Researchers from the University of Cambridge have harnessed the power of artificial intelligence (AI) to analyze the healthiness of menus at food outlets across Britain.
The findings, published in Health & Place, shed light on the disparity in food environments between affluent and deprived areas, emphasizing the challenges faced by individuals residing in economically disadvantaged neighborhoods.
AI predicts menu healthiness
Utilizing data from Just Eat, an online food ordering platform, the researchers examined nearly 55,000 food outlets. Each menu was evaluated based on various factors including the presence of special offers, desserts, salads, chips, and the diversity of vegetables.
Through an AI-driven deep learning model, trained on a subset of Just Eat data, the team predicted the healthiness of menus for almost 180,000 out-of-home food outlets across the country.
The study revealed a clear correlation between an area’s level of deprivation and the healthiness of its out-of-home food outlets. Areas with higher levels of deprivation tended to have a greater concentration of food outlets, with a notable prevalence of less healthy options. Specifically, the most deprived areas exhibited more than double the number of food outlets per capita compared to their affluent counterparts.
Triple burden for deprived communities
The impact of these disparities extends beyond mere accessibility, contributing to what researchers describe as a “double burden” for residents in deprived neighborhoods. Not only do these areas have a higher density of less healthy food outlets, but individuals with lower incomes residing in such areas are also more likely to face obesity-related challenges.
This phenomenon underscores the complex interplay between socioeconomic factors and dietary habits.
Geographical analysis revealed significant disparities in menu healthiness at the local authority level. While districts such as the City of London, Kensington and Chelsea, and Westminster boasted higher menu healthiness scores, areas like Northeast Lincolnshire, Luton, and Kingston upon Hull ranked lower on the healthiness scale.
These findings provide valuable insights for policymakers aiming to address health inequalities within their jurisdictions.
Implications for public health interventions
Despite the AI’s ability to predict menu healthiness based on outlet names and hygiene ratings, the researchers acknowledge the limitations of this approach. Factors such as portion sizes, cooking methods, and food processing levels were not captured by the model.
Thus, interventions aimed at improving the healthiness of food environments must consider these nuances, potentially incorporating measures such as smaller portion sizes and reduced salt content.
The study underscores the critical role of the food environment in shaping dietary habits and health outcomes. By leveraging AI technology, researchers have identified disparities in menu healthiness across different neighborhoods, highlighting the challenges faced by individuals in deprived areas.
These findings provide a compelling rationale for targeted interventions aimed at promoting healthier food choices and reducing health inequalities within communities.
