Impacts of the United Kingdom’s Soft Drinks Industry Levy: a systems-thinking informed systematic scoping review
Bibliographic record
Abstract
SUMMARY Background Consumption of sugar-sweetened beverages (SSBs) is associated with weight, weight gain and incidence of a number of chronic diseases. The World Health Organization recommends taxation on SSBs to reduce consumption. In 2018 the United Kingdom introduced the Soft Drinks Industry Levy (SDIL), a tiered tax on manufacturers and importers of SSBs. We aimed to review the consequences of the SDIL across all potential outcomes, informed by a systems thinking approach, to understand the range and importance of its effects. Methods We undertook a systematic scoping review of empirical studies of the SDIL. We used a conceptual systems map of the hypothesised pathways of effect to inform data extraction and narrative synthesis. Findings are presented in an evidence map and their consistency assessed. Results 38 studies met our inclusion criteria. The SDIL was consistently associated with reformulation of soft drinks to reduce sugar content. It was also consistently associated with: reduced purchasing of sugar from eligible drinks without increasing purchasing of substitute products such as alcohol and confectionary; longer-term improvements in acute and chronic health outcomes; and reduced health and social care costs, with few negative economic impacts for industry. Conclusions By systematically mapping all outcomes evaluated, we have demonstrated the systemic and interconnected impacts of the SDIL. Further research should seek deeper understanding of how to evaluate such interventions as events in complex adaptive systems.
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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 | 0.035 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".