In this section, we're going to talk about one example of an International Public Health Surveillance System. The scope of public health surveillance systems varies, some are truly global in nature, some are regional, some are national, just within one country, some are even at a state or provincial level, some are even more localized than that. Really, if you think about how you choose, it depends on where you're going to act, or do you want to act at a local, state, national, regional or global level? It depends on the problem that you're tackling. But in this example, we want to talk about an international system, and one that is conducted at a regional level, in Europe, in particular. So, we're going to talk a bit about the European Antimicrobial Resistance Surveillance Network. It's called EARS Net for short. The link to the site for EARS Net is below. So, you can go on the website and see all of the information about this incredible network there. The first iteration of this surveillance system began in 1998, and currently there are 30 European countries participating. So, just imagine the scale of that. Antimicrobial resistance is important because these bugs as the name suggests, are resistant to many different types of antibiotics. Antibiotics are important treatment for carrying these infections. So, when we find bacteria that are resistant, this is an important public health problem that we need to address. Because they're easily transmitted across country borders, it makes sense for many countries in Europe to work together to track and monitor the spread of these infections, and try to come to agreement on how to combat them. So, overall, the networks objectives are to collect comparable, representative, and accurate antimicrobial resistance data, to analyze temporal and spatial trends of AMR in Europe. Recall that we're interested in who is being infected, when and where. They want to provide timely data for policy decisions. Recall that action is very important in public health surveillance. They want to encourage the implementation, maintenance, and improvement of national AMR surveillance programs. So, you have a regional network that has as one of its objectives to specifically improve the surveillance at the national level, and support those programs. They also want to support national systems and their efforts to improve diagnostic accuracy by offering annual external quality assessments. So, again, in support of national programs, they band together all of these countries to help improve the quality of the overall surveillance. So, what kind of data do they collect? So, laboratories from those 30 different countries share results from the cultures that they grove and the bacterial isolates the culture from blood, and cerebral spinal fluid from patients in those countries. They're really thinking about eight different bacterial pathogens, which we're often concerned about for antimicrobial resistance. The rest of my talk about EARS Net, I'm going to focus on just one of these, klebsiella pneumoniae, it can cause many different types of invasive infections in humans. I want to review together with you a couple of different data points from EARS Net on klebsiella pneumoniae and antimicrobial resistant klebsiella pneumoniae. We're going to look at the geographic distribution of resistant klebsiella isolates from across Europe. Then also look at the data that they've produced over changes in time in those resistance patterns. All of these are available on the website. If you want to go and look at all of the pathogens, it's all there, and they have a really fantastic dataset. So, first, this slide shows us something about the geographic distribution of resistant isolates. When you look at a figure, it's always important to identify the legend and to look at the different axes to orient yourself to the data that are being presented. So, first, let's look at the legend which is at the very bottom, and we have different colors. There's the green, the different shades of grey, and the black. Now, in green, it shows the isolates that are fully susceptible. That means that antibiotics we use for these infections work. Then in various shades of grey through to black, we have increasing resistance to different types of antibiotics. So, in the light grey, it shows the isolates that are resistant to one antimicrobial group. Then all the way on the other side in black, it shows the isolates that are resistant to five antimicrobial groups, which would be highly, highly resistant. So, now let's look at, this is just a portion of the table with a few of the countries that are in EARS Net represented. Along the x-axis, so along the bottom you see different proportions, they're from zero to a 100. Those are the percentage of the total. For each country, it's showing us what proportion of the isolates of klebsiella pneumoniae are susceptible to all antimicrobial groups, or resistant to a number of antimicrobial groups. Let's look at one particular example just to make sure we're following how to read these data. Let's look at Hungary here at the bottom. Hungary, the bottom line shows that of all the isolates submitted from Hungary, more than half it looks to be about 58 percent are fully susceptible to antibiotics. There's a small number that are resistant to just one antimicrobial group, a bit larger proportion but less than 10 percent that are resistant to two antimicrobial groups. But then we see a large proportion, almost 30 percent are resistant to three antimicrobial groups. So, go through just to be sure that you can read that and understand what the data are saying for Hungary. So, what they've done is arranged countries here by the proportion that are fully susceptible. So, at the bottom of this list, you would see the countries that have the greatest problems with antimicrobial resistance when it comes to klebsiella pneumoniae. So, we can see here, for example, Germany's at the top. Almost 80 percent of their isolates were fully susceptible. Whereas in Hungary it was less than 60 percent. Now, let's look at changes over time. So, this map is also giving us an overview of geographic distribution of isolates, but we can look at this map and also look at how things have changed over time. So, this figure is showing us the percentage of invasive isolates with resistance just to third-generation cephalosporins, fluoroquinolones, and aminoglycosides in 2012. These are the participating countries in EARS Net. Here on the left, again these data from 2012. So, on the left side you see you can see the legend for this map. It starts at the top with green, goes down to dark red, and then you have some gray. If you look at the legend, the green means that less than one percent of the isolates in 2012 from that country were resistant to these three antimicrobials listed in the title. If you go down to the dark red, you see there are few countries in dark red. That means that more than 50 percent of the isolates were resistant to those antimicrobials. Then they have some grey with no reported data. That's good for all maps to have some indication of who actually didn't report data. So, we're going to compare that with the picture in 2015, which you'll now see on the right side. So, the map on the right side also has a legend, but those colors mean exactly the same thing at 2015 as they did in 2012, and that's what allows us to compare these two maps. So, when we see the bright green, which really indicates almost no problem with any microbial resistance. You can see between 2012 and 2015 there are fewer countries with bright green. We can also see that there seem to be more countries with red, darker red, countries like Spain, for example, which is yellow, and the five to less than 10 percent in 2012, that has now become orange which means 10 to 25 percent of the isolates were resistant. So, this seems to suggest that there are more isolates that are resistant in 2015 compared to 2012. Overall, the map is becoming less green and more red. So, you can see through this surveillance system how the European Union and the participating countries in the surveillance system are able to use data from the surveillance system to track resistance patterns geographically, and over time to help them develop interventions that might be useful, and to track the spread of these pathogens and help stop them across Europe.
