Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.
Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Paediatr Perinat Epidemiol. 2024 Feb;38(2):102-110. doi: 10.1111/ppe.13021. Epub 2023 Nov 15.
Systematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates.
To 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.
We 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.
Out 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.
This 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.
生命统计记录中并非总是有系统记录的吸烟数据，即使有这些数据，也可能会低估真实的吸烟率。
基于出生证明信息，利用自我报告或提供者报告的吸烟情况和新生儿血斑中的生物标志物（吸烟代谢物）相结合，开发一种用于预测妊娠晚期产妇吸烟的模型，将其作为合金金标准。
我们设计了一项病例对照研究，从加利福尼亚癌症登记处确定儿童癌症病例，从 1983 年至 2011 年的加利福尼亚出生记录中选择无癌症且在 6 岁之前无癌症的对照，对其新生儿干血斑进行高分辨率代谢组学分析，从这些血斑中提取可替宁[质荷比（m/z）=177.1023]和羟基可替宁（m/z=193.0973）。从加利福尼亚出生证明中选择吸烟的潜在预测因子。使用逐步向后选择的逻辑回归建立预测模型。在训练样本、自举样本和外部验证样本中评估模型性能。
在输入逻辑模型的七个预测变量中，有五个通过逐步程序被选中：母亲的种族/民族、母亲的教育程度、孩子的出生年份、产次和孩子的出生体重。我们在训练集中计算出总体判别准确率为 0.72，接收者操作特征曲线（ROC）下面积（AUC）为 0.81（95%置信区间[CI]0.77，0.84）。在内部（AUC 0.81，95%CI 0.77，0.84）和外部（AUC 0.69，95%CI 0.64，0.74）验证集中也获得了相似的准确率。
当生命登记处缺乏产妇吸烟信息时，这种易于应用的模型可能会使未来基于出生登记处的研究受益；然而，当仅使用出生证明中的变量来预测产妇吸烟情况时，仍存在一些吸烟状况的错误分类问题。
