Data integration for causal inference in behavioral health

行为健康，广义地定义为包括精神健康和物质使用，包括许多最 我们这个时代迫切的公共卫生问题。向数据丰富、网络互联社会的过渡带来了 产生了生成解决方案的机会，但它需要在以下方面实现劳动力培训的范式转变 数据分析。该培训计划的目标是培养学者成为使用先进技术的领导者 评估行为健康因果效应的计算方法和设计。为了实现这一目标， 我们将在以下方面提供严格的培训和高质量的指导：1)行为健康科学；2) 用于管理、分析和集成复杂数据源的计算和分析工具；以及3)因果关系 充分利用这些数据的推理方法。学员将获得基于跨学科团队的 培训，并将对所有这三个领域有深入的了解。这项培训计划将利用 约翰斯的行为健康、分析和计算方法以及生物统计学方面的丰富资源 霍普金斯大学彭博公共卫生学院(JHSPH)和更广泛的大学。该计划将被安置在 但每年的5名实习生将来自四个社会科学中的任何一个 JHSPH面向的部门：1)精神健康；2)健康行为和社会；3)健康政策和 管理；4)人口、家庭和生殖健康。此外，培训补助金将利用CLOSE 与来自大学各地的数据科学家、统计学家和计算机科学家的联系。实习生将 获得领导多学科协作研究团队所需的技能和经验。实习生将 在公共健康和行为健康的核心领域进行严格的课程设计 包括行为和社会科学、流行病学、生物统计学、数据科学、人口健康 信息学、因果推论和研究伦理。此外，每名学员还将选修额外的课程。 在关于精神健康和物质使用、信息学和计算技能的社会和行为视角中， 以及因果和统计推断。学员将参加为期一年的行为分析研讨会 健康，两周一次的研讨会，讨论正在进行的研究和专业发展，持续指导 研究项目和综合活动，以补充他们的教学课程。的重点领域 计划建立在JHSPH的优势之上；这些领域也被OBSSR、NIMH、 和NIDA。受训人员将得到由21名核心教员组成的经验丰富的小组的支持，每个受训人员将 由9名具有方法论专长的附属教员之一担任共同顾问。培训项目主任Dr。 伊丽莎白·斯图尔特是行为健康分析工具的全国领导者，她将得到4- 成员内部执行委员会和由5名成员组成的外部咨询委员会。最重要的目标 该计划的目的是确定和培训将成为使用各种先进技术的领导者的学者 用于回答行为健康关键问题的分析工具和数据。

Behavioral health, broadly defined to include mental health and substance use, includes many of the most pressing public health problems of our time. The transition to a data-rich, web-interconnected society has generated an opportunity to generate solutions, but it necessitates a paradigm shift in workforce training in data analytics. The goal of this training program is to train scholars to become leaders in the use of advanced computational methods and designs to estimate causal effects in behavioral health. To accomplish this goal, we will provide rigorous training and high-quality mentorship in: 1) the science of behavioral health; 2) computational and analytic tools to manage, analyze, and integrate complex data sources; and 3) causal inference methods to take full advantage of these data. Trainees will receive interdisciplinary team-based training and will acquire a deep understanding of all three areas. This training program will capitalize on the rich resources for behavioral health, analytic and computational methods, and biostatistics at the Johns Hopkins Bloomberg School of Public Health (JHSPH) and the broader University. The program will be housed in the Department of Mental Health but the 5 trainees per year will come from any of the four social science oriented departments at JHSPH: 1) Mental Health; 2) Health Behavior & Society; 3) Health Policy & Management; and 4) Population Family & Reproductive Health. Further, the training grant will leverage close connections with data scientists, statisticians, and computer scientists from across the University. Trainees will obtain the skills and experiences needed to lead multi-disciplinary, collaborative research teams. Trainees will undertake a rigorous program of coursework in the core domains of public health and behavioral health including behavioral and social science, epidemiology, biostatistics, data science, population health informatics, causal inference, and research ethics. In addition, each trainee will take additional elective courses in social and behavioral perspectives on mental health and substance use, informatics and computational skills, and causal and statistical inference. Trainees will participate in a year-long seminar on analytics for behavioral health, a bi-weekly seminar to discuss research in progress and professional development, ongoing mentored research projects, and integrative activities to complement their didactic curriculum. The focus area of the program builds on strengths within JHSPH; these areas also are highlighted as priorities by OBSSR, NIMH, and NIDA. The trainees will be supported by an experienced group of 21 core faculty and each trainee will be co-advised by one of 9 affiliated faculty with methodological expertise. The training program director, Dr. Elizabeth Stuart, is a national leader in analytic tools for behavioral health, and will be supported by a 4- member internal Executive Committee and a 5-member external Advisory Committee. The overarching aim of the program is to identify and train scholars who will become leaders in using a diversity of advanced analytic tools and data to answer key questions in behavioral health.