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Daniel Vigo
Daniel Vigo
Assistant Professor MD, Lic. Psych, DrPH
604-822-2772
daniel.vigo@ubc.ca
2206 East Mall Vancouver, BC V6T 1Z3
About
Dr. Vigo is a psychiatrist, clinical psychologist, and public health specialist, originally from Argentina. He is currently an Assistant Professor at UBC, a Lecturer at Harvard Medical School, an Advisor to PAHO and WHO, as well as the Chair of the Services and Policy Workgroup of the WHO-World Mental Health Surveys Initiative. He is also the Regional Lead Psychiatrist for Assertive Community Treatment for the Province of BC and the incoming Editor-in-Chief of the International Journal of Mental Health Systems.
He has worked in clinical, research, teaching, and leadership positions across both the public and private sector. In these roles, he has published peer-reviewed articles, book chapters, and reports on public health, health systems, global mental health, psychiatric epidemiology, psychopharmacology, psychotherapy, and e-mental health. Dr. Vigo also leads several projects in BC and globally, including on E-Mental Health, prediction of risk of adverse mental health and substance use outcomes, and needs-based planning for mental and substance use disorder services. More information can be found at Dr. Vigo’s Mental Health Systems and Services Laboratory webpage.
Chair, Services and Policy Workgroup of the WHO-World Mental Health Surveys Initiative
Scientist, Centre for Advancing Health Outcomes
Dr. Vigo leads several projects at UBC, including the Needs-Based Planning for Mental and Substance Use Disorder Project, the Student E-Mental Health Project, and several psychiatric epidemiology studies of regional, national, and global scope. The goal of his e-Mental Health portfolio (which includes CIHR and Health Canada funded projects) is to create, administer and evaluate online e-interventions and screening tools, as well as to integrate them with existing brick and mortar services. He is also working in collaboration with the Computer Science department to develop algorithms to predict the risk of developing mental and substance use disorders due to COVID-19. By using machine learning techniques and applying those to health service utilization data, the ultimate goal is to facilitate access to effective treatments.