Injury & Violence Prevention Data - A Look Beyond the Numbers--Part 2

Mighty Fine  0:14  
Welcome to Injury and Violence Prevention INdepth, a Safe States Alliance podcast. I'm your host Mighty Fine. And this is a platform for us to engage in dialogue on a variety of topics to help inform you on the latest trends in injury and violence prevention. Today's episode is sponsored by the American Trauma Society, an organization that has been dedicated to the elimination of needless death and disability from injury for the past 50 years. Learn more about the work that they're doing by logging on to their website@www.amtrauma.org.

Thanks again for spending time with us today for part two of our discussion on data and injury violence prevention. I'm so happy to welcome back our data gurus as I deem them, an epidemiologist from UNC Chapel Hill Injury Prevention Research Center, and Dr. Mike Dolan Fliss, and a senior mph epidemiologist from the Alaska Division of Public Health, Dr. Jared Parrish. So Mike and Jared, let's pick up where we left off. So my next question for you data gurus has to do with really just being an epidemiologist, and I know it's great, you love it. But if there's one thing you change within that role, what is it like the main thing that you would change with that role of an epidemiologist? 

Mike Dolan Fliss  1:35  
This was the last question. I don't know, it was I'm so curious what Jared's gonna say, because, okay, I filled out pre responses, you know, thinking ahead to these questions I wanted to get get some thought I did not know, this is the last one I like, literally just put a couple thoughts in. So it's tough, because there's a lot of stressors, but I think when it came to I feel good about, I started thinking about our COVID team had to draw on every head, all hands on deck, from every, you know, from injury from everywhere, right? I mean, and we had people not sleeping for a year, you know, it's just like, we were just, it was just people really stretched way, way, way too thin, you know, waking up early in the morning to run the numbers, you know, it's like, it's like waking up to make the donuts or whatever from that old commercial. It was like that, you know, like it was when we need daily answers, but we don't have infrastructure support daily flows. And so I think it's, it's sort of a strange answer to me to end up having to come to us, but I think it's infrastructure, I think, letting giving epidemiologists time to think critically, because you're helping them automate and schedule the tasks that they've designed. You know, I appreciate our IT group a lot, but the, you know, the question will just tell us the business logic, and we'll do it for you. And like, the epidemiologist, like, I can't explain ICD 10 codes to you, like, you know, where there's a change in case definition at CDC right now, and CST you're collaborating on, you know, and I can't give this up, you know, I need to be able to participate in the, in the flow of data, from databases to processing and analysis and back and not give away the task, you know, to be automated without me. And so I think, you know, having epidemiologist be able to participate in scheduling the most common tasks, promoting a flow up into, you know, these are these are typical software design needs now, you know, like things like data repositories, and what version is your code, code versioning software, these are all challenging changes to make as a field, but they then, you know, ideally let epidemiologist think critically do one off analyses. They're not chasing down, you know, this month's worth of data over and over and over, you know, I think we have to free up epidemiologists to be brain trusts, as well as number crunchers. And, you know, pushing the data down the pipeline kind of thing. So, and that would have given so many hundreds of hundreds of hours of sleep back to our epidemiologists here, if we had done that work before. COVID, you know, and now we feel the pain, but hopefully we can continue to make headway there. Jared, what do you think I cannot wait. 

