Department of Health Behavior and Policy, Virginia Commonwealth University, Richmond, VA.
RAND Corporation, Arlington, VA.
Nicotine Tob Res. 2021 Jun 8;23(7):1217-1223. doi: 10.1093/ntr/ntaa254.
Many children suffer from secondhand smoke exposure (SHSe), which leads to a variety of negative health consequences. However, there is no consensus on how clinicians can best query parents for possible SHSe among children. We employed a data-driven approach to create an efficient screening tool for clinicians to quickly and correctly identify children at risk for SHSe.
Survey data from mothers and biospecimens from children were ascertained from the Neurodevelopment and Improving Children's Health following Environmental Tobacco Smoke Exposure (NICHES) study. Included were mothers and their children whose saliva were assayed for cotinine (n = 351 pairs, mean child age = 5.6 years). Elastic net regression predicting SHSe, as indicated from cotinine concentration, was conducted on available smoking-related questions and cross-validated with 2015-2016 National Health and Nutrition Examination Survey (NHANES) data to select the most predictive items of SHSe among children (n = 1670, mean child age = 8.4 years).
Answering positively to at least one of the two final items ("During the past 30 days, did you smoke cigarettes at all?" and "Has anyone, including yourself, smoked tobacco in your home in the past 7 days?") showed area under the curve = .82, and good specificity (.88) and sensitivity (.74). These results were validated with similar items in the nationally representative NHANES sample, area under the curve = .82, specificity = .78, and sensitivity = .77.
Our data-driven approach identified and validated two items that may be useful as a screening tool for a speedy and accurate assessment of SHSe among children.
The current study used a rigorous data-driven approach to identify questions that could reliably predict SHSe among children. Using saliva cotinine concentration levels as a gold standard for determining SHSe, our analysis employing elastic net regression identified two questions that served as good classifier for distinguishing children who might be at risk for SHSe. The two items that we validated in the current study can be readily used by clinicians, such as pediatricians, as part of screening procedures to quickly identify whether children might be at risk for SHSe.
许多儿童遭受二手烟暴露（SHSe），这导致了各种负面的健康后果。然而，对于临床医生如何最好地询问父母儿童可能的 SHSe，目前还没有共识。我们采用数据驱动的方法为临床医生创建了一种有效的筛查工具，以便快速准确地识别有 SHSe 风险的儿童。
从环境烟草烟雾暴露后神经发育和改善儿童健康（NICHES）研究中的母亲和儿童的调查数据以及儿童的生物样本中确定了包括母亲和其唾液被检测出可替宁（n = 351 对，儿童平均年龄 = 5.6 岁）的儿童。在可利用的与吸烟有关的问题上进行预测可替宁浓度表明的 SHSe 的弹性网络回归，并与 2015-2016 年国家健康和营养检查调查（NHANES）数据进行交叉验证，以选择儿童 SHSe 最具预测性的项目（n = 1670，儿童平均年龄 = 8.4 岁）。
至少回答两个最终项目之一的“在过去的 30 天中，您是否吸烟？”和“在过去的 7 天中，包括您自己在内，是否有人在您家中吸烟？”的阳性答案显示曲线下面积为.82，特异性（.88）和敏感性（.74）良好。这些结果在具有代表性的 NHANES 样本中使用类似的项目进行了验证，曲线下面积为.82，特异性为.78，敏感性为.77。
我们的数据驱动方法确定并验证了两个项目，这些项目可能是一种有用的筛查工具，可快速准确地评估儿童的 SHSe。
本研究采用严格的数据驱动方法来识别可以可靠地预测儿童 SHSe 的问题。使用唾液可替宁浓度水平作为确定 SHSe 的金标准，我们的弹性网络回归分析确定了两个问题，它们可以作为区分可能有 SHSe 风险的儿童的良好分类器。我们在本研究中验证的两个项目可以由临床医生（如儿科医生）作为筛查程序的一部分方便地使用，以快速识别儿童是否有 SHSe 风险。
