WEBVTT 00:00:03.682 --> 00:00:10.277 - to this traveling university event. Thank you for choosing to spend part of this beautiful afternoon 00:00:10.277 --> 00:00:17.394 - here inside with us. The traveling university has a three-fold mission, to share our knowledge and expertise 00:00:17.394 --> 00:00:24.054 - with community-based audiences, to provide up-to-date information about topics of civic interest, and 00:00:24.054 --> 00:00:30.910 - to preserve and disseminate knowledge and understanding that is under threat of being lost or forgotten. 00:00:31.490 --> 00:00:37.828 - The traveling university was established by retired university faculty in response to increasing constraints 00:00:37.828 --> 00:00:44.108 - on the production and dissemination of knowledge and understanding at University of Islam. We were inspired 00:00:44.108 --> 00:00:50.330 - to act by Polish dissidents who, in several different times in Polish history, resisted autocratic attacks 00:00:50.330 --> 00:00:56.261 - on the Polish educational system by taking education out of the classroom and into their communities. 00:00:56.261 --> 00:00:58.238 - They called themselves the Flying 00:00:58.498 --> 00:01:05.496 - because our courses moved from place to place to avoid being discovered. We adopted a similar name to 00:01:05.496 --> 00:01:12.563 - emphasize our connection to its history and our support for its cause. We have a website where you can 00:01:12.563 --> 00:01:19.630 - sign up to be notified of future events, next to which is a presentation by Archie Hershey on election 00:01:19.630 --> 00:01:23.198 - security, which will be held on November 1st, 2020. 00:01:29.506 --> 00:01:36.080 - For our future churvas, we will speak on fruit fly research and talk in time on why should grown-up 00:01:36.080 --> 00:01:43.049 - flavor to influence. This year, we're offering presentations on topics of civic interest, such as today's 00:01:43.049 --> 00:01:49.623 - presentation, as well as presentations from fields and disciplines that are under threat, including 00:01:49.623 --> 00:01:56.329 - basic science. We have cards in the back for one of our future presentations that have information on 00:01:56.329 --> 00:01:57.118 - our website 00:02:00.802 --> 00:02:08.899 - In May of 2027, we plan to begin offering deliberative dialogue sessions in which we'll present alternative 00:02:08.899 --> 00:02:16.846 - perspectives on a social issue, such as the efforts to remove TBI from educational institutions, followed 00:02:16.846 --> 00:02:18.046 - by facilitating 00:02:31.362 --> 00:02:37.573 - We're grateful to Drs. Londergan and Victoria for agreeing to present for us today. Steve and Tim's 00:02:37.573 --> 00:02:43.846 - presentation is titled, Live Free and Die, Why Are Voters in Red Counties Dying Younger Than in Blue 00:02:43.846 --> 00:02:50.243 - Counties? Tim Londergan is an emeritus professor of physics at Indiana University, former chair of the 00:02:50.243 --> 00:02:56.765 - Department of Physics, associate dean of the graduate school, director of the Indiana University Nuclear 00:02:56.765 --> 00:02:59.870 - Theory Center, and director of the Wells Scholars 00:03:01.890 --> 00:03:07.805 - He's also a co-founder of Concerned Scientists of India at the University of Cooke. It supports 00:03:07.805 --> 00:03:13.843 - evidence-based science, funding for science research, and policy-based, best available scientific 00:03:13.843 --> 00:03:20.190 - information. Our faculty member, Tim, also served as a consultant for numerous research centers around 00:03:20.190 --> 00:03:26.660 - the world, including the Baba Atomic Research Center in Mumbai, India, Los Alamos Laboratory, University 00:03:26.660 --> 00:03:30.110 - of Adelaide. Steve Vigdor is also an emeritus professor 00:03:33.474 --> 00:03:40.422 - at the Brookhaven National Library for Nuclear and Particle Physics. At Brookhaven, he oversaw operations 00:03:40.422 --> 00:03:47.173 - of the relativistic heavy-ion glider and all high-energy physics research in the laboratory, including 00:03:47.173 --> 00:03:53.727 - extensive contributions to the Atlas detector and the large-capacity glider. He's the author of the 00:03:53.727 --> 00:04:00.478 - book Signatures of the Artist, The Vital Perfections that Make Our University Powerful. Steve and Tim, 00:04:11.463 --> 00:04:20.228 - the interface between science and policy. They expose the cynical label of science to the language. 00:04:20.228 --> 00:04:30.046 - Please join me in welcoming them. Thank you, Jane. Thanks so much for all of your efforts and for the traveling 00:04:33.314 --> 00:04:40.330 - title of our talk, Live, Free, and Die. Why are voters in red counties dying younger than those in blue 00:04:40.330 --> 00:04:47.143 - counties? We'll bombard you with data today to try to prove our point, show you what's going on, and 00:04:47.143 --> 00:04:53.956 - you can draw your own conclusions as to the reasons behind the invincible show. Many of you nowadays 00:04:53.956 --> 00:05:00.837 - may think that you're living in actually two countries, a red state country and a blue state country, 00:05:00.837 --> 00:05:03.198 - and what we want to show you today 00:05:04.034 --> 00:05:11.954 - to health outcomes, that's actually quite literally true, that you find very different outcomes in red 00:05:11.954 --> 00:05:19.798 - and blue areas. We're trying to show you all of this and why in our talk today. Here is an example of 00:05:19.798 --> 00:05:27.718 - this. This graph shows the life expectancy in two states, Connecticut and Oklahoma, from 1959 to 2019. 00:05:27.718 --> 00:05:31.486 - And you see that in 1959, a resident of Oklahoma 00:05:31.714 --> 00:05:39.588 - would live on average about one year longer than one in Connecticut. However, they started to diverge. 00:05:39.588 --> 00:05:47.461 - They have continued to diverge. So that today, our resident in Connecticut has a four year longer life 00:05:47.461 --> 00:05:55.335 - expectancy than one in Oklahoma. And it's getting bigger. And as we'll show you, if the current trends 00:05:55.335 --> 00:05:59.998 - continue, we expect these disparities to grow substantially. 00:06:00.514 --> 00:06:10.099 - on our blogdebunkingdenial.com, specifically these two, Live Free and Die and the Deployment Divide. 00:06:10.099 --> 00:06:19.588 - So, here's a disclaimer that we use. I don't represent official news of anybody who hears anything. 00:06:19.588 --> 00:06:28.414 - Although they should. So here is a page from our blog, Debunking Denial. By now we have over 00:06:29.058 --> 00:06:38.219 - 100 posts on our blog on the following categories. And the two categories relevant today are demographics 00:06:38.219 --> 00:06:47.121 - and partisan divides over science. I want to mention that our talk follows basically after Rob Stone's 00:06:47.121 --> 00:06:56.455 - talk, Medicare for All. And it precedes a public panel that will be held on Tuesday, Vaccines in a Changing 00:06:56.455 --> 00:06:58.270 - Political Landscape. 00:06:58.626 --> 00:07:08.427 - 5 to 6.30 p.m. on Tuesday here in the Monroe County Public Library. You've got a typo in the title. 00:07:08.427 --> 00:07:19.011 - Yeah, I just noticed that. Thank you. Oh, and by the way, that reminds me. If you have comments, questions, 00:07:19.011 --> 00:07:28.126 - challenges, please feel free during the talk. You can fix that before the next presentation. 00:07:28.258 --> 00:07:36.322 - Yes, thank you. Okay, so here's an outline of our presentation. We'll show you that life expectancy 00:07:36.322 --> 00:07:44.467 - in general is increasing worldwide, but in the U.S. there's evidence of a growing partisan mortality 00:07:44.467 --> 00:07:52.692 - gap and education gap in the U.S. We'll talk a little bit about the impact of COVID-19 on this. We'll 00:07:52.692 --> 00:07:58.014 - talk in great detail about chronic disease prevalence in the U.S. 00:07:58.178 --> 00:08:04.951 - And we'll show you the impact of state government control, state government laws and attitudes. And 00:08:04.951 --> 00:08:12.198 - then we'll talk about some specific situations, smoking restrictions and lung cancer, gun laws and firearm 00:08:12.198 --> 00:08:19.512 - deaths, abortion laws and maternal infant mortality, medication expansion, constant medical infrastructure. 00:08:19.512 --> 00:08:26.014 - And we'll just mention at the end, vaccine hesitancy and Robert F. Kennedy, Jr., which could be 00:08:26.434 --> 00:08:38.213 - top all on its own. And then future trends in summary. So life expectancy has been generally increasing 00:08:38.213 --> 00:08:49.765 - worldwide. Here's a graph from 1800 to 2023. The red curve here is US population. The blue is Europe. 00:08:49.765 --> 00:08:53.502 - The green is Asia and the black. 00:08:54.178 --> 00:09:02.543 - Sorry, I'm colorblind, so please turn around. It's beautiful. Or you can shout out if I miss that. Anyway, 00:09:02.543 --> 00:09:10.440 - until about the start of the Industrial Revolution, populations were more or less static. They began 00:09:10.440 --> 00:09:18.570 - to increase in Europe and the US rapidly after, in about 1870 or thereabouts, and have increased since. 