Trauma System Evaluation with Survival Time Models

描述（由申请人提供）： 概要/摘要拟议的目标和方法：使用公共数据和生存时间统计模型，确定和评估有助于改善急性受伤人员结局的紧急医疗服务（EMS）和创伤系统的组成部分。重要性：伤害是死亡和残疾的重要原因，其中一些可以通过有效的EMS和创伤系统来预防。然而，这些系统的评价是复杂的集中在城市地区的医疗资源，并可能不可避免的延误，在开始治疗，由于地理差异。为了克服这个方法问题，我们建议使用生存时间模型，明确调整受伤，干预和死亡之间的时间长度。现在有数据来源，使我们能够应用这些创新的方法，在EMS系统，其特定组件和其他潜在的可修改因素之间进行适当的比较。具体目标和假设：1）利用公共数据和生存时间分析的标准方法，构建交通事故或故意伤害后受试者的生存曲线; 2）确定EMS和医院干预对这些曲线和模型的总体影响; 3）在控制了可识别的固定因素后，确定EMS和医院干预的效果在特定创伤系统中有多大差异，并使用该剩余可变性作为单个系统性能的度量。研究设计：我们将使用生存时间建模技术来分析致命交通事故，凶杀和自杀的现有数据。受伤后至死亡时间的数据将从国家生命统计系统（死亡证明）、国家公路交通安全管理局死亡分析报告系统（NHTSA/法尔斯，致命车祸普查）和国家暴力死亡报告系统（凶杀和自杀普查）获得。将使用法尔斯（包括涉及致命碰撞的其他人员）、国家公路交通安全管理局国家数据系统（警方碰撞报告）或国家住院患者数据库（住院）来确定类似损伤后的相应死亡风险人群。EMS或住院治疗的发生将分别作为随时间变化的协变量（前0，后1）添加到标准比例风险生存时间模型中，我们预计EMS的风险降低，住院治疗的风险进一步降低。这将证实最小化反应时间和院前时间的重要性，并有助于解释观察到的农村地区与受伤后死亡率之间的关联。最后，每个州都将作为辅助因子纳入模型中，使我们能够在考虑其地理环境差异后估计每个特定创伤系统的剩余影响。这些模型将进一步完善，包括EMS和创伤系统在相应地区的已知结构，并估计特定组件的影响。环境和参与者：我们的研究人群将包括25个州最近两年的受伤人员，这些州在上述五个公共数据库中至少有四个数据（或目前正在开发的国家EMS信息系统）。干预：无。结果测量：主要结果是存活率和从受伤到死亡的时间。我们还将估计EMS和医院治疗的影响，EMS和创伤系统的一般特征，以及特定区域系统的影响，使用来自生存时间模型的系数。 公共卫生相关性： 该项目提供了一个新的机会，收集现有的数据，并应用创新的统计方法来评估创伤系统。这将使我们能够区分创伤系统发展水平的影响和影响农村和城市创伤死亡率差异的其他因素。

DESCRIPTION (provided by applicant): SUMMARY / ABSTRACT Proposed Objective and Approach: To determine and evaluate components of Emergency Medical Services (EMS) and trauma systems that contribute to improved outcomes for acutely injured persons, using public data and survival-time statistical models. Importance: Injuries are a substantial cause of death and disability, some of which may be prevented by effective EMS and trauma systems. However, evaluation of these systems is complicated by the concentration of medical resources in urban areas, and possibly unavoidable delays in initiating therapy due to geographic disparities. To overcome this methodological problem, we propose using survival-time models that explicitly adjust for the length of time between injury, intervention, and death. Data sources are now available that allow us to apply these innovative methods to make appropriate comparisons among EMS systems, their specific components, and other potentially modifiable factors. Specific Aims and Hypotheses: 1) To construct survival curves for subjects after traffic crashes or intentional injuries, using public data and standard methods of survival-time analysis; 2) To determine the overall effects of EMS and hospital intervention on these curves and models; and 3) After controlling for identifiable fixed factors, to determine how much the effects of EMS and hospital intervention vary among specific trauma systems, and use this residual variability as a measure of individual system performance. Study Design: We will use survival-time modeling techniques to analyze existing data from fatal traffic crashes, homicides, and suicides. Data on time to death following injury will be obtained from the National Vital Statistics System (death certificates), National Highway Traffic Safety Administration Fatality Analysis Reporting System (NHTSA/FARS, a census of fatal crashes), and National Violent Death Reporting System (a census of homicides and suicides). Corresponding populations at risk for death following similar injuries will be identified using FARS (which includes the other persons involved in a fatal crash), the NHTSA State Data System (police crash reports), or State Inpatient Databases (hospitalizations). The onset of EMS or hospital treatment will each be added to standard proportional hazards survival-time models as a time-varying covariate (0 before, 1 after), and we expect to show a decrease in the hazard with EMS and a further decrease with hospital treatment. This will confirm the importance of minimizing response time and prehospital time, and help explain the observed association between rural location and mortality after injury. Finally, each state will be incorporated as a cofactor in the models, allowing us to estimate the residual effect of each specific trauma system, after accounting for the disparities in their geographic settings. These models will be further refined by including the known structure of EMS and trauma systems in the corresponding regions and estimating the effects of specific components. Setting and Participants: Our study population will include injured persons during the two most recently available years from the 25 states that have data in at least four of the five public databases cited above (or the National EMS Information System currently under development). Interventions: None. Outcome Measures: The primary outcomes are survival and time elapsed from injury to death. We will also estimate the effects of EMS and hospital treatment, general characteristics of EMS and trauma systems, and effects of specific regional systems, using coefficients derived from survival-time models. PUBLIC HEALTH RELEVANCE: This project presents a new opportunity to assemble data already available and apply innovative statistical methods to evaluate trauma systems. This will enable us to distinguish between the impact of level of trauma system development and other factors affecting the disparity in rural and urban trauma mortality.