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

描述(申请人提供)：在美国，人们反复观察到健康和死亡率的地区差异，但很少有人试图解释这些差异。最好的情况是，通常会提供轶事解释。例如，南方人的健康状况较差通常被归因于没有任何经验支持的饮食。现存的文学还存在几个额外的缺陷。首先，许多研究关注单一的健康结果，如中风死亡率，因此低估了健康的地区差异的全部程度。第二，许多研究粗略地衡量区域。通常，只有一个地区与所有其他地区形成对比。这种方法还导致低估了健康方面的所有区域差异，并阻碍了我们理解造成这种差异的确切机制的能力，因为区域内的文化和结构异质性广泛存在。第三，对地区差异的研究通常没有从生命过程的角度来看待，而是将其视为存在于时间真空中。拟议的研究将解决这些不足之处，首先采用生命历程的观点。生命历程观点认识到，无论是居住区域还是健康状况，或者它们之间的关系，在不同年龄的个人层面上都不是静止的。此外，不同社会历史时期的区域特征和健康结果的分布也不同，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，生命历程视角提供了一个更全面和更详细的视角，通过它来开始解释健康方面的地区差异。鉴于这一观点，拟议的研究将使用适用于至少三个具有全国代表性的大样本数据集的各种纵向统计方法来确定区域健康差异的全面程度：一般社会调查、健康和退休研究以及国家健康流行病学后续调查。这些数据将通过收集医生密度、气候等区域年背景变量来扩大。总的来说，这些调查包含各种健康衡量标准，包括自我评估的健康状况、身体功能、抑郁症状、死亡率和糖尿病，以及改进的地区衡量标准(即九类人口普查衡量标准)。重要的是，这三项调查还包含至少一项关于早期生活(出生和青春期)居住地区的衡量标准，从生命历程的角度来看，这有助于区分早期生活社会化在区域文化中的作用，以及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，这一早期生命区域测量以及纵向方法的使用将使人们能够调查健康对区域流动性的影响程度，这是研究中通常忽视的一个问题(即内生性)。将使用基本描述性方法、典型回归模型、面板和横截面数据的多状态生命表法以及分层增长模型(包括自回归潜在轨迹模型)来充实健康状况的地区差异程度以及解释这些差异的机制。

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.