Understanding US regional health & mortality disparities: A Life Course Approach

描述（由申请人提供）：在美国，健康和死亡率的地区差异已被多次观察到，但很少有人试图解释它们。充其量，通常会提供一些轶事般的解释。例如，南方人的健康状况较差往往被归因于饮食，而没有任何经验支持。现存的文献还有几个其他的缺点。首先，许多研究关注单一的健康结果，如中风死亡率，从而低估了健康的区域差异的全部程度。其次，许多研究对区域进行了粗略的测量。通常，只有一个区域与所有其他区域形成对比。这种方法还导致低估了健康的全方位区域差异，并阻碍了我们理解其确切机制的能力，因为区域内的文化和结构异质性是广泛的。第三，区域差异的研究通常没有采取生命过程的视角，而是将其视为存在于时间真空中。拟议的研究将解决这些缺点，首先采用生命历程的观点。生命历程的观点承认，居住地区、健康状况以及两者之间的关系在个人层面上都不是跨越年龄的静止不变的。此外，区域特征和健康结果的分布也因社会历史时间而异，这意味着区域与健康之间的关系可能因出生队列而异。因此，生命过程的观点提供了一个更全面和详细的透镜，通过它开始解释健康方面的区域差异。鉴于这一观点，拟议的研究将使用各种纵向统计方法，应用于至少三个具有全国代表性的大样本数据集：综合社会调查，健康和退休研究，以及全国健康流行病学随访调查，建立健康的地区差异的全面程度。这些数据将通过收集地区年背景变量（如医生密度、气候等）来增强。总的来说，这些调查包含各种各样的健康指标，包括自测健康、身体功能、抑郁症状、死亡率和糖尿病，以及地区的精细指标（即，九类人口普查措施）。重要的是，这三项调查还包含至少一项衡量生命早期（出生和青春期）居住地区的措施，从生命历程的角度来看，这有助于区分生命早期社会化在区域文化中的作用和个人当前居住地区的结构特征在影响健康方面的作用。此外，这种早期生活区域措施，以及使用纵向方法，将使调查的程度，健康影响区域流动性，一个问题（即，在研究中通常被忽视。基本的描述性方法，典型的回归模型，面板和横截面数据的多状态生命表方法，以及分层增长模型，包括自回归潜在轨迹模型，将用于充实健康的区域差异的程度以及解释它们的机制。

DESCRIPTION (provided by applicant): Regional disparities in health and mortality in the U.S. have been observed repeatedly, but little attempt has been made to explain them. At best, anecdotal explanations are usually offered. For example, poorer health among southerners is often attributed to diet without any empirical support. Extant literature suffers from several additional shortcomings. First, many studies focus on a single health outcome, like stroke mortality, thereby underestimating the full extent of regional variation in health. Second, many studies measure region coarsely. Often, only one region is contrasted against all others. This approach also leads to underestimation of the full range of regional variation in health and hinders our ability to understand the precise mechanisms that account for it, because within-region cultural and structural heterogeneity is extensive. Third, studies of regional disparities have generally failed to take a life course perspective, instead treating them as existing in a temporal vacuum. The proposed research will address these shortcomings, first by adopting a life course perspective. The life course perspective recognizes that neither region of residence, nor health, nor the relationship between them, is static at the individual level across age. Furthermore, regional characteristics and the distribution of health outcomes also vary across sociohistoric time, implying that the relationship between region and health may differ across birth cohorts. The life course perspective therefore provides a more comprehensive and detailed lens through which to begin to explain regional differences in health. Given this perspective, the proposed research will establish the full extent of regional disparities in health using a variety of longitudinal statistical methods applied to at least three nationally-representative, large sample data sets: the General Social Survey, the Health and Retirement Study, and the National Health Epidemiologic Follow-up Surveys. These data will be augmented via the collection of region-year contextual variables like physician density, climate, etc. Collectively, these surveys contain a wide variety of health measures, including self-rated health, physical functioning, depressive symptoms, mortality, and diabetes, as well as refined measures of region (i.e., the nine-category Census measure). Importantly, these three surveys also contain at least one measure of region of residence in early life (birth and adolescence), which, from a life course perspective, is useful in helping differentiate the role of early life socialization into regional culture from the role of structural characteristics of an individual's current region of residence in influencing health. In addition, this early life region measure, as well as the use of longitudinal methods, will enable the investigation of the extent to which health influences regional mobility, an issue (i.e., endogeneity) commonly ignored in research. Basic descriptive methods, typical regression models, multistate life table methods for both panel and cross-sectional data, and hierarchical growth models, including autoregressive latent trajectory models, will be used to flesh out the extent of regional differences in health as well as the mechanisms that account for them.