00:09:18.570 --> 00:09:21.854 - Populations in Asia, for example, remains 00:09:22.082 --> 00:09:29.510 - for a longer period of time that have now shot up. There's many reasons for this increase. Improved 00:09:29.510 --> 00:09:37.385 - vaccines and medical treatments. Improved air and water quality. And in the U.S., that's the particularly 00:09:37.385 --> 00:09:45.407 - environmental protection agencies, clean air and clean water laws. Improved personal hygiene and nutrition. 00:09:45.407 --> 00:09:51.870 - Improved access to hospitals and healthcare and insurance. Yes. What happened in Asia? 00:09:52.610 --> 00:10:09.327 - Chinese family. That is all Chinese. And this is Spanish food. So in the U.S., the health care, mainly 00:10:09.327 --> 00:10:21.662 - government, Medicare, Medicaid, and Obamacare, and then improved education. 00:10:21.794 --> 00:10:29.399 - All of these have led to this increase in life expectancy in the US, Europe, and around the world. Yes? 00:10:29.399 --> 00:10:36.857 - I was an electorate in Sweden by an English public health president who said hygienic behavior is the 00:10:36.857 --> 00:10:44.681 - number one reason for the change from about 1920. Really? Could be. Could be. Can you repeat the question, 00:10:44.681 --> 00:10:50.750 - please? I'm sorry. I'll repeat it. The statement was that probably the single most 00:10:58.018 --> 00:11:08.655 - which I don't know. It certainly sounds believable. Yes? Yeah, Rob. There's a marvelous book that I 00:11:08.655 --> 00:11:20.250 - always recommend called Mirage of Health from 1959 by, what's his name? Rene Dubot. He was an epidemiologist 00:11:20.250 --> 00:11:26.526 - and microbiologist. So he knew a lot about the antibiotics 00:11:27.074 --> 00:11:35.598 - He wrote this book making this exact point that actually it's been public health that is the biggest 00:11:35.598 --> 00:11:44.797 - thing, hand washing and cleaning, particularly separating sewage from fresh water. But he had this wonderful 00:11:44.797 --> 00:11:53.321 - quote in there where he said, it is easy to believe one is emptying the ocean with a bucket when the 00:11:53.321 --> 00:11:56.190 - tide is going out. Thank you. OK. 00:11:58.018 --> 00:12:05.724 - So lately, however, the US has fallen behind with respect to other developed countries, the OECD countries. 00:12:05.724 --> 00:12:13.288 - This shows from 2003 to 2023, life expectancy, the light blue dots are the average of the OECD countries, 00:12:13.288 --> 00:12:20.781 - and the dark blue are the US. And you'll see that they're essentially equal in 2003, but then they begin 00:12:20.781 --> 00:12:26.846 - to diverge. And they diverge even more during COVID, where the US responds to COVID. 00:12:27.298 --> 00:12:36.436 - particularly in terms of deaths, was much higher than in the other OACP countries. And here, Rob Stone 00:12:36.436 --> 00:12:45.485 - showed this as well, despite this poor health outcomes in the US, despite the fact that in the US, we 00:12:45.485 --> 00:12:55.422 - spent our capita two and a half times more than the OACP average, and 10 times more than countries like Turkey. 00:12:55.714 --> 00:13:06.418 - Yes. What is those? OECD? Organization for Economic Cooperation and Development. That's in the fine 00:13:06.418 --> 00:13:17.230 - print. It's in the fine print, yeah. This is a test. I test, yeah. The U.S. currently ranks 30th out 00:13:17.230 --> 00:13:24.830 - of 30. Oh yeah, Organization for Economic Cooperation and Development. 00:13:26.690 --> 00:13:35.994 - It's a group of 38 countries, developed countries. And we ran out of 38 OECD countries in life expectancy 00:13:35.994 --> 00:13:45.122 - despite the tremendous amount that we spend on health care. Now, in the US, life expectancy is strongly 00:13:45.122 --> 00:13:53.987 - correlated with the income and education. And here is a slide showing how much it depends on income. 00:13:53.987 --> 00:13:55.742 - So this slide shows 00:13:56.322 --> 00:14:08.469 - the expected age of death for US citizens who reach the age of 40 versus the household income percentile 00:14:08.469 --> 00:14:20.500 - from zero to 100. So for example, men whose income is in the bottom 1% in the US have a life expectancy 00:14:20.500 --> 00:14:24.318 - of 72.7 years, men in the top 1% 00:14:24.738 --> 00:14:33.507 - have a life expectancy of 87.3 years. They have a 15-year difference. Women in the US, in the lowest 00:14:33.507 --> 00:14:42.188 - 1%, 78.8 years life expectancy. In the top 1%, 88.9 years. So it's a 10-year difference from around 00:14:42.188 --> 00:14:50.870 - 15 years from now. So you see the real strong effect of income here. Also, it depends significantly 00:14:50.870 --> 00:14:53.214 - on educational attainment. 00:14:53.890 --> 00:15:01.807 - So the bottom curve here is life expectancy for Americans who have less than a high school education. 00:15:01.807 --> 00:15:09.569 - Here, high school graduates in the US. Here, Americans who have some college. And here is Americans 00:15:09.569 --> 00:15:17.409 - with a college degree. So you see that there is about 74 to 84, about a 10 year difference depending 00:15:17.409 --> 00:15:23.230 - on educational attainment here. And furthermore, you see that between 2000 00:15:23.618 --> 00:15:31.477 - And in 2019, the only one piece that is actually increasing by an invisible amount is those with college 00:15:31.477 --> 00:15:39.411 - degree. So there is what Steve calls a diploma divide in the US. And higher income means access to higher 00:15:39.411 --> 00:15:47.345 - quality health care. We don't have a national health service. We don't have Medicare for all. And college 00:15:47.345 --> 00:15:52.510 - graduates have better information about health, less dangerous jobs. 00:15:52.770 --> 00:16:01.076 - They have generally better access to eyeballing health care, and they can work remotely during COVID. 00:16:01.076 --> 00:16:09.871 - So a lot of advantages here that lead to these discrepancies. But there is an increasing partisan mortality 00:16:09.871 --> 00:16:18.422 - gap in the US. This is the free environment we're talking about. So here, from 2001 to 2019, just before 00:16:18.422 --> 00:16:22.494 - COVID, the red dots here show the mortality rate. 00:16:23.266 --> 00:16:31.846 - deaths for 100,000 Americans in Republican counties, and the blue dots are the mortality rate in Democratic 00:16:31.846 --> 00:16:39.869 - counties. And we see that while there was a small gap between them in 2001, there's a really big gap 00:16:39.869 --> 00:16:48.290 - in 2019, and it's growing. And we'll argue later in this talk that you can expect it to grow even further 00:16:48.290 --> 00:16:51.070 - if current policies stay in place. 00:16:51.650 --> 00:16:59.310 - The effect was largest among white voters of both sexes. It was predominantly a discrepancy among white 00:16:59.310 --> 00:17:07.338 - voters. Here we show that. So here are the mortality rates by race or ethnicity in Republican and Democratic 00:17:07.338 --> 00:17:14.998 - counties from 2001 to 2019. The highest mortality rate, i.e., the lowest life expectancy is for blacks. 00:17:14.998 --> 00:17:21.406 - And you see there is some difference between red and blue counties, but it's not hard. 00:17:22.690 --> 00:17:32.673 - And it is dropping more rapidly than the white Hispanic. Yes? Have you made any attempt to disaggregate 00:17:32.673 --> 00:17:42.847 - this and to ask the question, what should the gap be given the educational circumstances and the revenue? 00:17:42.847 --> 00:17:50.238 - Well, actually, have we made any effort to actually unpack this to find out? 00:17:50.338 --> 00:17:59.301 - You know, the only thing we can say is that, as we'll show you, as the talk goes on, there are actually 00:17:59.301 --> 00:18:08.609 - a lot more different elements in play here than just education. And it turns out there are many intertwined 00:18:08.609 --> 00:18:17.745 - contributions. So it's not so trivial to unravel. But here, you see, the large discrepancy is Republicans 00:18:17.745 --> 00:18:20.158 - versus Democratic counties. 00:18:20.546 --> 00:18:29.108 - And thirdly, you see the Hispanic case, where you see very little difference between red and blue counties. 00:18:29.108 --> 00:18:37.116 - But you also see what's called the Hispanic parasites, which is the following. That Hispanics in the 00:18:37.116 --> 00:18:45.440 - US, as a group, have less educational attainment, less household income, and less access to high quality 00:18:45.440 --> 00:18:49.246 - medical care than whites. But they live longer. 00:18:51.650 --> 00:19:00.561 - Now, we're told that this difference between Hispanic and white became much smaller during COVID. So 00:19:00.561 --> 00:19:09.383 - we don't know whether post-COVID, this will once again separate out or whether they'll remain about 00:19:09.383 --> 00:19:18.559 - the same in the future. So mortality rates are also strongly correlated with community education level. 