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by facilitating
We're grateful to Drs. Londergan and Victoria for agreeing to present for us today. Steve and Tim's
presentation is titled, Live Free and Die, Why Are Voters in Red Counties Dying Younger Than in Blue
Counties? Tim Londergan is an emeritus professor of physics at Indiana University, former chair of the
Department of Physics, associate dean of the graduate school, director of the Indiana University Nuclear
Theory Center, and director of the Wells Scholars
He's also a co-founder of Concerned Scientists of India at the University of Cooke. It supports
evidence-based science, funding for science research, and policy-based, best available scientific
information. Our faculty member, Tim, also served as a consultant for numerous research centers around
the world, including the Baba Atomic Research Center in Mumbai, India, Los Alamos Laboratory, University
of Adelaide. Steve Vigdor is also an emeritus professor
at the Brookhaven National Library for Nuclear and Particle Physics. At Brookhaven, he oversaw operations
of the relativistic heavy-ion glider and all high-energy physics research in the laboratory, including
extensive contributions to the Atlas detector and the large-capacity glider. He's the author of the
book Signatures of the Artist, The Vital Perfections that Make Our University Powerful. Steve and Tim,
the interface between science and policy. They expose the cynical label of science to the language.
Please join me in welcoming them. Thank you, Jane. Thanks so much for all of your efforts and for the traveling
title of our talk, Live, Free, and Die. Why are voters in red counties dying younger than those in blue
counties? We'll bombard you with data today to try to prove our point, show you what's going on, and
you can draw your own conclusions as to the reasons behind the invincible show. Many of you nowadays
may think that you're living in actually two countries, a red state country and a blue state country,
and what we want to show you today
to health outcomes, that's actually quite literally true, that you find very different outcomes in red
and blue areas. We're trying to show you all of this and why in our talk today. Here is an example of
this. This graph shows the life expectancy in two states, Connecticut and Oklahoma, from 1959 to 2019.
And you see that in 1959, a resident of Oklahoma
would live on average about one year longer than one in Connecticut. However, they started to diverge.
They have continued to diverge. So that today, our resident in Connecticut has a four year longer life
expectancy than one in Oklahoma. And it's getting bigger. And as we'll show you, if the current trends
continue, we expect these disparities to grow substantially.
on our blogdebunkingdenial.com, specifically these two, Live Free and Die and the Deployment Divide.
So, here's a disclaimer that we use. I don't represent official news of anybody who hears anything.
Although they should. So here is a page from our blog, Debunking Denial. By now we have over
100 posts on our blog on the following categories. And the two categories relevant today are demographics
and partisan divides over science. I want to mention that our talk follows basically after Rob Stone's
talk, Medicare for All. And it precedes a public panel that will be held on Tuesday, Vaccines in a Changing
Political Landscape.
5 to 6.30 p.m. on Tuesday here in the Monroe County Public Library. You've got a typo in the title.
Yeah, I just noticed that. Thank you. Oh, and by the way, that reminds me. If you have comments, questions,
challenges, please feel free during the talk. You can fix that before the next presentation.
Yes, thank you. Okay, so here's an outline of our presentation. We'll show you that life expectancy
in general is increasing worldwide, but in the U.S. there's evidence of a growing partisan mortality
gap and education gap in the U.S. We'll talk a little bit about the impact of COVID-19 on this. We'll
talk in great detail about chronic disease prevalence in the U.S.
And we'll show you the impact of state government control, state government laws and attitudes. And
then we'll talk about some specific situations, smoking restrictions and lung cancer, gun laws and firearm
deaths, abortion laws and maternal infant mortality, medication expansion, constant medical infrastructure.
And we'll just mention at the end, vaccine hesitancy and Robert F. Kennedy, Jr., which could be
top all on its own. And then future trends in summary. So life expectancy has been generally increasing
worldwide. Here's a graph from 1800 to 2023. The red curve here is US population. The blue is Europe.
The green is Asia and the black.
Sorry, I'm colorblind, so please turn around. It's beautiful. Or you can shout out if I miss that. Anyway,
until about the start of the Industrial Revolution, populations were more or less static. They began
to increase in Europe and the US rapidly after, in about 1870 or thereabouts, and have increased since.
Populations in Asia, for example, remains
for a longer period of time that have now shot up. There's many reasons for this increase. Improved
vaccines and medical treatments. Improved air and water quality. And in the U.S., that's the particularly
environmental protection agencies, clean air and clean water laws. Improved personal hygiene and nutrition.
Improved access to hospitals and healthcare and insurance. Yes. What happened in Asia?
Chinese family. That is all Chinese. And this is Spanish food. So in the U.S., the health care, mainly
government, Medicare, Medicaid, and Obamacare, and then improved education.
All of these have led to this increase in life expectancy in the US, Europe, and around the world. Yes?
I was an electorate in Sweden by an English public health president who said hygienic behavior is the
number one reason for the change from about 1920. Really? Could be. Could be. Can you repeat the question,
please? I'm sorry. I'll repeat it. The statement was that probably the single most
which I don't know. It certainly sounds believable. Yes? Yeah, Rob. There's a marvelous book that I
always recommend called Mirage of Health from 1959 by, what's his name? Rene Dubot. He was an epidemiologist
and microbiologist. So he knew a lot about the antibiotics
He wrote this book making this exact point that actually it's been public health that is the biggest
thing, hand washing and cleaning, particularly separating sewage from fresh water. But he had this wonderful
quote in there where he said, it is easy to believe one is emptying the ocean with a bucket when the
tide is going out. Thank you. OK.
