%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 6
%N 2
%P e33941
%T Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis
%A Wakamiya,Shoko
%A Morimoto,Osamu
%A Omichi,Katsuhiro
%A Hara,Hideyuki
%A Kawase,Ichiro
%A Koshiba,Ryuji
%A Aramaki,Eiji
%+ Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 6300192, Japan, 81 743 72 5250, wakamiya@is.naist.jp
%K infoveillance
%K social media
%K Twitter
%K over-the-counter drugs
%K allergic rhinitis
%K hay fever
%K drug
%K treatment
%K allergy
%K immunology
%K surveillance
%K monitoring
%K prevalence
%K motivation
%K Japan
%K symptom
%D 2022
%7 2.2.2022
%9 Original Paper
%J JMIR Form Res
%G English
%X Background: Health-related social media data are increasingly being used in disease surveillance studies. In particular, surveillance of infectious diseases such as influenza has demonstrated high correlations between the number of social media posts mentioning the disease and the number of patients who went to the hospital and were diagnosed with the disease. However, the prevalence of some diseases, such as allergic rhinitis, cannot be estimated based on the number of patients alone. Specifically, individuals with allergic rhinitis typically self-medicate by taking over-the-counter (OTC) medications without going to the hospital. Although allergic rhinitis is not a life-threatening disease, it represents a major social problem because it reduces people’s quality of life, making it essential to understand its prevalence and people’s motives for self-medication behavior. Objective: This study aims to explore the relationship between the number of social media posts mentioning the main symptoms of allergic rhinitis and the sales volume of OTC rhinitis medications in Japan. Methods: We collected tweets over 4 years (from 2017 to 2020) that included keywords corresponding to the main nasal symptoms of allergic rhinitis: “sneezing,” “runny nose,” and “stuffy nose.” We also obtained the sales volume of OTC drugs, including oral medications and nasal sprays, for the same period. We then calculated the Pearson correlation coefficient between time series data on the number of tweets per week and time series data on the sales volume of OTC drugs per week. Results: The results showed a much higher correlation (r=0.8432) between the time series data on the number of tweets mentioning “stuffy nose” and the time series data on the sales volume of nasal sprays than for the other two symptoms. There was also a high correlation (r=0.9317) between the seasonal components of these time series data. Conclusions: We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships can help us understand the prevalence of allergic rhinitis and the motives for self-care treatment using social media data, which would be useful as a marketing indicator to reduce the number of out-of-stocks in stores, provide (sell) rhinitis medicines to consumers in a stable manner, and reduce the loss of sales opportunities. In the future, in-depth investigations are required to estimate sales volume using social media data, and future research could investigate other diseases and countries.
%M 35107434
%R 10.2196/33941
%U https://formative.jmir.org/2022/2/e33941
%U https://doi.org/10.2196/33941
%U http://www.ncbi.nlm.nih.gov/pubmed/35107434
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