Exploring ways to respond to rising obesity and diabetes in the Caribbean using a system dynamics model
Bibliographic record
Abstract
Diabetes and obesity present a high and increasing burden of disease in the Caribbean that have failed to respond to prevention policies and interventions. These conditions are the result of a complex system of drivers and determinants that can make it difficult to predict the impact of interventions. In partnership with stakeholders, we developed a system dynamics simulation model to map the system driving diabetes and obesity prevalence in the Caribbean using Jamaica as a test case. The study aims to use the model to assess the magnitude changes necessary in physical activity and dietary intake to achieve global targets set by the WHO Global Action plan and to test scenarios for interventions to reduce the burden of diabetes and obesity. Continuing current trends in diet, physical activity, and demographics, the model predicts diabetes in Jamaican adults (20+ years) to rise from 12% in 2018 to 15.4% in 2030 and 20.9% by 2050. For obesity, it predicts prevalence to rise from 28.6% in 2018 to 32.1% by 2030 and 39.2% by 2050. The magnitude change necessary to achieve the global targets set by the World Health Organization is so great as to be unachievable. However, a combination of measures both upstream (including reducing the consumption of sugar sweetened beverages and ultra processed foods, increasing fruit and vegetable intake, and increasing moderate-to-vigorous activity) at the population level, and downstream (targeting people at high risk and with diabetes) can significantly reduce the future burden of diabetes and obesity in the region. No single intervention reduces the prevalence of these conditions as much as a combination of interventions. Thus, the findings of this model strongly support adopting a sustained and coordinated approach across various sectors to synergistically maximise the benefits of interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".