- [Nelson] And on that note,
I'd actually like to introduce John Hayes
from our Geography Department.
Take it away, John.
- Thank you, Nelson. (coughs)
Good morning and thank you all for coming today.
The Geography and Sustainability Department is honored
to host the visit of Dr. Molly Brown
to the 43rd Annual Darwin Festival of Salem State.
Dr. Brown is a Research Professor
in the Department of Geographical Sciences
at the University of Maryland.
She received her Bachelor of Science degree in Biology
from Tufts University
and her master's and PhD in Geography
from the University of Maryland.
Dr. Brown worked at the NASA Space Flight Center
in the Biospheric Sciences branch for over 15 years.
She's published over 120 journal articles
and book chapters and two books.
The Geography and Sustainability Department
of Salem State has been hosting an invited speaker
at the Darwin Festival since February of 2002.
And we are very pleased to have Dr. Brown
with us today to continue this tradition.
We would also like to thank the Charles Albert Reid Trust
for sponsoring our invited speakers over the past 20 years.
And we as especially thank the Biology Department
and the Darwin Festival Planning Committee
for giving the Geography and Sustainability Department,
the opportunity to host a speaker every year.
We really do appreciate it.
Dr. Brown's research interests intersect a number
of interdisciplinary fields including
climate change science, crop production,
remote sensing and digital image processing,
mathematical modeling, and food security
and insecurity and nutrition.
Today, she will describe data sets
that she has used to measure climate extremes,
explain and define what food security is,
and to share data sets that demonstrate
the impact of climate change on food security and children.
Another part of Dr Brown's life is,
she's also the Chief Science Officer at 6th Grain,
a company that develops software solutions
with data gained from remote sensing in the field.
She, today is gonna present her work regarding
climate extremes, food security,
and the nutrition of children in Africa and Asia.
Again, we are thrilled that Dr. Brown is with us today
to speak on the topic,
"Climate change and Food Security,
Evidence that Connects Climate Change of Temperature,
Rainfall, and Vegetation Dynamics
to Nutrition and Human Health Outcomes."
So thank you, Dr. Brown,
looking forward to your talk, take care.
(paper rustling)
- Okay.
So I assume you all can see my screen.
Yeah, so thank you very much
for attending this lecture.
And as the introduction said,
I'm going to be talking about human health and nutrition,
and we're going to be using satellite data.
And you know, satellite data is an amazing tool
that we can use to look back in time
and to connect what's happening with,
what's happening with people,
with what was happening with the climate
when those people were spoken to.
A little bit about me before I start.
I grew up on a Connecticut dairy farm,
and this really has changed how I've looked at the world
because I've been fascinated by food and food production
and growing things
and understanding how the weather affects that process.
As you heard,
I also studied biology and environmental studies,
and then I went into the Peace Corps in Senegal,
where I worked on environmental education
and was continually amazed by how incredible it was
that people could grow food in a semi-arid ecosystem,
so profoundly different than that,
where we all have lived in New England.
After studying geography,
I went and became a civil servant and then in 2015,
I left NASA Goddard
so that I can focus on more social
and economic aspects of food security
than what I was able to look at, at Goddard.
So today, I'm gonna be speaking a little bit about,
first, the conceptual frameworks.
How do we link climate and food security?
And then I'll be talking a little bit
about what the data sets are that measure
climate in the past, the present,
and how we can see into the future.
And then, I will be defining food security
and speak in a little bit,
giving you a little bit of background
about how the food security indicators can be used
to connect to food security,
according to the literature.
And then right when you're totally exhausted,
I will give a bit results from three different papers,
one Niles et al in 2020,
and drought and stunting with Cooper et al in 2019.
And then finally, a paper about birth weight,
children's birth weight, temperature, and precipitation.
The overall message just to give you the punchline here
start at the beginning is that temperature changes
and temperature increases will have a profound effect
on food, food security,
malnutrition, and children's birth weight.
And that is really what I wanna convince you all of
before the end of this, the talk.
(keyboard clanks)
Okay, so what is food security?
"Food security exists when all people,
at all times, have physical, social,
and economic access to sufficient, safe,
nutritious foods that meets their dietary needs
and food preferences for an active and healthy life."
