June 23, 2024 • 34 mins
PhD candidate Hannah Zadeh and Dr. Martha Carvour tackle the methodological complexities of modeling racial, rural, and occupational disparities during the first wave of the COVID-19 pandemic. Their paper, “Epidemiological approaches to multivariable models of health inequity: A study of race, rurality, and occupation during the COVID-19 pandemic” can be found in the June 2024 (Vol. 94) issue of Annals of Epidemiology.
Read the full article here:
https://www.sciencedirect.com/science/article/pii/S1047279724000589?via=ihub
Episode Credits:
- Executive Producer: Sabrina Debas
- Technical Producer: Paula Burrows
- Annals of Epidemiology is published by Elsevier.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Patrick Sullivan (00:12):
Hello, you're listening to EPITalk
Paper, a monthly podcast fromthe Annals of Epidemiology.
I'm Patrick Sullivan, Editor-in-Chief of the journal,
and in this series we take youbehind the scenes of some of the
latest epidemiologic researchfeatured in our journal.
Today we're talking with Ms.
(00:34):
Zadeh and Dr.
Carver about their article "Epidemiological Approaches to
Multivariable Models of HealthInequity a Study of Race,
rurality and Occupation Duringthe COVID-19 Pandemic.
You can find the full articleonline in the June 2024 issue of
the journal atwwwanalystofepidemiologyorg.
(00:55):
Hannah Zada is a PhD candidateat the University of Iowa
Department of Sociology andCriminology and a current NSF
GRFP fellow.
Their research centers on theuse of race and statistics in
medicine and they're currentlyworking on a dissertation
project about the history ofrisk prediction in medicine over
(01:15):
the 20th century.
Welcome, H annah and A Dr.
Martha Carver is an assistantprofessor of internal medicine,
infectious diseases andepidemiology at the at the
University of Iowa, carver CCollege of Medicine and College
of Public Health.
Her research combinesepidemiological and
community-engaged researchmethods to promote health among
people with diabetes and improvethe outcomes of
(01:38):
diabetes-related infections,including COVID-19, with a focus
on designing and implementingequitable systems in healthcare
and public health.
Reading your bios, you'reexactly the two people I want to
be talking to today, because Ilove where your professional
focus is and I'm so glad youcould join us today.
Martha Carvour (01:56):
Thank you so.
much.
It's great to be here.
Yeah, happy to be here.
Patrick Sullivan (02:00):
Great.
Thanks.
So we're going to talk aboutthis article that you submitted
to Annals of Epidemiology t hatreally captures up a lot of very
current things like, obviously,the COVID-19 pandemic, but also
you're really working onapproaches to multivariable
modeling of health inequity andjust taking the complexity of
(02:20):
how these inequities occur andreally representing it through
modeling methods.
So I'm excited to talk to youabout this.
Just starting out with what thepurpose of the study was, what
research question you wereaiming to answer?
Martha Carvour (02:32):
Sure, yeah.
Thank you so much, D r.
Sullivan, for the opportunityto be here today.
We're really excited to talkmore about this paper.
So I can talk sort of brieflyabout the impetus behind the
study and that was really whatwe were seeing initially in that
first year, especially in thefirst six months of the pandemic
, in a public health context andin a clinical context.
Clearly we were seeing healthinequities.
(02:53):
In our own state, in Iowa, wewere seeing inequities
particularly in rural areas andrural counties, and many of
those inequities wereparticularly affecting people in
frontline occupations meatpacking, meat processing.
There were highly publicizedreports of this across lots of
states and lots of regions, butmany rural areas and rural
(03:16):
counties have substantialindustrial reliance on some of
these frontline occupations andso we were seeing that this was
one of the factors contributingto these inequities.
We also saw, in rural contextsas well, the persistence of
otherwise well-documentedinequities by race and ethnicity
.
So really we set out tounderstand the intersections of
(03:41):
these different factors inhealth inequity and COVID and
understand more about whathappens with racial and
occupational inequities in ruralcontexts like here in Iowa and
in other largely rural states.
Patrick Sullivan (03:55):
So you really described then sort of these
multiple layers of exposures andepi terms and inequities, and I
wonder if you could tell usabout what some of the
methodologic hurdles were whenyou're really trying to
understand some of thestructural characteristics that
may be associated withdisparities.
And you talk a little bit aboutthis in the introduction, but
what were some of thosemethodologic hurdles?
Hannah Zadeh (03:55):
Thanks.
Yeah.
(04:17):
So in our introduction we kindof lay out three of the common
methodological problems that wesometimes see in these kinds of
analyses.
And the first one a really bigone and one that I'm really
interested in coming from, likethe sociology of race, is the
way that race is modeled in alot of these statistical
analyses.
So you know, even though in thepast several years at least,
(04:42):
it's coming to be moresolidified in the mainstream
that the truth that race is nota biological variable, it's not
a biological characteristic,it's a social, structural level
system of racism, we still see,despite that understanding, we
still see methodologically sortof a dissonance between that
(05:03):
idea and how race is actuallymodeled.
We still see race modeled as anindividual level variable.
So we were really interested inhow do we operationalize racism
really in these models?
And one way that we wereinterested in doing that is by
using county level data to focuson where racism is occurring,
(05:26):
via the proportion, racialproportion in different counties
.
So that was kind of a firstthing is modeling structural
racism through these statisticalanalyses.
A second thing was thinkingabout how this period
immediately after the pandemicbegan in the United States,
(05:46):
period immediately after thepandemic began in the United
States.
It's kind of a unique period forthinking about what kinds of
resources counties have in termsof health infrastructure, which
is everything from counties'ability to distribute PPE to be
able to deliver remote educationto students, everything like
that.
And so we were really interestedin sort of this first initial
(06:08):
period, this first six months,as a way of sort of looking at
this as sort of a test of likewhat kinds of resources counties
have.
Because, you know, our kind ofthinking is from the literature
that counties with higherproportions of non-white people
are maybe going to have fewercounty resources because of how
we know that systemic racismworks in the United States.
(06:31):
And then a third thing, lastly,is just that we have seen that
there is a general paucity ofstatistical epidemiological
analyses that are looking at theintersection between racism and
workplace-related processesthat affect health in rural
areas.
I think there's still sort ofthis idea that rural places are
(06:53):
only populated by white peopleand that, like, these racial
disparities aren't and theseracial processes aren't
happening in rural areas, and weknow that that's not true.
And then I think there hasn'tbeen, as I think, as much
attention as we would like tosort of the health-related
processes happening due toprocesses happening in the
workplace.
Patrick Sullivan (07:14):
Yeah, I mean thank you for that.
You really have sort of putsome meat on the bones around
the statement in yourintroduction, which is that race
variables aren't representingbiological mechanism in this
case.
Maybe in some cases there maybe some very specific cases they
do, like sickle cell or someother biological mechanism, but
(07:34):
here race variables are reallyrepresenting structural racism,
you say, in the form ofinequitable resource
distribution or access.
So you've really sort of talkeda little bit about you know how
that plays out.
And then I think the kinds ofvariables that you brought into
the analysis animate that ideaof inequitable resource
distribution when you get intothe analysis.