So lately, however, the US has fallen behind with respect to other developed countries, the OECD countries.
This shows from 2003 to 2023, life expectancy, the light blue dots are the average of the OECD countries,
and the dark blue are the US. And you'll see that they're essentially equal in 2003, but then they begin
to diverge. And they diverge even more during COVID, where the US responds to COVID.
particularly in terms of deaths, was much higher than in the other OACP countries. And here, Rob Stone
showed this as well, despite this poor health outcomes in the US, despite the fact that in the US, we
spent our capita two and a half times more than the OACP average, and 10 times more than countries like Turkey.
Yes. What is those? OECD? Organization for Economic Cooperation and Development. That's in the fine
print. It's in the fine print, yeah. This is a test. I test, yeah. The U.S. currently ranks 30th out
of 30. Oh yeah, Organization for Economic Cooperation and Development.
It's a group of 38 countries, developed countries. And we ran out of 38 OECD countries in life expectancy
despite the tremendous amount that we spend on health care. Now, in the US, life expectancy is strongly
correlated with the income and education. And here is a slide showing how much it depends on income.
So this slide shows
the expected age of death for US citizens who reach the age of 40 versus the household income percentile
from zero to 100. So for example, men whose income is in the bottom 1% in the US have a life expectancy
of 72.7 years, men in the top 1%
have a life expectancy of 87.3 years. They have a 15-year difference. Women in the US, in the lowest
1%, 78.8 years life expectancy. In the top 1%, 88.9 years. So it's a 10-year difference from around
15 years from now. So you see the real strong effect of income here. Also, it depends significantly
on educational attainment.
So the bottom curve here is life expectancy for Americans who have less than a high school education.
Here, high school graduates in the US. Here, Americans who have some college. And here is Americans
with a college degree. So you see that there is about 74 to 84, about a 10 year difference depending
on educational attainment here. And furthermore, you see that between 2000
And in 2019, the only one piece that is actually increasing by an invisible amount is those with college
degree. So there is what Steve calls a diploma divide in the US. And higher income means access to higher
quality health care. We don't have a national health service. We don't have Medicare for all. And college
graduates have better information about health, less dangerous jobs.
They have generally better access to eyeballing health care, and they can work remotely during COVID.
So a lot of advantages here that lead to these discrepancies. But there is an increasing partisan mortality
gap in the US. This is the free environment we're talking about. So here, from 2001 to 2019, just before
COVID, the red dots here show the mortality rate.
deaths for 100,000 Americans in Republican counties, and the blue dots are the mortality rate in Democratic
counties. And we see that while there was a small gap between them in 2001, there's a really big gap
in 2019, and it's growing. And we'll argue later in this talk that you can expect it to grow even further
if current policies stay in place.
The effect was largest among white voters of both sexes. It was predominantly a discrepancy among white
voters. Here we show that. So here are the mortality rates by race or ethnicity in Republican and Democratic
counties from 2001 to 2019. The highest mortality rate, i.e., the lowest life expectancy is for blacks.
And you see there is some difference between red and blue counties, but it's not hard.
And it is dropping more rapidly than the white Hispanic. Yes? Have you made any attempt to disaggregate
this and to ask the question, what should the gap be given the educational circumstances and the revenue?
Well, actually, have we made any effort to actually unpack this to find out?
You know, the only thing we can say is that, as we'll show you, as the talk goes on, there are actually
a lot more different elements in play here than just education. And it turns out there are many intertwined
contributions. So it's not so trivial to unravel. But here, you see, the large discrepancy is Republicans
versus Democratic counties.
And thirdly, you see the Hispanic case, where you see very little difference between red and blue counties.
But you also see what's called the Hispanic parasites, which is the following. That Hispanics in the
US, as a group, have less educational attainment, less household income, and less access to high quality
medical care than whites. But they live longer.
Now, we're told that this difference between Hispanic and white became much smaller during COVID. So
we don't know whether post-COVID, this will once again separate out or whether they'll remain about
the same in the future. So mortality rates are also strongly correlated with community education level.
So what this graph here shows is
the mortality rate per 100,000 people versus the share of the local population of college degrees going
from 10% at the bottom here to 50% at the top. So they divided the US into 20 different groups every
5% of the population. And each of those is shown as a dot. The blue dots are from 1990. And the straight
blue line is the average of those blue dots.
The yellow are 2000, and the brown are 2010. And you see, first of all, that this difference in mortality
rates is increasing as we go from 1990 to 2010. And you see that it's actually a very strong correlation.
