Relationship between RSV-hospitalized children and meteorological factors: a time series analysis from 2017 to 2023.

Abstract

Objectives: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for lower respiratory tract infections amongst infants under 1 year, posing a significant global health challenge. The incidence of RSV exhibits marked seasonality and is influenced by various meteorological factors, which vary across regions and climates. This study aimed to analyze seasonal trends in RSV-related hospitalization in Tianjin, a region with a semi-arid and semi-humid monsoon climate, and to explore the relationship between these trends and meteorological factors. This research intends to inform RSV prevention strategies, optimize public health policies and medical resource allocation while also promoting vaccine and therapeutic drug development.

Methods: This study analyzed data from a cohort of 6222 children hospitalized with RSV-related infections. Meteorological data were collected from the Tianjin Binhai International Airport meteorological station, encompassing temperature (℃), air pressure (mmHg), wind speed (m/s), humidity (%), and precipitation (mm). We employed seasonal ARIMA and GAM models to investigate the association between meteorological factors and RSV-related hospitalizations.

Results: The SARIMA (1,0,0) (0,1,2)12 model effectively predicted RSV-related hospital admissions. Spearman correlation and GAM analysis revealed a significant negative association between the monthly average temperature and RSV hospitalizations.

Conclusions: Our findings indicated that meteorological factors influence RSV infection-related hospital admissions, with higher monthly average temperatures associated with fewer hospitalizations. The predictive capabilities of the SARIMA model bolster the formulation of targeted RSV prevention strategies, enhancing public health policy and medical resource allocation. Furthermore, continued research into vaccines and therapeutic drugs remains indispensable for augmenting public health outcomes.