Field validity and spatial accuracy of Food Standards Agency Food Hygiene Rating scheme data for England

Planning policies to restrict fast food and inequalities in child weight in England: a quasi-experimental analysis

OBJECTIVE

England has one of the highest childhood obesity rates in Europe. To promote a healthier food environment in 2015, Gateshead Council in North East England introduced planning guidelines effectively banning any new fast-food outlets. Our aim was to investigate whether this policy led to any reductions in childhood overweight and obesity prevalence and the inequalities in these outcomes.

METHODS

We used data from the National Child Measurement Programme, the Food Standards Agency Food Hygiene Rating Scheme data, and the Office of National Statistics between 2012 and 2020. We estimated a difference-in-differences model employing propensity score matching to identify a control group.

RESULTS

We found no significant change in population-level childhood overweight and obesity in Gateshead compared with control areas. In subgroup analysis by area-level deprivation, we found that the quintile of deprivation with the highest proportion of fast-food outlets had a statistically significant reduction of 4.8% in the prevalence of childhood overweight and obesity compared with control areas.

CONCLUSIONS

Restricting fast-food outlets in areas with a high concentration of such outlets as part of a package of policies to reduce childhood obesity may help to reduce prevalence and inequalities in childhood overweight and obesity.

A mixed-method systematic review and meta-analysis of the influences of food environments and food insecurity on obesity in high-income countries

Obesity remains a serious public health concern in rich countries and the current obesogenic food environments and food insecurity are predictors of this disease. The impact of these variables on rising obesity trends is, however, mixed and inconsistent, due to measurement issues and cross-sectional study designs. To further the work in this area, this review aimed to summarize quantitative and qualitative data on the relationship between these variables, among adults and children across high-income countries. A mixed-method systematic review was conducted using 13 electronic databases, up to August 2021. Two authors independently extracted data and evaluated quality of publications. Random-effects meta-analysis was used to estimate the odds ratio (OR) for the association between food insecurity and obesity. Where statistical pooling for extracted statistics related to food environments was not possible due to heterogeneity, a narrative synthesis was performed. Meta-analysis of 36,113 adults and children showed statistically significant associations between food insecurity and obesity (OR: 1.503, 95% confidence interval: 1.432-1.577, p < .05). Narrative synthesis showed association between different types of food environments and obesity. Findings from qualitative studies regarding a reliance on energy-dense, nutrient-poor foods owing to their affordability and accessibility aligned with findings from quantitative studies. Results from both qualitative and quantitative studies regarding the potential links between increased body weight and participation in food assistance programs such as food banks were supportive of weight gain. To address obesity among individuals experiencing food insecurity, wide-reaching approaches are required, especially among those surrounded by unhealthy food environments which could potentially influence food choice.

Automatic classification of takeaway food outlet cuisine type using machine (deep) learning

BACKGROUND AND PURPOSE

Researchers have not disaggregated neighbourhood exposure to takeaway ('fast-') food outlets by cuisine type sold, which would otherwise permit examination of differential impacts on diet, obesity and related disease. This is partly due to the substantial resource challenge of manual classification of unclassified takeaway outlets at scale. We describe the development of a new model to automatically classify takeaway food outlets, by 10 major cuisine types, based on business name alone.

MATERIAL AND METHODS

We used machine (deep) learning, and specifically a Long Short Term Memory variant of a Recurrent Neural Network, to develop a predictive model trained on labelled outlets (n = 14,145), from an online takeaway food ordering platform. We validated the accuracy of predictions on unseen labelled outlets (n = 4,000) from the same source.

RESULTS

Although accuracy of prediction varied by cuisine type, overall the model (or 'classifier') made a correct prediction approximately three out of four times. We demonstrated the potential of the classifier to public health researchers and for surveillance to support decision-making, through using it to characterise nearly 55,000 takeaway food outlets in England by cuisine type, for the first time.

CONCLUSIONS

Although imperfect, we successfully developed a model to classify takeaway food outlets, by 10 major cuisine types, from business name alone, using innovative data science methods. We have made the model available for use elsewhere by others, including in other contexts and to characterise other types of food outlets, and for further development.

