Identifying and Validating Complex Comorbidity Clusters in OEF-OIF Veterans

项目摘要/摘要 背景：许多研究描述了OEF-OIF退伍军人的身体或精神状况。它是 然而，越来越明显的是，许多退伍军人同时患有多种疾病，这增加了 他们治疗的复杂性。越来越多的关于OEF-OIF退伍军人的研究表明，有 聚集在一起的共病情况的模式。退伍军人事务部HSR&D资助创伤性脑损伤状态 2008年5月的艺术会议(TBI Sota)，突出了我们目前知识和认识上的差距 对常见的共病簇(下称共病簇)的系统识别是 需要了解长期的合并症模式并预测这位退伍军人的医疗需求 队列，特别是那些患有脑外伤的人。 目标： 目标1.在基线时确定OEF-OIF退伍军人的共病集群，并描述模式 三年来共病轨迹(稳定与恶化)的比例。 目标2.确定恶化轨迹与稳定性轨迹的风险因素。 目标3.比较具有稳定共病轨迹的个体与 那些表现出恶化模式的人。 目标4.检验在弹性应对、社会支持和心理健康测量方面的差异 OEF-OIF VA患者在每个共病轨迹(稳定或恶化)中的随机抽样 分层以表示每个初始共病群组内的轨迹。 方法：这是一项流行的队列研究，将结合来自退伍军人事务部国家数据仓库(奥斯汀， 药房福利管理数据库、TBI筛选数据库、OEF-OIF数据库)、原始数据 收集(调查)和国家Vista网络图表摘要，以实现我们的目标。这项研究将 确定在退伍军人管理局护理(FY02-08)中首次见到的个体的初始共病群。然后我们将使用潜伏期 结合专家临床意见进行分类分析，以确定稳定性或非稳定性的共病轨迹 每个共病群组的病情恶化。我们将通过检查共病组来验证共病组 战斗相关暴露与症状之间的关系，并通过比较 在每个共病群组内具有稳定和恶化共病轨迹的个体。到时候我们会的 确定按初始共病集群和轨迹分层的随机个体样本，以进行更多 利用调查和图表摘要数据进行全面检查。使用改进的Dillman方法，我们 将从大约2,000名OEF-OIF VA患者那里获得调查数据。调查将衡量精神健康状况 症状、应对策略、社会支持、人际关系的结构性方面，以及就业状况。我们 将从部署前和部署后健康评估/重新评估(PDHRA)获得数据 吸烟史、慢性病家族史和其他相关临床变量通过Vista Web图表 抽象。PDHRA将提供关于自我报告的健康、关系问题和 心理健康筛查仪器。我们将使用描述性和双变量统计来检查 自述弹性应对策略、社会支持、抑郁严重程度与心理健康的关系 共病轨迹，控制部署前和部署后的初始自我报告健康状况和部署 担忧。将进行广义线性混合效果建模，以检查 共病轨迹和弹性应对、社会支持和心理健康措施。

PROJECT SUMMARY/ABSTRACT BACKGROUND: A number of studies describe physical or mental conditions among OEF-OIF veterans. It is becoming clear, however, that many veterans have multiple conditions concomitantly which increases the complexity of their treatment. The growing body of research on OEF-OIF veterans suggests that there are patterns of comorbid conditions that cluster together. The VA HSR&D funded a Traumatic Brain Injury State of the Art Conference (TBI SOTA) in May, 2008 that highlighted gaps in our current knowledge and recognized that a systematic identification of common clusters of comorbidities (hereafter comorbidity clusters) is needed to understand the long-term comorbidity patterns and project the health care needs of this veteran cohort, particularly those with TBI. OBJECTIVES: Objective 1. Identify comorbidity clusters among OEF-OIF veterans at baseline and describe patterns of comorbidity trajectories (stable vs. deterioration) over 3 years. Objective 2. Identify risk factors for trajectories of deterioration vs. stability. Objective 3. Compare VA health care utilization for individuals with stable comorbidity trajectories vs. those who exhibit patterns of deterioration. Objective 4. Examine differences in resilient coping, social support and measures of mental health for OEF-OIF VA patients in each comorbidity trajectory (stable or deterioration) in a random sample stratified to represent trajectories within each initial comorbidity cluster. METHODS: This is a prevalent cohort study that will combine data from VA national data repositories (Austin, Pharmacy Benefits Management database, TBI screening database, OEF-OIF database), primary data collection (survey) and national VistA Web chart abstraction to accomplish our objectives. This study will identify initial comorbidity clusters for individuals first seen in VA care (FY02-08). We will then use latent class analysis in conjunction with expert clinical opinion to identify comorbidity trajectories of stablility or deterioration within each comorbidity cluster. We will validate comorbidity clusters by examining the relationship between combat-related exposures and symptoms, and by comparing utilization among individuals with stable and deterioration comorbidity trajectories within each comorbidity cluster. We will then identify a random sample of individuals stratified by initial comorbidity cluster and trajectory to conduct more comprehensive examination using survey and chart abstraction data. Using a Modified Dillman approach, we will obtain survey data from approximately 2,000 OEF-OIF VA patients. Surveys will measure mental health symptoms, coping strategies, social support, structural aspects of relationships, and employment status. We will obtain data from pre- and post-deployment health assessments/ re-assessments (PDHRA) in addition to smoking history, family history of chronic disease, and other relevant clinical variables via VistA Web chart abstraction. The PDHRA will provide baseline information on self-reported health, relationship concerns, and mental health screening instruments. We will use descriptive and bivariate statistics to examine the relationships among self-reported resilient coping strategies, social support, depression severity and comorbidity trajectory, controlling for pre-and post-deployment initial self-reported health and deployment concerns. Generalized linear mixed effects modeling will be conducted to examine the associations between comorbidity trajectory and resilient coping, social support, and measures of mental health.