So the higher percentage in your community
college degrees, the longer their life expectancy. And it's getting bigger. Here, we also have a growing
partisan college education gap. So what's shown here is all of the presidential elections from 1984
through 2020. So this red, I hope, is people without a college degree.
here is people with a college degree. And the zero line, which is right here, means equal numbers of
people voting as Republican and Democratic. You see that up until 2004, there were not extremely large
differences between people with college degrees and without college degrees and how they voted
for Republicans and Democrats. Starting in 2004, you see this gap
increasing such that in the 2020 election, people without a college degree, 20% more, voted Republican
than Democrat, and people with a college degree, about 13% more, voted Democrat than Republican. So
there is this partisan divide, which I suspect you're familiar with, and it's growing. So here, that's actually
Here we see that college educated Americans tend to cluster geographically. So what's shown here is
a graph of the U.S. and the red areas represent areas where you have more college graduates than the U.S.
average. And blue represent areas where you have fewer college graduates than the U.S. average.
And the color, the darker red means more college graduates, the darker blue.
It's fewer, even fewer college graduates. And you see, there is this clustering, Eastern Seaboard, New
England, then individual Chicago, Madison, Wisconsin, Minneapolis, Colorado, Pacific Northwest, Northern
California, and so on to our surprise, Southwestern Montana have high clusters
Is it? Yeah. It's impressive. Furthermore, you see this area where you have significantly fewer college
graduates than the U.S. average. And I want you to look at it carefully because you're going to see
this area of the country again and again and again in all the slides that show evidence, okay? So it's
the deep south.
Actually, we'll show, yes, we'll show that with respect to certain health care outcomes later. Yeah,
there is a definite effect. But in any case, this area, we will be seeing more and more the deep south
and then going up into Appalachia.
You know, some states will occur in various other slides, and they won't, but this particular area is
really outstanding in terms of having poorer health outcomes than the rest of the country. Now, this
is the best jobs are here. College graduates will tend to cluster here. The hotspots for college grads,
the red, have the best access to high quality.
health care, whereas the cold spots are called diuretics, aligned with geographic areas with the worst
health outcomes. So there are significant state by state differences in life expectancy. This is 2022,
and the state with the highest life expectancy in that year was Hawaii, life expectancy of 80.
And the state with the lowest was West Virginia, 72.2. So there is a 7.8 year difference in life expectancy,
depending on whether you live in West Virginia or live in Hawaii. And you see the same cluster of states
that occurred with respect to college graduates, deep south, going up into the Appalachian. Once again,
yes? I don't know whether it's possible, but can you separate out
In West Virginia, we'll have huge drug-related deaths during that time period. Whether it's possible
to attribute any of that. Yeah, we'll go through various outcomes, and we'll see that coming up again
later. So just for comparison, life expectancy of 80 is comparable with life expectancies in Western
European countries. Life expectancy of 72.2 is about that of Egypt.
However, you haven't seen anything yet. Now let's break it down by counties. So these are counties in
the US. Once again, the blue are counties that have higher life expectancy than average. And the red,
once again, the same. Deep South, South, Appalachia.
And the difference. Is there a blue dot there? Actually, quite possibly. Quite possibly. So the lowest,
the county with the lowest life expectancy, life expectancy is 66.8 years, and the county with the highest
life expectancy, 86.8 years. That's a 20 year difference. 86.8 is comparable to the countries like Monaco
and Singapore,
Longest life expectancy in any country in the world. 66.8 is about equal to Afghanistan. And where are
those counties? Ah, OK. They're the darkest red. So you see here, here, here. These are Native American
reservations. And you'll see also down here, almost equal, southwestern West Virginia.
a little bit. Yes? I keep thinking as you're presenting these statistics, we would be great to see by
census track, because thinking about our own county, for example, there are enormous discrepancies between
urban and rural parts of the county. Is some of this data available at the census track? Well,
we'll be sure. So there are these enormous differences that we see in a life expectancy county.
By the way, which state
has the county with the highest life expectancy. Where is Connecticut? Massachusetts. That's a good
choice, yes. It's blue out there. Massachusetts? It's Colorado. A couple of suburbs of Denver have the
highest. Sorry, in 2020. It varies from year to year. As does the state.
The state with the highest life expectancy tends to compare it to Hawaii and California. And for people
who are asking, the darkest blue dot in Indiana is Hamilton. It is. It is. It is. It is. It is. It is.
It is. It is. It is. It is. It is. It is. It is. It is. It is. It is. It is. It is. It is. It is.
It is. It is. It is.
How have these changed? That's the next slide. Thank you. So here is changes in life expectancy between
1980 and 2014. So over a 35-year period, once again, you see the same thing, right? You see this area
of the country by far the smallest life expectancy in that other areas of the country, the larger. And
therefore, you see.
And once again, we mentioned that, perhaps surprisingly, the growth in this mortality gap has occurred
mostly among white males and white females. Okay, I'm going to turn it over to Steve, who will unpack
a little more of the story. They're happy, apparently. Oh yeah, that's not all of it. There we go. So
Tim has shown you the statistics. I'm going to try to understand two things.
What are the causes of mortality that are leading to this mortality gap? And second, what are the causes?
So first, let's consider what features of health account for the growing mortality gap. And the first
thing I want to show you is something which doesn't contribute very much, and it answers one of the
questions that came before. There was a very influential book that came out in 2020
by economists Angus Heaton and Case, which argued that life expectancy in the US was stagnating largely
because white middle-aged American males without a college degree were dying what they called deaths
of despair, meaning deaths from suicide, drugs, and alcohol, from opioid deaths. Somebody asked before
about the opioid deaths.
