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Climate's eye: A 20-year study on climate and pollution impacts on fungal keratitis incidence
Zijun Zhang1, Jing Shang2, Feng Guo1
1Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing 100005, China.
Background:
Fungal keratitis (FK) is a vision-threatening corneal infection whose seasonal and long-term dynamics may be shaped by climate change and air pollution, yet exact environmental impact still unclear. This study the effects of meteorological and air pollution variables on FK incidence in Beijing, China, from 2000 to 2019 and developed an environmental risk index for prospective guidance.
Methods:
A retrospective hospital-based time-series analysis of microbiologically confirmed FK cases at a tertiary eye center was conducted. Monthly meteorological variables (air temperature, precipitation, relative humidity, wind speed, ultraviolet irradiance) and air pollutants (SO₂, NO, NO₂, NOx, CO, O₃, PM₂.₅; 2006-2019) were compiled. Annual FK counts were forecast using a generalized additive model (GAM). Spearman correlation and Distributed Lag Non-linear Model (DLNM) were employed to analyze associations and lagged effects of environmental variables on FK. Fungal Active Index (FAI) was developed using the Random Forest model to predict infection risks.
Results:
FK incidence increased over time, with GAM forecasting continued growth to ∼301 cases annually by 2029. DLNM identified significant non-linear, delayed effects of multiple meteorological factors and pollutants. High temperature increased FK risk (cumulative RR 1.79, 75th vs. median over lag 0-6 months), while precipitation and wind speed showed complex risk patterns across lags. Men, young adults, and middle-aged individuals were identified as the most vulnerable groups. The FAI demonstrated a moderate correlation with monthly fungal infection cases (ρ=0.867, P < 0.001).
Conclusions:
Climate and air pollution have non-linear, delayed effects on FK, varying by pathogen type and patient demographics. The FAI serves as a predictive tool for real-time guidance on fungal infection risks.
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