Background People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on Internet and search engines to look for terms related to their disease, and its possible causes, symptoms and treatments. “Infodemiology” and “infoveillance” are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining1. Different clinical and epidemiological studies have been conducted on seasonality of SLE, and focused on flare, fatigue and periodicity of onset of clinical manifestations. Overall, SLE disease activity has been associated with specific seasonal patterns in both, Northern and Southern hemispheres, possibly in relation to sun exposure, meteorological factors and vitamin D levels2,3.
Objectives In this study, we aimed to investigate trends of Internet research linked to SLE and symptoms associated to the disease seasonality by applying a Big Data monitoring approach.
Methods We analyzed the large amount of data generated by Google Trends, considering “lupus”, “relapse” and “fatigue” in a 10-year web-based research. Google Trends automatically normalized data for the overall number of searches, and presented them as relative search volumes, in order to compare variations of different search terms across regions and periods. The Menn-Kendall test was used to evaluate the overall seasonal trend of each search term and possible correlation between search terms.
Results We observed a seasonality for Google search volumes for lupus-related terms (Figure 1). In Northern hemisphere, relative search volumes for “lupus” were correlated with “relapse” (τ=0.85; p=0.019) and with fatigue (τ=0.82; p=0.003), whereas in Southern hemisphere we observed a significant correlation between “fatigue” and “relapse” (τ=0.85; p=0.018). Similarly, a significant correlation between “fatigue” and “relapse” (τ=0.70; p<0.001) was seen also in the Northern hemisphere.
Conclusions Despite the intrinsic limitations of this approach, the current study provides additional evidence for seasonality of lupus by using Google Trends. Internet-acquired data might represent a real-time surveillance tool and an alert for healthcare systems in order to plan the most appropriate resources in specific moments with higher disease burden
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Disclosure of Interest None declared