00:19:18.559 --> 00:19:21.470 - So what this graph here shows is 00:19:22.306 --> 00:19:31.061 - the mortality rate per 100,000 people versus the share of the local population of college degrees going 00:19:31.061 --> 00:19:39.563 - from 10% at the bottom here to 50% at the top. So they divided the US into 20 different groups every 00:19:39.563 --> 00:19:48.402 - 5% of the population. And each of those is shown as a dot. The blue dots are from 1990. And the straight 00:19:48.402 --> 00:19:52.190 - blue line is the average of those blue dots. 00:19:53.698 --> 00:20:06.054 - The yellow are 2000, and the brown are 2010. And you see, first of all, that this difference in mortality 00:20:06.054 --> 00:20:18.410 - rates is increasing as we go from 1990 to 2010. And you see that it's actually a very strong correlation. 00:20:18.410 --> 00:20:23.422 - So the higher percentage in your community 00:20:23.906 --> 00:20:35.214 - college degrees, the longer their life expectancy. And it's getting bigger. Here, we also have a growing 00:20:35.214 --> 00:20:45.984 - partisan college education gap. So what's shown here is all of the presidential elections from 1984 00:20:45.984 --> 00:20:53.630 - through 2020. So this red, I hope, is people without a college degree. 00:20:55.106 --> 00:21:02.454 - here is people with a college degree. And the zero line, which is right here, means equal numbers of 00:21:02.454 --> 00:21:09.948 - people voting as Republican and Democratic. You see that up until 2004, there were not extremely large 00:21:09.948 --> 00:21:16.860 - differences between people with college degrees and without college degrees and how they voted 00:21:16.860 --> 00:21:21.662 - for Republicans and Democrats. Starting in 2004, you see this gap 00:21:22.786 --> 00:21:31.354 - increasing such that in the 2020 election, people without a college degree, 20% more, voted Republican 00:21:31.354 --> 00:21:39.673 - than Democrat, and people with a college degree, about 13% more, voted Democrat than Republican. So 00:21:39.673 --> 00:21:48.990 - there is this partisan divide, which I suspect you're familiar with, and it's growing. So here, that's actually 00:21:57.858 --> 00:22:05.608 - Here we see that college educated Americans tend to cluster geographically. So what's shown here is 00:22:05.608 --> 00:22:13.823 - a graph of the U.S. and the red areas represent areas where you have more college graduates than the U.S. 00:22:13.823 --> 00:22:21.263 - average. And blue represent areas where you have fewer college graduates than the U.S. average. 00:22:21.263 --> 00:22:27.230 - And the color, the darker red means more college graduates, the darker blue. 00:22:27.618 --> 00:22:38.067 - It's fewer, even fewer college graduates. And you see, there is this clustering, Eastern Seaboard, New 00:22:38.067 --> 00:22:48.719 - England, then individual Chicago, Madison, Wisconsin, Minneapolis, Colorado, Pacific Northwest, Northern 00:22:48.719 --> 00:22:56.734 - California, and so on to our surprise, Southwestern Montana have high clusters 00:22:57.250 --> 00:23:06.501 - Is it? Yeah. It's impressive. Furthermore, you see this area where you have significantly fewer college 00:23:06.501 --> 00:23:15.397 - graduates than the U.S. average. And I want you to look at it carefully because you're going to see 00:23:15.397 --> 00:23:24.559 - this area of the country again and again and again in all the slides that show evidence, okay? So it's 00:23:24.559 --> 00:23:25.982 - the deep south. 00:23:28.322 --> 00:23:40.369 - Actually, we'll show, yes, we'll show that with respect to certain health care outcomes later. Yeah, 00:23:40.369 --> 00:23:52.655 - there is a definite effect. But in any case, this area, we will be seeing more and more the deep south 00:23:52.655 --> 00:23:56.830 - and then going up into Appalachia. 00:23:56.994 --> 00:24:05.559 - You know, some states will occur in various other slides, and they won't, but this particular area is 00:24:05.559 --> 00:24:14.039 - really outstanding in terms of having poorer health outcomes than the rest of the country. Now, this 00:24:14.039 --> 00:24:22.772 - is the best jobs are here. College graduates will tend to cluster here. The hotspots for college grads, 00:24:22.772 --> 00:24:26.718 - the red, have the best access to high quality. 00:24:26.882 --> 00:24:36.342 - health care, whereas the cold spots are called diuretics, aligned with geographic areas with the worst 00:24:36.342 --> 00:24:45.801 - health outcomes. So there are significant state by state differences in life expectancy. This is 2022, 00:24:45.801 --> 00:24:54.526 - and the state with the highest life expectancy in that year was Hawaii, life expectancy of 80. 00:24:54.754 --> 00:25:02.701 - And the state with the lowest was West Virginia, 72.2. So there is a 7.8 year difference in life expectancy, 00:25:02.701 --> 00:25:10.356 - depending on whether you live in West Virginia or live in Hawaii. And you see the same cluster of states 00:25:10.356 --> 00:25:17.938 - that occurred with respect to college graduates, deep south, going up into the Appalachian. Once again, 00:25:17.938 --> 00:25:22.750 - yes? I don't know whether it's possible, but can you separate out 00:25:23.650 --> 00:25:31.373 - In West Virginia, we'll have huge drug-related deaths during that time period. Whether it's possible 00:25:31.373 --> 00:25:39.172 - to attribute any of that. Yeah, we'll go through various outcomes, and we'll see that coming up again 00:25:39.172 --> 00:25:46.895 - later. So just for comparison, life expectancy of 80 is comparable with life expectancies in Western 00:25:46.895 --> 00:25:52.094 - European countries. Life expectancy of 72.2 is about that of Egypt. 00:25:53.026 --> 00:26:03.578 - However, you haven't seen anything yet. Now let's break it down by counties. So these are counties in 00:26:03.578 --> 00:26:14.131 - the US. Once again, the blue are counties that have higher life expectancy than average. And the red, 00:26:14.131 --> 00:26:19.614 - once again, the same. Deep South, South, Appalachia. 00:26:21.090 --> 00:26:30.231 - And the difference. Is there a blue dot there? Actually, quite possibly. Quite possibly. So the lowest, 00:26:30.231 --> 00:26:39.635 - the county with the lowest life expectancy, life expectancy is 66.8 years, and the county with the highest 00:26:39.635 --> 00:26:48.952 - life expectancy, 86.8 years. That's a 20 year difference. 86.8 is comparable to the countries like Monaco 00:26:48.952 --> 00:26:50.270 - and Singapore, 00:26:51.010 --> 00:27:01.480 - Longest life expectancy in any country in the world. 66.8 is about equal to Afghanistan. And where are 00:27:01.480 --> 00:27:12.051 - those counties? Ah, OK. They're the darkest red. So you see here, here, here. These are Native American 00:27:12.051 --> 00:27:20.894 - reservations. And you'll see also down here, almost equal, southwestern West Virginia. 00:27:21.666 --> 00:27:28.670 - a little bit. Yes? I keep thinking as you're presenting these statistics, we would be great to see by 00:27:28.670 --> 00:27:36.018 - census track, because thinking about our own county, for example, there are enormous discrepancies between 00:27:36.018 --> 00:27:42.542 - urban and rural parts of the county. Is some of this data available at the census track? Well, 00:27:42.542 --> 00:27:49.134 - we'll be sure. So there are these enormous differences that we see in a life expectancy county. 00:27:49.134 --> 00:27:50.782 - By the way, which state 00:27:51.394 --> 00:28:02.106 - has the county with the highest life expectancy. Where is Connecticut? Massachusetts. That's a good 00:28:02.106 --> 00:28:13.139 - choice, yes. It's blue out there. Massachusetts? It's Colorado. A couple of suburbs of Denver have the 00:28:13.139 --> 00:28:20.958 - highest. Sorry, in 2020. It varies from year to year. As does the state. 00:28:21.218 --> 00:28:30.394 - The state with the highest life expectancy tends to compare it to Hawaii and California. And for people 00:28:30.394 --> 00:28:39.482 - who are asking, the darkest blue dot in Indiana is Hamilton. It is. It is. It is. It is. It is. It is. 00:28:39.482 --> 00:28:48.129 - 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. 00:28:48.129 --> 00:28:49.982 - It is. It is. It is. 00:28:50.306 --> 00:28:57.912 - How have these changed? That's the next slide. Thank you. So here is changes in life expectancy between 00:28:57.912 --> 00:29:05.371 - 1980 and 2014. So over a 35-year period, once again, you see the same thing, right? You see this area 00:29:05.371 --> 00:29:12.903 - of the country by far the smallest life expectancy in that other areas of the country, the larger. And 00:29:12.903 --> 00:29:14.366 - therefore, you see. 00:29:22.850 --> 00:29:30.874 - And once again, we mentioned that, perhaps surprisingly, the growth in this mortality gap has occurred 00:29:30.874 --> 00:29:38.821 - mostly among white males and white females. Okay, I'm going to turn it over to Steve, who will unpack 00:29:38.821 --> 00:29:46.767 - a little more of the story. They're happy, apparently. Oh yeah, that's not all of it. There we go. So 00:29:46.767 --> 00:29:52.766 - Tim has shown you the statistics. I'm going to try to understand two things. 