Objectives/hypothesis: Sinusitis significantly affects the population of the United States, exacting direct cost and lost productivity. Patients are likely to search the Internet for information related to their health before seeking care by a healthcare professional. Utilizing data generated from these searches may serve as an epidemiologic surrogate.
Study Design: A retrospective time series analysis was performed.
Methods: Google search trend data from the Dallas-Fort Worth metro region for the years 2012 and 2013 were collected from www.google.com/trends for terms related to sinusitis based on literature outlining the most important symptoms for diagnosis. Additional terms were selected based on common English language terms used to describe the disease. Twelve months of data from the same time period and location for common pollutants (nitrogen dioxide, ozone, sulfur dioxide, and particulates), pollen and mold counts, and influenza-like illness were also collected. Statistical analysis was performed using Pearson correlation coefficients, and potential search activity predictors were assessed using autoregressive integrated moving average.
Results: Pearson correlation was strongest between the terms congestion and influenza-like illness (r=0.615), and sinus and influenza-like illness (r=0.534) and nitrogen dioxide (r=0.487). Autoregressive integrated moving average analysis revealed ozone, influenza-like illness, and nitrogen dioxide levels to be potential predictors for sinus pressure searches, with estimates of 0.118, 0.349, and 0.438, respectively. Nitrogen dioxide was also a potential predictor for the terms congestion and sinus, with estimates of 0.191 and 0.272, respectively.
Conclusions: Google search activity for related terms follow the pattern of seasonal influenza-like illness and nitrogen dioxide. These data highlight the epidemiologic potential of this novel surveillance method.
Level Of Evidence: NA.
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