(07:55):
So I think it's just soimportant, especially when
dealing with race and ethnicityas social determinants of health
, to ask that next layer down ofquestions and really
operationalize it in ways thatare changeable.
You know that could beaddressed and maybe we'll get
into some of those outcomes.
So in this research then talk alittle bit about your study
design and sort of your unit ofanalysis here, because you
(08:19):
relied on some population datasources.
So what was the design and whatwas the unit of analysis?
Martha Carvour (08:31):
Sure, yeah, so this study really emphasized the
county level.
So we measured exposures andoutcomes on the county level.
And we did that for two reasons.
One is we have more countylevel data available to look at
social and structural reserves,as I think Hannah outlined
really nicely.
Part of what we were thinkingis, every county going into the
pandemic, that early initialshock period had some set of
social and structural reservesand our hypothesis is that the
(08:54):
outcomes, or the disparities inCOVID-related outcomes, could
depend in some way on thosesocial and structural reserves.
So we were really focused onthe county level for that reason
.
The other reason is alsoconnected with what Hannah
mentioned, which is we wanted toget away from this idea that
everything we were looking atwas exclusively an individual
(09:15):
level outcome.
So what is it about thesestructural factors?
Thinking in public health terms, what can we do to make a big
impact right, to have an impacton a county level or population
level, by identifying thosegroup level or county level
types of characteristics?
What are those social andstructural reserves that might
be amenable to intervention?
(09:36):
So we did a county levelanalysis.
We had focused entirely on ruralcounties in the US and we
matched counties that wereidentified as case counties,
which were counties withdisparities, so at least one
standard deviation above themean for COVID incidents or for
COVID mortality.
We did models of each and thencontrol counties and those
(09:58):
controls were both within thesame state as the cases we
recognized.
Of course there's a lot ofstate to state variation and
different types of policies andsocial and structural reserves,
so we had controlled for thatand we also matched on the
rural-urban continuum codedefined by the USDA to try to
have direct comparisons of thesocial and structural variables
(10:19):
that we were looking at.
Then we used a form ofhierarchical logistic regression
modeling.
And this is, I think, one ofthe aspects of the study that I,
as an epidemiologist, felt likeI learned the most from which
was really splitting thisdeliberately into two phases or
two sections.
One phase the first phase wasfocused on who was disparately
(10:40):
affected, so what are thedemographic characteristics of
counties that experienceddisparities?
And then, as the second phase,what are the social and
structural factors that seem tobe connected to those
disparities, to remind us thatthose are the modifiable factors
in a potential public healthintervention.
Patrick Sullivan (11:00):
Great.
So, just building on that, I'mlooking at your table four,
which are the final adjustedmodels, and I'm not sure if
that's where you would go next.
There are some really for meunexpected associations with
both incidence and mortality.
So what were some of the mainfindings or key takeaways from
these analyses that you'redescribing?
Hannah Zadeh (11:20):
I can talk a little bit about that.
For COVID-19 incidents.
We found that hospital closures, hospital size and industry
reliance on mining ormanufacturing, as well as
baseline metric related tosmoking, all correlated with
county disparities.
We also found evidence ofinteractions between racial
(11:42):
disparities and industryreliance disparities and
industry reliance.
And so industry reliance is oneof the variables that we were,
I think, really interested tosee what we would find in this
analysis and just as a littlebit of background on that
variable.
So this is from the USDA'sEconomic Research Service and
it's basically measuringcounties' economic dependence on
(12:03):
certain industries.
So more precisely it's if acounty takes over 23% of its
earnings or if over 60% ofcounty residents are employed in
those industries, and so wewere interested in if a county
is really sort of economicallydependent on a certain industry,
like what kinds of COVID-19outcomes might that be
(12:24):
associated with?
So we did find for counties inwhich mining and manufacturing,
respectively, are predominant.
We found that was significantfor incidents.
Like I was saying, we foundevidence of interactions between
racial disparities and industryalliance.
More specifically, we foundthat in counties with higher
proportions of Hispanicresidents that had an economic
(12:44):
reliance on manufacturing, theretended to be higher racial
disparities in COVID-19incidents.
Also, for incidents, we foundthat disparities persisted in
counties with higher proportionsof Black or Hispanic residents.
So that was incidents.
And then for COVID-19 mortality, we found that a couple of
variables made it into the model.
(13:05):
So the proportion of residentsin the county classified as
unemployed or disabled was one,and then the proportion
classified as quote unquoteobese was one.
The proportion reliant onpublic transportation was one,
and a variable that was an indexof poverty segregation, all
correlated with non-metropolitancounty disparities, all
correlated with non-metropolitancounty disparities.
(13:26):
And you know, maybe we can talkabout this later.
But, as we sort of said in thebeginning, like we were using a
lot of these variables that arecommonly modeled as individual
level variables and trying tosort of contextualize them more
and think of them as signs ofcounty level resources, right.
(13:47):
So I think one thing that wesee a lot in the literature is a
focus on these sort of quoteunquote lifestyle associated
variables and sort of a push tosort of shift the locus of blame
onto individuals.
And we're sort of interested intaking these kinds of data
(14:09):
because that is the kind of datathat is publicly available and
recontextualizing it as a signof county level resources.
So that's just a bit ofbackground on why we included
some of these variables in theinitial pool, and that was some
of the findings.
Dr.
Carvour, I don't know if youhave anything to add.
Martha Carvour (14:33):
No, I think that's a great summary, H hannah
, thank you, and I would reallyreinforce that piece about what
we can understand about thesevariables on a county level or
sort of a group level.
HOne of the things we talkedabout as an example is the
public transit, which wassomething we didn't really
expect to see sort of inverselyassociated, because it makes
perfect sense just in terms oftransmission models and the way
that we were reading about otherrisk factors for exposure to
(14:55):
COVID or to SARS-CoV-2, being inenvironments where you might
have more exposure to otherindividuals, including certain
transportation modes, where thatmight be more likely.
It has the opposite effect,right, it's more likely to drive
at least incidents, and we sawthis was inversely correlated
with mortality.
(15:15):
And the way we're kind ofputting that together is
thinking that this could be asign of infrastructure,
transportation infrastructure toget access to healthcare, for
example, from rural areas wherethere are often geographic
barriers to care, and so that's,I think, a different issue in
terms of what this variablerepresents on a county level
versus what the exposure mightrepresent for an individual, and
(15:38):
that's an example, too, thathas borne out in community-based
discussions, you know, withcommunity partners.
We hear this time and againabout the issue with, very
specifically with,transportation, which I think
was helpful to kind of add tothe context of what we were
seeing with these publiclyavailable data.
Patrick Sullivan (15:55):
Or you know, higher access to public transit
may also be confounded withother sort of positive actions
in communities.
And I think one of these kindsof analyses I'm a fan of, and so
they let you get at some sortof big issues within communities
, but also have the disadvantagethat you can't really link the
people, the individuals who areusing, for example, public
(16:17):
transportation systems.
So there may be some need tosupplement with some other kinds
of studies.
But yeah, I was struck by Iencourage everybody to look at
table four, particularly in themortality column, like some of
the, maybe because of small cellsizes, but there are some
impressive adjusted odds ratiosand maybe some findings that
might not be what you might'vepredicted.
(16:38):
So an interesting table.
So can you talk just in generalabout some of the strengths and
limitations of the approach?
