46 Micronutrient deficiencies in children and youth with autism spectrum disorder: Findings from a national surveillance study
Bibliographic record
Abstract
Abstract Background Children and youth with autism spectrum disorder (ASD) may be at risk of micronutrient deficiencies secondary to feeding problems such as limited dietary repertoire and high-frequency single food intake. The epidemiology of micronutrient deficiencies in the paediatric ASD population has not been studied. Objectives To determine minimum incidence, clinical characteristics, and health care utilization for four micronutrient deficiencies in children and youth with ASD in Canada: (1) vitamin A deficiency/xerophthalmia, (2) scurvy, (3) severe, symptomatic vitamin D deficiency, and (4) severe iron-deficiency anemia. Design/Methods We conducted a study via the Canadian Paediatric Surveillance Program, an active, population-based surveillance platform, between January 2020 and December 2022. More than 2,500 paediatricians and paediatric subspecialists were asked, monthly, to report cases of children and youth <18 years of age with ASD and one or more of the micronutrient deficiencies under surveillance. Case information was obtained via a detailed clinical questionnaire. Descriptive statistics were used to summarize the data. For average yearly minimum incidence estimates, we used case counts over the 3 year surveillance period, 2021 census data and recent ASD prevalence estimates (2% of children/youth aged 1 to 17 years living in private dwellings in Canada). Results Twenty-seven children and youth met the case definition (most male; median age 7.7 years, range 1.8–14.9 years). Multiple deficiencies were diagnosed concurrently in 9/27 (33%). Minimum incidence, per 100,000 children and youth with ASD per year, was 5.1 for scurvy (17 diagnoses); 3.0 for severe iron deficiency anemia (10 diagnoses); 2.1 for severe, symptomatic vitamin D deficiency (7 diagnoses); and <1 for vitamin A deficiency/xerophthalmia (<5 diagnoses). Restricted diet/limited food repertoire, attributed to the patient, was universal (27/27). Number of different foods in the patient’s diet was <10 in nearly all (20/23, 87%). Most patients were non-verbal (19/27, 70%) and of normal weight (18/26, 69%). Two-thirds (18/27, 67%) were admitted to hospital (median duration 8 days, range 3–32 days). One-third (9/27, 33%) underwent an invasive procedure as part of their diagnostic workup. Conclusion This surveillance study represents the first population-level examination of the epidemiology of micronutrient deficiencies in children and youth with ASD. Results suggest that micronutrient deficiencies in ASD are rare but clinically important, leading not infrequently to hospital admission and invasive investigations. Clinical characteristics of cases should inform anticipatory guidance and prevention efforts tailored to the paediatric ASD population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".