1
00:00:00.000 --> 00:00:12.440
[upbeat music] Hi, everyone, and welcome to Climate Proofers, the show all about those on the front lines of adaptation finance, tech, and policy.
2
00:00:13.080 --> 00:00:18.800
My name's Louis Woodall, editor of Climate Proof, which you can read at www.climateproof.news.
3
00:00:19.320 --> 00:00:26.980
Today, we're getting into an area of climate adaptation I've been meaning to cover for a while, namely the nexus of climate change and health.
4
00:00:27.500 --> 00:00:38.380
Back in July 1995, a heat wave in Chicago killed more than 700 people in less than a week. 30 years on, the city has a new tool designed to stop that from ever happening again.
5
00:00:38.860 --> 00:00:48.460
It's a heat vulnerability index that pulls in hospital and public health records to show which communities are most at risk when temperatures spike. My guest today helped build it.
6
00:00:49.020 --> 00:01:00.580
Peter Graffe is a pulmonary and critical care postdoctoral fellow at the University of Chicago, and he's about to join Oregon Health & Science University as a professor of informatics and clinical epidemiology.
7
00:01:01.060 --> 00:01:09.260
During his doctorate at Northwestern, he was the electronic health records specialist on the Diffusing Disasters Working Group, the team behind Chicago's index.
8
00:01:09.800 --> 00:01:22.060
A little over a year ago, he co-founded Stria Health, a startup that links environmental and health data so employers, health systems, and governments can see who is most exposed to heat and air pollution and what to do about it.
9
00:01:22.560 --> 00:01:31.860
We talk about how healthcare providers can leverage patient data in a safe, secure way to generate insights on climate health risks and inform potentially life-saving interventions.
10
00:01:32.380 --> 00:01:40.680
We also cover how Stria's HeatScope tool works and the possible benefits of climate health data being made more readily accessible, as well as the potential pitfalls.
11
00:01:41.200 --> 00:01:49.780
Before we get started, my weekly reminder to all you climate proofers out there to please give us a star rating on Apple Podcasts or Spotify and drop a review too if you have the time.
12
00:01:50.440 --> 00:01:59.540
Also, remember, you can get lots more climate adaptation finance, tech, and policy content by becoming a Climate Proof member at www.climateproof.news/upgrade.
13
00:02:00.040 --> 00:02:11.020
Not only do you get more, it really helps keep the lights on here at Climate Proof HQ too. Right, in a moment, my conversation with Peter, but first, a rundown of last week's top adaptation and resilience stories.
14
00:02:11.180 --> 00:02:19.420
[swoosh] In finance, adaptation allocations in blended finance deals more than doubled in 2025, according to Convergence's latest State of Blended Finance report.
15
00:02:19.980 --> 00:02:31.780
The $5.6 billion total for last year represents the theme's strongest annual increase in a decade. While mitigation still drew the most blended finance at $9.9 billion, this was down 26% year on year.
16
00:02:32.300 --> 00:02:42.940
The wider blended finance market, covering deals involving public and private contributions, held steady at $25.8 billion, while the number of transactions leapt to 181 from 160.
17
00:02:43.100 --> 00:02:55.080
[swoosh] In policy, the US Supreme Court appeared divided last Monday over whether local governments can sue oil companies under state law for climate damages in a case that could decide the fate of dozens of lawsuits playing out across the US.
18
00:02:55.700 --> 00:03:05.300
Boulder, Colorado, sued ExxonMobil and Suncor Energy in 2018 to make them pay for the costs of heat waves, wildfires, droughts, and floods exacerbated by climate change.
19
00:03:05.860 --> 00:03:11.160
The suit alleges the companies misled the public about climate risks while producing and promoting fossil fuels.
20
00:03:11.700 --> 00:03:19.840
The case before the Supreme Court is not to determine whether the oil majors are liable, rather it is to determine whether such cases should be heard in state or federal courts.
21
00:03:20.380 --> 00:03:31.220
Ahead of the hearing, Justice Samuel Alito recused himself from the case. His absence makes a 4-4 split a possibility. A tie would leave in place a Colorado Supreme Court ruling that allowed Boulder's case to proceed.
22
00:03:31.600 --> 00:03:36.800
However, it would not set a national precedent. A ruling is expected by late June 2027.
23
00:03:36.920 --> 00:03:46.360
[swoosh] And in tech, The Weather Company will open access to its forecasting data and meteorological briefs to more than 11,000 employers through a new partnership with the Health Action Alliance.
24
00:03:46.900 --> 00:03:54.760
The tie-up will equip businesses and workers with extreme weather risk data in advance of a winter season likely to be made more volatile by a strong El Niño.
25
00:03:55.300 --> 00:04:06.340
The joint initiative sees The Weather Company become the official weather intelligence partner of the alliance's Extreme Weather and Work Initiative, whose founding members include Google, Disney, CVS Health, and Bristol Myers Squibb.
26
00:04:06.840 --> 00:04:16.519
Members will receive custom risk data and workforce resilience workshops together with research designed to encourage businesses to incorporate weather intelligence into their business continuity planning.
27
00:04:16.620 --> 00:04:30.520
[swoosh] Right, that's it for this week's roundup. Now onto my conversation with Peter. [upbeat music] Hi, Peter. Where are you calling from today, and what do you do? I am calling today from Portland, Oregon.
28
00:04:30.720 --> 00:04:37.060
I just moved here recently. I actually spent the last few many years in Chicago, Illinois, and in the Midwest, where I was born and raised.
29
00:04:37.840 --> 00:04:51.340
I am currently a pulmonary and critical care postdoctoral fellow at the University of Chicago, however, going to be transitioning to a faculty role as a professor of informatics and clinical epidemiology at OHSU here in Portland.
30
00:04:51.800 --> 00:05:01.720
I've spent the last 10, 15 years working in academic medicine. You perhaps have the longest credentials I've had on this podcast [chuckles] so far, so congratulations on that.
31
00:05:02.180 --> 00:05:19.940
But you're also here to talk about the climate health data nexus, which is an area I've mean- been meaning to examine in more detail at Climate Proof and Climate Proofers because we know how significant the health and medical effects of climate change can be to vulnerable populations.
32
00:05:20.480 --> 00:05:30.420
So you're also involved in this startup called Stria Health- Mm-hmm... which we'll get to in a minute, but perhaps we can first start off by unpacking some of those credentials you have.
33
00:05:30.820 --> 00:05:41.580
What has your career looked like to date, and what led you to focus on climate health impacts in particular? Yeah. I've had kind of, like, a wide and varied range of experience in working in, in research.
