In this course, we'll build on the previous lessons in this specialization to focus on some very specific skills related to public health surveillance. We'll learn how to get the most out of surveillance data analysis, focusing specifically on interpreting time trend data to detect temporal aberrations as well as person, place, and time in the context of surveillance data. We'll also explore strategies for the presentation of surveillance data and some of the complex legal elements that affect its use. We'll then turn our attention to surveillance of non-communicable chronic diseases and how the data can be used to support prevention efforts. Finally, we'll explore special surveillance systems, such as syndromic surveillance, antimicrobial resistance, and event-related surveillance. This course is designed for public health practitioners with a focus on those working on health surveillance in municipal, regional, state, provincial, or even national public health agencies. We really think that this course will help those with an interest in health surveillance to see which approaches are used in actual practice of public health.
Analyses of Surveillance Data
-In this first module, we're going to focus on the analyses used in health surveillance. Specifically, we're going to talk about how to interpret time trend data that's harnessed as part of health surveillance programs and strategies used to detect temporal aberrations. We're also going to build on some of your earlier specialization lessons on descriptive epidemiology with a real focus on how it can be used in the analyses of person, place, and time in the context of surveillance data. I really want to highlight throughout the module how we often underutilize surveillance data to analyze complex issues, but I also want to talk about the some of the limitations that exist within the data sets.
Dissemination Strategies and Communication Frameworks
-In this module, we will explore strategies for the presentation of surveillance data and some of the complex legal elements that affect the use of health surveillance data. Now, surveillance data are fundamentally different from research data. In research studies, people provide explicit consent for how their data are going to be used. For surveillance data, the collection doesn't include explicit consent. As a result, there are very specific laws governing how the data can and should be used to drive public health programs. We'll also discuss communication strategies in health surveillance given how sensitive it can be to message health surveillance data, and the associated recommendations that come along with communicating these data.
Chronic Disease Surveillance Systems
-So we're switching gears today from communicable diseases to noncommunicable diseases, or often called, chronic diseases. I'll present some of the frameworks and approaches that have been developed to guide chronic disease surveillance and then I'll focus on giving different examples of these, ranging from cancer to cardiovascular health systems. Taking you back to the surveillance cycle from Dr. Gurley's course, "Surveillance Systems: Building Blocks," I'll provide some context on how these chronic disease surveillance systems can really impact chronic disease prevention strategies if communicated effectively. And that brings us back to communication, where we will again focus on approaches to communicate these complex issues and then give an example of how to apply communication framework in the context of a chronic disease.
Special Surveillance Systems
-In this module, I will discuss some of the really interesting special surveillance systems. Although having standardized approaches for infectious diseases and non-communicable diseases is fundamentally important, there are also some health issues that do not fit nicely into these boxes, and it's here where these special surveillance systems can really play an important role. As you will see, having a framework or approach to the issue is still relevant, but the approaches are really tailored to the issue at hand. We will start by discussing syndromic surveillance systems, including when to use them and also how to design them. We'll then really evaluate the differences between indicator versus event-driven surveillance systems. We'll then move on to anti-microbial resistance surveillance systems and then talk in more detail about event-related surveillance systems.
Stefan Baral MD MPH MBA CCFP FRCPC