With roots in system dynamics and group model building, Community-Based System Dynamics (CBSD) is a participatory method for engaging and working with communities and organizations to address complex issues across a variety of contexts and cultures. CBSD is employed in research and practice settings globally to tackle “messy problems” by engaging community members. Over a 5-day period, this course introduces participants to CBSD, with emphasis on applications of CBSD to promote equity. The course provides participants with facilitation techniques for managing and engaging stakeholders; designing and structuring workshops; and opportunities to gain feedback on applying CBSD to a problem of interest or ongoing work. This course also develops skill sets for tools central to the use of system dynamics, such as qualitative causal mapping and creating a dynamic hypothesis.

This course is targeted towards researchers or practitioners in public health or related disciplines that address complex problems. A health disparities focus will also be present across examples in the course, so individuals interested in using these tools for disparities and equity issues are also encouraged. No previous experience or exposure to system dynamics is necessary for this course.

After completing this course, participants will be able to:

• Understand CBSD and its relationship to other methods of systems science, systems thinking, and system dynamics in particular.
• Identify and define problems most appropriate for CBSD.
• Utilize best practices to design a community-based participatory workshop.
• Identify facilitation strategies and scripts to enhance group-based participatory experiences.
• Understand how to use resources, such as Scriptapedia, in workshop design.
• Utilize qualitative causal loop diagrams as a tool to discuss and illustrate system behavior.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: The free simulation software program Vensim may be used in this course.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Community-based participatory research (CBPR) is an orientation to research that begins with community informational and research interests and seeks to use the knowledge gained to inform action for community health improvements and social change. Although this approach to research is widely adopted in urban health and generally familiar to both community-based organizations and researchers, many still struggle with the lack of knowledge or experience with applying practical CBPR approaches to a pressing community health issue or potential research partnership. This course will review the history and principles of CBPR and introduce participants to practical approaches and tools for equitable and authentic community-researcher engagement in all phases of research: from the creation of research questions, to study design, data collection, data analysis, and dissemination. Participants will also learn strategies for participatory grant-writing, budgeting, and opportunities for workforce development and capacity building for community residents and leaders in research. This course is open to researchers and community-based organizations.

After completing this course, participants will be able to:

• Learn about the history and principles of CBPR, and what distinguishes it from community-placed research.
• Understand the importance of and practical approaches for community-researcher engagement in all phases of a research process.
• Apply CBPR principles to research partnership development and grant-seeking processes.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: There is no required software for this course.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Increasingly data mined from the electronic health record (EHR) are being used in epidemiological research. But more data does not equate to better quality research. This workshop will cover the basics of working with EHRs and designing valid epidemiological analyses. The workshop will include a mix of didactic lecture and interactive group exercises. Participants are requested to provide planned or active research questions in advance, as these will form the basis of the group exercises.

After completing this course, participants will be able to:

• Identify appropriate epidemiological study designs using EHR data for both inpatient and outpatient settings, with particular focus on urban locations.
• Understand the architecture of the EHR along with terminology and data standards.
• Discuss data requirements in the EHR, including data export, linkage, and variable manipulation (e.g., parsing data from free text).
• Identify common pitfalls in working with EHR data and where to go for additional resources.

Prerequisite knowledge: Intermediate knowledge of epidemiology is required for this course.

Technical requirements: There is no required software for this course.

Continuing Education Credits*: 0.3 CEU or 3 CPH

Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing. Special consideration will be given to methods for spatial data analysis.

After completing this course, participants will be able to:

• Understand the fundamentals of Bayesian inference and the differences between Bayesian and frequentist (classical) methods.
• Formulate research questions and develop Bayesian approaches to address these questions.
• Become familiar with the available software for implementing Bayesian methods.
• Understand advanced Bayesian methods used in the scientific literature.

Prerequisite knowledge: Basic understanding of linear models (e.g., regression) and “generalized linear models” (e.g., logistic regression) is required. Basic familiarity with R programming language is recommended for this course.

Technical requirements: R and WinBUGS are required for the course. The free software is available for participants to install on their computers. A Windows computer is preferred for use of this software.

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course introduces quantitative tools to evaluate programs and public health policies in the context of urban health. The course provides hands-on training in the theoretical approach and practical application of evaluation methodology, ranging from methodologically rigorous quasi-experimental designs (including novel applications of the Differences-in-Differences design) to small tests of changing using a Plan-Do-Study-Act (PDSA) approach. We will overview how to conduct various study design and analytical approaches, and the strengths and weaknesses of these methods.

