Barriers to, and facilitators of, the adoption of a sugar sweetened beverage tax to prevent non-communicable diseases in Uganda: a policy landscape analysis
Bibliographic record
Abstract
BACKGROUND: Uganda is experiencing an increase in nutrition-related non-communicable diseases. Risk factors include overconsumption of sugar-sweetened beverages. Fiscal and taxation policies aim to make the consumption of healthier foods easier. However, the adoption and implementation of fiscal policies by countries are constrained by political and economic challenges. OBJECTIVE: We investigated the policy and political landscape related to the prevention of nutrition-related non-communicable diseases in Uganda to identify barriers to and facilitators of the adoption of sugar-sweetened beverage taxation in Uganda. METHODS: A desk-based policy analysis of policies related to nutrition-related non-communicable diseases and sugar-sweetened beverage taxation was conducted. Four key informant consultations (n = 4) were conducted to verify the policy review and to gain further insight into the policy and stakeholder contexts. Analysis was framed by Kingdon's theory of agenda setting and policy change. RESULTS: Nutrition-related non-communicable diseases were recognised as an emerging problem in Uganda. The Government has adopted a comprehensive approach to improve diets, but implementation is slow. There is limited recognition of the consumption of sugar and sugar-sweetened beverages as a contributor to the nutrition-related non-communicable disease burden in policy documents. Existing taxes on soft drinks are lower than the World Health Organization's recommended rate of 20% and do not target sugar content. The soft drink industry has been influential in framing the taxation debate, and the Ministry of Finance previously reduced taxation of sugar-sweetened beverages. Maintaining competitiveness in a regional market is an important business strategy. However, the Ministry of Health and other public health actors in civil society have been successful (albeit marginally) in countering reductions in taxation, which are supported by industry. CONCLUSIONS: An established platform for sugar-sweetened beverage taxation advocacy exists in Uganda. Compelling local research that explicitly links soft drink taxes to health goals is essential to advance sugar-sweetened beverage taxation.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".