So it turns out there is much more detail than we have time to show you in this. It turns out that is
a minor contribution to the growing mortality gap. If you look at the map again to see where the highest
rate of opioid deaths were, it's the red and yellow dots. So it's sort of the mid-Atlantic but high
incidence in New England and some in the Midwest.
not much in the South and Appalachia. So it's a different geographical distribution and turns out to
be only a minor contribution to this growing mortality gap. So it's not really opioid deaths that's
causing the problem. Okay, so while these males, white, middle-aged American males without a college
degree, often vote Republican,
These are secondary contributions. So now what we want to look at is this is a list of some of the leading
causes of mortality in the US. So heart disease on the left, cancer, those are the two big ones. Strokes,
chronic lung disease, and so forth. And the blue bars here represent the average mortality
Democratic voting counties, and the red in Republican voting counties. And the dark blue and red are
for 2001, and the lighter blue and red are for 2019. And what you see, for example, is that the mortality
gap, the difference between blue and red increases for heart disease from 2001 to 2019. It increases
for cancer.
It gets worse for lung disease. It gets worse for diabetes. It gets worse for suicide. This is a very
broad-based contribution of mortality causes to the mortality gap. So we want to investigate that in
more detail now. And let's start with coke.
So we showed you before life expectancy and mortalities up to 2019. That cut off before COVID. This
is now including COVID. You see that the life expectancy in the US dipped much more during COVID than
it did in a comparable country. Average, the US has 4.2% of the world population, but it had 17% of
COVID deaths around the world.
And that's because the U.S. government response was one, to put it. Okay, and that contributed very
strongly to a partisan mortality gap. So what is shown here, this is from May to December of 2021. So
after the first COVID vaccines were widely available,
The left plot here shows that every dot in this plot corresponds to one county in the US. And this shows
the percent of adults vaccinated in the county versus the percent that voted for Trump in the
2020 presidential election. This is vaccinated for COVID, yes. It's COVID deaths per 100,000 again in
every county.
versus the percent voted for Trump. You see a very strong correlation so that the vaccination rate dropped
as the vote for Trump increased. In average of Biden voting counties, the blue cross here, the average
vaccine uptake was 75%, which was above the herd immunity level for COVID, excuse me.
the average of Trump voting counties, the Red Cross, the Red Cross is an unfortunate allusion, but the
Red Cross there corresponds to a vaccine uptake of 50%, which is below the herd immunity threshold for
COVID. And because of that, the death rate from COVID was about three times higher in the average Trump
voting county than in the average Biden voting county.
And the reason for that is that once the vaccines were available, the death rate in red here for unvaccinated
individuals was 10 times higher than the death rate for COVID for vaccinated individuals. So COVID only
made that mortality gap grow much more. It accelerated the mortality gap. Now we're going to go through
a number of chronic diseases.
So first, consider obesity. The darker colors here are where obesity in the states with the highest
obesity rates. So the leaders are in terms of most obesity are Mississippi, Arkansas, and West Virginia.
But in general, a lot of the red states here, which also have relatively high obesity rates, are Republican
voting states. Obesity is often correlated with diabetes.
And again, the darker colors here are the states with the highest diabetes rate, diabetes prevalence.
And it's very similar to that map that Tim showed you earlier with the states that have, or the clustering,
the low clusters of college graduates. Basically, again, the deep south up into Appalachia at the highest
prevalence of diabetes.
You're going to see a lot of maps that all look basically the same. This is chronic heart disease, still
the number one killer in the US. This is age-adjusted heart disease death rates per 100,000 people for
inhabitants over 35 years of age. County by county in the US, the darker red are the highest heart disease
death rates. And again, it's very much the same region
from the south up into Appalachia. This is chronic obstructive pulmonary disease, COPD. Again,
very similar. The darker colors, again, have the highest rate of COPD. And they're, again, in the south
and up into Appalachia. When we gave this talk to a different audience, they asked, can you correlate
the COPD rates
with the level of fine particulate matter air pollution. It turns out that there is a correlation, but
it's weak, because fine particulate matter air pollution is also quite bad in the industrial Midwest
and Southern California, where you don't see high incidences. Yeah. What would a California world have been
Yeah, it's a good question. I don't know the answer to that. There are not a lot of people there in
North America. They all snore. Maybe they all snore. I really don't know. It's a good question. So the
one thing is if you look in detail now, you do see that the South and Appalachia are not solid.
There are lighter areas in there. And that addresses a question that Pat asked earlier. And we're going
to show a lot of maps in this talk, but this is probably the single most important map. So this addresses
also a question of vital grains. Benavides et al, a couple of years ago, did an analysis by zip code.
So not even by county, but by zip code in the US.
and they analyze the prevalence of the 10 most costly chronic diseases. So that is obesity, hypertension,
high cholesterol, coronary heart disease, COPD, asthma, chronic kidney disease, diabetes, cancer, except
for skin cancer, and depression. And the brown areas here are hot spots. Those are areas where the
incidence of all these chronic diseases is much higher than the national average. And the blue dots
are cold spots. They are regions where the incidence of all these chronic diseases is much lower than
the average. And what you see is that, again, the south and up into Appalachia are regions where there's
a high prevalence of chronic diseases. But there are blue dots.
