The purpose of this course is to develop Philadelphia community-based organizations’ and community members’ knowledge about grants, grant-writing, and increase community capacity to take charge of the research process. Learning Objectives:

• Describe what a grant is, the reasons for applying for a grant, and how grant-writing fits into your program or intervention planning process.
• Understand how to carefully read a grant announcement and develop the key components of grant proposals, such as the problem or needs statement, evaluation plan, and budget.
• Initiate the grant application process, including how to effectively search for grant opportunities and identify grants that align with the goals and missions of your organization. 

This course is for beginners, and so there are no pre-requisites or any expectations. This course is open to members of a community-based organization looking to learn the basics of grant writing. 

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

The purpose of this course is to develop Philadelphia community-based organizations’ and community members’ knowledge about budgeting and financial requirements of the grant application process. Learning Objectives:

• Understand budgetary requirements for grants based on reading RFPs Practice building a draft grant budget, budget justification, and other documents.

No software required, course will not be dependent on participants having computers. Although having a computer is a plus. This course is not necessarily a follow up to the grant writing course, but should go later in the week than the grant writing course. We would like to waive registration fees for up to 8-10 participants. 

Prerequisite Knowledge: None, however, some experience or knowledge of grant application processes would be helpful. While not required, participants would benefit from taking the introductory grant writing course above to get a basic introduction to grants and the grant application process if they have not had any direct experience developing or writing grant proposals.  

Technical Requirements: This course will not be dependent on participants having computers, although having a computer is a plus.

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, which 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. 

Prerequisite Knowledge: None

Technical Requirements: A temporary license for Esri's ArcGIS Pro will be provided for participants to install on their computers, if needed. ArcGIS Pro system requirements are available here. Students with a MacBook with the Apple Silicon (i.e., M-processors) will need to install Parallels or use the alternative software, QGIS or ArcGIS Online. QGIS is open software that can be installed on PC or Mac computers and has most of the same functions as ArcGIS. ArcGIS Online can be used to complete most of the exercises that will be completed during the course.

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will provide an overview of

1. The public health impacts of global climate change in urban areas.
2. Urban policies for climate change mitigation and adaptation and the tradeoffs with public health, and
3. Principles and methods to evaluate scientific evidence and apply this evidence to local public health interventions.

Learning Objectives:

• Explain the scientific basis of observed and projected climate changes.
• Describe the broad spectrum of public health impacts of climate hazards in urban areas (heat waves, floods, air pollution, sea level rise, population displacement, etc.) and distinguish the ways in which climate exposures and health impacts are distributed differentially within and across urban populations.
• Identify urban policies to mitigate and adapt to climate change and characterize the public health benefits and harms of these policies, with a focus on vulnerable populations
• Define key concepts and methods to evaluate existing scientific evidence and apply this evidence to local urban policies 

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will support participants in moving from racial equity as a theory into practice. We will develop a shared language and understanding of equity-centered health theories and how we came to be where we are. Participants will then gain an understanding of existing measures of structural, institutional, and interpersonal racism and how they are associated with health. We will then examine existing efforts within cities and communities to apply equity principles and where efforts are sustained through shared power. 

Prerequisite knowledge: Familiarity with basic quantitative analytic approaches — regression, descriptive epidemiology. We will cover some things but not in detail. 

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will discuss the rationale for multilevel studies and multilevel analysis in public health, as well as differences with other study designs and 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 the 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.

Prerequisite Knowledge: Knowledge of regression analysis (linear, logistic, Poisson) is required.

Technical Requirements: Participants will need access to SAS, Stata, or R (students can choose which; R is publicly available for free).

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will provide a basic introduction to person-centered analysis, especially latent class analysis, and its application in public health research. This course will provide a hands-on, step-by-step guide to conducting latent class analysis using SAS. The learning objectives of this course are for students to gain the ability to:

1. Describe the goal of conducting person-centered analysis, especially latent class analysis.
2. Conduct latent class analysis using SAS 3.
3. Propose a research questions that can be addressed using latent class analysis .

Prerequisite Knowledge: None

Technical Requirements: Students will need to have access to SAS

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course teaches students how to write clean and reproducible code in R, utilizing Git and GitHub. Although this course is taught in R, the concepts explored apply to any programming language used for statistics, such as SAS and Python. Students perform increasingly complex analyses, while prioritizing clarity in the structure, documentation, and generalizability of their project. Additional emphasis is placed on building an analysis workflow that is compatible with collaboration an external review. By the end of this course students should understand how to write clear and consistent code and how to utilize Git/GitHub to track changes in their own analysis and review the analyses of their collaborators.

Prerequisite Knowledge: Basic familiarity with entry-level statistics and R.

Technical Requirements: Students will need to download R and RStudio, both of which are free and publicly available.   

