Using the Internet Big Data to Investigate the Epidemiological Characteristics of Allergic Rhinitis and Allergic Conjunctivitis.

BACKGROUND

To explore the epidemiological characteristics of allergic rhinitis (AR) and allergic conjunctivitis (AC) based on the Internet big data.

METHODS

The Baidu index (BDI) of keywords "allergic rhinitis" and "allergic conjunctivitis" in Mandarin, the daily pollen concentration (PC) released by the Beijing Meteorological Bureau and the volumes of outpatient visits (OV) of the Beijing Tongren Hospital (Beijing) and the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou) from 2017 to 2020 were obtained. The temporal and spatial changes of AR and AC were discussed. The correlations between BDI and PC/OV were analyzed by Spearman correlation analysis.

RESULTS

The trends of BDI of "AR"/"AC" in Beijing showed obvious seasonal variations, but not in Guangzhou. The BDI of "AR" and "AC" was consistent with the OV in both cities (r1AR-BJ=0.580, P<0.001; r1AR-GZ=0.360, P=0.031; r1AC-BJ=0.885, P<0.001; r1AC-GZ=0.694, P<0.001). The BDI of "AR" and "AC" was highly consistent with the change of the PC in Beijing (r AR-Pollen=0.826, P<0.001; r AC-Pollen=0.564, P<0.001). The OV of AR in Beijing and Guangzhou decreased significantly in the first half of 2020, but there was no significant change in AC. In the first half of 2020, the OV of AC in Beijing was significantly higher than that of AR, while that of AC in Guangzhou was slightly higher than that of AR.

CONCLUSION

The BDI could reflect the real-world situation to some extent and has the potential to predict the epidemiological characteristics of AR and AC. The BDI and OV of AR decreased significantly, but those of AC were still at a high level, during the COVID-19 pandemic, in the environment where most people in Beijing and Guangzhou wore masks without eye protection.