Initial diagnosis patterns of coexisting mental health and neurodevelopmental conditions in autistic children and youth: Evidence from a nationally representative sample in Canada
Bibliographic record
Abstract
BACKGROUND: Elevated prevalence of coexisting health conditions has been observed in autistic people, yet how the timing of their initial diagnoses varies by sex and age of autism diagnosis remains understudied. Using a person-centered approach, we examined the patterns of initial diagnosis for mental health and neurodevelopmental conditions among autistic children and youth identified from the general population. METHODS: The sample was drawn from the 2019 Canadian Health Survey on Children and Youth (CHSCY) cohort (N = 47,781), consisting of 776 5-17-year-olds (82% assigned-male-at-birth) with a caregiver-reported diagnosis of autism. Multigroup latent class analysis was used to identify subgroups based on ages of initial diagnoses of autism, anxiety, mood, learning, and attention-deficit/hyperactivity disorders stratified by sex assigned at birth. Functional difficulties and multimorbidity status, including the number and types of coexisting conditions, were compared across the subgroups. RESULTS: Four latent subgroups were identified for each sex group, primarily differentiated by the age of autism diagnosis. The most prevalent class (46%) was characterized by an initial autism diagnosis at ages 3-5 years. The remaining subgroups, with autism diagnosed primarily before age 3, at 6-8, and at 9-17 years, each comprised ~20% of the sample. Subgroups with autism diagnosed after age 6 tended to have more coexisting conditions, with females showing heightened probabilities of mental health diagnoses across age windows from birth to age 17 years. The temporal order of coexisting diagnoses relative to autism diagnosis varied across subgroups, with sex differences more evident for anxiety and attention-deficit/hyperactivity disorders. CONCLUSIONS: There were nuanced variations in the timing of initial diagnoses of coexisting conditions based on the age of autism diagnosis. The sex-varying patterns highlight the importance of continuous monitoring and evaluation of the neurodevelopmental and mental health needs of autistic children and youth, with supports tailored to sex and the timing of autism diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".