There are the big cities in the south are not affected by this chronic disease prevalence. So you see
cold spots in the Atlanta region, Nashville, Charlotte, North Carolina, Raleigh-Durham, North Carolina,
Dallas, Fort Worth, Austin, San Antonio, Houston, Texas, and at least average behavior in Little Rock
and Oklahoma City
So what we're looking at is primarily a rural South and Appalachian high prevalence of all chronic diseases.
That is a large contribution to the mortality, yes. The term costly chronic diseases, does that mean
the incident of death is the cost you're referring to?
I don't think so. I'd actually have to go back. That's the term that's used in the Benavides paper.
And I'd have to go back to see exactly what they define it as. But I think it's just overall health
care costs. It's not financially costly. It's not financially costly, is it? I would guess
it's hospitalization costs. It may be hospitalization. It's an intensive care unit, specifically,
particularly related to cancer and heart disease. There is a financial cost issue.
Huge cost, that's where most of healthcare is spending its time. White areas are areas I think are just
active, no data. Either no data or active. I suspect there's data.
Are there various places like Oxford and Mississippi that are high in the same situation, but low in
the same situation? Are Bloomington would be the same? We're not high in the same situation.
High in college. All that I can tell you is a place like Bloomington, I'm not sure exactly where Oxford
is in Mississippi, are about average.
I'm not sure what you thought. I thought that was Jackson. That's probably Jackson. Oxford is down.
Where is Oxford? Down through the south. Down through the south. I don't know.
I don't know. But basically we have a high prevalence of chronic disease, especially in rural areas
of the South and Appalachia. Turns out chronic disease prevalence is only partially correlated with
educational attainment. So we showed you educational attainment from the high end of the spectrum earlier,
the clustering of college graduates. This map is the opposite.
It's the map of where there are the largest portions of the population that have less than a high school
diploma or the equivalent. And again, you do see in the red areas the South and Appalachia, but also
a very intense area along the Texas-Mexico border, along the Indian reservations, Native American
reservations in northern Mexico and Arizona.
and in the San Joaquin Valley in California. And you don't see the chronic disease prevalence here or
here. That may be an indication of this Hispanic paradox that Tim showed you earlier because those regions
are very heavily Hispanic. So it's not only education which goes here. So next issue is how do state
It's with a reaction to a question. I'm just looking. So now we're going to go into causes. So one cause
you can ask about is how does state government impact health? So this is a complicated graph, but let
me try to take you through. These are all cause age-adjusted mortality rates in states, the dark blue
are states which have unified democratic control. That is, a democratic governor and democratic control
of both houses of the state legislature. The dark red are the opposite. They are Republican governors
and full Republican control. And the other colors are mixed situations that you don't have to worry
about right now. So focus on dark blue.
compared to the dark red 2015 through 2021. Full Republican control gives you a higher mortality rate
by a significant margin than full Democratic control of the state. And it only gets worse as we've seen
before during COVID in 2021. So state government seems to have some effect. And now we can ask why.
So we're gonna go through a number of things that the state government can do.
First we're going to look at smoking restrictions and lung cancer deaths. So the map on the left, the
gray states are the states with the least restrictions on smoking behavior. The green states
have comprehensive smoking ban laws, including in restaurants and bars. The right hand map is the
shows the prevalence of lung cancer deaths from 2016 to 2020. And what you see is that among the gray
states with weak smoking restrictions, there is a heavier concentration of lung cancer deaths. Then
you can ask about gun restrictions. So here it is the
map on the right that shows you the stringency of gun laws. The white end is the least stringent gun
laws, and states like Louisiana, Arkansas, Kentucky, Virginia, Virginia has a big tobacco opinion on
it, especially here, Montana, and Wyoming, and other
left, unfortunately, with the reverse color coding. The states in red have the highest firearm death
rates. So in general, there's a correlation between weak gun restrictions in the state laws and heavy
firearm deaths. It's not surprising, because if you actually look at it, there's a very strong correlation
between the prevalence of guns in a state
and the firearm deaths. Next, I'm going to turn it back over to Tim, and we're going to talk about abortion
laws and maternal mortality and infant mortality. Thank you, Steve. So in 2022, the Supreme Court and
the Dobbs decision left it up to individual states to decide what their abortion laws, abortion restrictions would be.
In May of this year, 17 Republican-controlled states currently ban all abortions or those after the
first six weeks of pregnancy. So these are the dark, permanent ones. So then there are a few states
that have bans after 12 weeks. And then in blue are the states with fewer restrictions on abortions.
And the darker the blue, the looser
Now, let us look at the correlation between strict abortion laws and the prevalence of maternity care
providers in different states. So the level of restriction is shown here. This looks like brown to me,
but orange. Not orange, orange, thank you. So the orange is states that have a full abortion ban or
a ban after six weeks, and what looks like pink
the states that have orange. It's light. You can tell me, hey. It's really embarrassing. And then the
color of the state is the number of maternity care providers per 100,000 female population age 15 to
44. So the lightest color states
have maternity care providers 105 to 160 per 100,000 population. The darkest-colored states have the
least 52 to 69 abortion care providers per 100,000. And you see there's a strong correlation between
the severity of the abortion ban and lacking maternal care providers. And that's not really surprising.
