Canadian childhood obesity estimates based on WHO, IOTF and CDC cut-points
Bibliographic record
Abstract
OBJECTIVE: This article compares prevalence estimates of excess weight among Canadian children and youth according to three sets of body mass index (BMI) reference cut-points. The cut-points are based on growth curves generated by the World Health Organization (WHO), the International Obesity Task Force (IOTF), and the US Centers for Disease Control (CDC). A secondary objective is to compare estimates by method of data collection. METHODS: Prevalence estimates of overweight and obesity were produced for 2- to 17-year-olds using the three sets of BMI cut-points. Estimates are based on data from 8 661 respondents from the 2004 Canadian Community Health Survey and 1 840 respondents from the 1978/79 Canada Health Survey. In both surveys, the height and weight of children were measured. RESULTS: The 2004 prevalence estimate for the combined overweight/obese category is higher (35%) when based on the WHO cut-points compared with the IOTF (26%) or CDC (28%) cut-points. Estimates of the prevalence of obesity are similar based on WHO and CDC cut-points (13%), but lower when based on IOTF cut-points (8%). Absolute differences in excess weight estimates between 1978/79 and 2004 are similar based on the three sets of cut-points, but the relative increase is greater when based on the IOTF cut-points. Estimates vary substantially by method of data collection. CONCLUSION: When interpreting prevalence estimates of overweight and obesity for children and youth, it is important to consider the definitions used and the method of data collection.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".