Impact of multiple environmental factors on influenza–like illness in Fujian Province, China, 2015–2023: a multicity study - Yao, Xiaoyan; Zhang, Yanhua; Hu, Yuemei; Cui, Enjun; Lin, Fen; Chen, Guangmin; Xie, Jianfeng; Weng, Yuwei; Zheng, Kuicheng (Frontiers in Public Health, 2025)
IntroductionInfluenza-like illness (ILI) represents a significant global public health challenge influenced by environmental factors. While previous studies have demonstrated associations, most have been limited to single-city analyses with inconsistent findings. This multi-city study systematically examines the effects of meteorological and air pollution factors on ILI across diverse urban environments.Study designWe analyzed daily ILI surveillance data (2015–2023) from 18 sentinel hospitals across nine Fujian Province cities, combined with concurrent air quality and meteorological data. Using LASSO regression for variable selection, we employed distributed lag non-linear models (DLNMs) to characterize exposure-response relationships in each city, followed by random-effects multivariate meta-analysis to pool estimates and assess heterogeneity.ResultsA total of 2,995,909 ILI cases were collected. ILI cases in nine cities of Fujian Province exhibited significant seasonal fluctuations, peaking in winter or early summer. LASSO regression identified temperature, NO₂, and SO₂ as key environmental factors. Our results indicated that the combined cumulative effect of average temperature on ILI across nine cities decreased with rising temperatures, with a risk peak at −0.1°C. The NO₂–ILI association was non–linear, resembling an inverted “U” shape, with a risk peak at 40.5 μg/m3. SO₂ exposure had a large degree of heterogeneity in its effect on ILI.ConclusionThis study provides robust evidence that ambient temperature and NO₂ levels significantly influence ILI risk in Fujian Province, with distinct exposure-response patterns. Public health strategies should prioritize cold-weather preparedness and air quality management, particularly during high-risk seasons. Our two-stage analytical approach addresses previous limitations in multi-city environmental health studies.