PhD candidate Maria Gueltzow shares the compelling findings from her article, “Childhood obesity's influence on socioeconomic disparities in young adolescents’ mental health,” published in the June 2024 (Vol. 94) issue of Annals of Epidemiology. In this study, the researchers estimate the contribution of the mediating and moderating effects of obesity to the disparity in adolescents’ mental health.
Read the full article here:
https://www.sciencedirect.com/science/article/pii/S1047279724000486?via%3Dihub
Episode Credits:
- Executive Producer: Sabrina Debas
- Technical Producer: Paula Burrows
- Annals of Epidemiology is published by Elsevier.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Patrick Sullivan (00:11):
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 to Ms.
(00:35):
Gueltzow about her article"Childhood obesity's influence
on socioeconomic disparities inyoung adolescents' mental health
.
" You can find the full articleonline in the June 2024 issue of
the journal at www.
annalsofepidemiology.
org.
Maria Gueltzow is a PhDcandidate in Public Health at
the Max Planck Institute forDemographic Research in Rostock,
(00:58):
Germany, in affiliation withthe Erasmus Medical Center,
Rotterdam, the Netherlands,which is where she will receive
her PhD.
Her research focuses on gaininga deeper understanding of
subgroup differences in mentalhealth through the use of the
potential outcomes framework andgenetically informed designs.
Ms.
Gueltzow, thank you for joiningus today.
Maria Gueltzow (01:18):
Thank you so much for inviting me.
Patrick Sullivan (01:20):
I want to start out by just seeing if
you'll give us some backgroundabout the problem you described
in the paper.
Even in the title, you know, wecan see that there's a lot
going on here.
We've got childhood obesity,we've got socioeconomic
disparities, got mental health,and so give us a little
background about why this is animportant and complex issue to
(01:41):
investigate.
Maria Gueltzow (01:44):
Yes, sure.
So, as you mentioned already,there's a lot of factors that
play into this project and intothis problem that we were trying
to get kind of a deeperunderstanding of.
So the general idea was that wewere interested in looking at
socioeconomic inequalities inmental health among young
adolescents, specifically in aDutch population, and we were
specifically interested to gainkind of a bit more insights into
(02:07):
possible underlying mechanismsthat lead kind of socioeconomic
position or that linksocioeconomic position to mental
health.
Now there's a number of factorsthat likely all contribute or
explain this or underlying this,and these could be factors like
financial stress of thehousehold, so children who grew
up in lower socioeconomicsettings, they could have worse
mental health because of higherlevels of financial stress in
(02:29):
the household.
It could also be related tomental health problems among the
parents.
It might be more likely amonglow socioeconomic settings,
which could all lead to kind ofthe socioeconomic gradient that
we see in mental health.
Now what we were interested inin this paper specifically is we
wanted to get a deeperunderstanding to what extent
obesity contributes to thesocioeconomic disparities in
(02:50):
mental health.
And now you might ask yourselfhow obesity plays a role in this
, and there could essentially betwo things that are happening
here, or there are specificallytwo mechanisms that we were
interested in.
So what could be happening isthat obesity is just much more
common among certainsocioeconomic settings, for
example, low socioeconomicsettings, and since we know that
(03:11):
there is a link between obesityand mental health, that might
be a reason why we see kind ofmore mental health problems
among lower socioeconomicsettings, which then contribute
to these disparities.
Now this is known as mediation.
When you talk about methods, orwhen you talk about it
theoretically, it's also calleddifferential exposure or
differential prevalence, forexample.
Now what could also behappening is it could be that
(03:33):
obesity is actually equallycommon among socioeconomic
settings, but it could be thatthe effect of obesity on mental
health is just worse amongcertain settings, and now this
could be due to different levelof discrimination in the family
if a child is obese.
So there could be a lot ofthings going on there, and this
mechanism is also known asmoderation or interaction.
(03:54):
If you talk about it in termsof method or in the theoretical
way, it would be calleddifferential impact or
differential susceptibility.
So this is kind of what we weretrying to get a deeper
understanding of in this paper.
Patrick Sullivan (04:06):
So you have these multiple potential
exposures that have their owncomplex relationships and an
important public health questionhere.
So, in terms of this sort ofrange of exposures, this
important outcome, can you talkto us a little bit about the
study design and the methodsthat you use to answer the
(04:26):
question that you've posed?
Maria Gueltzow (04:30):
Yeah, of course.
So first of all, the cohortstudy that we used to analyze
this problem was the GenerationR study, which is a
population-based cohort studybased in Rotterdam in the
Netherlands, in a very specificdistrict, and this study is
pretty interesting because inthis study pregnant women were
recruited and then theirchildren are essentially
(04:50):
followed up until adolescence,so up until age 16.
And we have the data collectionnow up until age 13, which is
where we measure our outcome.
So our outcome measure Imentioned that we're interested
in mental health we are lookingat internalizing or emotional
problems and externalizing orbehavioral problems, and these
were measured with childbehavior checklist 6 to 18,
(05:12):
which is kind of a questionnairethat is filled in by one of the
caregivers so a parent oranother caregiver and it
consists of, I think, a numberof items.
It's about 112 items, I think.
So this was our outcome measure.
So we did our analysisseparately for internalizing and
externalizing symptoms.
Then in terms of our exposure.
So we measured socioeconomicposition through two separate
(05:34):
measures.
So firstly we looked atmaternal education, which we
measured at age five, and thenwe also looked at household
income, which was also measuredaround age five.
Now I mentioned that we'reinterested in obesity as well.
So our kind of mediator in thissense, or like the factor that
we're interested in to see howthis kind of contributes to the
disparities that we're lookingat, was measured at age five and
(05:55):
we measured obesity actually inan objective way.
So we measured the body fatpercentage with the DXA
measurement, which is called, Ithink, the dual X-ray
absorptiometry.
It sounds very complicated- itsounds like a very precise way
to measure this.
It's a very precise way tomeasure.
Yes, so that's what's greatabout this.
So we have a very objective wayto measure body fat percentage
(06:17):
and get an indication of obesity, yeah, so these were kind of
our variables of interest, andthen we had to control for a
large number of confounders,because this problem is kind of
highly complex.
Obesity and mental health arevery interrelated and so it's
also kind of socioeconomicposition with these two factors.
So, I mentioned, we wereinterested in understanding
these two different mechanisms,so differential impact and
(06:39):
differential exposure.
So to quantify these, weperformed a four-way
decomposition analysis, and afour-way decomposition analysis
is essentially a method that wasdeveloped by Vanderweel, I
think, in 2014.
