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

基本信息

批准号：
10216165
负责人：
ALEXANDRA NOEL HOUSTON-LUDLAM
金额：
$ 3.2万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
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/10216165
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 Minority Groups 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 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

项目摘要

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) 进行风险调查 - 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人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；美国原住民母亲的新数据收集）以及州个人层面的活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 影响的相互作用十万个出生人口，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；并交叉验证出生记录 SDP 报告；以及 (2) 从州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，并优化了标准评估的使用。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，并使用拟议的培训计划提供了新的概念和技术培训，并概述了这一点。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。