Abstract:Based on the outpatient and inpatient data of heatstroke in Tianjin from 2016 to 2020, a comparative analysis of the relationship between meteorological factors and the heatstroke visit rate was conducted using the Generalized Additive Model (GAM) and Distributed Lag Nonlinear Model (DLNM). By introducing the human heat balance model and predicted mean vote (PMV), a localized heatstroke meteorological risk warning index was established. The results show that the number of outpatient and inpatient cases of heatstroke in Tianjin is concentrated from late June to early August each year, with 84% of the peak heatstroke days occurring in 6 continuous weather process over the 5 years. The occurrence is most correlated with the meteorological conditions of the current day and the previous day, with a significant increase in heatstroke cases when the maximum temperature exceeds 35°C. Males are more susceptible to heatstroke than females, and the visit rate of the elderly is significantly higher than the general population. The heatstroke visit rate is positively correlated with average temperature, maximum temperature, relative humidity, and solar radiation intensity, with the strongest correlation with average temperature and a negative correlation with wind speed. Introducing the human heat balance model, PMV shows a higher correlation with the heatstroke visit rate than any single meteorological factor, indicating PMV has a clear advantage in evaluating heatstroke meteorological risk. A forecasting equation with PMV as the key indicator was developed, which demonstrated significant advantages in predicting heatstroke high-incidence events.