34
00:05:42.060 --> 00:05:46.460
Back kind of at the beginning, which was, like, 10, 15 years ago, I was...
35
00:05:46.520 --> 00:05:59.800
I got trained in epidemiology and biostatistics, and that was at the University of Wisconsin-Madison, where I took a lot of different courses and studied a lot of different things, the primary one being in ele- in electronic health record data.
36
00:06:00.020 --> 00:06:11.380
Could you apply AI tools, like convolutional neural networks, to CT scans to be able to pull out imaging features and then look to see how well those corresponded to, like, future events?
37
00:06:11.440 --> 00:06:23.060
Like, if I measure your a- abdominal aortic calcification using, like, an AI tool, how well does that correspond to, like, you having a cardiovascular event in five years or 10 years, right? That sort of thing.
38
00:06:23.500 --> 00:06:30.100
And so it was a lot of, like, longitudinal health records analysis that I was doing, and it was great. I learned a ton.
39
00:06:30.400 --> 00:06:49.020
And then that, I took that and kind of rolled it over, and then in my doctorate at Northwestern- I got pulled into this project called Defusing Disasters, where they were really interested in building some tools for the city of Chicago that were all based around the idea of, like, preventing or, like, you know, assessing heat vulnerability.
40
00:06:49.100 --> 00:06:54.200
Because I don't know if you knew this, but Chicago had the deadliest heat wave back in '95. I think a lot of people do.
41
00:06:54.300 --> 00:07:03.820
But, um, Chicago had about 1,000 people die in just a few days, and so their 30-year kind of commemoration was, like, they wanted to make sure that that sort of thing wasn't gonna happen again.
42
00:07:04.360 --> 00:07:16.840
To do so, they enlisted the help of Defusing Disasters, which is this group of-- It's a consortia of, of researchers and, like, you know, like, public officials and a bunch of other people who are trying to figure out ways that we could prevent this.
43
00:07:17.240 --> 00:07:28.800
One of the ideas they came up with was, "Hey, let's look at, you know, a wide range of health data, right? And, and look at those outcomes over the last however, like, 30 years, and see how well we've been doing."
44
00:07:29.040 --> 00:07:47.720
And also use that same data to, like, forecast in the future and, and make, like I said, tools for the future just to identify, like, what are the kind of pain points, things that we could be doing better, and bringing the city kind of up to the, you know, the, the more modern era and, and, like, kind of maybe even forward-thinking era of, um, climate resilience.
45
00:07:48.100 --> 00:07:52.380
And so they pulled me into that because of my extensive experience with electronic health record data.
46
00:07:52.960 --> 00:08:02.460
So my doctorate ended up being really focused around environmental epidemiology and clinical informatics, like, this really weird combination of things that, like, you don't typically see together.
47
00:08:02.840 --> 00:08:21.560
And I just kind of was the person to, like, glue them both together and, and ultimately help them develop a couple different iterations of their heat vulnerability index, which they rolled out just, you know, last year, and ended up being really successful, presented by the mayor, and had a lot of community involvement, led to some policy changes in the city over the last year.
48
00:08:22.060 --> 00:08:26.740
And then I took that, and then that was, like, kind of the impetus for starting Strive.
49
00:08:26.860 --> 00:08:37.120
It was, like, a lot of the things that I was seeing there, which was, like, I was bringing a lot of this informatics and kind of the more new age methods to this, like, climate health space.
50
00:08:37.420 --> 00:08:42.180
It hadn't really gotten that much attention. It hadn't really had that sort of exposure before.
51
00:08:42.760 --> 00:09:23.500
So me doing that was-- it was kind of like the new blood, new angle that people hadn't seen, and, and in the end, I kinda came to the conclusion that there was a real, like, business case to be made for people in the overall community, both in the, you know, public health space, but also, like, kind of in the industry space and maybe even, you know, the, the government space certainly, to, to make a case for the fact that, like, when we are protecting people and being preventative in the way that we, like, provision our health system to adapt to the healthcare or, or sorry, to the environmental, you know, like, situation context, we are going to save lives, and we're gonna save a bunch of money, and we're going to help people across the world.
52
00:09:24.100 --> 00:09:24.780
Wonderful.
53
00:09:24.880 --> 00:09:50.660
And yes, this intersection of informatics and the medical profession is of interest to me because I'm curious to know what the barriers are, the bottlenecks are to translating medical and health data, which is, for good reason, highly bureaucratized and, and protected and shrouded in legal requirements and legalese that can cross even the smartest lawyer's eyes.
54
00:09:51.160 --> 00:09:59.360
And this push that we're seeing accelerated now because of AI to turn all forms of raw data into actionable insights, right?
55
00:09:59.880 --> 00:10:11.660
And it seems to me that these two worlds, data informatics, AI, and medical health seem incompatible [chuckles] to a degree because of they're coming from these two different starting points.
56
00:10:11.780 --> 00:10:19.300
One, very focused on patient confidentiality and, and the complexity of, of health data, and then the other one is just like, let's just put AI to everything and see what happens.
57
00:10:19.340 --> 00:10:32.200
So can you talk a little bit about your experiences with health data and, and what do you understand to be the challenges maybe making it AI-ready or insight-ready even? Yeah. Well, your feeling is not...
58
00:10:32.460 --> 00:10:34.240
I mean, it is, it's very common, right?
59
00:10:34.280 --> 00:10:46.360
Like, and, and I think that many people have, have believed that that's the case, where it is essentially almost impossible to harness healthcare data or any sort of health data kind of in a large scale, right?
60
00:10:46.420 --> 00:10:51.819
In a way that makes it so that you can create these, like, large population insights if, if you really want to.
61
00:10:52.360 --> 00:11:07.860
I think one of the things that I'm trying to help, like, change, you know, like what I'm, what I'm trying to do is, like, kind of initiate this new wave of understanding that there are ways that we can do it, so there are methods, and which I'll talk about in a second, and that some of it is just simply, you know, like, putting in the work to do it, right?
62
00:11:07.900 --> 00:11:15.200
Like, not-- You don't have to be, like, fancy, right? Some of it is you just have to ask, and you have to be persistent, right? And you have to kinda have the gumption to, like, wanna go and do it.
63
00:11:15.720 --> 00:11:22.840
But one of the issues that you brought up, right, is that, like, a lot of data, like health data, is very siloed, right?
64
00:11:22.940 --> 00:11:29.760
You're gonna keep it at in-individual institutions, very hard to do any sort of like broader connections to other places in a lot of ways.
65
00:11:30.260 --> 00:11:39.080
And especially when it comes to, you know, like, linking to external data like, like environmental data, right? Like heat or, like, you know, flooding or whatever, right?
