The "Residential" Effect Fallacy in Neighborhood and Health Studies Formal Definition, Empirical Identification, and Correction
The "Residential" Effect Fallacy in Neighborhood and Health Studies Formal Definition, Empirical Identification, and Correction
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DOI：
10.1097/ede.0000000000000726
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发表时间：
2017-11-01
期刊：
影响因子：
5.4
通讯作者：
Kestens, Yan
中科院分区：
文献类型：
--
作者：
Chaix, Basile;Duncan, Dustin;Kestens, Yan
背景资料：由于混杂的城市/农村和社会经济组织的领土和住宅和非住宅暴露之间的相关性，经典估计的住宅邻里结果协会捕捉非住宅环境的影响，高估了住宅干预的影响。我们的研究诊断和纠正这种“住宅”效应谬误的偏见适用于一个大部分的邻里和健康study.Methods：我们的实证应用研究的影响，假设的干预措施，提高住宅数量的服务将有一个旅行的概率是走。使用全球定位系统跟踪和流动情况调查7天以上（227名参与者和7440次旅行），我们采用了一个多层次线性概率模型来估计居民服务数量和步行之间的旅行水平关联，以得出一个朴素的干预效果估计和一个校正模型，该模型考虑了居民，旅行起源，和出行目的地，以确定一个校正的干预效果估计（真正的效果条件的假设）。结果：有一个很强的相关性，在服务密度之间的居民区和非居民的地方。从朴素的模型，假设干预措施，提高住宅数量的服务，200，500，1000与增加0.020，0.055，0.109的概率在干预组的步行。校正后的估计值分别为0.007、0.019和0.039。因此，天真的估计被高估的乘数为3.0，2.9和2.8。结论：通常估计的住宅干预结果的关联大大高估了真正的效果。我们有些矛盾的结论是，估计住宅的影响，调查人员迫切需要非住宅访问的地方的信息。
Background: Because of confounding from the urban/rural and socioeconomic organizations of territories and resulting correlation between residential and nonresidential exposures, classically estimated residential neighborhood-outcome associations capture nonresidential environment effects, overestimating residential intervention effects. Our study diagnosed and corrected this "residential" effect fallacy bias applicable to a large fraction of neighborhood and health studies.Methods: Our empirical application investigated the effect that hypothetical interventions raising the residential number of services would have on the probability that a trip is walked. Using global positioning systems tracking and mobility surveys over 7 days (227 participants and 7440 trips), we employed a multilevel linear probability model to estimate the trip-level association between residential number of services and walking to derive a naive intervention effect estimate and a corrected model accounting for numbers of services at the residence, trip origin, and trip destination to determine a corrected intervention effect estimate (true effect conditional on assumptions).Results: There was a strong correlation in service densities between the residential neighborhood and nonresidential places. From the naive model, hypothetical interventions raising the residential number of services to 200, 500, and 1000 were associated with an increase by 0.020, 0.055, and 0.109 of the probability of walking in the intervention groups. Corrected estimates were of 0.007, 0.019, and 0.039. Thus, naive estimates were overestimated by multiplicative factors of 3.0, 2.9, and 2.8.Conclusions: Commonly estimated residential intervention-outcome associations substantially overestimate true effects. Our somewhat paradoxical conclusion is that to estimate residential effects, investigators critically need information on nonresidential places visited.