Assessing the impact of a mandatory calorie labelling policy in out-of-home food outlets in England on consumer behaviour: a natural experimental study
Bibliographic record
Abstract
Abstract Background Out-of-home (OOH) food tends to be energy-dense and nutrient-poor. In response, England implemented a mandatory calorie labelling policy in the OOH sector. We evaluated changes in consumer behaviours after the policy was implemented in April 2022. Methods We employed a natural experimental design to assess pre-post changes in noticing and using nutrition information, and behaviours associated with menu labelling. We compared changes in England to comparator jurisdictions without similar policies. Data included four consecutive years (2019-2022) from the International Food Policy Study; participants were adults aged 18 years or older. Mixed effects logistic regression models assessed pre-post changes in binary outcomes, and mixed effects negative binomial regression assessed changes in frequency of OOH eating. Results In England, noticing nutrition information increased from 16.0% (15.6 to 16.4) in 2020 to 19.7% (19.1 to 20.2) in 2021 and to 25.8% (25.5 to 26.1) in 2022. This increase was 4.8 percentage points (95% CI 2.5 to 7.1) higher in England versus the comparator group. Using nutrition information increased in England from 8.0% (7.5% to 8.4%) in 2020 to 11.8% (10.9% to 12.6%) in 2021 and to 13.5% (13.1% to 13.9%) in 2022. There was a 2.7 percentage point (95% CI 2.0 to 3.4) greater increase in England versus the comparator group from 2020 to 2021. Ordering something different was the only behaviour associated with menu labelling that increased after the policy in England: from 12.6% (12.4 to 12.7) in 2020 to 15.2% (14.7 to 15.6) in 2021 and to 17.7% (17.6 to 17.8) in 2022. There was a 2.8 percentage points (95% CI 1.8 to 3.9) greater increase in England versus the comparator group from 2021 to 2022. Frequency of OOH eating did not change after policy implementation. Conclusions The introduction of mandatory calorie labelling in England led to increases in self-reported noticing and using, with the key behavioural impact on ordering something different. This suggests that while calorie labelling can enhance awareness, translating this into behaviour change remains limited to shifting orders. Additional strategies may be required to maximize the public health benefits of calorie labelling.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".