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AB1110 What can google trends can tell us about a disease? big data trends analysis in systemic lupus erythematosus
  1. S Sciascia,
  2. M Radin
  1. Department of Clinical and Biological Sciences, Center of Research of Immunopathology and Rare Diseases- Coordinating Center of Piemonte and Valle d'Aosta Network for Rare Diseases, Torino, Italy

Abstract

BackgroundInfodemiology” and “infoveillance” are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining, applicable to provide new insights into unmet needs, such as the epidemiology of an uncommon disease like Systemic Lupus Erythematosus (SLE)[1].

Analysing how people (including patients, researchers, physicians) search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behaviour of populations.

Objectives In this study we aimed to investigate trends of Internet search volumes linked to SLE, on-going clinical trials and research developments associated to the disease, using Big Data monitoring and data mining. We also aimed to analyse peak-related information to investigate knowledge translation of novel therapies for SLE and the influence of media on health-related information seeking.

Methods We performed a longitudinal analysis based on the large amount of data generated by Google Trends, scientific search tools (SCOPUS, Medline/Pubmed/ClinicalTrails.gov) considering “SLE”, and “lupus” in a 5-year web-based research.

Results We observed an overall higher distribution of search volumes from Google Trends in United States, South America, Canada, South Africa, Australia and Europe (mainly Italy, United Kingdom, Spain, France, Germany), showing a geographically heterogeneity in insight into health-related behaviour of the different populations towards SLE. Data from Medline/Pubmed, SCOPUS and ClinicalTrials.gov were also analysed in order to monitor public health relevant publications and on-going trials. When comparing these results to the distribution of search volumes of Google Trends, we observed an overall similar distribution of Big Data for United States and Europe, while South America, Canada, Australia and South Africa were less represented. We observed a misbalance between search volumes for Google Trends compared to Medline/Pubmed, SCOPUS and ClinicalTrials.gov in some areas, suggesting a recent and expanding interest on SLE-related health issues in some countries.

Conclusions We observed in some countries a misbalance between the search volumes generated by Google Trends and those analyzed through scientific search tools. This new approach, merging togheter informatics and epidemiology, is able to investigate health information seeking. In the near future it might give an estimate of the health-related demand and even of the health-related behaviour of patients with SLE.

References

  1. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 2009;11:e11. doi:10.2196/jmir.1157.

References

Disclosure of Interest None declared

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