00:29:53.314 --> 00:30:01.187 - What are the causes of mortality that are leading to this mortality gap? And second, what are the causes? 00:30:01.187 --> 00:30:08.763 - So first, let's consider what features of health account for the growing mortality gap. And the first 00:30:08.763 --> 00:30:16.191 - thing I want to show you is something which doesn't contribute very much, and it answers one of the 00:30:16.191 --> 00:30:22.430 - questions that came before. There was a very influential book that came out in 2020 00:30:23.106 --> 00:30:31.939 - by economists Angus Heaton and Case, which argued that life expectancy in the US was stagnating largely 00:30:31.939 --> 00:30:40.518 - because white middle-aged American males without a college degree were dying what they called deaths 00:30:40.518 --> 00:30:49.267 - of despair, meaning deaths from suicide, drugs, and alcohol, from opioid deaths. Somebody asked before 00:30:49.267 --> 00:30:51.390 - about the opioid deaths. 00:30:53.730 --> 00:31:01.573 - So it turns out there is much more detail than we have time to show you in this. It turns out that is 00:31:01.573 --> 00:31:09.648 - a minor contribution to the growing mortality gap. If you look at the map again to see where the highest 00:31:09.648 --> 00:31:17.337 - rate of opioid deaths were, it's the red and yellow dots. So it's sort of the mid-Atlantic but high 00:31:17.337 --> 00:31:21.182 - incidence in New England and some in the Midwest. 00:31:21.570 --> 00:31:30.595 - not much in the South and Appalachia. So it's a different geographical distribution and turns out to 00:31:30.595 --> 00:31:39.531 - be only a minor contribution to this growing mortality gap. So it's not really opioid deaths that's 00:31:39.531 --> 00:31:48.556 - causing the problem. Okay, so while these males, white, middle-aged American males without a college 00:31:48.556 --> 00:31:51.326 - degree, often vote Republican, 00:31:51.682 --> 00:32:01.840 - These are secondary contributions. So now what we want to look at is this is a list of some of the leading 00:32:01.840 --> 00:32:11.903 - causes of mortality in the US. So heart disease on the left, cancer, those are the two big ones. Strokes, 00:32:11.903 --> 00:32:20.542 - chronic lung disease, and so forth. And the blue bars here represent the average mortality 00:32:21.154 --> 00:32:30.223 - Democratic voting counties, and the red in Republican voting counties. And the dark blue and red are 00:32:30.223 --> 00:32:39.740 - for 2001, and the lighter blue and red are for 2019. And what you see, for example, is that the mortality 00:32:39.740 --> 00:32:48.809 - gap, the difference between blue and red increases for heart disease from 2001 to 2019. It increases 00:32:48.809 --> 00:32:49.886 - for cancer. 00:32:50.242 --> 00:33:00.733 - It gets worse for lung disease. It gets worse for diabetes. It gets worse for suicide. This is a very 00:33:00.733 --> 00:33:11.121 - broad-based contribution of mortality causes to the mortality gap. So we want to investigate that in 00:33:11.121 --> 00:33:15.646 - more detail now. And let's start with coke. 00:33:16.258 --> 00:33:24.540 - So we showed you before life expectancy and mortalities up to 2019. That cut off before COVID. This 00:33:24.540 --> 00:33:32.988 - is now including COVID. You see that the life expectancy in the US dipped much more during COVID than 00:33:32.988 --> 00:33:41.270 - it did in a comparable country. Average, the US has 4.2% of the world population, but it had 17% of 00:33:41.270 --> 00:33:43.838 - COVID deaths around the world. 00:33:44.290 --> 00:33:55.076 - And that's because the U.S. government response was one, to put it. Okay, and that contributed very 00:33:55.076 --> 00:34:06.078 - strongly to a partisan mortality gap. So what is shown here, this is from May to December of 2021. So 00:34:06.078 --> 00:34:11.902 - after the first COVID vaccines were widely available, 00:34:12.610 --> 00:34:21.931 - The left plot here shows that every dot in this plot corresponds to one county in the US. And this shows 00:34:21.931 --> 00:34:30.276 - the percent of adults vaccinated in the county versus the percent that voted for Trump in the 00:34:30.276 --> 00:34:39.331 - 2020 presidential election. This is vaccinated for COVID, yes. It's COVID deaths per 100,000 again in 00:34:39.331 --> 00:34:40.574 - every county. 00:34:40.962 --> 00:34:50.990 - versus the percent voted for Trump. You see a very strong correlation so that the vaccination rate dropped 00:34:50.990 --> 00:35:00.642 - as the vote for Trump increased. In average of Biden voting counties, the blue cross here, the average 00:35:00.642 --> 00:35:08.702 - vaccine uptake was 75%, which was above the herd immunity level for COVID, excuse me. 00:35:09.218 --> 00:35:17.488 - the average of Trump voting counties, the Red Cross, the Red Cross is an unfortunate allusion, but the 00:35:17.488 --> 00:35:25.759 - Red Cross there corresponds to a vaccine uptake of 50%, which is below the herd immunity threshold for 00:35:25.759 --> 00:35:34.110 - COVID. And because of that, the death rate from COVID was about three times higher in the average Trump 00:35:34.110 --> 00:35:38.526 - voting county than in the average Biden voting county. 00:35:39.682 --> 00:35:48.450 - And the reason for that is that once the vaccines were available, the death rate in red here for unvaccinated 00:35:48.450 --> 00:35:56.741 - individuals was 10 times higher than the death rate for COVID for vaccinated individuals. So COVID only 00:35:56.741 --> 00:36:05.031 - made that mortality gap grow much more. It accelerated the mortality gap. Now we're going to go through 00:36:05.031 --> 00:36:07.422 - a number of chronic diseases. 00:36:09.122 --> 00:36:16.711 - So first, consider obesity. The darker colors here are where obesity in the states with the highest 00:36:16.711 --> 00:36:24.680 - obesity rates. So the leaders are in terms of most obesity are Mississippi, Arkansas, and West Virginia. 00:36:24.680 --> 00:36:32.876 - But in general, a lot of the red states here, which also have relatively high obesity rates, are Republican 00:36:32.876 --> 00:36:37.278 - voting states. Obesity is often correlated with diabetes. 00:36:37.762 --> 00:36:46.026 - And again, the darker colors here are the states with the highest diabetes rate, diabetes prevalence. 00:36:46.026 --> 00:36:54.777 - And it's very similar to that map that Tim showed you earlier with the states that have, or the clustering, 00:36:54.777 --> 00:37:03.365 - the low clusters of college graduates. Basically, again, the deep south up into Appalachia at the highest 00:37:03.365 --> 00:37:05.310 - prevalence of diabetes. 00:37:06.786 --> 00:37:15.094 - You're going to see a lot of maps that all look basically the same. This is chronic heart disease, still 00:37:15.094 --> 00:37:23.244 - the number one killer in the US. This is age-adjusted heart disease death rates per 100,000 people for 00:37:23.244 --> 00:37:31.710 - inhabitants over 35 years of age. County by county in the US, the darker red are the highest heart disease 00:37:31.710 --> 00:37:36.062 - death rates. And again, it's very much the same region 00:37:36.226 --> 00:37:45.008 - from the south up into Appalachia. This is chronic obstructive pulmonary disease, COPD. Again, 00:37:45.008 --> 00:37:54.622 - very similar. The darker colors, again, have the highest rate of COPD. And they're, again, in the south 00:37:54.622 --> 00:38:04.051 - and up into Appalachia. When we gave this talk to a different audience, they asked, can you correlate 00:38:04.051 --> 00:38:05.438 - the COPD rates 00:38:06.050 --> 00:38:14.373 - with the level of fine particulate matter air pollution. It turns out that there is a correlation, but 00:38:14.373 --> 00:38:22.535 - it's weak, because fine particulate matter air pollution is also quite bad in the industrial Midwest 00:38:22.535 --> 00:38:31.262 - and Southern California, where you don't see high incidences. Yeah. What would a California world have been 00:38:33.410 --> 00:38:42.923 - Yeah, it's a good question. I don't know the answer to that. There are not a lot of people there in 00:38:42.923 --> 00:38:52.721 - North America. They all snore. Maybe they all snore. I really don't know. It's a good question. So the 00:38:52.721 --> 00:39:01.854 - one thing is if you look in detail now, you do see that the South and Appalachia are not solid. 