Literature DB >> 25234631

[On the seasonality of dermatoses: a retrospective analysis of search engine query data depending on the season].

M J Köhler1, S Springer, M Kaatz.   

Abstract

BACKGROUND: The volume of search engine queries about disease-relevant items reflects public interest and correlates with disease prevalence as proven by the example of flu (influenza). Other influences include media attention or holidays. STUDY GOAL: The present work investigates if the seasonality of prevalence or symptom severity of dermatoses correlates with search engine query data.
METHODS: The relative weekly volume of dermatological relevant search terms was assessed by the online tool Google Trends for the years 2009-2013. For each item, the degree of seasonality was calculated via frequency analysis and a geometric approach.
RESULTS: Many dermatoses show a marked seasonality, reflected by search engine query volumes. Unexpected seasonal variations of these queries suggest a previously unknown variability of the respective disease prevalence. Furthermore, using the example of allergic rhinitis, a close correlation of search engine query data with actual pollen count can be demonstrated. DISCUSSION: In many cases, search engine query data are appropriate to estimate seasonal variability in prevalence of common dermatoses. This finding may be useful for real-time analysis and formation of hypotheses concerning pathogenetic or symptom aggravating mechanisms and may thus contribute to improvement of diagnostics and prevention of skin diseases.

Entities:  

Mesh:

Year:  2014        PMID: 25234631     DOI: 10.1007/s00105-014-2848-6

Source DB:  PubMed          Journal:  Hautarzt        ISSN: 0017-8470            Impact factor:   0.751


  6 in total

1.  What Google® knows about the pollen season.

Authors:  R Mösges; M Adrian; E El Hassan; V König
Journal:  Allergy       Date:  2011-01-17       Impact factor: 13.146

2.  Estimating the impact of the 2009 influenza A(H1N1) pandemic on mortality in the elderly in Navarre, Spain.

Authors:  L Josseran; A Fouillet
Journal:  Euro Surveill       Date:  2010-03-04

3.  Medical nowcasting using Google Trends: application in otolaryngology.

Authors:  Thomas Braun; Ulrich Harréus
Journal:  Eur Arch Otorhinolaryngol       Date:  2013-04-30       Impact factor: 2.503

4.  Using search engine query data to track pharmaceutical utilization: a study of statins.

Authors:  Nathaniel M Schuster; Mary A M Rogers; Laurence F McMahon
Journal:  Am J Manag Care       Date:  2010-08       Impact factor: 2.229

5.  Do seasons have an influence on the incidence of depression? The use of an internet search engine query data as a proxy of human affect.

Authors:  Albert C Yang; Norden E Huang; Chung-Kang Peng; Shih-Jen Tsai
Journal:  PLoS One       Date:  2010-10-28       Impact factor: 3.240

6.  Detecting influenza epidemics using search engine query data.

Authors:  Jeremy Ginsberg; Matthew H Mohebbi; Rajan S Patel; Lynnette Brammer; Mark S Smolinski; Larry Brilliant
Journal:  Nature       Date:  2009-02-19       Impact factor: 49.962

  6 in total
  2 in total

1.  Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.

Authors:  Amaryllis Mavragani; Gabriela Ochoa; Konstantinos P Tsagarakis
Journal:  J Med Internet Res       Date:  2018-11-06       Impact factor: 5.428

2.  Seasonal Patterns and Trends in Dermatoses in Poland.

Authors:  Krzysztof Bartosz Klimiuk; Dawid Krefta; Karol Kołkowski; Karol Flisikowski; Małgorzata Sokołowska-Wojdyło; Łukasz Balwicki
Journal:  Int J Environ Res Public Health       Date:  2022-07-22       Impact factor: 4.614

  2 in total

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