1. A prediction model for classifying maternal pregnancy smoking using California state birth certificate information
- Author
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He, Di, Huang, Xiwen, Arah, Onyebuchi A, Walker, Douglas I, Jones, Dean P, Ritz, Beate, and Heck, Julia E
- Subjects
Medical Biochemistry and Metabolomics ,Reproductive Medicine ,Biomedical and Clinical Sciences ,Health Sciences ,Clinical Research ,Pediatric ,Tobacco ,Perinatal Period - Conditions Originating in Perinatal Period ,Cancer ,Tobacco Smoke and Health ,Prevention ,Good Health and Well Being ,Child ,Female ,Humans ,Infant ,Newborn ,Pregnancy ,Birth Certificates ,California ,Case-Control Studies ,Neoplasms ,Smoking ,Tobacco Smoking ,Models ,Statistical ,birth certificates ,maternal pregnancy smoking ,neonatal blood spots ,prediction model ,tobacco biomarkers ,Paediatrics and Reproductive Medicine ,Public Health and Health Services ,Epidemiology ,Paediatrics ,Reproductive medicine - Abstract
BackgroundSystematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates.ObjectiveTo develop a prediction model for maternal tobacco smoking in late pregnancy based on birth certificate information using a combination of self- or provider-reported smoking and biomarkers (smoking metabolites) in neonatal blood spots as the alloyed gold standard.MethodsWe designed a case-control study where childhood cancer cases were identified from the California Cancer Registry and controls were from the California birth rolls between 1983 and 2011 who were cancer-free by the age of six. In this analysis, we included 894 control participants and performed high-resolution metabolomics analyses in their neonatal dried blood spots, where we extracted cotinine [mass-to-charge ratio (m/z) = 177.1023] and hydroxycotinine (m/z = 193.0973). Potential predictors of smoking were selected from California birth certificates. Logistic regression with stepwise backward selection was used to build a prediction model. Model performance was evaluated in a training sample, a bootstrapped sample, and an external validation sample.ResultsOut of seven predictor variables entered into the logistic model, five were selected by the stepwise procedure: maternal race/ethnicity, maternal education, child's birth year, parity, and child's birth weight. We calculated an overall discrimination accuracy of 0.72 and an area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval [CI] 0.77, 0.84) in the training set. Similar accuracies were achieved in the internal (AUC 0.81, 95% CI 0.77, 0.84) and external (AUC 0.69, 95% CI 0.64, 0.74) validation sets.ConclusionsThis easy-to-apply model may benefit future birth registry-based studies when there is missing maternal smoking information; however, some smoking status misclassification remains a concern when only variables from the birth certificate are used to predict maternal smoking.
- Published
- 2024