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Surveillance Data

Watch as Svea Closser reviews the various uses and impacts of polio surveillance data in different contexts (Step 3.3)
SVEA CLOSSER: First, let’s look at the data collected through the AFP surveillance system. So polio eradication has built the most comprehensive surveillance system in the history of the world. It covers pretty much the entire globe, and it looks tirelessly for cases of polio. This is critical for an eradication program because in order to know whether you’ve eradicated a disease, you have to be confident that you’ve found every last case. So there’s a number of ways that the data collected through the surveillance system gets used. The way that polio surveillance works is that data is collected in all children in a given population, usually under 15, although it varies somewhat by country.
For all cases of acute flaccid paralysis, or AFP, this means that any child that has sudden-onset floppy paralysis gets picked up by the surveillance system. So this kind of paralysis is mostly not due to polio. There’s a number of reasons that children would get paralyzed in this way. And so this surveillance system picks up both kids with polio and a much larger number of kids without polio that happen to be paralyzed for one reason or another. In addition, polio eradication does surveillance in the environment, looking at sewers and water sources to see if they can find polio virus in those contexts.
People within polio eradication use the data from this surveillance system to understand why and whether their program is succeeding or failing, and a lot of these strategies could be used in other infectious disease-control programs, depending on the epidemiology and biology of the infectious disease in question. So the first way that polio eradication uses surveillance data is to understand the quality of the surveillance system itself. And one way it does that is by looking at the AFP rate. So acute-flaccid paralysis is something that occurs in every population, regardless of whether polio exists. Polio is a relatively small percentage, as I mentioned, of AFP cases in most contexts.
So any surveillance system should be picking up a fair number of AFP cases, and the minimum number for a functional surveillance system that’s generally used is 2 per 100,000. So this slide is from Syria, and what it shows is differences in this AFP-detection rate. On the bottom-left here, we have a map of Syria, and it’s color-coded by the accessibility of different parts of the country. So some parts of the country, in 2019, are green– this means that they’re easily accessible– but some parts of the country are red, and this means that they’re fully inaccessible. It’s very difficult to get into these areas.
So the table on top of the screen presents a lot of information pulled from the surveillance system, broken down by weather districts are accessible or inaccessible. And rather than get stuck in this data, what I want to focus on is this bar chart on the lower right. And what that shows is that in fully inaccessible districts, the AFP-notification rate is 3.2 per 100,000. In fully accessible districts, it’s 5.5 per 100,000. This means that in accessible districts, the surveillance system is picking up almost twice as many cases of AFP as in the inaccessible districts. And this means that polio eradicators should be aware that they might be missing some cases of polio in the inaccessible areas.
The fact that they’re missing cases of AFP means they might also be missing cases of polio. So understanding your AFP rate is a good way of understanding how good your surveillance system is. Now, we’ll talk about something a little more obvious. We’re going to look at actual polio cases. So this particular map is from Pakistan and Afghanistan, and it has little people on it. There’s red people and blue people. We’ll talk about the colors in a moment. But each little person on here represents a polio case picked up by the surveillance system. So for a moment, think about what this tells you and what kinds of decisions you could make based on this information about where polio cases are.
There are, of course, obvious decisions you could make here. You could decide to focus your door-to-door vaccination campaigns on areas with polio cases. That would be a clear operational decision. But you might also want to dig a little deeper. Now that you know, for example, that, in Pakistan, most of the cases in this part of 2019 were focused along the Afghanistan border, you might want to think about why that’s the case and look deeper into what’s happening in those areas. So now let’s take a look at the right side of this slide.
The back story here is that each polio virus isolated in a child is genetically sequenced in a lab, and this allows polio staff to see which polio cases are closely related. What they did here is they color-coded them, so each different color represents polio cases that are closely related to each other. So the blue cases are closely related to each other, the red cases are closely related to each other, and the light-blue cases are closely related. So take a minute and think about what kinds of decisions this additional information might help you make.
