How prices and income influence global patterns in saturated fat intake by age, sex and world region: a cross-sectional analysis of 160 countries
Bibliographic record
Abstract
Objective When considering proposals to improve diets, it is important to understand how factors like price and income can affect saturated fat (SF) intake and demand. In this study, we examine and estimate the influence of price and income on intake across 160 countries, by age and sex, and derive sensitivity measures (price elasticities) that vary by age, sex and world region.\n\nDesign We econometrically estimate intake responsiveness to income and prices across countries, accounting for differences by world region, age and sex. Intake data by age, sex and country were obtained from the 2018 Global Dietary Database. These data were then linked to global price data for select food groups from the World Bank International Comparison Programme and income data from the World Development Indicators Databank (World Bank).\n\nResults Intake differences due to price were highly significant, with a 1% increase in price associated with a lower SF intake (% energy/d) of about 4.3 percentage points. We also find significant differences across regions. In high- income countries, median (age 40) intake reductions were 1.4, 0.8 and 0.2 percentage points, given a 1% increase in the price of meat, dairy, and oils and fats, respectively. Price elasticities varied with age but not sex. Intake differences due to income were insignificant when regional binary variables were included in the analysis.\n\nConclusion The results of this study show heterogeneous associations among prices and intake within and across countries. Policymakers should consider these heterogeneous effects as they address global nutrition and health challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".