Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis

Abstract

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.

12-year observation of tweets about rubella in Japan: A retrospective infodemiology study

“…Not only infectious diseases but also hay fever has been reported correlation ( r = 0.97) in the UK [ 41 ]. In Japan’s case, tweets related to allergic rhinitis were highly correlated ( r = 0.84) with their drug sales [ 42 ]. We reached similar results using longer-term data.…”

Social Media Utilization in Otolaryngology: A Scoping Review

“…In a more recent clinical context, SM was analyzed to assess the international incidence of COVID‐19 infection and infection‐related deaths amongst otolaryngologists 74 . Six studies specifically examined the use of Tweet patterns to trend pollen counts and assess correlation with rates of allergic rhinitis 3,32,33,42,53,86 . Wakamiya et al found the number of Tweets including the keywords “allergic rhinitis” to be positively correlated with the number of patient visits during the main seasonal allergic rhinitis phase 33 .…”

“…74 Six studies specifically examined the use of Tweet patterns to trend pollen counts and assess correlation with rates of allergic rhinitis. 3,32,33,42,53,86 Wakamiya et al found the number of Tweets including the keywords "allergic rhinitis" to be positively correlated with the number of patient visits during the main seasonal allergic rhinitis phase. 33 Overall, studies analyzing these platforms can gain potentially useful data to map spatiotemporal trends of environmental forces.…”