Health and economic burden of disease of sugar-sweetened beverage consumption in four Latin American and Caribbean countries: a modelling study
Bibliographic record
Abstract
OBJECTIVE: Overweight and obesity are important contributors to the non-communicable disease burden. The consumption of sugar-sweetened beverages (SSBs) has been associated with an increased risk of type 2 diabetes mellitus (T2DM), cardiovascular disease, cancer and other conditions. The objective of this study was to estimate the burden of disease attributable to the consumption of SSBs and the costs to the healthcare systems in Argentina, Brazil, El Salvador, and Trinidad and Tobago. DESIGN: Following a systematic review of models, a comparative risk assessment framework was developed to estimate the health and economic impact associated with the consumption of SSBs. SETTING: Argentina, Brazil, El Salvador, and Trinidad and Tobago. PARTICIPANTS: Overall population. PRIMARY AND SECONDARY OUTCOME MEASURES: The model estimated the effects of SSB consumption on health through two causal pathways: one mediated by body mass index (BMI) and health conditions associated with BMI and another that reflected the independent effects of SSB consumption on T2DM and cardiovascular diseases. RESULTS: The model results indicated that for all four countries, in 1 year, SSB consumption was associated with 18 000 deaths (3.2% of the total disease-related deaths), seven million disease events (3.3% of the total disease-related events), a half-million DALYs and US$2 billion in direct medical costs. This included 1.5 million cases of overweight and obesity in children/adolescents (12% of the excess weight cases) and 2.8 million cases in adults (2.8%); 2.2 million cases of type 2 diabetes (19%); 200 000 cases of heart disease (3.8%); 124 000 strokes (3.9%); 116 000 cases of musculoskeletal disease (0.2%); 102 000 cases of kidney disease (0.9%); and 45 000 episodes of asthma (0.4%). The Trinidad and Tobago population were the most affected by disease events. CONCLUSIONS: The study results indicate that the consumption of SSBs is associated with a significant burden of disease and death in Latin America and the Caribbean.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".