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Using Internal Migration to Estimate the Causal Effect of Neighborhood Socioeconomic Context on Health: A Longitudinal Analysis, England, 1995-2008

机译:利用内部迁移来估计邻里社会经济背景对健康的因果效应:纵向分析,英格兰,1995-2008

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摘要

There is long-standing evidence for the existence of geographical inequalities in health. Multiple conceptual frameworks have been proposed to explain why such patterns persist. The methodological design for these studies is often not appropriate for identifying causal effects of neighborhood context, however. It is possible that findings that show the importance of neighborhoods could be subject to confounding of individual-level factors, neighborhood sorting effects (i.e., health-selective migration), or both. We present an approach to investigating neighborhood-level factors that provides a stronger examination for causal effects, as well as addressing issues of confounding and sorting. We use individual-level data from the British Household Panel Survey (1995-2008). Individuals were grouped into quintiles based on the median house price of an individual's lower super output area as our measure of neighborhood socioeconomic context. Multivariate propensity scores were used to match individuals to control for confounding factors, and logistic regression models were used to estimate the association between destination of migration and risk of poor health (up to ten years following migration). Initially, we found some evidence that poorer neighborhoods were associated with an increased risk of poor health. Following controlling for an individual's health status prior to migration, the influence of neighborhood socioeconomic context was statistically nonsignificant. Our findings suggest that health-selective migration might help to explain the association between neighborhood-level factors and individual-level health. Our study design appears useful for both identifying causal effects of neighborhoods and accounting for health-selective migration.
机译:有长期存在的证据,以存在健康的地理不平等。已经提出了多个概念框架来解释为什么这样的模式持续存在。然而,这些研究的方法论设计通常不适合识别邻域背景的因果效应。表明邻里的重要性可能会受到个体级别因素的混淆,邻域分类效果(即健康选择性迁移)或两者。我们提出了一种调查邻里级因子的方法,为因果效应提供更强的检查,以及解决混淆和排序的问题。我们使用英国家庭面板调查(1995-2008)的个人级别数据。基于个人较低的超级产出区域的中位数价格作为我们邻里社会经济背景的衡量标准,将个体分为昆泰。多变量倾向分数用于将个体匹配控制混杂因素,并使用逻辑回归模型来估计迁移目的地与健康状况不佳的风险(迁移后十年)之间的关联。最初,我们发现一些证据表明较贫穷的社区与健康差的风险增加有关。在迁移之前控制个人的健康状况后,邻里社会经济背景的影响是统计上无关紧要的。我们的研究结果表明,健康选择性迁移可能有助于解释邻里级别因素和个人级别健康之间的关联。我们的研究设计对于识别邻里的因果效应以及核算健康选择性迁移的原因。

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