Lebanon-Epidemiology: The NCS Replication Study

描述(由申请人提供) 这项研究将主要在黎巴嫩的巴拉曼德大学与Elie Karam合作进行，作为NIMH#U01 MH60220赠款的延伸。这项提议寻求支持在黎巴嫩推广国家共病复制的数据分析阶段。NCS-R是对美国精神障碍的患病率和相关因素进行的具有全国代表性的一般人口流行病学调查。NCS-R已经在黎巴嫩复制。然而，黎巴嫩的研究小组只得到资助，对数据进行基本的描述性分析。此外，黎巴嫩小组在进行复杂的流行病学调查分析方面缺乏经验。因此，在此请求提供支助，以便为黎巴嫩分析与新国家评估分析的协调提供资金。这将通过让美国调查人员担任黎巴嫩团队的顾问，并资助黎巴嫩团队执行美国团队正在为美国调查进行的相同类型的详细数据分析来实现。比较分析将对美国调查人员有价值，既可以为NCS-R中的重要NCS-R结果提供复制机会，也可以增加分析在两次调查中被发现具有可比性的结果的统计能力。这种合作将对黎巴嫩合作者创造一个机会，以加强他们在流行病学调查数据分析方面的专门知识，并为他们的数据提供比没有这种合作更详细的分析的资源，这将是非常宝贵的。

DESCRIPTION (provided by applicant) This research will be done primarily in Lebanon at the University of Balamand in collaboration with Elie Karam as an extension of NIMH grant # U01 MH60220. This proposal seeks support for a replication of the data analysis phase of the National Comorbidity Replication (NCS-R) in Lebanon. The NCS-R is a nationally representative general population epidemiological survey of the prevalence and correlates of mental disorders in the United States. The NCS-R has been replicated in Lebanon. However, the research team in Lebanon is funded only to carry out basic descriptive analyses of the data. Furthermore, the Lebanon team is inexperienced in carrying out sophisticated epidemiological survey analysis. Support is consequently requested here to fund coordination of the Lebanese analysis with the NCS-R analysis. This will be done by having the U.S. investigators serve as consultants-advisors to the Lebanese team and by funding the Lebanon team to carry out the same types of detailed data analyses that are being carried out by the U.S. team for the U.S. survey. Comparative analyses will be valuable to the U.S. investigators both in providing replication opportunities for important NCS-R results in the NCS-R and increasing statistical power for the analysis of results that are found to be comparable in the two surveys. The collaboration will be valuable to the Lebanese collaborators in creating an opportunity to enhance their expertise in epidemiological survey data analysis and in providing resources for much more detailed analyses of their data than they would have otherwise.