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.
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).
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.
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.
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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.
Editor’s Note: A commentary on this article appears on p. 798.
Submitted 18 June 2016; accepted 25 July 2017.
The authors report no conflicts of interest.
Supported by INPES (National Institute for Prevention and Health Education); the Ministry of Ecology (DGITM); Cerema (Centre for the Study of and Expertise on Risks, the Environment, Mobility, and Planning); ARS (Health Regional Agency) of Ile-de-France; STIF (Ile-de-France Transportation Authority); the Ile-de-France Regional Council; RATP (Paris Public Transportation Operator); and DRIEA (Regional and Interdepartmental Direction of Equipment and Planning) of Ile-de-France.
The code used for processing and analyzing the data is available from the first author upon request. The data are also available upon request, through a collaboration agreement.
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Basile Chaix, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75012 Paris, France. E-mail: email@example.com.