Interventional applications of a Stroke Heat Risk Prediction Model produce health benefits

Although heat exposure increases stroke risk, targeted individualized interventions remain limited. This study develops and validates a Stroke Heat Risk Grading Prediction Model for precision intervention using 28,116 stroke deaths from 304 Chinese counties. Meteorological and stroke mortality data from 2013–2018 are analyzed with time-series methods, revealing a nonlinear temperature-mortality relationship. Four risk levels are established and validated using 2019–2022 data through case-crossover and time-series analyses considering sex, age, and geography. At the highest risk level of our model, stroke mortality increases by 13.8% in the general population and 16.4% in older adults, whereas the China Meteorological Administration warning system poorly predicts stroke mortality. Interventions guided by our model achieve nearly a two-fold increase in the proportion of avoidable heat-attributable excess deaths compared to existing approaches. These findings support this model as a digital tool to mitigate heat-related stroke risk under climate change. Although heat is a major stroke risk factor, targeted intervention tools are lacking, so we developed and evaluated a Stroke Heat Risk Model for personalized prevention. Here, the authors show that it yields three times the preventable heat-attributable stroke deaths of current heat warning systems.