Projected impact of a reduction in sugar-sweetened beverage consumption on diabetes and cardiovascular disease in Argentina: A modeling study
Bibliographic record
Abstract
BACKGROUND: Sugar-sweetened beverage (SSB) consumption is associated with obesity, diabetes, and hypertension. Argentina is one of the major consumers of SSBs per capita worldwide. Determining the impact of SSB reduction on health will inform policy debates. METHODS AND FINDINGS: We used the Cardiovascular Disease Policy Model-Argentina (CVD Policy Model-Argentina), a local adaptation of a well-established computer simulation model that projects cardiovascular and mortality events for the population 35-94 years old, to estimate the impact of reducing SSB consumption on diabetes incidence, cardiovascular events, and mortality in Argentina during the period 2015-2024, using local demographic and consumption data. Given uncertainty regarding the exact amount of SSBs consumed by different age groups, we modeled 2 estimates of baseline consumption (low and high) under 2 different scenarios: a 10% and a 20% decrease in SSB consumption. We also included a range of caloric compensation in the model (0%, 39%, and 100%). We used Monte Carlo simulations to generate 95% uncertainty intervals (UIs) around our primary outcome measures for each intervention scenario. Over the 2015-2024 period, a 10% reduction in SSBs with a caloric compensation of 39% is projected to reduce incident diabetes cases by 13,300 (95% UI 10,800-15,600 [low SSB consumption estimate]) to 27,700 cases (95% UI 22,400-32,400 [high SSB consumption estimate]), i.e., 1.7% and 3.6% fewer cases, respectively, compared to a scenario of no change in SSB consumption. It would also reduce myocardial infarctions by 2,500 (95% UI 2,200-2,800) to 5,100 (95% UI 4,500-5,700) events and all-cause deaths by 2,700 (95% UI 2,200-3,200) to 5,600 (95% UI 4,600-6,600) for "low" and "high" estimates of SSB intake, respectively. A 20% reduction in SSB consumption with 39% caloric compensation is projected to result in 26,200 (95% UI 21,200-30,600) to 53,800 (95% UI 43,900-62,700) fewer cases of diabetes, 4,800 (95% UI 4,200-5,300) to 10,000 (95% UI 8,800-11,200) fewer myocardial infarctions, and 5,200 (95% UI 4,300-6,200) to 11,000 (95% UI 9,100-13,100) fewer deaths. The largest reductions in diabetes and cardiovascular events were observed in the youngest age group modeled (35-44 years) for both men and women; additionally, more events could be avoided in men compared to women in all age groups. The main limitations of our study are the limited availability of SSB consumption data in Argentina and the fact that we were only able to model the possible benefits of the interventions for the population older than 34 years. CONCLUSIONS: Our study finds that, even under conservative assumptions, a relatively small reduction in SSB consumption could lead to a substantial decrease in diabetes incidence, cardiovascular events, and mortality in Argentina.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".