SCCM Pod-524 PCCM: Impact of Neighborhood on Pediatric ICU Outcomes

Hello, and welcome to the Society of Critical Care Medicine podcast.

I'm your host, Maureen Madden.

Today, I'll be speaking with Dr. Michael C. McCrory, MDMS, about the article, Child Opportunity

Index and Pediatric Intensive Care Outcomes, a Multi-Center Retrospective Study in the

United States, published in the April 2024 issue of Pediatric Critical Care Medicine.

To access the full article, visit pccmjournal.org.

So Dr. McCrory is an associate professor in the Departments of Anesthesiology and Pediatrics

at Wake Forest University School of Medicine in Winston-Salem, North Carolina.

Welcome, Dr. McCrory.

Before we start, do you have any disclosures to report?

No disclosures, and thanks for having me, Maureen.

It's really my pleasure to have the opportunity to sit and chat with you today.

As we were saying earlier, you never know when you're going to run into people, and

this is a great opportunity to talk a little bit more about you and your research interests

and about the article.

So can you give me a little bit of background about yourself, and what is it that brought

you to this publication?

Yeah, thanks.

Well, you know, I did my training up in Baltimore at Johns Hopkins.

And it's certainly a city of neighborhoods and a city of challenges in terms of areas

of deprivation, poverty, different challenges that lead to health inequities.

And as I've been attending here at Wake Forest, I've had the opportunity to get involved with

the Virtual Pediatric Systems, or VPS, database, you know, doing some research with them, with

their data.

And in thinking about that, and also being a part of the Pediatric Acute Lung Injury

and Sepsis Investigators, the Polici Social Determinants of Health subgroup, the idea for

that is that we came out through a Polici SDOH group meeting a couple of years ago.

So we were just kind of discussing all these challenges in health equity and sort of this

idea that a significant portion of children's health outcomes is related to social determinants.

As much as we provide great clinical care, and that continues to advance in the pediatric

ICU setting, a significant portion of kids' spectrum of illness, whether they come to

the PICU in the first place, maybe how they do in the PICU, and then how they do after

the PICU, is likely related to a lot of other factors as well.

And depending on the study you look, not specifically in the PICU population, but across the board,

you know, people say between 20 and 80% of your health outcomes are related to social

determinants, depending on how you define that and healthy behaviors and access to care

and those sorts of things.

So anyway, as we were discussing those things, I was thinking about how we could potentially

use these VPS data to help kind of look at that.

I got into discussions with some of the leaders from the SDOH Polici subgroup, Manzi Akande,

Catherine Slane, and others who have thought about these things a lot.

And we started looking into it.

VPS historically collects some zip code data sort of optionally, I believe.

And when we were looking into it, it didn't seem like that was going to be possible to

collect across.

In other words, there wasn't going to be enough data across the entire database.

So we started talking about making a smaller project, trying to put together centers that

were interested in looking at address map to indices of social determinants and trying

to utilize that high quality clinical database to look at how clinical outcomes may differ

based on children's neighborhood and their socioeconomic factors.

Okay.

So that's what led you to the Child Opportunity Index, I'm assuming.

I didn't know a whole lot about the Child Opportunity Index when we first started talking

about this a couple of years ago.

Thanks to Manzi and Catherine, we were looking into these different area level indices and

the Social Vulnerability Index, the Area Deprivation Index, there are quite a few different

ways to look at area level socioeconomic status, you know, percent of households in poverty

and so forth.

And we settled on the Child Opportunity Index as the most pediatric specific one.

And I can go into more detail.

But I wanted to ask you to discuss a little bit more about the Child Opportunity Index

because I really don't think that there are that many people familiar with it.

So tell me if you could describe a little bit more why you thought this one, more than

you've already stated specifically, was going to fit well with your study.

Yeah, thanks.

Well, you know, the Child Opportunity Index, it's a 29 indicator area level indicator of

socioeconomic factors and so in three domains.

So that's educational opportunity, health and environmental opportunity, and social

and economic opportunity.

And it's housed up at Brandeis University, it's at diversitydatakids.org if anybody wants

to check it out.

