Abstract
BACKGROUND: Influenza poses a significant threat to public health, potentially influenced by environmental factors. However, the role of meteorological factors (MFs) on influenza risks in China remains underexplored. This study explored the effect of MFs on laboratory-confirmed influenza (LCI) cases in Anhui, China. METHODS: We analysed daily meteorological and influenza data between January 2015 and March 2023, to determine the relationship between temperature, relative humidity, wind speed and LCI cases, using two-stage time series analysis. First, we used distributed lag nonlinear models (DLNMs) to construct cross-basis functions capturing the non-linear and lagged effects of MFs, which were then incorporated into a generalized additive quasi-Poisson regression model for each city. Second, we conducted a random-effects meta-analysis to combine city-specific estimates. We further performed sub-group analysis by age and gender and explored effect modifications by population density, median MFs levels, longitude, and latitude through meta-regression. RESULTS: A total of 43,872 LCI cases were recorded in Anhui. A slight, non-significant negative association between temperature and influenza cases was observed at a single-day lag (RR = 0.9778; 95% CI: 0.9468-1.0098), but a positive association was found over cumulative lags (RR = 1.0263; 95% CI: 0.9721-1.0836). Relative humidity showed a positive association with influenza on single-day lag (RR = 1.0056; 95% CI: 0.9899-1.0216), but a slight negative association over cumulative lags (RR = 0.9974; 95% CI: 0.9927-1.0022). Wind speed displayed a slight, non-significant positive association at both single-day (RR = 1.0105; 95% CI: 0.9965-1.0246) and over cumulative lags (RR = 1.0083; 95% CI: 0.9498-1.0704). Temperature negatively associated with LCI cases across all genders and ages, at p = 0.0001, marginally moderated by population density (p = 0.0506). CONCLUSIONS: In conclusion, while MFs showed non-significant associations with influenza in general population, sub-group analysis showed statistically significant temperature-LCI cases association. Population density marginally modified this association. Our findings enhance evidence-based knowledge for developing targeted interventions like early warning systems to reduce influenza risks.