Development of a population-based microsimulation model of body mass index.
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence of overweight and obesity has necessitated the development of body mass index (BMI) projection models such as the POpulation HEalth Model (POHEM). This study describes the POHEM-BMI model, a microsimulation tool that can be used to support evidence-based health policy making for obesity reduction. DATA AND METHODS: The National Population Health Survey, the Canadian Community Health Survey (CCHS), and the Canadian Health Measures Survey (CHMS) were used to develop and validate a predictive model of BMI for adults and childhood BMI history. Models were incorporated into POHEM and used to transition BMI over time in a fully dynamic simulated Canadian population. RESULTS: POHEM-BMI projections of self-reported and measured adult BMI and childhood BMI history agree well with CCHS and CHMS validation estimates. Among men and women, average BMI is projected to increase by more than one BMI unit between 2001 and 2030. Projections of self-reported BMI show that 59% of the adult population will be overweight or obese by 2030; projections of measured BMI show that the percentage will be 66%. INTERPRETATION: Using empirically developed BMI prediction models for adults and childhood BMI history integrated into the POHEM framework, validated projections of BMI for the Canadian population can be produced. Projections of BMI trends could have important applications in tracking the prevalence of related diseases, and in planning and comparing intervention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".