We've touched on these a littlebit in terms of the sort of
ecological level approach, butwhat do you see as the strengths
and any of the limitations thatyou would want to highlight?
Hannah Zadeh (16:55):
I can get us started.
So I think one of the strengthsof this study and something
that was, I think, born out of alot of conversations that we
had with co-authors was, like Iwas talking about in the
beginning, thinking about how tooperationalize race in
epidemiological models.
Right, because, you know, wedefinitely do want to be
(17:18):
continuing to track the way thatracism is affecting public
health, but we want to be doingthat in a way that is really,
you know, wedding a structuraltheory of race to methodology.
You know, I think I reallyalways like to go back to Ruth
Wilson Gilmore's definition ofstructural racism, right,
(17:40):
because I feel like structuralracism is something that is
increasingly like it's kind ofbecome like a buzzword.
That is increasingly like it'skind of become like a buzzword,
and Ruth Wilson Gilmore definesit as the state sanctioned or
quote the state sanction and orextra legal production and
exploitation of groupdifferentiated vulnerability to
premature death.
And I just feel like thatdefinition always sort of
(18:02):
grounds me or grounds adiscussion, like when we're
thinking about what in ourmethodology we're trying to
represent, right, like it's veryserious.
And in some of ourconversations we talked about
how, you know, we werefrustrated with some of the
literature where it doesn't seemlike sometimes folks are
applying the same level of likemethodological rigor to really
(18:22):
representing racism in thesemodels.
So that was something that Ithink is a strength of the study
is we were trying to sort ofexplore approaches to doing that
.
You know here, what we weredoing was, you know, using the
data we have to at the countylevel, model a proportion of
residents of a given racialidentity and sort of use that as
(18:44):
a way to sort of follow wherethese resource distribution
mechanisms of racism arehappening.
So I think that's one strength,and we had a couple.
There's always limitations toany study.
I think that one thing that wasreally sort of a constant thing
that we were trying to navigateis the availability of data.
(19:11):
So, like we were talking about alittle bit before, a lot of the
publicly available data that isavailable is this sort of data
that is often thought about asindividual level data like
smoking, like BMI, even income,and so that was something that
we were aware of.
I know that at one point we werereally interested in looking at
how local union density wouldbe shaping COVID-19 transmission
(19:33):
and mortality, especially whenwe know that the workplace and
that workers' power within theirworkplace is something that
really impacted their ability toaccess PPE and necessary
protections, and that data wejust weren't able to find.
So it would be very interestedif others are able to get that
data and to do those kinds ofanalyses.
(19:53):
And then I think a lastlimitation is that, especially
in rural areas, the county levelis like pretty big and there
can be a lot that's happening ina given county.
A lot of like dynamics city tocity or town to town, that like
are just sort of being like allmixed up and smoothed over when
you're looking at the countylevel.
(20:14):
So I think that, like futureanalyses that are getting down
to a more localized level wouldbe really helpful to have.
Patrick Sullivan (20:23):
Yeah, I mean I think the idea that sometimes
ecological analysis at a broaderlevel can be hypothesis
generating.
But then you know you couldthink about smaller
administrative areas if data isavailable.
The data are available, or youknow, sometimes I think like the
next step could be as, on sucha different methodologic path,
to say, like key informantinterviews with around you know
(20:46):
some of the manufacturing or Idon't know the public
transportation, so you can justsee how this is like.
This rich hypothesis generationand the ecological approach is
great for that, and then it sortof answers several important
questions and then raises abunch more.
You know that you might need toexplore with different methods,
so great.
So in light of that, what doyou think the implications are
(21:10):
for this idea of advancinghealth equity?
You sort of talked some aboutinequities, but what should be
done with these data to advanceconversations about improving
health equity through policy orthrough some kind of public
health practice?
Martha Carvour (21:26):
Yeah, I think there are a couple of things
that I know Hannah and I havetalked about, and we've also had
a lot of, I think, reallyhelpful and rich discussions
with co-authors, who I also wantto acknowledge for a huge
contribution to the way that wethought about each of these
variables and kind of put theseresults together.
I think there are two sort oftakeaways for us, and one of
those is methodological and oneof those is sort of this
(21:48):
practical public health piece inrural settings.
On the methodological side, Ithink using the phased analysis
that we did here allowed us tointerrogate some of our own
assumptions about what racevariables represented.
I think we're used to inepidemiological models, we're
very used to sort of adjustingfor certain socially constructed
(22:10):
demographic variables, and inthis case we tested some of the
assumptions about what does racemean?
What is it actually measuringhere?
Where does it fit into adirected acyclic graph?
Are our assumptions?
Do they appear to be valid,based on what we're seeing with
the analysis and the order inwhich you put variables into the
model?
And we found that, as expected,there's a lot of variability in
(22:34):
what the race constructsmeasure, especially on the
county level like this, and soreally thinking carefully about
questioning those assumptionsand thinking carefully about
what are the mechanisms,socially and structurally, that
we're trying to measure, becausethat's going to speak to how we
can actually improve outcomes,reduce inequities, really
(22:55):
thinking critically about moremechanistic pieces.
The practical public healthpiece is that we really want to
work with community partners,work with other collaborators,
really work in communitysettings to learn more about
what the social and structuralfactors are that are important,
how to think about interveningon these modifiable factors to
(23:15):
improve outcomes.
We've been working in this area,motivated in part by the
research here that Hannah hasled and that we've been doing as
a group to ask those questionsat the community level right and
conduct qualitative,quantitative research,
interventional research, andstart to answer some of those
more specific questions.
A standout piece of that iswhat's still happening with
(23:39):
occupational disparities afterthat initial wave where so many
frontline workers were affected.
We don't talk nearly as muchabout frontline workers or hear
as much about frontline workerstoday as we did several years
back when this data wascollected or when the study was
sort of devised.
And yet we know that there arelong-term impacts on many of the
(24:00):
frontline workers in thosecommunities many persistent
inequities in access to care forthings like long COVID or just
other health conditions.
So this is something that Ithink has motivated us to see
frontline workers as a part ofthe public health workforce in
rural communities, the peoplewho are packing the food,
delivering food this is part ofwhat sustains life and health in
(24:22):
a public health emergency andso ensuring that we have
equitable access to resources.
Much like I had, I was quiteprivileged to have access as a
frontline healthcare worker todifferent types of occupational
protections, and I still havethose occupational protections
to this day as a healthcareworker, and those may not exist
(24:44):
for others who were also on thefrontlines.
Patrick Sullivan (24:46):
Yeah, I think this idea of extending frontline
workers to say that that doesinclude some very highly trained
, you know, colleagues, but alsothere's as critical capacity
for transportation, for food,for other things, and I think
some of the discussion aboutfrontline workers, you know, did
focus more in medical settings.
So I really appreciate youreframing that.
(25:07):
So we're going to transitionnow to a piece of the podcast
that to me is always at leastinteresting, which is called
Behind the Paper, Dr.
Carvour always someone whoremains grateful for the
mentorship that I got throughoutmy career and now see my role
as a mentor as the mostimportant thing that I do for
public health.
