Rare diseases such as systemic lupus erythematosus (SLE), which has an estimated prevalence of 20 to 150 per 100,000 cases, are typically not viewed as research priorities compared with more common diseases.2 As such, it can be difficult to elucidate disease patterns and develop novel treatments for these illnesses.
Advances in web analytics offer the opportunity to fill important knowledge gaps and identify trends among patients affected by rare conditions. The use of data mining and big data monitoring to learn about the determinants and distribution of online health information is called “infodemiology.”2 Applications of this approach include the use of internet search volumes to predict disease outbreaks, identifying publications pertaining to public health, and analyzing disparities in access to health information.3,4
Analysis of the public’s behaviors pertaining to searching, communicating, and sharing health-related information can provide information that would otherwise be unavailable. “Individuals with SLE frequently rely on internet searches for information about the disease and its management,” Savino Sciascia, MD, PhD, and Massimo Radin, MD, from the Center of Research of Immunopathology and Rare Diseases at the University of Turin in Italy, told Rheumatology Advisor. “Data mining might be applied to rare conditions to explore the distribution of systemic autoimmune diseases in different populations.”
In a study published in November 2017 in the International Journal of Medical Informatics, Dr Sciascia and Dr Radin examined data generated by Google Trends and scientific search tools over the course of a 5-year period, based on the search terms “SLE” and “lupus.”2 Earlier that year in Lupus, they reported the results of a similar analysis of data generated by the search terms “lupus,” “relapse,” and “fatigue” over the course of a 10-year period.4
In an interview with Rheumatology Advisor, the physicians summarized their findings, associated implications, and future directions in the field of infodemiology.
Rheumatology Advisor: What were the main findings of your study, and what are the broader implications of these observations?
Dr Sciascia and Dr Radin: In the first study, we observed a seasonality for Google search volumes for lupus-related terms generated over the course of a 10-year period.4 Specifically, we found a seasonal correlation between the search terms “lupus” and “relapse” and between “lupus” and “fatigue” in the Northern hemisphere (P =.019; P =.003, respectively), and a significant correlation between “relapse” and “fatigue” in both the Northern and Southern hemispheres (P <.001; P =.018, respectively).
In the most recent longitudinal analysis of data gathered from scientific databases and Google Trends over the course of a 5-year period, the search terms “SLE” and “lupus” indicated a geographic heterogeneity in populations’ SLE-related behaviors.2 This heterogeneity seemed to be influenced by the search engine, available publications, new treatment options, and celebrity culture. For instance, the peaks in Google Trend searches were closely linked to news reports of celebrities affected by SLE. These peaks also correlated with the approval of belimumab, the first drug approved for the disease in more than 50 years.
These studies support the notion that data mining and big data monitoring may provide insight into the search behavior for health-related information among patients with SLE. Taken together, one could speculate that infodemiology could have an effect on a global level by helping healthcare providers to allocate resources when and where they are most needed, implementing informational programs where health literacy for specific topics is still lacking, and, most important from the patients’ perspective, providing further insight to the impact of the disease on a personal level — aspects that are usually poorly estimated by conventional epidemiological tools.
Rheumatology Advisor: What are some ways in which infodemiology might prove to be a valuable tool for SLE and other rheumatic diseases?
Dr Sciascia and Dr Radin: Investigating the prevalence and features of rare conditions requires multicenter efforts. Collecting homogenous data is challenging, and because of the low prevalence of rare diseases, funding might be difficult. For similar reasons, most information on prevalence is based on registry data. In the era of the internet, new tools are available for researchers and clinicians, especially to study subgroups of low-prevalence conditions.
By extrapolating relative search data, clinicians can obtain information about how patients navigate the internet and how the spread of information on rare conditions is affecting clinicians and patients in different areas of the globe. Although not free of limitations, the infodemiology approach might help researchers fill some gray areas when investigating low-prevalence diseases.
Rheumatology Advisor: What should be the focus of additional research pertaining to this approach?
Dr Sciascia and Dr Radin: Questions and uncertainties remain about the consistency of research design, the gathering of data, and the analysis and interpretation of results. On one hand, this approach can provide and analyze data in near real-time with relatively no cost. On the other hand, this methodology has some intrinsic limitations: available data are based on a sample of web searches, with the potential for nonrepresentative sampling bias and differences in access to the internet, and as a consequence, the calculation of the search value index is dependent on several mathematical assumptions and approximations in search traffic.
In the near future, the challenge will be to combine this new approach with conventional epidemiological data to capture an overview of the global distribution of a disease and, from an individual perspective, a more comprehensive understating of patients’ needs to individually tailor their management in the era of “precision medicine.”
- Danchenko N, Satia JA, Anthony MS. Epidemiology of systemic lupus erythematosus: a comparison of worldwide disease burden.Lupus. 2006;15(5):308-318.
- Sciascia S, Radin M. What can Google and Wikipedia tell us about a disease? Big Data trends analysis in systemic lupus erythematosus. Int J Med Inform. 2017;107:65-69.
- Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012-1014
- Sciascia S, Radin M. Infodemiology of systemic lupus erythematous using Google Trends. Lupus. 2017;26(8):886-889.