Changes in the number and outcome of takeaway food outlet planning applications in response to adoption of management zones around schools in England: A time series analysis
Bibliographic record
Abstract
Physical exposure to takeaway food outlets ("takeaways") is associated with poor diet and excess weight, which are leading causes of excess morbidity and mortality. At the end of 2017, 35 local authorities (LAs) in England had adopted takeaway management zones (or "exclusion zones"), which is an urban planning intervention designed to reduce physical exposure to takeaways around schools. In this nationwide, natural experimental study, we used interrupted time series analyses to estimate the impact of this intervention on changes in the total number of takeaway planning applications received by LAs and the percentage rejected, at both first decision and after any appeal, within management zones, per quarter of calendar year. Changes in these proximal process measures would precede downstream retail and health impacts. We observed an overall decrease in the number of applications received by intervention LAs at 12 months post-intervention (6.3 fewer, 95% CI -0.1, -12.5), and an increase in the percentage of applications that were rejected at first (additional 18.8%, 95% CI 3.7, 33.9) and final (additional 19.6%, 95% CI 4.7, 34.6) decision, the latter taking into account any appeal outcomes. This effect size for the number of planning applications was maintained at 24 months, although it was not statistically significant. We also identified three distinct sub-types of management zone regulations (full, town centre exempt, and time management zones). The changes observed in rejections were most prominent for full management zones (where the regulations are applied irrespective of overlap with town centres), where the percentage of applications rejected was increased by an additional 46.1% at 24 months. Our findings suggest that takeaway management zone policies may have the potential to curb the proliferation of new takeaways near schools and subsequently impact on population health.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".