Association between the price of ultra-processed foods and obesity in Brazil
Bibliographic record
Abstract
Background and aims To estimate the relationship between the price of ultra-processed foods and prevalence of obesity in Brazil and examine whether the relationship differed according to socioeconomic status. Methods and results Data from the national Household Budget Survey from 2008/09 (n = 55 570 households, divided in 550 strata) were used. Weight and height of all individuals were used. Weight was measured by using portable electronic scales (maximum capacity of 150 kg). Height (or length) was measured using portable stadiometers (maximum capacity: 200 cm long) or infant anthropometers (maximum capacity: 105 cm long). Multivariate regression models (log-log) were used to estimate price elasticity. An inverse association was found between the price of ultra-processed foods (per kg) and the prevalence of overweight (Body mass index (BMI) ≥25 kg/m 2 ) and obesity (BMI ≥30 kg/m 2 ) in Brazil. The price elasticity for ultra-processed foods was −0.33 (95% CI: −0.46; −0.20) for overweight and −0.59 (95% CI: −0.83; −0.36) for obesity. This indicated that a 1.00% increase in the price of ultra-processed foods would lead to a decrease in the prevalence of overweight and obesity of 0.33% and 0.59%, respectively. For the lower income group, the price elasticity for price of ultra-processed foods was −0.34 (95% CI: −0.50; −0.18) for overweight and −0.63 (95% CI: −0.91; −0.36) for obesity. Conclusion The price of ultra-processed foods was inversely associated with the prevalence of overweight and obesity in Brazil, mainly in the lowest socioeconomic status population. Therefore, the taxation of ultra-processed foods emerges as a prominent tool in the control of obesity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".