Relationship of cash transfers with risk of overweight and obesity in children and adults: a systematic review
Bibliographic record
Abstract
Abstract Background Cash transfer (CT) programs are an important type of social protection meant to reduce poverty. Whether CT programs increase the risk of overweight and obesity is unclear. The objective was to characterize the relationship between CT programs and the risk of overweight and obesity in children and adults. Methods We searched articles in PubMed, Embase, Cochrane, EconLit, Global Health, CINAHL Plus, IBSS, Health & Medical Collection, Scopus, Web of Science, and WHO Global Index Medicus in August 2021. Studies involving CT as the intervention, a control group, body mass index, overweight, or obesity as an outcome, and sample size > 300 were included. The Newcastle–Ottawa Scale was used for quality assessment. Results Of 2355 articles identified, 20 met the inclusion criteria. Because of marked heterogeneity in methodology, a narrative synthesis was used to present results. Thirteen of the studies reported that CT programs were associated with a significantly lower risk of overweight and obesity, eight studies showed no significant association, and one study reported a significantly increased risk of obesity in women. Quality assessment showed that most studies lacked sample size and power calculations, validation of exposure, descriptions of non-respondents or those lost to follow-up, and blinded outcome assessment. Conclusions Overall, the studies were suggestive that CT programs either have no impact or decrease the risk of overweight and/or obesity in children, adolescents, and adults, but no firm conclusions can be drawn from the available evidence. This review demonstrated limitations in the available studies of CT programs and overweight/obesity.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".