What can Google and Wikipedia can tell us about a disease? Big Data trends analysis in Systemic Lupus Erythematosus.

OBJECTIVE

To investigate trends of Internet search volumes linked to Systemic Lupus Erythematosus (SLE), on-going clinical trials and research developments associated to the disease, using Big Data monitoring and data mining.

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. Wikipedia page views were also analysed using WikiTrends and the results were compared with the search volumes generated by Google Trends.

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. By comparing the search volumes analysing the Wikipedia page views of both SLE and belimumab, we found a close peak trend, reflecting the knowledge translation after the approval of belimumab for the treatment of SLE. When focusing on search volumes of Google Trends, we noticed that the highest peaks were related to news headlines that involved celebrities affected by SLE, also when comparing to the peak generated by the approval of belimumab.

CONCLUSION

This new approach, able to investigate health information seeking, might give an estimate of the health-related demand and even of the health-related behaviour of SLE, bringing new light to unanswered questions.