But I want to talk a little bitjust about, even in the way I
(25:30):
think you've prepared for thiswe're on Zoom, I'll just tell
Carvour listeners we're on Zoom,so that body language about
who's going to answer isn'tquite so evident.
But even down to the level Ithink of preparing for this,
it's clear that you've beenthoughtful about how you're
going to take these roles.
So I really just want to talk alittle bit about the
interaction and your worktogether on this and how you
(25:52):
sort of saw those roles and howyou work together obviously
collaborated in this, so eitherone of you can start.
But what was that workingrelationship like and how did
you handle the roles?
Sure, yeah, I can start.
We were just talking aboutbeforehand that we sort of
started.
.
We Carvour met each other inMarch 2020.
And I was saying that my Zoommeeting with Dr Carver D was.
(26:15):
Carvour first Zoom meeting I'd.
ever had in my life.
And look at us now.
Hannah Zadeh (26:19):
So, yeah, I H was in the University of Iowa what
they call a pre-MSTP medicalscientist training program as an
undergrad and it was supposedto be for summer 2020, but it
got shut down because of thepandemic.
But Dr Carver graciously agreedto still work with me on a
project anyway, and we had a lotof conversations early on about
(26:42):
racial health disparities andhealth equity.
And it was cool because I thinkwe sort of got to have some
conversations sort of in realtime as we were observing the
tragedies all across the US, allacross the world, that were
happening as a result of theCOVID-19 pandemic, and we were
also observing, I think, some ofthe things that were sort of
being overlooked in the mediaand also, I think, in the
(27:05):
scientific conversations aroundthe pandemic.
And I feel very lucky to havebeen able to have those
conversations with Dr Carver, aswell as all of our co-authors
throughout this project.
And yeah, I'll stop there, drCarver, if you have anything to
add.
Martha Carvour (27:22):
Yeah, thank you, hannah.
I mean I think I would echo alot of what you've just said.
I think this is a project thathas gone through its own phases,
as the pandemic has, and reallyrecognizing the significance
and the importance of the typesof questions and in many ways,
even though it was a longproject, as any research project
(27:43):
can be, it's also an urgenttopic, right.
And so thinking about how wecan still be balancing that with
the practical public healthwork and the practical community
engagement along the way.
Hannah has really approachedthis as a leader, intellectually
, collaboratively, has engaged alot of co-authors from
different disciplines to bringtheir perspectives, co-authors
(28:05):
from different disciplines tobring their perspectives.
I think that's really important,especially for scholars sort of
in training and in development.
I think we're all sort of intraining .
always Carvour and I advice-that's the good thing about the
field, but I think, reallyhaving that chance to lead.
and to ask those tough questions, to find answers to those tough
(28:27):
questions and to work through aprocess, even when there are
challenges and in Hannah's case,over several years and other
professional milestones, and soit's wonderful to sort of see it
come to this point, to see thatwe continue to have discussions
about not only this project butabout the profession, about the
(28:54):
direction of the profession,and I think those are, for me,
those are some of the mostimportant and gratifying parts
of doing this work.
I think the research, of course, is crucial.
The ability to have an impactis crucial, but one of the ways
to make the biggest impact isthrough mentorship of other
really, really skilled scholarslike Hannah.
Patrick Sullivan (29:12):
Thank you and I'm going to ask you one more
question and then Hannah, I'mgoing to come back to you which
is Dr Carver, what advice so youdescribe how this really
productive, professional,scientific relationship
developed and how impactfulthat's been?
What advice would you have forearly career researchers who
come in wanting this kind ofexperience?
(29:32):
How do they go about, you know,opening up those relationships
or putting themselves in aposition to be in this kind of
mentoring, collaborativerelationship?
Yeah, I think that's such animportant question.
I think I know from talkingwith so many students,
especially who have been inschool or in different training
programs over the last severalyears, this has also been a
really tough several years to bea student and be thinking about
(29:55):
what the future looks like andbe thinking about how to get
into a program or how to getresearch experience.
So, first and foremost, you'renot alone and don't be patient
with yourself.
Be patient with the process andseek out people who want you to
learn, want you to succeed, whoyou can, you feel comfortable
(30:16):
with and feel like you canadvocate for yourself with.
I think one of the key skillsto learn in a mentoring
relationship that I learned alsoas a student is how to
communicate about what I need orwhat I'm, what I'm, what's not
clear to me.
So it's just such a greatopportunity to do that in a
place that you should havesupport and and I think that's
(30:36):
really really important.
Martha Carvour (31:24):
Thanks.
I Hannah the other piece ofadvice I would really put out
there is there's no substitutefor critical thinking.
There are a lot of greatmethods, there are a lot of
great resources, but you reallydo have to think critically
about the data that's put infront of you, the questions that
you're asking, and I think, ifyou have in mind a career
trajectory or a research topicthat's of interest to you, there
may also be a different waythat you're approaching that or
a different way that you'rethinking about that, and I think
that it can actually besomething that the profession
needs right now.
We need people who thinkdifferently and critically, and
so don't give up on that piece.
Definitely it takes hard workand dedication and great mentors
, but don't give up on thinkingcritically for yourself.
Thanks.
Patrick Sullivan (31:24):
And Hedda, I'm going to give you the last word
, which is what advice would youhave for maybe more seasoned or
later career colleagues interms of mentoring and working
with students or youngerresearchers?
Yeah, I think like I've.
been really, Carvour reallylucky in the mentors that I've
had throughout my career so farand it's something that I think
(31:47):
sort of took for granted untillike the past couple of years.
.
Hannah Zadeh (31:51):
But I mean, like Dr Carver was saying, students,
especially in the past couple ofyears, have been dealing with a
lot, and I think like,especially if we want to, like
you know, make researchsomething that is accessible to
everyone including, you know,people who are coming from
low-income backgrounds, you know, who don't have like family
connections to research, or evento higher education, people who
(32:15):
are trying to care fordependents while they're
pursuing a career in research,we really need mentors who are
like willing to be flexible withstudents and patient with
students, even coming down tolike nitty gritty of like
scheduling stuff and studentshaving to miss meetings, stuff
like that.
I think like that kind of likepatience and flexibility is
(32:37):
really important to like do thework of making this profession
accessible and open to all kindsof students.
And again, I just have been solucky with Dr Carver and other
mentors that I have to reallysee that in practice.
Patrick Sullivan (32:52):
And you'll have a chance to pay it .
forward Carvour So keep yournotes.
So, do you have any lastthoughts that you'd like to
share with our listeners?
Hannah Zadeh (33:01):
I Carvour?
one thing that you know, DrCarver, and I talk about a lot
still to this day is thepandemic is not over.
We've talked in thisconversation about how the
implications of long COVID andyou know there are still
thousands of deaths every weekin the United States, and so
like, even though this projectwas just focusing on that
(33:22):
initial period.
Yeah, my final thought is thatthe pandemic is still ongoing
and more of this criticalthinking about health equity
needs to be done.
Patrick Sullivan (33:33):
Dr Carver.
Martha Carvour (33:34):
No, I think that's great.
Thank you very much and thanksHannah.
M.
Z.
.
T P t o j o t A C i o E.
(34:08):
F a t o t p o t r t a f i t e am f t j y c v u www.
annalsofepidemiology.
o
Patrick Sullivan (33:38):
All right, that brings us to the end of
this episode.