Socioeconomic inequalities in food outlet access through an online food delivery service in England: A cross-sectional descriptive analysis

Online food delivery services facilitate 'online' access to food outlets selling food prepared away-from-home. Online food outlet access has not previously been investigated in England or across an entire country. Systematic differences in online food outlet access could exacerbate existing health inequalities, which is a public health concern. However, this is not known. Across postcode districts in England (n = 2118), we identified and described the number of food outlets and unique cuisine types accessible online from the market leader (Just Eat). We investigated associations with area-level deprivation using adjusted negative binomial regression models. We also compared the number of food outlets accessible online with the number physically accessible in the neighbourhood (1600m Euclidean buffers of postcode district geographic centroids) and investigated associations with deprivation using an adjusted general linear model. For each outcome, we predicted means and 95% confidence intervals. In November 2019, 29,232 food outlets were registered to accept orders online. Overall, the median number of food outlets accessible online per postcode district was 63.5 (IQR; 16.0-156.0). For the number of food outlets accessible online as a percentage of the number accessible within the neighbourhood, the median was 63.4% (IQR; 35.6-96.5). Analysis using negative binomial regression showed that online food outlet access was highest in the most deprived postcode districts (n = 106.1; 95% CI: 91.9, 120.3). The number of food outlets accessible online as a percentage of those accessible within the neighbourhood was highest in the least deprived postcode districts (n = 86.2%; 95% CI: 78.6, 93.7). In England, online food outlet access is socioeconomically patterned. Further research is required to understand how online food outlet access is related to using online food delivery services.

The impact of school exclusion zone planning guidance on the number and type of food outlets in an English local authority: A longitudinal analysis

The use of planning policy to manage and create a healthy food environment has become a popular policy tool for local governments in England. To date there has been no evaluation of their short-term impact on the built environment. We assess if planning guidance restricting new fast food outlets within 400 m of a secondary school, influences the food environment in the local authority of Newcastle Upon Tyne, UK. We have administrative data on all food outlets in Newcastle 3 years pre-intervention 2012-2015, the intervention year 2016, and three years' post-intervention 2016-2019. We employ a difference-in-difference approach comparing postcodes within the school fast food outlet exclusion zone to those outside the fast-food exclusion zones. In the short term (3 years), planning guidance to limit the number of new fast-food outlets in a school exclusion zone did not have a statistically significant impact on the food environment when compared with a control zone.

Supporting a Healthier Takeaway Meal Choice: Creating a Universal Health Rating for Online Takeaway Fast-Food Outlets

Digital food ordering platforms are used by millions across the world and provide easy access to takeaway fast-food that is broadly, though not exclusively, characterised as energy dense and nutrient poor. Outlets are routinely rated for hygiene, but not for their healthiness. Nutritional information is mandatory in pre-packaged foods, with many companies voluntarily using traffic light labels to support making healthier choices. We wanted to identify a feasible universal method to objectively score takeaway fast-food outlets listed on Just Eat that could provide users with an accessible rating that can infer an outlet's 'healthiness'. Using a sample of takeaway outlets listed on Just Eat, we obtained four complete assessments by nutrition researchers of each outlet's healthiness to create a cumulative score that ranged from 4 to 12. We then identified and manually extracted nutritional attributes from each outlet's digital menu, e.g., number of vegetables that have the potential to be numerated. Using generalized linear modelling we identified which attributes were linear predictors of an outlet's healthiness assessment from nutritional researchers. The availability of water, salad, and the diversity of vegetables were positively associated with academic researchers' assessment of an outlet's healthiness, whereas the availability of chips, desserts, and multiple meal sizes were negatively associated. This study shows promise for the feasibility of an objective measure of healthiness that could be applied to all outlet listings on Just Eat and other digital food outlet aggregation platforms. However, further research is required to assess the metric's validity, its desirability and value to users, and ultimately its potential influence on food choice behaviour.