And the idea is that youdecompose the total effect of
something so, for example, ofeducation on mental health into
(07:00):
four separate effect componentsand if you then sum up, so the
four effect components are thecontrolled direct effect and the
mediated reference interaction,and then also the pure indirect
effect, and if you sum the twoof them together, you get the
differential exposure anddifferential impact.
Now, one of the things that weslightly tweaked in comparison
to the original approach is thatwe were not necessarily
(07:22):
interested in actuallyquantifying the causal effect of
socioeconomic position onmental health.
But we were rather interestedin trying to understand the
disparity.
So we wanted to understand thesocioeconomic disparities in
mental health and we wanted tosee what would happen if we
actually intervene on obesity.
So instead of assuming anintervention on socioeconomic
(07:42):
position, we wanted to interveneon obesity and see how this
could affect the socioeconomicinequalities and mental health.
Now for that we had to slightlytweak the approach and we
essentially had to just performa little bit more of a complex
analysis.
That's what it comes down to.
So we did the four-waydecomposition with the help of a
marginal structural model withinverse probability of treatment
(08:02):
, weighting and interventionalanalogues.
So it was just a little bitmore complicated than the
average for decomposition.
Patrick Sullivan (08:09):
Thanks for that explanation of the methods,
and I think getting at thisidea of what it lets you do is
to isolate the contribution ofobesity.
And so I think, in the midst ofall these methods I'm going to
ask you in a minute about thekey findings but in the midst of
all these methods, I think it'sreally important as
epidemiologists to sort of touchback on the idea that, like why
(08:31):
is it important to be able toisolate or to understand
particularly this impact ofobesity?
And it's exactly what you said,which is because then you can
have a conversation about likehow much good could we do for
health if we mitigated that?
And then obesity is a wholeother world where epi and
clinical trials and otherbranches of academic inquiry
(08:53):
have given us some tools toaddress that.
So I think the methods are alot and yet, at the end of the
day, they're so importantbecause it lets us tease out
something that asks a questionabout how to improve health and
we're going to get to that.
So what were some of the keyfindings?
And particularly this idea ofthe differential exposure to
obesity and how that accountedfor this total disparity in
(09:18):
emotional problems?
What was the role of obesity inthat.
Maria Gueltzow (09:23):
Yeah.
So talking about key findingsmaybe, to start off in general,
looking at the disparities thatwe found in our data set, so we
do find that they are moreinternalizing and also
externalizing, so emotional andbehavioral symptoms among the
low educated groups, so amongchildren that grow up in low
educated and low income settings.
So we do find that there is adisparity between the low and
(09:46):
the high educated groups and thelow and the high income groups,
and we find a point differenceof about 0.5 to 1.5 on the scale
for the internalizing andexternalizing symptoms.
So the disparity is there.
It's not very large in thissetting, which is, I guess, very
good news Now in terms of themechanisms and how obesity
contributes to this.
(10:08):
So what we found is thatdifferential exposure to obesity
contributes to the disparitiesin internalizing symptoms more
so than the disparities inexternalizing symptoms.
And specifically, what we findhere is that, looking at low
versus high educated families orlow versus high educated
(10:28):
maternal education, we find thatif you would set the obesity
levels in the low educatedgroups to that of the high
educated groups, thesocioeconomic disparity in terms
of maternal education would bereduced by about 50%.
And when we look at income, wefind a reduction of about 14%.
So differential exposure toobesity contributes.
(10:50):
So this differential prevalenceplays a role.
Now, when we look atdifferential impact, we don't
find any evidence fordifferential impact and this is
largely driven by the wideconfidence interval.
So we can't really make anysubstantive conclusions about
the differential impact and howit contributes.
Now we also did some genderstratified analysis and it
(11:10):
seemed that obesity plays a moreimportant role in explaining
the socioeconomic inequalitiesin mental health among girls
more so than boys, and, as Imentioned, it seems to be more
important for internalizing thanfor externalizing symptoms.
So I think that's the gist ofthe findings.
Patrick Sullivan (11:25):
Great thanks, I just wanted to follow up.
You said like there's a onepoint disparity, I think for
education, in terms of the rangeof this measure.
It looks to me like themeasures were like four to six
or something right, the absolutevalue, so it's like one point
out of four to six.
That's sort of the magnitude ofit.
(11:45):
Is that right?
Yeah, I think that's how Icould describe it Great.
So how did this relate to sortof what was known about this
topic before your analysis?
Maria Gueltzow (11:58):
Yeah.
So what's interesting about thestudy is that this is the first
of its kind.
At least we're not really awareof a study that has tried to
contribute kind of these twomechanisms simultaneously.
But if we look at previousliterature, overall it seems
that the link between obesityand internalizing symptoms is
stronger than the link betweenobesity and externalizing
(12:18):
symptoms, which could be areason why we might find larger
disparities and largercontributions for obesity to
internalizing symptomdisparities.
When we look at the differentialexposure which I mentioned can
also be called mediation.
So we found a few studies thatlooked at mediation essentially
or kind of inferred that thereis mediation present.
And there is a finding from astudy that kind of showed
(12:40):
already that there is some kindof co-occurrence of obesity and
mental health and specificallyin low socioeconomic settings,
which could be an indication fordifferential exposure.
And there's also a few studiesthat reported an attenuation of
the effect of socioeconomicstatus on mental health after
controlling for obesity, whichis another indicator for
differential exposure.
(13:00):
So we essentially confirmedthis finding and kind of give
more clearly quantifiedcontributions of like how much
does obesity actually matter Nowin terms of differential impact
?
There is some evidence outthere that obesity has kind of a
stronger effect on healthoutcomes among low socioeconomic
settings, but the literature onthis is definitely lacking in
(13:22):
terms of adolescence and also interms of mental health.
So this would definitely besomething that future research
could look into, in my opinion.
Patrick Sullivan (13:30):
Yeah, I think that sounds like a postdoc.
Maria Gueltzow (13:33):
Yeah, it could be someone's postdoc.
Patrick Sullivan (13:35):
I'd like to sort of wrap up this discussion
of the analysis.
Clearly you know part of thestrength here is, you know,
having this great cohort dataand some of the methods you use.
What are your thoughts aboutthe other strengths, but also
the limitations, of this type ofstudy?
Maria Gueltzow (13:52):
Yeah.
So I think one of the mainstrengths is that this is kind
of the first of its kind, as Ijust mentioned.
