Integrating data for causal inference in behavioral health

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 continued 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 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 receive interdisciplinary team-based training and acquire a deep understanding of all three areas. This training program capitalizes on the rich resources for behavioral health, analytic and computational methods, and biostatistics at the Johns Hopkins Bloomberg School of Public Health (BSPH) and the broader University. The program is housed in the Department of Mental Health; trainees come from any of the four social science oriented departments at BSPH: 1) Mental Health; 2) Health Behavior & Society; 3) Health Policy & Management; and 4) Population Family & Reproductive Health. Faculty from Biostatistics serve as Affiliated faculty and provide additional mentoring and research opportunities. Further, the training grant leverages close connections with data scientists, statisticians, and computer scientists from across the University. Trainees obtain the skills and experiences needed to lead multi-disciplinary, collaborative research teams. Trainees 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 takes additional elective courses in social and behavioral perspectives on mental health and substance use, informatics and computational skills, and causal and statistical inference. Trainees participate in a biweekly 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 BSPH; these areas also are highlighted as priorities by OBSSR, NIMH, and NIDA. The trainees are supported by an experienced group of 13 core faculty and each trainee will be co-advised by one of 9 affiliated faculty with methodological expertise. The training program directors, Dr. Elizabeth Stuart and Dr. Rashelle Musci are national leaders in analytic tools for behavioral health and will be supported by a 3-member internal Executive Committee and a 4-member external Advisory Committee. The overarching aim of the program is to identify and train scholars who will become leaders in using a variety of advanced analytic tools and data to answer key questions in behavioral health.