66
00:11:39.120 --> 00:11:42.000
Like, it's really hard to bring in external features.
67
00:11:42.720 --> 00:11:54.720
But there have been a variety of different methods that have come about that can preserve the protected health information aspect, like, so the PHI side, while still doing that sort of, like, linkage.
68
00:11:55.080 --> 00:11:58.420
There are programs that have been developed like DeGauss, which uses, like...
69
00:11:58.460 --> 00:12:10.520
It's in-- There's informatics techniques that are like token, tokenization and hashing techniques that are preserving the patient identity while linking to a variety of different things, including other patients.
70
00:12:11.040 --> 00:12:17.000
Let's say I want to link, you know, there are hospitals A, B, and C, right? And then they each has, you know, thousands and thousands of patients.
71
00:12:17.520 --> 00:12:25.960
You basically are creating a generic token for each individual person that doesn't necessarily reflect, you know, like, their, like, actual MRN.
72
00:12:26.360 --> 00:12:36.680
But what you're able to do is you're able to kind of pool all of those tokens together, and then you're able to collect them in kind of like a, you know, a central repository, and then you analyze them all in that one place.
73
00:12:36.720 --> 00:12:48.020
That's, like, one method. And another method, which is called federated or feder-federated learning or, or a federated system, which is where you can standardize all of that information, right?
74
00:12:48.100 --> 00:12:59.800
So, like, the same way that we have ICD codes, right, that are, like, kind of the standard classification for all diagnoses, well, you can do that for any given, like, lab, right? You can do that for any given procedure.
75
00:13:00.000 --> 00:13:07.260
You create this kind of standard language that exists across all institutions that you wanna, like, have participating in, in this thing.
76
00:13:07.540 --> 00:13:15.448
And then you're able to have each site run its-- the analysis, like, within its own little enclave. And then you pool all of those things.
77
00:13:15.468 --> 00:13:21.768
There are, like, pooling methods that you use statistically to, like, pool all of that information together to create, like, population-level insight.
78
00:13:22.268 --> 00:13:29.148
So those are two methods that typically are-- have been used, like, for other things, not used in, like, environmental epidemiology, right?
79
00:13:29.228 --> 00:13:47.648
They're not things that have historically been really, like, thought of too much, but they allow us to be able to, like, get that kind of, like, large, you know, like, multi-system, millions and millions of patients and millions of, you know, data points for-- within each patient to be able to get the, like, really deep insight on people.
80
00:13:48.188 --> 00:14:00.028
So you're saying through certain data methodologies, you can get around this perceived or actual challenge of accessing health data and, and making it useful? Yeah. You can use them.
81
00:14:00.168 --> 00:14:06.948
They're peer-reviewed, and they're used in a lot of different formats. But they haven't really been used in environmental epi-epidemiology. They're used for other things.
82
00:14:07.168 --> 00:14:23.688
And there is a real case to be made that this is gonna be one of the more important ways and reasons to be pooling and, like, you know, linking hospital data across, you know, a bunch of different institutions because of the very pressing need that we're seeing, like, all the time.
83
00:14:24.128 --> 00:14:29.388
And we're just, like, individual hospitals. You get a small snapshot of what's occurring at any given place.
84
00:14:29.428 --> 00:14:37.548
Like, I'm at University of Chicago, and I can see that, you know, like, this is what it looks like for the few thousand patients or however many that I'm going to be looking at. But that doesn't...
85
00:14:37.848 --> 00:14:48.288
You know, this is one hospital. There are, like, 12 different health systems across Chicago. So being able to have them all communicate in kind of like a standard language, then all of a sudden you get people on board.
86
00:14:48.548 --> 00:14:52.548
And so part of it is, like, creating the framework that they-- where they can all com-communicate.
87
00:14:52.968 --> 00:15:03.008
But then the other one is actually, like, doing the work of, like, building the relationships with the people because that is more-- that is, like, honestly, the larger barrier, is the political side of things.
88
00:15:03.068 --> 00:15:15.928
And not, like, government political, though that is a part of it, but, like, the politics of, like, knowing the people who hold the keys to the things that you're looking for and getting them on board and, like s- like, basically convincing them that this is a worthwhile cause, right?
89
00:15:15.968 --> 00:15:26.148
And just, like, being able to kinda get everybody on the same page. Now, I want to get into the intersection of, of climate and health and- Mm-hmm... environmental epidemiology, as you said.
90
00:15:26.668 --> 00:15:37.428
I was wondering, first of all, if you'd give a, a rundown of climate health impacts that you think are most pertinent, most pressing for the populations that you're looking into. Yeah.
91
00:15:38.128 --> 00:15:44.608
So there are a few different things, and I'll talk about it from both sides of the work that I do, right? From the academic side and then also the Stria side.
92
00:15:45.268 --> 00:15:58.588
So on the academic side, and, and this is really focused on, like, the patient population that I'm, I'm spending the most time, like, researching and looking at, which is people, those of us who are admitted to the ICU, right?
93
00:15:58.648 --> 00:16:08.628
Critical care. So these are typically, like, the sickest patients, right? Um, and my belief was that these are folks who are probably gonna be the most vulnerable to the effects of climate, right?
94
00:16:08.708 --> 00:16:10.828
Certain climate stressors. For example, air pollution.
95
00:16:11.128 --> 00:16:36.288
So one of the hypotheses that I had going into this postdoctoral fellowship was, I think that for a lot of these people in intensive care who get put on mechanical ventilation, right, who are constantly in, like, going in and out of organ failure, that the effects of, like, where they lived prior to ever being admitted to the ICU was gonna have a really strong and important effect on, you know, their eventual outcomes.
96
00:16:36.348 --> 00:16:40.248
Like, what would actually happen in the ICU, and would we see those, like, major differences? Mm.
97
00:16:40.288 --> 00:16:50.428
And so I've spent, you know, the last year plus really looking at the effects of air pollution in, in particular, both acute, like wildfire smoke, as well as chronic, you know, like long-term effects.
98
00:16:50.788 --> 00:16:59.188
Pr- fine particulate matter, which is PM two point five, NO2, like nitrogen dioxide, which is, you know, traffic-related, warehousing pollutant, ozone, right?
99
00:16:59.428 --> 00:17:13.068
And so you have, like, these major EPA criteria pollutants, and there's, you know, a, a really big shift that's occurred over the last few decades in our air quality standards. Was really bad a few decades ago. Yeah.
100
00:17:13.268 --> 00:17:22.408
Has gotten slightly better, but then in the US has started to get worse in some ways. And so what we're seeing is, in what I'm-- what I found in my research, is really big effects.
