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: 10511865
• 负责人: ALEXANDRA NOEL HOUSTON-LUDLAM
• 金额: $ 0.25万
• 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/10511865
• 关键词: Address; Adverse effects; African American population; 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; Minority Groups; Missouri; Mothers; Native Americans; Neighborhoods; Nicotine Dependence; Not Hispanic or Latino; Outcome; Patients; Perinatal mortality demographics; Persons; 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; Underserved Population; 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; risk prediction; 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：本研究使用怀孕期间吸烟（SDP）来研究风险 - 成瘾过程。而人口水平的SDP和SDP社会人口统计学风险因素的趋势 对与母体SDP相关的后代的不利影响已经建立得很好，这是更复杂的任务 了解个人风险预测及其临床应用仍然不完整。我将实施“大型 使用交联的研究数据（来自大型女性双胞胎队列的数据”方法 中位数15岁；与美国原住民母亲的新数据收集）和国家个人级别的生命和驾驶 记录数据（SDP，社会人口统计学，地理符号），以表征个人与 环境因素会导致在整个怀孕期间继续吸烟的风险或保护。 目的：（1.1）用状态数据来表征社会人口统计学和邻里对SDP的影响的相互作用 在100,000次出生中，并在SDP中检查了种族分布的潜在混杂因素； （1.2）使用交联 研究和状态数据以表征个人风险因素的影响（尼古丁依赖，精神病和 创伤历史）作为SDP的预测因素，包括与摘要社区/社会人口统计学的相互作用 来自（1.1）的风险评分；和跨估量出生记录SDP报告； （2）从国家至关重要 记录一个新的美国印第安人（AI）母亲（一个非常高的SDP率）的队列 回顾性研究，将1.2中的发现扩展到一个理解的小组，并解决现有的局限性 研究未能识别该组中与SDP相关的特定文化因素。 方法：州出生记录/驾驶执照数据（SDP，青少年出生，DUIS），汇总到人口普查区， 将用于预测SDP风险，在青少年时期和在 怀孕，以补充标准人口普查/美国社区调查社会经济障碍 预测指标。逻辑回归将在（1.1）中使用，以生成用于分析的摘要风险评分 （1.2）中的研究数据，降低了假阳性发现的风险。优化用于使用的标准评估 人工智能人群将用于对一系列AI妇女的回顾性采访。 预测结果：表明种族特异性互动贡献对个人的SDP风险的贡献， 社会文化，社会人口统计学和邻里特征，允许量身定制的个人风险预测。 候选人，培训：申请人是领先的培训机构的MD-PHD候选人 成功的独立身体科学家，与一个坚定的心理团队合作，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 一套职业发展活动，研讨会和正式的教学机会，以支持发展 一个独立的身体科学家，致力于改善成瘾的理解，评估和治疗， 特别是在理解/服务不足的群体中。