title: Neighborhood effects on health: correcting bias from neighborhood effects on participation.
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.
authors.affiliation: Inserm, U707, Research Unit in Epidemiology, Information Systems, and Modeling, Paris, France. chaix@u707.jussieu.fr
authors.forename: Basile
authors.initials: B
authors.lastname: Chaix
authors.forename: Nathalie
authors.initials: N
authors.lastname: Billaudeau
authors.forename: Frédérique
authors.initials: F
authors.lastname: Thomas
authors.forename: Sabrina
authors.initials: S
authors.lastname: Havard
authors.forename: David
authors.initials: D
authors.lastname: Evans
authors.forename: Yan
authors.initials: Y
authors.lastname: Kestens
authors.forename: Kathy
authors.initials: K
authors.lastname: Bean
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003256:Community Participation; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010297:Paris / Q000453:epidemiology; D012107:Research Design; D012111:Residence Characteristics; D015983:Selection Bias