Thank you again, ms Sada, andDr Carver, for joining us today.
It was so nice to have you onthe podcast, thank you, thank
you so much.
Today was so nice to have youon the podcast.
Thank you, thank you so much.
I'm your host, patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
EpiTalk, brought to you byAnnals of Epidemiology, us
(34:09):
online atwwwannalsofepidemiologyorg.
Hello, you're listening to EPITalk
Paper, a monthly podcast fromthe Annals of Epidemiology.
I'm Patrick Sullivan, Editor-in-Chief of the journal,
and in this series we take youbehind the scenes of some of the
latest epidemiologic researchfeatured in our journal.
Today we're talking with Ms.
(00:34):
Zadeh and Dr.
Carver about their article "Epidemiological Approaches to
Multivariable Models of HealthInequity a Study of Race,
rurality and Occupation Duringthe COVID-19 Pandemic.
You can find the full articleonline in the June 2024 issue of
the journal atwwwanalystofepidemiologyorg.
(00:55):
Hannah Zada is a PhD candidateat the University of Iowa
Department of Sociology andCriminology and a current NSF
GRFP fellow.
Their research centers on theuse of race and statistics in
medicine and they're currentlyworking on a dissertation
project about the history ofrisk prediction in medicine over
(01:15):
the 20th century.
Welcome, H annah and A Dr.
Martha Carver is an assistantprofessor of internal medicine,
infectious diseases andepidemiology at the at the
University of Iowa, carver CCollege of Medicine and College
of Public Health.
Her research combinesepidemiological and
community-engaged researchmethods to promote health among
people with diabetes and improvethe outcomes of
(01:38):
diabetes-related infections,including COVID-19, with a focus
on designing and implementingequitable systems in healthcare
and public health.
Reading your bios, you'reexactly the two people I want to
be talking to today, because Ilove where your professional
focus is and I'm so glad youcould join us today.
Martha Carvour (01:56):
Thank you so.
much.
It's great to be here.
Yeah, happy to be here.
Patrick Sullivan (02:00):
Great.
Thanks.
So we're going to talk aboutthis article that you submitted
to Annals of Epidemiology t hatreally captures up a lot of very
current things like, obviously,the COVID-19 pandemic, but also
you're really working onapproaches to multivariable
modeling of health inequity andjust taking the complexity of
(02:20):
how these inequities occur andreally representing it through
modeling methods.
So I'm excited to talk to youabout this.
Just starting out with what thepurpose of the study was, what
research question you wereaiming to answer?
Martha Carvour (02:32):
Sure, yeah.
Thank you so much, D r.
Sullivan, for the opportunityto be here today.
We're really excited to talkmore about this paper.
So I can talk sort of brieflyabout the impetus behind the
study and that was really whatwe were seeing initially in that
first year, especially in thefirst six months of the pandemic
, in a public health context andin a clinical context.
Clearly we were seeing healthinequities.
(02:53):
In our own state, in Iowa, wewere seeing inequities
particularly in rural areas andrural counties, and many of
those inequities wereparticularly affecting people in
frontline occupations meatpacking, meat processing.
There were highly publicizedreports of this across lots of
states and lots of regions, butmany rural areas and rural
(03:16):
counties have substantialindustrial reliance on some of
these frontline occupations andso we were seeing that this was
one of the factors contributingto these inequities.
We also saw, in rural contextsas well, the persistence of
otherwise well-documentedinequities by race and ethnicity
.
So really we set out tounderstand the intersections of
(03:41):
these different factors inhealth inequity and COVID and
understand more about whathappens with racial and
occupational inequities in ruralcontexts like here in Iowa and
in other largely rural states.
Patrick Sullivan (03:55):
So you really described then sort of these
multiple layers of exposures andepi terms and inequities, and I
wonder if you could tell usabout what some of the
methodologic hurdles were whenyou're really trying to
understand some of thestructural characteristics that
may be associated withdisparities.
And you talk a little bit aboutthis in the introduction, but
what were some of thosemethodologic hurdles?
Hannah Zadeh (03:55):
Thanks.
Yeah.
(04:17):
So in our introduction we kindof lay out three of the common
methodological problems that wesometimes see in these kinds of
analyses.
And the first one a really bigone and one that I'm really
interested in coming from, likethe sociology of race, is the
way that race is modeled in alot of these statistical
analyses.
So you know, even though in thepast several years at least,
(04:42):
it's coming to be moresolidified in the mainstream
that the truth that race is nota biological variable, it's not
a biological characteristic,it's a social, structural level
system of racism, we still see,despite that understanding, we
still see methodologically sortof a dissonance between that
(05:03):
idea and how race is actuallymodeled.
We still see race modeled as anindividual level variable.
So we were really interested inhow do we operationalize racism
really in these models?
And one way that we wereinterested in doing that is by
using county level data to focuson where racism is occurring,
(05:26):
via the proportion, racialproportion in different counties
.
So that was kind of a firstthing is modeling structural
racism through these statisticalanalyses.
A second thing was thinkingabout how this period
immediately after the pandemicbegan in the United States,
(05:46):
period immediately after thepandemic began in the United
States.
It's kind of a unique period forthinking about what kinds of
resources counties have in termsof health infrastructure, which
is everything from counties'ability to distribute PPE to be
able to deliver remote educationto students, everything like
that.
And so we were really interestedin sort of this first initial
(06:08):
period, this first six months,as a way of sort of looking at
this as sort of a test of likewhat kinds of resources counties
have.
Because, you know, our kind ofthinking is from the literature
that counties with higherproportions of non-white people
are maybe going to have fewercounty resources because of how
we know that systemic racismworks in the United States.
(06:31):
And then a third thing, lastly,is just that we have seen that
there is a general paucity ofstatistical epidemiological
analyses that are looking at theintersection between racism and
workplace-related processesthat affect health in rural
areas.
I think there's still sort ofthis idea that rural places are
(06:53):
only populated by white peopleand that, like, these racial
disparities aren't and theseracial processes aren't
happening in rural areas, and weknow that that's not true.
And then I think there hasn'tbeen, as I think, as much
attention as we would like tosort of the health-related
processes happening due toprocesses happening in the
workplace.
Patrick Sullivan (07:14):
Yeah, I mean thank you for that.
You really have sort of putsome meat on the bones around
the statement in yourintroduction, which is that race
variables aren't representingbiological mechanism in this
case.
Maybe in some cases there maybe some very specific cases they
do, like sickle cell or someother biological mechanism, but
(07:34):
here race variables are reallyrepresenting structural racism,
you say, in the form ofinequitable resource
distribution or access.
So you've really sort of talkeda little bit about you know how
that plays out.
And then I think the kinds ofvariables that you brought into
the analysis animate that ideaof inequitable resource
distribution when you get intothe analysis.
(07:55):
So I think it's just soimportant, especially when
dealing with race and ethnicityas social determinants of health
, to ask that next layer down ofquestions and really
operationalize it in ways thatare changeable.
You know that could beaddressed and maybe we'll get
into some of those outcomes.
So in this research then talk alittle bit about your study
design and sort of your unit ofanalysis here, because you
(08:19):
relied on some population datasources.
So what was the design and whatwas the unit of analysis?
