Incident cases and healthcare costs of non-communicable diseases attributable to industrialized and total sugar sweetened beverage intake in Mexico
Bibliographic record
Abstract
OBJECTIVES: To estimate incident cases and healthcare costs of non-communicable chronic diseases (NCD) attributable to sugar-sweetened beverages (SSB) intake in Mexican adults. METHODS: We estimated the population attributable fractions (PAF) of NCDs- including obesity, hypertension, type-2 diabetes, cardiovascular diseases and diet-related cancers- and healthcare costs attributable to the consumption of industrialized and all SSBs. Association measures for SSB-NCD were obtained from published literature. Data on SSB intake were obtained from the 2018 Mexican National Health and Nutrition Survey (ENSANUT), incident cases of NCDs from health registries, and annual NCDs healthcare costs from Mexican studies. RESULTS: Industrialized SSB intake contributed to 11.3% of obesity, 10.4% of hypertension, 17.4% of type-2 diabetes, 3.9% of cardiovascular disease, and 12.8% of diet-related cancers (PAFs are disease-specific and not additive due to multimorbidity), resulting in an annual excess healthcare cost of 202 million USD. CONCLUSIONS: Despite efforts to reduce SSB consumption, their consumption continues to affect population health and increase healthcare costs.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".