166: Using Electronic Medical Records to Estimate Overweight and Obesity Rates in Children in Ontario, Canada
Bibliographic record
Abstract
Few population-based systems exist to monitor child obesity prevalence in Canada. Data from electronic medical records (EMR) have been used in a small number of juridstictions worldwide to estimate obesity prevalence in children. To determine the frequency of height and weight documentation in EMRs in children, to describe the prevalence of child overweight and obesity, by age, and sex using data from the Electronic Medical Record Administrative data Linked Database (EMRALD) database in Ontario, and to determine if there are differences in prevalence based on visit type (well-child visit vs. other). We abstracted height and weight in children zero to 19 years of age in EMRALD who had at least one well-child visit from January 2010 to December 2011. Using the most recent visit with both a documented height and weight, we reported the proportion and 95% CIs of subjects defined as overweight, and obese, by age group and sex, using the WHO growth reference standards. We compared the proportion or overweight and obese children by visit type for all age groups, using χ2 tests. There were 28,083 well-child visits in 7705 children over this study period. 84.7% of children who attended well-child visits had both a height and weight documented. The prevalence of overweight and obesity, varied by age group from 12% to 32%, and 2% to 12%, respectively. Obesity rates were significantly higher in one- to four-year-olds compared to children <1 year of age (6.1% vs. 2.3%), and in 10- to 14-year-olds compared to five- to nine-year-olds (12% vs. 9%). Both one- to four-year-old (7.2% vs. 4.9%) and 10- to 14-year-old boys (14.5% vs. 9.6%) had higher obesity rates. The proportion of overweight and obese children was higher using heights and weights reported from other child visit types, compared to well-child visits, for all age groups (P<0.05), except for children less than one year of age (P=0.45). We documented a high rate of overweight and obesity in children. EMR may be a useful tool to conduct population-based surveillance of child overweight and obesity in Canada. The selection of visit type may be an important methodological consideration.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".