《邻里与健康研究中的“居住”效应谬误：正式定义、实证识别与纠正》
The "Residential" Effect Fallacy in Neighborhood and Health Studies: Formal Definition, Empirical Identification, and Correction.
机构信息
From the aInserm, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris, France; bSorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris, France; cDepartment of Population Health, New York University School of Medicine, New York, NY; dUMR Géographie-Cités, CNRS, Paris, France; eDepartment of Urban Design and Planning, Urban Form Lab, University of Washington, Seattle, WA; fDepartment of Family Medicine and Public Health & Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA; and gDepartment of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada.
出版信息
Epidemiology. 2017 Nov;28(6):789-797. doi: 10.1097/EDE.0000000000000726.
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 naïve 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 naïve 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, naïve 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.
背景
由于城乡和社会经济组织的混杂，以及居住和非居住暴露之间的相关性，经典的估算居住邻里-结果关联捕捉到了非居住环境的影响，高估了居住干预的效果。我们的研究诊断并纠正了这种适用于很大一部分邻里和健康研究的“居住”效应谬误偏差。
方法
我们的实证应用研究了假设干预措施增加居住服务数量对步行出行概率的影响。我们使用全球定位系统跟踪和 7 天的移动调查（227 名参与者和 7440 次出行），采用多层次线性概率模型来估计居住服务数量与步行出行之间的出行层面关联，得出一个幼稚的干预效果估计值，以及一个纠正模型，该模型考虑了居住地点、出行起点和出行目的地的服务数量，以确定一个纠正的干预效果估计值（在假设条件下的真实效果）。
结果
居住社区和非居住场所的服务密度之间存在很强的相关性。从幼稚模型来看，假设干预措施将居住服务数量提高到 200、500 和 1000，与干预组中步行概率增加 0.020、0.055 和 0.109 相关。纠正后的估计值分别为 0.007、0.019 和 0.039。因此，幼稚估计值被高估了 3.0、2.9 和 2.8 倍。
结论
通常估计的居住干预-结果关联大大高估了真实效果。我们得出了一个有些矛盾的结论，即要估计居住效果，调查人员迫切需要有关访问过的非居住场所的信息。
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