Historical and projected fractures associated with mobility scooters presenting to U.S. emergency departments: 2004-2025

Abstract

Introduction: The proportion of mobility scooter users in the United States continues to rise. However, these devices impart a substantial yet underappreciated risk of serious injury - namely, fractures - on users.

Methods: The purpose of this cross-sectional, retrospective study was to use the National Electronic Injury Surveillance System (NEISS) to quantify the national burden of these device-associated fractures between 2004 and 2017, analyzing in two-year intervals. We also projected estimates to 2025, and described the demographic characteristics of those injured.

Results: We found that national estimates of device-associated fractures increased significantly between the 2004-2005 (N = 2583; C.I. 1851 - 3316) and 2016-2017 (N = 6553; C.I. 5026 - 8081) periods (p < 0.001). A linear regression model (R² = 0.89; P = 0.002) was applied to project 9824 such fractures (C.I. 8273-11,328) in the 2024-2025 period. Injured patients were commonly over age 65 (63.5%; C.I. 57.7%-69.4%) and white (61.4%; C.I. 50.7% - 72.1). Fractures often occurred at home (28.6%; C.I. 22.0%-35.3%) or in public (26.0%; C.I. 21.1%-30.9%).

Conclusion: Our study suggests that osteoarthritic patients relying on mobility scooters to manage pain during ambulation should be considered candidates for total joint replacement procedures. This may help minimize the growing economic and health burden of mobility scooter fractures.

Keywords: Arthroplasty; Burden; Epidemiology; Fracture; Mobility scooter; Osteoarthritis; Surgical candidate; Total joint replacement; Trends.

Fig. 1

This graph demonstrates the increased trend in fractures associated with mobility scooter use across two-year intervals using a linear regression model (R² = 0.89, P = 0.002), at a rate of about 812 additional cases in each consecutive interval period. The blue, filled circles represent the estimates previously shown in Table 1. The linear regression is demonstrated using the dashed, black colored line.

Fig. 2

This figure extends the regression model from Fig. 1 to the 2024–2025 interval period, to demonstrate that the national estimate of cases would approach 10,000 in said period. The blue crosses represent historical estimates, which are identical to those plotted and described in Table 1 and Fig. 1. The blue, filled triangles represent our projected estimate of cases. The linear regression is demonstrated using the dashed, black colored line. The blue line connecting both historical and projected estimates is meant to illustrate goodness-of-fit with respect to our model.