Translational approaches to multilevel models of prenatal exposure to cigarettes

产前香烟暴露多层次模型的转化方法

• 批准号: 7764558
• 负责人: Vanja Dukic
• 金额: $ 19.74万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2010-08-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7764558
• 关键词: Accounting; Addictive Behavior; Address; Affect; Attention; Behavior; Behavioral; Biological; Biological Assay; Birth Weight; Blood; Boston; Brain; Calendar; Calibration; California; Child; Child Behavior; Cigarette; Classification; Collection; Communities; Complex; Computer software; Cotinine; Data; Data Collection; Data Set; Databases; Dental crowns; Development; Disclosure; Dose; Drug Kinetics; Epidemiologist; Error Sources; Exposure to; Family Study; Fetal Growth; Fetus; Funding; Future; Genes; Genetic; Genotype; Goals; Gold; Half-Life; Head circumference; Heel; Heterogeneity; Hour; Individual; Infant; Infant Development; Interview; Joints; Knowledge; Laboratories; Length; Light; Likelihood Functions; Literature; Longitudinal Studies; Manuals; Measurement; Measures; Metabolic; Metabolic Clearance Rate; Metabolism; Methodology; Methods; Modeling; Modification; National Institute of Drug Abuse; Nicotine; Nicotine Dependence; Outcome; Output; Parents; Patient Self-Report; Pattern; Perception; Performance; Perinatal; Phase; Physiological; Population; Population Heterogeneity; Pregnancy; Problem behavior; Prospective Studies; Protocols documentation; Publishing; Recommendation; Reporting; Research; Research Design; Research Personnel; Role; Saliva; Sampling; San Francisco; Schedule; Scheme; Self Disclosure; Serum; Smoke; Smoker; Smoking; Smoking Behavior; Smoking History; Source; Staging; Statistical Models; Techniques; Testing; Time; Universities; Urine; Validation; Variant; Woman; Work; Youth; base; computerized tools; cytochrome P-450 CYP2A6 (human); design; emotional disclosure; experience; fetal tobacco exposure; hydroxycotinine; improved; indexing; insight; interest; maternal cigarette smoking; model design; multilevel analysis; non-smoker; offspring; pharmacokinetic model; prenatal; prenatal exposure; prenatal smoking; pressure; prospective; psychological outcomes; public health relevance; research study; response; sample collection; self reported behavior; smoking cessation; social; software development; symposium; translational approach; trend; web site

DESCRIPTION (provided by applicant): Assessing smoking, like assessing and quantifying many other addictive behaviors, often suffers from recall bias, deliberate misreporting and even non-disclosure. This problem is particularly prevalent during pregnancy. For that reason, studies of effects of prenatal exposure to cigarettes frequently acquire both self- report and biologic assays (such as cotinine levels in urine or blood) of maternal smoking. However, although biological assays have been used to assert non-disclosure of smoking (simple "smoker" versus "non-smoker" status), little attention has been paid to using actual biological assays to calibrate self-reported measures. Methods for combining information from both self-report and bioassays would enhance the precision of smoking exposure measurement, and yield better understanding of the effects of smoking on a variety of physiological and psychological outcomes. For example, in pregnancy smoking studies, enhancing the quality of smoking exposure measurement could substantially advance the studies of teratologic effects of exposure on both physiological and behavioral development of offspring, as well as shed more light onto addictive behavior patterns in general. Recently, Dukic et al. (2007) have devised a method to combine self-report with biological assays (such as urine, serum or saliva cotinine), in order to extract a combined (and calibrated) measure of smoking exposure, based on the known metabolic models for decay of cotinine. Our findings thus far highlight the usefulness of using combined biological and self-reported measures as a predictor of child behavior outcomes. However, they also reveal some marked limitations of current designs for collection of biological measures of exposure in longitudinal studies, and the need for the development of better research protocols for assessing self-disclosure of smoking behavior. In the proposed research, some of the main limitation built into the original models will be alleviated in two main ways: 1) by proposing more realistic Bayesian models which incorporate metabolic and time variation, and information from other samples, and 2) by solving for optimal schemes for collection of biological samples. These results can have marked effects on research protocols designed to study effects of complex smoking behavior. The aims of the work in this project are thus three-fold: (R21-1) to obtain better models for smoking exposure that rely on both biological assays and self-report measures; (R21-2) use these models to recommend better data collection schedules for future studies and (R33-1) to validate and improve these models by predicting outcomes suggested to be related to smoking exposure (youth problem behavior and nicotine addiction), while accounting for genetic variability, in other prenatal and youth behavior datasets. It is important however to note that although this project focuses on smoking during pregnancy, the methodological advances developed would be directly applicable to non-pregnant smoker populations as well. PUBLIC HEALTH RELEVANCE: Though studies of effects of prenatal exposure to cigarettes frequently acquire both self-report and biologic assays (such as cotinine levels in urine or blood) of maternal smoking, little attention has been paid to methods for combining information from both sources in order to enhance the precision of exposure measurement. Both measures have their own source of bias -- single bioassay measure alone cannot reflect intricate metabolic mechanism over time, while self-report is subject to reporting, topographic, and metabolic biases - and information. This project is proposing to devise new Bayesian statistical models for prediction and calibration of smoking exposure measure, derived from combined biological and self-report information which can reflect metabolic differences among women and during pregnancy.

