Xia Xiaoling, Du Zhengjing, Liu Tao, Xu Ke, Yuan Chen
Guizhou New Meteorological Technology Co., Ltd, Guiyang, 550002, Guizhou, China.
Guizhou Meteorological Service Center, Guiyang, 550002, Guizhou, China.
Sci Rep. 2025 Jul 2;15(1):22676. doi: 10.1038/s41598-025-03611-6.
Cardiovascular and cerebrovascular diseases are critical public health challenges influenced by environmental and meteorological factors. Understanding the association between these factors and disease incidence can provide valuable insights for disease prevention and control.This study analyzed data from Anshun City, western Guizhou, collected between January 2018 and December 2022. A Distributed Lag Non-linear Model (DLNM) was employed to evaluate the lagged and non-linear effects of meteorological variables (e.g., temperature, precipitation, wind speed) and air pollutants (e.g., PM2.5, SO2) on the incidence of cardiovascular and cerebrovascular diseases. Covariates such as seasonality and time trends were included to adjust for confounding effects.The results revealed significant associations between meteorological factors, air pollution, and disease incidence. Increased precipitation and SO2 concentrations significantly elevated the risk of cardiovascular and cerebrovascular diseases, particularly at a lag of 25-30 days (e.g., RR for SO2 = 1.19, 95% CI: 1.10-1.28). Conversely, higher average and maximum wind speeds demonstrated a protective effect (e.g., RR for maximum wind speed = 0.70, 95% CI: 0.62-0.78). Seasonal patterns and temperature variations further influenced disease incidence.These findings highlight the complex interactions between meteorological factors and air pollution in influencing cardiovascular and cerebrovascular disease risk. The study provides evidence for targeted public health interventions and emphasizes the importance of incorporating meteorological and environmental data into disease prevention strategies.
心血管疾病是受环境和气象因素影响的重大公共卫生挑战。了解这些因素与疾病发病率之间的关联可为疾病预防和控制提供有价值的见解。本研究分析了2018年1月至2022年12月期间在贵州西部安顺市收集的数据。采用分布滞后非线性模型(DLNM)来评估气象变量(如温度、降水、风速)和空气污染物(如PM2.5、SO2)对心血管疾病发病率的滞后和非线性影响。纳入了季节性和时间趋势等协变量以调整混杂效应。结果显示气象因素、空气污染与疾病发病率之间存在显著关联。降水量增加和SO2浓度显著提高了心血管疾病的发病风险,尤其是在滞后25 - 30天的时候(例如,SO2的相对风险 = 1.19,95%置信区间:1.10 - 1.28)。相反,平均风速和最大风速较高则显示出保护作用(例如,最大风速的相对风险 = 0.70,95%置信区间:0.62 - 0.78)。季节模式和温度变化进一步影响疾病发病率。这些发现凸显了气象因素和空气污染在影响心血管疾病风险方面的复杂相互作用。该研究为有针对性的公共卫生干预提供了证据,并强调将气象和环境数据纳入疾病预防策略的重要性。