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Social Mobilization Data

In this section, Svea Closser shares examples of social mobilization data and how we might use it for decision making. (Step 3.8)
SVEA CLOSSER: So in addition to going door to door doing vaccination campaigns, polio eradication also does a lot of work for social mobilization, or promoting the use of polio vaccine through a variety of different means. They collect a lot of data to understand how well this social mobilization is working. So there’s a number of different sources of social mobilization data. There’s data collected from surveys, there’s data collected from the social mobilizers themselves, there may be information collected through the AFP system, and there’s information collected on reasons for refusals as part of campaigns. So all of these different data sources are used to understand how well social mobilization is working. Here’s an example from Pakistan in 2013.
This is a bar graph that shows the reasons for refusal in different provinces of Pakistan. Take a minute and look at this as you can see the different colors in the bar graph represent different reasons for refusal, whether religious misconception about the vaccine, frustration over repeated campaigns, demand refusals– these are cases when communities refuse polio vaccine not because they have any concern about polio vaccine but as a way to get the government to give them something else that they want. So take a minute and look at this graph and think about how this might affect how you would deal with refusals. Also think about what this graph doesn’t tell you.
Looking at some of the areas with the most refusals, KP and what on this graph is labeled BLN, which is the province of Baluchistan, you see that each one of those places has around 5,000 parents that are refusing vaccine because of hear what’s labeled as religious. So in some ways this information is really useful. Addressing those refusals is probably going to require working through religious leaders. On the other hand, there’s a lot of this information doesn’t tell you. We have no idea what anyone in this group’s religious objection might be to polio vaccine. For the most part, most religious leaders in Pakistan endorse it. So it doesn’t really tell us anything about specifically why these people are actually refusing.
Religious is a pretty broad category. So in some ways, this information is very helpful. In some ways, it just raises more questions. The same is true of demand refusals. You can see in FATA, there’s almost 1,000 parents refusing because of demand refusals. They want something from the government, and they refusing polio vaccine as leverage. They think that if they refuse polio vaccine the government will give them what they want. This is interesting but we don’t know with these 980 exactly what they’re demanding, what the political situation is around it. So in some ways, that is incredibly useful, in some ways, there’s a lot we don’t know. Here’s another example of a data source that could be used for social mobilization.
This one actually comes from post-campaign monitoring. This means after the campaign, someone went door to door seeing how many children were covered and recording the reasons why the child didn’t get vaccinated. So what we have here is a bar graph showing each month there’s a different campaign and the reasons that children were missed in all of those different campaigns. What we can see here is the most common reason that children are missed is that they were not available. This means they weren’t home when the team came by. It also means the team didn’t come back to cover them. So this is a programmatic failure.
The brown pieces of this, which are another big piece, means the team didn’t visit at all. That’s another programmatic failure. You can see the orange, which is refusals, is a relatively tiny slice of the children who are missed every round. So dealing with refusals, while important, might be less important in this particular scenario than improving the programmatic performance of your own vaccination teams. This kind of information is really important to think about. All the work you do on refusals while useful is only going to cover a small percentage of the overall children missed. So sometimes this question of what most children entail can be a little complicated. And we touched on this before.
But the kinds of data you collect might change your interpretation of those data. So I’m going to use this example that we mentioned briefly before of under-vaccinated children in the early 2000s in North India. And for a while in the early 2000s, in both India and Nigeria, under vaccinated children were predominantly from Muslim communities. So people started talking about, well, maybe there’s this link between Islam and resistance. And the programmatic response was to focus on communication interventions around providing accurate information and correcting misunderstandings. But this changed as the program got better data. So in 2004 in Nigeria, in one area of Nigeria, Kano, almost 3/4 of the kids that were recorded as missed were recorded as religious objections.
But when new categories for the reasons that kids weren’t vaccinated were added in 2006 and 2007, this went from 75% down to like 10%. So it turned out that Islam or being Muslim wasn’t really the risk factor. It was just that there weren’t enough categories, so Islam was getting selected as sort of a catch-all. So asking the right questions is really, really important. Otherwise, the data you collect can lead you astray. And a similar thing happened in India. Monitoring forms for most children in the early 2000s didn’t ask enough questions to be able to really drill down on why children might have been missed. So social mobilization data is really important.
If you want to encourage parents to vaccinate their children, it’s really important to understand what their possible objections are or why they might not be vaccinating. At the same time, collecting this data through a survey can be really difficult, because the kinds of categories that you have may not adequately reflect people’s experience. And as an example from Pakistan, sometimes a category like religion or demand refusal doesn’t give you quite enough information to design a good intervention. Sometimes in addition to some of the survey data, you have to actually go talk to people.

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

In 2004 in Kano, Nigeria, for almost 3/4 of the children that were recorded as missed, the reason was categorized as “religious objections”. However, when new categories for the reasons that children weren’t vaccinated were added in 2006 and 2007, this went from 75% down to approximately 10%. This led to the realization that religion wasn’t really the risk factor. The campaign got inaccurate data because there weren’t enough categories, so religion was getting selected as sort of a “catch-all” category.

As a result of this case study, we can see that asking the right questions is incredibly important. Otherwise, the data you collect can lead you astray.

Think about this situation. Based on the information that you’ve learned so far in this lecture, and on your own experience, what could have been some additional categories that might have been added that would help identify what was really going on? What might help draw out the ideas that were falling under the “religious objection” category?

Please take a moment to share your ideas in the discussion.

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

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