Mighty Fine  4:07  
Yeah, yeah, on pins and needles, right? Mike

Jared Parrish  4:11  
I got the same visceral reaction to this question, too. I went through them and I was like, boom, boom, boom. And this one, I just put a huge question mark. And I was like, I'm just gonna sleep on that one. And what I wrote, in my little note was data infrastructure. And I wrote that same thing as my number one issue that I ended up. And I guess I wasn't expected, as an epidemiologist, that I would have to understand data science, I'd have to understand informatics, I would have to understand to be able to speak what I t's talking about. I think that was an area where I just have always felt a little bit like I'm running on the fly here. I'm trying to learn as I go a little bit and keep up with these other professionals that devote their life. And then at the same time, when it creates a database, they often don't engage with epidemiologists, they're not an equal partner sitting there. And so we end up having these systems that we make work. And we end up having this really awkward situation where we're trying to paint a picture of a really complicated issue, using like one or two colors, I always feel like I'm trying to do an analysis with one hand to identify back and then it's just, it just can get incredibly frustrating and time consuming. So if I was able to change one thing, it would be the infrastructure of our data, and how we flow it. And I would start simple, I'd be like, Hey, we're working with this system and this system and get them moving in on a parallel, we can continue operating this way, then have that go and test it retest that mean, do it like a normal informatic process, right. So that would be the one thing I would change. 

Mike Dolan Fliss  5:39  
I love it that we ended up getting over it. But coming to comparable conclusion. Yeah, I'd love to give one specific example of this that happened recently, just because I don't think this is all doom and gloom, I think we've been able to make some headway. And I think I'd love it. If Jared has also made some headway recently can share an example of that, because so so our opioid Action Plan, which we spent a long time on harmonizes data, so to make the fields basically the same, so that it can be easily analyzed together, across gotta be at least 14, 15 different data sources, we wanted to pull out one or two indicators from each place, and put them in the same place so that we could get better situational awareness. I my punch line here is that the fact that we did that in advance meant that when I got a new data question that involves a different data set, and I wanted to see its association with the most current data around opioids, I can actually go pull that same data set that was prepped for that project, that was indicator level, it wasn't you know, it's not record level, it's just I want to know, for county and month or year, you know, overdose deaths, overdose, ED visits, people in treatment, prescriptions, you know, a long list of stuff, my punchline here is that we are our biggest data users. You know, I'm mostly prepping data for other epidemiologists in terms of saving time, and other or the researchers or other whatever. But I think a lot of the focus in infrastructure has been on sharing data publicly, which is a really important and good thing. But when you treat yourself as the public, you know, what do I need from my neighbor from my sister branch or section at the health department so that I can easily give them answers and they can get answers from me, it helps everybody immensely, you know, treat yourself as a data user, not just as a data producer, you know, we've made some headway that started all of a sudden started tastes really good. It's like, in five minutes, I can go get the data I want. And then I can tell you the answer to that question. Instead of starting over from scratch over and over, you know, we're just with the record level data that we already cleaned and coded, but not the indicators themselves that I really want to do the analysis on. 

Mighty Fine  7:38  
And and if you want to add anything there, Jared

Jared Parrish  7:41  
You saw me going, Yeah, going through my brain, it can become either really interesting or not so great. I yeah, I think I had just an incomplete thoughts when I was listening to Mike talk about this. And, and so I don't know if they're really well formulated to be discussing at this point, like, I just keep thinking through this role as an epidemiologist, the quote, unquote, credibility of the information. And do we like the clinical provider, the clinical medical provider has a well defined role within our society, and what they provide and who they treat, they treat an individual patient with their medical elements, that epidemiologists' patient is the community. And now the community is a variety of different definitions of what that looks like and who that is. And so I think that, that sometimes epidemiologists, and maybe this is broader to public health in general, especially right now in COVID, with people seeing it evolve in real time and see how science actually is kind of messy and dirty, that there's some not well defined understanding of what that role is, and what the legitimacy of the the science behind it is, and, and maybe there's some some education that can go on there as well, I don't and that's why I'm saying this is kind of an incomplete thought. But I've seen that, you know, so if you're a PhD epidemiologist who really understands data and an understanding of that, but you're not a medical doctor, are you somehow treated differently, right, with respect to data? I'm not saying with respect to clinical outcomes, have we have we, I think we're recognizing more and more the value of data and the importance of unbiased data that accounts for these isms and understands it, and the training that goes into that. And so, I think there's going to be this kind of shift that we'll see over time where because we now understand the value of good quality data, sure, the resources to identify people that can collect that data present that data their value and and validity of what they say is going to be recognized more and more and so I don't think I articulated very well but that was just a general concept of thought that's coming to my head. 

