"Edwina: I'm Edwina Yeung and I'm in the Epidemiology Branch of the Division of Intramural Population
Health Research here at the Eunice Kennedy Shriver National Institute of Child Health
and Human Development at NIH.
I'm primarily interested in obesity and its related metabolic risk factors.
I was born in Hong Kong and then I moved to Boston, the area around Boston when I was
around nine.
I grew up being encouraged to study science, go into medicine.
My parents gave us a microscope.
I remember using it a lot.
I wasn't that great.
We had after-school science programs and then I had just really great teachers in high school
too who taught biology very well.
Then I went to undergraduate education in Johns Hopkins University and that's where
I actually knew about epidemiology so before my college education, I had never even heard
of it.
After I got in, I actually changed from being a biology major to a public health major.
After I graduated from Hopkins, I actually continued in their graduate program to get
my Masters and my PhD, and to study obesity in a population health research setting.
I think everyone knows that obesity is a large public health program right now worldwide,
and so some of the metabolic risk factors I'm referring to, obviously, is heart disease
down the line but also just having bad cholesterol or high blood pressure, those types of things.
What I really want to understand is what are the early determinants of obesity.
How is it that kids become obese and then it puts them on their trajectory into becoming
overweight or obese, and what is it that we can do early on to stop this?
What we do mainly is collect data from large populations, so I lead a study.
It's called the Upstate KIDS Study, and what we do is ask women about their and their children's
health through questionnaires and we also try to collect biological specimens from our
participants as well as get data in other ways.
Then when we get that data together here, we do statistical analysis to look at associations
between those who do, for example, have a certain condition, in my case I'm interested
in obesity, versus those who do not have a condition, and make comparisons to see what
are the risk factors associated with this condition.
It's not just all about science and math, and actually enjoying writing could actually
be really helpful too just because we do write a lot of proposals and manuscripts and stuff
like that.
This is our diet recall computer module that the National Cancer Institute has made for
kids, and what I'm trying to figure out is whether or not this will work also in the
participants that we want to follow.
They have this menu, it has the penguin asking the kids to talk about what they've eaten
in the last 24 hours.
We do consult the bio-statisticians to come up with an analytic plan before we do the
analysis, and then the analysis is performed.
Either I do it myself or someone else will implement the analysis on the data that has
been collected.
It's a team effort and then we see the results and then the interpretation of the findings
is mainly based on who is writing the paper but we always, and of course, the co-author,
is when we come together and talk about what we found, and getting our findings out there
with publications.
A lot of it is also figuring out what has been done before and what we can build off
of what's been done and improve upon it, etc.
In 15 years, I'm hopeful that we can figure out also specifically some of the mechanisms
that go into how these life risk factors and if figure out those mechanisms, we can perhaps
come up with novel treatments and novel ways of prevention.
It's like a very multi-disciplinary job.
I get to interact a lot with people of different scientific and also mathematical statistical
expertise, and finding out new things about what I want to study, in this case, obesity,
and finding out these things to, again, help people.
I think that's why it becomes the most exciting job for me.