I think, like I said, I hopethere's other people that are
working on this and that theremight be other papers coming out
that at least use the same kindof methodological approach.
So this is one of the strengthsof our papers, for sure.
I also mentioned that we havean objective BMI measure, which
I think is quite rare and Ithink is definitely one of the
(14:14):
strengths of the paper as well.
So we don't have as manyproblems with kind of
self-reporting BMI, especiallyamong children, where it might
be the parent that reports BMI,whereas there might be some sort
of bias in kind of recall bias,and just the way that it's
reported might not be fullyaccurate.
Just the way that it's reportedmight not be fully accurate.
Then with our study that we use.
(14:35):
So the Generation R study is areally great study but not
really representative of theunderlying population, so we
have a little bit of a selectionbias.
So the Generation R study is ingeneral a little more highly
educated than the underlyingsample, which is something to
take into account, to take intoaccount and then because the
generation of our study has beengoing on for so many years I
(14:56):
think now it's about 15 yearsroundabout.
There's, of course, a littlebit of issue with attrition,
especially if, specifically,children who grew up in lower
socioeconomic settings are morelikely to drop out.
I think this could reallyaffect our study.
So this is something to keep inmind.
And then, in terms of thedifferential impact, we had some
issues with getting enoughcertainty to really quantify it.
(15:18):
So there seems to be somesample size issues.
So we had a sample of about5000 individuals.
We might need a few more toreally quantify differential
impact.
And then, lastly, this is acausal study, so we have to also
think about kind of theunderlying causal inference
assumptions and I will not gointo too much detail about this,
but one of them being theassumption of unmeasured
(15:38):
confounding.
So we controlled for quite alarge number of confounders.
But since obesity, mentalhealth and socioeconomic
position are so interrelatedwith each other, I think there's
a chance that we might havemissed some or that we had some
factors we couldn't control forjust because of data
availability.
So, for example, we couldn'tcontrol for paternal BMI, which
(15:59):
would have been an interestingfactor to control for.
Now, um there are a few otherassumptions, but I wanted to
mention one more, and that's theconsistency assumption, or the
assumption of well-definedinterventions.
So now what we are inherentlydoing is we are assuming a
hypothetical intervention onobesity, where we essentially
reduce obesity, for example,among the low socioeconomic
(16:22):
groups, right.
But this of course raises thequestion of how do we actually
achieve this reduction inobesity, and I think there's a
lot of discussion to be hadabout this, of whether obesity
in itself could be defined as awell-defined intervention or if
it would come down more to,maybe, lifestyle changes.
But then you always need tokind of take this into context
(16:44):
with how could we tacklesocioeconomic inequalities, and
I think this is a discussion Icould talk about for a very long
time, but I would just say,maybe have a read at the paper
if you're interested, andthere's also a few studies I can
recommend that would be reallyinteresting to read.
I think there's a study bySchwartz et al from maybe 2015
(17:04):
and a study by Kaufmann from2019, which really go into this
kind of discussion ofwell-defined interventions,
which is very interesting, Ithink.
Patrick Sullivan (17:13):
Great.
So we're going to turn now to asection of the podcast we call
Behind the Paper, and this isreally meant to think a little
bit more about how we work andlearn and collaborate as
epidemiologists and tounderstand what factors make it
possible for us to do this workor maybe influence how we do it
or how we see it or explain it.
So I'll just start by askingyou know what was not
(17:38):
necessarily from a technicalbasis, because there are a lot
of like technical pieces to youranalysis but, just like as a
researcher, what was challengingor frustrating about conducting
this particular analysis andwhat parts of it were kind of
enjoyable, you know, joyful foryou, and what parts were that
like things that you kind of hadto slog through.
Maria Gueltzow (18:00):
Mm, hmm, yeah, maybe starting with kind of the
negative, or something thatreally frustrated me is that the
method that we use now is quitecomputationally intensive, so
it just takes a very long timeto run the analysis, which I
don't know if this would be atechnical issue that you are
referring to, but yeah, it's alittle bit tricky.
So I think one set of analysisin the end took about five hours
(18:24):
to run, if I remember correctly, and just to get the main
results ready.
I think we had to run aboutfour sets of that.
So then it's very frustratingif you start running your
analysis and then you realizethat you forgot to include this
variable or like you saved itwrong and then it just didn't
get saved, even though you wererunning it overnight.
So I think this is something Iwas getting really frustrated
(18:46):
with, but it's kind of yeah-were you running it like on a
mainframe or on a local machine?
I was in Rotterdam at the timeso I was using I think it's
called a server.
Yeah, it's where you could atleast run things in parallel, so
it sped up things a little bit.
But yeah, there were definitelysome computational challenges,
just in terms of the space thatI needed to run this.
Patrick Sullivan (19:07):
There were different challenges too to
running.
I worked at a time when wesubmitted big jobs, like big
surveillance data jobs, tomainframe SAS and so we'd get in
a queue and then we would dialup.
We'd have to have a dial-upmode to see where you were in
the queue and you'd watch for afew hours and then it would
start running and immediatelystop, which usually meant I left
(19:28):
a semicolon out of SASsomeplace.
So you have to find that fixthat put it back in the queue.
So in some ways we do a lot ofstuff on our desktops now,
obviously, but like this is acomplicated enough analysis that
you're using that sort ofserver structure to do it, so
that's definitely adds a piece.
Conversely, what was sort ofmost joyful for you, most
(19:49):
engaging or like enjoyable aboutit, just like personally or
your scientific self?
Maria Gueltzow (19:55):
I wrote this paper when I was actually on a
research stay in Rotterdam.
So my second author, Joost OudeGröniger, I worked with him and
I worked very closely with himon this paper, which was
actually very interestingbecause I have never worked with
him before and I think we had avery interesting collaboration,
kind of engaging in all thesecausal discussions, so I think
it was very that was veryenjoyable for me and then also
(20:18):
so I mentioned that we used alittle bit more of a complex
approach to deal with some ofthe issues that I mentioned
earlier.
But it took me a while tofigure out how to actually apply
this forward decomposition ifwe're interested in explaining
disparities instead of lookingat causal effects, disparities
instead of looking at causaleffects and this was something
that I think it was equallychallenging or frustrating and
(20:39):
enjoyable, because it just tookme a while to figure out the
solution, which I didn't figureit out.
It was just in a papersomewhere.
I just had to find that paper.
So that was definitely aninteresting process to go
through.
Patrick Sullivan (20:50):
Great.
