Google Trends can improve surveillance of Type 2 diabetes

Abstract 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 a...

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Autores principales:	Nataliya Tkachenko, Sarunkorn Chotvijit, Neha Gupta, Emma Bradley, Charlotte Gilks, Weisi Guo, Henry Crosby, Eliot Shore, Malkiat Thiarai, Rob Procter, Stephen Jarvis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1601fd9166d24a948a296c2461d40921
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spelling	oai:doaj.org-article:1601fd9166d24a948a296c2461d409212021-12-02T12:32:30ZGoogle Trends can improve surveillance of Type 2 diabetes10.1038/s41598-017-05091-92045-2322https://doaj.org/article/1601fd9166d24a948a296c2461d409212017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05091-9https://doaj.org/toc/2045-2322Abstract 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.Nataliya TkachenkoSarunkorn ChotvijitNeha GuptaEmma BradleyCharlotte GilksWeisi GuoHenry CrosbyEliot ShoreMalkiat ThiaraiRob ProcterStephen JarvisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nataliya Tkachenko Sarunkorn Chotvijit Neha Gupta Emma Bradley Charlotte Gilks Weisi Guo Henry Crosby Eliot Shore Malkiat Thiarai Rob Procter Stephen Jarvis Google Trends can improve surveillance of Type 2 diabetes
description	Abstract 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.
format	article
author	Nataliya Tkachenko Sarunkorn Chotvijit Neha Gupta Emma Bradley Charlotte Gilks Weisi Guo Henry Crosby Eliot Shore Malkiat Thiarai Rob Procter Stephen Jarvis
author_facet	Nataliya Tkachenko Sarunkorn Chotvijit Neha Gupta Emma Bradley Charlotte Gilks Weisi Guo Henry Crosby Eliot Shore Malkiat Thiarai Rob Procter Stephen Jarvis
author_sort	Nataliya Tkachenko
title	Google Trends can improve surveillance of Type 2 diabetes
title_short	Google Trends can improve surveillance of Type 2 diabetes
title_full	Google Trends can improve surveillance of Type 2 diabetes
title_fullStr	Google Trends can improve surveillance of Type 2 diabetes
title_full_unstemmed	Google Trends can improve surveillance of Type 2 diabetes
title_sort	google trends can improve surveillance of type 2 diabetes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/1601fd9166d24a948a296c2461d40921
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Google Trends can improve surveillance of Type 2 diabetes

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