The legal feasibility of adopting a sugar-sweetened beverage tax in seven sub-Saharan African countries
Bibliographic record
Abstract
Background: A number of countries have adopted sugar-sweetened beverage taxes to prevent non-communicable diseases but there is variance in the structures and rates of the taxes. As interventions, sugar-sweetened beverage taxes could be cost-effective but must be compliant with existing legal and taxation systems.Objectives: To assess the legal feasibility of introducing or strengthening taxation laws related to sugar-sweetened beverages, for prevention of non-communicable diseases in seven countries: Botswana, Kenya, Namibia, Rwanda, Tanzania, Uganda and Zambia.Methods: We assessed the legal feasibility of adopting four types of sugar-sweetened beverage tax formulations in each of the seven countries, using the novel FELIP framework. We conducted a desk-based review of the legal system related to sugar-sweetened beverage taxation and assessed the barriers to, and facilitators and legal feasibility of, introducing each of the selected formulations by considering the existing laws, laws related to impacted sectors, legal infrastructure, and processes involved in adopting laws.Results: Six countries had legal mandates to prevent non-communicable diseases and protect the health of citizens. As of 2019, all countries had excise tax legislation. Five countries levied excise taxes on all soft drinks, but most did not exclusively target sugar-sweetened beverages, and taxation rates were well below the World Health Organization’s recommended 20%. In Uganda and Kenya, agricultural or HIV-related levies offered alternative mechanisms to disincentivise consumption of sugar-sweetened beverages without the introduction of new taxes. Nutrition-labelling laws in all countries made it feasible to adopt taxes linked to the sugar content of beverages, but there were lacunas in existing infrastructure for more sophisticated taxation structures.Conclusion: Sugar-sweetened beverage taxes are legally feasible in all seven countries Existing laws provide a means to implement taxes as a public health intervention.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".