101
00:17:22.568 --> 00:17:32.008
People who live in more polluted areas typically are having much worse pulmonary and cardiopulmonary outcomes compared to people who live in just kind of the cleaner areas, right?
102
00:17:32.307 --> 00:17:42.208
Places where the air is a little bit better. And I think it's a huge problem, right? Like, people's lives are being cut short, right? Because they would potentially be able to survive, right?
103
00:17:42.268 --> 00:17:53.048
They would potentially be able to, to get over whatever their affliction is, but they're not able to because of the fact that they live in a place where they're breathing in, like, warehouse gases, right, constantly.
104
00:17:53.468 --> 00:18:02.148
And it's affecting their lungs and their cardiopulmonary reserve. And so that is, like, kind of like a l-long-term chronic view, like air pollution, which is a great example.
105
00:18:02.568 --> 00:18:11.648
And then another one is, like, heat, which is, I think, what people are really thinking about a lot now and is really salient. And it's one of the big things that Stria started with.
106
00:18:11.728 --> 00:18:21.588
Like, that's one of the big things that we took a look at because we had a bunch of government partners who are coming to us saying, like, "We really wanna be able to quantify the effects of heat because we don't really know, you know, what it looks like."
107
00:18:22.008 --> 00:18:28.828
We had a bunch of industry people saying the same thing. And so one of the things that we did was we really looked at the acute effects of heat, right?
108
00:18:28.908 --> 00:18:44.908
When we get those heat waves, right, those really intense heat event days, what are we seeing as far as not just, like, the, the, the usual suspects, like in measuring those cardiovascular disease, I think the people kind of have a pretty good idea of, you know, that you see more ED visits, right?
109
00:18:45.328 --> 00:18:53.928
Or, like, renal disease or mental health visits, right? Things like that. But that wasn't good enough, in my opinion, because I think that you could go deeper.
110
00:18:54.208 --> 00:19:00.028
And so what we tried to do was more of, like, an inductive approach, where we kind of cast the net as over everything.
111
00:19:00.608 --> 00:19:10.548
We look to see what each individual context brings in, more than just cardiovascular disease, and maybe it's not even cardiovascular disease. North Carolina is gonna be different than California.
112
00:19:10.568 --> 00:19:16.848
It's gonna be different than Chicago and, and, you know, like Austin. These are all different contexts, and they have different vulnerabilities.
113
00:19:17.208 --> 00:19:31.320
And so being able to treat each as such is going to be real- a lot more powerful, and it's gonna- You know, empower the, the actual stakeholders to make more informed decisions that are, like, data-driven and actually, like, tailored to what they're working with.
114
00:19:31.460 --> 00:19:33.920
We're not, like, coming in saying, like, "We're looking at cardiovascular disease."
115
00:19:34.000 --> 00:19:44.820
We're looking at everything, and then when we wanna do a deeper dive, then we can look at individual people, right, and individual context and say, "Well, it's not even for this individual," right?
116
00:19:44.980 --> 00:20:04.960
We can say that they have an increased chance of whatever, 10% or 50% or whatever over whatever period of time, and the largest factors that are contributing to this person's risk are the tree canopy coverage outside, right, the fact that they're on ACE inhibitors, right, and the fact that they had a stent put in, you know, like three years ago, right?
117
00:20:05.000 --> 00:20:13.600
When you, like, put all of these external, right, and clinical factors together, you're creating just, like, a very strong and holistic picture of someone's health.
118
00:20:14.060 --> 00:20:30.100
So you're saying you can identify predictors of clinical conditions or high vulnerability populations by combining this data in innovative ways. Yeah. Precision ecologic medicine is kind of the best way to describe it.
119
00:20:30.120 --> 00:20:48.440
And overall, like, the, the, the grand scheme of everything is, I would say, climate adaptive health systems is, like, the main goal of Stria, is how do we focus on adapting our various health systems, which include healthcare as well as public health and everything in between, right?
120
00:20:48.780 --> 00:20:58.820
How do we prepare those systems so that they are adaptive to the climate that is occurring on a daily, day-to-day basis, but also progressively over the, you know, coming years.
121
00:20:59.420 --> 00:21:07.080
Yes, 'cause I wanted to get into how this is a moving target. Mm-hmm. Adaptation is a moving target for every sector of the economy and society.
122
00:21:07.340 --> 00:21:12.820
But for health particularly, I think it's fascinating because research is always ongoing. The climate is always changing.
123
00:21:12.920 --> 00:21:30.180
We might be unearthing new connections between our environment and our, and our health over time, and I'm wondering to what extent you are doing research or looking into how changing temperatures in the next 10, 20, 30 years could exacerbate some of those stressors you've identified.
124
00:21:30.480 --> 00:21:38.100
You mentioned air pollution, for example, and some folks would be like, "Oh, air pollution, that's just cars and density of emitting products."
125
00:21:38.140 --> 00:21:47.640
But actually, we know that higher temperatures in, in ci- in cities especially can mean that air pollution is trapped close to the ground for longer periods than there would be otherwise.
126
00:21:47.960 --> 00:21:57.680
Similarly, wildfires, right, we know climate change is creating the conditions for more regular and destructive wildfires that will then throw up more particulates that then get into the lungs of people.
127
00:21:58.360 --> 00:22:05.560
My sense is that, you know, hospitals and other medical facilities are long-lived institutions, and they have to plan for the long term as well.
128
00:22:05.600 --> 00:22:17.960
And to what extent are you making sure that you're not just looking at how climate can affect health outcomes today, but how in 10 years' time, maybe when the next hospital budget cycle comes up, they're being factored into then?
129
00:22:18.140 --> 00:22:27.660
There are a lot of really brilliant and people who are much smarter than myself when it comes to the prediction of climate events going forward, right?
130
00:22:27.700 --> 00:22:48.840
Like, there are a lot of people doing really great work in that space, a lot of researchers, academic and otherwise, who are putting together, you know, every single day, really highly detailed predictions of what, like, the world's gonna look like today, tomorrow, next year, 10 years, 15, 20, and they're constantly updating those things because that's their, you know, it's their line of research, and they're, you know, they're good at it.
131
00:22:49.360 --> 00:22:59.420
And so they generally make those public, right? Like, those are, like, publicly available data sets, and those are things that we have incorporated into our models, right, the stuff that we use at Stria.
132
00:22:59.900 --> 00:23:21.700
So having access to that updated forecast data and then bringing in real-time health data, all you have to do when you have this, you know, your health data, is you have your response functions that you've created all the way up until, you know, today or yesterday or whenever you have your health data, and then you are able to feed those into the upcoming predictions that you have, right?