Martha Carvour (08:31):
Sure, yeah, so this study really emphasized the
county level.
So we measured exposures andoutcomes on the county level.
And we did that for two reasons.
One is we have more countylevel data available to look at
social and structural reserves,as I think Hannah outlined
really nicely.
Part of what we were thinkingis, every county going into the
pandemic, that early initialshock period had some set of
social and structural reservesand our hypothesis is that the
(08:54):
outcomes, or the disparities inCOVID-related outcomes, could
depend in some way on thosesocial and structural reserves.
So we were really focused onthe county level for that reason
.
The other reason is alsoconnected with what Hannah
mentioned, which is we wanted toget away from this idea that
everything we were looking atwas exclusively an individual
(09:15):
level outcome.
So what is it about thesestructural factors?
Thinking in public health terms, what can we do to make a big
impact right, to have an impacton a county level or population
level, by identifying thosegroup level or county level
types of characteristics?
What are those social andstructural reserves that might
be amenable to intervention?
(09:36):
So we did a county levelanalysis.
We had focused entirely on ruralcounties in the US and we
matched counties that wereidentified as case counties,
which were counties withdisparities, so at least one
standard deviation above themean for COVID incidents or for
COVID mortality.
We did models of each and thencontrol counties and those
(09:58):
controls were both within thesame state as the cases we
recognized.
Of course there's a lot ofstate to state variation and
different types of policies andsocial and structural reserves,
so we had controlled for thatand we also matched on the
rural-urban continuum codedefined by the USDA to try to
have direct comparisons of thesocial and structural variables
(10:19):
that we were looking at.
Then we used a form ofhierarchical logistic regression
modeling.
And this is, I think, one ofthe aspects of the study that I,
as an epidemiologist, felt likeI learned the most from which
was really splitting thisdeliberately into two phases or
two sections.
One phase the first phase wasfocused on who was disparately
(10:40):
affected, so what are thedemographic characteristics of
counties that experienceddisparities?
And then, as the second phase,what are the social and
structural factors that seem tobe connected to those
disparities, to remind us thatthose are the modifiable factors
in a potential public healthintervention.
Patrick Sullivan (11:00):
Great.
So, just building on that, I'mlooking at your table four,
which are the final adjustedmodels, and I'm not sure if
that's where you would go next.
There are some really for meunexpected associations with
both incidence and mortality.
So what were some of the mainfindings or key takeaways from
these analyses that you'redescribing?
Hannah Zadeh (11:20):
I can talk a little bit about that.
For COVID-19 incidents.
We found that hospital closures, hospital size and industry
reliance on mining ormanufacturing, as well as
baseline metric related tosmoking, all correlated with
county disparities.
We also found evidence ofinteractions between racial
(11:42):
disparities and industryreliance disparities and
industry reliance.
And so industry reliance is oneof the variables that we were,
I think, really interested tosee what we would find in this
analysis and just as a littlebit of background on that
variable.
So this is from the USDA'sEconomic Research Service and
it's basically measuringcounties' economic dependence on
(12:03):
certain industries.
So more precisely it's if acounty takes over 23% of its
earnings or if over 60% ofcounty residents are employed in
those industries, and so wewere interested in if a county
is really sort of economicallydependent on a certain industry,
like what kinds of COVID-19outcomes might that be
(12:24):
associated with?
So we did find for counties inwhich mining and manufacturing,
respectively, are predominant.
We found that was significantfor incidents.
Like I was saying, we foundevidence of interactions between
racial disparities and industryalliance.
More specifically, we foundthat in counties with higher
proportions of Hispanicresidents that had an economic
(12:44):
reliance on manufacturing, theretended to be higher racial
disparities in COVID-19incidents.
Also, for incidents, we foundthat disparities persisted in
counties with higher proportionsof Black or Hispanic residents.
So that was incidents.
And then for COVID-19 mortality, we found that a couple of
variables made it into the model.
(13:05):
So the proportion of residentsin the county classified as
unemployed or disabled was one,and then the proportion
classified as quote unquoteobese was one.
The proportion reliant onpublic transportation was one,
and a variable that was an indexof poverty segregation, all
correlated with non-metropolitancounty disparities, all
correlated with non-metropolitancounty disparities.
(13:26):
And you know, maybe we can talkabout this later.
But, as we sort of said in thebeginning, like we were using a
lot of these variables that arecommonly modeled as individual
level variables and trying tosort of contextualize them more
and think of them as signs ofcounty level resources, right.
(13:47):
So I think one thing that wesee a lot in the literature is a
focus on these sort of quoteunquote lifestyle associated
variables and sort of a push tosort of shift the locus of blame
onto individuals.
And we're sort of interested intaking these kinds of data
(14:09):
because that is the kind of datathat is publicly available and
recontextualizing it as a signof county level resources.
So that's just a bit ofbackground on why we included
some of these variables in theinitial pool, and that was some
of the findings.
Dr.
Carvour, I don't know if youhave anything to add.
Martha Carvour (14:33):
No, I think that's a great summary, H hannah
, thank you, and I would reallyreinforce that piece about what
we can understand about thesevariables on a county level or
sort of a group level.
HOne of the things we talkedabout as an example is the
public transit, which wassomething we didn't really
expect to see sort of inverselyassociated, because it makes
perfect sense just in terms oftransmission models and the way
that we were reading about otherrisk factors for exposure to
(14:55):
COVID or to SARS-CoV-2, being inenvironments where you might
have more exposure to otherindividuals, including certain
transportation modes, where thatmight be more likely.
It has the opposite effect,right, it's more likely to drive
at least incidents, and we sawthis was inversely correlated
with mortality.
(15:15):
And the way we're kind ofputting that together is
thinking that this could be asign of infrastructure,
transportation infrastructure toget access to healthcare, for
example, from rural areas wherethere are often geographic
barriers to care, and so that's,I think, a different issue in
terms of what this variablerepresents on a county level
versus what the exposure mightrepresent for an individual, and
(15:38):
that's an example, too, thathas borne out in community-based
discussions, you know, withcommunity partners.
We hear this time and againabout the issue with, very
specifically with,transportation, which I think
was helpful to kind of add tothe context of what we were
seeing with these publiclyavailable data.
Patrick Sullivan (15:55):
Or you know, higher access to public transit
may also be confounded withother sort of positive actions
in communities.
And I think one of these kindsof analyses I'm a fan of, and so
they let you get at some sortof big issues within communities
, but also have the disadvantagethat you can't really link the
people, the individuals who areusing, for example, public
(16:17):
transportation systems.
So there may be some need tosupplement with some other kinds
of studies.
But yeah, I was struck by Iencourage everybody to look at
table four, particularly in themortality column, like some of
the, maybe because of small cellsizes, but there are some
impressive adjusted odds ratiosand maybe some findings that
might not be what you might'vepredicted.
(16:38):
So an interesting table.
So can you talk just in generalabout some of the strengths and
limitations of the approach?
We've touched on these a littlebit in terms of the sort of
ecological level approach, butwhat do you see as the strengths
and any of the limitations thatyou would want to highlight?
Hannah Zadeh (16:55):
I can get us started.
So I think one of the strengthsof this study and something
that was, I think, born out of alot of conversations that we
had with co-authors was, like Iwas talking about in the
beginning, thinking about how tooperationalize race in
epidemiological models.
