Epidemiological characteristics of RSV in pediatric inpatients with lower respiratory tract infections in Suzhou and their correlation with meteorology and atmospheric pollutants

Abstract

Background

Lower respiratory infections are the leading cause of illness and death in children under 5, primarily due to respiratory syncytial virus(RSV). Climate and pollution influence disease and pathogen prevalence. This study investigates the correlation between meteorological factors, atmospheric pollutants, and RSV infections in children, aiming to implement effective clinical measures and reduce RSV risk in children by enhancing the environment.

Methods

This study included patients with lower respiratory tract infections who were hospitalized in the Department of Respiratory Medicine at Children’s Hospital of Soochow University from January 2006 to December 2019 as the research subjects. This study analyzed detection rates across different ages, genders, and seasons, while also examining the relationship of RSV infection between meteorological factors and atmospheric pollutants. RSV was detected using direct immunofluorescence, and an LS-SVM prediction model with lag nonlinear curves was established in conjunction with meteorological data. In this model, monthly average temperature, atmospheric pollutant levels, and average monthly wind speed were used as predictive variables for construction and prediction. A distributed lag nonlinear model (DLNM) was developed, which included the creation of a lag nonlinear curve by integrating meteorological data.

Results

A total of 19,637 pediatric cases of lower respiratory tract infections were included in this study. The detection rate of RSV over 14 years averaged 14.9% (2934/19637). The male-to-female ratios for positive detection was 1.2:1. The primary detection season for RSV is winter, with a detection rate of 33.7%. The prevalence of RSV was correlated with climatic factors and atmospheric pollution. Utilizing the monthly average temperature, monthly average wind speed, and levels of atmospheric pollutants as the predictive factors in LS-SVM for model construction and prediction, a DLNM identified that the relative risk (RR) of RSV infection fluctuated with changes in the temperature and wind speed.

Conclusion

RSV has the highest detection rate in infants and is often detected during winter.The influence of meteorological factors and atmospheric pollutants on RSV infection rates cannot be overlooked, with observation of a lag effect.