133
00:23:21.780 --> 00:23:31.100
So it's like if, you know, NOAA says that in five years, this is what air pollution looks like, you have your response functions, and you can model what it would look like based on the current trends, right?
134
00:23:31.460 --> 00:23:35.560
And then you can change those response functions, you know, based on updated health data.
135
00:23:35.640 --> 00:23:47.100
So if you get, like, real-time data, then you're recursively going to be updating your models all the time, every single day, right, improving the function to reflect the most, like, you know, the most real time trends in health.
136
00:23:47.380 --> 00:23:53.000
And then you're able to have a, like, a dynamic risk prediction tool, which is what we've created.
137
00:23:53.300 --> 00:24:01.500
It's our Heat Scope tool for Stria, which is a dynamic risk prediction tool that recursively learns as more health data is fed into it.
138
00:24:01.520 --> 00:24:15.740
Like, more and more recent health data will improve the function, and then you're able to basically layer any sort of, like, forecast or, like, five-year climate prediction or whatever you want on top of it to understand, like, this is what it's gonna look like over whatever period of time.
139
00:24:16.140 --> 00:24:20.060
Well, let's talk now a little bit more about Stria Health, the startup you co-founded.
140
00:24:20.400 --> 00:24:33.500
Can you talk a little bit about the mission of the company and what does it mean to you to provide, as you say yourself on the website of Stria Health, every community business and health system in the world to have access to climate health intelligence?
141
00:24:34.040 --> 00:24:40.160
Um, well, I started this a little over a year ago. I started with Josh Soren. He's my co-founder, a brilliant guy.
142
00:24:40.420 --> 00:24:48.560
He is kind of like our business intelligence side of things, while I'm, like, kind of our technical side, and it's been a great pairing. We work super well together.
143
00:24:48.980 --> 00:24:59.200
We met through a mutual friend, just because she knew that we were both interested in this same idea. But he was coming at it from the more of entrepreneurship space, and I came from the academic medicine space, right?
144
00:24:59.240 --> 00:25:01.120
And we came together and found that it worked great.
145
00:25:01.600 --> 00:25:16.436
And the idea that we both had coming into it- Was this idea that Stria could be that bridge between the massive amount of environmental and health data, right, that exists, but are in opposite spheres, right?
146
00:25:16.576 --> 00:25:33.856
And if we were able to be the people that could kind of be that link between them, right, and be the catalyst for generating really important insights and new cutting-edge sorts of methods to be able to broaden the field, then we would have the real ability to make change in people's lives.
147
00:25:33.916 --> 00:25:40.616
Because what I found in my academic research was, like, you can have some translational ability, but not a ton, right?
148
00:25:40.656 --> 00:25:47.956
Like, you don't-- sometimes we get stuck in our, you know, like, s- academic silo, and we-- our findings don't really make it out. And that was frustrating to me.
149
00:25:48.056 --> 00:25:53.136
I wanted to be able to have, like, an impact and be able to, like, make, you know, real change and do real things in the world.
150
00:25:53.356 --> 00:26:02.736
Not that other people in academic medicine don't, but I just really wanted to, like, you know, move quickly and, and be able to do it. And Josh felt the same way, just not, you know, from coming from the academic side.
151
00:26:03.236 --> 00:26:26.936
So the, the focus of creating this, like, climate super intelligence, you know, like hub, right, this nexus that everyone could feed into is, like, if we create these resources and tools like I was talking about, where if you bring in these new informatics tools that people hadn't thought of, right, publish all your code for making a variety of different tools, right, and a variety of different, you know, like, different types of studies and designs, right- Mm-hmm...
152
00:26:27.016 --> 00:26:28.636
that people haven't maybe thought of.
153
00:26:28.896 --> 00:26:38.636
Like, you're shining a light in this new area in environmental epidemiology and informatics that people haven't really been looking at, especially, like, in the, in the business sense.
154
00:26:39.156 --> 00:26:58.976
And so when you take those, right, and then you actually apply them in a setting, like, for, like, a self-insured employer, right, who's looking to protect their, their employees, you can basically-- you can, you can advise them on how to provision the sort of healthcare that they're giving their, their employees so that they're better protecting the people who are gonna be most vulnerable.
155
00:26:59.116 --> 00:27:08.536
You can identify, right, segments of their, you know, workforce that are really going to be struggling given, you know, like, a heat wave, right?
156
00:27:08.676 --> 00:27:17.276
Given, you know, like, a huge humidity spike as well as, like, a, you know, wildfire, you know, part- fine particular matter, you know, giant cloud that settles over your city.
157
00:27:18.076 --> 00:27:31.096
If you have an idea of, you know, which people are going to be the most at risk, these people are going to be able to save their workforce from huge and potentially, like, really long and catastrophic health sequences that are gonna come down the line.
158
00:27:31.536 --> 00:27:35.216
And there's a benefit, you know, like, that's the nice thing about this, is that it's a benefit for everyone, right?
159
00:27:35.296 --> 00:27:52.656
It's like with a small provision of preventative resources and, and giving them advice on, like, how to do all these different things, you are able to effectively keep people off of what would be a potentially terrible route where they would show up on the other side of my research, right, in, in critical care medicine.
160
00:27:52.996 --> 00:28:06.716
That answers your question? Yes. I wanted to get into the use cases and potential applications of more fine-grained understanding of populations' vulnerability to climate-related health impacts. Yeah.
161
00:28:06.736 --> 00:28:22.316
Because on the one hand, yes, I can see a future whereby employers can better plan resilience actions to keep their workforces safe, and indeed there is a, a Health Action Alliance that is set up by employers with, with this in mind.
162
00:28:22.836 --> 00:28:26.656
I can also see the kind of negative side, the adverse selection that might happen- Mm-hmm...
163
00:28:26.676 --> 00:28:37.736
new forms of discrimination arising, insurers finding ways to carve people out of certain policies because, "Oh, actually, you're more at risk of a heat-related illness, and therefore, we're gonna charge you more for your healthcare."
164
00:28:38.196 --> 00:28:42.716
I could see in a, in a vicious free market system with no regulation, these things potentially happening.
165
00:28:42.756 --> 00:28:51.256
Of course, we don't live in that, that world, but there are obviously understandable concerns with data like this being used to create more new forms of health discrimination.
166
00:28:51.336 --> 00:28:56.356
So I was wondering if you'd put any thought into that or the research shows, et cetera. Definitely have put a lot of thought into that.
