The Potential Effect of Sugar-Sweetened Beverages Tax on Obesity Prevalence in Tanzania
Bibliographic record
Abstract
Background: Obesity and the associated non-communicable diseases contribute significantly to the disease burden in Tanzania. Obesity can be attributed to the consumption of Sugar Sweetened Beverages (SSB) due to their high sugar content that leads to high caloric intakes. This study estimates the effect of SSB tax on the prevalence of obesity. Methods: A mathematical model that compares the reference population which is unchanged and a counterfactual population in which tax intervention has been introduced is developed. Changes in price and consumption of SSBs, and subsequent changes in energy intake are applied to estimate the body mass change by age groups. The change in body mass by age groups is merged with the reference population to estimate changes in body mass index and obesity. Results: Imposing a 20% SSB tax in Tanzania is estimated to reduce the average overall energy intake by 76.1 kJ per person per day. This change is associated with an overall reduction of prevalence of obesity by 6.6%; and by 12.9% and 5.2% in adult males and adult females, respectively. The number of obese people will potentially decrease by about 47,000 among adult males and about 85,000 among adult females from the current levels. Conclusions: The SSB tax is a potential strategy to complement efforts to reduce obesity prevalence in Tanzania. The revenue generated from the tax should be channelled towards public health promotion programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".