The Effects of the COVID-19 Pandemic on Physician Delivery of Virtual and In-person Mental Health Care Services across Reformed Primary Care Payment Models in Ontario

Led by Dr. Nibene Somé, the project aims to improve access to mental health care in primary care settings using lessons learned during the COVID-19 pandemic, the Ontario Ministry of Health (MOH) approved new physician billing codes for virtual assessments by telephone or video. These changes in the delivery of care and the payment received through different primary care models may have affected family physicians' behaviour regarding the quantity of virtual and in-person mental health care provided. This research will mobilize knowledge to inform the MOH policymakers on how mental health services can be effectively integrated and delivered to Ontarians during and after the pandemic through primary care. We will determine which payment model maximizes virtual and in-person mental health care and how important are the temporary virtual visit codes recently introduced for mental health care delivery. This study will provide the initial groundwork for a more extensive examination of patient-level experiences and mental health outcomes observed before and after the pandemic, including clinical data on patient mental health and diverse patient perspectives to understand population experiences accessing and receiving virtual care in future research. The main objectives of this study are to compare physician provision of virtual and in-person mental health care services before and after the COVID-19 pandemic in Ontario's dominant primary care delivery models: blended fee-for-service, blended capitation, and team-based primary care. We will also apply a sex and age lens in the proposed research to provide new evidence about the impact of sex and age differences in primary care physicians' mental health care delivery and propose action to address them. To achieve these objectives, we will use Ontario health administrative data held at ICES and work collaboratively with the MOH to compare patterns overtime before and during the first two years of the pandemic.

Dr. Nibene Somé's project, COVID-19 Risk Factors, Inequalities and Health-Care Utilization and Costs in People with Mental Health and Substance Use/Addiction Problems, involves the utilization of machine learning algorithms to identify the most important risk factors for COVID-19 illness, hospitalization, and mortality due to COVID-19 among individuals with mental health and addiction problems. This research also aims to evaluate potential inequalities in access to COVID-19 testing and estimate health care costs and utilization patterns before and during COVID-19. The Ontario Ministry of Health funded this project through its research program: The COVID-19 Challenge Initiative: Challenge Questions Research Areas. Although this research has many components and involves many studies, the ultimate goal is to help shape future interventions for people with mental health and addiction problems by (i) supporting health authorities in developing preventative strategies for people with mental health and addiction problems; (ii) characterizing people at high-risk for contracting COVID-19 and estimating the potential costs of care associated to these individuals; and (iii) highlighting potentially avoidable inequalities in testing including geographical inequities. This study will use several linked health administrative databases from ICES.

Dr. Nibene H. Somé is an Independent Scientist and Health Economist with the Institute for Mental Health Policy Research at CAMH. He is also an Adjunct Assistant Professor in the Department of Epidemiology and Biostatistics at Western University, an Adjunct Lecturer in the Institute of Health Policy, Management and Evaluation at the University of Toronto, and an ICES fellow.

Areas of research

Dr. Somé applies economic, statistical/econometric, and machine learning methods in health economics and health policy and practice. His research covers three specific areas using administrative and survey data: (i) evaluating health care policies that are designed to affect the provision of health and mental health services – physician’s compensation policy and government financial incentives programs, (ii) optimal delivery of mental health care into primary care settings, and (iii) understanding the impact of COVID-19 on mental health, substance use (i.e., alcohol and cannabis), health care costs, and utilization. He is interested in applying machine learning techniques in healthcare, particularly predicting suicide and suicidal behaviour using administrative and survey data.