Social Epidemiology Course

Instructors: Arjumand Siddiqi, University of Toronto and The Hospital for Sick Children and Silvia Stringhini, University of British Columbia 

Module Description: Social determinants of health (SDOH) play a fundamental role in shaping health outcomes and driving health inequities. This module provides an in-depth exploration of (a) the state of the empirical evidence, with a focus on socioeconomic inequities, (b) the social-to-biological mechanisms that operate to connect social determinants to health and, (c) the broader theoretical lessons that we can glean from this research, which explain why and how social determinants have such a powerful role for health and health inequities.  Through this module, learners will gain a comprehensive understanding of SDOH and their relevance for research, policy, and public health interventions.  

Intructors: Arjumand Siddiqi, University of Toronto and The Hospital for Sick Children and Silvia Stringhini, University of British Columbia 

Module Description: 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. The focus then shifted to understanding mechanisms through mediation analysis and causal inference techniques, while econometric and quasi-experimental methods enhanced our ability to assess policy impacts. More recently, AI and machine learning have entered the field, offering new analytical opportunities but also raising challenges related to bias and interpretability. 

Instructor: Laura Rosella, Professor and Division Head of Epidemiology, Dalla Lana School of Public Health, University of Toronto Canada Research Chair in Population Health Analytics 

Module Description: Understanding and measuring socioeconomic disparities is essential for advancing health equity and informing policy. This module explores key considerations in measuring socioeconomic status (SES) in health research and using health services data, highlighting commonly used SES indicators, their strengths, and limitations. It examines different approaches to quantifying disparities, including absolute and relative measures, emphasizing the importance of distributional analyses. Special attention is given to applying these methods in Canadian population data, including ICES, health services data, and Statistics Canada datasets. The module also addresses ethical and practical challenges in SES measurement and reporting, discussing how disparity data can be used responsibly to inform interventions and improve health outcomes.  

Instructor: Kara Rudolph, Associate Professor of Epidemiology in the Mailman School of Public Health at Columbia University 

Module Description: Causal mediation analysis can provide a mechanistic understanding of how an exposure impacts an outcome. However, rapid methodologic developments coupled with few formal courses presents challenges to implementation. Beginning with an overview of classical direct and indirect effects, this workshop will present recent advances that overcome limitations of previous methods. We will emphasize how researchers can think through how to choose among the different types of causal mediation effects to answer their research question under reasonable assumptions. To ensure translation to real-world data analysis, this workshop will incorporate hands-on R programming exercises to allow participants practice in implementing robust, nonparametric estimators of these causal effects, using the open source R package medoutcon. 

Instructor: Rita Hamad, Social Epidemiologist and the Director of the Social Policies for Health Equity Research (SPHERE) Center at the Harvard School of Public Health 

Module Description: This module delves into the pivotal role of quasi-experimental designs in policy evaluation, focusing on their application, assessment, and limitations. We begin by exploring the motivations behind using quasi-experimental approaches in policy evaluation, particularly their ability to provide insights when randomized controlled trials are impractical or unethical. Following this, we conduct a comprehensive review of prevalent quasi-experimental study designs employed in the field, such as difference-in-differences, instrumental variables, and regression discontinuity techniques. Participants will learn to critically assess these methods by comparing and contrasting their strengths and weaknesses. We will also discuss the practical challenges encountered in implementing these designs, including issues related to data availability and potential biases. Finally, the module will conclude with an exploration of current literature gaps, encouraging participants to identify and propose areas for future research and innovation in quasi-experimental policy evaluation.