描述（由申请人提供）：评估吸烟，例如评估和量化许多其他成瘾性行为，通常会遭受召回偏见，故意错误报告甚至不披露。怀孕期间这个问题尤其普遍。因此，对产前暴露于香烟的影响的研究经常获得孕产妇吸烟的自我报告和生物学测定（例如尿液或血液中的可替宁水平）。但是，尽管已经使用生物学测定来主张吸烟的披露（简单的“吸烟者”与“非吸烟”状态），但很少关注使用实际的生物学测定来校准自我报告的措施。结合自我报告和生物测定信息的信息的方法将增强吸烟暴露测量的精度，并更好地了解吸烟对各种生理和心理结果的影响。例如，在妊娠吸烟研究中，提高吸烟暴露测量的质量可能会大大提高暴露对后代生理和行为发展的致力作用的研究，并在总体上向成瘾行为模式提供了更多的光线。最近，Dukic等人。 （2007年）设计了一种将自我报告与生物学测定（例如尿液，血清或唾液Cotinine）相结合的方法，以基于已知的cotinine衰减代谢模型提取吸烟暴露的合并（和校准）测量。迄今为止，我们的发现强调了将合并的生物学和自我报告措施作为儿童行为结果的预测指标的有用性。但是，他们还揭示了当前设计的一些明显局限性，用于收集纵向研究中的生物学暴露量，以及开发更好的研究方案以评估吸烟行为的自我披露。在拟议的研究中，原始模型中内置的一些主要限制将通过两种主要方式来缓解：1）提出更现实的贝叶斯模型，这些模型结合了代谢和时间变化，以及来自其他样品的信息，以及2）通过求解以收集生物样品的最佳方案。这些结果可能对旨在研究复杂吸烟行为影响的研究方案产生明显影响。因此，该项目中该作品的目的是三个方面：（R21-1）获得依赖生物学测定和自我报告措施的吸烟暴露模型的更好模型； （R21-2）使用这些模型为未来的研究推荐更好的数据收集时间表，（R33-1）通过预测建议与吸烟暴露有关的结果（青年问题行为和尼古丁成瘾）来验证和改进这些模型，同时考虑遗传变异性，在其他产前和青年行为数据集中。然而，重要的是要注意，尽管该项目重点是在怀孕期间吸烟，但开发的方法学进步也将直接适用于非孕妇吸烟者人群。 公共卫生相关性：尽管对产前暴露于卷烟的影响的研究经常获得孕产妇吸烟的自我报告和生物学测定（例如尿液或血液中的可替宁水平），但很少关注将两种来源组合信息组合的方法，以增强暴露的精度测量。这两种措施都有自己的偏见来源 - 单独的单个生物测定措施不能随着时间的流逝反映复杂的代谢机制，而自我报告则受到报告，地形和代谢偏见的约束 - 以及信息。该项目提议设计新的贝叶斯统计模型，以预测和校准吸烟暴露措施，这些模型是由合并的生物学和自我报告信息衍生而来的，这些信息可以反映妇女和怀孕期间的代谢差异。