A Conversation with Stats Guru Bill Davenhall

“If I wanted to have a heart attack, I’d been in the right places.”

That was Bill Davenhall describing in his Ted Talk how he’d learned, after his heart attack, that he’d spent his life living in counties with high rates of “acute myocardial infarction.” Thus, it’s small wonder he developed an interest in health data, especially in what came to be known as “geomedicine,” the geography of health. Some of Bill’s best known quotes…

Clinical data account for only ten percent of the factors that determine a person’s health.

We are just creatures of our genes; we are creatures of our environment.

Genetics plus lifestyle plus environment equals risk.

If you look at the fine print on the screenshots you’ll see that the Ted Talk was in ’09 and now has over a million views. For many years before and after that talk, Bill was publishing articles about the better use of health data, including his being a regular contributor to the IINews. As next month will be the 100th edition of this publication, we thought it was about time we profiled the data guru who regularly shares his insights with us.

“I GOT SIDETRACKED”
When we got Bill to talk about himself, he described how he stumbled into a career of numbers: “I went to school for what I thought was going to be a clinical practice in the behavioral sciences. That’s what I thought. But, what happened to me is that I got sidetracked by this whole novel thing when my employer said, ‘Oh, we want you to play with computers. We want you to figure out how to take all this monstrous data and do something useful with it.’ In my particular case, it was in the health and human service area. And that’s what I did.”

THE DOG THAT DIDN’T BARK
But, we wondered, where the “geo” part of the data come in? “It’s not that I love geography. I loved what the geography can do to add value to a piece of information.” Asked for an example, Bill lit up, saying…

“Take the dog that didn’t bark solution. You take a database and profile it. Like the database of immunizations. You can find out who are the people that like a certain kind of immunizations. By ‘like’ I mean who gets it and who doesn’t get it? And if you know who gets it, you can do reverse geocoding and you can get all the addresses that are not in your database. So in other words, let’s say you have a 100 million household addresses of people who got a vaccination, I can find out the 10 million household addresses you’re missing. And then suddenly you have the addresses and you can start to look at the household characteristics of those who don’t get immunizations.”

THE KEYS TO THE DATA KINGDOM
And that brought Bill to one of his favorite topics, the U.S. Census, adding, “What we’re talking about is the households, what goes on in a household. The Census Bureau has been very generous in the kind of information they’ve told us about what’s in a household. They don’t tell us about the individual, but they tell us in a summary fashion what’s in that household. We know how many people are in it. We know how much income they probably have, the number of children, the number of vehicles, their educational attainment and much more. The Census Bureau keeps individual data protected. So the household is like the keys to the kingdom. When you’re a behavioral scientist, this is what you’re looking for. You’re not looking at individual data necessarily, but its aggregate – you want the data collected at a very low level of geographic granularity, even though you don’t necessarily need to identify it personally. We call this address geocoding and its use is huge?”

THE DATA PIONEER
Indeed, it was the U.S. Census data that was a siren call to Bill early on, shaping his career.
Bill looked back on his career and concluded this: “How did I arrive at the place where I’ve arrived? I call myself a data pioneer because I first worked with the digital version of the 1970 census. That was the first census that was made commercially available that was relatively easy-to-grasp by the public. Prior to that, the 1960 Census was the first one that was digitized and put on magnetic tape, but it was only sold to firms that had a lot of money because you had to have a staff and computers to deal with it. By the time 1970 rolled around, they’d got their act together and it was like the ground floor. That’s when I finished my college work. I’d had all the book learning and I suddenly said, ‘Well, I want to apply all this new address matching technology and at that moment, things began to happen.’”

That early work evolved into a long, unexpected career that Bill summed up this way: “So as I look back at the projects in which people typically hired me — this is going to sound crazy – but it was to help them understand and use their own data. Most of them had data but didn’t know what to do with it. And they often looked at their data as nothing but transactional information, like sales data and inventory counts; they never used it for strategic purposes. They never linked it to other kinds of data that would tell them more about their customers or the marketplace.

“Of course, I wanted to spend all my time in health care, right? Because that’s what I was trained in. Well, that was great, but the industry itself wasn’t ready to spend money in that space. Hospitals were some of the last people to get on what I call the big data bandwagon. They’re on it now for sure, but they weren’t back then. So who was? I worked for literally thousands of third-party companies — for-profits, not-for-profits. The work was pretty much the same because I was always looking at what the transactional data was telling me about the behavioral and household characteristics of either the people that were using their organization’s product or services, the people they sold or served, or their competitors. So, I would say nothing has really changed. It’s still that way today and new information technologies have made it easier to carry out, faster and more economically.”

THE BIG GATOR
While Bill continues his explorations into new uses of data, including in the IINews, he recently moved to Florida, where he’s still keeping an eye out for new information.

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STATS OF THE MONTH
“If You Change The Way You Look At Things,
The Things You Look At Change!”*
By Bill Davenhall, Geomedicine Science
*Wayne Dyer, circa 1970

By 2030, there will be fewer children under 5 years of age in the population than in a very long time.

In 2025, about 18.9 million children under 5 years of age live in the United States.
In 2030, its estimated that there will be about 353,000 fewer.

Surprised? A decline has not been experienced in a very long time. It’s not surprising why there is a bit of panic among economists over this evidence of slower population growth. In my experience working with population data in health settings, how you “see” these numbers depends on where you’re looking – and where you’re looking could suggest changes in how you go about addressing or adjusting to its variable implications.

There is little doubt that the immunization road ahead is a bit bumpy: The national immunization recommendations to parents and the targets set by national and state governments all could be changing; add to that challenges to vaccine efficacy, panic over a declining birth rate, and the risks of a slowing economy looming. These generate significant implications to the wellness futures of children for years to come.

So, take a long and thought provoking “look” at the future of Series 7 immunizations for those under 5 years of age in the map below. Look at the states now experiencing some of the highest rates for Series 7 in the US today — MA 92%, CT 90%, RI 84%, NH 83%, and ND 81% — all well above the national average in 2024 of 72.8% . Now look closely at the 41 states that lie below the national average. What are you thinking now?

Practical implications
There will be 353,417 fewer children under 5 in 2030, and only 26,795 more from 10 states – what are the implications if this trend holds steady even for the next five years:
• Shrinking demand for Series 7 immunizations in the majority of states?
• Slowing purchases of baby products?
• Fewer hospital programs for newborns and children?
• More households moving in and out of states with varying immunization resources for immunization programs and lower numerical immunization goals?
• Changes of immunization advocacy and promotional strategies?

And, the things we look toward: Will the sobering elaboration of these observed and projected trends move states and communities toward defining specifically what a “great start” actually means to the future generations of children, no matter how large or small the under 5 population will be? That would be my most reasonable reaction to what and where I am looking! How about you?

Making data do something is an important outcome of collecting, reporting, and using tables, charts, graphs, and maps. Don’t miss the opportunity to change the view of what you’re really looking at!

As always, I appreciate 2nd Opinions.