Changes in food consumption and prevalence of overweight and obesity in Brazilian adults between 2008 and 2018
Bibliographic record
Abstract
ABSTRACT Objective To assess dietary intake and weight status changes among Brazilian adults. Methods In this dietary survey, data from the food consumption modules of the 2008-2009 (n=21,003 adults) and the 2017-2018 (n=28,153 adults) Household Budget Survey were evaluated to estimate the mean consumption (g/day) of 20 food groups. The body mass index was calculated to classify the weight status of adults and estimate the prevalence of overweight and obesity. Differences between surveys were identified when the 95% confidence intervals were not interspersed. All analyses were stratified by gender and considered the sample weight and the complexity of the sample design. Results The prevalence of overweight increased both among men (38.4%; 95% CI: 36.8-40.0, in 2008-2009 vs. 42.2%; 95% CI: 40.9-43.5, in 2017-2018) and women (29.5%; 95% CI: 58.0-30.9 vs. 35.2%; 95% CI: 34.0-36.4, respectively). Mean consumption of poultry and eggs (57.6g/day vs. 77.9 g/day in men and 43.5g/day vs. 57.3g/day in women, p<0.05) and fast foods (31.3g/day vs. and 48.7g/day in men and 25.3g/day in 2008-2009 vs. 34.8g/day in women, p<0.05) increased between the two surveys, while the mean consumption of rice, beans, fruits, coffee and tea, fish and seafood, processed meats, milk and dairy products, sweets and desserts, sugary drinks, and soups declined. Conclusion The Brazilian food consumption pattern follows the increased prevalence of overweight and reinforces the need to encourage healthy patterns that revive our country's food culture and eating habits.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".