Weather patterns forecast the severity of cervical spinal cord injuries

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.