Impact of extreme temperature on hospitalization for cardiovascular diseases in Lanzhou, China

The purpose of this study was to examine the relationship between extreme temperature and the risk of hospitalization for cardiovascular diseases (CVDs). A distributed lag nonlinear model (DLNM) in combination with a quasi-Poisson regression model was employed to assess the relationship between extreme temperature and risk of hospitalization for CVDs. This approach can be utilized to deal with lag and nonlinear effects. By comprehensively leveraging data information, it can explain the influencing factors from multiple aspects. Due to the complexity of the model, parameter estimation and model fitting typically require significant computational resources and extended processing time. Additionally, we identified the sensitive populations through subgroup analyses based on age and sex. Extremely low temperature (≤-10℃) (Relative Risk (RR) = 1.156, with a 95% confidence interval (CI) of 1.095–1.221), moderately low temperature (> 10℃, ≤-2℃) (RR = 1.132 95% CI: 1.091–1.174), moderately high temperature (≥ 20℃, < 28℃) (RR = 1.061 95% CI 1.039–1.084) and extremely high temperature (≥ 28℃) (RR = 1.124 95% CI: 1.080–1.169) were all associated with the increased risk of hospitalization for CVDs in the total population analysis, and low temperatures have a stronger effect than high temperatures. In the subgroup analysis, extremely low temperatures appeared to have a greater impact on females (RR = 1.280 95% CI:1.171-1.400) and < 65 age group (RR = 1.238 95% CI:1.147–1.339). Under extremely high temperature conditions, those more affected were males (RR = 1.130 95% CI: 1.073–1.190) and < 65 age group (RR = 1.222 95% CI: 1.163–1.285). Low and high temperatures lead to an increased risk of hospitalization for CVDs with a lagged effect. Subgroup analyses indicated that females and < 65 age group were more sensitive to low temperatures, whereas males and < 65 age group were more sensitive to high temperatures.