00:39:02.722 --> 00:39:10.241 - There are lighter areas in there. And that addresses a question that Pat asked earlier. And we're going 00:39:10.241 --> 00:39:17.905 - to show a lot of maps in this talk, but this is probably the single most important map. So this addresses 00:39:17.905 --> 00:39:25.279 - also a question of vital grains. Benavides et al, a couple of years ago, did an analysis by zip code. 00:39:25.279 --> 00:39:28.894 - So not even by county, but by zip code in the US. 00:39:29.282 --> 00:39:38.548 - and they analyze the prevalence of the 10 most costly chronic diseases. So that is obesity, hypertension, 00:39:38.548 --> 00:39:47.727 - high cholesterol, coronary heart disease, COPD, asthma, chronic kidney disease, diabetes, cancer, except 00:39:47.727 --> 00:39:56.382 - for skin cancer, and depression. And the brown areas here are hot spots. Those are areas where the 00:39:56.546 --> 00:40:04.273 - incidence of all these chronic diseases is much higher than the national average. And the blue dots 00:40:04.273 --> 00:40:12.155 - are cold spots. They are regions where the incidence of all these chronic diseases is much lower than 00:40:12.155 --> 00:40:20.269 - the average. And what you see is that, again, the south and up into Appalachia are regions where there's 00:40:20.269 --> 00:40:25.214 - a high prevalence of chronic diseases. But there are blue dots. 00:40:25.922 --> 00:40:34.852 - There are the big cities in the south are not affected by this chronic disease prevalence. So you see 00:40:34.852 --> 00:40:43.956 - cold spots in the Atlanta region, Nashville, Charlotte, North Carolina, Raleigh-Durham, North Carolina, 00:40:43.956 --> 00:40:52.886 - Dallas, Fort Worth, Austin, San Antonio, Houston, Texas, and at least average behavior in Little Rock 00:40:52.886 --> 00:40:54.462 - and Oklahoma City 00:40:55.042 --> 00:41:05.412 - So what we're looking at is primarily a rural South and Appalachian high prevalence of all chronic diseases. 00:41:05.412 --> 00:41:15.021 - That is a large contribution to the mortality, yes. The term costly chronic diseases, does that mean 00:41:15.021 --> 00:41:20.254 - the incident of death is the cost you're referring to? 00:41:20.610 --> 00:41:26.758 - I don't think so. I'd actually have to go back. That's the term that's used in the Benavides paper. 00:41:26.758 --> 00:41:32.907 - And I'd have to go back to see exactly what they define it as. But I think it's just overall health 00:41:32.907 --> 00:41:38.502 - care costs. It's not financially costly. It's not financially costly, is it? I would guess 00:41:38.502 --> 00:41:44.527 - it's hospitalization costs. It may be hospitalization. It's an intensive care unit, specifically, 00:41:44.527 --> 00:41:49.630 - particularly related to cancer and heart disease. There is a financial cost issue. 00:41:49.794 --> 00:42:02.442 - Huge cost, that's where most of healthcare is spending its time. White areas are areas I think are just 00:42:02.442 --> 00:42:10.590 - active, no data. Either no data or active. I suspect there's data. 00:42:22.402 --> 00:42:31.241 - Are there various places like Oxford and Mississippi that are high in the same situation, but low in 00:42:31.241 --> 00:42:39.379 - the same situation? Are Bloomington would be the same? We're not high in the same situation. 00:42:39.379 --> 00:42:48.481 - High in college. All that I can tell you is a place like Bloomington, I'm not sure exactly where Oxford 00:42:48.481 --> 00:42:51.806 - is in Mississippi, are about average. 00:42:58.850 --> 00:43:15.456 - I'm not sure what you thought. I thought that was Jackson. That's probably Jackson. Oxford is down. 00:43:15.456 --> 00:43:28.574 - Where is Oxford? Down through the south. Down through the south. I don't know. 00:43:29.122 --> 00:43:36.974 - I don't know. But basically we have a high prevalence of chronic disease, especially in rural areas 00:43:36.974 --> 00:43:44.826 - of the South and Appalachia. Turns out chronic disease prevalence is only partially correlated with 00:43:44.826 --> 00:43:53.227 - educational attainment. So we showed you educational attainment from the high end of the spectrum earlier, 00:43:53.227 --> 00:43:58.174 - the clustering of college graduates. This map is the opposite. 00:43:58.690 --> 00:44:07.508 - It's the map of where there are the largest portions of the population that have less than a high school 00:44:07.508 --> 00:44:15.989 - diploma or the equivalent. And again, you do see in the red areas the South and Appalachia, but also 00:44:15.989 --> 00:44:24.219 - a very intense area along the Texas-Mexico border, along the Indian reservations, Native American 00:44:24.219 --> 00:44:27.998 - reservations in northern Mexico and Arizona. 00:44:28.258 --> 00:44:37.922 - and in the San Joaquin Valley in California. And you don't see the chronic disease prevalence here or 00:44:37.922 --> 00:44:48.060 - here. That may be an indication of this Hispanic paradox that Tim showed you earlier because those regions 00:44:48.060 --> 00:44:57.630 - are very heavily Hispanic. So it's not only education which goes here. So next issue is how do state 00:45:00.002 --> 00:45:09.613 - It's with a reaction to a question. I'm just looking. So now we're going to go into causes. So one cause 00:45:09.613 --> 00:45:18.950 - you can ask about is how does state government impact health? So this is a complicated graph, but let 00:45:18.950 --> 00:45:28.286 - me try to take you through. These are all cause age-adjusted mortality rates in states, the dark blue 00:45:28.610 --> 00:45:36.756 - are states which have unified democratic control. That is, a democratic governor and democratic control 00:45:36.756 --> 00:45:44.745 - of both houses of the state legislature. The dark red are the opposite. They are Republican governors 00:45:44.745 --> 00:45:52.577 - and full Republican control. And the other colors are mixed situations that you don't have to worry 00:45:52.577 --> 00:45:55.710 - about right now. So focus on dark blue. 00:45:56.066 --> 00:46:03.771 - compared to the dark red 2015 through 2021. Full Republican control gives you a higher mortality rate 00:46:03.771 --> 00:46:11.628 - by a significant margin than full Democratic control of the state. And it only gets worse as we've seen 00:46:11.628 --> 00:46:19.182 - before during COVID in 2021. So state government seems to have some effect. And now we can ask why. 00:46:19.182 --> 00:46:25.150 - So we're gonna go through a number of things that the state government can do. 00:46:26.018 --> 00:46:35.953 - First we're going to look at smoking restrictions and lung cancer deaths. So the map on the left, the 00:46:35.953 --> 00:46:45.012 - gray states are the states with the least restrictions on smoking behavior. The green states 00:46:45.012 --> 00:46:54.558 - have comprehensive smoking ban laws, including in restaurants and bars. The right hand map is the 00:46:54.818 --> 00:47:05.603 - shows the prevalence of lung cancer deaths from 2016 to 2020. And what you see is that among the gray 00:47:05.603 --> 00:47:16.176 - states with weak smoking restrictions, there is a heavier concentration of lung cancer deaths. Then 00:47:16.176 --> 00:47:21.886 - you can ask about gun restrictions. So here it is the 00:47:22.146 --> 00:47:33.792 - map on the right that shows you the stringency of gun laws. The white end is the least stringent gun 00:47:33.792 --> 00:47:45.439 - laws, and states like Louisiana, Arkansas, Kentucky, Virginia, Virginia has a big tobacco opinion on 00:47:45.439 --> 00:47:51.550 - it, especially here, Montana, and Wyoming, and other 00:47:52.066 --> 00:48:00.641 - left, unfortunately, with the reverse color coding. The states in red have the highest firearm death 00:48:00.641 --> 00:48:09.300 - rates. So in general, there's a correlation between weak gun restrictions in the state laws and heavy 00:48:09.300 --> 00:48:18.384 - firearm deaths. It's not surprising, because if you actually look at it, there's a very strong correlation 00:48:18.384 --> 00:48:21.950 - between the prevalence of guns in a state 00:48:22.082 --> 00:48:31.818 - and the firearm deaths. Next, I'm going to turn it back over to Tim, and we're going to talk about abortion 00:48:31.818 --> 00:48:41.014 - laws and maternal mortality and infant mortality. Thank you, Steve. So in 2022, the Supreme Court and 00:48:41.014 --> 00:48:51.742 - the Dobbs decision left it up to individual states to decide what their abortion laws, abortion restrictions would be. 00:48:51.842 --> 00:49:00.463 - In May of this year, 17 Republican-controlled states currently ban all abortions or those after the 00:49:00.463 --> 00:49:09.084 - first six weeks of pregnancy. So these are the dark, permanent ones. So then there are a few states 00:49:09.084 --> 00:49:17.791 - that have bans after 12 weeks. And then in blue are the states with fewer restrictions on abortions. 