SOCIAL INTEGRATION, AGING AND STROKE

描述：（改编自研究者摘要）中风是第三种 导致死亡和残疾的主要原因 States. 先前的研究表明，社交网络和 社会支持和全因死亡率以及从严重 病 虽然一些关于社会融合和心血管疾病的研究 疾病已经进行，关于社会作用的证据 整合在中风中是罕见和矛盾的。 这是合理的，同样的， 社会融合与心血管疾病之间的联系机制也 参与中风。 以前试图确定这一联系的努力受到了影响 从社会关系的测量差，或从不充分的 统计力量 本研究的主要目的是研究 社交网络和社会支持对几种卒中结局的影响 包括发病率、死亡率、病死率、功能恢复和 卸货目的地 来自三个大规模前瞻性队列的数据 研究和一项心理社会干预研究将进行分析。 四 研究包括：1）EPESE研究的纽黑文研究中心（n = 2，182）; 2） 杜克大学EPESE研究中心（n = 4，163）; 3）卫生专业人员随访研究（n = 32，624）;和4）中风恢复家庭试验（n = 290）。 的 待检验的五个假设是：1）较高的社会支持水平， 较强的社交网络与较低的中风发病风险相关 2）较强的社交网络与 在社区居住的成年人中，中风死亡率的风险较低; 3） 更高水平的社会支持和更强的社交网络是相关的 改善中风后的功能恢复; 4）更高的社会水平 支持和更强大的社交网络与所有人的风险降低有关 在那些已经中风的人中， 5）更高水平的社会支持和更强的社会 网络与较低的风险出院到一个熟练的护理 长期安置设施。 致命性和非致命性中风将通过与医学匹配来确认。 记录，国家死亡指数，以及HCFA A部分医疗保险数据。 两项研究（HPFS和FIRST）正在进行数据收集活动 这将导致增强的统计能力。 主要的独立 在所有四项研究中可用的变量是Berkman-Syme社会网络 索引（SNI）。 一系列社会支助和网络的补充措施 也将被利用。 将使用各种统计模型 包括：1）考克斯比例风险模型（假设1、2、4）; 2）OLS 自回归模型（假设3）;和3）多分类logistic 回归（假设5）。 将分析每项研究的数据 分开 研究人员指出，这项研究的强度将是 多个国家和地区的大量中风病例的可用性 问题研究 他们进一步指出，拟议的研究将是最 社会融合和中风的全面研究，并将产生 临床和政策相关的结果。 他们得出结论， 关于社会背景如何影响其病因和过程 流行病将导致低成本的干预战略。

DESCRIPTION: (Adapted from Investigator's Abstract) Stroke is the third leading cause of death and the leading cause of disability in the United States. Previous studies have shown a link between social networks and social supports and all-cause mortality as well as to recovery from serious illness. While several studies of social integration and cardiovascular disease have been conducted, evidence regarding the role of social integration in stroke is rare and conflicted. It is plausible that the same mechanisms linking social integration and cardiovascular disease are also involved in stroke. Previous attempts to identify this link have suffered from poor measurement of social relationships, or from inadequate statistical power. The central aim of this research is to examine the impact of social networks and social support on several stroke outcomes including incidence, mortality, case fatality, functional recovery and discharge destination. Data from three large-scale prospective cohort studies and one psychosocial intervention study will be analyzed. The four studies are: 1) the New Haven site of the EPESE study (n = 2,182); 2) the Duke EPESE site (n = 4,163); 3) the Health Professionals Follow-up Study (n = 32,624); and 4) the Families in Recovery from Stroke trial (n = 290). The five hypotheses to be tested are: 1) higher levels of social support and stronger social networks are associated with lower risk of incidence stroke among community-dwelling adults; 2) stronger social networks are associated with a lower risk of stroke mortality among community-dwelling adults; 3) higher levels of social support and stronger social networks are associated with improved functional recovery after stroke; 4) higher levels of social support and stronger social networks are associated with lower risk of all cause mortality among those who have sustained a stroke and who are discharged alive; and 5) higher levels of social support and stronger social networks are associated with a lower risk of discharge to a skilled nursing facility for long-term placement. Fatal and non-fatal strokes will be confirmed by matching with medical records, the national death index, as well as HCFA Part A Medicare data. Data gathering activities ongoing in two of the studies (HPFS and FIRST) which will lead to enhanced statistical power. The main independent variable, available in all four studies, is the Berkman-Syme Social Network Index (SNI). A range of additional measures of social support and networks will be also be utilized. A variety of statistical models will be used including: 1) Cox proportional hazards model (hypotheses 1, 2, 4); 2) OLS autoregression models (hypothesis 3); and 3) polychotomous logistic regression (hypothesis 5). Data from each study will be analyzed separately. The investigators state that the strength of this study will be the availability of substantial numbers of stroke cases across multiple studies. They further state that the proposed research will be the most comprehensive study of social integration and stroke to date, and will yield both clinically and policy relevant results. They conclude that information about how the social context impacts the etiology and course of this prevalent disease will lead to low-cost intervention strategies.