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Neighborhood Effects on Health: Correcting Bias From Neighborhood Effects on Participation

Chaix, Basilea,b; Billaudeau, Nathaliea,b; Thomas, Frédériquec; Havard, Sabrinaa,b; Evans, Davida,b,d; Kestens, Yane,f; Bean, Kathyc

doi: 10.1097/EDE.0b013e3181fd2961
Methods: Original Article

Background: Studies of neighborhood effects on health that are based on cohort data are subject to bias induced by neighborhood-related selective study participation.

Methods: We used data from the RECORD Cohort Study (REsidential Environment and CORonary heart Disease) carried out in the Paris metropolitan area, France (n = 7233). We performed separate and joint modeling of neighborhood determinants of study participation and type-2 diabetes. We sought to identify selective participation related to neighborhood, and account for any biasing effect on the associations with diabetes.

Results: After controlling for individual characteristics, study participation was higher for people residing close to the health centers and in neighborhoods with high income, high property values, high proportion of the population looking for work, and low built surface and low building height (contextual effects adjusted for each other). After individual-level adjustment, the prevalence of diabetes was elevated in neighborhoods with the lowest levels of educational attainment (prevalence odds ratio = 1.56 [95% credible interval = 1.06-2.31]). Neighborhood effects on participation did not bias the association between neighborhood education and diabetes. However, residual geographic variations in participation weakly biased the neighborhood education-diabetes association. Bias correction through the joint modeling of neighborhood determinants of participation and diabetes resulted in an 18% decrease in the log prevalence odds ratio for low versus high neighborhood education.

Conclusions: Researchers should develop a comprehensive, theory-based model of neighborhood determinants of participation in their study, investigate resulting biases for the environment-health associations, and check that unexplained geographic variations in participation do not bias these environment-health relationships.


From the aInserm, U707, Research Unit in Epidemiology, Information Systems, and Modeling, Paris, France; bUniversité Pierre et Marie Curie-Paris6, UMR-S 707, Paris, France; cCentre d'Investigations Préventives et Cliniques, Paris, France; dEHESP School of Public Health, Rennes, France; eCentre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada; and fDepartment of Social and Preventive Medicine, Université de Montréal, Montreal, Canada.

Submitted 23 January 2010; accepted 2 July 2010.

Supported, as part of the RECORD project, by the National Research Agency (Agence Nationale de la Recherche) (Health-Environment Program 2005, 00153 05); the Institute for Public Health Research (Institut de Recherche en Santé Publique); the National Institute for Prevention and Health Education (Institut National de Prévention et d'Education pour la Santé) (Prevention Program 2007 074/07-DAS); the National Institute of Public Health Surveillance (Institut de Veille Sanitaire) (Territory and Health Program); the French Ministries of Research and Health (Epidemiologic Cohorts Grant 2008); the National Health Insurance Office for Salaried Workers (Caisse Nationale d'Assurance Maladie des Travailleurs Salariés); the Ile-de-France Health and Social Affairs Regional Direction (Direction Régionale des Affaires Sanitaires et Sociales d'Île-de-France); the Ile-de-France Public Health Regional Group (Groupement Régional de Santé Publique); the City of Paris (Ville de Paris); and the Ile-de-France Youth and Sports Regional Direction (Direction Régionale de la Jeunesse et des Sports).

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Editors' note: Commentaries on this article appear on pages 36 and 40.

Correspondence: Basile Chaix, Inserm U707, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75012, Paris, France. E-mail:

© 2011 Lippincott Williams & Wilkins, Inc.