Mighty Fine  10:01  
No, definitely got my wheels turning in my brain. I was gonna say tinkering I'm sure of your brain tinker's. But mind that, but I was I was what you were putting down? I was I was picking up, you know. So, Mike, what are your thoughts based on what Jared responded? 

Mike Dolan Fliss  10:17  
Yeah, I'm reminded of there's there are skill sets that epidemiologists may not realize that they're getting in a formal education program. And I think, you know, and I, I'm very thankful for my education. I think, Jared, I share a lot of education here in the background, but you know, causal causal inference, you know, like, what causes what, how do you adjust for confounding? How do you design a study? That's been one of the ways that we frame what an epidemiologist can do. But I think those same questions translate actually very well for thinking about how to improve a data flow in order to get the questions answered that you want, how to improve data quality, how to have conversations with partners about bias, and about different ways to triangulate from that bias for practical public health. And I think we're not really necessarily framing it that way. And if you're thinking about my job was to put out one paper about this study, that's as a high quality study, design and can work. If your question becomes how do I produce a flow of information that can turn it into good knowledge and wisdom up the chain? I actually think we actually were getting a fair bit of education about that. It's just we're not framing maybe our work in that way, even though that's what practical public health has to do is, is a flow of knowledge, not a one off report. But you know, how do we do surveillance? How do we do sort of situational awareness? How do we act on a regular basis to improve public health? And that's the thing that I think epidemiologist can stick a flag in and say, you know, this is part of our training and one that we can represent, you know, how do we keep knowledgeable about this? How do we gain and keep that knowledge? 

Mighty Fine  11:53  
Yeah, there were a few things that you said that, again, got me tinkering. But there are some other questions that I want to get to. So if there's time, I'll come back to that. But you mentioned surveillance and thinking about data collection. And so thinking about us as a field IVP? How do we change or influence the change to ensure that we're capturing demographic demographic information, excuse me, that we may not already be doing or may not be doing it traditionally? What are from your perspective? What? What more should we be doing? So we're intentionally collecting data? Like demographic? 

Jared Parrish  12:33  
I think I think Mike already kind of started us down that road and talking about the switching the question a little bit to the information flow and how we're doing it and collecting that information just from that structural so the systems approach to this a little bit more. And I think, Mike, they made a great point here is that we've kind of harped a little bit on our education, but we've got great education. And we've we've, we've benefited a lot. And I think that the transition from the academic setting to the applied setting is really what we're focusing on, it's not the actual training itself, that maybe there's so few things that can be developed there. But when I think about how the IVP field can change or influence change, to better capture demographics, we've already mentioned some of the key components, the systems and the bias, right? And so we all have bias. And I think this is one area where epidemiologists really shine because we emphasize that what are other possible explanations for what you're seeing, other than what you're seeing, right? So thinking through the bias critically and carefully. And I think that if we applied that same amount of critical and careful thinking to our systems and information flow, we would drastically improve the way that the demographics are collected and represented and the meaningfulness behind them. 