So it's not enough to be merely food.
It has to be food that you want to eat,
that your family wants to eat, et cetera.
So there are three,
there are four linked concepts of food security.
There's availability, access, utilization,
and the stability of these.
Availability is whether or not food is actually present.
Oops, excuse me,
present in the place you are.
And we can see availability looking at food production,
distribution exchange, using satellite data as proxies.
We can use satellite observations of crop area
and crop health to figure out
whether or not a particular area has sufficient food
or if they're having a big reduction in availability.
Then there's access.
Access is about food affordability,
allocation, and preference.
There's not enough to have food where you are
if you can't afford to buy it.
So it's basically, the whole foods effect.
You go into whole foods, you can only afford a candy bar.
It's not exactly sufficient.
And then we can use data sets on prices,
trade, household resources,
and wealth to understand access variations.
And then finally, there's utilization.
You can think of utilization as more at the individual.
Whereas availability is at the region
and access is at the household.
Utilization is at the individual.
Are you well enough to use the food
that you consume to have an active and healthy life?
If you're too ill,
or if the food isn't safe, then you want food secure.
So, and we often use proxies of healthcare, food,
and status nutrition levels.
And then finally, that yellow dot in the middle,
these all have to be stable.
It's not enough to have all three of these aspects
nine months of the year,
and then be starving three months of the year.
It's just in, or it's just not sufficient.
Okay, so when we're connecting climate change to help,
what we do is we often use this complex set of parameters.
(claps) And I know it looks very hard and complicated,
but I wanted to just give you some idea
about the different aspects
to the impact of extreme temperatures
or precipitation extremes, like droughts or floods.
So here we have internal socioeconomic vulnerability.
This is your culture, your social networks.
If you can be vulnerable to climate change
because of economic considerations.
And then there's internal biophysical vulnerability,
your age, your sex, your nutritional status,
your personal, your health, right?
So this is like, so if you are disabled,
you're more vulnerable to extremes
than if you were a healthy 20-year-old, right?
And then you have external socioeconomic vulnerability,
your income, your food affordability, social aid.
And then external,
is your house located in a flood plain?
If your house is located in a flood plain,
doesn't matter if you're a healthy 20-year-old,
if your house has been flooded,
you're still vulnerable, right?
So you need to think about the impact of temperature
and climate extremes across all these factors.
So even if you're physically safe,
maybe you're economically not safe, right?
Another thing we might think about is like the difference
between twins, a 13-year-old girl and a 13-year-old boy,
they have very different vulnerabilities
because they have different social roles,
different cultures, they do different things.
All of those things affect your ability to adapt to extremes
and your exposure.
(keyboard clanks)
Okay, so let's go back
and talk about the biophysical side for a minute
now that I've confused you about the socioeconomic.
So on the biophysical side,
we can really see amazing stuff right back
to the early '80s
about temperature, rainfall, clouds,
humidity, soil, moisture, et cetera.
But today is a theme is temperature.
So I really wanted to focus on temperature
because most people think about climate extremes
that affect food security regarding drought.
They think, ah, you have a drought,
you're gonna have loss of crops,
and therefore you're gonna have food deficits
and therefore food insecurity.
And that's true.
But recently in the work that I've been doing,
we've been finding
that temperature is really the driving factor.
It might be that temperature's driving the droughts
or I'm not exactly sure, (dogs barking)
but it's quite,
sorry about the dogs.
You can really see these big trends in temperature changes
over the last,
from 1880 to 2019.
But I wanted to bring this home a little bit more.
So there's this great tool that shows the number of days
between birth today and when you're 80.
So here, if you happen to have been born
in Hartford, Connecticut in 1969,
then the average number of days
at or above 90 degrees would be eight.
By the time 2017 rolls around, it would be 10 days over 90.
However, by the time this per theoretical person is 80
and they still live in Hartford, Connecticut,
it would be 25.
So you can see that between 8 and 25,
this really is driving a lot of choices that you're making
in the kind of places that you're living,
but it isn't hugely transformative.
There's still a lot more heat waves.
