Medication use in normal weight and overweight children in a nationally representative sample of Canadian children
Bibliographic record
Abstract
BACKGROUND: As overweight and obese children are more likely to develop serious medical conditions, they incur higher doctor and hospital costs compared to their normal weight counterparts. Consequently, the differential healthcare costs between obese and normal weight children may be even greater if medication use is considered. OBJECTIVE: To compare medication use between normal weight and overweight children in a nationally representative sample from Canada. METHODS: Data from the Canadian Health Measures Survey 2007/2009, a cross-sectional survey assessing indicators of health and wellness in Canadians, was used in the current study. The analysis included 2087 children and adolescents between 6-19 years of age with valid measures of body mass index (BMI). Poisson/negative binomial regression was used to examine the association between weight status and the number of medications taken in the last month. RESULT: For 6-11 year olds, the frequency of prescription, over-the-counter and natural health product (NHP) medication use did not differ between normal weight and overweight/obese children. For 12-19 year olds, overweight/obese children used prescription medication significantly more often than their normal weight peers (adjusted incidence rate ratio (IRR), 1.59; 95% CI 1.19 to 2.14), whereas for NHP the opposite was the case (adjusted IRR, 0.52; 95% CI 0.32 to 0.82). These children also used nervous system and respiratory medications more frequently than their normal weight peers. CONCLUSION: The findings of the present study suggest that the differential usage of prescription drugs among overweight/obese children underline the need to develop effective obesity prevention programmes and policies that may reduce the health and economic burden of childhood obesity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".