Seasonal Variation in Public Interest in Gout: Longitudinal Infodemiology Study Using Google Trends (2014–2024)

Abstract

Background: Gout, the most common inflammatory arthritis worldwide, shows clear seasonal variation in flares. Traditional epidemiology provides important insights but often lacks real-time resolution. Digital behavior, such as online search patterns, offers a scalable, timely complement that can capture seasonal trends in disease-related activity. Objective: This study aimed to determine whether public interest in gout, as expressed through Google (Google LLC) search queries, exhibits seasonal variation across countries, US states, and metropolitan areas, and to assess the influence of symptom- and language-specific search terms. We evaluated whether a bimodal (semiannual) seasonal pattern better described certain queries, providing further insight into complex behavioral rhythms. Methods: We retrieved monthly Google Trends data for gout-related queries from January 1, 2014, to December 31, 2024, covering 70 countries, all 50 US states, and 36 major cities in the United States and Canada. Queries included generic terms, symptom descriptors, and language-specific translations in 14 languages. We applied cosinor modeling to assess seasonality and calculated the amplitude and phase of fitted sinusoidal curves. Significance was assessed using P values and adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) within and across subgroups, and the Bonferroni method. To explore bimodal patterns, we compared 12- and 6-month harmonics using changes in Akaike and Bayesian information criteria. Results: We found robust seasonal variation in gout-related search interest across multiple geographic and linguistic categories. Statistically significant seasonality (original P