Causal Estimates of Neighborhood Poverty on Health and Mortality

社区贫困对健康和死亡率的因果估计

• 批准号: 7658417
• 负责人: D. PHUONG DO
• 金额: $ 19.55万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7658417
• 关键词: Accounting; Address; Affect; Attention; Behavior; Characteristics; Critical Pathways; Data; Economically Deprived Population; Education; Employment; Employment Status; Environment; Goals; Health; Health Policy; Health Status; Health behavior; Heterogeneity; Income; Individual; Knowledge; Lead; Life; Link; Literature; Marital Status; Marriage; Mediating; Mediator of activation protein; Methodology; Modeling; Neighborhoods; Observational Study; Outcome; Patient Self-Report; Plague; Poverty; Public Health; Race; Reliance; Research; Research Personnel; Risk; Services; Smoking; Sorting - Cell Movement; Source; Statistical Methods; Structural Models; Time; Work; analytical method; base; design; hazard; health disparity; mortality; novel; policy implication; public health relevance; social; socioeconomics

DESCRIPTION (provided by applicant): The disparities in the distribution of goods and services, and hazards and opportunities across space are increasing, underscoring the growing connection between place and health. Although ample evidence confirms that living in an economically disadvantaged neighborhood is associated with adverse health outcomes, the reliance on cross-sectional data and inadequate attention to two main sources of bias make causal inferences problematic. Residents tend to sort themselves into different types of neighborhoods based on a multitude of characteristics. Not accounting for all characteristics that are correlated to both the outcome and neighborhood context would likely lead to over-estimations of neighborhood effects. Because regression models cannot possibly account for all relevant factors, the strong possibility of unobserved heterogeneity make neighborhood effect studies open to criticisms of omitted variable bias. Yet, at the same time, neighborhood effect studies are also just as likely to be susceptible to bias due to over- adjustment. Many factors that are controlled for in neighborhood effect models, such as educational attainment, income, and employment, may arguably have been influenced by past neighborhood context. Adjusting for these factors eliminate possible critical pathways through which neighborhoods affect health, likely yielding overly conservative estimates of neighborhood effects. These two sources of bias, working in opposing directions, have plagued extant neighborhood-health research; consequently, results from current research yield tenuous and ambiguous inferences. This proposed project will use novel analytical methods and longitudinal data from an existing observational study to address the two major limitations described above and recover causal estimates of neighborhood poverty on self-rated health and mortality. We will 1) use marginal structural modeling to appropriately adjust for covariates that are simultaneously confounders as well as mediators and 2) conduct a sensitivity analysis to determine the robustness of the neighborhood effect findings to unobserved heterogeneity. Applying this combined methodology to neighborhood-health research has the potential to significantly advance our knowledge of the relationship between place and health, yielding far reaching policy implications. PUBLIC HEALTH RELEVANCE: Relevance Robust findings of a causal connection between residential context and health can help health policymakers judge the extent and magnitude of neighborhood impacts on health and guide public health policies. Evidence that the social and structural environment influences life-chances, and ultimately health outcomes, suggests that health policy, traditionally targeted at the individual level with little regard to neighborhood context, should consider underlying constraints or opportunities present in the residential environment in designing and implementing the most effective and efficient public health policies.

描述(由申请者提供)：商品和服务分配的不平等以及空间中的危险和机会正在增加，突显了地点与健康之间日益密切的联系。尽管大量证据证实，生活在经济条件较差的社区与不利的健康后果相关，但对横断面数据的依赖以及对偏见的两个主要来源的不充分关注，使得因果推断存在问题。居民倾向于根据多种特征将自己划分为不同类型的社区。如果不考虑与结果和邻里环境相关的所有特征，很可能会导致对邻里效应的高估。由于回归模型不可能解释所有相关因素，未观察到的异质性的强烈可能性使邻里效应研究容易受到遗漏变量偏差的批评。然而，与此同时，由于过度调整，邻里效应研究也同样容易受到偏见的影响。在邻里效应模型中控制的许多因素，如教育程度、收入和就业，可能会受到过去邻里环境的影响。对这些因素进行调整，消除了社区影响健康的可能关键途径，可能会导致对社区影响的过度保守估计。这两个偏向相反的来源，一直困扰着现有的邻里健康研究；因此，当前研究的结果产生了含糊不清的推论。这个拟议的项目将使用新的分析方法和来自现有观察性研究的纵向数据，以解决上述两个主要限制，并恢复社区贫困对自评健康和死亡率的因果估计。我们将1)使用边际结构模型对同时是混杂因素和中介变量的协变量进行适当调整，以及2)进行敏感性分析，以确定邻域效应研究结果对未观察到的异质性的稳健性。将这种结合的方法应用于邻里健康研究，有可能显著提高我们对地点和健康之间关系的了解，产生深远的政策影响。公共卫生相关性：关于居住环境和健康之间因果关系的可靠发现，可以帮助卫生政策制定者判断社区对健康的影响的程度和程度，并指导公共卫生政策。有证据表明，社会和结构环境影响生活机会，并最终影响健康结果，这表明，传统上以个人层面为目标的健康政策，很少考虑邻里环境，在设计和实施最有效和最高效的公共卫生政策时，应考虑居住环境中存在的潜在限制或机会。