A Systematic Review of the Recent Consumption Levels of Sugar-Sweetened Beverages in Children and Adolescents From the World Health Organization Regions With High Dietary–Related Burden of Disease
Bibliographic record
Abstract
This review aimed to investigate national estimates of sugar-sweetened beverage (SSB) consumption in children and adolescents aged two to 18 years, from countries in regions particularly burdened by dietary-related chronic illnesses. The most recent studies or reports from included countries (n = 73) with national-level consumption data of SSBs in children and adolescents, collected between January 2010 and October 2019, were considered for inclusion. A random effects meta-analysis was used to calculate pooled estimates of the mean consumption of SSB in millimeters per day. Heterogeneity between national estimates was assessed using the I 2 statistic and explored via subgroup analyses by the World Health Organization region, age groups, and country-level income. Forty-eight studies were included in the review reporting national estimates of consumption for 51 countries. The highest estimate of daily consumption was in China at 710.0 mL (95% confidence interval (CI) [698.8, 721.2], while the lowest was in Australia at 115.1 mL (95% CI [111.2, 119.1]). Pooled synthesis of daily SSB consumption of the 51 countries was 326.0 mL (95% CI [288.3, 363.8]), although heterogeneity was high, and was not explained by subgroup analyses. While there is considerable variability between countries, intake of SSB remains high among children and adolescents internationally underscoring the need for public health efforts to reduce SSBs consumption.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".