Curriculum

Description: This module will cover basics of epidemiology, including methods and research designs.  In addition, we will discuss how do obtain large datasets, how to assure that the data are appropriately matured, and how data and information systems can be used to understand public health issues in populations.  

Learning Objectives:

1. Describe epidemiologic methods and research designs
2. Explain incidence and prevalence rates
3. Discuss the importance of understanding the denominator in any epidemiological study
4. List several data sources 
5. Describe important issues in data management
6. Provide an overview of information systems currently used in epidemiology
7. Identify opportunities for more effective epidemiology investigation and disease prevention opportunities through implementation of informatic practices
8. Describe challenges and opportunities presented by integration of information systems for epidemiology and disease prevention.

Description: The course introduces methods in health data science – defining the problem, accessing, and loading the data, formatting into data structures required for analysis. This course covers the basics of computational thinking to define a computational solution, methods to access healthcare data from variety of sources (EHR data, UMLS, Medline, etc.), and in different data formats. The students will apply methods for data wrangling and data quality assessments to structure the data for analysis. The students will be introduced to basics of design and evaluation of algorithms and application of data structures for healthcare data. The course will use Python programming language and basic python libraries for data sciences such as numpy, scipy, matplotlib and pandas. Students should expect a good amount of programming exercises for each week. This course is not an introduction to programming, and not a course to improve programming skills. Students are expected to have some experience with introductory / beginner level Python programming.

Learning Objectives:

1. Abstract a business need for data analysis and define appropriate computational problem
2. Design and analysis (time complexity) of simple algorithms
3. List basic data structures and their characteristics, applications in biomedicine
4. Retrieve biomedical data from multiple sources formats – specifically flat files (text), tabular data (CSV), structured data (JSON, XML)
5. Implement Python programs to load data and apply basic data wrangling to structure output.

Description: This module will cover basics of epidemiology, including methods and research designs.  In addition, we will discuss how do obtain large datasets, how to assure that the data are appropriately matured, and how data and information systems can be used to understand public health issues in populations.

Learning Objectives:

1. Describe epidemiologic methods and research designs
2. Explain incidence and prevalence rates
3. Discuss the importance of understanding the denominator in any epidemiological study
4. List several data sources
5. Describe important issues in data management
6. Provide an overview of information systems currently used in epidemiology
7. Identify opportunities for more effective epidemiology investigation and disease prevention opportunities through implementation of informatic practices
8. Describe challenges and opportunities presented by integration of information systems for epidemiology and disease prevention.

Description:  This module will train students to critically understand how the nature of data collection, interpretation and use shape health outcomes.  Students will review approaches used to detect and reduce health disparities among various groups of individuals across the global sociodemographic spectrum.

Learning Objectives:

1. Discuss the evolution of health equity and health disparities as subjects of study and public health action. 
2. Identify the multiple dimensions of health disparities as described in Healthy People 2030 (US Dept of Health and Human Services, Office of Disease Prevention and Health Promotion; data-driven national objectives to improve health   https://health.gov/healthypeople ) and be able to describe and critique approaches for studying the Social Determinants of Health.
3. Discuss and analyze why the socioecological framework is critical for addressing health equity and reducing health disparities.
4. Compare and contrast ethnic and racial influences on specific health inequities/health disparities in the U.S. population. 
5. Describe how the selection of reference groups can affect the measurement of health disparities and analyze, interpret, and present health equity data in tabular and graphic form. 

Description: This course aims to establish the foundations of public health analytics to transition data from information to knowledge and actionable wisdom. National data standards, data sources, data management, and approaches to analytics relevant to public health will be covered. This course builds on a foundation of statistics, basic analytics, evidence-based practice, and implementation science.

Learning Objectives:

1. Identify and define key terms and concepts associated with data analytics and public health data.
2. Identify primary and secondary data sources of relevance to public health.
3. Understand and contrast the functionality, advantages, and limitations of descriptive, predictive, prescriptive, and inferential analytical approaches.
4. Identify public domain, census data, and associated resources to assess a public health problem.
5. Explore the use of software solutions (R/Python, Commercial Off the Shelf) to manage and visualize public health datasets.
6. Develop a data analytics component for a community assessment of a public health problem.

Description:  This is a short course that introduces learners to the key elements of Surveillance in Public Health.  This course will introduce learners to the different types of surveillance used in public health; the role of reporting in public health surveillance and introduce some of the informatics tools used in surveillance.

Learning Objectives:

1. Define public health surveillance
2. Describe the uses of a public health surveillance system
3. Compare active and passive public health surveillance
4. Identify informatics tools used in active and passive public health surveillance
5. Identify sources of data commonly used for public health surveillance

Describe the public health surveillance process

Description: This is a short course that will introduce learners to the key elements of public health reporting and its importance.  This course will introduce learners to the types of data typically reported, how they are reported, and to whom they are reported. Learners will recognize the types of data and mechanisms of reporting, and how reporting is related to surveillance.

Learning Objectives:

1. Describe the history and importance of public health reporting of communicable and noncommunicable disease
2. Differentiate between individual level and aggregate level data to be reported
3. Differentiate between PHI vs. Non-PHI data
4. Identify the need for secure level transmission and different codes for reporting (HL7 Vs FTP), as well as selection of appropriate levels of security. 
5. Identify reporting health agencies at the city, county, state, and federal levels
6. Contrast reporting with surveillance 

Description: The effective utilization of data in public health requires it to come from many sources without losing meaning or context, that is with semantic interoperability. This module will explore what constitutes semantic interoperability, how critical semantic interoperability is to public health informatics research, and what is needed to support semantic interoperability for public health purposes. Standards such as HL7’s Fast Healthcare Interoperability Resources, as well as Logical Observation Identifiers Names and Codes (LOINC), the International Classification of Diseases, SNOMED-CT, and others will be explored. 

