Abstract 08: The Impact of Food Taxation and Subsidy Policies on Cardiometabolic Disparities in the US
Bibliographic record
Abstract
Background: Food taxes and subsidies are promising strategies for improving diets and reducing cardiometabolic diseases (CMD). Both dietary habits and CMD burdens are unequally distributed in the US, with major disparities by socioeconomic status (SES). Information on impacts of national food price policies on disparities is lacking. Aim: To estimate the impact on CMD deaths and health disparities in US adults of price interventions (taxes, subsidies) targeting 7 key dietary factors. Methods: Using nationally representative data, we conducted comparative risk assessment analysis to estimate the impact of a 10% price subsidy on fruits, vegetables, whole grains, and nuts and a 10% tax on processed meat, unprocessed red meat, and sugar-sweetened beverages, on CMD deaths and disparities in SES subgroups. We evaluated 18% (based on global price elasticity data) and 50% (based on recent experience from soda taxes in Mexico) greater price responsiveness in lowest vs. highest SES groups. Results: Each separate price intervention would reduce CMD deaths (Figure). Jointly subsidizing and taxing these 7 dietary factors (10% price change each) and assuming 18% greater price-responsiveness in lowest vs. highest SES, this intervention would prevent 5.27% of CMD deaths in those with Conclusions: Introducing modest price changes on key dietary factors could reduce CMD burdens and improve disparities in the US. Policy-based strategies targeting disparities will require considering both baseline dietary habits as well as price responsiveness in specific population subgroups.
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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".