Distribution, Patterns and Severity of Musculoskeletal Injuries Among Motorcycle Crash Victims at Kenyatta National Hospital

Abstract

Background: Road traffic crash injuries constitute a major public health burden and has led to an increase in morbidity, disability and mortality for the victims. Motorcycle crashes (MCC) contribute a large proportion of road traffic crashes (RTC) in Kenya. Use of motorcycles as a means of transport has increased explosively in the last decade due to their affordability and convenience. Road traffic crashes due to MCC have also concomitantly increased, with an increased burden in trauma management at hospitals and the health care system in Kenya as a whole. The injuries include musculoskeletal trauma which result in debilitating and life threating injuries to the victims. MCC-related musculoskeletal injury distribution, fracture patterns, severity as well as mechanisms of injury are not well described in Kenya and local studies describing the injuries are scarce. Study objective: To determine the distribution, patterns and severity of musculoskeletal injuries among motorcycle crash victims at Kenyatta National Hospital. Study design and setting: Descriptive prospective cross-sectional study at Kenyatta National Hospital. Study population: All motorcycle riders, pillion passengers and pedestrians involved in motorcycle crashes presenting at KNH A&E Department, admitted in Orthopaedic wards and ICU with musculoskeletal injuries between April and June 2021. Methods: One hundred and twenty six consecutive patients with motorcycle crash-related musculoskeletal injuries were prospectively studied. Data were obtained by interviews, physical and radiological examination and recorded in a pre-designed questionnaire. The data were stratified and analyzed on age, sex, anatomical fracture distribution, mechanism of injury, pattern and severity of fractures classified under AO/OTA classification and Gustilo-Anderson open fracture classification, associated injuries and type of protective gear used by the operators and the pillion passengers. Data analysis was done using Statistical Package for Social Sciences (SPSS®).................................................................