Health, Neighborhood Context, and Mobility

社区环境可能是经济流动性的上游原因，但很少有社区研究是这样 实验性的，削弱了因果推理，限制了政策翻译。我们的研究建议分析新的-- 可获得的数据来自一家大型联邦政府发起的志愿社区社会试验 在美国5个城市使用住房代金券进行搬迁(The Moving to Opportunity，MTO，Study)。我们的项目将测试 是否、如何以及在谁中间随机分配一个提议搬到一个较低贫困的社区 在15年的时间里，这对4600个低收入家庭的经济流动性产生了影响。MTO的目标是 中断邻里贫困对少数民族低收入家庭的连锁效应，增加面积-- 以获得机会为基础，从而促进经济流动性。尽管MTO代金券 干预并没有始终如一地影响经济结果，它确实深刻地影响了健康。不幸的是，健康 研究人员对MTO数据的访问有限，损害了7000多万美元的科学回报 投资。因此，到目前为止，我们还不清楚为什么结果可能会因结果、组 或者是什么机制在起作用，因为很少有研究测试调解或缓和。因为健康是 低收入父母经济自给自足的最重要障碍是，可以想象 经济和就业的增长集中在首先经历健康的家庭中 改进。然而，测试随机分配后发生的因素是否是 其他结果在方法上是具有挑战性的。幸运的是，最近在方法学上的发展 流行病学学科提供了令人信服的解决方案来模拟这种复杂性，并限制了任何偏见。我们建议 使用本实验新获得的15年随访数据进行二次数据分析，以测试 健康状况影响随机MTO住房代金券治疗的经济效果。再配上这个 实验设计，我们建议应用创新的因果方法和机器学习技术来 评估调解和效果修改，以克服传统的限制和潜在偏见 方法，加强因果推理。我们的R01项目建立在一个高效的跨学科团队的基础上， 具有MTO数据的经验，并借鉴了就业壁垒框架。我们提出四个目标： 确定：MTO对经济结果的影响是否受到基线健康脆弱性的影响； 如果他们或他们的孩子经历了健康增长，MTO是否改善了父母的经济结果； MTO是否改善了儿童的就业和教育，如果他们的健康有所改善；MTO是否 如果家庭搬到就业空间障碍较小的社区，经济结果会有所改善。 我们的发现具有直接的政策相关性，因为它们解决了住房代金券的一个关键政策问题 执行：纳入非住房部门的要素(例如，保健、就业、教育)，以 促进向机会更高的社区转移，最终改善低收入家庭的结果。

Neighborhood context may be an upstream cause of economic mobility, yet few neighborhood studies are experimental, weakening causal inference and limiting policy translation. Our study proposes to analyze newly- available data from a large, federal government initiated social experiment of voluntary neighborhood relocation using housing vouchers in 5 US cities (the Moving to Opportunity, MTO, Study). Our project will test whether, how, and among whom random assignment of an offer to move to a lower-poverty neighborhood influenced the economic mobility of 4600 low-income families over a 15 year period. The goal of MTO was to interrupt the cascading effects of neighborhood poverty for minority low-income families, by increasing area- based access to opportunities, and thereby promote economic mobility. Although the MTO voucher intervention did not consistently affect economic outcomes, it did profoundly affect health. Unfortunately, health researchers have had limited access to MTO data, compromising the scientific payoff of the $70+ million investment. So as of yet, we have no clear understanding of why results might differ across outcomes, groups, or what mechanisms are at play, since few studies have tested mediation or moderation. Since health is one of the most important barriers to economic self-sufficiency among low-income parents, it is conceivable that economic and employment gains were concentrated among families who first experienced health improvements. However testing whether factors that occurred after random assignment are precursors for other outcomes is methodologically challenging. Fortunately, recent methodological developments in the epidemiology discipline offer cogent solutions to model this complexity and bound any bias. We propose secondary data analyses with newly available 15 year follow-up data from this experiment, to test whether health influenced the economic effects of the randomized MTO housing voucher treatment. Paired with this experimental design, we propose to apply innovative causal methods and machine learning techniques for assessing mediation and effect modification, to overcome limitations and potential bias of traditional approaches, and strengthen causal inference. Our R01 project builds on a productive, interdisciplinary team, experienced with the MTO data, and draws on a barriers-to-employment framework. We propose 4 aims to determine: whether the effect of MTO on economic outcomes was modified by baseline health vulnerability; whether MTO improved parental economic outcomes, if they or their children experienced health gains; whether MTO improved children’s employment and education, if they experienced health gains; whether MTO improved economic outcomes, if families moved to neighborhoods with fewer spatial barriers to employment. Our findings have direct policy relevance since they address a key policy question in housing voucher implementation: incorporating elements from non-housing sectors (e.g., health, employment, education) to promote moves to higher opportunity neighborhoods, to ultimately improve outcomes for low income families.