The compound effect of acute respiratory infections and temperature variability on excess mortality
Numerous studies have thoroughly documented the individual impacts of non-optimal temperatures and acute respiratory infections (ARIs) on increasing mortality rates. However, there’s a crucial gap in understanding how these factors work together to affect excess mortality, particularly during the cold season. Additionally, the influence of these factors on population susceptibility to heat waves in the summer remains unclear.
In this study we are going to conduct an analysis for the Czech Republic utilizing the data set available for a period spanning 38 years (1982–2019). The data includes: a) daily all-cause mortality counts, b) daily acute respiratory infections (ARIs) incidence proxies, interpolated from weekly healthcare surveillance data, focusing on three dominant influenza viruses (A/H3N2, A/H1N1, and B), and c) a suite of weather variables, sourced from E-OBS gridded data, including daily mean, maximum, and minimum temperatures, daily precipitation, daily mean sea level pressure, daily mean wind speed, daily mean relative humidity, and radiation level.
To investigate the complex associations between mortality rates, ARI incidence, and weather variability, we employ a quasi-Poisson regression model with a multiple distributed lag non-linear model (DLNMs) framework. Notably, the DLNMs framework is adept at adjusting for confounding meteorological variables, thereby enhancing our understanding of their role as fluctuating confounders in the overall mortality context.
From these refined models, we aim to extract the fraction of mortality attributable to ARIs and low temperatures. This will enable a precise quantification of their collective impact on excess mortality during the colder seasons. Furthermore, our study extends to analyze the shifts in seasonal mortality patterns in relation to meteorological and epidemiological characteristics. We also assess the temporal associations between air temperature and mortality in the summer, taking into account factors such as the intensity of ARI outbreaks and the mean winter temperature of the preceding cold season.
The results of this study are expected to contribute significantly to the scientific understanding of the associations between temperature variability, respiratory infection dynamics, and seasonal mortality variations. This knowledge will also be crucial for those involved in public health strategy and policy development, as it will assist in creating more effective approaches to address the health risks associated with seasonal temperature fluctuations and respiratory infections.
Stay healthy! :)
