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

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

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Abstract

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.

© 2011 Lippincott Williams & Wilkins, Inc.

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