Objective To explore a suitable heat wave definition for assessing heatstroke risk in Chongqing, China, and to identify vulnerable populations.

Methods Data on reported heatstroke cases and meteorological variables in Chongqing from May to September between 2014 and 2019 were collected.Segmented regression models, observed/expected analysis, and distributed lag non-linear models were applied to determine the temperature thresholds for heat waves leading to heatstroke.Heat waves were defined based on threshold and duration, and the lagged effects and non-linear characteristics under different definitions were analyzed.The most suitable heatwave definition was selected by comparing different definitions, and vulnerable populations were identified accordingly.

Results Daily maximum temperature was non-linearly and positively associated with daily heatstroke cases.When the daily maximum temperature exceeded 34.5℃, heatstroke cases increased significantly.All heat waves with different definitions significantly elevated heatstroke risk, with the strongest effect observed on the day of heat wave occurrence, lasting for 1 day.A heat wave defined by a temperature exceeding 34.5℃ for ≥2 consecutive days had the lowest Q-AIC value of 347.15, representing the optimal definition.Under this heat wave definition, the cumulative relative risk value for lag01 days was 6.74(95% confidence interval: 5.18-8.77) for the total population.All heatstroke risk estimated were statistically significant across sex-and age-specific subgroups.However, no significant differences in heatstroke risk were found by sex or age.

Conclusion Defining heat waves using calculated temperature thresholds is suitable for heatstroke risk identification.Under heat wave exposure, the risk of heatstroke is present across various populations.