Moradzadeh R, et al. - Using a Bayesian approach that incorporated exposure misclassification probability estimates from the literature, researchers studied the link between childhood asthma and self-reported Maternal Smoking During Pregnancy (MSDP) after adjusting for a range of exposure misclassification scenarios. They noted that after adjusting for misclassification, the estimated impact of MSDP on asthma was strengthened in many scenarios given the assumptions and the accuracy of the bias model.

Methods

• Using National Health and Nutrition Examination Survey (NHANES) 2011-2012, researchers obtained self-reported MSDP and asthma data.
• They used a Bayesian bias model approach to determine the link between self-reported MSDP and asthma adjusted for exposure misclassification.

Results

• A total of 3074 subjects who were 1-15 years of age, including 492 asthma cases, were analyzed.
• Data showed that, among asthmatic and non-asthmatic groups, the mean (SD) of age of the participants was 8.5 (4.1) and 7.1 (4.2) years and the number (percentage) of female was 205 (42%) and 1314 (51%), respectively.
• In logistic regression adjusted for confounders, 1.28 (95% confidence interval (CI): 0.92, 1.77) was reported as the odds ratio (OR) for the link between self-reported MSDP and asthma.
• Between MSDP and asthma, different ORs were found by applying different priors (posterior ORs 0.90 (95% credible interval (CRI): 0.47, 1.60) to 3.05 (95% CRI: 1.73, 5.53) in differential and 1.22 (CRI 95%: 0.62, 2.25) to 1.60 CRI: 1.18, 2.19)in non-differential misclassification settings in a Bayesian analysis that adjusted for exposure misclassification using external data.