Pollen exposure and mild allergy: A multi-city study in China

Area of Science:

• Environmental Health
• Public Health Surveillance
• Computational Epidemiology

Background:

• Pollen is a major allergen, but traditional monitoring methods struggle to capture immediate public health impacts.
• Mild allergy symptoms and limited real-time data hinder conventional pollen surveillance systems.

Purpose of the Study:

• To investigate the real-time association between pollen exposure and allergy-related social media activity in China.
• To assess the immediate health impact of pollen on various allergy symptoms using novel data sources.

Main Methods:

• Collected pollen data and daily allergy-related posts from Weibo across 25 Chinese cities (May 2022-Dec 2023).
• Utilized machine learning for classifying social media posts and generalized linear models for statistical analysis.
• Employed meta-regression to pool city-specific effect estimates.

Main Results:

• A 10 grains/10³ mm² increase in pollen concentration was linked to a 0.3% rise in allergy risk (RR: 1.003).
• Upper respiratory tract symptoms showed the strongest association with pollen exposure (RR: 1.010).
• Pollen's impact was more pronounced in northern cities, during autumn, and with shorter lag times.

Conclusions:

• Integrating social media and environmental data offers timely insights into pollen's short-term health effects.
• Findings underscore the need for tailored, region- and season-specific public health interventions for allergies.
• This approach enhances allergy surveillance by capturing real-time public sentiment and exposure data.