Modelling the potential impact of a sugar-sweetened beverage tax on stroke mortality, costs and health-adjusted life years in South Africa
Bibliographic record
Abstract
BACKGROUND: Stroke poses a growing human and economic burden in South Africa. Excess sugar consumption, especially from sugar-sweetened beverages (SSBs), has been associated with increased obesity and stroke risk. Research shows that price increases for SSBs can influence consumption and modelling evidence suggests that taxing SSBs has the potential to reduce obesity and related diseases. This study estimates the potential impact of an SSB tax on stroke-related mortality, costs and health-adjusted life years in South Africa. METHODS: A proportional multi-state life table-based model was constructed in Microsoft Excel (2010). We used consumption data from the 2012 South African National Health and Nutrition Examination Survey, previously published own and cross price elasticities of SSBs and energy balance equations to estimate changes in daily energy intake and BMI arising from increased SSB prices. Stroke relative risk, and prevalent years lived with disability estimates from the Global Burden of Disease Study and modelled disease epidemiology estimates from a previous study, were used to estimate the effect of the BMI changes on the burden of stroke. RESULTS: Our model predicts that an SSB tax may avert approximately 72 000 deaths, 550 000 stroke-related health-adjusted life years and over ZAR5 billion, (USD400 million) in health care costs over 20 years (USD296-576 million). Over 20 years, the number of incident stroke cases may be reduced by approximately 85 000 and prevalent cases by about 13 000. CONCLUSIONS: Fiscal policy has the potential, as part of a multi-faceted approach, to mitigate the growing burden of stroke in South Africa and contribute to the achievement of the target set by the Department of Health to reduce relative premature mortality (less than 60 years) from non-communicable diseases by the year 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".