Impacts of Heat Stress on Infectious Disease – Insights From the Study of Climate and Diarrheal Pathogens

Abstract:

Extreme heat events stress physiology, influencing disease transmission and leading to adverse impacts. Expanding understanding of risks associated with heat extremes is essential for effective mitigation strategies and requires collaboration across multidisciplinary fields. This is particularly evident when considering relationships between heat stresses, vulnerable populations and infectious disease. Research is needed to characterize associations between temperature extremes and other meteorological exposures and individual pathogens. Enteric pathogens are some of the most climate-sensitive infectious microorganisms and cause diarrheal disease, a major contributor to childhood mortality and morbidity. Recently, improved diagnostic methods, and several ambitious multi-site, population-based studies are shedding light on pathogen-specific etiologies of diarrheal disease. Earth Observation (EO) climate data derived from satellites and model-based reanalysis are increasing in availability and accuracy and show the potential to address these knowledge gaps. Under a NASA-funded collaboration, data from multiple multi-site studies have been compiled into a dataset consisting of results from over 70,000 stool samples from 28,000 infants in >100 different locations around the world, which were tested using quantitative polymerase chain reaction (qPCR) for infection status with 10 high-burden enteric pathogens – 5 viruses, 3 bacteria and 2 protozoa. Samples were matched by date and location to EO-derived estimates of hydrometeorological parameters extracted from the GLDAS global model. By fitting generalized linear models to model associations with specific enteric pathogens adjusting for confounders we found complex, non-linear, species- and syndrome-specific effects of linearly increasing temperature on pathogen transmission risk. However, the extent to which this might be explained by host susceptibility at the upper extreme is unclear. Meanwhile, causal inference methods such as case-crossover or interrupted time series (ITS) analyses have been used to quantify effects of extreme rainfall events on infectious disease outcomes and have the potential to be extended to heatwaves.