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

利用管理和研究数据调查怀孕期间吸烟的风险和保护因素

• 批准号: 9908687
• 负责人: ALEXANDRA NOEL HOUSTON-LUDLAM
• 金额: $ 3.14万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-10 至 2024-03-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9908687
• 关键词: Address; Adverse effects; African American; Age; Agreement; Alcohol abuse; Alcohol consumption; Alcohol dependence; American; American Indians; Automobile Driving; Big Data; Birth; Birth Records; Birth Weight; Censuses; Characteristics; Childbirth; Communities; Community Surveys; Complex; Data; Data Collection; Data Sources; Development; Doctor of Philosophy; Educational workshop; Elderly; Environmental Risk Factor; Ethnic Origin; Ethnic group; Exposure to; Failure; Family; Female; First Pregnancy Trimester; Health; Health Status; Individual; Individual Adjustment; Infant Mortality; Institution; Intervention; Interview; Licensing; Literature; Logistic Regressions; Low Birth Weight Infant; Marriage; Maternal Health; Measures; Mentors; Minority; Missouri; Mothers; Native Americans; Neighborhoods; Nicotine Dependence; Not Hispanic or Latino; Outcome; Patients; Perinatal mortality demographics; Phenotype; Physicians; Pilot Projects; Population; Pregnancy; Pregnancy Outcome; Prenatal care; Process; Prospective Studies; Psychopathology; Public Health; Race; Recording of previous events; Records; Reporting; Reproduction; Research; Research Project Summaries; Resources; Retrospective Studies; Retrospective cohort; Risk; Risk Behaviors; Risk Factors; Sampling; Scientist; Series; Siblings; Smoker; Smoking; Smoking History; Systematic Bias; Teenagers; Testing; Time; Tobacco Dependence; Tobacco use; Training; Translating; Trauma; Twin Multiple Birth; Woman; Work; addiction; career development; child bearing; cigarette smoking; clinical application; clinical practice; cohort; crosslink; demographics; ethnic difference; high risk; improved; informant; innovation; maternal cigarette smoking; mortality; offspring; predictive modeling; premature; prepregnancy; prospective; protective factors; racial disparity; racial identity; reproductive; residence; resilience; smoking cessation; smoking during pregnancy; social culture; sociodemographics; socioeconomic disadvantage; socioeconomics; teen birth; trend

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.

项目摘要：本研究利用怀孕期间吸烟 (SDP) 来调查风险 - 成瘾过程。虽然 SDP 和 SDP 的社会人口风险因素的人口水平趋势 与母体 SDP 相关的对后代的不利影响已得到充分证实，但更复杂的任务是 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“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，前瞻性地跟踪 中位年龄 15 岁；与美洲原住民母亲的新数据收集）以及国家个人层面的生命力和驾驶能力 记录数据（SDP、社会人口统计、地理坐标）来描述个体和群体之间的相互作用 环境因素赋予在整个怀孕期间持续吸烟的风险或保护。 目标：(1.1) 使用状态数据描述社会人口统计和邻里关系对 SDP 影响的相互作用 对十万个出生人口进行研究，并研究 SDP 中种族差异的潜在混杂因素； (1.2)使用交联 研究和国家数据来描述个体危险因素（尼古丁依赖、精神和疾病）的影响 创伤史）作为 SDP 的预测因素，包括它们与汇总社区/社会人口统计的相互作用 源自 (1.1) 的风险评分；交叉验证出生记录 SDP 报告； (2) 从国家重要信息中确定 为飞行员记录了一个新的美洲印第安人 (AI) 母亲群体（SDP 比率非常高的群体） 回顾性研究，将 1.2 中的发现扩展到未充分研究的群体，并解决现有研究的局限性 研究未能确定该群体中与 SDP 相关的特定文化因素。 方法：州出生记录/驾照数据（SDP、青少年出生、酒驾），汇总到人口普查区， 将用于预测 SDP 风险，分别针对青少年时期和成年时母亲的居住地 怀孕，以补充标准人口普查/美国社区调查的社会经济劣势 预测因子。逻辑回归将在 (1.1) 中用于生成分析的汇总风险评分 (1.2) 中的研究数据，降低了假阳性结果的风险。标准评估经过优化，可用于 人工智能人群将被用于对一小部分人工智能女性进行回顾性采访。 预测结果：展示特定种族的互动对个人 SDP 风险的贡献， 社会文化、社会人口统计和邻里特征，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候选人 成功的独立医师科学家，与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了新的概念和技术培训，并概述了 一系列职业发展活动、研讨会和正式的教学机会，以支持发展 一位独立的医师科学家，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。