Right, because, you know, wedefinitely do want to be
(17:18):
continuing to track the way thatracism is affecting public
health, but we want to be doingthat in a way that is really,
you know, wedding a structuraltheory of race to methodology.
You know, I think I reallyalways like to go back to Ruth
Wilson Gilmore's definition ofstructural racism, right,
(17:40):
because I feel like structuralracism is something that is
increasingly like it's kind ofbecome like a buzzword.
That is increasingly like it'skind of become like a buzzword,
and Ruth Wilson Gilmore definesit as the state sanctioned or
quote the state sanction and orextra legal production and
exploitation of groupdifferentiated vulnerability to
premature death.
And I just feel like thatdefinition always sort of
(18:02):
grounds me or grounds adiscussion, like when we're
thinking about what in ourmethodology we're trying to
represent, right, like it's veryserious.
And in some of ourconversations we talked about
how, you know, we werefrustrated with some of the
literature where it doesn't seemlike sometimes folks are
applying the same level of likemethodological rigor to really
(18:22):
representing racism in thesemodels.
So that was something that Ithink is a strength of the study
is we were trying to sort ofexplore approaches to doing that
.
You know here, what we weredoing was, you know, using the
data we have to at the countylevel, model a proportion of
residents of a given racialidentity and sort of use that as
(18:44):
a way to sort of follow wherethese resource distribution
mechanisms of racism arehappening.
So I think that's one strength,and we had a couple.
There's always limitations toany study.
I think that one thing that wasreally sort of a constant thing
that we were trying to navigateis the availability of data.
(19:11):
So, like we were talking about alittle bit before, a lot of the
publicly available data that isavailable is this sort of data
that is often thought about asindividual level data like
smoking, like BMI, even income,and so that was something that
we were aware of.
I know that at one point we werereally interested in looking at
how local union density wouldbe shaping COVID-19 transmission
(19:33):
and mortality, especially whenwe know that the workplace and
that workers' power within theirworkplace is something that
really impacted their ability toaccess PPE and necessary
protections, and that data wejust weren't able to find.
So it would be very interestedif others are able to get that
data and to do those kinds ofanalyses.
(19:53):
And then I think a lastlimitation is that, especially
in rural areas, the county levelis like pretty big and there
can be a lot that's happening ina given county.
A lot of like dynamics city tocity or town to town, that like
are just sort of being like allmixed up and smoothed over when
you're looking at the countylevel.
(20:14):
So I think that, like futureanalyses that are getting down
to a more localized level wouldbe really helpful to have.
Patrick Sullivan (20:23):
Yeah, I mean I think the idea that sometimes
ecological analysis at a broaderlevel can be hypothesis
generating.
But then you know you couldthink about smaller
administrative areas if data isavailable.
The data are available, or youknow, sometimes I think like the
next step could be as, on sucha different methodologic path,
to say, like key informantinterviews with around you know
(20:46):
some of the manufacturing or Idon't know the public
transportation, so you can justsee how this is like.
This rich hypothesis generationand the ecological approach is
great for that, and then it sortof answers several important
questions and then raises abunch more.
You know that you might need toexplore with different methods,
so great.
So in light of that, what doyou think the implications are
(21:10):
for this idea of advancinghealth equity?
You sort of talked some aboutinequities, but what should be
done with these data to advanceconversations about improving
health equity through policy orthrough some kind of public
health practice?
Martha Carvour (21:26):
Yeah, I think there are a couple of things
that I know Hannah and I havetalked about, and we've also had
a lot of, I think, reallyhelpful and rich discussions
with co-authors, who I also wantto acknowledge for a huge
contribution to the way that wethought about each of these
variables and kind of put theseresults together.
I think there are two sort oftakeaways for us, and one of
those is methodological and oneof those is sort of this
(21:48):
practical public health piece inrural settings.
On the methodological side, Ithink using the phased analysis
that we did here allowed us tointerrogate some of our own
assumptions about what racevariables represented.
I think we're used to inepidemiological models, we're
very used to sort of adjustingfor certain socially constructed
(22:10):
demographic variables, and inthis case we tested some of the
assumptions about what does racemean?
What is it actually measuringhere?
Where does it fit into adirected acyclic graph?
Are our assumptions?
Do they appear to be valid,based on what we're seeing with
the analysis and the order inwhich you put variables into the
model?
And we found that, as expected,there's a lot of variability in
(22:34):
what the race constructsmeasure, especially on the
county level like this, and soreally thinking carefully about
questioning those assumptionsand thinking carefully about
what are the mechanisms,socially and structurally, that
we're trying to measure, becausethat's going to speak to how we
can actually improve outcomes,reduce inequities, really
(22:55):
thinking critically about moremechanistic pieces.
The practical public healthpiece is that we really want to
work with community partners,work with other collaborators,
really work in communitysettings to learn more about
what the social and structuralfactors are that are important,
how to think about interveningon these modifiable factors to
(23:15):
improve outcomes.
We've been working in this area,motivated in part by the
research here that Hannah hasled and that we've been doing as
a group to ask those questionsat the community level right and
conduct qualitative,quantitative research,
interventional research, andstart to answer some of those
more specific questions.
A standout piece of that iswhat's still happening with
(23:39):
occupational disparities afterthat initial wave where so many
frontline workers were affected.
We don't talk nearly as muchabout frontline workers or hear
as much about frontline workerstoday as we did several years
back when this data wascollected or when the study was
sort of devised.
And yet we know that there arelong-term impacts on many of the
(24:00):
frontline workers in thosecommunities many persistent
inequities in access to care forthings like long COVID or just
other health conditions.
So this is something that Ithink has motivated us to see
frontline workers as a part ofthe public health workforce in
rural communities, the peoplewho are packing the food,
delivering food this is part ofwhat sustains life and health in
(24:22):
a public health emergency andso ensuring that we have
equitable access to resources.
Much like I had, I was quiteprivileged to have access as a
frontline healthcare worker todifferent types of occupational
protections, and I still havethose occupational protections
to this day as a healthcareworker, and those may not exist
(24:44):
for others who were also on thefrontlines.
Patrick Sullivan (24:46):
Yeah, I think this idea of extending frontline
workers to say that that doesinclude some very highly trained
, you know, colleagues, but alsothere's as critical capacity
for transportation, for food,for other things, and I think
some of the discussion aboutfrontline workers, you know, did
focus more in medical settings.
So I really appreciate youreframing that.
(25:07):
So we're going to transitionnow to a piece of the podcast
that to me is always at leastinteresting, which is called
Behind the Paper, Dr.
Carvour always someone whoremains grateful for the
mentorship that I got throughoutmy career and now see my role
as a mentor as the mostimportant thing that I do for
public health.
But I want to talk a little bitjust about, even in the way I
(25:30):
think you've prepared for thiswe're on Zoom, I'll just tell
Carvour listeners we're on Zoom,so that body language about
who's going to answer isn'tquite so evident.
But even down to the level Ithink of preparing for this,
it's clear that you've beenthoughtful about how you're
going to take these roles.
So I really just want to talk alittle bit about the
interaction and your worktogether on this and how you
(25:52):
sort of saw those roles and howyou work together obviously
collaborated in this, so eitherone of you can start.
