Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

Abstract

Background: Health-related social media data are increasingly used in disease surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients to increase the credibility of disease surveillance based on social media data. Objective: To clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of SAR patients in the real world. Methods: This analysis was conducted using datasets of pollen count, tweet numbers, and SAR patient numbers from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of SAR in Japan) and hinoki cypress (which commonly complicates SAR) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword "kafunshō" (or SAR) were calculated between January 1 and May 31, 2017. Daily SAR patient numbers from January 1 to May 31, 2017 were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of SAR patients during the study period from February to May 2017. To determine if time-variant factors affect these causal relationships, we also analyzed the main SAR phase (February to April) when Japanese cedar trees actively produce and release pollen. Results: Increases in pollen count were found to cause increases in the number of tweets during the overall study period (P = .04), but not the main SAR phase (P = . 05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P = .04) and the main SAR phase (P = .01). In addition, increases in the number of tweets also caused increases in patient numbers during the main SAR phase (P = .02), but not the overall study period (P = .89). Patient numbers did not affect the number of tweets in both the overall study period (P = . 24) and the main SAR phase (P = .47). Conclusions: Understanding the causal relationships between these factors is an important step to increase the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis.