Overview

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This fourth of five courses examines the methodological approaches and research tools that inform our understanding of best practices in working with science teams, communities, and with data. The topics of this course include: 1. Community-Based Participatory Research 2. Team Science 3. Community-Level Data 4. Informatics and the Social Determinants of Health: Knowledge Management 5. Data Applications: ANOVA Analysis and Line Graph Visualization

Syllabus

Community-Based Participatory Research

The purpose of this module is to examine community-based participatory research (CBPR) and evaluate its potential applications in data-to-action initiatives. Lesson one will define CBPR, as we discuss its origin and relation to collective impact. We will also review the goals, purpose, benefits and characteristics of CBPR as we compare it to traditional research methods. In lesson two, we will explore potential measurement strategies for analyzing CBPR outcomes and data-to-action interventions. We will also consider how CBPR success is measured and how data can be used to amplify community voices.

Team Science

This module will introduce the principles of team science and examine how team science can be used to enhance data-to-action initiatives. In lesson one, we will define team science, as we discuss how it is related to collective impact and CBPR. We will also review recommendations made by the National Academies Committee on the Science of Team Science for improving team science effectiveness. Lesson two will focus on the opportunities and challenges for team science in communities, as we further discuss incorporating community perspectives into team science research. We will also evaluate how to measure team science outcomes, as we consider how team science can add perspective and voice to data.

Community-Level Data

This module will focus on the importance of community partnerships in collecting and analyzing community-level data that can be integrated into data-to-action initiatives. Lesson one will define key terms, and introduce the concept of whole-person health. We will also explore how community data can be used to advocate, influence and create policy to support health equity. In lesson two, we will examine the use of simplified plain language in the context of health literacy, as we discuss how to assess the usability of community-validated plain language terms. Lesson three will introduce the MyStrengths+MyHealth assessment, as we review the implications of collecting community-based social determinant of health data. Finally in lesson four, we will evaluate a community-level data exemplar, as we consider how to translate whole person health and community-level data into community-driven health initiatives.

Informatics and the Social Determinants of Health

In this module, we will examine informatics as a potential methodology and resource to inform data-to-action initiatives. Lesson one will define key concepts including informatics, knowledge complexity, and knowledge management. Building on these concepts, we will investigate the levels of knowledge management proposed by Verna Allee. We will also consider the different perspectives on the proposed creation of a new social informatics specialty. Building on our understanding of knowledge management, in lesson two, we will explore knowledge representation structures. We will also analyze the use of publicly available population health records as contextual information to manage knowledge and data for action to reduce health disparities. In addition, we will evaluate knowledge representation structures of evidence-based social determinants of health interventions. Finally, we will explore some informatics applications including the Population Health Record and the WHO Health Equity Assessment Toolkit (HEAT).

Data Applications: ANOVA Analysis and Line Graph Visualization

This module will focus on analyzing, displaying and interpreting social determinants of health data, with a particular focus on comparing health outcomes by groups. Lesson one will provide an overview of ANOVA analysis and line graph visualization. In lesson two, we will learn how to conduct ANOVA analyses and create line graphs in R. Using the NHANES dataset, we will compare the mean Hgb a1c by education level. Using the Omaha System dataset, we will compare the mean change in status by number of problems. Finally, we will discuss how to interpret the results of our analysis as we visualize our findings using line graphs.