You would be more restricted in such states, and you could also
So here's something which is really, I think, sort of a national disgrace.
The maternal mortality rate in the US versus various other countries is the maternal deaths per 100,000
live births in select countries for 2018. This is before the Dow's decision. So US 17.4, which is twice
the next largest, which is France at 8.7, but it's 10 times the maternal mortality rate in Norway. So
we somehow are tolerating embarrassing
high maternal mortality rate. And it's getting worse. Now this, unfortunately, there's a lot on this
graph, but what we have here are the maternal mortality rates for every state in the nation. From California
up to Louisiana, we're missing data from three states, Delaware, New Hampshire, Vermont. There are 47
states here.
So California is about four deaths per 100,000 births. Louisiana is about 47. And here is the average U.S.
maternal mortality rate. It's about 25. That in Iran is shown here in blue. This group of nine developed
countries here, this is the maternal mortality rate here.
The top is the maternal mortality rate in Mexico and Nicaragua, which is actually lower than the maternal
mortality rate in Louisiana. Okay, so once again, now also, you might ask how this is correlated with
the poverty level in these states. And the X's here, and this right-hand axis, show the poverty level
in these states. And you see there's very little correlation between the poverty level
Yes, two things. Indiana, which I'm proud of this, is the third highest maternal mortality rate in the
country. And Texas, yes. So from 2019 to 2022, maternal mortality rate in Texas
Okay, so this shows infant and perinatal mortality rates for 1,000 births by race, ethnicity, and abortion
policy in the states.
The states with the strongest abortion restrictions are here in, orange again? Sam. Sam, okay. And the
states with fewer abortion restrictions are in need. Okay, this is broken down by, so this is, left-hand
one is for all groups, non-Hispanic white, non-Hispanic black, non-Hispanic Asian, Hispanic, and this
is perinatal.
which is the combination of infant mortality and fetal mortality. And you see that in every case, the
states with stronger abortion restrictions have higher infant mortality rates. In the year following
Texas's 2021 abortion law, infant mortality rates rose by 13%. And in the year following the 2022 DOPS
decision, national infant mortality
between these increased abortion restrictions that came in after Dow's and the rise of infant mortality.
The U.S. currently ranks number 32 among the 38 OECD countries in infant mortality. So don't we like
to frighten ourselves on our wonderful medical? Do you know who's worse? Huh? Do you know who's worse?
Oh, the citizens.
No, I don't know. I don't know if 32 is. So here, this is the rate of clinical abortions in the US as
a function of time. Starting with 1973, Roe v. Wade decision. And going up to 2024, although there's
a gap here during the COVID years. But you see that starting in 1973, there was a very rapid rise in
abortions.
in about 1980 at 1.6 billion portions per year, and remaining constant for about 10 years. And then
you see a fairly rapid decline for some time. Part of this is due to better access to birth control.
A major factor in this decline is that we've seen a very rapid decline in the US in teenage births,
women 21 and younger. That has decreased very rapidly.
It was out in 2017, but you see between 2020 and 2024, it increases by 10%. This is post-docs. Amazingly
enough, these draconian abortion restrictions have managed to increase abortions, increase maternal
mortality, and increase infant mortality.
There's also the question of the uninsured people in each state. Starting in 2014 with the Ford Care
Act, Obama Care, states were able to increase their Medicaid with relatively little contribution from
the states, mainly federal. And many states did, but there were also many states
And what this graph shows is every state in the union. And the coloring here, the lightest color, this
is the percentage of uninsured people in the state who live below the poverty line. So it ranges from
6% to 13% for the lightest, up to 28.5% to 46.9% for the darkest. And once again, you see, with a few
exceptions, the deep south here,
going up somewhat into the prairie states. This map also shows for each state whether or not they expanded
their Medicare and if so when. And you'll see that there are a lot of the states here with the highest
rates of uninsured, Kansas, Texas, Mississippi, Alabama, Georgia, Florida, South Carolina, and Wyoming,
which have never raised their
expanded their Medicare coverage, and which have the highest rates of uninsured for Medicaid. Is that
Medicare? I'm sorry. The exception here is Wisconsin, which has its own Badger Care Plus. Now, red states
also have the most rural hospitals at risk.
In the past 15 years, 180 rural hospitals have closed or stopped providing an approach to care, and
another 432 are currently valued. Stuller for rule of law, okay? And here, so the color coding here,
the lightest color is 0% of the rural hospitals in that state at risk of closure, going up to the highest,
which is over 41%.
And once again, we see this cluster in the south growing up in the prairie regions. And it's likely
to get worse because the one big, beautiful bill, Trump's bill, contains a trillion dollars in cuts
to Medicaid, most of which will not occur until after the midterm.
So we're going to see this dramatic drop in Medicaid covered by Medicaid. And many of these rural hospitals
depend on Medicaid income to stay open. So we expect to see many rural hospitals in the United States
go out of business if things don't change.
So the 92 million Medicaid and CHIP beneficiaries, 26% of these were females ages 15 to 44,
as of child-bearing years. And in 2021, 41% of U.S. births were paid for.
shows the percentage of birth that were paid for by Medicaid in 2021. The lightest color is here, 21.7% to 34.5%.
