Young from CTBH on Vimeo.

November 18, 2016

Social media for predicting and changing behavioral health

Sean Young, PhD
Associate Professor
Executive Director
University of California Institute for Prediction Technology (UCIPT)
Department of Family Medicine
UCLA


 

About the Presenter:  

Sean D. Young, PhD, MS is an Associate Professor in the UCLA Department of Family Medicine. Trained as a social psychologist, health economist, and social entrepreneur, Dr. Young’s research focuses on two main areas: 1) using social “big data” to monitor and predict public health issues such as HIV, drug addiction, and crime, and 2) designing and testing technologies to address public health and medical issues among at-risk populations, including African Americans, Latinos, and men who have sex with men (MSM). He teaches a course each Spring quarter on how to quickly build low-cost technologies to address global health and poverty-related issues. He designed a technology platform (healthcheckins.com) being tested among UCLA Health System patients to improve their behavioral and mental health.


November 4, 2016

Comparing e-health vs. in-person delivered family psycho-educational treatment for schizophrenia

Armando J. Rotondi, Ph.D.
Associate Professor, Department of Critical Care Medicine and Department of Health Policy and Management


 

About the Presenter:

Dr. Rotondi focuses on health systems engineering and health services research methods. His research involves the design, development, and testing of systems to improve the quality and cost performance of health, mental health, and social services and access to these services. His work to date has included the evaluation of the experiences of patients receiving intensive care services; the design of e-health systems to provide therapy and adjunctive services to persons with severe chronic illness and their families and to deliver these services to consumers' homes; and the assessment of patient and family service needs at a state-wide level.


 

O'Malley from CTBH on Vimeo.

October 28, 2016

Modeling a Bivariate Residential-Workplace Neighborhood Effect when Estimating the Effect of Proximity to Fast-Food Establishments on Body Mass Index

James O’Malley, PhD
Professor of Biostatistics
Department of Biomedical Data Science
The Dartmouth Institute for Health Policy and Clinical Practice
Geisel School of Medicine at Dartmouth


 

About the Presentation:  

Hierarchical modeling is the preferred approach of modeling neighborhood effects. Given residential and workplace location indicators, a bivariate (residential-workplace) neighborhood random effect whose correlation quantifies the extent that a neighborhood's residential effect correlates with its workplace effect may be specified. However, statistical model-estimation software typically does not allow correlations between the effects of different clustering variables. We develop a Bayesian model with a bivariate random effect for neighborhood and an accompanying estimation procedure. The model accounts for individuals who reside or work in multiple neighborhoods across their observations, individual heterogeneity (random intercepts, random slopes), and serial correlation between observations on the same individual. We apply the models to the motivating Framingham heart study linked food establishment data to examine whether (i) Proximity to fast-food establishments is associated with Body Mass Index (BMI); (ii) Workplace neighborhood exposure associations are larger than those for residential exposure; (iii) Residential neighborhood exposure associations correlate with workplace neighborhood exposure. For robustness, we perform analyses under multiple specifications of the prior distribution of the neighborhood random effect covariance matrix. In addition, we show that allowing for time varying neighborhood membership, individual heterogeneity, and serial correlation across time yields more precise neighborhood level estimates.

About the Presenter:  

Dr. O’Malley’s methodological research interests have centered on social network analysis, causal inference (comparative effectiveness research), multivariate-hierarchical modeling, and previously the design and analysis of medical device clinical trials. In these, he has developed novel statistical methods, often involving novel use of Bayesian statistical methods, to solve important methodological and applied problems in health policy and health services research, including the evaluation of treatments and outcomes of health care in multiple areas of medicine. This has led to advances in interventional cardiology, vascular surgery, measuring quality of health care, mental health and long-term care.

