Recent trends in adult body mass index and prevalence of excess weight
Bibliographic record
Abstract
<h3>Objective</h3> To explore recent body mass index (BMI) trends over time among Canadian adults seen in primary care to identify the best target groups for preventive interventions. <h3>Design</h3> Retrospective descriptive cohort design. <h3>Setting</h3> Data for this study were derived from the Canadian Primary Care Sentinel Surveillance Network database. <h3>Participants</h3> All patients aged 18 years and older who had BMI measurements available between 2011 and 2016 were identified. A closed cohort (N = 243 078 unique patients) with a start date of January 1, 2011, was defined. Patients were excluded if key variables were missing or if BMI measurements were 15 kg/m<sup>2</sup> or less or 50 kg/m<sup>2</sup> or greater. <h3>Main outcome measures</h3> The dependent variable for this study was BMI (kg/m<sup>2</sup>). Measured BMI values recorded in electronic medical records were used. A linear mixed-effect estimate was fit to model changes in BMI over time with control of baseline age and sex. <h3>Results</h3> Patients in the Canadian Primary Care Sentinel Surveillance Network database experienced a modest increase in mean (95% CI) BMI by 2.1% from 28.5 (28.4 to 28.6) kg/m<sup>2</sup> in 2011 to 29.1 (28.9 to 29.2) kg/m<sup>2</sup> in 2016 (<i>P</i> < .0001). This increase is not a measured difference in BMI in the same individual but reflects the difference in the average BMI of the population in 2011 versus 2016. Male patients had BMI values that were on average 1.02 kg/m<sup>2</sup> higher than those of female patients (<i>P</i> < .0001). Mean BMI values increased most rapidly in young adults (18 to 34 years) compared with older adults. <h3>Conclusion</h3> The findings indicate that current obesity management in primary care is failing to moderate weight trajectories in different groups by age and sex. The results also suggest that younger age groups, in whom accelerated weight gain occurred, should be the target of prevention initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".