Abstract P266: The Local Food Environment and Obesity: A Systematic Review
Bibliographic record
Abstract
Introduction: Numerous studies have explored the relationship of the local food environment and obesity. However, results have been inconsistent, and existing literature reviews have not taken into account study quality or the heterogeneity of measures of the local food environment. Methods: We used systematic keyword searches in Pubmed and Scopus to identify studies conducted in the US and Canada that assessed the relationship of obesity to the local availability of supermarkets, grocery stores, convenience stores, fast food restaurants or indices combining these measures. We developed a quality metric based on study design, exposure and outcome measurement and analysis, and then assigned each study a score. Results: We identified 71 studies representing 65 cohorts. Overall, study quality was low; 60 studies were cross-sectional. The approach to measuring local food environments varied: fast food availability was measured 31 ways in 44 studies. Associations between food outlet availability and obesity were predominantly null. In adults, we saw a trend among the non-null associations toward inverse associations between supermarket availability and obesity (22 inverse, 4 direct, 67 null) and direct associations between fast food and obesity (29 direct, 6 inverse, 71 null). In children, we saw robust direct associations between fast food availability and obesity in lower income populations only (12 direct, 7 null). In adults, indices made up of multiple types of outlets had resulted in the most consistent associations with obesity (18 expected, 23 null). Limiting analyses to higher quality studies did not affect results. Conclusions: We found limited evidence for associations between the local food environment and obesity. Quality issues, however, make causal inference difficult. Absent compelling direct evidence linking local food environments to obesity, policy makers will need to rely on other types of evidence as they address the environmental changes that contribute to the steep increase in obesity in the US.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".