Mike Dolan Fliss  13:56  
Yeah, that's great. That lets me skip parts of my answer, as usual, which I love to be able to do. Working with Jared. I'll maybe I'll boil down to some specific questions. So I think collecting, for instance, very crudely collecting separate race, ethnicity, data, race and ethnicity separately, but then potentially presenting them together. Because if you don't present them together, you can actually create systematic reductions and disparities if you're comparing, you know, non Hispanics, both white and black to Hispanics, or vice versa. But thinking critically about how you collect as much as you can and then use critically what you do get, you know, is race ethnicity being is itself defined, is it a nurse or police officer perceiving race and ethnicity and tagging it? I mean, these are kind of basics for data governance and data generation processes, data providence, but important here, how does that differ by region, like we have data that like the south, you know, self identification of what we what a person, the same ancestry and same language use in the southwest is very different than here in the southeast for me, you know what? The description of by, you know, when when someone else says, you know, oh, you're white, non Hispanic or no, you're white Hispanic, in South Carolina, but you'd b e white, non Hispanic, you know, you're not passing as as white in a different area. So conceiving of race, ethnicity, and more complex way helps us have those conversations in a more critical and concrete way. And I'll hold up the work of particularly of Shandra Ford and Erica Hinduja, on the public health, critical race practice, which I think helps a lot with this for me personally, and yeah, I mean, think just, you know, and that's just the race, ethnicity data, you know, gender, you can have that same conversation, you know, is that biological sex? Is that what what is this? You know, what are we calling this? And how is it going to be used? And how, who doesn't make it visible? I think, you know, suppression is a part of this, too. And maybe we'll get to that in a future question. But suppressing small numbers can systematically leave out small groups who are in some of the sometimes some of the groups who need that data the most. And so, you know, that can get to, you know, you need to work with lawyers to say, hey, if I've got two years of data, I'm going to go ahead and share whatever I want, or, you know, just you need you need outs, or some health equity strategy that informs your suppression guidelines, because if you just make suppression guidelines, you know, generically to protect people, you're going to systematically leave out groups of people, and small counties and communities. 

Mighty Fine  16:20  
Yeah. And actually speaking out about suppression. And this is, I think fits well within this question. I wanted to ask you both thinking about looking ahead and how we use data to influence injury and violence prevention programs, or how funding is distributed or where the focus area is, how do we ensure that we're doing that and making it equitable in terms of the disparities and some of the inequities that we witness? 

Mike Dolan Fliss  16:51  
Yeah, I'll continue the thread on suppression, and I'm super interested in Jared's answer, I think not just sharing equity issue of blood sister, we're sharing equity issues, what it takes to do that, you know, to talk about equity disparities, you got to have a critical conversation with lawyers about suppressing small numbers and ways to protect individuals, while still allowing communities to speak on their own behalf and use your data to speak on their behalf. That's not a trivial answer. I mean, we literally have an email thread right now with with lawyers and our, you know, informatics and epidemiologist leads about whether we can share small combinations of age and race, ethnicity, maybe if we get multiple years of data, can we do that in a regular way, versus always having a fight with when we have three counties, we have to leave out some of the time but not all the time that it depends on how, you know, not not having an equity informed strategy means that we're doing a lot of a lot of protection, a lot of ad hoc protection, you know, that sort of level want to tell the story, but we don't know how we can tell the story. And you know, and if it's hard to tell an equity story, some groups might included may not get there, because it's just such a lift. And so having a standardized way of making sure data suppression works. But then let's say you can share, you know, questions about historical redlining, or in the case of South the southeast here, like we have communities that historically used to be slave plantations. And those same communities, typically black communities now are exposed to hog cafos. You know, giant ponds of hog feces. And that's a historical, you know, we need to interpret those disparities a little bit to help tell that story or, you know, or in the case of urban areas, you got redlining, whatever it is, and so not just sharing equity disparity issues, but helping the public interpret them to Jared's point earlier around, like, I know, they want to get on board with talking about systemic racism, but don't know how. And so, you know, we have to both share disparities and interpret them or help others interpret them. 

Mighty Fine  18:39  
Yeah. And I think yes, like, what you're saying, help them help folks interpret and understand it, beyond kind of the intellectualization of it. I don't know if I just made that word up or not, but so that we can see what actually can be done right? If we fully understand it, and the root causes and what's driving it, then that helps us to percolate potential solutions to address it. 

Mike Dolan Fliss  19:02  
So yeah, and I wanted to end with exactly that action, you know, like that the the ADPH, APHA who call for any race action by public health people call for action call for analysis and actually not just, you know, spitting out data for people to maybe interpret or help interpret or whatever but help you drive people to actions around anti racist action, particularly. 