So this is really exciting workthat you've done, and I wonder,
as you move forward towardsyour graduation, what you hope
to accomplish in the long termof your public health career.
What's your big "hy, like whatdo you want to get done in the
world with these methods?
Maria Gueltzow (21:10):
Yeah, that's-that's a very big
question to ask.
I think, ultimately somethingthat I've been starting to tell
people.
So maybe it will become mymission, maybe not, maybe I will
change my mind, but I think Iwould definitely like to bring
these more complex methods moreinto public health, because I
feel like in epidemiology peopleare kind of more and more using
kind of causal inferencemethods and more complex methods
and machine learning and so on.
(21:31):
But I have a feeling that inpublic health this is still
something that's really to come,even though a lot of these more
complicated methods, they willactually give us the answers to
the questions that we'reactually asking.
So, for example, with thispaper here, I've been talking
about hypothetical interventionsand I think when we think about
public health, ultimately we'retrying to improve health, right
(21:54):
, which would kind of come downto what can we recommend to
improve public health or likereduce problems that we have in
public health?
So I think these methods aregreat and I think ultimately I
want to try to kind of make thema little bit more accessible
and have people not be so scaredabout these methods, which I
used to be one of them so Iunderstand.
So I think that's my mission.
Patrick Sullivan (22:16):
Yeah, I think there's two pieces to that sort
of bridge.
I'm so glad you mentioned thebridge to impact, because these
methods are complex and we canspend a half hour podcast
talking about the methods, butin the end, the question is the
(22:37):
one that you identified, whichis you know, how does this give
us information that's actionable, that lets us be more targeted
or more impactful in the thingsthat we do to improve health?
So, I think, as a sort ofco-career goal of like learning
more about these methods butalso their impact and there are
some folks who in their careers,like to live in the
methodologic bubble and that's aplace to live but I think
(22:58):
there's also a model for havingone foot in sort of complex
methods but also asking at eachstep, like you know, how does
this lead to improving health?
And I think, for my ownexperience, is that you get that
second part by the time thatyou spend with people who are
running programs for these kids.
Or you know school systems thatare trying to intervene in
(23:21):
structural ways, and it's a lotof times it's those
conversations with people whomay know more than you know
about the intervention side, oryou know the lives of the people
whose health you're trying toimprove.
So I'm really glad that youmentioned like bringing together
like these kinds of methodswith that, you know, connection
with where the action is goingto happen, like is it at the
(23:44):
school level, is it at thefamily level, is it the kid
level?
So that's great.
So I want to end just talkingabout mentorship, because when
we talk about being in a degreeprogram, your mentor, or your
team of mentors, is such a hugedeterminant of, like, how fast
you get through it and howimpactful it is and the quality
of your life.
So can you talk a little bitabout one or more mentors who've
(24:06):
been, like, really key for youin the process of doing this
work and why they were greatmentors?
What about their mentorship wasgreat for you?
Maria Gueltzow (24:15):
Yeah, so I have a team I would call it a team of
three supervisors that kind ofhelped me through this whole PhD
process and kind of gettingthrough it somewhat intact, yeah
.
So I think all of them theyreally helped me a lot, starting
with one of my promoters Idon't know if you use the word
promoter, too in the US.
Patrick Sullivan (24:37):
I was going to say we don't use the term
promoters, but maybe we should,so tell us what a promoter is.
Maria Gueltzow (24:43):
Yeah, so promoter is usually a professor
that will be officially on yourdissertation, but then you can
also have co-promoters, or Iwould call it a supervisor,
which is more like someone youtalk to kind of on a weekly
basis, whereas the professorsyou might talk to on a more like
a monthly basis.
(25:05):
But yeah, so I want to talkabout my two promoters, very
strong in statistics and verycritical, which is very helpful
to kind of improve and kind ofget more insights and see where
I can make some changes, and theother one is very supportive.
So it was a very goodcombination and in terms of my
supervisor, I think he probablyhelped me the most, also because
(25:28):
I had kind of more contact withhim.
I had like a weekly back andforth with him and he's
essentially the person thatreally brought me into this
field of causal inference andnot being scared of causal
inference, which I think I'mreally grateful for, and I think
it kind of brought me to whereI am today at like kind of this
intersection of methods and howdoes this translate to like the
(25:52):
bigger picture.
Patrick Sullivan (25:53):
it's really interesting how you talk about
the team and I think, for thosewho may be listening, who are
mentors, thinking about thesedifferent roles.
You know these different rolesthat are that the earlier career
people that we work with need,some of which are around
strategy and some are of whichare around just like how to get
through this experience, andsome of those other needs might
(26:16):
be quite technical about how tomake this code work or how to
interpret that, and so I thinkin some settings, in some
situations, it ends up as beingin one, those needs being in one
person, but I think the abilityto talk about you know what's
needed because you've done areally challenging thing, and so
we need some academic supportsand sometimes we need some like
(26:36):
interpersonal supports for that,and so thinking about those two
roles is interesting.
So I'm going to move to a lastquestion here, which is you are
about to graduate with yourdoctoral program, and
congratulations.
What advice do you have forstudents who are at that point
writing?
What have you learned that youthink might be helpful to other
(26:57):
students who are at this placein their journey?
Maria Gueltzow (27:00):
Yeah, I think it depends a little bit where you
write your dissertation, so Ithink it might differ.
So for me my dissertation iscumulative, so I've been kind of
doing research, writing papers,and then a few months ago I
started writing the dissertationand I think one kind of advice
that I can give is that at leastfor me, this felt like this
last chore that I have to do,like writing everything down and
(27:21):
making it come together.
But I actually think that itwas very insightful and I would
advise everyone to kind of tryand have fun with it a little
bit, because I think that's kindof your chance to talk a little
bit more about, like I said,the bigger picture.
What are the bigger issues here?
What are kind of the challengesthat I encountered that I never
(27:42):
had the space to talk about?
So I think it's kind of alittle bit of a mindset shift,
which that's something thathelped me a lot to see kind of
more the positive instead ofbeing like, okay, I just have to
get this over with and thenthis was a really enjoyable
process for me actually.
Patrick Sullivan (27:56):
Great.
Well, congratulations on whereyou are in your career,
congratulations on themanuscript and on your upcoming
graduation.
Which bring us to the end ofanother episode.
Thank you again, Ms.
Gueltzow, for joining us today.
It's such a pleasure to haveyou on the podcast.
Maria Gueltzow (28:13):
Thank you so much for inviting me again.
It was a pleasure.