Dr. O’Malley’s primary teaching activities currently include teaching a statistics class to medical and health policy students, guest lectures in other courses, and presenting short courses at conferences and other forums. His educational activities include a large number of invited seminars; mentoring colleagues, post-doctoral fellows and students; and service to the statistics profession. In addition, his service to the statistics profession includes eight-years at the forefront of the Health Policy Statistics Section of the ASA, associate editorships at both Statistics in Medicine and Health Services and Outcomes Research Methodology, and as reviewer for over 20 respected academic journals.

In recognition of many of Dr. O’Malley’s above contributions, he was elected fellow of the American Statistical Association (ASA) in 2012 and awarded the 2011 Mid-Career Award by the Health Policy Statistics Section of the ASA (a single award is given biannually).


October 12, 2016

Sponsored by the Center for Social Brain Sciences, the Center for Cognitive Neuroscience, and CTBH

The future of fMRI in cognitive neuroscience 

Russell A. Poldrack, PhD
Albert Ray Lang Professor of Psychology
Stanford University


About the Presentation:  

Cognitive neuroscience has witnessed two decades of rapid growth, thanks in large part to the continued development of fMRI methods.  In this talk, Dr. Poldrack will question what this work has told us about brain function, and will propose three new directions that he sees as being crucial to the ultimate success of cognitive neuroscience. First, Dr. Poldrack will discuss the need for approaches that allow selective associations between mental operations and representations and brain activity.  Second, he will discuss whether current ontologies of mental function are sufficient to support a robust cognitive neuroscience, and ask whether new ontologies might be developed in a data-driven way.  Finally, he will discuss the need for neuroimaging researchers to adopt practices to improve reproducibility and transparency.

About the Presenter:  

Russell Poldrack is the Albert Ray Lang Professor of Psychology at Stanford, and Director of the Center for Reproducible Neuroscience.  He received his Ph.D in cognitive psychology from the University of Illinois, and has previously held faculty positions at Harvard Medical School, UCLA, and the University of Texas.  His research uses neuroimaging to investigate the brain systems underlying decision making and executive function.  His lab has also developed or contributed to a number of tools to improve transparency and reproducibility in neuroimaging research, including the OpenfMRI and Neurovault data sharing projects, the Brain Imaging Data Structure (BIDS) initiative, the Neurosynth project, and the Cognitive Atlas ontology.


May 6, 2016 

Using the Electronic Health Record as a Platform for Research and Clinical Care 

Co-hosted by the Northeast Node of the Clinical Trials Network 

Constance M. Weisner, DrPH, MSW
Research Scientist, Kaiser Permanente Northern California Division of Research
Professor, Department of Psychiatry, University of California, San Francisco


 

About the Presentation:  

The presentation will address the role of the electronic health record (EHR) in conducting research in “learning healthcare systems”, and how research can be combined with clinical care. It describes the different types of studies that can be conducted through the EHR platform and the potential for Health-IT interventions and research more generally.  Case studies of research on adults and adolescents in primary care and in specialty substance use treatment are presented, as well as descriptions of other types of studies, including comparative effectiveness research and registries.

About the Presenter:

Constance Weisner, DrPH, MSW, is the Chief of Behavioral Health, Aging, and Infectious Diseases at the Division of Research, Kaiser Permanente and a Professor at the Department of Psychiatry, University of California, San Francisco. She has a doctorate in Public Health from the University of California, Berkeley and a Masters in Social Work from the University of Minnesota. She directs a research program addressing access, outcome, and cost effectiveness of substance use and mental health treatment. She is a member of the International Expert Advisory Group on Alcohol and Drug Dependence of the World Health Organization and the NIAAA National Advisory Council. She has also been a member of the National Advisory Council of the National Institute on Drug Abuse and of the National Advisory Council for the Center for Substance Abuse Treatment. She has participated on several Institute of Medicine committees, most recently Improving the Quality of Health Care for Mental and Substance-Use Conditions, and Prevention, Diagnosis, Treatment and Management of Substance Use Disorders in the U.S. Armed Forces. Her on-going work focuses on integrating alcohol, drug, and mental health services with health care.