Quon Robert J, Feler Josh, Wang Ziqi, Leary Owen P, Murillo Anarina, Fridley Jared S
Department of Neurosurgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, APC 6, Providence, RI, 02903, USA.
Department of Biostatistics, School of Public Health of Brown University, 121 S Main Street, Providence, RI, 02903, USA.
Sci Rep. 2025 Jul 1;15(1):21234. doi: 10.1038/s41598-025-03465-y.
To determine if regional weather patterns could predict the severity of cervical spinal cord injuries (CSCI) across the United States. Non-elective CSCI patients from 2015 to 2020 were identified in the National Inpatient Sample. Linear mixed-effects models explored associations between CSCI features and weather patterns, with random effects for hospitals. Granger causality tests were performed for each region to assess if weather could predict complete CSCI. Cross-correlation analyses examined temporal trends. Logistic mixed-effects models evaluated correlations between CSCI severity and seasonal or transitional temperature months. Average regional temperature (p = 0.016) and precipitation (p = 0.038) were positively correlated with complete CSCI admissions. Granger causality tests showed that average regional temperature (p = 0.046) and precipitation (p = 0.039) could predict complete CSCI in the Midwest but not in other regions. There was no seasonal association with complete CSCI, but acute temperature drops in the West were significantly correlated with increased complete CSCI (OR 2.98, 95% CI 1.36-4.61, p < 0.01). Weather trends, including regional temperature, precipitation, and acute temperature transitions, may predict CSCI severity in certain regions. These findings suggest weather trends could inform resource allocation for spinal cord injuries, thereby enhancing patient outcomes and optimizing healthcare resource management.
为了确定区域天气模式是否能够预测美国各地颈脊髓损伤（CSCI）的严重程度。在全国住院患者样本中识别出2015年至2020年的非选择性CSCI患者。线性混合效应模型探讨了CSCI特征与天气模式之间的关联，并对医院进行随机效应分析。对每个地区进行格兰杰因果检验，以评估天气是否能预测完全性CSCI。交叉相关分析检查了时间趋势。逻辑混合效应模型评估了CSCI严重程度与季节性或过渡性温度月份之间的相关性。区域平均温度（p = 0.016）和降水量（p = 0.038）与完全性CSCI入院呈正相关。格兰杰因果检验表明，区域平均温度（p = 0.046）和降水量（p = 0.039）可以预测中西部地区的完全性CSCI，但在其他地区则不然。完全性CSCI与季节无关联，但西部的急性温度下降与完全性CSCI增加显著相关（OR 2.98，95%CI 1.36 - 4.61，p < 0.01）。包括区域温度、降水量和急性温度变化在内的天气趋势可能预测某些地区的CSCI严重程度。这些发现表明，天气趋势可为脊髓损伤的资源分配提供参考，从而改善患者预后并优化医疗资源管理。