Patrick Sullivan (28:17):
I'm your host, Patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
EPI Talk, brought to you byAnnals of Epidemiology, the
official journal of the AmericanCollege of Epidemiology.
For a transcript of thispodcast or to read the article
featured on this episode andmore from the journal, you can
(28:40):
visit us online at www.
annalsofepidemiology.
org.
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 to Ms.
(00:35):
Gueltzow about her article"Childhood obesity's influence
on socioeconomic disparities inyoung adolescents' mental health
.
" You can find the full articleonline in the June 2024 issue of
the journal at www.
annalsofepidemiology.
org.
Maria Gueltzow is a PhDcandidate in Public Health at
the Max Planck Institute forDemographic Research in Rostock,
(00:58):
Germany, in affiliation withthe Erasmus Medical Center,
Rotterdam, the Netherlands,which is where she will receive
her PhD.
Her research focuses on gaininga deeper understanding of
subgroup differences in mentalhealth through the use of the
potential outcomes framework andgenetically informed designs.
Ms.
Gueltzow, thank you for joiningus today.
Maria Gueltzow (01:18):
Thank you so much for inviting me.
Patrick Sullivan (01:20):
I want to start out by just seeing if
you'll give us some backgroundabout the problem you described
in the paper.
Even in the title, you know, wecan see that there's a lot
going on here.
We've got childhood obesity,we've got socioeconomic
disparities, got mental health,and so give us a little
background about why this is animportant and complex issue to
(01:41):
investigate.
Maria Gueltzow (01:44):
Yes, sure.
So, as you mentioned already,there's a lot of factors that
play into this project and intothis problem that we were trying
to get kind of a deeperunderstanding of.
So the general idea was that wewere interested in looking at
socioeconomic inequalities inmental health among young
adolescents, specifically in aDutch population, and we were
specifically interested to gainkind of a bit more insights into
(02:07):
possible underlying mechanismsthat lead kind of socioeconomic
position or that linksocioeconomic position to mental
health.
Now there's a number of factorsthat likely all contribute or
explain this or underlying this,and these could be factors like
financial stress of thehousehold, so children who grew
up in lower socioeconomicsettings, they could have worse
mental health because of higherlevels of financial stress in
(02:29):
the household.
It could also be related tomental health problems among the
parents.
It might be more likely amonglow socioeconomic settings,
which could all lead to kind ofthe socioeconomic gradient that
we see in mental health.
Now what we were interested inin this paper specifically is we
wanted to get a deeperunderstanding to what extent
obesity contributes to thesocioeconomic disparities in
(02:50):
mental health.
And now you might ask yourselfhow obesity plays a role in this
, and there could essentially betwo things that are happening
here, or there are specificallytwo mechanisms that we were
interested in.
So what could be happening isthat obesity is just much more
common among certainsocioeconomic settings, for
example, low socioeconomicsettings, and since we know that
(03:11):
there is a link between obesityand mental health, that might
be a reason why we see kind ofmore mental health problems
among lower socioeconomicsettings, which then contribute
to these disparities.
Now this is known as mediation.
When you talk about methods, orwhen you talk about it
theoretically, it's also calleddifferential exposure or
differential prevalence, forexample.
Now what could also behappening is it could be that
(03:33):
obesity is actually equallycommon among socioeconomic
settings, but it could be thatthe effect of obesity on mental
health is just worse amongcertain settings, and now this
could be due to different levelof discrimination in the family
if a child is obese.
So there could be a lot ofthings going on there, and this
mechanism is also known asmoderation or interaction.
(03:54):
If you talk about it in termsof method or in the theoretical
way, it would be calleddifferential impact or
differential susceptibility.
So this is kind of what we weretrying to get a deeper
understanding of in this paper.
Patrick Sullivan (04:06):
So you have these multiple potential
exposures that have their owncomplex relationships and an
important public health questionhere.
So, in terms of this sort ofrange of exposures, this
important outcome, can you talkto us a little bit about the
study design and the methodsthat you use to answer the
(04:26):
question that you've posed?
Maria Gueltzow (04:30):
Yeah, of course.
So first of all, the cohortstudy that we used to analyze
this problem was the GenerationR study, which is a
population-based cohort studybased in Rotterdam in the
Netherlands, in a very specificdistrict, and this study is
pretty interesting because inthis study pregnant women were
recruited and then theirchildren are essentially
(04:50):
followed up until adolescence,so up until age 16.
And we have the data collectionnow up until age 13, which is
where we measure our outcome.
So our outcome measure Imentioned that we're interested
in mental health we are lookingat internalizing or emotional
problems and externalizing orbehavioral problems, and these
were measured with childbehavior checklist 6 to 18,
(05:12):
which is kind of a questionnairethat is filled in by one of the
caregivers so a parent oranother caregiver and it
consists of, I think, a numberof items.
It's about 112 items, I think.
So this was our outcome measure.
So we did our analysisseparately for internalizing and
externalizing symptoms.
Then in terms of our exposure.
So we measured socioeconomicposition through two separate
(05:34):
measures.
So firstly we looked atmaternal education, which we
measured at age five, and thenwe also looked at household
income, which was also measuredaround age five.
Now I mentioned that we'reinterested in obesity as well.
So our kind of mediator in thissense, or like the factor that
we're interested in to see howthis kind of contributes to the
disparities that we're lookingat, was measured at age five and
(05:55):
we measured obesity actually inan objective way.
So we measured the body fatpercentage with the DXA
measurement, which is called, Ithink, the dual X-ray
absorptiometry.
It sounds very complicated- itsounds like a very precise way
to measure this.
It's a very precise way tomeasure.
Yes, so that's what's greatabout this.
So we have a very objective wayto measure body fat percentage
(06:17):
and get an indication of obesity, yeah, so these were kind of
our variables of interest, andthen we had to control for a
large number of confounders,because this problem is kind of
highly complex.
Obesity and mental health arevery interrelated and so it's
also kind of socioeconomicposition with these two factors.
So, I mentioned, we wereinterested in understanding
these two different mechanisms,so differential impact and
(06:39):
differential exposure.
So to quantify these, weperformed a four-way
decomposition analysis, and afour-way decomposition analysis
is essentially a method that wasdeveloped by Vanderweel, I
think, in 2014.
And the idea is that youdecompose the total effect of
something so, for example, ofeducation on mental health into
(07:00):
four separate effect componentsand if you then sum up, so the
four effect components are thecontrolled direct effect and the
mediated reference interaction,and then also the pure indirect
effect, and if you sum the twoof them together, you get the
differential exposure anddifferential impact.