But what was that workingrelationship like and how did
you handle the roles?
Sure, yeah, I can start.
We were just talking aboutbeforehand that we sort of
started.
.
We Carvour met each other inMarch 2020.
And I was saying that my Zoommeeting with Dr Carver D was.
(26:15):
Carvour first Zoom meeting I'd.
ever had in my life.
And look at us now.
Hannah Zadeh (26:19):
So, yeah, I H was in the University of Iowa what
they call a pre-MSTP medicalscientist training program as an
undergrad and it was supposedto be for summer 2020, but it
got shut down because of thepandemic.
But Dr Carver graciously agreedto still work with me on a
project anyway, and we had a lotof conversations early on about
(26:42):
racial health disparities andhealth equity.
And it was cool because I thinkwe sort of got to have some
conversations sort of in realtime as we were observing the
tragedies all across the US, allacross the world, that were
happening as a result of theCOVID-19 pandemic, and we were
also observing, I think, some ofthe things that were sort of
being overlooked in the mediaand also, I think, in the
(27:05):
scientific conversations aroundthe pandemic.
And I feel very lucky to havebeen able to have those
conversations with Dr Carver, aswell as all of our co-authors
throughout this project.
And yeah, I'll stop there, drCarver, if you have anything to
add.
Martha Carvour (27:22):
Yeah, thank you, hannah.
I mean I think I would echo alot of what you've just said.
I think this is a project thathas gone through its own phases,
as the pandemic has, and reallyrecognizing the significance
and the importance of the typesof questions and in many ways,
even though it was a longproject, as any research project
(27:43):
can be, it's also an urgenttopic, right.
And so thinking about how wecan still be balancing that with
the practical public healthwork and the practical community
engagement along the way.
Hannah has really approachedthis as a leader, intellectually
, collaboratively, has engaged alot of co-authors from
different disciplines to bringtheir perspectives, co-authors
(28:05):
from different disciplines tobring their perspectives.
I think that's really important,especially for scholars sort of
in training and in development.
I think we're all sort of intraining .
always Carvour and I advice-that's the good thing about the
field, but I think, reallyhaving that chance to lead.
and to ask those tough questions, to find answers to those tough
(28:27):
questions and to work through aprocess, even when there are
challenges and in Hannah's case,over several years and other
professional milestones, and soit's wonderful to sort of see it
come to this point, to see thatwe continue to have discussions
about not only this project butabout the profession, about the
(28:54):
direction of the profession,and I think those are, for me,
those are some of the mostimportant and gratifying parts
of doing this work.
I think the research, of course, is crucial.
The ability to have an impactis crucial, but one of the ways
to make the biggest impact isthrough mentorship of other
really, really skilled scholarslike Hannah.
Patrick Sullivan (29:12):
Thank you and I'm going to ask you one more
question and then Hannah, I'mgoing to come back to you which
is Dr Carver, what advice so youdescribe how this really
productive, professional,scientific relationship
developed and how impactfulthat's been?
What advice would you have forearly career researchers who
come in wanting this kind ofexperience?
(29:32):
How do they go about, you know,opening up those relationships
or putting themselves in aposition to be in this kind of
mentoring, collaborativerelationship?
Yeah, I think that's such animportant question.
I think I know from talkingwith so many students,
especially who have been inschool or in different training
programs over the last severalyears, this has also been a
really tough several years to bea student and be thinking about
(29:55):
what the future looks like andbe thinking about how to get
into a program or how to getresearch experience.
So, first and foremost, you'renot alone and don't be patient
with yourself.
Be patient with the process andseek out people who want you to
learn, want you to succeed, whoyou can, you feel comfortable
(30:16):
with and feel like you canadvocate for yourself with.
I think one of the key skillsto learn in a mentoring
relationship that I learned alsoas a student is how to
communicate about what I need orwhat I'm, what I'm, what's not
clear to me.
So it's just such a greatopportunity to do that in a
place that you should havesupport and and I think that's
(30:36):
really really important.
Martha Carvour (31:24):
Thanks.
I Hannah the other piece ofadvice I would really put out
there is there's no substitutefor critical thinking.
There are a lot of greatmethods, there are a lot of
great resources, but you reallydo have to think critically
about the data that's put infront of you, the questions that
you're asking, and I think, ifyou have in mind a career
trajectory or a research topicthat's of interest to you, there
may also be a different waythat you're approaching that or
a different way that you'rethinking about that, and I think
that it can actually besomething that the profession
needs right now.
We need people who thinkdifferently and critically, and
so don't give up on that piece.
Definitely it takes hard workand dedication and great mentors
, but don't give up on thinkingcritically for yourself.
Thanks.
Patrick Sullivan (31:24):
And Hedda, I'm going to give you the last word
, which is what advice would youhave for maybe more seasoned or
later career colleagues interms of mentoring and working
with students or youngerresearchers?
Yeah, I think like I've.
been really, Carvour reallylucky in the mentors that I've
had throughout my career so farand it's something that I think
(31:47):
sort of took for granted untillike the past couple of years.
.
Hannah Zadeh (31:51):
But I mean, like Dr Carver was saying, students,
especially in the past couple ofyears, have been dealing with a
lot, and I think like,especially if we want to, like
you know, make researchsomething that is accessible to
everyone including, you know,people who are coming from
low-income backgrounds, you know, who don't have like family
connections to research, or evento higher education, people who
(32:15):
are trying to care fordependents while they're
pursuing a career in research,we really need mentors who are
like willing to be flexible withstudents and patient with
students, even coming down tolike nitty gritty of like
scheduling stuff and studentshaving to miss meetings, stuff
like that.
I think like that kind of likepatience and flexibility is
(32:37):
really important to like do thework of making this profession
accessible and open to all kindsof students.
And again, I just have been solucky with Dr Carver and other
mentors that I have to reallysee that in practice.
Patrick Sullivan (32:52):
And you'll have a chance to pay it .
forward Carvour So keep yournotes.
So, do you have any lastthoughts that you'd like to
share with our listeners?
Hannah Zadeh (33:01):
I Carvour?
one thing that you know, DrCarver, and I talk about a lot
still to this day is thepandemic is not over.
We've talked in thisconversation about how the
implications of long COVID andyou know there are still
thousands of deaths every weekin the United States, and so
like, even though this projectwas just focusing on that
(33:22):
initial period.
Yeah, my final thought is thatthe pandemic is still ongoing
and more of this criticalthinking about health equity
needs to be done.
Patrick Sullivan (33:33):
Dr Carver.
Martha Carvour (33:34):
No, I think that's great.
Thank you very much and thanksHannah.
M.
Z.
.
T P t o j o t A C i o E.
(34:08):
F a t o t p o t r t a f i t e am f t j y c v u www.
annalsofepidemiology.
o
Patrick Sullivan (33:38):
All right, that brings us to the end of
this episode.
Thank you again, ms Sada, andDr Carver, for joining us today.
It was so nice to have you onthe podcast, thank you, thank
you so much.
Today was so nice to have youon the podcast.
Thank you, thank you so much.
I'm your host, patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
EpiTalk, brought to you byAnnals of Epidemiology, us
(34:09):
online atwwwannalsofepidemiologyorg.
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