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Geographic cluster analysis of injury severity and hospital resource use in a regional trauma system.

机译：区域创伤系统中伤害严重程度和医院资源使用的地理聚类分析。

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OBJECTIVES: To determine clusters of trauma incidents with high injury severity and resource utilization and to test their association with census demographic information. METHODS: Using "trauma band" unique identifiers and probabilistic linkage for unmatched cases, we matched injury location information collected from a centralized regional trauma communications center to the state trauma system registry for patients directly transported to two level I trauma centers for the years 2001-2003 in a three-county area. The injury locations were aggregated at the census tract level using a geographic information system (GIS). Moran's I analysis was used to determine clusters of census tracts that had a high incidence of either total trauma injuries, Injury Severity Scores (ISSs) >15, or high resource use (in-hospital mortality, admission to the intensive care unit, or major nonorthopedic surgery). These clusters were then tested for association with census tract demographics using logistic regression. RESULTS: Eight thousand seven hundred fifty-one injured persons were directly transported from the tricounty area to a trauma center during the study period. The mean (+/- standard deviation) age was 37 +/- 21 years, 67.4% were male, 18.9% had ISSs >15, and 29.8% had a high-resource-use indicator. Moran's I analysis demonstrated a single large cluster of incidents for total injuries, ISS >15, and occurrence of a high-resource-use indictor that overlapped except for one small census tract. Logistic regression revealed that the high-risk cluster was associated with a higher prevalence of nonwhite population and vacant housing and a lower prevalence of foreign-born residents and family housing. CONCLUSIONS: GIS cluster analysis demonstrated high-risk census tracts for trauma incidents and associated population demographics. Geospatial analyses may assist injury prevention interventions and emergency medical services deployment strategies for trauma.
机译：目的：确定具有严重伤害严重性和资源利用率的外伤事件群，并测试其与人口普查人口统计信息的关联。方法：对于不匹配的病例，使用“创伤带”唯一标识符和概率链接，我们将从中央区域性创伤交流中心收集的伤害位置信息与州创伤系统注册中心进行了匹配，以直接将患者转移到二级I创伤中心，时间为2001- 2003年在三县区。使用地理信息系统（GIS）在普查区域汇总受伤地点。 Moran's I分析用于确定人口普查区群，这些人口群的总外伤率，伤害严重度评分（ISS）> 15或资源使用率高（医院内死亡率，重症监护病房或重症监护室）的发生率都很高非骨科手术）。然后使用logistic回归测试这些聚类与普查地区人口统计学的相关性。结果：在研究期间，八千七百一十五的受伤人员被直接从三县转移到创伤中心。平均年龄（+/-标准偏差）为37 +/- 21岁，男性为67.4％，ISS> 15的为18.9％，高资源利用指标为29.8％。莫兰（Moran）的I分析显示，一宗大事故造成了全部伤害，国际空间站（ISS）> 15，以及一个高资源消耗指标的发生，除了一个小人口普查区外，其他指标都重叠了。 Logistic回归显示，高风险群与非白人人口和空置住房的患病率较高，以及外国出生居民和家庭住房的患病率较低有关。结论：GIS聚类分析显示了针对创伤事件和相关人口统计学的高风险普查区域。地理空间分析可能有助于预防伤害干预措施和针对创伤的紧急医疗服务部署策略。

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