Cost Associated with Adherence to the EAT-Lancet Score in Brazil
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Food prices are a crucial factor in food choices, especially for more vulnerable populations. To estimate the association between diet cost and quality, as measured by the EAT-Lancet score, across demographic groups in Brazil. METHODS: Data from the 2017/18 Household Budget Survey were used to calculate the EAT-Lancet score, comprising 14 components. Scores ranged from 0 (low adherence) to 42 (high adherence), with emphasized components (e.g., vegetables, fruits, legumes) and limited components (e.g., red meat, sugar, eggs). Results were stratified by per capita income, geographic region, and area of residence and compared using linear regression adjusted for high and low costs. In addition, the association between the EAT-Lancet score (and its emphasized and limited components) and diet cost (continuous) was analyzed for the total population and for income tertiles. RESULTS: The mean EAT-Lancet score was 18.65 points (range: 7 to 25) and the mean diet cost was BRL$0.65/100 kcal. Total scores showed no significant difference between low- and high-cost diets. However, limited intake was more pronounced in low-cost diets, while high-cost diets featured emphasized foods such as fruits, vegetables, and seafood. High-cost diets also included sugars and red meat, while unsaturated fats scored higher in low-cost diets. Each one-point increase in the EAT-Lancet score was associated with a BRL$0.38 reduction in cost, driven by lower costs in the Limited component, especially among the lowest-income strata (reductions of BRL$1.58 and BRL$1.55 in the lowest income and middle income tertiles, respectively). However, higher scores for emphasized foods increased costs (BRL$0.89) in the lowest tertile. CONCLUSIONS: Higher EAT-Lancet scores were associated with reduced diet costs, likely influenced by the lower Limited component costs in low-income groups. Emphasized foods, however, tended to increase costs, particularly among the lowest-income group. These findings suggest that the role of diet composition plays a significant role in cost differences and underscore the challenges that low-income groups face in accessing affordable, healthy diets.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".