Social Epidemiology Learning Series
Welcome to the Social Epidemiology Learning Series, a collection of recorded lectures developed through a collaboration between the Edwin S.H. Leong Centre for Healthy Children and the Edwin S.H. Leong Centre for Healthy Aging.
This series brings together leading experts from Canada and the United States to share both foundational, and more advanced concepts in social epidemiology. Across five recorded sessions, viewers will learn about different theories, methodological approaches, and applied examples used to understand and address health inequities.
The learning series is designed to support a deeper understanding of how social conditions shape health and why inequities persist. It introduces core concepts in social epidemiology alongside practical research tools and shows how these approaches are used to study complex health challenges in applied settings.
Each lecture can be watched on its own and at a time that works for you. Suggested readings are included for those who would like to explore topics in more depth.
Meet the Speakers
Rita Hamad, MD, PhD
Director, Social Policies for Health Equity Research (SPHERE) Center, Harvard School of Public Health
Read Rita’s Bio
Dr. Rita Hamad is a social epidemiologist and Director of the Social Policies for Health Equity Research (SPHERE) Center at the Harvard School of Public Health. Her research examines how social factors like poverty and education contribute to racial and socioeconomic health disparities across the life course. She specializes in evaluating the health effects of social and economic policies using interdisciplinary quasi-experimental methods to generate actionable evidence for policymaking.
Dr. Hamad serves as an Advisory Editor for Social Science & Medicine and an Associate Editor at Health Affairs Scholar. From 2020 to 2022, she was the James C. Puffer American Board of Family Medicine/National Academy of Medicine Fellow. She also provides consultation to state and federal legislators on poverty alleviation and social safety net policies.
In addition to mentoring trainees in population health and health equity research, Dr. Hamad has lectured extensively on the impact of social policies on health disparities. Previously, she worked as a family physician for 10 years in safety-net clinics throughout the San Francisco Bay Area.
Laura C. Rosella, MHSc, PhD
Professor and Division Head of Epidemiology, Dalla Lana School of Public Health, University of Toronto & Canada Research Chair in Population Health Analytics
Read Laura’s Bio
Dr. Laura C. Rosella is a Professor and Division Head of Epidemiology at the Dalla Lana School of Public Health, University of Toronto, where she holds a Canada Research Chair in Population Health Analytics. She leads the Population Health Analytics Laboratory, which focuses on advancing sustainable and equitable health systems while improving population health and well-being.
A recognized leader in public health research, Dr. Rosella is a member of the Royal Society of Canada’s College of New Scholars and serves as Editor-in-Chief of the Canadian Journal of Public Health. Her research leverages linked data sources to demonstrate their impact on critical population health and health system outcomes. She has authored over 310 peer-reviewed publications and was named one of Canada’s Top 40 Under 40, recognizing her contributions to epidemiology and public health.
Kara E. Rudolph, MHS, PhD
Associate Professor of Epidemiology, Mailman School of Public Health, Columbia University
Read Kara’s Bio
Dr. Kara Rudolph is an Associate Professor of Epidemiology at the Mailman School of Public Health, Columbia University. Her research focuses on developing and applying causal inference methods to study mental health, substance use, and violence in the United States.
Her methodological work centers on transportability and mediation, with a focus on generalizing findings from study samples to target populations and identifying subpopulations most likely to benefit from interventions. Her research contributes to optimizing the targeting of policy and program resources to enhance public health outcomes.
Arjumand Siddiqi, MPH, ScD
Professor and Canada Research Chair in Population Health Equity, Dalla Lana School of Public Health, University of Toronto & Senior Scientist and Edwin S.H. Leong Chair of Child Policy Research, The Hospital for Sick Children
Read Arjumand’s Bio
Dr. Arjumand Siddiqi is a Professor and Canada Research Chair in Population Health Equity at the Dalla Lana School of Public Health, University of Toronto, and a Senior Scientist and Edwin S.H. Leong Chair of Child Policy Research at the Hospital for Sick Children, Toronto. She also holds appointments in Sociology, Public Policy, and Women and Gender Studies at the University of Toronto and is a Senior Fellow at Massey College. Additionally, she serves as an Adjunct Professor at Harvard University and the University of North Carolina – Chapel Hill.