Again, I have no formal affiliation with them, but they've been super helpful to me throughout

this process.

And they've helped generate a lot of health policy research as well as some medical studies

using that.

But you know, it has some aspects of it that are more pediatric specific than the other

indices that have been commonly used in adults, for example, proximity to early childhood

education centers, third grade reading level and so forth.

Some of the other things like in social and economic.

Are quite similar to the other indices like foreclosure rates, poverty rates, you know,

and also there had been some studies, especially using some using phys and administrative data,

some with smaller center studies, but we hadn't seen one that had used a larger clinical database

like BPS.

And that's part of why we saw this opportunity to potentially put those two together and

try to get some interesting answers or at least insights into how that neighborhood

may affect PICU outcomes.

You know, it like any area level index, just because they live in a certain spot doesn't

mean they have the same exact challenges as someone next door to them.

So it's obviously imperfect like any of those.

But another advantage of it is that it is indexed to census tract, which is a little

bit more specific to the neighborhood than zip code.

For example, there are over 70,000 census tracts in the US.

I believe for zip codes, there's maybe 40,000 or so.

So it's a bit more of a specific area that's hopefully possibly more indicative of kind

of that family's environment.

And that was another reason that we thought it would be a good one to use.

Okay.

So we're going to dive into the actual study now.

So looking at it, you had set up some purpose to evaluate for the association between the

neighborhood as you spoke about where the child lives and characteristics and outcomes

of PICU admission from a large multicenter geographically diverse cohort using that census

tract designated COI and clinical data and the items you focused on really the objectives

were to see whether COI was associated with PICU mortality, severity of illness on PICU

admission.

And then you also looked at the PICU length of stay and admitted to the cohort of USPICUs.

So when you looked at it, first of all, interestingly enough, your retrospective study went from

January 1, 2019 to December 31, 2020, and included 15 PICUs across the United States.

So talk a little bit about your timeframe that you included, because we all know the

pandemic certainly altered pediatric ICU admissions.

Yes.

Well, you know, when we first started putting this together, it was in, I believe the spring,

around the spring policing meeting.

In 2022.

So we wanted to make sure that we had closed cases.

In other words, a lot of centers, including us, sometimes have trouble staying up to date

with their VPS cases.

And we try to get them in as soon as we can, but we wanted everybody to have complete data

on the timeframe.

And that's why we went through the end of 2020, because you know, some places may not

have had more recent data.

Now, of course, the data seemed pretty old.

And of course, there was the pandemic situation.

And we did look at the in our appendix, we looked at a secondary analysis of just the

March to December.

2020 timeframe, because we know that the epidemiology of PICU admissions in the world

in general was quite different place at that time.

And we didn't see any significant differences from what we looked at.

But still, we realized that, you know, there are a number of reasons, it would be interesting

to look at more recent data as well as, as an aside, they've also just within the past

month released a new version of the COI with now 44 indicators instead of 29.

It's called the COI 3.0, we use the 2.0.

So you know, things move fast, what if we had a little bit more recent indices and data

and stuff?

That's what moves us forward to continue to do more studies.

So I was gonna say, there's your next publication.

But looking at that, you included 15 PICUs in the United States.

Can you discuss a little bit how you chose those PICUs?

Or why that specific number?

Yeah, so we actually didn't expect we expected to have about five PICUs.

So again, we were discussing this in the social determinants of health policing meeting.

And we had a few centers that were interested that our VPS members are contributing centers,

as well as just had, you know, PIs who would be interested in in doing that.

And in doing the work, which involves pulling the admissions, and then having to go back

to the electronic medical record to get the addresses and map them to the census tract

and the COI.

And we also wanted to pull insurance payer and so forth, as well as primary language

spoken so that we could get some important covariates there.

So first, we just sort of use that group we and reached out to some people in the VPS

group that I'd worked with before that I thought might be interested.

Then during the main meeting of Polici, Dr. Slane and Nakande mentioned it to the whole

group.

And we ended up with several more centers, luckily, that were interested.

I unfortunately wasn't there.

I was I was supposed to be attending remotely, but my son had a GI illness, and we were actually

in the emergency department.