The darkest, 45.7% to 67%. Once again, you see this cluster of states. So these are the states that
disproportionately lose rural hospitals, especially hard hit and about 2 thirds of rural
already lack hospital-based obstetrics. The future doesn't work. And Steve will now move into other
medical facts and figures and summarize them. Sorry, yes. One question. Did you discriminate as to whether
more babies are dying
or after they're born, because of, you know, the seizures. So the slide that Tim showed before, the
feeble and newborn deaths were the perinatal in the last column. The others were in mortality, so an
early childhood mortality. The difference
I don't want to bore you with too many more things. So it turns out many Republican states also have
fewer primary care physicians who hear the
beige and red states, and the fewest primary care for the physicians per capita. Another thing that's
really important is that COVID has increased Republican hesitancy about other vaccines. They were hesitant
about COVID vaccines, but it changed their view of all vaccines. So this is the result of Pew surveys
taken in 2016
2019 was before COVID and 2023 after COVID. And the question asked was, should healthy children be required
to be vaccinated to attend public schools because of a potential risk for others? That's not positive.
Or alternatively, should parents be able to decide for themselves whether or not their children get
vaccinated?
Democratic voters and mean Democratic, it's always about 85% who favor mandated vaccination for school
children. Before COVID, about 79% of Republican voters favored that, but it dropped to 57% after COVID.
So general vaccine hesitancy was introduced or increased by COVID.
Turns out red states also have below average rates of the human papillomavirus vaccination. So here
the darkest states are the ones with the lowest uptake of HPV vaccination. And vaccine hesitancy has
consequences. So these are cervical cancer rates in the various states. The darker colors have the highest
cervical cancer prevalence
Also, again, largely in the south and up into Appalachia. Yeah? What does the tan Indiana mean? I think
that's MoData. That's MoData. It's not MoData. Then a couple of other things. We also looked at suicide rates.
So again, darker blue here is the highest suicide rates per county. There is some localization of high
suicide rates in the South and Appalachia, but largely in the West. And it turns out it's largely in
rural counties in the West, in Alaska. So it's believed that it's feelings of isolation and lack of
opportunity, along with access to guns.
which is increasing the suicide rates. And finally, if we look at traffic mentality, which to our surprise,
show up most prevalent in South and Appalachia. So I'm going to skip this now so we can have some time
for questions. Let me go to the question. Yeah.
Is a certain type of vaccine, as in the state, able to operate seatbelts? It could be. I mean, we haven't
looked into the real causes of this. There's also some correlation with just the number of miles driven,
which tends to be larger in those areas. But it may also be seatbelt. It may be drunk driving. There
are many possible ways.
So the average life expectancy of Americans in Republican counties is significantly less than in Democratic
counties, and the gap has been growing steadily throughout this century. The gap is growing fastest
among white voters of both sexes, and it's especially localized in the rural South and Appalachia. The
symptoms include higher incidences in red counties
of obesity, diabetes, heart disease, COPD, other chronic diseases, lung cancer, cervical cancer, firearm
deaths, maternal and infant mortality, COVID deaths, suicides, and even traffic deaths. It's a very
broad-based problem that we're seeing. The contributing factors include, and addressing the question
that was asked in the beginning, we haven't been able to unravel all of these
are all contributions. Educational disparities play an important role. So do lifestyle and diet. Access
to quality health care services, hospitals, and insurance is a very important contribution. Decreased
trust in scientists
and doctors and vaccines is an important contribution. State laws concerning Medicaid, smoking and guns.
State laws regarding abortion rights and women's healthcare. Susceptibility to medical misinformation
also is a contributor. Isolation and lack of opportunity in rural areas. And poverty does contribute,
but there are many regions of the country that have as much poverty
as these regions in the South and Appalachia, but not these problems with chronic disease.
Choosing Robert F. Kennedy Jr. as Secretary of Health and Human Services, and the Medicaid cuts in the
one big, beautiful bill, the termination of the Subsidies for the Affordable Care Act, are all going
to accelerate mortality
There's no way around that. So finally, and we had a slide on the problems with RFK, but that's Tim
Smith. Many Republican voters interpret freedom today to include a growing distrust of experts. And
that has a substantial cost in death rate. That's why we called our talk Live Free and Die. Our other
alternative title was
liberty and give me death. In order to shrink the mortality gap, what we need are policies that enhance
rural health care and support rural hospitals, that improve health education and combat medical
misinformation, which is rampant right now and in many cases promulgated by our present government,
promote medicine and science as inherently non-partisan approaches to truth about human interactions
with nature and with disease, and educate voters that election preferences impact their health. I don't
think this has been widely understood by voters in this country. They tend generally to assume that
the elections will affect their economic output, their economic condition.
not their health condition, and it really does. Unfortunately, those changes are largely opposed to
the directions of the current Republican state and federal policies, including the One Big Beautiful
Bill Act. And that is a problem that we face right now. So the health care system in the US, as Rob
Stone told you a few weeks ago, is kind of the best right now.
The problem we have right now is we're in a hole and we're still digging. You know, we first have to
stop digging and then try to start climbing out. Whether that's through a Medicare for All or some more
incremental policy change, one can debate, but we have to stop digging.