Jared Parrish  19:25  
This topic area, we could probably do a whole podcast on this. Absolutely. So I'll just try to focus a couple of comments and first is just yesterday, I was talking with a researcher in Fairbanks who does a lot of Transportation Research. And he reiterated to me that the best source of info, the most accurate source of information is able to collect related to no off road vehicle type of injuries was the newspaper and collecting it systematically and just documenting what's in there because they publish this. Oh, there wasn't that there was a crash on this place, and it has some basic details around it. And so like the date of it, the time, the location, the general occurrence of it was all contained in a newspaper article. And if he collected that any mined those articles that was more accurate than our hospital discharge data, then our trauma registry data, because it had that those critical pieces of information that the newspaper can put out there. And so we have like, what Mike was talking, we have these rules that we have to play by and do to protect the enemy, and they need to be there. But we also need to also we're, you know, we're an evolving creature, and are we evolving with with the way we're approaching our information the same way? And are we are we giving ourselves the opportunity to succeed and provide that information to those communities or not. And so that's, that's just one general thought that I've had that I thought was a, something that was just yesterday that happened working in, in Alaska, I've definitely seen how, you know, I have a lot of bias that that I would just have with me. And so recognizing that, that those those biases and influenced the way that data systems were structured based on people who created them, are things that are just gonna have to be considered. So anyhow, I think that the the idea of being able to recognize that that information that the information does exist, and some people can't put that information out there, but are we capturing it in a regular and systematic way is an important thing. I also participated at the it's the knowledge data and discovery conference recently, it's a big machine learning group organization, and it was here in Anchorage, like Google and Apple, all these people were here, it's like the biggest conference Anchorage has ever had, I weaseled my way on to a meeting there with them. And I was blown away at the the information that these private industry have, that people are willingly agreeing to provide. And so when I think about who's the biggest watchdog on people, right now, it's not government, it's private industry that's looking to sell products and, and and help you know, give them and is there. Is there a way to understand that model? And can we leverage and work closer with those those private industries to do things and we see in COVID-19, private industry really stepped up Google did tracking the a lot of like, what was it the proximity apps and things like that, that were being developed, there was a lot of big push to try to help solve that issue. So I think that we can try to be a little bit more collaborative and innovative where we think and and from that equity lens and when I first got here, and when I'm still here in Alaska, there's this concept I started coming up with the disparity fatigue, yeah, okay. But right now, there's this disparity resurgence right now is that we do want to talk about it. But But what I'm really starting seeing, it's about who's talking about it, and the ref and how it's being talked about. And so when I work with our Tribal Health Partners, as I've really tried to strengthen those relationships, I realized that they don't, they don't necessarily not want to talk about the disparity and understand it. They're just kind of sick of the state, putting out these big disparities, say this populations, you know, worse about this and worse mess. And what happens is that when you're worse at this versus this worse, this you start getting this collective is that it's bad to be me. And that is the opposite message that we want, because they're our country is strengthened by its diversity. And we have power in that diversity and their solutions. And that diversity isn't realized that, instead of me putting that out, is actually supporting that other entity to put that information out and share that. And so I've also had to kind of transition how I approach that. 

Mighty Fine  23:43  
Yeah, and even that's a great point, Jared, thinking about how it's shared, because we're not trying to reinforce stereotypes or folks being lesser than or focusing on the individual. And sometimes those narratives can do that. So I totally agree, I think the messenger is critically important. And then, you know, I don't know how to say this without saying is, you don't want to look at a chart and be the most at risk for everything you see on the chart or the graph, right? And because you know, there's resilience, and there's a lot of positive things about who you represent as a people. So I agree, I think we have to be really delicate with Yes, showcasing what the disparities are, but not reinforcing us versus them mentality. Mike, I don't know if there's anything you wanted to add to that combo. 

