The second stage of our analysis examined the association between biomarkers of tobacco smoke exposure and infant birth weight. We chose to examine birth weight because there is a well-established inverse relationship between serum cotinine concentrations and infant birth weight [23 (link)-28 (link)].
We compared the results for the different biomarkers of tobacco smoke exposure since many cohorts only have the resources to collect one exposure measurement. We estimated and compared the associations between continuous log10-transformed prenatal serum cotinine and meconium tobacco smoke metabolite concentrations and infant birth weight using linear regression. Coefficients from these analyses represent the mean change in infant birth outcome for a 10-fold increase in tobacco smoke biomarker concentration. In addition, we examined the association between categorical serum and meconium tobacco smoke metabolite concentrations and infant birth weight. Serum cotinine concentrations were categorized using the thresholds described above. Several different meconium tobacco smoke metabolite concentrations were used to discriminate secondhand from active tobacco smoke exposure based on sensitivity and specificity analyses.
In all of the analyses examining the association between prenatal tobacco smoke exposure and infant birth weight, we adjusted for confounders identified using a directed acyclic graph (DAG) [29 (link)]. DAGs are a better method to assess the role of confounding variables compared to change in estimate and significance testing approaches [30 (link)]. Based on our DAG, all models included maternal age, maternal education, maternal race, marital status, depression (), and maternal weight (kg). We did not adjust for gestational age since it was an intermediary on the causal pathway between prenatal tobacco smoke exposure and infant birth outcomes.
We compared the results for the different biomarkers of tobacco smoke exposure since many cohorts only have the resources to collect one exposure measurement. We estimated and compared the associations between continuous log10-transformed prenatal serum cotinine and meconium tobacco smoke metabolite concentrations and infant birth weight using linear regression. Coefficients from these analyses represent the mean change in infant birth outcome for a 10-fold increase in tobacco smoke biomarker concentration. In addition, we examined the association between categorical serum and meconium tobacco smoke metabolite concentrations and infant birth weight. Serum cotinine concentrations were categorized using the thresholds described above. Several different meconium tobacco smoke metabolite concentrations were used to discriminate secondhand from active tobacco smoke exposure based on sensitivity and specificity analyses.
In all of the analyses examining the association between prenatal tobacco smoke exposure and infant birth weight, we adjusted for confounders identified using a directed acyclic graph (DAG) [29 (link)]. DAGs are a better method to assess the role of confounding variables compared to change in estimate and significance testing approaches [30 (link)]. Based on our DAG, all models included maternal age, maternal education, maternal race, marital status, depression (), and maternal weight (kg). We did not adjust for gestational age since it was an intermediary on the causal pathway between prenatal tobacco smoke exposure and infant birth outcomes.
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