Evidence map›Paper›PMID 38827983›Full record

ArticleJCPP advances2024

The distribution of parent-reported attention-deficit/hyperactivity disorder and subclinical autistic traits in children with and without an ADHD diagnosis.

Tracey Chau, Jeggan Tiego, Louise E Brown, Olivia J Mellahn, Beth P Johnson, Aurina Arnatkeviciute, Ben D Fulcher, Natasha Matthews, Mark A Bellgrove

Abstract read
In one paragraph

Article in JCPP advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tracey ChauTurner Institute for Brain and Mental Health School of Psychological Sciences Monash University Clayton Victoria Australia.ORCID https://orcid.org/0000-0001-6867-3533
Jeggan TiegoTurner Institute for Brain and Mental Health School of Psychological Sciences Monash University Clayton Victoria Australia.ORCID https://orcid.org/0000-0001-7835-6398
Louise E BrownSchool of Nursing, Midwifery & Paramedicine Curtin University Bentley Western Australia Australia.
Olivia J MellahnTurner Institute for Brain and Mental Health School of Psychological Sciences Monash University Clayton Victoria Australia.ORCID https://orcid.org/0000-0002-9049-976X
Beth P JohnsonTurner Institute for Brain and Mental Health School of Psychological Sciences Monash University Clayton Victoria Australia.ORCID https://orcid.org/0000-0002-6336-0092
Aurina ArnatkeviciuteTurner Institute for Brain and Mental Health School of Psychological Sciences Monash University Clayton Victoria Australia.ORCID https://orcid.org/0000-0003-4098-7084
Ben D FulcherSchool of Physics The University of Sydney Camperdown Western Australia Australia.ORCID https://orcid.org/0000-0002-3003-4055
Natasha MatthewsSchool of Psychology The University of Queensland Saint Lucia Queensland Australia.ORCID https://orcid.org/0000-0001-5683-7289
Mark A BellgroveTurner Institute for Brain and Mental Health School of Psychological Sciences Monash University Clayton Victoria Australia.ORCID https://orcid.org/0000-0003-0186-8349

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Autistic traits are often reported to be elevated in children diagnosed with attention-deficit/hyperactivity disorder (ADHD). However, the distribution of subclinical autistic traits in children with ADHD has not yet been established; knowing this may have important implications for diagnostic and intervention processes. The present study proposes a preliminary model of the distribution of parent-reported ADHD and subclinical autistic traits in two independent samples of Australian children with and without an ADHD diagnosis. Methods: Factor mixture modelling was applied to Autism Quotient and Conners' Parent Rating Scale - Revised responses from parents of Australian children aged 6-15 years who participated in one of two independent studies. Results: A 2-factor, 2-class factor mixture model with class varying factor variances and intercepts demonstrated the best fit to the data in both discovery and replication samples. The factors corresponded to the latent constructs of 'autism' and 'ADHD', respectively. Class 1 was characterised by low levels of both ADHD and autistic traits. Class 2 was characterised by high levels of ADHD traits and low-to-moderate levels of autistic traits. The classes were largely separated along diagnostic boundaries. The largest effect size for differences between classes on the Autism Quotient was on the Social Communication subscale. Conclusions: Our findings support the conceptualisation of ADHD as a continuum, whilst confirming the utility of current categorical diagnostic criteria. Results suggest that subclinical autistic traits, particularly in the social communication domain, are unevenly distributed across children with clinically significant levels of ADHD traits. These traits might be profitably screened for in assessments of children with high ADHD symptoms and may also represent useful targets for intervention.

Indexed as

attention‐deficit/hyperactivity disorderfactor mixture modellinglatent structuresubclinical autism

Identifiers

PMID38827983
PMCPMC11143953

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.