And a lot of this warming that we expect is in the future
between 2017 and 2049,
there's a much bigger change than between '69 and 2017.
Now, if a person was born in Dakar, Senegal,
right about 1969 or 1970,
there was 105 days over 90 degrees.
This is not all that surprising.
It's pretty warm there,
but 105, so that's one third of the year.
By the time 2017 has happened,
significant warming over double the number of days,
226 days at 90 degrees or more.
And you see that
most of this significant warming has happened in the past
instead of in the future,
which means that the vulnerability
that people in tropical areas are experiencing
the food security caused by increasing
temperatures has already happened.
We can see that now,
and we don't need to wait until 2050
to see the impacts of climate change.
And this insight is driving a lot of the literature
and my research in particular,
that in tropical areas
that are already on the high end of the cusp.
As the climate has been warming,
they're already experiencing
a great deal of change. (sniffs)
Okay, so when we're measuring food security,
there's a number of ways that we can show the impact
of this change on outcome.
One of of the big ways is through surveys,
these things called Demographic and Health Surveys.
Currently, the most common,
reliable data source are these DHS.
And it's really about looking at the individual
instead of using average statistics.
I'm sure you all have seen the FAO saying 35%
of this and that have experienced food insecurity.
It's very difficult to relate,
like the average in Mauritania or the average in Russia,
such a gigantic area to the-
what is happening with the food security of an individual
or even a community or even a neighborhood,
because it's just too average,
'cause you're averaging dry places and wet places,
cold places and hot places.
And so you get this milk-toast number, right?
So by having individual observation,
we're able to really see the geospatial,
connecting that geospatial data
and the changes we see in the satellites
to the changes we're seeing on the ground.
The DHS data is extensively available.
This, you can find the data at dhsprogram.com
and it's funded by your tax dollars and mine.
So it started in 1985.
It's continuing to go on.
It covers an extensive amount of the world.
And so we're able to use this data to gather
an understanding of how individual observation
of nutrition outcomes,
which is a direct measure of food security can be connected
to extreme temperatures.
So this is really the foundation of the work
that I'm presenting today.
When we're looking at the individuals,
you have to connect them to a population.
So if you look at this figure on the right,
you can see that what we do is we take a huge number
of individuals and we plot them on a graph
and what we expect in a healthy community,
in a perfectly healthy community,
you would have the average,
the peak here on this line would be zero.
You would have some skinny people,
some larger people,
but you basically have this distribution.
In a community with food insecurity,
this entire area is shifted to the left.
And one experiencing an obesity epidemic,
like some places that we would, that we know,
this whole thing would be shifted to the right.
So many of the communities that I study,
this distribution of children
under the age of five is shifted to the left.
And you can see this figure on the bottom,
the light gray areas are normal.
And then you see
these dark gray areas show
this distribution has shifted to the left.
And so this negative three,
that's this number here.
And so this shifting to the left,
this is talking about the center of this distribution.
We have very, very malnourished people
and less malnourished people,
but what we want is to have that distribution
right in the center instead of on the left.
So there are a few measures of nutrition outcomes.
There's stunting,
which is what we're talking about here,
which is basically when you're too short for your age.
Wasting is when you're too skinny for your age,
but your height is average.
Underweight, which means that your weight
for your age is low
and there's finally also low birth weight.
So I'm gonna be talking about these stunting,
wasting and underweight,
and essentially, they're all proxies
for your food security status
for one of the most vulnerable groups,
which is children under five.
Okay, so if we were live,
you could then ask me whether or not I'm going too fast,
but because we're not live,
you can ask me to repeat any of this later.
Okay, so when you're linking these individual observations
to the environment,
what we need to do is to,
we need to connect the geospatial coordinate
or the latitude, longitude point to the satellite data.
And we do that by averaging
the satellite data over to small area,
but not the whole continent.
So 10 by 10 or 5 by 5 kilometer area
where we have this latitude, longitude point,
which has been displaced so that you cannot find the village
or the community or the household on the map.
But we know there's been some displacement
to protect confidentiality,
but it works pretty good
because we're able to show the mean temperature
and precipitation for that spatial area,
but only for this little area inside.
