SOCIAL INTEGRATION, AGING AND STROKE

描述：(改编自《调查者摘要》)中风是第三次 美国人的主要死因和致残原因 各州。此前的研究表明，社交网络和 社会支持和各种原因造成的死亡以及从严重疾病中恢复 生病了。而一些关于社会融合和心血管疾病的研究 已经进行的疾病，关于社会作用的证据 中风中的整合是罕见的，也是相互冲突的。有道理的是，同样的 将社会融合和心血管疾病联系起来的机制也 与中风有关。之前识别这种联系的尝试都失败了 是因为对社会关系的衡量不力，还是因为不充分 统计学上的力量。这项研究的中心目的是检查 社会网络和社会支持对几种中风预后的影响 包括发病率、死亡率、病死率、功能恢复和 卸货目的地。来自三个大型前瞻性队列的数据 将对研究和一项心理社会干预研究进行分析。四个 研究是：1)EPESE研究的纽黑文地点(n=2,182)；2) Duke EPESE网站(n=4,163)；3)卫生专业人员随访研究(N) =32,624)；4)卒中恢复期家庭(n=290)。这个 需要检验的五个假设是：1)更高的社会支持水平和 较强的社会网络与较低的中风发病率相关 在社区居住的成年人中；2)更强大的社交网络 社区成年人中风死亡风险较低；3) 更高水平的社会支持和更强大的社会网络是相关的 中风后功能恢复的改善；4)更高的社会水平 支持和更强大的社交网络与ALL风险较低相关 导致中风患者和 活着出狱；以及5)更高水平的社会支持和更强大的社会 人际关系网与熟练护理人员出院的风险较低有关 适合长期安置的设施。 致命和非致命的中风将通过与医疗人员的匹配来确认 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)中正在进行的数据收集活动 这将导致统计能力的增强。主力独立 在所有四项研究中都可以得到的变量是Berkman-Syme社交网络 指数(SNI)。一系列额外的社会支持和网络措施 也将被利用。将使用各种统计模型 包括：1)Cox比例风险模型(假设1、2、4)；2)OLS 自回归模型(假设3)；3)多分类Logistic模型 回归(假设5)。每项研究的数据都将被分析 分开的。调查人员表示，这项研究的力量将是 相当数量的中风病例可跨多个 学习。他们进一步表示，拟议中的研究将是 到目前为止，对社会融合和中风的全面研究，将产生 与临床和政策相关的结果。他们得出结论，这些信息 关于社会环境如何影响这一现象的起因和过程 流行的疾病将导致低成本的干预策略。

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.