Luckily, he was fine.

But it was a bizarre situation, because people started texting me like, Oh, yeah, we're excited

about this.

I was like, I can't talk right now.

So anyway, he was fine.

But I just remember it clearly because of that challenging situation at the time.

One of the things I wanted to ask when you dive into the publication, it talks about

that the data was really pulled from academic children's hospitals.

So meaning freestanding children's hospitals.

Is that correct?

Yeah, I didn't realize that I've maybe hadn't made that until you were mentioning

earlier that you were trying to figure it out.

I didn't realize that I could have made that more clear up front.

You know, we would have taken whatever variety of children's hospitals we could get, we wanted

to have some geographic, you know, variability or diversity, because you can see clearly

if you look at our well, it's on our figure, if you go to diversity data, kids.org, it's

clear that that child opportunity index varies a lot over the country, but of course, also

across the country.

So we wanted to make sure we had some from different areas of the country.

So generally, it's, you know, academic children's hospitals that are contributing to VPS, not

always, but we basically just needed places that had the VPS data, and we're willing to

also map the addresses.

And I actually didn't really, like I said, I thought it was going to be more like five

to seven centers, I did not realize we were going to get 15 centers.

And that introduced some additional challenges regarding just coordinating things and finding

out, you know, differences in how people do things or how different challenges that we

didn't anticipate.

So we're happy to get that many together and able to contribute.

But so sorry, to answer your question, no, they're not all freestanding.

They're all pediatric ICUs that contribute to VPS, and either tertiary or quaternary

pediatric ICUs, but not necessarily freestanding children's hospitals.

A weakness of the study that's come up a lot with the reviewers and others is, is this

really representative of who presents to PICUs, you know, across the country and other types

of places?

And we don't know for sure.

Okay.

Well, at least I'm, you know, aligned with what you're saying.

I'm aligned with what some of the other people are discussing, because I've worked in a variety

of different PICUs and can recognize the resources that may or may not be present, depending

upon what type of setting you're in.

So I thought it certainly might influence some of your findings, but you did list it

as a limitation.

So you've already recognized that you and the other authors, but let's get to the meat

of the study.

What you really ended up finding, really what you found that children without insurance

coverage, irrespective of the COI neighborhoods that they were at, had significantly higher

odds of PICU mortality.

So let's talk about that.

I mean, there's such a push for every child to have insurance.

And we know in the recent years within the United States that, you know, the goal has

been to ensure that everybody truly has access to insurance.

But, you know, here, this sample is already showing you that that's still not the case.

Yeah.

So that was interesting and not our, our main focus.

We wanted it to be a covariate and ended up being the strongest association with mortality,

besides severity of illness.

So as you alluded to, you know, the very low Child Opportunity Index group had an odds

ratio of 1.3, but it was not statistically significant.

So it looked like there may be increased severity adjusted mortality in that group, but it didn't

reach statistical significance.

Whereas the group with none missing insurance, none or missing insurance was a ratio of 3.5.

So that's a really eye-popping increased mortality for those patients.

It wasn't a huge part of the study, but it was, I want to say 5%, it was over 1500 patients.

And we don't know exactly why those patients didn't have PICU.

We don't know exactly why those patients didn't have insurance.

In our supplement, we looked a little bit at the demographics of those patients and there

wasn't a clear cut case or ethnicity or origin or origin of admission or other signal as

to who, or, you know, how these patients might be different than the rest of the cohort.

But in talking with at least our social worker here about what she observes when families

don't have insurance, you know, sometimes it's just entered incorrectly.

Sometimes the family doesn't qualify because they're above the Medicaid threshold.

But according to our social worker in our population, at least the most common scenario

is that the family qualified, but hasn't applied or has let it lapse.

So it may just sort of be a signal.

And again, I don't know, this is sort of speculation, but interesting to look into further.

It may just be indicative of a family that has particular difficulties in filling out

the paperwork, getting the insurance coverage set up, you know, or, or keeping it set up.

And that probably translates to other healthy behaviors, preventive care visits, vaccines,

and any number of other things that may influence the child's health and lead to them ending

up with worse outcomes.