Mike Dolan Fliss  24:29  
Yeah, I would just want a second jarrods point around community data. And I think it's a particularly interesting and weird tension that we may acknowledge, the community is going to just post in a newspaper, it's going to post the name of the person who just overdosed. And I can't share that there's a count of one that year. You know, I can't tell you it was one a year but you're gonna get a date and the name in the newspaper. And I think that tension is particularly frustrating when we then aren't allowed to respect community data and use it and talk about it and treat it as a valid source of public health data, you know, like, so we can't use to close the gap is what I'm getting at, you know, you know, you can't say we know, well, we can't share emergency room visits, but we can share is that the newspaper had reported this many overdoses in the last two weeks or something like that, that is already out there. And it's ready for epidemiologists interpretation. But you know, you maybe need to be funded or you need to you need some help scraping it or, you know, there's and then again, some communities groups already post these public datasets, I'm thinking about police files, datasets, there are four or five different community groups posting police datasets, police violence datasets, crash data, you know, like that stuff could be collected by locals under different HIPAA expectations. And we could use it if we, you know, respected it. And if we if we made a plan a strategy to incorporate it into our surveillance flows.

Mighty Fine  25:49  
Yeah, yes. I was gonna say in there posting real time, you know,

Mike Dolan Fliss  25:54  
yeah, no, it's like, you know, you get alerts, you know, I could set up an alert on my own for some of this stuff, you know, newspaper articles, and then, you know, get some helpers to encode that. And, you know, at least for some questions that could really help in some small communities and a small number issues. Yeah, I wanted to also mention sort of the private data and disparity fatigue, I love both those concepts. Particularly, I'm thinking about mobility data, which I think, you know, being key in some ways to talk about COVID data I think Jared was getting at. And on the flip side, for me, that data disappeared, once we started making some headway, or was like, we'll give you a one time snapshot. And, you know, I'm like that, that feels like a sales pitch. And I think if there are taxes, funding, in some cases, some of these entities, private entities to do good, or I'm thinking about the Google - Google for good maps campaign, I'd love it if you would allow me to geocode some data, but you know, you can't they're they have very restrictive use public use of some of their tools. And so you know, my calling up the up the chain all the way to the national level, like, Can we get some help from some of these private entities that states you know, that isn't, it isn't maybe like, around for a time, and then it's meant to sell something in the end, or, you know, I'm open to more collaborations there. But if you pull the rug from under me, later on, that's not so great. And yeah, so the last note off Jared's springboard here was around, not just individuals, but communities being you know, this is the this is the community that has the highest level of cancer incidence, blah, blah, blah, you know, exposure to pollution, whatever. And I think this is a different perspective than Jared's, I think they're complimentary is that if we don't talk about what are sometimes called sacrifice zones, or places that we're just going to keep dumping stuff and keep harming health, and if we don't talk about them holistically, then all we've ever done is talk about each thing over and over, then that I think produces a lot of a lot of fatigue around talking about disparities, because you're not talking about them all together. But you know, in an explicit way, we've been informed by the community, it becomes 20 separate conversations, when it really needs to be one conversation that actually might get somewhere. And that's a that's a subtlety. And I don't have the answer how to do that other than I think it's can be very dangerous to separately analyze questions, you know, around pollution, or built environment or whatever, you know, over and over and over separately, and you're not going upstream to talk about funding of communities, or you're not going upstream to talk about health, promoting a broader level. Yeah.

Mighty Fine  28:22  
Yeah. And I think you've run the risk, to your point, Mike, about ignoring kind of the structural manifestations of those things that we talk about them in silos, and some that will be missed on on some people for sure. Well, I'm, I've thoroughly enjoyed this conversation in this dialogue, I just have one sort of last question for you both. If there's something that you feel like folks IVP professionals are doing in relation to data collection and interpretation, what would you caution against? Or what would you promote doing instead? And if there's any other kind of closing thoughts that you have, that you didn't get a chance to share during our talk? 

