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.

项目概述：这项研究使用怀孕期间吸烟(SDP)来调查风险- 上瘾的过程。虽然人口水平的社会人口风险因素对SDP和 与母亲SDP相关的对后代的不利影响是公认的，更复杂的任务是 对个体风险预测及其临床应用的理解仍不完整。我会实施一个“大的 使用交叉链接研究数据的方法(来自一大批女性双胞胎队列，前瞻性地从 中位年龄15岁；新的数据收集与美洲原住民母亲)和州个人层面的生命和驾驶 记录数据(SDP、社会人口统计、地理坐标)，以表征个人和 在怀孕期间持续吸烟的危险或保护环境因素。 目标：(1.1)使用州数据描述社会人口和邻里关系对SDP的影响 对100,000名新生儿进行调查，并检查SDP中种族差异的潜在混杂因素；(1.2)使用交叉链接 研究和国家数据，以表征个人风险因素的影响(尼古丁依赖、精神疾病和 创伤病史)作为SDP的预测因素，包括它们与社区/社会人口统计的相互作用 风险评分源自IN(1.1)；交叉验证出生记录SDP报告；以及(2)根据生命垂危状态确定 为飞行员记录了一组新的美国印第安人(AI)母亲(SDP率非常高的队列) 回溯性研究，将1.2中的发现扩展到研究不足的群体，并解决现有 研究未能确定与这一群体中的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、酒后驾驶)，汇总到人口普查区域， 将用于预测SDP风险，分别用于青少年时期的母亲住所和 怀孕，以补充标准人口普查/美国社区调查的社会经济劣势 预测者。在(1.1)中将使用Logistic回归来生成用于以下分析的汇总风险分数 (1.2)中的研究数据，降低了假阳性结果的风险。针对与一起使用而优化的标准评估 人工智能人口将被用于对一小系列人工智能女性的回顾采访。 预测结果：显示特定种族的交互作用对个人的SDP风险的影响， 社会文化、社会人口学和邻里特征，允许量身定做的个人风险预测。 应聘者，培训：申请人是一家顶尖培训机构的医学博士研究生 成功的独立医生-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，概述了 一套职业发展活动、讲习班和正式的教学机会，以支持发展 一位致力于改善对成瘾的理解、评估和治疗的独立内科科学家， 特别是在研究不足/服务不足的群体中。