通过整合交通记录和地理空间分析来加强创伤登记处，以提高自行车骑行者的安全性。
Enhancing trauma registries by integrating traffic records and geospatial analysis to improve bicyclist safety.
机构信息
From the Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, Department of Surgery (J.J.D., L.N.G., L.K., A.E.B., A.E.L., M.A.J.E.R., J.W., A.S., T.C.), University of California San Diego Health, San Diego, California; and Lloyd L. Gregory School of Pharmacy (J.W.D.), Palm Beach Atlantic University, West Palm Beach, Florida.
出版信息
J Trauma Acute Care Surg. 2021 Apr 1;90(4):631-640. doi: 10.1097/TA.0000000000003075.
BACKGROUND
Trauma registries are used to identify modifiable injury risk factors for trauma prevention efforts. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, street conditions, and neighborhood characteristics. We hypothesize that (GIS) analysis of trauma registry data matched with a traffic accident database could identify risk factors for bicycle-automobile injuries and better inform injury prevention efforts.
METHODS
The trauma registry of a US Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, vitals, injury severity scores, toxicology, helmet use, and mortality.Matching with the Statewide Integrated Traffic Records System was done to provide collision, victim and GIS information. The GIS mapping of collisions was done with census tract data including poverty level scoring. Incident hot spot analysis to identify statistically significant incident clusters was done using the Getis Ord Gi* statistic.
RESULTS
Of 25,535 registry admissions, 531 (2.1%) were bicyclists struck by automobiles, 425 (80.0%) were matched to Statewide Integrated Traffic Records System. Younger age (odds ratio [OR], 1.026; 95% confidence interval [CI], 1.013-1.040, p < 0.001), higher census tract poverty level percentage (OR, 0.976; 95% CI, 0.959-0.993, p = 0.007), and high school or less education (OR, 0.60; 95 CI, 0.381-0.968; p = 0.036) were predictive of not wearing a helmet. Higher census tract poverty level percentage (OR, 1.019; 95% CI, 1.004-1.034; p = 0.012) but not educational level was predictive of toxicology positive-bicyclists in automobile collisions. Geographic information systems analysis identified hot spots in the catchment area for toxicology-positive bicyclists and lack of helmet use.
CONCLUSION
Combining trauma registry data and matched traffic accident records data with GIS analysis identifies additional risk factors for bicyclist injury. Trauma centers should champion efforts to prospectively link public traffic accident data to their trauma registries.
LEVEL OF EVIDENCE
Prognostic and Epidemiological, level III.
背景
创伤登记处用于确定可修改的创伤危险因素，以进行创伤预防工作。然而，这些可能会错过对预防自行车-汽车碰撞有用的因素，例如车辆速度、驾驶员醉酒、街道状况和社区特征。我们假设（GIS）对创伤登记处数据的分析与交通事故数据库相匹配，可以确定自行车-汽车受伤的危险因素，并更好地为伤害预防工作提供信息。
方法
回顾性地使用美国一级创伤中心的创伤登记处，从 2010 年 1 月 1 日至 2018 年 12 月 31 日，确定自行车-机动车碰撞入院患者。收集的数据包括人口统计学资料、生命体征、伤害严重程度评分、毒理学、头盔使用和死亡率。与全州综合交通记录系统进行匹配，以提供碰撞、受害者和 GIS 信息。使用包括贫困程度评分在内的普查区数据进行碰撞 GIS 制图。使用 Getis Ord Gi*统计量进行事件热点分析，以确定具有统计学意义的事件集群。
结果
在 25535 例登记入院患者中，有 531 例（2.1%）为被汽车撞击的骑自行车者，其中 425 例（80.0%）与全州综合交通记录系统相匹配。年龄较小（比值比[OR]，1.026；95%置信区间[CI]，1.013-1.040，p < 0.001）、较高的普查区贫困程度百分比（OR，0.976；95%CI，0.959-0.993，p = 0.007）和高中或以下教育程度（OR，0.60；95 CI，0.381-0.968；p = 0.036）是不戴头盔的预测因素。较高的普查区贫困程度百分比（OR，1.019；95%CI，1.004-1.034；p = 0.012）而不是教育水平是毒理学阳性自行车与汽车碰撞中阳性的预测因素。地理信息系统分析确定了毒理学阳性自行车患者和头盔使用率不足的集水区热点地区。
结论
将创伤登记处数据与匹配的交通事故记录数据与 GIS 分析相结合，确定了自行车受伤的其他危险因素。创伤中心应倡导努力前瞻性地将公共交通事故数据与创伤登记处相联系。
证据水平
预后和流行病学，三级。
Zotero 插件