Infodemiology of systemic lupus erythematous using Google Trends

Abstract

Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the 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 mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach. Methods We analysed 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. In the 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 the 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. Conclusion Despite the intrinsic limitations of this approach, 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.

Population’s health information-seeking behaviors and geographic variations of stroke in Malaysia: an ecological correlation and time series study

“…A correlation of − 1 indicates that the two variables are negatively linearly related, a correlation of 0 means that the two variables do not have any linear relations, while a correlation coefficient of 1 means that two variables are perfectly positively linearly related 40,41 . Consistent with these statistical theories, we followed trends of recent time series studies that utilized Google Trends to explore www.nature.com/scientificreports/ correlations within search terms or between search terms and counts data of different diseases by employing Pearson's correlation analyses 2,7,8,10 . Subsequently, we performed an ecological correlation analysis 42,43 to test whether search volumes were correlated with the actual incidence of stroke at state and country level using Pearson's correlation coefficient analysis.…”

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

“…Some researchers studied mental health [53] and depression [54] queries. Other topics studied using GT data are skin cancer [55], sunscreen use [56], sunburn [57], seasonality of bruxism [58], multiple sclerosis [59], cancer [60], stroke [61], HIV [62], lupus [63], norovirus [64], sepsis [65], pertussis [66], epistaxis [67], plague [68], rheumatoid arthritis [69], and prostate cancer [70]. In terms of general population behavior, research was done using GT data on pharmaceutical data [71], vaccinations [72], movement disorders [73], digital epidemiology [74], kidney stone surgery [75], foot and ankle pain [76], knee injuries [77], osteoarthritis [78], seasonality of cellulitis [79], tracking influenza epidemics using climate data [80], palliative care [81], cosmetic body procedures [82], and anesthesia [83].…”

Infodemiology of Crohn’s disease and Ulcerative colitis using Google Trends – an approach to investigate patient needs

“…To address people's needs for IBD, both diseases should be cross-linked on information providing websites. For both diseases, no seasonal search pattern can be detected, which is contrary to e. g., rheumatoid diseases or irritable bowel syndrome, where a seasonal variety of search volumes has been described [16,18,19], while being in line with the known medical course of IBD, which is unpredictable rather than season-or weather-dependent. Several "stars" or well-known personalities seem to have a deep influence on disease-related searches concerning UC as well as CD.…”