Now, one of the things that weslightly tweaked in comparison
to the original approach is thatwe were not necessarily
(07:22):
interested in actuallyquantifying the causal effect of
socioeconomic position onmental health.
But we were rather interestedin trying to understand the
disparity.
So we wanted to understand thesocioeconomic disparities in
mental health and we wanted tosee what would happen if we
actually intervene on obesity.
So instead of assuming anintervention on socioeconomic
(07:42):
position, we wanted to interveneon obesity and see how this
could affect the socioeconomicinequalities and mental health.
Now for that we had to slightlytweak the approach and we
essentially had to just performa little bit more of a complex
analysis.
That's what it comes down to.
So we did the four-waydecomposition with the help of a
marginal structural model withinverse probability of treatment
(08:02):
, weighting and interventionalanalogues.
So it was just a little bitmore complicated than the
average for decomposition.
Patrick Sullivan (08:09):
Thanks for that explanation of the methods,
and I think getting at thisidea of what it lets you do is
to isolate the contribution ofobesity.
And so I think, in the midst ofall these methods I'm going to
ask you in a minute about thekey findings but in the midst of
all these methods, I think it'sreally important as
epidemiologists to sort of touchback on the idea that, like why
(08:31):
is it important to be able toisolate or to understand
particularly this impact ofobesity?
And it's exactly what you said,which is because then you can
have a conversation about likehow much good could we do for
health if we mitigated that?
And then obesity is a wholeother world where epi and
clinical trials and otherbranches of academic inquiry
(08:53):
have given us some tools toaddress that.
So I think the methods are alot and yet, at the end of the
day, they're so importantbecause it lets us tease out
something that asks a questionabout how to improve health and
we're going to get to that.
So what were some of the keyfindings?
And particularly this idea ofthe differential exposure to
obesity and how that accountedfor this total disparity in
(09:18):
emotional problems?
What was the role of obesity inthat.
Maria Gueltzow (09:23):
Yeah.
So talking about key findingsmaybe, to start off in general,
looking at the disparities thatwe found in our data set, so we
do find that they are moreinternalizing and also
externalizing, so emotional andbehavioral symptoms among the
low educated groups, so amongchildren that grow up in low
educated and low income settings.
So we do find that there is adisparity between the low and
(09:46):
the high educated groups and thelow and the high income groups,
and we find a point differenceof about 0.5 to 1.5 on the scale
for the internalizing andexternalizing symptoms.
So the disparity is there.
It's not very large in thissetting, which is, I guess, very
good news Now in terms of themechanisms and how obesity
contributes to this.
(10:08):
So what we found is thatdifferential exposure to obesity
contributes to the disparitiesin internalizing symptoms more
so than the disparities inexternalizing symptoms.
And specifically, what we findhere is that, looking at low
versus high educated families orlow versus high educated
(10:28):
maternal education, we find thatif you would set the obesity
levels in the low educatedgroups to that of the high
educated groups, thesocioeconomic disparity in terms
of maternal education would bereduced by about 50%.
And when we look at income, wefind a reduction of about 14%.
So differential exposure toobesity contributes.
(10:50):
So this differential prevalenceplays a role.
Now, when we look atdifferential impact, we don't
find any evidence fordifferential impact and this is
largely driven by the wideconfidence interval.
So we can't really make anysubstantive conclusions about
the differential impact and howit contributes.
Now we also did some genderstratified analysis and it
(11:10):
seemed that obesity plays a moreimportant role in explaining
the socioeconomic inequalitiesin mental health among girls
more so than boys, and, as Imentioned, it seems to be more
important for internalizing thanfor externalizing symptoms.
So I think that's the gist ofthe findings.
Patrick Sullivan (11:25):
Great thanks, I just wanted to follow up.
You said like there's a onepoint disparity, I think for
education, in terms of the rangeof this measure.
It looks to me like themeasures were like four to six
or something right, the absolutevalue, so it's like one point
out of four to six.
That's sort of the magnitude ofit.
(11:45):
Is that right?
Yeah, I think that's how Icould describe it Great.
So how did this relate to sortof what was known about this
topic before your analysis?
Maria Gueltzow (11:58):
Yeah.
So what's interesting about thestudy is that this is the first
of its kind.
At least we're not really awareof a study that has tried to
contribute kind of these twomechanisms simultaneously.
But if we look at previousliterature, overall it seems
that the link between obesityand internalizing symptoms is
stronger than the link betweenobesity and externalizing
(12:18):
symptoms, which could be areason why we might find larger
disparities and largercontributions for obesity to
internalizing symptomdisparities.
When we look at the differentialexposure which I mentioned can
also be called mediation.
So we found a few studies thatlooked at mediation essentially
or kind of inferred that thereis mediation present.
And there is a finding from astudy that kind of showed
(12:40):
already that there is some kindof co-occurrence of obesity and
mental health and specificallyin low socioeconomic settings,
which could be an indication fordifferential exposure.
And there's also a few studiesthat reported an attenuation of
the effect of socioeconomicstatus on mental health after
controlling for obesity, whichis another indicator for
differential exposure.
(13:00):
So we essentially confirmedthis finding and kind of give
more clearly quantifiedcontributions of like how much
does obesity actually matter Nowin terms of differential impact
?
There is some evidence outthere that obesity has kind of a
stronger effect on healthoutcomes among low socioeconomic
settings, but the literature onthis is definitely lacking in
(13:22):
terms of adolescence and also interms of mental health.
So this would definitely besomething that future research
could look into, in my opinion.
Patrick Sullivan (13:30):
Yeah, I think that sounds like a postdoc.
Maria Gueltzow (13:33):
Yeah, it could be someone's postdoc.
Patrick Sullivan (13:35):
I'd like to sort of wrap up this discussion
of the analysis.
Clearly you know part of thestrength here is, you know,
having this great cohort dataand some of the methods you use.
What are your thoughts aboutthe other strengths, but also
the limitations, of this type ofstudy?
Maria Gueltzow (13:52):
Yeah.
So I think one of the mainstrengths is that this is kind
of the first of its kind, as Ijust mentioned.
I think, like I said, I hopethere's other people that are
working on this and that theremight be other papers coming out
that at least use the same kindof methodological approach.
So this is one of the strengthsof our papers, for sure.
I also mentioned that we havean objective BMI measure, which
I think is quite rare and Ithink is definitely one of the
(14:14):
strengths of the paper as well.