Continuing Education Credits*: 1.5 CEU or 15 CPH

Citizen Science methods and theories help Urban Health Researchers and Practitioners by providing knowledge and ideas from regular citizens, engaging vulnerable populations and improving public health communication. Citizen Science improves the social and scientific impact of public health research by engaging with citizens and policy makers and pushes our research to become more policy and action oriented. This Drexel summer training will share previously conducted projects, theories and methods and will offer specific training in Photovoice as one Citizen Science method with a long-standing experience in Urban Health. Learning objectives are:

1. Gaining basic knowledge on the foundations and principles of Citizen Science and its application in Urban Health Research and Practice.
2. Sharing of Citizen Science projects in different Urban Health areas.
3. Training in Photovoice as one Participatory Action Research and Citizen Science method.
4. Building on Citizen Science to highlight and practice Communication in Urban Health Research .
   
   This training may be of interest for any Urban Health researcher or practitioner and definitely for public health trainees.

Prerequisite Knowledge: None 

Continuing Education Credits*: 1.5 CEU or 15 CPH

Community violence, with gun violence as its most devastating manifestation, is a pervasive public health problem across the US. Black and Latino youth disproportionally experience violence. The resulting mental health consequences, including PTSD and depression, often go untreated, especially among impoverished youth. Untreated mental health challenges related to violence can derail youths' healthy development, often resulting in poor outcomes across settings. Hospital-based violence intervention programs (HVIPs) were designed to prevent reinjury and retaliation among youth. More recently, HVIPs have shown promising outcomes for improving mental health outcomes for children who experience community violence through the inclusion of trauma-focused evidence-based treatments and peer support services.

This course is designed for people interested in learning the fundamentals of violence intervention and potentially pursuing a career in the field. Learning Objectives:

1. Participants will learn about the prevalence, associated factors, and root causes of community violence (firearm violence) and its devastating consequences for children and families in the US.
2. Participants will learn about the mental health challenges in children associated with violence, PTSD, depression, and disrupted attachments.
3. Participants will learn about evidence-based and promising practices to improve mental health outcomes for children who experience violence.
4. Participants will learn about the different pathways, e.g., practice, research, policy, to careers in the violence prevention and intervention field.  

This course is designed for students, community members, and professionals interested in learning the fundamentals of violence intervention and potentially pursuing a career in the field. 

Prerequisite Knowledge: None

Technical Requirements: None 

Continuing Education Credits*: 1.5 CEU or 15 CPH

Estimating the public health impacts of new policies or programs is one of the core functions of epidemiology and health services research. This course will offer an overview of the most common quasi-experimental methods used for policy and program evaluation, including difference-in-differences, interrupted time series, and regression discontinuity approaches. Lectures will be complemented by hands-on labs in R. Students will leave the course with frameworks for how to conceptualize and operationalize policy exposures and design policy and program evaluations to improve population health. 

Prerequisite Knowledge: Students should have some familiarity with coding and regression models, though no R experience is required.

Technical Requirements: Students will need to download R and RStudio, both of which are free and publicly available. 

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will introduce researchers and practitioners to artificial intelligence (AI), focused on deep learning, including generative AI and agents. We will review and discuss use cases for urban health practice and research. The course will provide the historical context and development of AI models in use today, the key concepts and design of these models, what resources are needed to implement them in your research or practice, and collaborating with AI experts.

You will also have hands-on case examples of use for both research and practice, such as collecting built environment data and prompting generative AI GPT models to provide text, images, videos or other outputs for your practice or research use as we will focus on prompting interfaces. No programming knowledge is needed or required for this course, though suggested resources will be provided to those interested in that aspect.

The learning objectives of the course are:

• Describe some of the primary AI algorithmic approaches in plain language.
• Assess the quality and reliability of AI models and output.
• Identify use cases of AI for your area of research or practice.
• Learn, improve and practice prompting and interaction with AI tools, platforms and agents.

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

A comprehensive introduction to the statistical methods used in the analysis of geo-referenced spatial data. The course covers the topics of disease mapping (relative risk estimation), disease clustering, and ecological analysis. The methods covered are mainly in the area of generalized linear models and mixed models. The course addresses the use of appropriate software packages for the analysis of disease incidence data.

The progression of methods begins with simple Poisson regression (log-linear models) and logistic linear models, moves to Bayesian hierarchical modeling for mapped data, and finally models with spatially correlated prior distributions only available in advanced software. If time permits, we also examine space-time modeling, multivariate analysis, and survival modeling. Knowledge of intermediate statistics and basic proficiency in R is expected.

Prerequisite Knowledge: Basic knowledge of statistics and R 

Technical Requirements: Participants will need access to R

Continuing Education Credits*: 1.5 CEU or 15 CPH