In this section, we will talk about risk factors in public health and then take some time to create a visualization of our own. Much of public health involves the monitoring and studying of health outcomes. These can be deaths or mortality and also diseases, both incidence and prevalence of certain diseases. But an important component of both public health and epidemiologic work, is the assessment of risk factors. These are those factors that might contribute to someone, having a particular outcome or progressing more rapidly, to death or another negative consequence. Risk factors can be in several domains. There are genetic risk factors, having a particular gene that predisposes one to having a particular outcome. There are social risk factors such as exposure to crime or access to healthy foods. There are behavioral risk factors, tobacco or other substance use or sexual contact without protection. There are environmental risk factors such as poor water quality or air pollution. And there are health indicators such as hypertension or immunizations that might indicate an increased or decreased chance of having a particular outcome. The surveillance for risk factors is incredibly important but it can be very difficult to do. Outcome data can often be collected from compilation sources such as registries, vital statistics, medical records or other such blood collection methods. Risk factor data in contrast generally needs to come from self report by individuals. Most countries have large service or major surveillance efforts that they undertake in order to gather risk factor data. Some examples from the United States are the Behavioral Risk Factor Surveillance System, directed by the centers for disease control and prevention, and the National Health and Nutrition Examination Survey. One global example of the compilation of risk factor data is the Global Disease Burden Project. The Global Disease Burden Project was initiated in 1990 by the World Health Organization. It's aim is to provide a consistent and comparative description of the burden of diseases and injuries and the risk factors that cause them. Starting in 2010, the Institute for Health Metrics and Evaluation or IHME and several academic partners began participating in the process. The GBD provides mortality estimates at the country, regional as well as global levels from the years 2000 through the year 2015 currently. The method they use is to use vital statistics data collected from countries where the quality is considered usable. Estimates are then provided for other countries, extracted from geographically and demographically similar areas. They provide estimates back to the country for review and refinement before finalization of the numbers. The data is accessible both from the World Health Organization as well as from participating partners, such as the IHME. The Institute for Health Metrics and Evaluation is a population health research center that is part of the University of Washington in the United States. The link to their website is provided on the lecture page. The IHME generates country-level of risk factor estimates using known data modelling and statistical estimation methods. They have a Data Visualization Hub, where this information can be explored. The link to that hub is also provided on your lecture page. Please take a moment now to go to the IHME Visualization Hub website. You will see that you have several choices of data to explore. Select visualization of social determinants of health. You can then select different specific determinants, locations, scales, and years. Take a moment to spend some time exploring the coverage of DPT3, which is three doses of the diphtheria, pertussis, and tetanus vaccines. You will notice immunization coverage, how it differs over different locations. As you explore this visualization, consider the following questions. What global trends do you notice in immunization coverage over time from 1980 onwards? Second, what specific pockets of lower immunization rates do you notice on the world map? Third, click on the uncertainty box. What does this mean? When I think about the global trends in immunization coverage, certainly it's heartening to see the rapid increases in coverage that we've noticed over the past 30 to 40 years. However, there remains specific pockets of lower immunization rates and there are many global efforts happening to focus on improving immunization coverage specifically in sub-Saharan Africa and Southeast Asia. The uncertainty box, because of poor data quality, immunization coverage often has to be estimated. The uncertainty displays the mathematical uncertainty around the calculated estimates. You can say with some confidence, for example 95%, that the actual coverage lies somewhere between the shaded area surrounding each data point on the graph. Now let's consider how we might explore the burden of risk factors in specific areas. Go back to the same data visualization page and this time, select GBD compare, which is a comparison of global disease burden estimates. Select Arrow diagram on the left-most column. Select Compare on the top most row and Deaths as your measure. These choices are circled on the depicted example of the visualization page. This is the graph that I was able to produce. I can see the rankings of the specific causes of deaths for 1990 on the left side compared to 2016 on the right side. As you might note, cardiovascular disease was the top cause of death both in 1990 and in 2016. In contrast, neonatal disorders went from the fifth leading cause of death in 1990, to the ninth cause of death in 2016. This depiction also groups the different types of disease causes by type such that in the orange are communicable diseases, maternal, and neonatal diseases and nutritional diseases. In the pale blue are non-communicable or chronic diseases, and in the pale green are injuries. Now, explore changes in risk factors that you might see. Hone in on risk factor burden comparisons by location, category, age and sex, or year. Consider the following two questions. How could an arrow diagram or another depiction of change over time be useful in public health messaging? Second, what could some unintended consequences be of seeing data displayed in this way? When I considered these two questions, I think of the following, the arrow diagram is a positive way to show change, areas in which we've improved overtime and areas in which we perhaps have gone in the wrong direction overtime. However, I can imagine one unintended consequence that you might think that we have a particular problem solved just because it has gone down in ranking of the leading cause of death. This may result in the removal of resources or energy towards a particular cause of death or a particular risk factor that would then result in its subsequent increase. As you think about these data visualizations, imagine what a change this has made in making this information accessible to individuals and much more useful to people that are doing on the ground or public health or epidemiologic work making decisions about the day-to-day allotment of resources. Data visualizations have brought us a long way in the use and actual interaction with data.