Yeah.

So it's, it's a lot of information to sift through to try and figure out the reasons

why.

And then we also know that the bureaucracy associated sometimes with trying to obtain

the insurance pieces really is challenging.

And as you're talking about COI and you talked about education and such, it's not just, you

know, it's the education of the child, but it's the education of the parents and caregivers

as well.

But now I want to talk about another thing.

Because as both of us clinicians in the ICU, when one of the things you did want to look

at.

It was mortality.

The study had found approximately 2% of an overall PICU mortality from this and the mortality

difference of 0.8% higher in the moderate and 0.5 to 0.6 higher in the very low and

low groups as compared with the very high group.

And particularly it's not statistically significant, but you had brought out in your paper that

it may be clinically significant.

So we know that PICU mortality overall isn't really high, particularly in comparison to

adults.

Can you tell us a little bit more about the clinical significance of the differences?

Yeah, well, that's a great point.

You know, over time, the PICU mortality seems to have trended down, which is good.

We hope we're providing better care.

We may also be admitting a different overall case mix, but either way, you know, if you're

thinking about something like below 2% in the high and very high group and well above

in the very low through moderate groups, you know, and you're looking at 15 sites and we

had 50,000 overall admissions.

There's only 33,000 that were index admissions that were able to have their COI map.

But anyway, I mean, that's a significant number of children and you're talking about, again,

children.

We all know, you know, years of life lost and so forth.

Another really interesting thing that I'd like to mention about that is a study came

out last year, like since we started working on this by Slope and Adele in Pediatrics,

where they looked at death and childhood.

So by linking the American Community Survey and the National Death Index, they looked

at do children in lower COI neighborhoods, are they more likely to die in childhood or

in the next?

11 years?

Or is it just kind of they're having years of life lost, you know, later in life, and

they did find a very similar odds ratio of something like 1.3 for the lower COI groups

to die within the next 11 years.

And the other thing I'll mention is that the high and the very high group were very similar.

So it seems like maybe these bins, these quintiles that people tend to use with COI of very low,

low, moderate, high, very high, and some of our authors, especially Adrian Zirka and our

author group are raising this just because they're divided into these quintiles doesn't

mean that the very high group.

You know, has it made in the next group is totally different and so forth.

And maybe that the and this came up with the reviewers too, and maybe that the high and

very high groups sort of have the resources they need.

And then there's a step off, because we definitely saw that the moderate group had the highest

severity of illness and the highest mortality.

So it is interesting to see there.

And it kind of lines up with some of that other data that the high and very high group

together had the lowest mortality and lowest severity of illness compared with the other

three groups.

Yeah, so one of the things I was wondering about when I had looked at this and trying

to understand the COI, and as we talked about, you know, it's the neighborhoods and have

your map up and you can look at it.

And it's, you know, it's a snapshot with the 15 PICUs, even though there's geographic diversity

there, and you had severity of illness in there as well.

But at the same time, diving a little bit deeper, were there more prevalent diagnoses,

the one in particular, there wasn't any discussion if these were all just acute episodic illnesses

versus their underlying chronic conditions, and you know, how severe that chronicity may

be.

Yeah, good point.

In our table two, we talked about primary diagnosis category, and we did see that respiratory

diagnoses were more common in the very low group.

And that's been consistent with what's described in the pre-hospital and emergency department

setting regarding lower SES groups, that there's more often respiratory diagnoses.

And in this paper, we didn't dive into it, but you know, asthma, bronchiolitis, some

of these common ones, and specifically have also been implicated as more commonly coming

from very low or SES or COI groups.

So it does seem to be more commonly the unscheduled admissions, the respiratory admissions.

As far as the chronic complex conditions, we felt like we had so much to jam into this

article that we didn't get into that.

We do have the diagnoses for these patients.

We have coded in the chronic complex conditions and are working on that as a secondary manuscript.

So hopefully we'll have more information on that soon, because we know, as you have alluded

to that a significant proportion, like 50% of our patients have some kind of chronic

complex condition, and we need to think about how that may work out when there are limited

resources to get, you know, healthcare follow-up or therapies or medications or any number

of challenges related to those ongoing health conditions.