So we don't have as manyproblems with kind of
self-reporting BMI, especiallyamong children, where it might
be the parent that reports BMI,whereas there might be some sort
of bias in kind of recall bias,and just the way that it's
reported might not be fullyaccurate.
Just the way that it's reportedmight not be fully accurate.
Then with our study that we use.
(14:35):
So the Generation R study is areally great study but not
really representative of theunderlying population, so we
have a little bit of a selectionbias.
So the Generation R study is ingeneral a little more highly
educated than the underlyingsample, which is something to
take into account, to take intoaccount and then because the
generation of our study has beengoing on for so many years I
(14:56):
think now it's about 15 yearsroundabout.
There's, of course, a littlebit of issue with attrition,
especially if, specifically,children who grew up in lower
socioeconomic settings are morelikely to drop out.
I think this could reallyaffect our study.
So this is something to keep inmind.
And then, in terms of thedifferential impact, we had some
issues with getting enoughcertainty to really quantify it.
(15:18):
So there seems to be somesample size issues.
So we had a sample of about5000 individuals.
We might need a few more toreally quantify differential
impact.
And then, lastly, this is acausal study, so we have to also
think about kind of theunderlying causal inference
assumptions and I will not gointo too much detail about this,
but one of them being theassumption of unmeasured
(15:38):
confounding.
So we controlled for quite alarge number of confounders.
But since obesity, mentalhealth and socioeconomic
position are so interrelatedwith each other, I think there's
a chance that we might havemissed some or that we had some
factors we couldn't control forjust because of data
availability.
So, for example, we couldn'tcontrol for paternal BMI, which
(15:59):
would have been an interestingfactor to control for.
Now, um there are a few otherassumptions, but I wanted to
mention one more, and that's theconsistency assumption, or the
assumption of well-definedinterventions.
So now what we are inherentlydoing is we are assuming a
hypothetical intervention onobesity, where we essentially
reduce obesity, for example,among the low socioeconomic
(16:22):
groups, right.
But this of course raises thequestion of how do we actually
achieve this reduction inobesity, and I think there's a
lot of discussion to be hadabout this, of whether obesity
in itself could be defined as awell-defined intervention or if
it would come down more to,maybe, lifestyle changes.
But then you always need tokind of take this into context
(16:44):
with how could we tacklesocioeconomic inequalities, and
I think this is a discussion Icould talk about for a very long
time, but I would just say,maybe have a read at the paper
if you're interested, andthere's also a few studies I can
recommend that would be reallyinteresting to read.
I think there's a study bySchwartz et al from maybe 2015
(17:04):
and a study by Kaufmann from2019, which really go into this
kind of discussion ofwell-defined interventions,
which is very interesting, Ithink.
Patrick Sullivan (17:13):
Great.
So we're going to turn now to asection of the podcast we call
Behind the Paper, and this isreally meant to think a little
bit more about how we work andlearn and collaborate as
epidemiologists and tounderstand what factors make it
possible for us to do this workor maybe influence how we do it
or how we see it or explain it.
So I'll just start by askingyou know what was not
(17:38):
necessarily from a technicalbasis, because there are a lot
of like technical pieces to youranalysis but, just like as a
researcher, what was challengingor frustrating about conducting
this particular analysis andwhat parts of it were kind of
enjoyable, you know, joyful foryou, and what parts were that
like things that you kind of hadto slog through.
Maria Gueltzow (18:00):
Mm, hmm, yeah, maybe starting with kind of the
negative, or something thatreally frustrated me is that the
method that we use now is quitecomputationally intensive, so
it just takes a very long timeto run the analysis, which I
don't know if this would be atechnical issue that you are
referring to, but yeah, it's alittle bit tricky.
So I think one set of analysisin the end took about five hours
(18:24):
to run, if I remember correctly, and just to get the main
results ready.
I think we had to run aboutfour sets of that.
So then it's very frustratingif you start running your
analysis and then you realizethat you forgot to include this
variable or like you saved itwrong and then it just didn't
get saved, even though you wererunning it overnight.
So I think this is something Iwas getting really frustrated
(18:46):
with, but it's kind of yeah-were you running it like on a
mainframe or on a local machine?
I was in Rotterdam at the timeso I was using I think it's
called a server.
Yeah, it's where you could atleast run things in parallel, so
it sped up things a little bit.
But yeah, there were definitelysome computational challenges,
just in terms of the space thatI needed to run this.
Patrick Sullivan (19:07):
There were different challenges too to
running.
I worked at a time when wesubmitted big jobs, like big
surveillance data jobs, tomainframe SAS and so we'd get in
a queue and then we would dialup.
We'd have to have a dial-upmode to see where you were in
the queue and you'd watch for afew hours and then it would
start running and immediatelystop, which usually meant I left
(19:28):
a semicolon out of SASsomeplace.
So you have to find that fixthat put it back in the queue.
So in some ways we do a lot ofstuff on our desktops now,
obviously, but like this is acomplicated enough analysis that
you're using that sort ofserver structure to do it, so
that's definitely adds a piece.
Conversely, what was sort ofmost joyful for you, most
(19:49):
engaging or like enjoyable aboutit, just like personally or
your scientific self?
Maria Gueltzow (19:55):
I wrote this paper when I was actually on a
research stay in Rotterdam.
So my second author, Joost OudeGröniger, I worked with him and
I worked very closely with himon this paper, which was
actually very interestingbecause I have never worked with
him before and I think we had avery interesting collaboration,
kind of engaging in all thesecausal discussions, so I think
it was very that was veryenjoyable for me and then also
(20:18):
so I mentioned that we used alittle bit more of a complex
approach to deal with some ofthe issues that I mentioned
earlier.
But it took me a while tofigure out how to actually apply
this forward decomposition ifwe're interested in explaining
disparities instead of lookingat causal effects, disparities
instead of looking at causaleffects and this was something
that I think it was equallychallenging or frustrating and
(20:39):
enjoyable, because it just tookme a while to figure out the
solution, which I didn't figureit out.
It was just in a papersomewhere.
I just had to find that paper.
So that was definitely aninteresting process to go
through.
Patrick Sullivan (20:50):
Great.
So this is really exciting workthat you've done, and I wonder,
as you move forward towardsyour graduation, what you hope
to accomplish in the long termof your public health career.
What's your big "hy, like whatdo you want to get done in the
world with these methods?
Maria Gueltzow (21:10):
Yeah, that's-that's a very big
question to ask.
I think, ultimately somethingthat I've been starting to tell
people.
