Health and economic burden of sugar-sweetened beverages consumption in Brazil
Bibliographic record
Abstract
Sugar-sweetened beverages (SSBs) are a major source of added sugar and are associated with noncommunicable diseases (NCDs) such as obesity and diabetes. This study assessed the impact of SSBs consumption on disease burden in Brazil, including deaths, disability-adjusted life years (DALYs), and healthcare costs. A 3-stage methodology was used to assess the direct effects of SSBs on diabetes, cardiovascular diseases, and body mass index (BMI), along with the influence of BMI on disease incidence. These assessments were then used to estimate the economic and health burden using population-attributable factors. Results showed that 2.7% and 11% of adult and children overweight/obesity cases were attributable to SSBs, respectively. SSBs consumption in Brazil led to 1,814,486 cases, 12,942 deaths, 362,088 DALYs, and USD 2,915.91 million in medical costs related to diabetes, cardiovascular diseases, oncological diseases, and other NCDs. Urgent implementation of public policies is crucial to address the consumption of SSBs, recognized as a key risk factor for NCDs.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".