Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation

Long, Gavin; Nica-Avram, Georgiana; Harvey, John; Lukinova, Evgeniya; Mansilla, Roberto; Welham, Simon; Engelmann, Gregor; Dolan, Elizabeth; Makokoro, Kuzivakwashe; Thomas, Michelle; Powell, Edward; Goulding, James

doi:10.1016/j.foodpol.2025.102826

All Outputs (2)

Nutritional Implications of Alternative Proteins: A commentary (2025)
Journal Article
Davis, U., Bobo, J., Wilson, P., Noy, P., Mansilla Lobos, R., Long, G., Welham, S., Harvey, J., Lukinova, E., Nica‑Avram, G., Smith, G., Salt, D., Smith, A., & Goulding, J. (2025). Nutritional Implications of Alternative Proteins: A commentary. Public Health Nutrition, 28(1), Article e69. https://doi.org/10.1017/S1368980025000242

Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation (2025)
Journal Article
Long, G., Nica-Avram, G., Harvey, J., Lukinova, E., Mansilla, R., Welham, S., Engelmann, G., Dolan, E., Makokoro, K., Thomas, M., Powell, E., & Goulding, J. (2025). Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation. Food Policy, 131, Article 102826. https://doi.org/10.1016/j.foodpol.2025.102826

Deprivation pushes people to choose cheap, calorie-dense foods instead of nutritious but expensive alternatives. Diseases, such as obesity, cardiovascular disease, and diabetes, resulting from these poor dietary choices place a significant burden on... Read More about Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation.