title: Association between variables used in the field substitution and post-stratification adjustment in the Belgian health interview survey and non-response.
abstract: OBJECTIVE
  Field substitution and post-stratification adjustment have been proposed to reduce non-response bias in population surveys. We investigated if variables involved in those techniques in the Belgian health interview survey 2004 are associated with non-response and assessed the impact of field substitution and post-stratification adjustment on the survey results.
  
  
  METHODS
  Data were obtained from all selected households (n = 12.204). The association between non-response and the selected variables was explored through multilevel logistic regression models with municipality and statistical sector as random effects.
  
  
  RESULTS
  All investigated variables were significantly related with non-response. Especially households that could not be contacted differed substantially from those who participated. Only post-stratification had a clear impact on the survey results.
  
  
  CONCLUSIONS
  Even if variables used in the field substitution procedure of health surveys are strongly associated with non-response, the impact of field substitution on the survey results may be minimal, either because there was no bias of relevance or it was not captured. The usefulness of field substitution to correct for non-response bias in population health surveys seems to be quite limited.
authors.affiliation: Scientific Institute of Public Health, Brussels, Belgium, johan.vanderheyden@wiv-isp.be.
authors.forename: Johan
authors.initials: J
authors.lastname: Van der Heyden
authors.forename: Stefaan
authors.initials: S
authors.lastname: Demarest
authors.forename: Koen
authors.initials: K
authors.lastname: Van Herck
authors.forename: Dirk
authors.initials: D
authors.lastname: De Bacquer
authors.forename: Jean
authors.initials: J
authors.lastname: Tafforeau
authors.forename: Herman
authors.initials: H
authors.lastname: Van Oyen
mesh_terms: D000328:Adult; D000368:Aged; D001530:Belgium; D015982:Bias; D003627:Data Interpretation, Statistical; D005260:Female; D006306:Health Surveys / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D007407:Interviews as Topic; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D055815:Young Adult