Neighbourhood out-of-home food environment, menu healthiness, and their associations with meal purchasing and diet quality: a multiverse analysis
Bibliographic record
Abstract
BACKGROUND: Governments worldwide have implemented various interventions to improve the healthiness of food offered by out-of-home outlets. However, there is limited evidence on whether healthier menus would influence individual dietary behaviours and quality. In this cross-sectional study, we investigated associations between different measures of the neighbourhood out-of-home food environment, incorporating menu healthiness, and out-of-home meal purchasing and diet quality. METHODS: We used a sample of 3,481 adults in Great Britain (GB) with valid home postcodes from the 2021 International Food Policy Study. We linked this sample to a national database of food outlet geographical locations to characterise individuals' exposure to the out-of-home food environment. The exposure metrics included menu healthiness scores, availability, proximity, and relative composition of out-of-home food outlets in various neighbourhood buffers around the home (i.e., 500 - 1600 m). Outcomes considered were out-of-home meal consumption and overall diet quality. Using multiverse analyses, where multiple reasonable analytical choices can be tested, we investigated the associations between different exposure measures and these outcomes. RESULTS: GB adults had access to an average of 97 (95% CI 91, 104) out-of-home food outlets within 1600 m of their homes. The number of both healthier and less healthy out-of-home food outlets was positively associated with the number of meals purchased out-of-home across all neighbourhood buffers, e.g., every 10 additional less healthy out-of-home food outlets within 500 m of the home corresponded to a 6% (95% CI = 2, 11) increase in the frequency of out-of-home meal purchases in the previous week. Proximity, relative composition, and menu healthiness of neighbourhood out-of-home outlets were not associated with out-of-home meal purchase frequency after adjusting for multiple comparisons. There were no consistent associations between out-of-home food environment exposures and diet quality. CONCLUSION: The only aspect of the neighbourhood out-of-home food environment associated with out-of-home meal purchase frequency was the number of out-of-home food outlets. Menu healthiness of out-of-home food outlets was not associated with how often people purchased out-of-home meals or overall diet quality. Interventions focusing on mitigating the proliferation of out-of-home food outlets may be more effective in changing individual dietary behaviour than those focusing on food served.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".