Object The main objective of this study was to analyze the epidemiological data obtained from patients with traumatic spinal fracture at 2 university-affiliated hospitals in Chongqing, China. Methods The authors retrospectively reviewed the hospital records of all patients who suffered traumatic spinal fracture and were treated at Xinqiao Hospital and Southwest Hospital (both affiliated with The Third Military Medical University) between January 2001 and December 2010. The demographic characteristics, injury characteristics, and clinical outcomes of patients over this 10-year period were compared. Results A total of 3142 patients (mean age 45.7 years, range 1–92 years) with traumatic spinal fractures were identified; 65.5% of the patients were male. The peak frequency of these injuries occurred in the 31- to 40-year-old age group. Accidental falls and traffic accidents were the most common causes of spinal fractures (58.9% and 20.9%, respectively). Traffic accidents tended to occur in younger patients, whereas accidental falls tended to occur in older patients. The most common area of fracture was the thoracolumbar spine (54.9%). Cervical spinal fractures were significantly more common in patients injured in traffic accidents, while lumbar spinal fractures were more common in accidental fall patients. Using the American Spinal Injury Association (ASIA) classification, 479 (15.3%) patients were classified as having ASIA A injuries; 913 (29.1%), ASIA B, ASIA C, or ASIA D; and 1750 (55.7%), ASIA E. ASIA A injuries were more common in patients who suffered thoracic spinal fractures (15.09%) than in those with fractures in other areas of the spine. A total of 954 (30.4%) patients had associated nonspinal injuries. Of these patients, 389 (40.78%) suffered a thoracic injury, and 191 (20.02%) sustained a head and neck injury. The length of hospitalization differed significantly between the accidental falls from high heights and falls from low heights, as did the mean cost of hospitalization (p < 0.05), but no significant difference was found between accidental falls from high heights and traffic accidents (p > 0.05). The length of hospitalization differed significantly among the 3 groups according to the ASIA classification, as did the mean cost of hospitalization (p < 0.05). Of patients with incomplete lesions, 39.3% improved 1 or more grades in ASIA classification during hospitalization. Conclusions Accidental falls emerged as the leading cause of traumatic spinal fracture in this study, and the numbers of fall-induced and sports-related injuries increased steadily with age. These results indicate that there should be increased concern for the consequences of fall- and sports-related injuries among the elderly.

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In this paper, we consider a variant of the location-routing problem (LRP), namely, the regional low-carbon LRP with reality constraint conditions (RLCLRPRCC), which is characterized by clients and depots that located in nested zones with different speed limits. The RLCLRPRCC aims at reducing the logistics total cost and carbon emission and improving clients satisfactory by replacing the travel distance/time with fuel consumption and carbon emission costs under considering heterogeneous fleet, simultaneous pickup and delivery, and hard time windows. Aiming at this project, a novel approach is proposed: hyperheuristic (HH), which manipulates the space, consisted of a fixed pool of simple operators such as “shift” and “swap” for directly modifying the space of solutions. In proposed framework of HH, a kind of shared mechanism-based self-adaptive selection strategy and self-adaptive acceptance criterion are developed to improve its performance, accelerate convergence, and improve algorithm accuracy. The results show that the proposed HH effectively solves LRP/LRPSPD/RLCLRPRCC within reasonable computing time and the proposed mathematical model can reduce 2.6% logistics total cost, 27.6% carbon emission/fuel consumption, and 13.6% travel distance. Additionally, several managerial insights are presented for logistics enterprises to plan and design the distribution network by extensively analyzing the effects of various problem parameters such as depot cost and location, clients’ distribution, heterogeneous vehicles, and time windows allowance, on the key performance indicators, including fuel consumption, carbon emissions, operational costs, travel distance, and time.