One thing that knowing how polio viruses are related to each other tells you is what kind of population movements might be spreading polio. So for example, cases in southern Afghanistan are related to cases right across the border in Pakistan. This tells you that population movements across that border are transmitting polio. So in addition to the kinds of interventions we just talked about, this information might also push you toward, for example, having a vaccination post at border crossings. So here, we see exactly the same map of Pakistan and Afghanistan, except with the addition of some triangles. These triangles are environmentally positive samples, and they’re color-coded, again, by virus type.
So what new information does this environmental surveillance give you that you couldn’t have gotten otherwise? Take a minute to think about that. In most cases, this doesn’t really give you any new information. The majority of the environmental samples testing positive are in the same places where we already knew there were polio cases, so it’s not going to change our operational decisions that much. However, there are a few areas where you see environmentally positive samples but no polio cases, and that’s useful because it means that these are areas we need to be concerned about and focusing on, as well, since there is polio virus in the environment. Now, this slide here shows exactly the same information as the previous two slides.
It’s taken from the same slide deck. But instead of showing polio cases on a map, instead, here, we have them by month, the month that the child was paralyzed. So this gives you a different way of thinking about what’s happening. Polio is a seasonal disease, so all else being equal, you’re going to see more cases during the hot parts of the year in Pakistan and Afghanistan– that’s the summer– June, July, August– than you would in the cold months– December, January, February. That’s not necessarily the case in this particular year. Instead, you see particular virus types spreading in different months.
Looking at the particular virus types color-coded differently– red, brown, blue, light blue– allows you to look at different outbreaks and their extent and fluctuation over time and the extent to which you’ve been able to stop that outbreak. So information on polio cases is obviously useful, but the AFP surveillance system– the vast majority of the information it collects is about cases of paralysis that don’t turn out to be polio. This is a surprisingly useful treasure trove of information because AFP cases represent a reasonably random sample of a population, usually the children in the population, since most AFP surveillance systems focus on children. So what we see here is a sample data-collection sheet that surveillance officers fill out about every AFP case.
And there’s a wide variety of information on this sheet, from whether the family lives in an urban or rural setting, to their immunization history. So what we have here is, a more or less, random sample of a population describing all of these factors. This can be really useful in understanding population issues, like routine-immunization coverage or polio-immunization coverage in a population. In many cases, non-polio AFP data is more comprehensive than some other sources. For example, administrative-coverage data is sometimes inflated because people are rewarded for high-coverage levels. And other data sources, such as the DHS survey, may not have a lot of information about certain small populations.
And the AFP surveillance system can be a really useful resource in these kinds of cases. This might be easier to understand with an example, so let’s look at this example. This is looking at nomadic or pastoralist populations in the Horn of Africa around 2015. So these populations were covered by the AFP surveillance system, and the AFP surveillance system was used to understand polio vaccination coverage in these populations, nomadic populations. So what you see here is a bar graph.
So if we look at AFP data between January and June of 2014, what that AFP data revealed is that more than 40% of the nomadic children who showed up in the AFP surveillance system with paralysis– they did not have polio– had never been vaccinated for polio. So this tells us that probably around 40% of the entire population of vaccinated children had never been vaccinated against polio. This is obviously a concern. Decisions were made, and steps were taken based on that information so that by July to December of 2014, the next six-month period, looking at the AFP cases, something around 15% to 20% of those children were totally unvaccinated.
So planners were able to use the AFP surveillance-system data to track how effective their polio vaccination had been. So they’re using this data on kids that were paralyzed by something other than polio to understand how good their immunization is. This is a strategy you can’t use in every surveillance system. It’s maybe somewhat specific to AFP. But it’s also worth thinking about that as long as there’s an AFP surveillance system in place, there’s this information on vaccination coverage, and potentially, other health indicators, depending on what you want to ask in your surveillance form, that could be useful to a wide range of health programs.

Svea Closser, MPH, PhD
Bloomberg School of Public Health, Johns Hopkins University, USA

What types of surveillance data described in the lecture might be helpful in your work or educational context? Why do you think those might be useful data to have? How might you use them?

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Collecting and Using Data for Disease Control and Global Health Decision-Making

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