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Web search query data and prescription volumes of antidepressants

Published online by Cambridge University Press:  23 March 2020

M. Gahr ,
Z. Uzelac ,
R. Zeiss ,
B.J. Connemann ,
D. Lang  and
C. Schönfeldt-Lecuona
M. Gahr
Affiliation:
University Hospital of Ulm, Psychiatry and Psychotherapy III, Ulm, Germany
Z. Uzelac
Affiliation:
University Hospital of Ulm, Psychiatry and Psychotherapy III, Ulm, Germany
R. Zeiss
Affiliation:
University Hospital of Ulm, Psychiatry and Psychotherapy III, Ulm, Germany
B.J. Connemann
Affiliation:
University Hospital of Ulm, Psychiatry and Psychotherapy III, Ulm, Germany
D. Lang
Affiliation:
University Hospital of Ulm, Psychosomatic Medicine and Psychotherapy, Ulm, Germany
C. Schönfeldt-Lecuona
Affiliation:
University Hospital of Ulm, Psychiatry and Psychotherapy III, Ulm, Germany

Abstract

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Introduction

Persons using the Internet generate large amounts of health-related data, which are increasingly used in modern health sciences.

Objectives/aims

We analysed the relation between annual prescription volumes (APV) of several antidepressants with marketing approval in Germany and corresponding web search query data generated in Google to test, if web search query volume may be a proxy for medical prescription practice.

Methods

We obtained APVs of several antidepressants related to corresponding prescriptions at the expense of the statutory health insurance in Germany from 2004–2013. 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 (Pearson's r) between the APVs of each substance and the respective annual “search share” values; coefficients of determination (R2) were computed to determine the amount of variability shared by the two variables.

Results

Significant and strong correlations between substance-specific APVs and corresponding annual query volume were found for each substance during the observational interval: agomelatine (r = 0.968; R2 = 0.932; P = 0.01), bupropion (r = 0.962; R2 = 0.925; P = 0.01), citalopram (r = 0.970; R2 = 0.941; P = 0.01), escitalopram (r = 0.824; R2 = 0.682; P = 0.01), fluoxetine (r = 0.885; R2 = 0.783; P = 0.01), paroxetine (r = 0.801; R2 = 0.641; P = 0.01), and sertraline (r = 0.880; R2 = 0.689; P = 0.01).

Conclusions

Although the used data did not allow to perform an analysis with a higher temporal resolution our results suggest that web search query volume may be a proxy for corresponding prescription behaviour. However, further studies analysing other pharmacologic agents and prescription data that facilitates an increased temporal resolution are needed to confirm this hypothesis.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
e-Poster Walk: Psychopharmacology and pharmacoeconomics and psychoneuroimmunology
Information
European Psychiatry , Volume 41 , Issue S1: Abstract of the 25th European Congress of Psychiatry , April 2017 , pp. S369
Copyright
Copyright © European Psychiatric Association 2017
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