Overview

This first of five courses introduces students to the social determinants of health, and provides an overview of the definitions and theoretical perspectives that will form the foundation of this specialization. The topics of this course include: 1. Introduction to the Social Determinants of Health 2. Theoretical Perspectives and Knowledge Complexity 3. Data Driven Collective Impact 4. Minority Stress Theory 5. Data Applications: Frequency Analysis and Bar Chart Visualization

Syllabus

Introduction to Social Determinants of Health

The purpose of this module is to provide an introduction to the social determinants of health in the context of this specialization. In lesson one, we will define the social determinants of health, explore how our understanding of social determinants has changed over time, and analyze the impact health inequity has on society. We will also consider the variety of transformational ideas that can be used to address health inequities. In lesson two, we will review different ways of knowing and how community knowledge can be augmented with data to influence policy. We will also evaluate defining characteristics of data, as we assess how data, analysis and partnership can be leveraged to create power for transformative change.

Theoretical Perspective

The purpose of this module is to provide a foundation of theoretical knowledge to support systems thinking and knowledge management principles applied to determinants of health. Systems thinking involves making distinctions, understanding systems, relationships, points of view and perspective taking. In lesson one, we will learn about the DSRP theory in regard to developing a systems thinking mindset. In lesson two, we introduce the Data to Action Hourglass model as a conceptual framework and a way to think about the different logical levels and relationships between and among determinants of health.

Collective Impact

The purpose of this module is to introduce the concept of collective impact as a model and method for designing data driven collective impact initiatives. The principles and phases of collective impact are described and explained. Collective impact thinking requires a shift in mind that requires attention to systems thinking. Using a collective impact mindset supports and encourages collaboration and team science and the use of standardized data sets to understand and support knowledge work and translation with community and population data sets. Example case studies illustrate the power and potential of collective impact efforts to create transformational changes to support desired health care futures.

Minority Stress Theory

In this module we will define minority stress theory as it relates to the social determinants of health. In lesson one, we will define minority stress, and examine how systemic discrimination contributes to minority stress. We will also look at how minority stress can lead to health disparities. In lesson two, we will discuss the effects of structural inequalities on both advantage and disadvantaged groups. We will also explore the personal, interpersonal and social effects of minority stress. Finally, we will evaluate the personal and social resources available to counteract minority stress, as well as the ways in which data can be used to enact transformative changes.

Data Applications: Frequency Analysis and Bar Chart Visualization

This module will focus on analyzing, displaying and interpreting social determinants of health data, with a particular focus on identifying social determinants of health in large datasets. Lesson one will provide an overview of frequency analyses and bar chart visualizations. In lesson two, we will learn how to use the R environment in Coursera. Lesson three will introduce us to the datasets, NHANES and Omaha System, which we will use throughout the Data Application modules in this specialization. In lesson four, we will learn how to conduct frequency analyses and create bar charts in R. Using the NHANES dataset, we will obtain the frequencies of income, education, family savings, depression and insurance by race. Using the Omaha System dataset, we will obtain the frequencies of common social determinants by both race and ethnicity. Finally, we will discuss how to interpret the results of our analysis as we visualize our findings using bar plots.