Abstract / Summary
Acute respiratory infections represent a major global public health challenge, and their pathogen epidemiological characteristics exhibit substantial spatial and temporal heterogeneity. However, local multi-pathogen surveillance data from inland cities in Central China remain scarce. This study aimed to analyze the overall detection rate, demographic distribution characteristics, and seasonal epidemic patterns of respiratory pathogens among patients with acute respiratory infections in Kaifeng, and to preliminarily explore associations between pathogens and environmental factors, thereby providing data support for routine prevention and control of respiratory infectious diseases in this region. This single-center retrospective cross-sectional study enrolled 1,716 patients with acute respiratory infections admitted to a sentinel hospital in Kaifeng between 2023 and 2025. Seventeen common respiratory pathogens were detected using real-time fluorescence quantitative PCR. The distribution characteristics of the pathogen spectrum across sex, age groups, regions, and seasons were analyzed. Spearman correlation analysis was used to preliminarily explore associations between pathogen detection rates and temperature, precipitation, and PM2.5 concentration. The overall pathogen detection rate was 55.77% (957/1,716), with viral pathogens showing the highest detection rate (40.44%), followed by bacterial pathogens (28.03%). Single-pathogen infections predominated (34.15%), while mixed infections accounted for 21.51%. Detection rates differed significantly across age groups and regions: the pediatric group showed the highest rate (69.81%), while the older adult group (≥ 66 years) showed the lowest (35.68%); urban detection rates (62.10%) were significantly higher than rural rates (20.28%) (χ²=132.522, P < 0.005). Male and female detection rates were 55.78% (487/873) and 55.75% (470/843), respectively, with no statistically significant difference (χ²=0.000, P > 0.05). Seasonal analysis showed that influenza A virus detection peaked in winter (20.40%) and rhinovirus detection peaked in autumn (15.63%), with all major pathogens displaying significant seasonal patterns. Exploratory Spearman correlation analysis suggested a moderate negative correlation trend between influenza B virus detection and precipitation (rs = − 0.591, P < 0.05) and a moderate positive correlation trend between Bordetella pertussis detection and PM2.5 concentration (rs = 0.597, P < 0.05); however, after Bonferroni correction for multiple comparisons (α′ = 0.05/45 = 0.0011), neither association reached statistical significance. The pathogen spectrum of acute respiratory infections in Kaifeng is complex, with substantial heterogeneity in population distribution and seasonal characteristics. In this retrospective single-center study, most respiratory pathogens (13 of 15) showed no statistically significant correlations with temperature, precipitation, or PM2.5 after Bonferroni correction. Only influenza B virus and B. pertussis exhibited marginal associations with precipitation and PM2.5, respectively, which likely represent chance findings given the number of comparisons. This study establishes the current epidemiological baseline of local respiratory pathogens, providing reliable evidence for targeted, population-specific, and seasonally adapted prevention and control measures.