Most people in this room are aware that the university is under pressure from the state with the threat
of dire consequences to the university to prove that education is worth it. On your third or fourth
slide, the fact that the outcomes of your entire life are helped by your education might be something
you would have said to Pam Witten.
A good point to note, that slide, by the way, did not have graduate degrees, which only increased the
life expectancy further. So graduate school also had value. I was struck by your very first slide, which
compared Connecticut and Oklahoma. Today, many of you probably saw in the New York Times a story about
people in Oklahoma who could afford their utility bills. Yeah. Threatfully hot summer.
Data centers being built everywhere. And it happened that the family they focused on, the wife had,
was on oxygen. The husband had to sleep with a CPAP machine. And if they couldn't pay for their utilities
during this, they would die. And it's especially noted that Oklahoma, every county in Oklahoma in the
last three elections, might have overwhelmed
from that first slide, I mean, Oklahoma and Connecticut are not alkaline. They were just basically
representative red states and blue states. Yeah, Mike. I'm still worried there's something missing in
the granularity of the data looking at counties or states. So, I mean, one example, for example, is
chronic diseases in urban areas, particularly affecting black
white populations, I think those levels of those chronic diseases and the life expectancy statistics
are not good for those either zip codes or census spec. Yeah, so we, you know, the most granular study
we found of this study by Benavides, which looked wholly at the zip code,
They didn't break down the incident codes by ethnicity or race. But that's something one could look
at further. I mean, there's a lot of data. It just has to be handled. The slides and graphs wouldn't
be available on any of the websites or anything? They are already available on our website.
So if you go to debunkingdenial.com, there's a page called Our Presentations. And this presentation
is there. You can look at the slides, or you can download them. They're free to use. Yeah? So in correlation
with the conclusions that you brought that, and global climate change, most of the vast majority
Most of the WhatsApp has bad stuff. Climate change is going to make all of this worse for a number of
reasons. One of which is that as the globe warms, lots of species are shifting their range, and among
the species
shifting their ranges are disease-bearing mosquitoes, disease-bearing ticks. They are moving northward.
So things that one used to think of as southern hemisphere diseases, Zika, dengue fever, things like
that, West Nile virus, are moving north. So that's going to affect
But there are all sorts of, we have lots of posts on our site about climate change effects,
about biodiversity loss. All of these things are intertwined. And basically, the bottom line message
is that humans have become a dominant species on the planet, and ecosystems
when there is one dominant species. They tend to exhaust the resources and mess up things. You know,
that's what we're doing in climate change, biodiversity, in health. It's a very broad-faced problem.
I was just noting that you pointed out that Colorado was kind of an exception. Colorado is an altitude.
So how does that affect you? Colorado is an altitude.
It also has attracted a lot of educated people with high paying jobs. So whether it's the climate in
Colorado that makes it better or just the high density of college graduates.
There's not a complete understanding. Let me answer the second question first. The second question,
it is believed that family structure in Hispanic communities is a large contribution to this so-called
Hispanic paradox, but it's not understood
It did, as Tim mentioned earlier, it largely disappeared during COVID because Hispanics were hit by
COVID much more than whites, Hispanic communities. So there was still a small gap between white communities
and Hispanic, but much smaller than pre-COVID. In terms of the effect in Mexico,
ever looking to the physics department? Or is there an interesting story on how you got into it? Basically,
we talk about the interesting problems that come up. We assign ourselves reading assignments. And that's
how we attack these problems. We learn about things we don't know about. That's certainly something
we didn't know about.
So I'll tell you how we got to this. We wrote a different book addressing the question of whether partisan
differences that exist in the country now, which are fairly extreme, are from a demographic point of
view likely to get better or worse in states.
And it turns out things that matter there are birth rates versus blue counties, death rates, and also
childbirth, whether the children tend to have the same partisan preferences as their parents. And it
was when we were working on that that I saw this large mortality gap break. There's also a birth rate
which goes in the opposite direction.
thinking that this is going to get rigged, Republicans won't. That's how we got here. There are all
sorts of things that trigger our interest. My son was an economist, and he told me at one point he came
to a conference on the economic impacts of the declining birth rates
I see on your website you quoted Carl Sagan and mentioning some aspects of
the belief in pseudoscience, superstition. Did you have a breakout of the statistics of those who believe
in such things, including their religiosity? So, I'm now scrolling mentally through our posts. I've
been thinking about posts, you know, we have posts on the,
growing partisan gap in trust in science, which does have some data on trust in science as a function
of religiosity. So for example, Christian nationalists have very low trust in science. So there are
data on our site in that. I don't have any slides.
probably one or two other folks that also have relevant data for that. Can I just say a word about that?
There's a famous Pew research study about religious denominations and, well, it's not so much about
new medicine, but belief in evolution and interest in environmental, support for environmental movements.
And as you might imagine, there's an enormous spectrum. There's a correlation between those two.
but an enormous spectrum, depending on what particular religious group you might belong to. And in many
cases, religious communities have been very strong supporters of scientific and medical advances and
other cases. The other direction is quite interesting and complicated. I want to take a moment and thank
Steve and Tim again. They're going to be hanging around.