Climate variability and seasonal patterns of paediatric parainfluenza infections in the tropics: An ecological study in Singapore

Global epidemiology, seasonality and climatic drivers of the four human parainfluenza virus types

OBJECTIVES

Human parainfluenza viruses (hPIV) are a common cause of acute respiratory infections, especially in children under five years and the elderly. hPIV can be subclassified as types 1-4: these showed various seasonality patterns worldwide, and it is unclear how climatic factors might consistently explain their global epidemiology.

METHODS

This study collected time-series incidence data from the literature and hPIV surveillance programs worldwide (47 locations). Wavelet analysis and circular statistics were used to detect the seasonality and the months of peak incidence for each hPIV type. Relationships between climatic drivers and incidence peaks were assessed using a generalized estimating equation.

RESULTS

The average positive rate of hPIV among patients with respiratory symptoms was 5.6% and ranged between 0.69-3.48% for different types. In the northern temperate region, the median peak incidence months for hPIV1, hPIV2, and hPIV4 were from September to October, while for hPIV3, it was in late May. Seasonal peaks of hPIV3 were associated with higher monthly temperatures and lower diurnal temperatures range throughout the year; hPIV4 peaks appeared to correlate with lower monthly temperatures and higher precipitation throughout the year. Different hPIV types exhibit different patterns of global epidemiology and transmission.

CONCLUSIONS

Climate drivers may play a role in hPIV transmission. More comprehensive and coherent surveillance of hPIV types would enable more in-depth analyses and inform the timing of preventive measures.

Seasonal Patterns of Common Respiratory Viral Infections in Immunocompetent and Immunosuppressed Patients

INTRODUCTION

Several respiratory viruses have been shown to have seasonal patterns. The aim of our study was to evaluate and compare these patterns in immunocompetent and immunosuppressed patients for five different respiratory viruses.

METHODS

We performed a retrospective analysis of results for 13,591 respiratory tract samples for human metapneumovirus (HMPV), influenza virus, parainfluenza virus (PIV) and respiratory syncytial virus (RSV) in immunocompetent and immunosuppressed patients. A seasonal pattern was aligned to the data of immunocompetent patients through a logistic regression model of positive and negative test results.

RESULTS

A narrow seasonal pattern (January to March) was documented for HMPV. Most RSV infections were detected in the winter and early spring months, from December to March, but occasional cases of RSV could be found throughout the year. The peak season for PIV-3 was during the summer months, and that for PIV-4 was mostly in autumn. A narrow seasonal pattern emerged for influenza virus as most infections were detected in the winter, in January and February. The seasonal patterns of HMPV, RSV, PIV, and influenza virus were similar for both immunocompetent and immunocompromised patients.

CONCLUSIONS

We found no difference in the seasonality of HMPV, RSV, PIV, and influenza virus infections between immunosuppressed and immunocompetent hosts.

Climate change and population health in Singapore: a systematic review

Gaseous emissions have contributed to global warming, an increase in the frequency of extreme weather events and poorer air quality. The associated health impacts have been well reported in temperate regions. In Singapore, key climate change adaptation measures and activities include coastal and flood protection, and mitigating heat impacts. We systematically reviewed studies examining climate variability and air quality with population health in Singapore, a tropical city-state in South-East Asia (SEA), with the aim to identify evidence gaps for policymakers. We included 14 studies with respiratory illnesses, cardiovascular outcomes, foodborne disease and dengue. Absolute humidity (3 studies) and rainfall (2 studies) were positively associated with adverse health. Extreme heat (2 studies) was inversely associated with adverse health. The effects of mean ambient temperature and relative humidity on adverse health were inconsistent. Nitrogen dioxide and ozone were positively associated with adverse health. Climate variability and air quality may have disease-specific, differing directions of effect in Singapore. Additional high quality studies are required to strengthen the evidence for policymaking. Research on effective climate action advocacy and adaptation measures for community activities should be strengthened.

Funding

There was no funding source for this study.

Epidemiological Characteristics of Parainfluenza Virus Type 3 and the Effects of Meteorological Factors in Hospitalized Children With Lower Respiratory Tract Infection

Objective

To investigate the relationship between meteorological factors and Human parainfluenza virus type 3 (HPIV-3) infection among hospitalized children.

Methods

All hospitalized children with acute lower respiratory tract infections were tested for viral pathogens and enrolled, at the second affiliated hospital of Wenzhou medical university, between 2008 and 2017. Meteorological data were directly obtained from Wenzhou Meteorology Bureau's nine weather stations and expressed as the mean exposure for each 10-day segment (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed). The correlation between meteorological factors and the incidence of HPIV-3 was analyzed, with an autoregressive integrated moving average model (ARIMA), generalized additive model (GAM), and least absolute shrinkage and selection operator (LASSO).

Results

A total of 89,898 respiratory specimens were tested with rapid antigen tests, and HPIV-3 was detected in 3,619 children. HPIV-3 was detected year-round, but peak activities occurred most frequently from March to August. The GAM and LASSO-based model had revealed that HPIV-3 activity correlated positively with temperature and rainfall day, but negatively with wind speed. The ARIMA (1,0,0)(0,1,1) model well-matched the observed data, with a steady R2 reaching 0.708 (Ljung-Box Q = 21.178, P = 0.172).

Conclusion

Our study suggests that temperature, rainfall days, and wind speed have significant impacts on the activity of HPIV-3. GAM, ARIMA, and LASSO-based models can well predict the seasonality of HPIV-3 infection among hospitalized children. Further understanding of its mechanism would help facilitate the monitoring and early warning of HPIV-3 infection.