Evaluating sugar-sweetened beverage tax effects: online price and sales data from grocers in Canada
Bibliographic record
Abstract
Newfoundland and Labrador (NL) introduced Canada's first sugar-sweetened beverage (SSB) tax in September 2022. Compared with national averages, NL has higher intakes of SSBs, lower intakes of plain water and milk, with higher rates of overweight and obesity and diabetes. Taxing SSBs is a recommended intervention but real-world effectiveness of SSB taxes requires more investigation. We evaluated changes in weekly beverage prices and sales pre- and post-tax implementation, comparing NL (intervention) to non-tax regions in Canada (control). We used a controlled interrupted time series to evaluate prices from grocery store websites 3 months pre- and post-tax. We observed no differences-in-differences in the intercept [β = -0.024, 95% confidence interval (CI) -0.15-0.10, P = .70] or slope (β = 0.00, 95% CI -0.02-0.02, P = .99) of price changes. We used a repeat cross-sectional study to compare total annual sales of beverage categories in the year pre- and post-tax. Per capita sales in litres of taxable SSB decreased more in NL (-11.6%) than non-tax regions (-6.7%). Per capita sales of diet beverages (+4.4%) and unsweetened water (+2.2%) increased in NL. The NL SSB tax had no immediate impact on retail prices of taxable SSBs measured on product selection pages on grocery websites. Beverage purchasing shifted in NL since the SSB tax start date, however, it is difficult to isolate the impact of the SSB tax from broader market trends or other influencing factors. Long-term evaluation of the NL SSB tax is needed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".