Incidence of Physical Health Conditions in Autistic Children Within 5 Years After Their Autism Diagnosis
Bibliographic record
Abstract
This study aimed to investigate the incidence of physical illnesses of autistic young children compared with children in the general population. This population-based study included children (aged ≤ 5 years) with newly diagnosed autism (autism group), followed up for 5 years after their autism diagnoses. Data were collected from Taiwan's National Health Insurance Research Database in the period of 2000-2019. Autistic children (n = 45,680) were matched (1:20; by age and sex [assigned at birth]) with a comparison group from the general population (n = 913,600). We calculated incidence rate ratios (IRRs) for physical illnesses diagnosed within 5 years after autism diagnoses. Data were analyzed using Poisson regression models adjusted for person-time and stratified by sex and the presence/absence of intellectual disabilities. The prevalence of almost all illnesses across major organ systems after 1 year of autism diagnosis was higher in the autism group than in the comparison group. The autism group exhibited significantly elevated incidence of cardiovascular disorders, cerebrovascular disorders, and endocrine diseases within 1 year after autism diagnosis (IRR 2.30-71.42). Although the incidence rates of these illnesses decreased over the 5-year follow-up period in the autism group, they remained higher than those in the comparison group, with most IRRs exceeding 2 in the fifth year after autism diagnosis. The IRRs were significant in both autistic male and female children and those with and without intellectual disabilities, although those with intellectual disabilities displayed descriptively larger IRRs. Autistic young children have heightened risks of being diagnosed with physical illnesses soon after their autism diagnoses. Future research should understand the etiological associations between autism and physical illnesses to offer tailored care from early in life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".