LAUSR

AbstractGoogle Trends (GT) describes the variation of the relevant interest of internet searches toward medical conditions and related symptoms. Allergic rhinitis symptom levels result from the intensity of exposure to aeroallergens in combination with relevant medication use. We analyze data from Germany to examine the relationship between hay fever-related Google search terms, symptom levels, medication use, and pollen count levels. For doing so, we also employ the new definitions on pollen season and peak pollen period start and end as proposed by the European Academy of Allergy and Clinical Immunology in a recently published position paper. We extract GT data for a number of search terms related to allergic rhinitis for Germany. We use total nasal symptom and mediation scores as reported by patients via a patient hay fever diary in the Berlin and Brandenburg areas in Germany for 3 years (2014–2016), accompanied by pollen data. Then a Pearson and Spearman correlation analysis is performed between symptom data and GT data. A graphical analysis is conducted, and the identification of pollen season and peak pollen periods is done based on the EAACI criteria. The analysis reveals that GT data are highly correlated with symptom levels and follow peak pollen period start–end, concerning grass and birch pollen-induced allergic rhinitis symptoms. GT data can be used as a proxy for the identification of the onset and variation of nasal symptom and medication score for allergic rhinitis sufferers.