Using Administrative and Research Data to Investigate Risk and Protective Factors for Smoking During Pregnancy

项目总结：本研究使用妊娠期间吸烟（SDP）调查风险- 成瘾的过程。虽然SDP的社会人口风险因素的人口水平趋势和 与母体SDP相关的对后代的不利影响是公认的， 对个体风险预测及其临床应用的理解仍然不完整。我将实施一项"大 数据"方法使用交叉链接的研究数据（来自一个大型女性双胞胎队列，前瞻性地从 中位年龄15岁;与美国土著母亲的新数据收集）和国家个人水平的活力和驾驶 记录数据（SDP，社会人口统计学，地理坐标），以表征个人和 环境因素赋予风险或保护在整个怀孕期间继续吸烟。 目标：（1.1）使用状态数据表征社会人口统计学和邻里对SDP的影响的相互作用 100，000例出生，并检查SDP中种族差异的潜在混杂因素;（1.2）使用交联 研究和国家数据，以表征个别风险因素（尼古丁依赖，精神和 创伤史）作为SDP的预测因子，包括它们与社区/社会人口统计学汇总的相互作用 从（1.1）中得出的风险评分;交叉验证出生记录SDP报告;以及（2）从状态重要性确定 记录了一个新的队列的美国印第安人（AI）的母亲（一个队列具有非常高的SDP率）的试点 回顾性研究，将1.2中的发现扩展到研究不足的组，并解决现有研究的局限性。 研究未能确定特定的文化因素与SDP在这一组。 方法：国家出生记录/驾驶执照数据（SDP，青少年出生，DUI），汇总到人口普查区， 将用于预测SDP风险，分别用于母亲在青少年时期和 怀孕，以补充标准的人口普查/美国社区调查的社会经济劣势 预测器逻辑回归将用于（1.1）中，以生成用于分析的汇总风险评分， 研究数据（1.2），降低假阳性结果的风险。标准评估经过优化， 人工智能人群将用于对一小部分人工智能女性的回顾性访谈。 预测结果：证明种族特异性交互作用对个体SDP风险的贡献， 社会文化，社会人口和邻里特征，允许量身定制的个人风险预测。 候选人，培训：申请人是领先培训机构的医学博士-博士候选人 成功的独立医生科学家，与一个坚定承诺的指导团队合作，并使用 独特的资源。拟议的培训计划提供新的概念和技术培训， 一系列职业发展活动、讲习班和正式的教学机会，以支持发展， 一个独立的医生科学家，致力于提高对成瘾的理解，评估和治疗， 特别是在研究不足/服务不足的群体中。

PROJECT SUMMARY: This research uses cigarette smoking during pregnancy (SDP) for investigating risk- processes in addiction. While population-level trends on sociodemographic risk factors for SDP and the adverse effects on offspring associated with maternal SDP are well established, the more complex task of understanding individual risk-prediction and its clinical application remains incomplete. I will implement a “big data” approach using cross-linked research data (from a large female twin cohort, followed prospectively from median age 15; new data collection with Native American mothers) and state individual-level vital and driving records data (SDP, sociodemographics, geocoordinates) to characterize the interplay between individual and environmental factors conferring risk or protection for continued smoking throughout pregnancy. AIMS: (1.1) characterize interplay of sociodemographic and neighborhood influences on SDP using state data on 100,000s of births, and examine potential confounders of racial disparities in SDP; (1.2) use cross-linked research and state data to characterize effects of individual risk-factors (nicotine dependence, psychiatric and trauma history) as predictors of SDP, including their interplay with a summary neighborhood/sociodemographic risk-score derived from in (1.1); and cross-validate birth record SDP report; and (2) ascertain from state vital records a new cohort of American Indian (AI) mothers (a cohort with very high rates of SDP) for a pilot retrospective study, to extend findings in 1.2 to an understudied group and to address limitations of existing research’s failure to identify specific cultural factors associated with SDP in this group. METHODS: State birth record/driver’s license data (SDP, teen births, DUIs), aggregated to the census tract, will be used for prediction of SDP risk, separately for mother’s residence during teen years and at time of pregnancy, to supplement standard Census/American Community Survey socioeconomic disadvantage predictors. Logistic regression will be used in (1.1) to generate a summary risk score for analyses with research data in (1.2), reducing the risk of false-positive findings. Standard assessments optimized for use with AI populations will be used in retrospective interviews of a small series of AI women. PREDICTED RESULTS: Demonstration of race-specific interactive contributions to SDP risk of individual, sociocultural, sociodemographic, and neighborhood characteristics, allowing tailored individual risk prediction. CANDIDATE, TRAINING: The applicant is an MD-PhD candidate at a leading institution for training successful independent physician-scientists, working with a strongly committed mentoring team, and using unique resources. The proposed training plan provides new conceptual and technical training with an outlined set of career development activities, workshops, and formal didactic opportunities, to support development as an independent physician-scientist working to improve understanding, assessment, and treatment of addiction, particularly in understudied/underserved groups.