Wu Di, Shi Yu, Wang ChenChen, Li Cheng, Lu Yaoqin, Wang Chunfang, Zhu Weidong, Sun Tingting, Han Junjie, Zheng Yanling, Zhang Liping
School of Public Health, Xinjiang Medical University, Urumqi, China.
Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, China.
Arch Public Health. 2024 Aug 19;82(1):128. doi: 10.1186/s13690-024-01361-x.
The impact of weather on human health has been proven, but the impact of extreme weather events on cardiometabolic multimorbidity (CMM) needs to be urgently explored.
Investigating the impact of extreme temperature, relative humidity (RH), and laboratory testing parameters at admission on adverse events in CMM hospitalizations.
Time-stratified case-crossover design.
A distributional lag nonlinear model with a time-stratified case-crossover design was used to explore the nonlinear lagged association between environmental factors and CMM. Subsequently, unbalanced data were processed by 1:2 propensity score matching (PSM) and conditional logistic regression was employed to analyze the association between laboratory indicators and unplanned readmissions for CMM. Finally, the previously identified environmental factors and relevant laboratory indicators were incorporated into different machine learning models to predict the risk of unplanned readmission for CMM.
There are nonlinear associations and hysteresis effects between temperature, RH and hospital admissions for a variety of CMM. In addition, the risk of admission is higher under low temperature and high RH conditions with the addition of particulate matter (PM, PM and PM) and O_8h. The risk is greater for females and adults aged 65 and older. Compared with first quartile (Q1), the fourth quartile (Q4) had a higher association between serum calcium (HR = 1.3632, 95% CI: 1.0732 ~ 1.7334), serum creatinine (HR = 1.7987, 95% CI: 1.3528 ~ 2.3958), fasting plasma glucose (HR = 1.2579, 95% CI: 1.0839 ~ 1.4770), aspartate aminotransferase/ alanine aminotransferase ratio (HR = 2.3131, 95% CI: 1.9844 ~ 2.6418), alanine aminotransferase (HR = 1.7687, 95% CI: 1.2388 ~ 2.2986), and gamma-glutamyltransferase (HR = 1.4951, 95% CI: 1.2551 ~ 1.7351) were independently and positively associated with unplanned readmission for CMM. However, serum total bilirubin and High-Density Lipoprotein (HDL) showed negative correlations. After incorporating environmental factors and their lagged terms, eXtreme Gradient Boosting (XGBoost) demonstrated a more prominent predictive performance for unplanned readmission of CMM patients, with an average area under the receiver operating characteristic curve (AUC) of 0.767 (95% CI:0.7486 ~ 0.7854).
Extreme cold or wet weather is linked to worsened adverse health effects in female patients with CMM and in individuals aged 65 years and older. Moreover, meteorologic factors and environmental pollutants may elevate the likelihood of unplanned readmissions for CMM.
天气对人类健康的影响已得到证实，但极端天气事件对心脏代谢多重疾病（CMM）的影响亟待探索。
研究极端温度、相对湿度（RH）及入院时的实验室检测参数对CMM住院患者不良事件的影响。
时间分层病例交叉设计。
采用具有时间分层病例交叉设计的分布滞后非线性模型，探讨环境因素与CMM之间的非线性滞后关联。随后，通过1:2倾向得分匹配（PSM）处理不平衡数据，并采用条件逻辑回归分析实验室指标与CMM计划外再入院之间的关联。最后，将先前确定的环境因素和相关实验室指标纳入不同的机器学习模型，以预测CMM计划外再入院的风险。
温度、RH与多种CMM的住院情况之间存在非线性关联和滞后效应。此外，在低温和高RH条件下，加上颗粒物（PM、PM和PM）和O_8h，入院风险更高。女性和65岁及以上成年人的风险更大。与第一四分位数（Q1）相比，第四四分位数（Q4）时血清钙（HR = 1.3632，95%CI：1.0732～1.7334）、血清肌酐（HR = 1.7987，95%CI：1.3528～2.3958）、空腹血糖（HR = 1.2579，95%CI：1.0839～1.4770）、天冬氨酸转氨酶/丙氨酸转氨酶比值（HR = 2.3131，95%CI：1.9844～2.6418）、丙氨酸转氨酶（HR = 1.7687，95%CI：1.2388～2.2986）和γ-谷氨酰转移酶（HR = 1.4951，95%CI：1.2551～1.7351）与CMM计划外再入院独立正相关。然而，血清总胆红素和高密度脂蛋白（HDL）呈负相关。纳入环境因素及其滞后项后，极端梯度提升（XGBoost）对CMM患者计划外再入院的预测性能更为突出，受试者工作特征曲线下面积（AUC）平均为0.767（95%CI：0.7486～0.7854）。
极端寒冷或潮湿天气与CMM女性患者及65岁及以上个体的不良健康影响恶化有关。此外，气象因素和环境污染物可能增加CMM计划外再入院的可能性。
