Google Trends can improve surveillance of Type 2 diabetes

• Published in: Scientific reports Vol. 7; no. 1; pp. 4993 - 10
• Main Authors: Tkachenko, Nataliya, Chotvijit, Sarunkorn, Gupta, Neha, Bradley, Emma, Gilks, Charlotte, Guo, Weisi, Crosby, Henry, Shore, Eliot, Thiarai, Malkiat, Procter, Rob, Jarvis, Stephen
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.07.2017; Nature Publishing Group; Nature Portfolio
• Subjects: 692/499; 692/700/459/284; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - epidemiology; Early Diagnosis; Humanities and Social Sciences; Humans; Hypertension; Information Seeking Behavior; Internet - statistics & numerical data; Living conditions; multidisciplinary; Population Surveillance - methods; Science; Science (multidisciplinary); Search Engine - methods; Semantics; Smoking; Trends
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-05091-9

Recent studies demonstrate that people are increasingly looking online to assess their health, with reasons varying from personal preferences and beliefs to inability to book a timely appointment with their local medical practice. Records of these activities represent a new source of data about the health of populations, but which is currently unaccounted for by disease surveillance models. This could potentially be useful as evidence of individuals’ perception of bodily changes and self-diagnosis of early symptoms of an emerging disease. We make use of the Experian geodemographic Mosaic dataset in order to extract Type 2 diabetes candidate risk variables and compare their temporal relationships with the search keywords, used to describe early symptoms of the disease on Google. Our results demonstrate that Google Trends can detect early signs of diabetes by monitoring combinations of keywords, associated with searches for hypertension treatment and poor living conditions ; Combined search semantics, related to obesity , how to quit smoking and improve living conditions (deprivation) can be also employed, however, may lead to less accurate results.