Analyzing and predicting patient admissions related to acute heat at the Chemnitz Hospital (Germany).

BACKGROUND: Climate change is leading to an increase in acute heatwaves, thereby raising risks to human health and healthcare systems. The aim of this study was to quantify the relationship between heat and hospital admissions, and to develop a predictive model for patient admissions. METHODS: A retrospective secondary data analysis was conducted for the period 2018-2023, using data from Chemnitz Hospital and the Regional Climate Information Service of TU Dresden. Hospital admissions with the following diagnoses were analyzed: E86 (Volume depletion), I20 (Angina pectoris), I21 (Acute myocardial infarction), I63 (Cerebral infarction), I64 (Stroke, unspecified as hemorrhagic or ischemic), J44 (COPD), L55 (Sunburn), T67 (Effects of heat and light). Meteorological variables considered included temperature, humidity, air pressure, and daily sunshine hours. Analyses included correlations between daily maximum temperatures (≥ 23°C and ≥ 30°C) and hospital admissions, as well as linear and non-linear regression analyses., both univariate (maximum temperature) and multivariate (humidity, air pressure, sunshine hours). RESULTS: A positive correlation between maximum temperature and hospital admissions was observed: r = 0.44 (p > 0.05) for ≥ 23°C and r = 0.88 (p < 0.01) for ≥ 30°C. Regression analyses suggested a non-linear relationship (U-shaped pattern with alternating curvature) between temperature and hospital admissions for days with a maximum temperature between 23°C and 29°C, and a linear effect for days above 30°C, additionally influenced by air pressure, humidity, and sunshine hours. CONCLUSION: The results indicate a complex association between heat and hospital admissions. Based on these findings, a predictive model was developed to forecast patient admissions. The study highlights the importance of primary preventive measures and the consideration of moderate heat stress in healthcare planning.