Frontiers in cellular and infection microbiology · 2026
Analysis of pathogenic characteristics of acute respiratory tract infections in children and the influence of environmental factors in Shijiazhuang area.
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Liu Shangze, Han Guangyue, Jiang Caixiao, Geng Minghao, Wu Wentao, Guo Nana, Han Xu, Li Yan, Li Qi
Аннотация
OBJECTIVE: To investigate the epidemiological patterns of acute respiratory infections (ARI) in children (0-14 years) in Shijiazhuang and the impact of meteorological factors.
METHODS: Fourteen respiratory pathogens were detected by fluorescent PCR melting curve analysis. LASSO regression screened environmental factors; generalized additive model (GAM) analyzed nonlinear associations between meteorological conditions and ARI incidence; distributed lag nonlinear model (DLNM) explored lagged/cumulative effects of meteorological factors and pollutants. Pathogen distribution differences were tested by χ² or Fisher's exact test.
RESULTS: A total of 1,961 ARI cases were included, with an overall pathogen positivity rate of 78.23%. Positivity rates differed significantly by age, season, and case classification (all P < 0.05), being highest in 3-6-year-olds, summer, and outpatient/emergency cases. Single infections (59.19%) were dominant; SP and Hi were prone to mixed infections, EV to quadruple+ infections. Pathogen spectrum varied by age and season. Time-series analysis showed seasonal ARI peaks in winter. DLNM revealed nonlinear associations between all environmental factors and ARI: O3 (33.5 μg/m³, RR = 4.3051) and SO2 (39.4 μg/m³) had strong effects; high CO (49.40 mg/m³), high temperature (31.1 °C), and low-moderate humidity (27%-42.5%) increased risk; high NO2 had moderate lagged risk. GAM confirmed nonlinear associations, with highest ARI risk at 3.63 °C (RR = 1.89, 95% CI:1.74-2.05).
CONCLUSION: High pathogen detection rate was observed in 2024-2025. ARI had distinct age/season patterns, with winter peaks. Low/high temperature, low humidity, and high SO2 were key risk factors with immediate + cumulative effects, providing data for prevention and early warning.
METHODS: Fourteen respiratory pathogens were detected by fluorescent PCR melting curve analysis. LASSO regression screened environmental factors; generalized additive model (GAM) analyzed nonlinear associations between meteorological conditions and ARI incidence; distributed lag nonlinear model (DLNM) explored lagged/cumulative effects of meteorological factors and pollutants. Pathogen distribution differences were tested by χ² or Fisher's exact test.
RESULTS: A total of 1,961 ARI cases were included, with an overall pathogen positivity rate of 78.23%. Positivity rates differed significantly by age, season, and case classification (all P < 0.05), being highest in 3-6-year-olds, summer, and outpatient/emergency cases. Single infections (59.19%) were dominant; SP and Hi were prone to mixed infections, EV to quadruple+ infections. Pathogen spectrum varied by age and season. Time-series analysis showed seasonal ARI peaks in winter. DLNM revealed nonlinear associations between all environmental factors and ARI: O3 (33.5 μg/m³, RR = 4.3051) and SO2 (39.4 μg/m³) had strong effects; high CO (49.40 mg/m³), high temperature (31.1 °C), and low-moderate humidity (27%-42.5%) increased risk; high NO2 had moderate lagged risk. GAM confirmed nonlinear associations, with highest ARI risk at 3.63 °C (RR = 1.89, 95% CI:1.74-2.05).
CONCLUSION: High pathogen detection rate was observed in 2024-2025. ARI had distinct age/season patterns, with winter peaks. Low/high temperature, low humidity, and high SO2 were key risk factors with immediate + cumulative effects, providing data for prevention and early warning.