title: Using bayesian models to assess the effects of under-reporting of cannabis use on the association with birth defects, national birth defects prevention study, 1997-2005.
abstract: BACKGROUND
  Studies on associations between periconceptional cannabis exposure and birth defects have mainly relied on self-reported exposure. Therefore, the results may be biased due to under-reporting of the exposure. The aim of this study was to quantify the potential effects of this form of exposure misclassification.
  
  
  METHODS
  Using multivariable logistic regression, we re-analysed associations between periconceptional cannabis use and 20 specific birth defects using data from the National Birth Defects Prevention Study from 1997-2005 for 13 859 case infants and 6556 control infants. For seven birth defects, we implemented four Bayesian models based on various assumptions concerning the sensitivity of self-reported cannabis use to estimate odds ratios (ORs), adjusted for confounding and under-reporting of the exposure. We used information on sensitivity of self-reported cannabis use from the literature for prior assumptions.
  
  
  RESULTS
  The results unadjusted for under-reporting of the exposure showed an association between cannabis use and anencephaly (posterior OR 1.9 [95% credible interval (CRI) 1.1, 3.2]) which persisted after adjustment for potential exposure misclassification. Initially, no statistically significant associations were observed between cannabis use and the other birth defect categories studied. Although adjustment for under-reporting did not notably change these effect estimates, cannabis use was associated with esophageal atresia (posterior OR 1.7 [95% CRI 1.0, 2.9]), diaphragmatic hernia (posterior OR 1.8 [95% CRI 1.1, 3.0]), and gastroschisis (posterior OR 1.7 [95% CRI 1.2, 2.3]) after correction for exposure misclassification.
  
  
  CONCLUSIONS
  Under-reporting of the exposure may have obscured some cannabis-birth defect associations in previous studies. However, the resulting bias is likely to be limited.
authors.affiliation: Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Marleen M H J
authors.initials: MM
authors.lastname: van Gelder
authors.forename: A Rogier T
authors.initials: AR
authors.lastname: Donders
authors.forename: Owen
authors.initials: O
authors.lastname: Devine
authors.forename: Nel
authors.initials: N
authors.lastname: Roeleveld
authors.forename: Jennita
authors.initials: J
authors.lastname: Reefhuis
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D015982:Bias; D000013:Congenital Abnormalities / Q000453:epidemiology*; D005260:Female; D006801:Humans; D002189:Marijuana Abuse / Q000453:epidemiology*; D018811:Maternal Exposure / Q000706:statistics & numerical data*; D012650:Self Disclosure; D057566:Self Report; D014481:United States / Q000453:epidemiology
keywords: Anencephaly; Bayes theorem; Bias (epidemiology); Birth defects; Cannabis; Diaphragmatic hernia; Epidemiologic methods; Esophageal atresia; Gastroschisis; Maternal exposure