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However, online studies are limited in resolution: they are carried out at country or regional level and do not capture precisely the composition of the food consumed. We study the association between food consumption (derived from the loyalty cards of the main grocery retailer in London) and health outcomes (derived from publicly-available medical prescription records of all general practitioners in the city). The scale and granularity of our analysis is unprecedented: we analyze 1.6B food item purchases and 1.1B medical prescriptions for the entire city of London over the course of one year. By studying food consumption down to the level of nutrients, we show that nutrient diversity and amount of calories are the two strongest predictors of the prevalence of three diseases related to what is called the \u201cmetabolic syndrome\u201d: hypertension, high cholesterol, and diabetes. This syndrome is a cluster of symptoms generally associated with obesity, is common across the rich world, and affects one in four adults in the UK. Our linear regression models achieve an<jats:inline-formula><jats:alternatives><jats:tex-math>$R^{2}$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:msup><mml:mi>R<\/mml:mi><mml:mn>2<\/mml:mn><\/mml:msup><\/mml:math><\/jats:alternatives><\/jats:inline-formula>of 0.6 when estimating the prevalence of diabetes in nearly 1000 census areas in London, and a classifier can identify (un)healthy areas with up to 91% accuracy. Interestingly, healthy areas are not necessarily well-off (income matters less than what one would expect) and have distinctive features: they tend to systematically eat less carbohydrates and sugar, diversify nutrients, and avoid large quantities. More generally, our study shows that analytics of digital records of grocery purchases can be used as a cheap and scalable tool for health surveillance and, upon these records, different stakeholders from governments to insurance companies to food companies could implement effective prevention strategies.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-019-0191-y","type":"journal-article","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T17:41:30Z","timestamp":1556646090000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Large-scale and high-resolution analysis of food purchases and health outcomes"],"prefix":"10.1140","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0654-2527","authenticated-orcid":false,"given":"Luca Maria","family":"Aiello","sequence":"first","affiliation":[]},{"given":"Rossano","family":"Schifanella","sequence":"additional","affiliation":[]},{"given":"Daniele","family":"Quercia","sequence":"additional","affiliation":[]},{"given":"Lucia","family":"Del Prete","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,30]]},"reference":[{"issue":"3","key":"191_CR1","doi-asserted-by":"crossref","first-page":"613","DOI":"10.3945\/ajcn.114.100065","volume":"101","author":"U Ekelund","year":"2015","unstructured":"Ekelund U, Ward HA, Norat T, Luan J, May AM, Weiderpass E, Sharp SJ, Overvad K, \u00d8stergaard JN, Tj\u00f8nneland A et al. 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Am J Lifestyle Med 5(4):338\u2013345","journal-title":"Am J Lifestyle Med"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-019-0191-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-019-0191-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-019-0191-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T05:56:55Z","timestamp":1694843815000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-019-0191-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,30]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["191"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-019-0191-y","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,30]]},"assertion":[{"value":"25 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests. This work was done while LMA and DQ were employees of Nokia Bell Labs and LDP was employee of Tesco Labs. The authors\u2019 employers provided support in the form of salaries but did not have any additional role in the study design, data collection and analysis, or preparation of the manuscript. All work was done as part of the respective authors\u2019 research, with no additional or external funding.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}