Jared Parrish  29:03  
I'll go I'll go Mike, and you can i this this is a great question Mighty for you to be able to let us wrap up on I think there's a lot of things that we I really think of things that we should be doing. And and what I think that some of the things that we should be doing is being a systematic piece of information into this evolving conversation related to equality. We are well trained in looking at bias and that's what I've talked about. And that's what we should be emphasizing. We should know how our role can really help with this conversation. And so some of the things that we can do to help with that is, is we don't need to overly rely on statistical significance. And we can we can interpret information like we look at trends over a 20 year period and it let's say it's built on small numbers. So there's instantly that number, but we see that that number follows a very stable number it's not jumping wildly. We have good information there. Even though we have some statistically unreliable annual results over the totality of it, we have information there. And we can interpret that. So I think we need to stop over relying on statistical significance, and start relying more on our training related to understanding and interpreting information in the context of potential bias. 

Mighty Fine  30:22  
I can definitely get behind that for sure. Mike, what, Woody, what are some of your parting thoughts for your fellow IVP professionals?

Mike Dolan Fliss  30:33  
How tough I mean, I think we've covered a lot of ground this conversation that again, we're really thankful to have been part of it, you know, for as many times as I'm agreeing, like, 110%, with what Jared saying, I think this is true of lots of people who are working ivpp is that we maybe share language and can see a lot of these challenges together, and can maybe work with each other on some of these solutions. You know, I think we do a very good job, I feel really thankful to be part of a broader network of public health professionals working on injury, it's an amazing group, who share strengths all the time, and really, you know, great ways know, one or two parting thoughts kind of quickly, maybe around equality and infrastructure in particular, I think it's important to not take no for an answer, no matter what organization you're in. And what I mean by that is, if if there's pushback at the state level on talking about something sensitive, and you know, and politics determine what's sensitive, largely speaking, and that can be really, I think, frustrating for some epidemiologists and certainly has been for me on you know, how do I talk about disparities and drug related arrests while I talk about the opioid overdose epidemic? How do we talk about police violence when we talk about firearm violence, but I think don't take no for an answer as and that might require strategizing with partners, it might require it might, you might not be able to be the voice that does all things, but you then have to be in collaboration in coalition with others who are working on that. To do that, you know, we're or, you know, are thinking of an example for us where one branch, you know, that does work around, explicitly does work around disparities and minority health for the globe, we currently call minority health was able to make some inroads on some questions. And it was only when we could point to their wins, that we were able to move into that same space. And so you know, every time you know, we make an injury win, or someone else makes a win that we can point to and say, Hey, others of the state have been doing this or, or this research group did this or that we get to make a little bit more headway in that direction, too. So I think that's really just praise for what we're already doing in a way but and continue to sort of emphasize that strength that you know, if we're if you're making headway, and and groups can find a way to move forward in some specific way that works for them, and helps us all, and I think you know, so it's not just your branch or your section, you know, or your state, even when you're able to make some, some interesting little bit of innovation here and there. And that's part of the scientific process, but it's also part of the community organizing and change making process. That's great. Let's keep doing it.

Mighty Fine  32:57  
I like that. Well, I certainly consider myself I was gonna say, lucky to be occupying this space right now, this virtual space with you both, but also in the broader space of injury and violence prevention. So again, thank you for being guests on the podcast today. And I look for for a time where we can actually get together in person, you know, maybe a APHA, SAVIR, or some other public convening in the very near future. So just thanks again and wishing you both well.

Jared Parrish  33:28  
Thank you Mighty

Mike Dolan Fliss  33:29  
Thanks Mighty

Unknown Speaker  33:34  
Thanks again for listening to IVP in depth with your host me mighty fine. Be sure to subscribe and listen to us on Apple podcasts, Spotify and Google podcasts. You can also follow Safe States on Twitter at Safe States, follow us on LinkedIn and check out our website www dot Safe states.org to learn about the wonderful work that we're doing. I like to also again, thank our sponsor, the American Trauma Society, for supporting Safe States and helping us to bring you programs such as this. Until next time, stay safe and injury free.

Transcribed by https://otter.ai