Learning Objectives:

1. Describe the difference between interoperability and semantic interoperability.
2. Explain why semantic interoperability is necessary for public health.
3. Utilize HL7 FHIR in a given use case.
4. Compare and contrast the uses of various health-related standards and select the best one for a given scenario.

Description: This course will introduce learners to the public policy process with a special focus on the foundations, history, and principles guiding health informatics policy. The course will discuss various landmark health information technology legislation and will integrate the roles that federal, state, and local agencies play in influencing health information technology infrastructure and policies related to public health. Special attention will be given to addressing current issues in health informatics policy and their impact on population health equity and the current state of infrastructure.

Learning Objectives:

1. Describe the public policy process in the United States
2. Discuss the history of the health information technology infrastructure and landmark legislation and their impact on public and population health
3. Analyze the different stakeholder perspectives shaping health informatics policy
4. Examine current issues relevant to health informatics policy, with a particular focus on recent health challenges
5. Discuss the role of health informatics policies in influencing health disparities and inequities
6. Develop a health informatics policy analysis

Description: Public health informatics relies on synchronous and asynchronous processing of data in real-time and from data warehouses. This multi-source data is called multi-stream data. The course covers how to apply data science methods to leverage multi-stream data for public health and manage the pipeline from data source to analysis to applications. The course will use Python programming language and data science modules for acquiring, exploring, preparing multi-stream data for inferential, exploratory, and predictive analysis. Students will develop and implement a real-time public health dashboard as an example of the application of multi-stream data.  The course will introduce students to use open-source tools to develop the dashboard and connect multi-stream data sources.

Learning Objectives:

1. Define and describe multi-stream data in public health
2. Describe and illustrate the architecture for managing multi-stream data
3. Utilize data science methods to analyze multi-stream data
4. Prepare multi-stream data for predictive analysis
5. Apply multi-stream data for real-time public health dashboards

Course Description: This module provides an overview on how social media and other emerging communication technologies can connect millions of voices to increase the timely dissemination and potential impact of public health and safety information, leverage audience networks to facilitate information sharing, facilitate interactive communication, connection, and public engagement, and to create a more adaptive, timely and effective public health response. 

Learning Objectives:

1. Describe the influence of social media in the dissemination of health and safety information to create a more adaptive and effective public health response
2. Explore the use of social media programmatic approaches to improve community/population health
3. Investigate tools, techniques, and measures for public health social listening initiatives 
4. Describe best practices and tools for social media listening, network analysis and visualization for public health professionals 
5. Recognize challenges regarding privacy and security measures behind using social media platforms
6. Identify critical challenges and opportunities for advancing social media listening for public health

Description: This module provides an overview of the role played by health literacy in public health and safety and the importance of people’s access to information they can use to protect and promote their health. Learners will understand the basics of health literacy and the tools and technologies that could be leveraged to help the population and public health officials have an overall impact on reducing gaps in disparities and improving population health. Different levels of health literacy will be introduced by connecting them with the US Healthy People, the Centers for Disease Control and Prevention (CDC) Health Literacy Action Plan, and the World Health Organization (WHO) goals.

Learning Objectives:

1. Define health literacy and describe the role health literacy plays in meeting core public health services and health equity
2. Identify who is affected by public health literacy and the consequences of limited public health literacy
3. Identify health outcomes among people with low health literacy
4. Describe why people, regardless of literacy skills, may fail to understand health information
5. Recognize public health data, information and knowledge and the role played by misinformation and disinformation and how it can affect public health interventions
6. Describe health literacy approaches or interventions that could be conducted to combat misinformation and disinformation

Description: This course introduces students to how racism influences data science/informatics and how data science/informatics influences racism. The course will review: 1) the origins of racial classifications in the United States, 2) how race has been historically used to inform policy and practices,3) how classifications of racial groups are currently overtly and covertly used in data analytics, data science, and public health policies; 4) the advantages and disadvantages of demographic descriptors in data science and public health, and 5) practical ways to identify and address practices that perpetuate racism in data science. The course consists of lectures, case studies, and practice in identifying and responding to racism in data science.

Learning Objectives:

1. Define racism, implicit bias, explicit bias, structural racism, and antiracism 
2. Describe the historical origins of racial classifications in the United States
3. Explain how racial classifications have historical influenced public policies 
4. Classify whether racial categories promote racism or promote equity
5. Articulate ways to mitigate bias in data science 
6. Outline the essential role of data scientists in tackling racism and discrimination

Description: This course provides an overview of bias in machine learning (ML) and artificial intelligence (AI).

Learning Objectives:

1. Explain how explicit and implicit human biases can contribute to data biases
2. Explain how the way in which data is sampled can introduce data biases even if there are no human biases in the data
3. List methods to minimize the effects of bias in ML algorithms
4. Describe multi-level approval for minimizing biases

Description: This course will introduce a community-engaged model for public health research. The course will introduce various quantitative and qualitative research methods (scientific, sociological, historical, computational) and outline how they are utilized in public health research. The course will detail a community-engaged research framework, and guide learners through an application of this framework to public health research questions. Concepts of research ethics, health equity, and data privacy are embedded throughout the course.

Learning Objectives 

1. Describe the history and impact of bias in public health research in the United States.
2. Compare qualitative, quantitative, and mixed methods frameworks for conducting public health research.
3. Apply a community-engaged research model to a public health research question.
4. Discuss concepts of research ethics, health equity, and data privacy in relation to research projects.

Comprehend the sources, utility, and limitations of study data