Dr. Siddiqi’s research focuses on understanding the causes of health inequities, with a particular emphasis on how social policies and societal conditions influence population health. She frequently collaborates with governments and international agencies to address issues related to health inequalities and the social determinants of health.
Silvia Stringhini, PhD
Associate Professor, School of Population and Public Health and Investigator, Edwin S.H. Leong Centre for Healthy Aging, University of British Columbia
Read Silvia’s Bio
Dr. Silvia Stringhini is an Associate Professor at the School of Population and Public Health at the University of British Columbia, Investigator of the Edwin S.H. Leong Centre for Healthy Aging, and a Senior Epidemiologist at Geneva University Hospitals. Her research examines the social determinants of health and aging, with a focus on how socioeconomic conditions influence biological processes and contribute to health disparities over the life course.
Dr. Stringhini has led pioneering studies on biological embedding, demonstrating how social adversity translates into physiological changes that accelerate disease risk. With extensive experience in large-scale population health studies and interdisciplinary research, she has published widely in high-impact journals and actively contributes to advancing epidemiological research.
Recorded Sessions
1. Understanding the Social Determinants of Health: Evidence, Mechanisms, and Biological Embedding
Presenters: Arjumand Siddiqi, University of Toronto and The Hospital for Sick Children and Silvia Stringhini, University of British Columbia
Social determinants of health play a central role in shaping health outcomes and driving health inequities. This session explores the state of empirical evidence, with a focus on socioeconomic inequities, the mechanisms that connect social conditions to health, and the broader theoretical insights that explain why these factors have such a strong influence on health.
The lecture introduces core concepts and shows how they inform research, policy, and public health practice.
This session is presented in four parts.
Additional Readings:
- Link, B.G. and Phelan, J., 1995. Social Conditions as Fundamental Causes of Disease. Journal of Health and Social Behavior, pp. 80-94.
- McEwen, B.S. and Gianaros, P.J., 2010. Central Role of the Brain in Stress and Adaptation: Links to Socioeconomic Status, Health, and Disease. Annals of the New York Academy of Sciences, 1186(1), pp.190-222.
- Posen, A., Siddiqi, A. and Hertzman, C., 2015. Nurturing Early Childhood Development in Times of Austerity in BC. Canadian Centre for Policy Alternatives.
- SDH WHO Team, 2010. A Conceptual Framework for Action on the Social Determinants of Health. Debates, Policy and Practice, Case Studies. World Health Organization.
2. Methods in Social Epidemiology
Presenters: Arjumand Siddiqi, University of Toronto and The Hospital for Sick Children and Silvia Stringhini, University of British Columbia
The methodological toolkit of social epidemiology has evolved significantly over time. Early approaches relied on basic regression models to identify associations between social factors and health, often in cross-sectional designs. Researchers then incorporated longitudinal and multilevel approaches to capture dynamic and contextual influences on health. More recently, artificial intelligence and machine learning have introduced new analytical possibilities alongside important considerations related to bias and interpretation.
The lecture provides an overview of commonly used methods and how they are applied to study complex health questions.
This session is presented in three parts.
Additional Readings:
- Kaufman, J.S., 2024. Causal Inference Challenges in the Relationship Between Social Determinants and Cardiovascular Outcomes. Canadian Journal of Cardiology, 40(6), pp.976-988.
- Shahidi, F.V., Sod-Erdene, O., Ramraj, C., Hildebrand, V. and Siddiqi, A., 2019. Government Social Assistance Programmes are Failing to Protect the Health of Low-Income Populations: Evidence from the USA and Canada (2003–2014). J Epidemiol Community Health, 73(3), pp.198-205.
- Deaton, A. and Cartwright, N., 2018. Understanding and Misunderstanding Randomized Controlled Trials. Social science & medicine, 210, pp.2-21.
3. Considerations for Measuring Socioeconomic Disparities for Health Outcomes
Presenter: Laura Rosella, Professor and Division Head of Epidemiology, Dalla Lana School of Public Health, University of Toronto Canada Research Chair in Population Health Analytics
Understanding and measuring socioeconomic disparities is important for advancing health equity and informing policy. This session examines how socioeconomic status is defined and measured in health research, including commonly used indicators and their limitations. It introduces approaches to quantifying disparities, including absolute and relative measures, and highlights their use in Canadian population data such as ICES and Statistics Canada datasets. It also considers ethical and practical issues in measurement and reporting.
This session is presented in three parts.
