The health and economic burden associated with sugar-sweetened beverage consumption in Trinidad and Tobago
Bibliographic record
Abstract
BackgroundIn Trinidad and Tobago, non-communicable diseases (NCDs) are the leading cause of death. Unhealthy diet is one modifiable NCD risk factor, which contributes to the NCD burden. The consumption of sugar-sweetened beverages (SSBs) has been associated with an increased risk of NCDs.AimThe aim of this paper is to estimate the burden of disease and economic costs associated with the consumption of SSBs in Trinidad and Tobago as evidence to support the implementation of health and fiscal policies on SSB consumption.MethodsThe results of this study were obtained through the use of a mathematical model which used a comparative risk assessment approach to estimate the health and economic burden associated with SSB intake, by sex and age.ResultsEstimates for one year showed that SSB consumption was associated with approximately 15,000 cases of overweight and obesity in adults and 11,700 cases in children, 28% of all the cases of diabetes and overall, an estimated 387 deaths and 9000 years of healthy life were lost due to premature death and disability. Approximately US$23.1 million was spent in the public healthcare system to treat diseases associated with consumption of sugary beverages.ConclusionsThe consumption of SSBs is associated with increases in diseases, deaths and rising healthcare costs in Trinidad and Tobago. It is hoped that the results of this study will provide an added rationale and impetus for the implementation of policies to reduce the consumption of SSBs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".