167
00:28:56.556 --> 00:29:07.296
And that's why part of Stria is the actual-- you know, we develop the platforms that people can use in order to increase their knowledge, right, and, and have access to information that they wouldn't otherwise.
168
00:29:07.496 --> 00:29:10.456
Access about their own health and how it relates to the environment.
169
00:29:10.976 --> 00:29:20.556
This includes, like I've said, kind of like the employer side, right, or the health system side, where you gain kind of those population level insights, maybe all the way down to, like, the individual person.
170
00:29:21.056 --> 00:29:35.576
But it also includes, like, the person, like the user side, the patient side, right, the employee side, where they are able to be empowered to using our platform to have access to a variety of different resources, right, that get pushed from their employer, right?
171
00:29:35.916 --> 00:29:43.936
So that they're able to have a lot more, you know, like, just an increased access to things that would potentially help them. Cooling resources, right?
172
00:29:44.016 --> 00:29:51.116
Being able to, you know, be o- in direct contact with people like healthcare providers, right, who are gonna be able to adjust their medication dosage.
173
00:29:51.416 --> 00:29:54.936
All sorts of, you know, aspects that would potentially help them down the line.
174
00:29:55.416 --> 00:30:10.536
And then if we're talking about the potential, you know, the use of this to, to identify vulnerable folks and, and, you know, for the purpose of making their life worse, that is definitely an issue, and it's something that we've thought about a lot and, like, how to make sure that that does not occur.
175
00:30:10.776 --> 00:30:13.816
And there's one story back when I lived in Madison.
176
00:30:14.256 --> 00:30:23.856
There was-- so they-- not a huge homeless population, but a sizable one that would, uh, constantly attend the University of Wisconsin Madison Emergency Department, right?
177
00:30:24.136 --> 00:30:34.016
And these were folks who, you know, they would go into the ED, they would have, you know, some sort of issue obviously, they would need to get treated, and then they would get kinda sent back out on the street, right?
178
00:30:34.076 --> 00:30:41.256
They have nowhere else to go. And so then they would kinda become frequent flyers. Like, they would go back in a few days later because the infection that they had wasn't cleared, right?
179
00:30:41.316 --> 00:30:50.876
Like, they didn't have, like, that long-term follow-up to be able to make sure that they were recovering effectively, right? And so University of Wisconsin, the hospital, they went down the street, right?
180
00:30:50.916 --> 00:30:54.636
There was, like, a Best Western down there, and they just bought it, right? Thirty million dollars, right?
181
00:30:54.696 --> 00:31:05.176
They just, like, bought the, the hotel with the idea being that they were gonna take that whole top floor of the hotel, the Best Western, and they were going to use it for the unhoused folks, right?
182
00:31:05.236 --> 00:31:12.396
And they were just gonna staff it with a few nurses, and those people could stay there as long as they needed to, essentially, to make sure that they were getting, like, fully healed, right?
183
00:31:12.496 --> 00:31:21.392
And then they were gonna go back, like, then, you know, off into the world again, right? Like find-- you know, trying to find their way out again. But it made sure that they were saving money.
184
00:31:21.432 --> 00:31:26.872
What they realized was that they were losing money, right, by having to treat these people who were, you know, underinsured, right?
185
00:31:26.932 --> 00:31:33.252
People who constantly were coming in, right, with a wide variety of things and, and, you know, re- needing a bunch of resources.
186
00:31:33.792 --> 00:31:42.952
And they were better off buying a thirty million dollar hotel down the street, right, and making sure that those people were effectively cared for. I believe that that's something that's similar here, right?
187
00:31:42.992 --> 00:31:51.832
Like, I think that... And it's not gonna be a thirty million dollar hotel that people are gonna need to buy, right? But it might be making sure that your employees have energy subsidies, right?
188
00:31:51.992 --> 00:31:54.612
So that they are able to run their air conditioners during heat events.
189
00:31:54.652 --> 00:32:09.352
And if you are able to make sure that the 65-year-old employee, right, who might not do super great during this upcoming heat wave and air pollution event, and you can make sure that they have an air purifier at home, right, which, like, runs you a hundred dollars, right?
190
00:32:09.412 --> 00:32:15.552
And that you're giving them, like, two hundred dollars worth of energy subsidies this month so that they can just run the AC and they're not gonna have to worry about it.
191
00:32:16.092 --> 00:32:25.672
I think that you're gonna find that there's a business case to be made that's gonna be better off than, you know, like, having to train a new person, right? Or charge that person extra money, right?
192
00:32:25.732 --> 00:32:31.392
All sorts of, you know, whatever terrible way that could be used. Our goal is to make the business case for the opposite.
193
00:32:31.432 --> 00:32:38.592
It's like, if you take care of these people, you're gonna save a ton of money and you're gonna be saving people's lives. Yes.
194
00:32:38.772 --> 00:32:48.252
Preventative interventions, [laughs] I suppose, uh, is the way to, to lower costs and also potentially lower liabilities if you're a insurer or indeed a hospital.
195
00:32:48.652 --> 00:33:01.332
Also, you know, example you said about energy subsidies also suggests that this data will be useful at the municipal and state level legislative arenas, right? So that policies can be crafted- Hundred percent...
196
00:33:01.472 --> 00:33:11.972
that are also preventative, right? And here in New York City, there are multiple schemes for subsidized or free air conditioning for vulnerable and, and low-income populations. We have cooling centers when it's hot.
197
00:33:12.012 --> 00:33:17.212
We have resiliency centers when floods are coming through. So there are lots of pre-positioning of resources taking place.
198
00:33:17.612 --> 00:33:22.672
So I'd absolutely see that as a valuable way to head off some of these health impacts before they occur.
199
00:33:23.052 --> 00:33:34.132
But I am interested actually in what the science and the data has shown you about the technologies and practices that can have a positive effect on health outcomes during the event itself.
200
00:33:34.372 --> 00:33:44.472
You know, there are a number of companies that I've been looking at who are developing wearable cooling technologies, for example, or heat alert bracelets and watches that tell you, "Get out, it's too hot.
201
00:33:44.512 --> 00:33:50.612
You need to take a break." And certain businesses and even local governments are implementing these technologies and practices.
202
00:33:51.192 --> 00:34:00.152
What has come across your radar in terms of technologies and practices that are impactful, not just under the heat example, although I know that's a big one, but other sorts of climate risks as well?
203
00:34:00.612 --> 00:34:10.952
I'll be honest, the most impactful thing that I've seen, and I don't think this is necessarily new, but I think it's overlooked, truly is like a, is community, right? Is like outreach.
