Department of Health Services Policy and Management, University of South Carolina, 800 Sumter St, Columbia, SC 29208, USA.
Soc Sci Med. 2013 Aug;91:58-66. doi: 10.1016/j.socscimed.2013.03.003. Epub 2013 Mar 14.
Extant observational studies generally support the existence of a link between neighborhood context and health. However, estimating the causal impact of neighborhood effects from observational data has proven to be a challenge. Omission of relevant factors may lead to overestimating the effects of neighborhoods on health while inclusion of time-varying confounders that may also be mediators (e.g., income, labor force status) may lead to underestimation. Using longitudinal data from the 1990 to 2007 years of the Panel Study of Income Dynamics, this study investigates the link between neighborhood poverty and overall mortality risk. A marginal structural modeling strategy is employed to appropriately adjust for simultaneous mediating and confounding factors. To address the issue of possible upward bias from the omission of key variables, sensitivity analysis to assess the robustness of results against unobserved confounding is conducted. We examine two continuous measures of neighborhood poverty - single-point and a running average. Both were specified as piece-wise linear splines with a knot at 20 percent. We found no evidence from the traditional naïve strategy that neighborhood context influences mortality risk. In contrast, for both the single-point and running average neighborhood poverty specifications, the marginal structural model estimates indicated a statistically significant increase in mortality risk with increasing neighborhood poverty above the 20 percent threshold. For example, below 20 percent neighborhood poverty, no association was found. However, after the 20 percent poverty threshold is reached, each 10 percentage point increase in running average neighborhood poverty was found to increase the odds for mortality by 89 percent [95% CI = 1.22, 2.91]. Sensitivity analysis indicated that estimates were moderately robust to omitted variable bias.
现有的观察性研究普遍支持邻里环境与健康之间存在关联。然而，从观察性数据中估计邻里效应的因果影响一直是一个挑战。遗漏相关因素可能导致高估邻里对健康的影响，而纳入可能也是中介因素（如收入、劳动力状况）的时变混杂因素则可能导致低估。本研究利用收入动态面板研究 1990 至 2007 年的纵向数据，调查邻里贫困与总体死亡率风险之间的关系。采用边缘结构建模策略，适当调整同时存在的中介和混杂因素。为了解决可能因关键变量缺失而导致向上偏差的问题，进行了敏感性分析以评估结果对未观察到的混杂因素的稳健性。我们考察了邻里贫困的两种连续衡量指标——单点和运行平均值。这两个指标都被指定为具有 20%节点的分段线性样条。我们没有从传统的简单策略中发现邻里环境会影响死亡率风险的证据。相反，对于单点和运行平均值的邻里贫困两种具体情况，边缘结构模型估计表明，随着邻里贫困程度超过 20%的门槛，死亡率风险呈显著上升趋势。例如，在邻里贫困低于 20%的情况下，没有发现关联。然而，在达到 20%的贫困门槛后，发现运行平均值邻里贫困每增加 10 个百分点，死亡率的几率就会增加 89%[95%CI=1.22,2.91]。敏感性分析表明，估计结果对遗漏变量偏差具有中等稳健性。
