Examining the themes of STD-related Internet searches to increase specificity of disease forecasting using Internet search terms.

Amy K Johnson,Tarek Mikati,Supriya D Mehta

doi:10.1038/srep36503

Abstract

US surveillance of sexually transmitted diseases (STDs) is often delayed and incomplete which creates missed opportunities to identify and respond to trends in disease. Internet search engine data has the potential to be an efficient, economical and representative enhancement to the established surveillance system. Google Trends allows the download of de-identified search engine data, which has been used to demonstrate the positive and statistically significant association between STD-related search terms and STD rates. In this study, search engine user content was identified by surveying specific exposure groups of individuals (STD clinic patients and university students) aged 18–35. Participants were asked to list the terms they use to search for STD-related information. Google Correlate was used to validate search term content. On average STD clinic participant queries were longer compared to student queries. STD clinic participants were more likely to report using search terms that were related to symptomatology such as describing symptoms of STDs, while students were more likely to report searching for general information. These differences in search terms by subpopulation have implications for STD surveillance in populations at most risk for disease acquisition.

Highlights

The Internet is an important source of health information, as it is anonymous, low- to-no cost, and can be accessed at any time
We found differences in user characteristics related to sexually transmitted diseases (STDs) search prevalence, that demographics were important predictors in a high-risk sample and sexual risk behavior was predictive in a low-risk sample
446 subjects were recruited from the public STD clinics and 279 students were recruited from the university

Summary

Introduction

The Internet is an important source of health information, as it is anonymous, low- to-no cost, and can be accessed at any time. Search terms used are reflective only of users with suspected or known disease, as opposed to Internet users in general This is unlikely, and an increase in search volume could indicate a true finding or may indicate the need to further train the model by revising or weighting the search terms included. When Google Flu Trends erroneously predicted an outbreak of influenza it was based on the contribution of volume of a single search term, which once corrected, the model performed at its peak[10]. If there are differences in the content of the search terms, by demographics or risk behavior, specificity can be increased in the application of Google Trends for STD surveillance which enhances this modeling technique

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Conclusion