204
00:34:11.372 --> 00:34:24.132
Because what you find in a lot of cases is that when people are, you know, by themselves, right, the amount of, of, you know, like preventative care that they might implement for themselves is actually pretty small.
205
00:34:24.292 --> 00:34:32.052
Like the amount that... Like people typically are like, "Uh, it's hot, but I'll be fine." You know, like, "I'm not gonna run the AC," or, "I'm not gonna put on my cooling vest," or whatever, right?
206
00:34:32.712 --> 00:34:39.552
But it's like in, you know, Kleinberg's book, right, Heat Wave, where he was, he was comparing neighborhoods that were right next to each other, and he's like, "Well, what's the difference?"
207
00:34:39.692 --> 00:34:44.112
You have East and West Lawndale, right? Like, why did one get hit and the other one did not?
208
00:34:44.532 --> 00:34:52.412
And generally, the main thing that he was trying to prove was, like, that there were m- major differences in the community aspect, right?
209
00:34:52.532 --> 00:35:00.492
Like, when you have people knocking on the doors of the folks who, you know, are a little bit older, right, who they're worried about, right? You're able to get help to those people, right?
210
00:35:00.552 --> 00:35:10.352
You're able to look out for those people. And I think that's the main point of, like, Stria, and, and one of, like, the main things that we push as our resource is, like, connection is the key.
211
00:35:10.552 --> 00:35:16.192
And so being able to connect the people who are going to be the most vulnerable with other people, right?
212
00:35:16.232 --> 00:35:26.972
And having outreach directly to them to give them the resources that are, you know, like, important and tailored to them. That's gonna be more impactful than, like, a cooling vest or a bracelet or something.
213
00:35:27.032 --> 00:35:31.192
Not saying that those things don't help. They do, right? And they can certainly in the right context.
214
00:35:31.552 --> 00:35:47.192
But the most important thing truly, I think, is having direct outreach, right, and direct connection with other people, right, and building community so that we create this, like, mesh of, you know, everyone looking out for each other when we know that, you know, there's gonna be certain people at risk.
215
00:35:47.832 --> 00:35:54.752
We can be kind of that framework to be able to allow that to happen. Uh, and that's why... That's a big part of our goal.
216
00:35:55.332 --> 00:36:06.412
It's so interesting in a tech-forward society like the US where there is a sense that technologies personally applied, personally tailored, can solve a lot of our ills.
217
00:36:06.732 --> 00:36:19.672
And then you make the point that actually no, just reaching out and making sure other folks are okay during times of extreme weather, extreme heat, can go a long way to reducing negative outcomes, and I think that's, it's something to bear in mind.
218
00:36:19.912 --> 00:36:26.592
Use, use the tech, like create... use the, the cutting edge technology to identify those people, right?
219
00:36:26.692 --> 00:36:35.892
Because that's where the work we've been lagging behind is, like the scientific aspect of creating this precision ecologic medicine paradigm that I've been talking about, right? Because we can...
220
00:36:35.932 --> 00:36:41.492
You can blast something out to like a whole community and say, "Hey, there's a heat wave." But that's not really helpful, right?
221
00:36:41.652 --> 00:36:53.112
What is helpful is you have this like individualized, tailored approach to identifying people saying, "Hey, you are at risk," right? We can say with empirical evidence that you are going to be at greater risk.
222
00:36:53.452 --> 00:37:03.672
Here are direct resources, here's direct connection to you, and this is like how it's going to overall like benefit, right? Just that a 15-minute phone call. And you can use technology like this, right?
223
00:37:03.712 --> 00:37:12.392
It doesn't necessarily have to be like a knock on a door, right? You and I are talking across the entire, you know, the country to each other right now. So if you have like a check-in, right?
224
00:37:12.452 --> 00:37:22.912
A person, I'm looking at you and you're saying, you know, like, hey... You know, you tell me, "Hey Peter, you know, it's like 95 degree day today. You need to go. Like where's the closest cooling center to you right now?
225
00:37:22.972 --> 00:37:30.692
Is it the one down the street? Okay, I need you to go there starting at 10:00 AM. I want you to call me when you get there," right? And then we're gonna... You know, we can talk about what we're gonna do after that.
226
00:37:30.932 --> 00:37:33.152
That's powerful, right? Like, that's something that's powerful.
227
00:37:33.692 --> 00:37:41.272
But having someone have, you know, like a cooling bracelet sitting on their dresser, you know, that they may or may not put on, the adherence to those things is usually pretty low.
228
00:37:41.960 --> 00:37:46.580
So Peter, you're working at the cutting edge of climate health intelligence, right?
229
00:37:46.840 --> 00:38:08.920
And I'm wondering, when you look at your peers and colleagues in, in the academic and, and research community, and you look to those who are actually operating in the, in health professions and medical professions, how aware are these professions of climate changes impacts on health outcomes, and indeed on their own day-to-day working environment, how it's changing their profession?
230
00:38:09.400 --> 00:38:18.920
What more needs to be done to kind of educate those professions and make sure that they're ready for the challenges to come? That's a great question, and it's a spectrum for sure.
231
00:38:19.180 --> 00:38:27.580
What you see in the environmental epi side of things is that people have, I think, been feeling like they've been yelling into the void, right, for a while.
232
00:38:27.960 --> 00:38:39.880
And I think that they've seen that we're, you know, we're seeing a lot of increases in a lot of different, you know, heat-related or just environment-related diseases over the last few years, certainly, right?
233
00:38:40.000 --> 00:38:49.000
We've seen more papers get published in this kind of environmental epi side of things in the last five years than in the previous 50. So we have...
234
00:38:49.040 --> 00:38:52.080
You know, there's, there's, there's definitely a boom happening in that space.
235
00:38:52.500 --> 00:39:00.740
But when it comes to the translational side, like, and when we're actually applying it to, like, a healthcare situation or, or, you know, whatever, we haven't figured that out yet.
236
00:39:00.880 --> 00:39:10.020
We haven't figured out how to kind of really cross that, you know, cross into the, what do we do, right? We know that there's maybe an issue, but the question is, what are we gonna do about it?
237
00:39:10.620 --> 00:39:14.400
And I really see that a lot with the, the clinicians that I work with, right?
238
00:39:14.480 --> 00:39:22.080
For example, like in the ICU, if I tell a clinician, "Hey, the patient that you're seeing right now, they come from a super polluted environment, right?
239
00:39:22.120 --> 00:39:29.880
They're also a construction worker or whatever, and their lungs just cannot handle much mechanical ventilation that needs to happen, right?
240
00:39:30.040 --> 00:39:35.340
The odds of them dying in the next 28 days is 57%," or something like that, right?