00:49:17.791 --> 00:49:20.894 - And the darker the blue, the looser 00:49:25.698 --> 00:49:33.584 - Now, let us look at the correlation between strict abortion laws and the prevalence of maternity care 00:49:33.584 --> 00:49:41.547 - providers in different states. So the level of restriction is shown here. This looks like brown to me, 00:49:41.547 --> 00:49:49.279 - but orange. Not orange, orange, thank you. So the orange is states that have a full abortion ban or 00:49:49.279 --> 00:49:52.990 - a ban after six weeks, and what looks like pink 00:49:55.298 --> 00:50:07.412 - the states that have orange. It's light. You can tell me, hey. It's really embarrassing. And then the 00:50:07.412 --> 00:50:19.407 - color of the state is the number of maternity care providers per 100,000 female population age 15 to 00:50:19.407 --> 00:50:23.326 - 44. So the lightest color states 00:50:23.778 --> 00:50:31.857 - have maternity care providers 105 to 160 per 100,000 population. The darkest-colored states have the 00:50:31.857 --> 00:50:39.936 - least 52 to 69 abortion care providers per 100,000. And you see there's a strong correlation between 00:50:39.936 --> 00:50:48.255 - the severity of the abortion ban and lacking maternal care providers. And that's not really surprising. 00:50:48.255 --> 00:50:53.374 - You would be more restricted in such states, and you could also 00:50:57.666 --> 00:51:26.942 - So here's something which is really, I think, sort of a national disgrace. 00:51:28.578 --> 00:51:37.474 - The maternal mortality rate in the US versus various other countries is the maternal deaths per 100,000 00:51:37.474 --> 00:51:46.369 - live births in select countries for 2018. This is before the Dow's decision. So US 17.4, which is twice 00:51:46.369 --> 00:51:55.094 - the next largest, which is France at 8.7, but it's 10 times the maternal mortality rate in Norway. So 00:51:55.094 --> 00:51:58.430 - we somehow are tolerating embarrassing 00:51:58.818 --> 00:52:07.787 - high maternal mortality rate. And it's getting worse. Now this, unfortunately, there's a lot on this 00:52:07.787 --> 00:52:17.466 - graph, but what we have here are the maternal mortality rates for every state in the nation. From California 00:52:17.466 --> 00:52:26.524 - up to Louisiana, we're missing data from three states, Delaware, New Hampshire, Vermont. There are 47 00:52:26.524 --> 00:52:27.678 - states here. 00:52:28.962 --> 00:52:40.716 - So California is about four deaths per 100,000 births. Louisiana is about 47. And here is the average U.S. 00:52:40.716 --> 00:52:52.251 - maternal mortality rate. It's about 25. That in Iran is shown here in blue. This group of nine developed 00:52:52.251 --> 00:52:58.622 - countries here, this is the maternal mortality rate here. 00:52:58.754 --> 00:53:06.742 - The top is the maternal mortality rate in Mexico and Nicaragua, which is actually lower than the maternal 00:53:06.742 --> 00:53:14.428 - mortality rate in Louisiana. Okay, so once again, now also, you might ask how this is correlated with 00:53:14.428 --> 00:53:22.114 - the poverty level in these states. And the X's here, and this right-hand axis, show the poverty level 00:53:22.114 --> 00:53:28.670 - in these states. And you see there's very little correlation between the poverty level 00:53:37.122 --> 00:53:48.629 - Yes, two things. Indiana, which I'm proud of this, is the third highest maternal mortality rate in the 00:53:48.629 --> 00:53:57.566 - country. And Texas, yes. So from 2019 to 2022, maternal mortality rate in Texas 00:54:07.874 --> 00:54:32.449 - Okay, so this shows infant and perinatal mortality rates for 1,000 births by race, ethnicity, and abortion 00:54:32.449 --> 00:54:37.502 - policy in the states. 00:54:38.754 --> 00:54:48.142 - The states with the strongest abortion restrictions are here in, orange again? Sam. Sam, okay. And the 00:54:48.142 --> 00:54:57.713 - states with fewer abortion restrictions are in need. Okay, this is broken down by, so this is, left-hand 00:54:57.713 --> 00:55:07.010 - one is for all groups, non-Hispanic white, non-Hispanic black, non-Hispanic Asian, Hispanic, and this 00:55:07.010 --> 00:55:08.286 - is perinatal. 00:55:08.418 --> 00:55:17.169 - which is the combination of infant mortality and fetal mortality. And you see that in every case, the 00:55:17.169 --> 00:55:25.833 - states with stronger abortion restrictions have higher infant mortality rates. In the year following 00:55:25.833 --> 00:55:34.670 - Texas's 2021 abortion law, infant mortality rates rose by 13%. And in the year following the 2022 DOPS 00:55:34.670 --> 00:55:37.758 - decision, national infant mortality 00:55:46.402 --> 00:55:54.169 - between these increased abortion restrictions that came in after Dow's and the rise of infant mortality. 00:55:54.169 --> 00:56:01.640 - The U.S. currently ranks number 32 among the 38 OECD countries in infant mortality. So don't we like 00:56:01.640 --> 00:56:09.259 - to frighten ourselves on our wonderful medical? Do you know who's worse? Huh? Do you know who's worse? 00:56:09.259 --> 00:56:10.590 - Oh, the citizens. 00:56:15.074 --> 00:56:24.595 - 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 00:56:24.595 --> 00:56:34.023 - a function of time. Starting with 1973, Roe v. Wade decision. And going up to 2024, although there's 00:56:34.023 --> 00:56:43.451 - a gap here during the COVID years. But you see that starting in 1973, there was a very rapid rise in 00:56:43.451 --> 00:56:44.478 - abortions. 00:56:45.442 --> 00:56:53.603 - in about 1980 at 1.6 billion portions per year, and remaining constant for about 10 years. And then 00:56:53.603 --> 00:57:01.845 - you see a fairly rapid decline for some time. Part of this is due to better access to birth control. 00:57:01.845 --> 00:57:10.006 - A major factor in this decline is that we've seen a very rapid decline in the US in teenage births, 00:57:10.006 --> 00:57:14.494 - women 21 and younger. That has decreased very rapidly. 00:57:17.282 --> 00:57:27.972 - It was out in 2017, but you see between 2020 and 2024, it increases by 10%. This is post-docs. Amazingly 00:57:27.972 --> 00:57:38.154 - enough, these draconian abortion restrictions have managed to increase abortions, increase maternal 00:57:38.154 --> 00:57:42.430 - mortality, and increase infant mortality. 00:57:50.274 --> 00:57:58.569 - There's also the question of the uninsured people in each state. Starting in 2014 with the Ford Care 00:57:58.569 --> 00:58:06.946 - Act, Obama Care, states were able to increase their Medicaid with relatively little contribution from 00:58:06.946 --> 00:58:13.598 - the states, mainly federal. And many states did, but there were also many states 00:58:19.906 --> 00:58:28.494 - And what this graph shows is every state in the union. And the coloring here, the lightest color, this 00:58:28.494 --> 00:58:36.998 - is the percentage of uninsured people in the state who live below the poverty line. So it ranges from 00:58:36.998 --> 00:58:45.503 - 6% to 13% for the lightest, up to 28.5% to 46.9% for the darkest. And once again, you see, with a few 00:58:45.503 --> 00:58:48.254 - exceptions, the deep south here, 00:58:48.482 --> 00:58:57.130 - going up somewhat into the prairie states. This map also shows for each state whether or not they expanded 00:58:57.130 --> 00:59:05.455 - their Medicare and if so when. And you'll see that there are a lot of the states here with the highest 00:59:05.455 --> 00:59:13.861 - rates of uninsured, Kansas, Texas, Mississippi, Alabama, Georgia, Florida, South Carolina, and Wyoming, 00:59:13.861 --> 00:59:16.286 - which have never raised their 00:59:17.698 --> 00:59:29.613 - expanded their Medicare coverage, and which have the highest rates of uninsured for Medicaid. Is that 00:59:29.613 --> 00:59:41.994 - Medicare? I'm sorry. The exception here is Wisconsin, which has its own Badger Care Plus. Now, red states 00:59:41.994 --> 00:59:47.134 - also have the most rural hospitals at risk. 00:59:49.154 --> 00:59:57.804 - In the past 15 years, 180 rural hospitals have closed or stopped providing an approach to care, and 00:59:57.804 --> 01:00:06.540 - another 432 are currently valued. Stuller for rule of law, okay? And here, so the color coding here, 01:00:06.540 --> 01:00:15.795 - the lightest color is 0% of the rural hospitals in that state at risk of closure, going up to the highest, 01:00:15.795 --> 01:00:17.438 - which is over 41%. 01:00:19.906 --> 01:00:30.319 - And once again, we see this cluster in the south growing up in the prairie regions. And it's likely 01:00:30.319 --> 01:00:40.733 - to get worse because the one big, beautiful bill, Trump's bill, contains a trillion dollars in cuts 01:00:40.733 --> 01:00:47.710 - to Medicaid, most of which will not occur until after the midterm. 01:00:48.930 --> 01:00:58.094 - So we're going to see this dramatic drop in Medicaid covered by Medicaid. And many of these rural hospitals 01:00:58.094 --> 01:01:06.749 - depend on Medicaid income to stay open. So we expect to see many rural hospitals in the United States 01:01:06.749 --> 01:01:10.398 - go out of business if things don't change. 01:01:27.138 --> 01:01:37.777 - So the 92 million Medicaid and CHIP beneficiaries, 26% of these were females ages 15 to 44, 01:01:37.777 --> 01:01:46.334 - as of child-bearing years. And in 2021, 41% of U.S. births were paid for. 01:02:05.602 --> 01:02:16.641 - shows the percentage of birth that were paid for by Medicaid in 2021. The lightest color is here, 21.7% to 34.5%. 