Social Status and Psychopathology over the Life Course

描述（由申请人提供）：拟议研究将检查 社会经济地位（SES）作为成年人精神病理学的风险因素 生命历程最近NIH呼吁在这一领域进行研究（国家研究所 1998年卫生部）非常适合二级数据分析，因为许多数据 地址已经存在，但尚未从生命过程中分析 perspective.拟议中的项目将利用现有数据来研究年龄 条件的影响，社会经济地位作为一个危险因素， 抑郁、焦虑和酒精依赖等“常见精神障碍”-- 最常见的精神疾病。调查数据包括长期 巴尔的摩流行病学集水区研究中心的随访（数据 （不属于公共领域）、全国科摩罗研究报告和 美国人改变生活的研究这些数据包括DSM标准 精神障碍的诊断和症状量表， 精神病理学指标 拟议的分析集中在两个一般性的研究问题。一是 分析将调查SES指标之间的关联变化， 从18岁到90岁的精神病理学。生命历程理论和 初步研究表明，这些协会将 随着年龄增长。 为了检验这一假设，分析将使用传统的OLS和Logistic回归分析。 回归，以及广义估计方程（GEE）。分析 将研究年龄与社会经济地位指标相互作用的程度， 精神病理学的预测，使用横截面和纵向 模型 第二，分析将调查如何连接SES和 精神病理学随年龄变化。同时使用横截面和纵向 模型，分析将检查年龄条件的程度， 社会经济地位与身体健康等干预机制之间的关联， 应对资源和经济压力。反过来，分析还将研究 年龄条件的方式，这些调解人之间的关联， 精神病理学

DESCRIPTION (provided by applicant): The proposed study will examine socioeconomic status (SES) as a risk-factor for psychopathology over the adult life course. The recent NIH call for research in this area (National Institutes of Health 1998) is well-suited for secondary data analysis because much data to address it already exists but has not yet been analyzed from a life-course perspective. The proposed project will use existing data to examine how age conditions the influence of socioeconomic status as a risk factor for the "common mental disorders" of depression, anxiety, and alcohol dependence - the most prevalent mental illnesses. Data for the investigation consist of a long-term follow-up of Epidemiologic Catchment Area Study site in Baltimore (data that is not in the public domain), the National Comorbidity Study, and the Americans' Changing Lives Study. These data encompass both DSM-criteria diagnoses of mental disorders and symptom scales that provide dimensional measures of psychopathology. The proposed analysis centers on two general research questions. First, the analysis will investigate changes in the association between SES indicators and psychopathology across adulthood, from ages 18 to 90. Life course theory and preliminary research lead to the expectation that the associations will increase with age. To test this hypothesis the analysis will use conventional OLS and logistic regressions, as well as Generalized Estimating Equations (GEE). The analysis will examine the extent to which age interacts with SES indicators in the prediction of psychopathology, using both cross-sectional and longitudinal models. Second, the analysis will investigate how the mechanisms linking SES and psychopathology change with age. Using both cross-sectional and longitudinal models, the analysis will examine the extent to which age conditions the association between SES and intervening mechanisms such as physical health, coping resources, and economic strain. In turn, the analysis will also examine ways in which age conditions the association between these mediators and psychopathology.