Speaker
Description
Climate change is increasingly recognized as a critical driver of public health risks, particularly through its influence on waterborne diseases. Diarrheal illnesses, a significant cause of morbidity and mortality in low-resource settings, are especially sensitive to climatic factors such as extreme precipitation and temperature fluctuations. In response to the need for robust, standardized tools to monitor and quantify these impacts, a new statistical framework has been developed to estimate the burden of climate-attributable diarrheal disease. This framework introduces two core indicators: the incidence of diarrhea attributable to extreme precipitation and to extreme temperature. It employs a spatiotemporal Bayesian hierarchical model integrated with Distributed Lag Nonlinear Models (DLNM) to assess the exposure-lag-response relationship. The approach captures both immediate and delayed effects of climate hazards on disease incidence while accounting for seasonality, spatial heterogeneity, and unmeasured confounders. The methodology leverages routinely collected health surveillance data, high-resolution meteorological datasets such as ERA5, and census-based population statistics. It supports disaggregation by age, sex, region, and socioeconomic status, enabling targeted insights into population vulnerability. Outputs include estimates of relative risk as well as the attributable number and rate of diarrhea cases per 100,000 population. This work contributes a scalable and replicable approach to climate-health monitoring, providing decision-makers with evidence to design adaptive interventions and improve resilience to climate-sensitive diseases. It aligns with international public health goals and advances the development of actionable indicators for global climate-health surveillance.
