Sugar-sweetened beverage consumption and socioeconomic status: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Sugar-sweetened beverages (SSB) are an independent risk factor for obesity and other non-communicable diseases. Socioeconomic status (SES) is one of the key drivers for the purchase and consumption of SSBs among children and adults; however, there is a lack of strong evidence. This study aims to determine the association between SES and consumption patterns of SSBs across populations. Results The review was conducted according to PRISMA guidelines. PubMed, MEDLINE, Scopus, EMBASE, LILACS, Web of Science, Cochrane, and CINHAL databases were searched for relevant articles until 2022. Participants included children, adolescents, and adults who consumed different SSBs and were assessed based on their SES. The random-effects model was used to obtain the pooled odds ratio (OR). Twenty-one studies (152,070 participants) met the inclusion criteria. The risk of bias was assessed using the Newcastle-Ottawa tool, with the majority of the studies indicating medium to high quality. Eight ORs from four studies (34,454 participants) were considered for meta-analysis. Results showed those belonging to high SES had 48% lower odds of consuming the SSBs (OR 0.52; 95% CI: 0.42–0.61; p = 0.017). The overall quality of evidence was ascertained using GRADE criteria, illustrating a moderate certainty of evidence between SSB consumption and SES. Conclusion Meta-analysis suggests that SES influences the consumption pattern of SSBs, with high SES having lower odds of SSB 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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".