Right.

And now we have started to turn our focus, as you're showing, to social determinants

of health.

But we're also trying to look at outcomes and beyond, you know, survival and really

what are meaningful outcomes and such.

And I know you didn't have the opportunity to delve into this and see them, you know,

beyond their admission and such.

But all of these elements are playing into the children that we see.

So longer-term mortality, as you said, in those lower COI neighborhoods seem to have

a higher risk of death in childhood, but also functional decline, you know, those that came

in and now had a complete change.

It'd be very interesting to be able to put those together at some point in time.

I know it's a huge data set, but to see what correlations truly are there.

Yeah, absolutely.

And I definitely think that's where we need to go down the road.

We're trying to understand some of the features carefully from what we have so far.

But yeah, down the road, we definitely need to know more about those longer-term outcomes.

We are going to try to look at readmissions because, you know, some of these patients

that came in, these indexed admissions may have later been admitted to another PICU,

but we can get some idea of were they admitted to the same PICU during the study period.

And we're looking at that.

It's going to be severely limited, again, getting back to our discussion about the COVID

times.

The admissions were thankfully down and they were different.

So it may be very, very different, but we can at least look at what we have and get

an idea.

It'd be interesting to see if we only have the two-year follow-up period, but each child,

we know the month they were admitted in, so they'll have certain months at risk for the

rest of the study period.

And we can look at were they more likely to be readmitted to the PICU just as a small portion

of what you're talking about.

Right.

And then the other thing to think about too, we're talking about, you know, the makeup

of the population.

And it was discussed that it didn't really match the U.S. population.

Right.

The U.S. population in terms of white, Hispanic, and Black.

So how do you think that impacted some of this?

I know you brought it out as a limitation.

Yes, that was an observation by the reviewers, which certainly is valid that our racial and

ethnic distribution didn't exactly match the U.S. population.

We did have over 19,000 census tracts and there's something like 73,000 in the U.S.

So it was like 27 or 28% of U.S. census tracts that we matched.

So we were happy with that.

But again, some of it may be reflective of...

Even though we had good geographic representation in some sense, you know, we had East, Northeast,

West, Midwest sites, we were still in major children's hospitals and cities.

So we really in the future need to look at some of these rural and urban differences

or non-academic PICUs and so forth to try to get a broader representation.

Yeah, I come from when I first started, I was out in a relatively rural environment

and people had to travel an enormous distance potentially to find pediatric ICUs.

So there's a huge gap in there.

So that's partly where my...

Where my interest stems from understanding the level of care that's available as well

as, you know, what time or distance, et cetera, that brings them into the ICU.

That's just me though.

Yeah.

So, you know...

I don't know if you were one of the reviewers, Maureen, but we got some questions

about that too and distance to care.

You know, unfortunately we did know if they came from an outside ED or ICU or floor, but

we really had no way of knowing how far they had to come to get to...

Even though that we had looked up addresses, you know, oftentimes they hadn't come straight

to that ED.

Or we may not know for sure.

I think there's a lot of literature about distance to care and the literature regarding

births or pregnant moms and so forth and trying to get to care.

In this population though, there's not a whole lot and that may be an interesting avenue.

Yeah.

And I'll disclose to you, I was not a reviewer.

So...

Okay.

Well, you're hitting on a lot of the points that they also hit on, so they're excellent

points.

Thank you.

No, you're welcome.

So our time's about up, but I wanted to give you the opportunity maybe to bring up some

lessons learned and then moving forward.

Yeah.

So you can use the information that you found.

I'd love you to try and bring it all together.

Yeah.

Well, you know, this was my first time trying to organize multiple other sites for a study

and it was quite a bit harder than I expected.

You know, it's a little bit frustrating.

The IRBs were okay, the DUAs were really challenging and that's part of the reason it took us so

long and then you end up, you know, publishing data that are over two years old.

And so that was frustrating.

Even though we were all using the same database, VPS, there's differences in, you know, what

statistical software, what kind of support they had to kind of...

Move the data along or clean it up or have it ready, you know, map the addresses and

so forth.