The course is designed to prepare practitioners and academics to identify appropriate approaches and quantitative methods for evaluating programs and policies, and to determine when, how, and under what circumstances an evaluation should be conducted. Course activities will include discussions about the strengths and weaknesses of various methods, problem sets practicing analysis methods, and presentations critically reviewing published evaluation studies. This course will not cover qualitative or participatory methods, or methods of metric creation, identifying target populations, data collection and measurement error.

After completing this course, participants will be able to:

• Understand the fundamentals and applications of policy and program evaluation.
• Understand the most common methodological approaches used to evaluate policies and programs in public health, and strengths and limitations of each approach.
• Critically assess the appropriate study design and analysis method, based on the policy context, data availability, stakeholder buy-in, and timeline. 

Prerequisite knowledge: Basic knowledge of statistics, probability, and regression techniques is required for this course. 

Technical requirements: Instructors will use R and Stata software to demonstrate analytical approaches used in program and policy evaluation. Access to R or Stata is recommended if participants want to follow along but not required. R free software is available for participants to install on their computers. Code will be provided in the course material.

Continuing Education Credits*: 1.5 CEU or 15 CPH

The goal of this intermediate course is to familiarize students with the applications of Geographic Information Systems (GIS) to assess or evaluate urban health challenges, and potential solutions, with a focus on the Philadelphia context. Students are expected to have some prior experience with using GIS. Through hands-on exercises based on local case studies, students will gain practice in assessing the relationship between aspects of our environments (e.g., housing, trees and parks, food/nutrition) and health in Philadelphia. Class sessions will consist of briefs lectures and extensive hands-on computer lab exercises that students can tailor based on geographic location or health outcome interests.

After completing this course, participants will be able to:

• Use GIS tools such as spatial overlays, interpolation and zonal statistics with both vector and raster data.
• Utilize GIS tools in combination with basic statistics and graphing to answer urban health research questions.

Prerequisite knowledge: Knowledge of basic concepts and techniques of GIS and some basic knowledge of ESRI's ArcGIS Pro software is required for this course.

Technical requirements: A temporary license for Esri's ArcGIS Pro will be provided for participants to install on their computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will explore, from a public health perspective, the communication of scientific findings to the general public, as well as to other targeted audiences. Methods of effective (as well as misleading or ineffective) communication of public health results will be discussed, using historical and current-day examples, including examples of misinformation, such as: the current pandemic; autism and vaccines, climate change, and often-conflicting dietary recommendations. Health risk perception and scientific communication in the era of social media and “fake news” will also be discussed. This course will be valuable to public health practitioners, researchers, and scientists interested in better communication strategies to the public. Students will learn how to communicate health risks through discussions of current headlines and findings, media training guidelines, and practical applications.

After completing this course, participants will be able to:

• Be able to describe the meaning and impact of a scientific finding.
• Be able to translate a scientific finding to multiple audiences, including: lay, stakeholder, and scientific audiences, demonstrating how approaches to communicating the same results differ for each audience.
• Understand the process and value of effective communication to the media.

Prerequisite knowledge: Some background in public health, basic epidemiology, or knowledge of study designs is recommended for this course.

Technical requirements: There is no required software for the course. 

Continuing Education Credits*: 0.3 CEU or 3 CPH

Agent based modeling is a complex systems simulation approach that public health researchers use to understand how individuals interact with each other and their environments in ways that influence health. Agent based models can complement other methodological approaches because they can explicitly “build in” forms of complexity (e.g., feedback loops, change over time, interdependence) that are important for population health but difficult to examine with other methods. They can also provide evidence to inform policy and intervention decisions (e.g., which policies to combine, in what intensities, targeted at whom). This course will introduce theory and use of agent-based models within the context of urban health research and non-infectious disease models. The course will include three parts: introductory readings to be completed out of class; a didactic lecture component to introduce key concepts in agent-based modeling (e.g., agents and their properties, rules, environment) and provide examples; and a hands-on lab component in which the class will implement their first agent-based model using the AnyLogic simulation platform. The in-class labs will take students through the steps of developing an agent-based model of diet behaviors in cities using AnyLogic.

After completing this course, participants will be able to:

• Describe foundational principles in complex systems thinking.
• Understand key design attributes of existing agent-based models.
• Apply complex thinking principles within an agent-based modeling framework.
• Develop basic proficiency in AnyLogic software.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: Participants will need a computer that can run AnyLogic. Participants will use a personal learning edition of AnyLogic, a free simulation software.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Why do life expectancies differ by gender and race? How did the United States move to most people giving birth in hospitals? What does having public or private insurance mean for access to doctors? Health is not the same for everyone in the United States. There are inequalities based on how much you make, your identities, where you live, and how healthcare works in your community. How do these inequalities happen, and what can we do to make things better? This course provides a sociological foundation to understand how healthcare inequalities function in the United States. We will cover historical context, current causes of differential outcomes, and how the healthcare system works. As well as highlighting barriers and concerns, this course will talk about solutions, including community-based efforts and empowering individuals. This course will utilize lecture, discussion, media, and in class activities to develop skills to identify and confront barriers to health equity. The last day of the course will be reserved for topics most of interest as voted on by the class.

