Hello, I'm Karen Monsen, professor at the University of Minnesota School of Nursing. This is the first of five data applications modules for our social determinants of health data to action specialization. In this module, we will explain and conduct frequency analysis and use the bar chart to visualize our findings. These learning activities will be key to your success as we conduct our hands-on data analysis experiences. These additional learning activities are optional to help you deepen your understanding. In this course, we focus on hands-on experience describing social determinants of health indicators for individuals categorized by race ethnicity. In each data to action module, we'll consider ways to ensure we are ethical analysts and allies to the most vulnerable. For example, the actionable intelligence for social policy, AISP at the University of Pennsylvania, helps state and local governments to collaborate and responsibly use data to improve lives. Their guiding principles for data use, help us center our thinking responsibly as we approach our analyses. It is critical to ensure that our data use is focused on equity for all in order to prevent harm. Another valuable resource is open source bias audit toolkit for machine learning developers, analysts and policymakers, to audit machine-learning models for discrimination and bias and make informed and equitable decisions around developing and deploying predictive risk assessment tools. Recall important ideas surrounding race and racism. We know that human beings, that is the human race, is one group. But that racism persists despite scientific evidence that human beings do not differ genetically in substantive ways for socially constructed races. Watch this video to deepen your awareness. We as data to action enthusiasts are faced with a huge conundrum. That is, to understand the impact of racism on health, we need to be able to assign race ethnicity labels to data, while recognizing that doing so is fraught with issues embedded within socio-political structures. Therefore, we must be informed and consciously involve stakeholders who can ensure that race ethnicity data are collected and used ethically to advance health equity, rather than using the data to perpetuate racist structures and racism within our societies. The American Journal of Public Health offers some guidance in this regard. For articles published in the AJPH, if race ethnicity is reported, the authors should indicate why race ethnicity was assessed, how individuals were classified, what the classifications were, and whether the investigators or the participants selected the classifications. Also fundamental to our data analysis is the notion of measuring social determinants of health. As we have seen, there are many perspectives on this matter. Just as with race ethnicity, governments and scientists are not likely to reach consensus soon on what should be measured and how. I encourage you to explore this and other websites to learn more about various social determinants of health measurement approaches. Now, we can begin our actual hands-on data work. We provide two separate data sets in which you can work. The publicly available and NHANES dataset and a fictitious de-identified Omaha System dataset. I'll go into more depth about these as we go along. We're going to start with the simplest descriptive statistical analysis. In each module we'll add more complexity. We provide learning resources for those of you who would like to understand more deeply the actual statistics involved and also the complete coding that will create the correct results. First, here's a brief video about frequency analysis. As you watch this video, consider, we will be analyzing how various social determinants of health occur in a data sample. Our two textbooks are the Introduction to Statistics by Dr. Lane, which is freely available in this course. The optional text, Introduction to Biomedical Data Science by Doctors, Hoyt and Muenchen, which may be ordered online. For each book, we point you to optional readings pertaining to each module's analysis. Likewise, for each book, we point you to optional readings pertaining to each module's visualization techniques. In this module, we focus on bar graphs, also known as column charts, which use vertical or horizontal bars to represent data along both an x-axis and a y-axis visually. Each bar represents one value. When the bars are stacked next to one another, the viewer can compare the different bars or values at a glance. Just a quick reminder about descriptive analysis, in this case, frequencies. Frequencies are simple counts that do not account for any other factors. Therefore, we can count and describe how often social determinants of health variables were documented in our data sample, by race, and by ethnicity. We cannot make comparative or causal statements. Tying all of this into our course material, recall the notion of collective impact that is critical to the success of a data to action project. In each data to action module we'll also consider an example of collective impact using data to continuously learn, adapt, and improve. In this module, please read about the Memphis Fast Forward Project. I encourage you to pause this video now to quickly review it. As you read think about the data sources that the project used and how the team used data to tell the story to us as an inspiration and to decision-makers to create policy change. Recall also that we have positive system archetypes to guide us in our data to action efforts. For each collective impact example, we draw on a positive archetype that underlies that example's success. In this case, we see that the Memphis Fast-Forward Project success leverage that fixes that work archetype. Recall that a number of these positive system archetypes can be used to overcome our classic mental models that creates systems failures and perpetuate negative health and social outcomes. Again, please pause the video and review fixes that work versus fixes that fail. As you review the positive archetypes, do you agree that fixes that work positive archetype was instrumental in the success of the Memphis Fast Forward Project? Which other positive archetypes may have contributed to this success? With all of these exciting ideas, you're ready to move on to Part 2, Introduction to the R Environment in Coursera, in which we answer the question, where will I find the datasets and analyze them in this course?