You got your pro-vax and your anti-vax people, right? You got Anthony Fauci and RFK Jr. But what all those in between, the folks who don’t really give thought to vaccinations? Ask them how they feel about the subject and they’ll just shrug. Perhaps these people could make the difference in the success of immunization programs, the way Undecideds end up deciding elections. Said another way, we might need to care most about those who care least.

What got us thinking about the middles was hearing some surprising data from STChealth about “flu-floppers,” the people who get a flu shot some years but not others. Check this out: In one analysis of six years of data across a large data set,

THE PERCENTAGE OF THOSE WHO GOT A FLU SHOT
THREE YEARS IN A ROW = 30%
FOUR YEARS IN A ROW = 20%

We sat down with Dr. Sam McGee, one of the big brains of STChealth’s Analytics team, to discuss these results and he explained the immunization breakdown that their data suggests:  20-30% of the population can be counted on to routinely get a flu shot and 40-50% can be counted out, being highly unlikely to get one. So, using the mid-points of 25% of a population on one side and 45% on the other, that leaves 30% in the middle, our “flu-floppers.” 

Dr. Sam McGee

Further, Sam explained, when they did some recent modeling of vax program outcomes, they sub-divided the flu-floppers into those who tend to get a flu shot about half of the flu seasons and those who do so only occasionally. Thus, they used four groups:

25% regulars
15% semi-annual
15% infrequent
45% never

These data become highly useful when put into a new program that can simulate what might happen if some of those numbers could be changed. Sam explained that this is modeling uses ABM or agent-based modeling, which creates a population of individual “agents” who are sent out into their statistical world (with dozens or hundreds of variables we get to choose) then we sit back and watch what happens, something like Madden NFL but the opposing team is the flu.

So, using the new simulation software, let’s get back to our flu-floppers and see what happens if we can get them on Team Regular.

First, let’s look at how flu outbreaks might progress across five flu seasons, using a population of one million, with typical assumptions about the disease and with typical vaccination rates. The numbers in the blue boxes are the number of new infections each week. (In the first season, we see about a thousand new cases each week at the peak.)

Now, let’s change the assumptions. For the second run, let’s say that we were able to implement a campaign that succeeded in converting our “flu-floppers,” the 15% who get flu shots semi-annually, into regulars. Nothing else changed: while we now had 40% getting shots every year, we still had 15% in the “infrequent” group and 45% still “nevers.” Here’s how that scenario plays out:

OK. But it’s a not a giant surprise that more people getting flu shots results in few cases of flu. So what’s so useful about this simulation tool?

First, as Sam explained, what’s appealing about this particular run of the model is that that it shows what can happen when a program focuses solely on “flu-floppers”: the drop in flu cases over time didn’t require the converting of any anti-vaxxers; rather, this scenario merely got some of those people in the middle, the ones open to getting flu shots, to be more consistent.

Likewise, a team choosing among potential immunization campaigns can run a series of simulations to assess the potential outcomes and the potential payoff in terms of reduced disease and the cost savings that come with it. As Sam put it, discussing the five-year simulation, “You don’t see immediate results – the payoff is in years four and five. Go to someone making funding decisions and say, ‘It’ll take four or five years to see results, but, trust me, it’ll pay off.’ That’s a hard sell. Not everyone has the vision and the budget to wait five years. But if you can show them the data up front, it’s an easier sell.”

PLAYING AROUND WITH SIMULATIONS

Speaking of easy sells: Sam offered us the chance to play with the simulation program and we took him up on it by going back to something we wrote about recently, what might have been the world’s first pandemic, the Roman Plague. That one was likely some early version of smallpox and might have had a mortality rate as high as 30%. 

We played around with a few variables, but there was one that we wish we could go back in time and offer to the guy in charge at that time: Marcus Aurelius. First, the model told us that if the Romans did nothing, a population of a million people faced with something as deadly as smallpox could expect the death rate to peak 47 weeks into the outbreak, with 1800 deaths a week. However, if Marcus had been able to impose a strict stay-at-home order, with only essential workers being out among the public, the disease would peak in week 23, at about 70 deaths a week. I suspect that, given that the ancient Romans did not have stay-at-home options of Zoom meetings and Amazon deliveries, Marcus might have asked, “What else have you got?” And the model would offer hundreds of options, from cancelling certain holidays to different levels of masking to options on vaccine uptake and effectiveness.

We’ve read that the ancient Romans were said to use the eating habits of “sacred chickens” as auguries. We’re not sure  if Marcus Aurelius had chickens, but you have to think he would have welcomed the chance to test out a simulation model.

WHAT’S NEXT?

We asked Sam where the team sees this going. He replied,  “That might be up to us here in the middle; we know that we’d like to see more people get a flu vaccine annually, but we’re not the ones who need to start taking them annually (or maybe some of us are). But now we have a tool to help us observe the power of even a little more consistency, repeated again and again. With it, we might change our approach for where we put our energy and resources each flu season, so that we can protect more of our communities first and get folks onboard consistently, and then start working with more challenging groups. Any way we go, we’re going to need to do it together, and if you’d like to hear more about or collaborate on this project, contact the STChealth Analytics team at [email protected].”

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STATS OF THE MONTH

Do Birds of a Feather Flock Together?

By Bill Davenhall, Geospatial Advocate

“Birds of a feather flock together” is an saying suggesting that people who are similar in some way tend to associate with each other. The exact origin of the saying, while uncertain, has been traced back to ancient Greek times and was first recorded in English in 1545. So, what does it have to do with immunizations?

Most products and services have unique “mini” markets, essentially more specific and targeted segments. For example, a product might target “affluent, tech-savvy urban millennials” or “middle-aged suburban families with young children.” There is hardly a successful national product or service that has not been “segmented” by using customer-centric data, including geographic, psychographic, behavioral, and demographic. In recent decades, several huge consumer marketing research organizations have constructed statistically robust population or household segmentation models to target products or services better. It’s not magic, but a series of data fusion processes that are more accurate in identifying “flocks” and figuring out how they find and track the flock.

Market research organizations essentially meld this data together into statistical clusters or segments that more accurately identify the various “flocks” and put it into a geographical context – in other words, where can we find the many other flocks out there in terms of their size and geographical location, and can we more accurately describe their consuming behaviors? Most marketing segmentation algorithms over the last several decades have identified  65+ statistically unique clusters or, as we like to call them, “flocks.” Still with me?

Immunization is both a product and a service. The vaccine is one part of the product, as are the tools for administering the product. The services that health providers provide include immunization registration, vaccine administration, long-term data storage (the official repository) of the vaccine administered, and other personal contact information.

The map below illustrates one particular segment, identified by a popular market segmentation methodology of one of 72 identified consumer “flocks” representing 4.6 million households (out of 131.2 million nationwide). The segment is described as those “Influenced by Influencers”; that is, “an ambitious flock of hard workers who want to advance as quickly as possible, and while they don’t have much free time, but still find ways to support the liberal-leaning cause, are not likely registered to vote but are willing to volunteer for a good cause or a worthy protest if an issue moves them.”

Hopefully, you can quickly see, geographically,  the value of this type of research for those organizations involved in vaccine administration – as a way to help deliver an immunization product and service to various “flocks” with the best messages and promotions. The remaining 126.7 million “birds” that are out there flying in the remaining 71  different “flocks” are the new delivery challenge in a highly mobile and segmented society  – staying in touch with your customer forever!

As always, I appreciate  2^nd opinions!