After completing this course, participants will be able to:

• Understand health inequalities, who they effect, and how they impact our society.
• Examine the healthcare system and how it creates barriers to better health.
• Identify mechanisms for lessening inequalities in healthcare and develop concrete skills to recognize and address them.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: There is no required software for the course.

Continuing Education Credits*: 1.5 CEU or 15 CPH

The course is an introduction to the basic concepts and techniques of Geographic Information Systems (GIS). This course contains lectures and exercises in ArcGIS. The lectures introduce the basic concepts of GIS, data models, coordinate system and map projections, data management and processing, spatial analysis, spatial estimation, and GIS data visualization. Students will also learn how to use the fundamental knowledge and techniques of GIS to solve real-world problems. Through the exercises, students will get familiar with the interfaces and analysis tools in the Esri software package ArcGIS. Students will also practice applying GIS as a tool and a methodological approach to spatially analyze environment and public health data. This course is targeted towards anyone interested in gaining working knowledge of ArcGIS and GIS.

After completing this course, participants will be able to:

• Understand basic concepts and terms of GIS and know how to spatially analyze data using ArcGIS.
• Acquire spatial data that is relevant to urban health, e.g., census data, land use data, greenspace, road networks, satellite images, etc.
• Build knowledge of data structure, data management, and coordination and projection systems.
• Practice featuring symbols and making elegant maps in ArcGIS.
• Apply the knowledge of GIS to solve research questions in urban health research.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: A temporary license for Esri's ArcGIS Pro will be provided for participants to install on their computers. Students planning on using a Mac computer with Intel processors will need Boot Camp with Microsoft Windows installed in the partition or a virtual environment (e.g., VMWare or Parallels 15 or greater). Students with a MacBook with the new Apple Silicon (i.e., M1 processors), can complete the course assignments with an alternative software to ArcGIS, such as QGIS.

Continuing Education Credits*: 1.5 CEU or 15 CPH

The overall purpose of this course is to provide a broad understanding of urban health research and practice. The course will integrate conceptual frameworks and approaches used in urban health with practical examples that participants can identify in their everyday practice, through discussion and reflections from a transdisciplinary perspective. Participants in this course can come from a variety of disciplines including public health, health care, urban planning, engineering, sociology, anthropology, as well as community stakeholders interested in understanding the connections between urban environments and population health. Students will receive instruction through lectures, group discussions and group projects. Although not mandatory, attendees are encouraged to bring their own project ideas and develop them throughout the course.

After completing this course, participants will be able to:

• Understand frameworks and evidence for urban environment-health relations and the multiple levels of factors that may influence health.
• Understand, compare, and contrast leading urban health problems in domestic and global settings.
• Evaluate the relationship between health issues and the policies intended to reduce them.
• Develop recommendations for urban setting-based interventions that promote public health.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: There is no required software for this course. 

Continuing Education Credits*: 1.5 CEU or 15 CPH

Multilevel studies and multilevel analysis have been increasingly used in the public health field. This course will discuss the rationale for multilevel studies and multilevel analysis in public health as well as differences with other study designs and other analytical approaches. Although the course will not be heavily mathematical, the basics of fitting multilevel models for different types of outcomes as well as the interpretation of estimates obtained from multilevel models will be reviewed and practiced. Emphasis will be on conceptual understanding, application, and interpretation of multilevel analysis in the context of urban health research. The course will also review and critique empirical applications in urban health research and discuss conceptual and methodological challenges in using multilevel analysis.

After completing this course, participants will be able to:

• Understand the fundamentals of multilevel studies and multilevel analysis and their differences with other study designs and analytical approaches.
• Fit multilevel models and interpret estimates derived from them.
• Be familiar with applications of multilevel analysis in urban health research.
• Understand the strengths and limitations of multilevel analysis for urban health research.

Prerequisite knowledge: Knowledge of regression analysis (linear, logistic, Poisson) is required. Familiarity conducting regression analysis in SAS, Stata, or R software is recommended for this course.

Technical requirements: Participants will need access to SAS, Stata, or R software for the course. R free software is available for participants to install on their computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH