Piatt Joseph
Division of Neurosurgery, Nemours/A I duPont Hospital for Children, 1600 Rockland Road, Wilmington, DE, 19803, USA.
Departments of Neurological Surgery and Pediatrics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
Inj Epidemiol. 2022 Jan 24;9(1):5. doi: 10.1186/s40621-021-00366-2.
Studies of pedal cyclist injuries have largely focused on individual injury categories, but every region of the cyclist's body is exposed to potential trauma. Real-world injury patterns can be complex, and isolated injuries to one body part are uncommon among casualties requiring hospitalization. Latent class analysis (LCA) may identify important patterns in heterogeneous samples of qualitative data.
Data were taken from the Trauma Quality Improvement Program of the American College of Surgeons for 2017. Inclusion criteria were age 18 years or less and an external cause of injury code for pedal cyclist. Injuries were characterized by Abbreviated Injury Scale codes. Injury categories and the total number of injuries served as covariates for LCA. A model was selected on the basis of the Akaike and Bayesian information criteria and the interpretability of the classes. Associations were analyzed between class membership and demographic factors, circumstantial factors, metrics of injury severity, and helmet wear. Within-class associations of helmet wear with injury severity were analyzed as well.
There were 6151 injured pediatric pedal cyclists in the study sample. The mortality rate was 0.5%. The rate of helmet wear was 18%. LCA yielded a model with 6 classes: 'polytrauma' (5.5%), 'brain' (9.0%), 'abdomen' (11.0%), 'upper limb' (20.9%), 'lower limb' (12.4%), and 'head' (41.2%). Class membership had highly significant univariate associations with all covariates except insurance payer. Helmet wear was most common in the 'abdomen' class and least common in the 'polytrauma' and 'brain' classes. Within classes, there was no association of helmet wear with severity of injury.
LCA identified 6 clear and distinct patterns of injury with varying demographic and circumstantial associations that may be relevant for prevention. The rate of helmet wear was low, but it varied among classes in accordance with mechanistic expectations. LCA may be an underutilized tool in trauma epidemiology.
对踏板自行车骑行者受伤情况的研究主要集中在个别损伤类别上，但骑行者身体的每个部位都有遭受潜在创伤的风险。现实世界中的损伤模式可能很复杂，在需要住院治疗的伤亡人员中，单一身体部位的孤立损伤并不常见。潜在类别分析（LCA）可能会在定性数据的异质样本中识别出重要模式。
数据取自美国外科医师学会2017年的创伤质量改进项目。纳入标准为年龄18岁及以下且有踏板自行车骑行者的外部损伤原因编码。损伤用简略损伤量表编码进行描述。损伤类别和损伤总数作为LCA的协变量。根据赤池信息准则和贝叶斯信息准则以及类别的可解释性选择模型。分析类别归属与人口统计学因素、环境因素、损伤严重程度指标和头盔佩戴情况之间的关联。还分析了头盔佩戴与损伤严重程度在类别内的关联。
研究样本中有6151名受伤的儿童踏板自行车骑行者。死亡率为0.5%。头盔佩戴率为18%。LCA产生了一个有6个类别的模型：“多发伤”（5.5%）、“脑”（9.0%）、“腹部”（11.0%）、“上肢”（20.9%）、“下肢”（12.4%）和“头部”（41.2%）。类别归属与除保险支付方外的所有协变量均有高度显著的单变量关联。头盔佩戴在“腹部”类中最常见，在“多发伤”和“脑”类中最不常见。在各损伤类别中，头盔佩戴与损伤严重程度无关联。
LCA识别出6种清晰且不同的损伤模式，其人口统计学和环境关联各不相同，可能对预防工作具有相关性。头盔佩戴率较低，但根据机械原理预期在不同类别中有所差异。LCA可能是创伤流行病学中一种未得到充分利用的工具。
