Air quality, meteorological variability and pediatric respiratory syncytial virus infections in Singapore

Abstract

Respiratory syncytial virus (RSV) is an important cause of respiratory illness among children. While studies have focused on the air-quality and climate dependence of RSV infections, few have been undertaken in South-East Asia where the burden of respiratory illness is among the highest across the globe. This study aimed to determine the relationships between climatic factors and air quality with RSV infections among children in Singapore. We obtained all laboratory-confirmed reports of RSV infections in children below 5 years old from the largest public hospital specializing in pediatric healthcare in Singapore. We assessed the independent cumulative effects of air quality and meteorological factors on RSV infection risk using the Distributed Lag Non-Linear Model (DLNM) framework in negative binomial models adjusted for long-term trend, seasonality and changes in the diagnostic systems. We included 15,715 laboratory-confirmed RSV reports from 2009 to 2019. Daily maximum temperature exhibited a complex, non-linear association with RSV infections. Absolute humidity (Relative Risk, 90th percentile [RR_{90th percentile}]: 1.170, 95% CI: [1.102, 1.242]) was positively associated with RSV risk. Higher levels of particulate matter of aerodynamic diameter of less than (i) 2.5 µm (PM_2.5), (ii) 10 µm (PM₁₀), carbon monoxide (CO) and sulfur dioxide (SO₂) were associated with lower RSV infection risk. RSV infections exhibited both annual and within-year seasonality. Our findings suggest that falls in ambient temperature and rises in absolute humidity exacerbated pediatric RSV infection risk while increases in air pollutant concentrations were associated with lowered infection risk. These meteorological factors, together with the predictable seasonality of RSV infections, can inform the timing of mitigation measures aimed at reducing transmission.

Figure 1

Dily measures of RSV incidence, meteorological factors and air quality from 2009 to 2019.

Figure 1

Dily measures of RSV incidence, meteorological factors and air quality from 2009 to 2019.

Figure 2

Time series of reported and predicted number of RSV infections, 2009–2019. The hollow circles represent the reported daily counts of RSV infections and the blue solid line represents the daily number of RSV infections predicted from the final PM_2.5 model.

Figure 3

The cumulative effect of (a) maximum temperature (b) absolute humidity (c) PM_2.5 (d) SO₂ (e) PM₁₀ and (f) CO on RSV infections over 8 days. (a–d) are from the final PM2.5 model, while (e) and (f) are from the PM₁₀ model and CO model, respectively. Solid lines represent relative risk (RR), grey shaded areas represent 95% confidence intervals (CIs).