Using a Distributed Lag Non-Linear Model to Forecast the Impact of Temperature on Cardiovascular Admissions: Implications for Meteorology and Health Systems

This study explores the application of the Distributed Lag Non-Linear Model (DLNM) to predict cardiovascular hospital admissions in relation to daily average air temperature fluctuations.With increasing concerns about weather-driven health risks, our aim is to analyse the delayed and non-linear effects of temperature changes on hospital admissions over time.By utilizing advanced statistical modelling techniques and historical weather data, we uncover critical insights into the sensitivity of cardiovascular admissions to temperature variations, particularly highlighting peak impacts occurring two days post-exposure.This research not only contributes to the understanding of temperature-health relationships but also enhances the development of health surveillance systems.Additionally, the integration of visual aids facilitates a clearer comprehension of weatherinduced health risks, offering valuable implications for public health politics preparedness and the effective use of meteorological and satellite data in health forecasting.