Association between soft drinks consumption and asthma: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives To carry out meta-analysis and systematic review on the association between soft drinks consumption and asthma prevalence among adults and children. Design Systematic review and meta-analysis of observational research. Data sources Medline, Scopus, ISI Web of Science and the Cochrane Library were searched up to December 2018. Eligibility criteria We included observational studies investigating the association between soft drinks consumption (including maternal consumption during pregnancy) and asthma or wheeze. Data extraction and synthesis Data were extracted by one author and reviewed independently by two other authors. The most adjusted estimate from each original study was used in the meta-analysis. Meta-analysis was conducted using random-effects model. The quality of studies was assessed using the Newcastle–Ottawa scale and heterogeneity was evaluated using I2 statistic. Results Of 725 publications originally identified, 19 were included in this systematic review, including 3 cohort studies and 16 cross-sectional studies. Ten articles reported on children up to 18 years, 5 articles on adults (>18 years) and 2 articles on prenatal exposure. In total, 468 836 participants were included, with more than 50 000 asthma cases. Soft drinks consumption was associated with significantly increased odds of asthma in both adults (OR=1.37; 95% CI, 1.23 to 1.52) and children (OR=1.14; 95% CI, 1.06 to 1.21). Prenatal exposure had marginally statistically significant association (OR=1.11; 95% CI, 1.00 to 1.23) with asthma in children. In subgroup analysis for childhood exposure, the association persists for sugar-sweetened soft drinks but not for carbonated drinks. Conclusion Our findings show a positive association between soft drinks consumption and asthma prevalence, mostly from cross-sectional studies. Therefore, more longitudinal research is required to establish causality.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.048 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".