Nonalcoholic and Alcoholic Beverage Intakes by Adults across 5 Upper-Middle- and High-Income Countries
Bibliographic record
Abstract
BACKGROUND: Despite considerable public health interest in sugary drink consumption, there has been little comparison of intake across countries. OBJECTIVES: This study aimed to compare the consumption frequency and amounts of commonly consumed beverages among adults in 5 upper-middle- and high-income countries, and examine differences in consumption between population subgroups. METHODS: Adults aged 18-65 y completed online surveys in December 2017 in Australia (n = 3264), Canada (n = 2745), Mexico (n = 3152), the United Kingdom (n = 3221), and the USA (n = 4015) as part of the International Food Policy Study. The frequency of consuming beverages from 22 categories in the past 7 d was estimated using the Beverage Frequency Questionnaire. Regression models were used to examine differences in the likelihood of any consumption and in the amounts consumed of sugar-sweetened beverages (SSBs), sugary drinks (SSBs and 100% juice), diet, and alcoholic beverages between countries and across sociodemographic subgroups. RESULTS: The prevalence of reported SSB consumption in the past 7 d ranged from 47% (United Kingdom) to 81% (Mexico), and that of sugary drinks ranged from 62% (United Kingdom) to 87% (Mexico). Rates of consumption of diet drinks ranged from 26% (Mexico) to 37% (United Kingdom), whereas alcoholic drink consumption rates ranged from 45% (USA) to 52% (Canada). Respondents in Mexico were more likely to consume SSBs and sugary drinks, and in greater amounts, than those in other countries. Respondents in the United Kingdom were more likely to consume diet drinks than those in Australia, Canada, and Mexico, and greater amounts of diet drinks were consumed in the United Kingdom and the USA. Across countries, younger respondents and males were more likely to consume greater amounts of SSBs and sugary drinks. CONCLUSIONS: Most adult respondents across all countries consumed SSBs and sugary drinks, with greater consumption in Mexico and the USA. Consumption varied greatly across countries, but patterns of association among subpopulations were relatively similar.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".