01:02:16.641 --> 01:02:26.324 - The darkest, 45.7% to 67%. Once again, you see this cluster of states. So these are the states that 01:02:26.324 --> 01:02:34.942 - disproportionately lose rural hospitals, especially hard hit and about 2 thirds of rural 01:02:35.650 --> 01:02:48.476 - already lack hospital-based obstetrics. The future doesn't work. And Steve will now move into other 01:02:48.476 --> 01:03:02.200 - medical facts and figures and summarize them. Sorry, yes. One question. Did you discriminate as to whether 01:03:02.200 --> 01:03:05.022 - more babies are dying 01:03:05.730 --> 01:03:17.119 - or after they're born, because of, you know, the seizures. So the slide that Tim showed before, the 01:03:17.119 --> 01:03:28.622 - feeble and newborn deaths were the perinatal in the last column. The others were in mortality, so an 01:03:28.622 --> 01:03:33.406 - early childhood mortality. The difference 01:03:33.698 --> 01:03:53.401 - I don't want to bore you with too many more things. So it turns out many Republican states also have 01:03:53.401 --> 01:04:01.790 - fewer primary care physicians who hear the 01:04:02.850 --> 01:04:11.506 - beige and red states, and the fewest primary care for the physicians per capita. Another thing that's 01:04:11.506 --> 01:04:20.586 - really important is that COVID has increased Republican hesitancy about other vaccines. They were hesitant 01:04:20.586 --> 01:04:29.242 - about COVID vaccines, but it changed their view of all vaccines. So this is the result of Pew surveys 01:04:29.242 --> 01:04:30.430 - taken in 2016 01:04:30.722 --> 01:04:39.890 - 2019 was before COVID and 2023 after COVID. And the question asked was, should healthy children be required 01:04:39.890 --> 01:04:48.634 - to be vaccinated to attend public schools because of a potential risk for others? That's not positive. 01:04:48.634 --> 01:04:57.123 - Or alternatively, should parents be able to decide for themselves whether or not their children get 01:04:57.123 --> 01:04:58.142 - vaccinated? 01:04:58.498 --> 01:05:08.781 - Democratic voters and mean Democratic, it's always about 85% who favor mandated vaccination for school 01:05:08.781 --> 01:05:19.165 - children. Before COVID, about 79% of Republican voters favored that, but it dropped to 57% after COVID. 01:05:19.165 --> 01:05:25.854 - So general vaccine hesitancy was introduced or increased by COVID. 01:05:26.626 --> 01:05:35.155 - Turns out red states also have below average rates of the human papillomavirus vaccination. So here 01:05:35.155 --> 01:05:43.769 - the darkest states are the ones with the lowest uptake of HPV vaccination. And vaccine hesitancy has 01:05:43.769 --> 01:05:52.895 - consequences. So these are cervical cancer rates in the various states. The darker colors have the highest 01:05:52.895 --> 01:05:55.198 - cervical cancer prevalence 01:05:55.618 --> 01:06:07.612 - Also, again, largely in the south and up into Appalachia. Yeah? What does the tan Indiana mean? I think 01:06:07.612 --> 01:06:20.414 - that's MoData. That's MoData. It's not MoData. Then a couple of other things. We also looked at suicide rates. 01:06:20.834 --> 01:06:29.514 - So again, darker blue here is the highest suicide rates per county. There is some localization of high 01:06:29.514 --> 01:06:38.025 - suicide rates in the South and Appalachia, but largely in the West. And it turns out it's largely in 01:06:38.025 --> 01:06:46.451 - rural counties in the West, in Alaska. So it's believed that it's feelings of isolation and lack of 01:06:46.451 --> 01:06:49.822 - opportunity, along with access to guns. 01:06:50.050 --> 01:07:02.311 - which is increasing the suicide rates. And finally, if we look at traffic mentality, which to our surprise, 01:07:02.311 --> 01:07:14.005 - show up most prevalent in South and Appalachia. So I'm going to skip this now so we can have some time 01:07:14.005 --> 01:07:19.454 - for questions. Let me go to the question. Yeah. 01:07:20.546 --> 01:07:28.984 - Is a certain type of vaccine, as in the state, able to operate seatbelts? It could be. I mean, we haven't 01:07:28.984 --> 01:07:37.343 - looked into the real causes of this. There's also some correlation with just the number of miles driven, 01:07:37.343 --> 01:07:45.383 - which tends to be larger in those areas. But it may also be seatbelt. It may be drunk driving. There 01:07:45.383 --> 01:07:47.294 - are many possible ways. 01:07:50.658 --> 01:07:59.564 - So the average life expectancy of Americans in Republican counties is significantly less than in Democratic 01:07:59.564 --> 01:08:07.811 - counties, and the gap has been growing steadily throughout this century. The gap is growing fastest 01:08:07.811 --> 01:08:16.304 - among white voters of both sexes, and it's especially localized in the rural South and Appalachia. The 01:08:16.304 --> 01:08:20.510 - symptoms include higher incidences in red counties 01:08:20.610 --> 01:08:28.711 - of obesity, diabetes, heart disease, COPD, other chronic diseases, lung cancer, cervical cancer, firearm 01:08:28.711 --> 01:08:36.427 - deaths, maternal and infant mortality, COVID deaths, suicides, and even traffic deaths. It's a very 01:08:36.427 --> 01:08:44.220 - broad-based problem that we're seeing. The contributing factors include, and addressing the question 01:08:44.220 --> 01:08:50.238 - that was asked in the beginning, we haven't been able to unravel all of these 01:08:50.370 --> 01:08:59.189 - are all contributions. Educational disparities play an important role. So do lifestyle and diet. Access 01:08:59.189 --> 01:09:07.838 - to quality health care services, hospitals, and insurance is a very important contribution. Decreased 01:09:07.838 --> 01:09:09.534 - trust in scientists 01:09:09.890 --> 01:09:17.723 - and doctors and vaccines is an important contribution. State laws concerning Medicaid, smoking and guns. 01:09:17.723 --> 01:09:25.332 - State laws regarding abortion rights and women's healthcare. Susceptibility to medical misinformation 01:09:25.332 --> 01:09:32.941 - also is a contributor. Isolation and lack of opportunity in rural areas. And poverty does contribute, 01:09:32.941 --> 01:09:38.014 - but there are many regions of the country that have as much poverty 01:09:38.338 --> 01:09:46.699 - as these regions in the South and Appalachia, but not these problems with chronic disease. 01:09:46.699 --> 01:09:56.162 - Choosing Robert F. Kennedy Jr. as Secretary of Health and Human Services, and the Medicaid cuts in the 01:09:56.162 --> 01:10:05.441 - one big, beautiful bill, the termination of the Subsidies for the Affordable Care Act, are all going 01:10:05.441 --> 01:10:07.646 - to accelerate mortality 01:10:08.674 --> 01:10:17.789 - There's no way around that. So finally, and we had a slide on the problems with RFK, but that's Tim 01:10:17.789 --> 01:10:26.904 - Smith. Many Republican voters interpret freedom today to include a growing distrust of experts. And 01:10:26.904 --> 01:10:36.201 - that has a substantial cost in death rate. That's why we called our talk Live Free and Die. Our other 01:10:36.201 --> 01:10:38.206 - alternative title was 01:10:38.594 --> 01:10:48.133 - liberty and give me death. In order to shrink the mortality gap, what we need are policies that enhance 01:10:48.133 --> 01:10:56.938 - rural health care and support rural hospitals, that improve health education and combat medical 01:10:56.938 --> 01:11:06.110 - misinformation, which is rampant right now and in many cases promulgated by our present government, 01:11:06.978 --> 01:11:14.351 - promote medicine and science as inherently non-partisan approaches to truth about human interactions 01:11:14.351 --> 01:11:21.943 - with nature and with disease, and educate voters that election preferences impact their health. I don't 01:11:21.943 --> 01:11:29.243 - think this has been widely understood by voters in this country. They tend generally to assume that 01:11:29.243 --> 01:11:34.718 - the elections will affect their economic output, their economic condition. 01:11:35.074 --> 01:11:42.396 - not their health condition, and it really does. Unfortunately, those changes are largely opposed to 01:11:42.396 --> 01:11:49.791 - the directions of the current Republican state and federal policies, including the One Big Beautiful 01:11:49.791 --> 01:11:57.113 - Bill Act. And that is a problem that we face right now. So the health care system in the US, as Rob 01:11:57.113 --> 01:12:01.726 - Stone told you a few weeks ago, is kind of the best right now. 01:12:02.210 --> 01:12:09.489 - The problem we have right now is we're in a hole and we're still digging. You know, we first have to 01:12:09.489 --> 01:12:16.985 - stop digging and then try to start climbing out. Whether that's through a Medicare for All or some more 01:12:16.985 --> 01:12:22.174 - incremental policy change, one can debate, but we have to stop digging. 