The distributional impact of taxing sugar-sweetened beverages: findings from an extended cost-effectiveness analysis in South Africa
Bibliographic record
Abstract
BACKGROUND: Facing increasing obesity prevalence and obesity-related disease burden, South Africa has devised an obesity prevention strategy that includes a recently implemented tax on the sugar content of sugar-sweetened beverages (SSB). We assess the potential distributional impact (across socioeconomic groups) of this tax on type 2 diabetes mellitus (T2DM) incidence and associated mortality and its financial burden on households. METHODS: We conducted an extended cost-effectiveness analysis of the new 10% tax on SSBs in South Africa, and estimated: the averted premature deaths related to T2DM, the financial benefits to households (out-of-pocket (OOP) medical costs and indirect costs due to productivity losses averted), the increased government tax revenues and healthcare savings for the government, all across income quintiles. FINDINGS: A 10% SSB tax increase would avert an estimated 8000 T2DM-related premature deaths over 20 years, with most deaths averted among the third and fourth income quintiles. The government would save about South African rand (ZAR) 2 billion (US$140 million) in subsidised healthcare over 20 years; and would raise ZAR6 billion (US$450 million) in tax revenues per annum. The bottom two quintiles would bear the smallest tax burden increase (36% of the additional taxes). The bottom two income quintiles would also have the lowest savings in OOP payments due to significant subsidisation provided by government healthcare. Lastly, an estimated 32 000 T2DM-related cases of catastrophic expenditures and 12 000 cases of poverty would be averted. CONCLUSIONS: SSB taxation would have a substantial distributional impact on obesity-related premature deaths, cost savings to the government and the financial outcomes of South Africa's population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".