Nutrition-related non-communicable disease and sugar-sweetened beverage policies: a landscape analysis in Kenya
Bibliographic record
Abstract
Background: The burden of undernutrition is significant in Kenya. Obesity and related non-communicable diseases are also on the increase. Government action to prevent non-communicable diseases is critical. Taxation of sugar-sweetened beverages has been identified as an effective mechanism to address nutrition-related non-communicable diseases, although Kenya is not yet committed to this.Objective: To assess the policy and stakeholder landscape relevant to nutrition related non -communicable diseases and sugar-sweetened beverage taxation in Kenya.Methods: A desk review of evidence and policies related to nutrition related non-communicable diseases and sugar-sweetened beverages was conducted. Data extraction matrices were used for analysis. Key informant interviews were conducted with 10 policy actors. Interviews were thematically analysed to identify enablers of, and barriers to, policy change towards nutrition-sweetened beverage taxation.Results: Although nutrition related non-communicable diseases are recognised as a growing problem in Kenya most food-related policies focus on undernutrition and food security, while underplaying the role of nutrition related non-communicable diseases. Policy development on communicable diseases is multi-sectoral, but implementation is biased towards curative rather than preventive services. An excise tax is charged on soft drinks, but is not specific to sugar-sweetened beverages. Government has competing roles: advocating for industrial growth, such as sugar and food processing industries to foster economic development, yet wanting to control nutrition related non-communicable diseases. There is no national consensus about the dangers posed by sugar-sweetened beverages.Conclusion: Nutrition related non-communicable diseases policies should reflect a continuum of issues, from undernutrition to food security, nutrition transition, and the escalation of nutrition related non-communicable diseases. A local advocacy case for sugar-sweetened beverage taxation has not been made. Public and policy maker education is critical to challenge the prevailing attitudes towards sugar-sweetened beverages and the western diet.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".