241
00:39:35.860 --> 00:39:44.840
And so you have this information that you've now given to this physician, but generally speaking, they're like, "What do I do as a clinician to change the way that I'm treating these people?"
242
00:39:44.960 --> 00:39:48.860
There isn't really the connection, right? So we have, like, the insight, right?
243
00:39:48.920 --> 00:39:56.920
We have, like, the data-driven kind of evidence, but we need to figure out what we're doing about it, like, at a clinical level, right, to, like, understand what we'd actually change and what we'd do.
244
00:39:57.400 --> 00:40:01.620
That's the kind of cutting edge research that's happening right now in the healthcare space because you have...
245
00:40:02.020 --> 00:40:16.740
And now it's, generally speaking, clinicians, like, actual, you know, like, care teams who really are not-- They have so many variables that they have to contend with in the hospital that external ones like environment, that's not on their radar, right?
246
00:40:16.780 --> 00:40:19.000
They're not thinking about what the temperature really was that day.
247
00:40:19.320 --> 00:40:29.000
They're not really thinking about the air pollution of that per- like, where that person's coming from or, you know, if this is, like, a long-term flood-related thing or rain or, you know, whatever, right? A, a drought.
248
00:40:29.540 --> 00:40:32.280
They're not thinking about that. They're thinking about treating the person in front of them.
249
00:40:32.580 --> 00:40:41.340
Whether or not we need to im- you know, make clinicians knowledgeable and to be able to recognize these things, that is a kind of a separate question.
250
00:40:41.800 --> 00:40:49.660
But whether or not we need to make health systems, right, more adaptive to these, and that is much easier to do, and that's, like, kind of the next phase.
251
00:40:49.760 --> 00:40:54.160
Because you can change clinician behavior if the overall health system behavior changes, right?
252
00:40:54.640 --> 00:41:02.280
You're not gonna change an individual doctor and the way that they, you know, provision healthcare, um, you know, like, on a one-to-one basis by saying like, "Hey, your patient's gonna do this," right?
253
00:41:02.440 --> 00:41:10.780
But if you're saying like, "Hey, we have..." For example, like, in Chicago, we have the Chicago Marathon, right? Just, like, happened this, you know, this past weekend.
254
00:41:11.320 --> 00:41:18.600
Let's say it was gonna be abnormally hot, right, a crazy hot weekend, right, and you have thousands and thousands of runners, you know, like, all out there.
255
00:41:19.060 --> 00:41:29.820
If you have an idea that there's going to be a 10% increase, right, in, like, these heat-related events, and these are the specific types of events that are gonna occur, then you can work on staffing resource needs, right?
256
00:41:29.920 --> 00:41:37.780
Then you're gonna be working on, like, okay, we know that these people typically have electrolyte imbalances, so we need to stock more, you know, like, more saline bags, right?
257
00:41:37.840 --> 00:41:46.200
We need to make sure that we have more IVs ready to go. We have-- need to make sure, you know, we have more renal replacement therapy in the ICU, right? You know, all sorts of things like that.
258
00:41:46.740 --> 00:41:48.040
I think that's where it starts, right?
259
00:41:48.100 --> 00:41:59.640
I think that's, like, where the next phase is, and then it will eventually kind of trickle down into, like, the individualized, like, precision health that would come from, like, individual doctors.
260
00:42:00.179 --> 00:42:02.260
But that is, I think, still a little bit away.
261
00:42:02.840 --> 00:42:19.320
Yes, if medical procedural dramas and soaps have taught me anything, [laughs] it's that, yes, the individual clinician has limited room to maneuver within the broader system, and indeed, even before they come to the hospital, right, [laughs] the conditions that their patients are subjected to can shape so much of their outcomes.
262
00:42:19.400 --> 00:42:29.020
Okay, and thinking about the future, what is next for Stria Health over the next 12 months or so? Where do you hope the company will be this time next year?
263
00:42:29.420 --> 00:42:37.760
By this time next year, our goal really is to kind of build out our, you know, all, all our different arms that we have.
264
00:42:38.220 --> 00:42:48.100
One, like I've mentioned before, is that, like, nonprofit arm where we're really trying to create public good and, and, and bunch of public goods for people who are interested in doing this sort of work, right?
265
00:42:48.400 --> 00:42:54.120
Helping to teach those who wanna do this sort- these sorts of studies, right, give them the tools so they can do them.
266
00:42:54.540 --> 00:43:02.560
You know, public health systems, right, who maybe don't have a ton of money, right, but they want-- they, you know, they, they have the data potentially and they wanna know how to use it, right?
267
00:43:02.640 --> 00:43:10.880
Being able to help advise them and, like, help foster their growth so that they're able to go on and do these things, I think is a really important aspect of, of part of our business.
268
00:43:11.240 --> 00:43:21.640
And then the other ones really is, in a year from now, I think the hope is that we've really built out and become, like, the game in town for any sort of, like, climate health super intelligence.
269
00:43:21.800 --> 00:43:46.980
So when a business really wants to understand the effects of climate on their, the people that work for them and their dependents, right, and any-anyone in their kind of sphere, that they know that we're the people that you're gonna go and talk to because we're gonna be able to not only help them quantify, right, and give them these tools so that they can have these, like, real-time tracking and updates, but we also give them the insights and the knowledge of, like, what you can actually do, right?
270
00:43:47.020 --> 00:44:03.360
Like, it's not enough to just give them the information, but we can also give them the eventual, like, this is what you can provision to help your workforce succeed, be better, right, in the long term, and be prepared for what will be a very potentially tumultuous and, um,
271
00:44:04.340 --> 00:44:14.060
potentially difficult future of climate change ahead if a lot of the climate projections stay what they are. It's a lofty goal, but what I expect from a data first startup.
272
00:44:14.460 --> 00:44:19.380
So I wish you and Josh and the rest of the team good luck with the mission of growing Stria Health.
273
00:44:19.800 --> 00:44:33.800
I plan to keep tabs on your work and do more coverage of the climate health space, as it is an area where, yes, there's increasing amounts of policy being built around the challenge, an increasing number of tech startups, uh, also looking at the space.
274
00:44:34.140 --> 00:44:45.700
So thank you for your time, Peter. Best of luck, and have a good rest of your week. Thank you. Appreciate you. Climate Proofers is a Climate Proof Media production. It is produced and presented by me, Louis Woodall.
275
00:44:45.980 --> 00:45:01.280
Subscribe to the Climate Proof newsletter by going to climateproof.news/subscribe. And to learn more about the publication, feel free to reach out directly using our LinkedIn page. Goodbye for now. [upbeat music]