So maybe it will become mymission, maybe not, maybe I will
change my mind, but I think Iwould definitely like to bring
these more complex methods moreinto public health, because I
feel like in epidemiology peopleare kind of more and more using
kind of causal inferencemethods and more complex methods
and machine learning and so on.
(21:31):
But I have a feeling that inpublic health this is still
something that's really to come,even though a lot of these more
complicated methods, they willactually give us the answers to
the questions that we'reactually asking.
So, for example, with thispaper here, I've been talking
about hypothetical interventionsand I think when we think about
public health, ultimately we'retrying to improve health, right
(21:54):
, which would kind of come downto what can we recommend to
improve public health or likereduce problems that we have in
public health?
So I think these methods aregreat and I think ultimately I
want to try to kind of make thema little bit more accessible
and have people not be so scaredabout these methods, which I
used to be one of them so Iunderstand.
So I think that's my mission.
Patrick Sullivan (22:16):
Yeah, I think there's two pieces to that sort
of bridge.
I'm so glad you mentioned thebridge to impact, because these
methods are complex and we canspend a half hour podcast
talking about the methods, butin the end, the question is the
(22:37):
one that you identified, whichis you know, how does this give
us information that's actionable, that lets us be more targeted
or more impactful in the thingsthat we do to improve health?
So, I think, as a sort ofco-career goal of like learning
more about these methods butalso their impact and there are
some folks who in their careers,like to live in the
methodologic bubble and that's aplace to live but I think
(22:58):
there's also a model for havingone foot in sort of complex
methods but also asking at eachstep, like you know, how does
this lead to improving health?
And I think, for my ownexperience, is that you get that
second part by the time thatyou spend with people who are
running programs for these kids.
Or you know school systems thatare trying to intervene in
(23:21):
structural ways, and it's a lotof times it's those
conversations with people whomay know more than you know
about the intervention side, oryou know the lives of the people
whose health you're trying toimprove.
So I'm really glad that youmentioned like bringing together
like these kinds of methodswith that, you know, connection
with where the action is goingto happen, like is it at the
(23:44):
school level, is it at thefamily level, is it the kid
level?
So that's great.
So I want to end just talkingabout mentorship, because when
we talk about being in a degreeprogram, your mentor, or your
team of mentors, is such a hugedeterminant of, like, how fast
you get through it and howimpactful it is and the quality
of your life.
So can you talk a little bitabout one or more mentors who've
(24:06):
been, like, really key for youin the process of doing this
work and why they were greatmentors?
What about their mentorship wasgreat for you?
Maria Gueltzow (24:15):
Yeah, so I have a team I would call it a team of
three supervisors that kind ofhelped me through this whole PhD
process and kind of gettingthrough it somewhat intact, yeah
.
So I think all of them theyreally helped me a lot, starting
with one of my promoters Idon't know if you use the word
promoter, too in the US.
Patrick Sullivan (24:37):
I was going to say we don't use the term
promoters, but maybe we should,so tell us what a promoter is.
Maria Gueltzow (24:43):
Yeah, so promoter is usually a professor
that will be officially on yourdissertation, but then you can
also have co-promoters, or Iwould call it a supervisor,
which is more like someone youtalk to kind of on a weekly
basis, whereas the professorsyou might talk to on a more like
a monthly basis.
(25:05):
But yeah, so I want to talkabout my two promoters, very
strong in statistics and verycritical, which is very helpful
to kind of improve and kind ofget more insights and see where
I can make some changes, and theother one is very supportive.
So it was a very goodcombination and in terms of my
supervisor, I think he probablyhelped me the most, also because
(25:28):
I had kind of more contact withhim.
I had like a weekly back andforth with him and he's
essentially the person thatreally brought me into this
field of causal inference andnot being scared of causal
inference, which I think I'mreally grateful for, and I think
it kind of brought me to whereI am today at like kind of this
intersection of methods and howdoes this translate to like the
(25:52):
bigger picture.
Patrick Sullivan (25:53):
it's really interesting how you talk about
the team and I think, for thosewho may be listening, who are
mentors, thinking about thesedifferent roles.
You know these different rolesthat are that the earlier career
people that we work with need,some of which are around
strategy and some are of whichare around just like how to get
through this experience, andsome of those other needs might
(26:16):
be quite technical about how tomake this code work or how to
interpret that, and so I thinkin some settings, in some
situations, it ends up as beingin one, those needs being in one
person, but I think the abilityto talk about you know what's
needed because you've done areally challenging thing, and so
we need some academic supportsand sometimes we need some like
(26:36):
interpersonal supports for that,and so thinking about those two
roles is interesting.
So I'm going to move to a lastquestion here, which is you are
about to graduate with yourdoctoral program, and
congratulations.
What advice do you have forstudents who are at that point
writing?
What have you learned that youthink might be helpful to other
(26:57):
students who are at this placein their journey?
Maria Gueltzow (27:00):
Yeah, I think it depends a little bit where you
write your dissertation, so Ithink it might differ.
So for me my dissertation iscumulative, so I've been kind of
doing research, writing papers,and then a few months ago I
started writing the dissertationand I think one kind of advice
that I can give is that at leastfor me, this felt like this
last chore that I have to do,like writing everything down and
(27:21):
making it come together.
But I actually think that itwas very insightful and I would
advise everyone to kind of tryand have fun with it a little
bit, because I think that's kindof your chance to talk a little
bit more about, like I said,the bigger picture.
What are the bigger issues here?
What are kind of the challengesthat I encountered that I never
(27:42):
had the space to talk about?
So I think it's kind of alittle bit of a mindset shift,
which that's something thathelped me a lot to see kind of
more the positive instead ofbeing like, okay, I just have to
get this over with and thenthis was a really enjoyable
process for me actually.
Patrick Sullivan (27:56):
Great.
Well, congratulations on whereyou are in your career,
congratulations on themanuscript and on your upcoming
graduation.
Which bring us to the end ofanother episode.
Thank you again, Ms.
Gueltzow, for joining us today.
It's such a pleasure to haveyou on the podcast.
Maria Gueltzow (28:13):
Thank you so much for inviting me again.
It was a pleasure.
Patrick Sullivan (28:17):
I'm your host, Patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
EPI Talk, brought to you byAnnals of Epidemiology, the
official journal of the AmericanCollege of Epidemiology.
For a transcript of thispodcast or to read the article
featured on this episode andmore from the journal, you can
(28:40):
visit us online at www.
annalsofepidemiology.
org.
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