58 Overweight and obesity in children with autism spectrum disorder: Findings from primary care electronic medical records
Bibliographic record
Abstract
Abstract Introduction/Background Paediatric overweight and obesity are important public health problems worldwide. Children with autism spectrum disorder (ASD) may be at increased risk compared to their typically-developing peers; however, prevalence estimates in ASD have varied widely and existing studies have largely been limited by use of an external comparison group. Objectives To compare prevalence of overweight and obesity in children and youth (<19 years of age) with and without ASD, using electronic medical record data from paediatric primary care visits. Design/Methods This was a cross-sectional analysis of EMRPC (Electronic Medical Records Primary Care) data, representing 385 family physicians in 43 clinics in Ontario, Canada. Age- and sex-standardized body mass index (BMI) z-scores were calculated using abstracted heights and weights from the most recent visit between January 2011 and December 2015. Weight status was determined using World Health Organization growth reference standards. ASD was defined using a previously-validated algorithm in EMRPC, based on an ASD-related term in the ‘Cumulative Patient Profile.’ Chi-square test statistics and multinomial logistic regression were used to compare weight status of those with and without ASD. Results In total, 44,625 children and youth were included, 632 [1.42%] with ASD. Distribution of weight status was significantly different between those with and without ASD (p<0.001) [Table 1]. Compared to their typically-developing peers, children with ASD had significantly higher odds of overweight (unadjusted odds ratio [OR] 1.52; 95% confidence interval [CI] 1.24-1.87), obesity (unadjusted OR 2.55 (2.00-3.26) and severe obesity (unadjusted OR 3.09; 95% CI 2.08-4.60); these associations persisted after adjusting for sex, age, neighborhood income quintile and rural residence (Table 2). Conclusion Data from a large primary care database suggest that children with ASD are at substantially increased risk of overweight, obesity and severe obesity. Findings support the need for anticipatory guidance, prevention and management strategies specific to this clinical population. Future work will aim to better understand at what age differences in weight status emerge, and what nutritional, behavioural, or medical factors differentially affect weight status in the 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.002 | 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".