Additional Readings:
- Asada Y. A Framework for Measuring Health Inequity.. J Epidemiol Community Health. 2005 Aug;59(8):700-5.
- Bilheimer, L.T. and Klein, R.J. (2010), Data and Measurement Issues in the Analysis of Health Disparities. Health Services Research. 45: 1489-1507.
- Braveman P. What are Health Disparities and Health Equity? We Need to be Clear.. Public Health Rep. 2014 Jan-Feb;129 Suppl 2(Suppl 2):5-8.
- Canadian Institute for Health Information: Measuring Health Inequalities: A Toolkit https://www.cihi.ca/en/measuring-health-inequalities-a-toolkit.
- Canadian Institute for Health Information. Trends in Income-Related Health Inequalities in Canada: Methodology Notes. 2015.
- Denny, K., & Davidson, M. J. (2012). Area-Based Socio-Economic Measures as Tools for Health Disparities Research, Policy and Planning.. Canadian Journal of Public Health, 103, S4-S6.
- Pauly, B., Revai, T., Marcellus, L. et al. “The Health Equity Curse”: Ethical Tensions in Promoting Health Equity.. BMC Public Health 21, 1567 (2021).
4. Introduction to Causal Mediation Analysis
Presenter: Kara Rudolph, Associate Professor of Epidemiology in the Mailman School of Public Health at Columbia University
Causal mediation analysis helps explain how an exposure impacts an outcome by examining underlying pathways. This session introduces classical concepts of direct and indirect effects and reviews newer approaches that address limitations of earlier methods. It focuses on how to select appropriate mediation effects based on a research question and underlying assumptions, and includes applied examples using R to support implementation with real data.
This session is presented in four parts.
Additional Readings:
- Petersen, M. L., Sinisi, S. E., & van der Laan, M. J. (2006). Estimation of Direct Causal Effects. Epidemiology, 17(3), 276-284.
- Rudolph, K. E., Williams, N. T., & Diaz, I. (2024). Practical Causal Mediation Analysis: Extending Nonparametric Estimators to Accommodate Multiple Mediators and Multiple Intermediate Confounders. Biostatistics, 25(4), 997-1014.
- VanderWeele, T. J. (2016). Mediation Analysis: A Practitioner's Guide. Annual review of public health, 37(1), 17-32.
- VanderWeele, T. J., Vansteelandt, S., & Robins, J. M. (2014). Effect Decomposition in the Presence of an Exposure-Induced Mediator-Outcome Confounder. Epidemiology, 25(2), 300-306.
5. Methods to Evaluate Health Effects of Policies
Presenter: Rita Hamad, Social Epidemiologist and the Director of the Social Policies for Health Equity Research (SPHERE) Center at the Harvard School of Public Health
Quasi-experimental designs play an important role in evaluating policies when randomized trials are not feasible or appropriate. This session introduces commonly used approaches such as difference-in-differences, instrumental variables, and regression discontinuity. It examines their applications, strengths and limitations, and discusses practical challenges, including data constraints and sources of bias. The session also highlights gaps in current research and areas for future development.
This session is presented in four parts.
Additional Readings:
- Batra, A., Jackson, K., & Hamad, R. (2023). Effects of the 2021 Expanded Child Tax Credit on Adults’ Mental Health: A Quasi-Experimental Study: Study Examines the Effects of the Expanded Child Tax Credit on Mental Health Among Low-Income Adults with Children and Racial and Ethnic Subgroup. Health Affairs, 42(1), 74-82.
- Bitler, M., Currie, J., Hoynes, H., Ruffini, K., Schulkind, L., & Willage, B. (2023). Mothers as Insurance: Family Spillovers in WIC. Journal of Health Economics, 91, 102784.
- Dore, E. C., Wright, E., White, J. S., & Hamad, R. (2025). Methods Used to Evaluate the Health Effects of Social Policies: A Systematic Review. Current Epidemiology Reports, 12(4).
- Glymour, M. M., Kawachi, I., Jencks, C. S., & Berkman, L. F. (2008). Does Childhood Schooling Affect Old Age Memory or Mental Status? Using State Schooling Laws as Natural Experiments. Journal of Epidemiology & Community Health, 62(6), 532-537.
- Hamad, R. (2020). Natural and Unnatural Experiments in Epidemiology. Epidemiology, 31(6), 768-770.