Effectiveness and Feasibility of Taxing Salt and Foods High in Sodium: A Systematic Review of the Evidence
Bibliographic record
Abstract
Diets high in salt are a leading risk for death and disability globally. Taxing unhealthy food is an effective means of influencing what people eat and improving population health. Although there is a growing body of evidence on taxing products high in sugar, and unhealthy foods more broadly, there is limited knowledge or experience of using fiscal measures to reduce salt consumption. We searched peer-reviewed databases [MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and the Cochrane Database of Systematic Reviews] and gray literature for studies published between January 2000 and October 2019. Studies were included if they provided information on the impact on salt consumption of: taxes on salt; taxes on foods high in salt, and taxes on unhealthy foods defined to include foods high in salt. Studies were excluded if their definition of unhealthy foods did not specify high salt or sodium. We found 18 relevant studies, including 15 studies reporting the effects of salt taxes through modeling (8), real-world evaluation (4), experimental design (2), or review of cost-effectiveness (1); 6 studies providing information relevant to country implementation of salt taxes; and 2 studies reporting stakeholder perceptions toward salt taxation. Although there is some evidence on the potential effectiveness and cost-effectiveness of salt taxation, especially from modeling studies, uptake of salt taxation is limited in practice. Some modeling studies suggested that food taxes can have unintended outcomes such as reduced consumption of healthy foods, or increased consumption of unhealthy, untaxed substitutes. In contrast, modeling studies that combined taxes for unhealthy foods with subsidies found that the benefits were increased. Modeling suggests that taxing all foods based on their salt content is likely to have more impact than taxing specific products high in salt given that salt is pervasive in the food chain. However, the limited experience we found suggests that policy-makers favor taxing specific products.
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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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".