The Census Geocoder is a nice free online resource, but took some doing to get that

to work.

I would say that the COI mapping is pretty easy.

The team there is really great.

So I just, I learned a lot of lessons about the FIPS geocoding, the 11 digit census tracks

about how to use the geocoder and the COI data.

I think Polici was super awesome as a, as a, just a grassroots research network for recruiting

centers.

You know, if you want to find people who are interested in all kinds of different things

and willing to spend their time working on these studies, like it was just awesome to

get all that help from my collaborators and all these centers.

I would say how to use the data going forward.

I think that's a big challenge.

It's hard for us as clinicians to change the world or change, you know, how, how insurance

works or anything like that.

But I do think it's more and more over the past decade, we're realizing how much our

clinical care, while very, very important is a, is a portion of a child's continuum

of health.

And it's so important if we're thinking about equity for, for children to have the

tools they need to not get critically ill or not have to come back to the PICU or sometimes

not come back to the PICU frequently.

And so the more we think about that, the more we advocate for social determinants of health

screening and, and for different programs to try to help folks with needs so that they

can have the best health outcomes possible.

At our center, we have, I have a great hospitalist that I'm working with Layla DeWitt, who's

doing a lot of food insecurity screening, and she's managed to convince our administrators

to have a free meal.

It's a meal tray program.

And so it's just really cool to see different individuals interested in advancing this line

of research and of need to help these families.

It is amazing what people are doing.

I'm in New Jersey and I had learned about more in Southern Jersey.

So the Camden area, there's actually been some projects that are looking at the neighborhoods

and social determinants of health and taking it from more of the wellness and preventative

strategy.

And they've embedded more clinics and such to look at the population there.

And try and focus on, as I said, the preventative medicine and if they're discharged, you know,

they're having increased visits, they're having increased access, whether or not they have

the insurance piece.

So we work in the ICU level, which is a very different focus and somehow to bring that

grassroots in the neighborhood to impact what we're doing in the ICU.

You know, I think we all love what we get to do and take care of critically ill children

and see them, you know, overcome what brought them into the ICU.

And we've seen that progress as you talked about, as we've changed some of it, you know,

as we've done some more preventative strategies, whether it's the respiratory and some of the

antivirals we have, but you know, seat belts and helmets and all of those pieces as well.

But I think we'll always have a population that will come and require the PICU.

So there's a lot more to investigate and try and continue to improve upon.

So I love the fact that you and all your collaborators have really started to focus on this and trying

to delve into.

Maybe where we can make an impact.

So I've loved the fact that I've had the chance to chat with you and talk about this and hopefully

we can talk about it some more, but before we conclude, is there any final things you

want to say?

Well, I just want to thank again, my collaborators at all these sites.

Like I said, it was kind of a grassroots effort.

People had to work with their local VPS folks, get the data, and then work with folks to

get more data out of the EMR and map it to the, you know, census tract and COI and so

forth.

So I really appreciate all of their hard work.

And thanks for having me Maureen.

Thanks to SCCM.

Yeah, it's been a pleasure.

So this concludes another episode of the Society of Critical Care Medicine podcast.

If you're listening on your favorite podcast app and you liked what you heard, consider

rating and leaving a review.

For the Society of Critical Care Medicine podcast, I'm Maureen Madden.

Maureen A. Madden, DNP, RN, CPNC, AC, CCRN, FCCM, is a professor of pediatrics at Rutgers

Robert Wood Johnson Medical School.

Maureen Madden, DNP, RN, CPNC, AC, CCRN, FCCM, is a professor of pediatrics at Rutgers Robert Wood Johnson Medical School.

Maureen Madden, DNP, RN, CPNC, AC, CCRN, FCCM, is a professor of pediatrics at Rutgers Robert

Wood Johnson Medical School.

Maureen Madden, DNP, RN, CPNC, AC, CCRN, FCCM, is a professor of pediatrics at Rutgers Robert

Wood Johnson Medical School.

Maureen Madden, DNP, RN, CPNC, AC, CCRN, FCCM, is a professor of pediatrics at Rutgers Robert

Wood Johnson Medical School.

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