Assessing the impact of the Barbados sugar-sweetened beverage tax on beverage sales: an observational study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization has advocated for sugar-sweetened beverage (SSB) taxes as part of a broader non-communicable disease prevention strategy, and these taxes have been recently introduced in a wide range of settings. However, much is still unknown about how SSB taxes operate in various contexts and as a result of different tax designs. In 2015, the Government of Barbados implemented a 10% ad valorem (value-based) tax on SSBs. It has been hypothesized that this tax structure may inadvertently encourage consumers to switch to cheaper sugary drinks. We aimed to assess whether and to what extent there has been a change in sales of SSBs following implementation of the SSB tax. METHODS: We used electronic point of sale data from a major grocery store chain and applied an interrupted time series (ITS) design to assess grocery store SSB and non-SSB sales from January 2013 to October 2016. We controlled for the underlying time trend, seasonality, inflation, tourism and holidays. We conducted sensitivity analyses using a cross-country control (Trinidad and Tobago) and a within-country control (vinegar). We included a post-hoc stratification by price tertile to assess the extent to which consumers may switch to cheaper sugary drinks. RESULTS: We found that average weekly sales of SSBs decreased by 4.3% (95%CI 3.6 to 4.9%) compared to expected sales without a tax, primarily driven by a decrease in carbonated SSBs sales of 3.6% (95%CI 2.9 to 4.4%). Sales of non-SSBs increased by 5.2% (95%CI 4.5 to 5.9%), with bottled water sales increasing by an average of 7.5% (95%CI 6.5 to 8.3%). The sensitivity analyses were consistent with the uncontrolled results. After stratifying by price, we found evidence of substitution to cheaper SSBs. CONCLUSIONS: This study suggests that the Barbados SSB tax was associated with decreased sales of SSBs in a major grocery store chain after controlling for underlying trends. This finding was robust to sensitivity analyses. We found evidence to suggest that consumers may have changed their behaviour in response to the tax by purchasing cheaper sugary drinks, in addition to substituting to untaxed products. This has important implications for the design of future SSB taxes.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".