Local Data for Obesity Prevention: Using National Data Sets
Bibliographic record
Abstract
One of the challenges in planning for obesity prevention is the dearth of relevant local data. We analyzed a large nationally representative data set, the Canadian Community Health Survey (CCHS) 2.2, to obtain regional and local distributions of physical activity and diet using statistical and spatial techniques. CCHS 2.2 contains information on health status, diet, physical activity, and measured body mass index (BMI), collected from January through December 2004. The total sample size is approximately 35,000; for the Hamilton metropolitan area it is approximately 600. The analyses were limited to descriptive statistics stratified by age group (2–11 years old, 12–17 years old, 18 years and older) and sex. For continuous variables we computed weighted means, standard errors, and coefficients of variation using the bootstrap macro (BOOTVARE_V3.1.SAS). For spatial analyses we used interpolation with inverse distance weighting. Analyses were conducted at the Research Data Center, McMaster University, using SAS Version 9 and ArcGIS 9.2, in compliance with Statistics Canada’s disclosure rules. Children 6 to 11 years old and 12 to 17 years old spent 2.6 and 5.8 hours per day, respectively, in sedentary activities. Children 2 to 11 years old consumed fruits and vegetables 5.3 times per day; however, 34 percent of that was from fruit juices, and 6 percent was from potatoes. Reported consumption of key nutrients such as fiber and saturated fat varied by neighborhood. We identified risk factors for obesity in the Hamilton population specific for age groups, sex, and location using a subset of national data. This information can guide programs and policies for obesity prevention at the local level.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.034 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".