Center for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Division of Emergency Medicine, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Traffic Inj Prev. 2022;23(sup1):S130-S136. doi: 10.1080/15389588.2022.2083612. Epub 2022 Jun 13.
The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type.
We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days.
In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists.
Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.
完整而准确的碰撞伤害数据对于预防和干预工作至关重要。仅依靠医院出院数据或警方碰撞报告可能会导致对伤害的严重低估。将医院数据与碰撞报告相联系，可以更准确地识别伤害，并了解在分析单一来源时可能遗漏的人群。我们使用新泽西州安全与健康结果（NJ-SHO）数据仓库，检查这两个数据源在总体和按年龄、种族/族裔、性别、伤害严重程度和道路使用者类型分类的情况下，各自识别的整个碰撞受伤人群中的比例。
我们利用了 NJ-SHO 仓库 2016-2017 年的数据。我们通过应用 ICD-10-CM 外伤病因矩阵，在医院出院数据中识别出与碰撞相关的个体。在与碰撞相关的个体中，我们确定了那些有伤害或疼痛相关诊断代码的个体为受伤者。我们还通过碰撞报告数据识别出与碰撞相关的个体，并使用 KABCO 量表识别出伤害。我们共同检查了这两个来源；如果碰撞报告的日期比医院入院日期提前不超过两天，则医院出院数据中记录的与碰撞相关的伤害被视为与碰撞报告中发现的伤害相关。
在研究期间，共有 262338 名与碰撞相关的个体在医院出院数据或碰撞报告中记录了受伤；根据医院出院数据，有 168874 人受伤，根据碰撞报告数据，有 164158 人受伤。只有 70694 人（26.9%）在两个来源中都有受伤。我们观察到了年龄、种族/族裔、伤害严重程度和道路使用者类型的差异：医院出院数据更能捕捉到 65 岁以上的人群、黑人和西班牙裔人群、伤害严重程度较高的人群以及骑自行车或骑摩托车的人群。
每个数据源孤立地捕获了大约三分之二的整个碰撞受伤人群；仅一个数据源就漏掉了大约三分之一的受伤个体。每个数据源都低估了某些群体的人数，因此仅依赖一个数据源可能无法实现有针对性的预防和干预工作。
