Sugar-sweetened beverage purchases in urban Peru before the implementation of taxation and warning label policies: a baseline study
Bibliographic record
Abstract
BACKGROUND: Sugar-sweetened beverage consumption is associated with obesity and chronic disease. In 2018, Peru increased the tax on high-sugar beverages (≥6 g of sugar per 100 mL) from 17 to 25%, yet little is known about pre-existing beverage trends or demographic characteristics associated with purchases in the country. The aim of this study was to explore beverage purchasing trends from 2016 to 2017 and examine variation in purchase volume by sociodemographic characteristics among urban households in Peru. METHODS: This study used monthly household purchase data from a panel of 5145 households from January 2016-December 2017 from Kantar WorldPanel Peru. Beverage purchases were categorized by type and tax status under the 2018 regulation (untaxed, lower-sugar taxed, high-sugar taxed). To assess beverage purchasing trends, per-capita volume purchases were regressed on a linear time trend, with month dummies for seasonality and clustered standard errors. Mean volume purchases by beverage tax status (total liters purchased per month), overall and by key demographic characteristics (education, socioeconomic status, and geographic region), were calculated. Mean volume by beverage type was assessed to identify the largest contributors to total beverage volume. RESULTS: The trends analysis showed a decline in total beverage volume of - 52 mL/capita/month (95% CI: - 72, - 32) during the 24-month study period. Over 99% of households purchased untaxed beverages in a month, while > 92% purchased high-sugar taxed beverages. Less than half of all households purchased low-sugar taxed beverages in a month and purchase volume was low (0.3 L/capita/month). Untaxed beverage purchases averaged 9.4 L/capita/month, while households purchased 2.8 L/capita/month of high-sugar taxed beverages in 2017. Across tax categories, volume purchases were largest in the high education and high socioeconomic (SES) groups, with substantial variation by geographic region. The highest volume taxed beverage was soda (2.3 L/capita/month), while the highest volume untaxed beverages were milk and bottled water (1.9 and 1.7 L/capita/month, respectively). CONCLUSIONS: Nearly all households purchased high-sugar taxed beverages, although volume purchases of taxed and untaxed beverages declined slightly from 2016 to 2017. Households with high SES and high education purchased the highest volume of taxed beverages, highlighting the need to consider possible differential impacts of the tax policy change by sub-population groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".