01:12:36.482 --> 01:12:45.167 - Most people in this room are aware that the university is under pressure from the state with the threat 01:12:45.167 --> 01:12:53.517 - of dire consequences to the university to prove that education is worth it. On your third or fourth 01:12:53.517 --> 01:13:02.035 - slide, the fact that the outcomes of your entire life are helped by your education might be something 01:13:02.035 --> 01:13:04.958 - you would have said to Pam Witten. 01:13:05.858 --> 01:13:13.100 - A good point to note, that slide, by the way, did not have graduate degrees, which only increased the 01:13:13.100 --> 01:13:20.556 - life expectancy further. So graduate school also had value. I was struck by your very first slide, which 01:13:20.556 --> 01:13:27.869 - compared Connecticut and Oklahoma. Today, many of you probably saw in the New York Times a story about 01:13:27.869 --> 01:13:34.046 - people in Oklahoma who could afford their utility bills. Yeah. Threatfully hot summer. 01:13:35.010 --> 01:13:42.760 - Data centers being built everywhere. And it happened that the family they focused on, the wife had, 01:13:42.760 --> 01:13:50.974 - was on oxygen. The husband had to sleep with a CPAP machine. And if they couldn't pay for their utilities 01:13:50.974 --> 01:13:58.879 - during this, they would die. And it's especially noted that Oklahoma, every county in Oklahoma in the 01:13:58.879 --> 01:14:02.366 - last three elections, might have overwhelmed 01:14:07.618 --> 01:14:15.533 - from that first slide, I mean, Oklahoma and Connecticut are not alkaline. They were just basically 01:14:15.533 --> 01:14:23.687 - representative red states and blue states. Yeah, Mike. I'm still worried there's something missing in 01:14:23.687 --> 01:14:31.681 - the granularity of the data looking at counties or states. So, I mean, one example, for example, is 01:14:31.681 --> 01:14:36.638 - chronic diseases in urban areas, particularly affecting black 01:14:37.698 --> 01:14:47.439 - white populations, I think those levels of those chronic diseases and the life expectancy statistics 01:14:47.439 --> 01:14:57.373 - are not good for those either zip codes or census spec. Yeah, so we, you know, the most granular study 01:14:57.373 --> 01:15:04.510 - we found of this study by Benavides, which looked wholly at the zip code, 01:15:05.186 --> 01:15:13.747 - They didn't break down the incident codes by ethnicity or race. But that's something one could look 01:15:13.747 --> 01:15:22.394 - at further. I mean, there's a lot of data. It just has to be handled. The slides and graphs wouldn't 01:15:22.394 --> 01:15:30.270 - be available on any of the websites or anything? They are already available on our website. 01:15:30.402 --> 01:15:39.823 - So if you go to debunkingdenial.com, there's a page called Our Presentations. And this presentation 01:15:39.823 --> 01:15:50.092 - is there. You can look at the slides, or you can download them. They're free to use. Yeah? So in correlation 01:15:50.092 --> 01:15:59.230 - with the conclusions that you brought that, and global climate change, most of the vast majority 01:16:03.522 --> 01:16:16.410 - Most of the WhatsApp has bad stuff. Climate change is going to make all of this worse for a number of 01:16:16.410 --> 01:16:29.298 - reasons. One of which is that as the globe warms, lots of species are shifting their range, and among 01:16:29.298 --> 01:16:30.814 - the species 01:16:31.298 --> 01:16:41.648 - shifting their ranges are disease-bearing mosquitoes, disease-bearing ticks. They are moving northward. 01:16:41.648 --> 01:16:51.699 - So things that one used to think of as southern hemisphere diseases, Zika, dengue fever, things like 01:16:51.699 --> 01:16:58.366 - that, West Nile virus, are moving north. So that's going to affect 01:17:00.770 --> 01:17:10.564 - But there are all sorts of, we have lots of posts on our site about climate change effects, 01:17:10.564 --> 01:17:21.316 - about biodiversity loss. All of these things are intertwined. And basically, the bottom line message 01:17:21.316 --> 01:17:29.406 - is that humans have become a dominant species on the planet, and ecosystems 01:17:30.882 --> 01:17:39.230 - when there is one dominant species. They tend to exhaust the resources and mess up things. You know, 01:17:39.230 --> 01:17:47.578 - that's what we're doing in climate change, biodiversity, in health. It's a very broad-faced problem. 01:17:47.578 --> 01:17:56.175 - I was just noting that you pointed out that Colorado was kind of an exception. Colorado is an altitude. 01:17:56.175 --> 01:18:00.638 - So how does that affect you? Colorado is an altitude. 01:18:00.962 --> 01:18:14.941 - It also has attracted a lot of educated people with high paying jobs. So whether it's the climate in 01:18:14.941 --> 01:18:25.598 - Colorado that makes it better or just the high density of college graduates. 01:18:47.650 --> 01:18:58.763 - There's not a complete understanding. Let me answer the second question first. The second question, 01:18:58.763 --> 01:19:10.210 - it is believed that family structure in Hispanic communities is a large contribution to this so-called 01:19:10.210 --> 01:19:14.878 - Hispanic paradox, but it's not understood 01:19:17.634 --> 01:19:25.306 - It did, as Tim mentioned earlier, it largely disappeared during COVID because Hispanics were hit by 01:19:25.306 --> 01:19:33.592 - COVID much more than whites, Hispanic communities. So there was still a small gap between white communities 01:19:33.592 --> 01:19:39.806 - and Hispanic, but much smaller than pre-COVID. In terms of the effect in Mexico, 01:19:55.938 --> 01:20:05.438 - ever looking to the physics department? Or is there an interesting story on how you got into it? Basically, 01:20:05.438 --> 01:20:14.674 - we talk about the interesting problems that come up. We assign ourselves reading assignments. And that's 01:20:14.674 --> 01:20:23.471 - how we attack these problems. We learn about things we don't know about. That's certainly something 01:20:23.471 --> 01:20:25.406 - we didn't know about. 01:20:25.826 --> 01:20:36.204 - So I'll tell you how we got to this. We wrote a different book addressing the question of whether partisan 01:20:36.204 --> 01:20:46.000 - differences that exist in the country now, which are fairly extreme, are from a demographic point of 01:20:46.000 --> 01:20:50.462 - view likely to get better or worse in states. 01:20:54.210 --> 01:21:02.488 - And it turns out things that matter there are birth rates versus blue counties, death rates, and also 01:21:02.488 --> 01:21:10.684 - childbirth, whether the children tend to have the same partisan preferences as their parents. And it 01:21:10.684 --> 01:21:18.962 - was when we were working on that that I saw this large mortality gap break. There's also a birth rate 01:21:18.962 --> 01:21:22.046 - which goes in the opposite direction. 01:21:23.010 --> 01:21:33.781 - thinking that this is going to get rigged, Republicans won't. That's how we got here. There are all 01:21:33.781 --> 01:21:44.982 - sorts of things that trigger our interest. My son was an economist, and he told me at one point he came 01:21:44.982 --> 01:21:52.414 - to a conference on the economic impacts of the declining birth rates 01:21:53.986 --> 01:22:21.694 - I see on your website you quoted Carl Sagan and mentioning some aspects of 01:22:22.338 --> 01:22:34.176 - the belief in pseudoscience, superstition. Did you have a breakout of the statistics of those who believe 01:22:34.176 --> 01:22:45.345 - in such things, including their religiosity? So, I'm now scrolling mentally through our posts. I've 01:22:45.345 --> 01:22:51.934 - been thinking about posts, you know, we have posts on the, 01:22:53.026 --> 01:23:04.777 - growing partisan gap in trust in science, which does have some data on trust in science as a function 01:23:04.777 --> 01:23:16.298 - of religiosity. So for example, Christian nationalists have very low trust in science. So there are 01:23:16.298 --> 01:23:22.174 - data on our site in that. I don't have any slides. 01:23:24.610 --> 01:23:32.202 - probably one or two other folks that also have relevant data for that. Can I just say a word about that? 01:23:32.202 --> 01:23:39.432 - There's a famous Pew research study about religious denominations and, well, it's not so much about 01:23:39.432 --> 01:23:47.097 - new medicine, but belief in evolution and interest in environmental, support for environmental movements. 01:23:47.097 --> 01:23:54.110 - And as you might imagine, there's an enormous spectrum. There's a correlation between those two. 01:23:54.242 --> 01:24:00.734 - but an enormous spectrum, depending on what particular religious group you might belong to. And in many 01:24:00.734 --> 01:24:07.038 - cases, religious communities have been very strong supporters of scientific and medical advances and 01:24:07.038 --> 01:24:13.592 - other cases. The other direction is quite interesting and complicated. I want to take a moment and thank 01:24:13.592 --> 01:24:17.150 - Steve and Tim again. They're going to be hanging around.