Linking Annual Prescription Volume of Antidepressants to Corresponding Web Search Query Data: A Possible Proxy for Medical Prescription Behavior?. Issue 6 (December 2015)

• Record Type: Journal Article
• Authors: Gahr, Maximilian; Uzelac, Zeljko; Zeiss, René; Connemann, Bernhard J.; Lang, Dirk; Schönfeldt-Lecuona, Carlos
• Is Part Of: Journal of clinical psychopharmacology. Volume 35:Issue 6(2015)
• Publication Date: 2015-12
• Subjects: depression -- drug safety -- Google -- pharmacovigilance; Psychopharmacology -- Periodicals; Psychopharmacology -- Periodicals; Psychopharmacologie -- Périodiques; Psychopharmacology; Periodicals; 615.78
• DOI: 10.1097/JCP.0000000000000397
• Languages: English
• ISSNs: 0271-0749

Abstract

Abstract : Abstract: Persons using the Internet to retrieve medical information generate large amounts of health-related data, which are increasingly used in modern health sciences. We analyzed the relation between annual prescription volumes (APVs) of several antidepressants with marketing approval in Germany and corresponding web search query data generated in Google to test whether web search query volume may be a proxy for medical prescription practice. We obtained APVs of several antidepressants related to corresponding prescriptions at the expense of the statutory health insurance in Germany from 2004 to 2013. Web search query data generated in Germany and related to defined search terms (active substance or brand name) were obtained with Google Trends. We calculated correlations (Person's r ) between the APVs of each substance and the respective annual "search share" values; coefficients of determination ( R 2 ) were computed to determine the amount of variability shared by the 2 variables. Significant and strong correlations between substance-specific APVs and corresponding annual query volumes were found for each substance during the observational interval: agomelatine ( r = 0.968, R 2 = 0.932, P = 0.01), bupropion ( r = 0.962, R 2 = 0.925, P = 0.01), citalopram ( r = 0.970, R 2 = 0.941, P = 0.01), escitalopram ( r = 0.824, R 2 = 0.682, P = 0.01), fluoxetine ( r = 0.885, R 2 = 0.783, P = 0.01), paroxetine ( r = 0.801, R 2 = 0.641, P = 0.01), and sertraline ( r = 0.880, R 2 = 0.689, P = 0.01). Although the used data did not allow to perform an analysis with a higher temporal resolution (quarters, months), our results suggest that web search query volume may be a proxy for corresponding prescription behavior. However, further studies analyzing other pharmacologic agents and prescription data that facilitate an increased temporal resolution are needed to confirm this hypothesis.