So we do a very careful connecting of this location,
where the child or that group of households is located
to the place on the map.
(keyboard clans)
And we carefully connect the time of the observation
of the child to the time.
So not only do we connect the space,
but we also connect the time.
And essentially what we do is we say,
okay, so if your five, four and a half-year-old child,
five-year-old child is measured here in 2011,
we then connect that the satellite data
from either a short or a long term period.
But not just any satellite data,
(hand thuds) we don't say,
for example, connect in Salem State in Massachusetts,
we do not take the January temperature data
and try to connect it to the food security
of a child living in Salem, right?
Because obviously nobody's growing anything
in January in Salem.
We're all inside hoping the snow goes away, right?
So we need to be very careful about taking-
not only the space, like where Salem is.
You're not averaging Montana with Salem, with Massachusetts.
You're taking only that one location.
And you're only taking July,
August and September growing period for Salem
to connect to the outcomes, right?
So here's a little graph
that shows if your child was observed here,
you wanna only take the relevant growing season
for just one year for wasting
or for many years for stunting.
The years that matter before the child was born,
when they were in utero,
all the way through to the year
when the child was observed, right.
But not the off season,
but only when the food is being grown.
So then you can connect the potential impact of temperature
and precipitation variability to the outcome.
Okay, so this is all explained in that paper in 2014,
if you want to see more about this.
Okay, so let's see.
So now we're gonna get into the brief literature review.
So you know why I'm making the humongous conclusions
that I'm making in the last three papers.
So we're getting there and it's yeah,
we have maybe 20 minutes more before I'll stop talking.
Okay, so this literature review looked
at 90 different papers.
We published it in 2020,
and we basically looked at papers
that provided quantitative outcomes.
And we were characterizing these papers
and each of the factors within each paper
as either a risk for food insecurity,
a mitigating factor or an inconclusive factor.
And basically, what we did is we looked
at four different geographic levels,
the child, the household,
the region community, and the country.
At the child level,
the factors that these 90 papers called out were whether
or not you were male or female, you're sex,
your age, the young, you know,
if anyone has tried to feed a two-year-old knows
how darn hard it is,
but when they're six,
they'll eat anything you put in front of them
or whatever it is this for your particular child.
Your age, your birth order,
whether or not you've been a multiple birth,
short birth interval, if you were early,
came early, vitamin A supplements,
so each of these parameters are either a risk
or a mitigating, or they could be in inclusive.
Inconclusive is where a factor appears in multiple studies,
but they are not consistently either positive or negative.
And this means that we just don't know what impact they have
or maybe they get observed and it's just confusing.
So for example, birth order is a risk for stunting,
but not for wasting or underweight.
Very important that here we can see
that if you were born in a hospital,
professional birth, we call it,
it is quite a big mitigating factor for stunting.
Same with vitamin A.
This means when you get sick later on,
if you're not doing well,
your mother brings you to the hospital or to the doctor
and you get looked at and you get,
and this is a huge mitigating factor.
So healthcare matters, right?
We all know that.
So at the individual level,
these are the parameters.
Now, when we go onto the household level,
remember that big, complicated, colorful figure I showed
at the beginning,
these are where the socioeconomics
of your household really matter.
So if your parents have had education,
your mother's weight and the mother's height,
wealth and assets, obviously,
if you're more wealthy,
you get more to eat and you're much less likely
to be wasted, stunted or underweight.
Also, we have rural indigenous
altitude being isolated from town,
more rural than urban, right?
There's the big risk factor for stunting
and rural is also a risk factor for underweight.
There are lot of inconclusive factors in here.
It's very complicated.
So a lot of these things were not conclusive all the time.
For example, household size,
female head, dependency ratio,
none of that was really conclusive.
Okay, so now we're getting into the region of the community
and here is where we get into
our great remote sensing observations,
rainfall, growing season rainfall,
extreme temperature, drought, and vegetation quality.
All of these are important for,
the excessive rainfall,
extreme temperature or heat waves,
and drought all are risk factors for underweight,
stunting and under nutrition,
and finally, conflict.
Conflict in the region, days, born during conflict,
these are all stunting risk factors.
And then finally, the GDP country level parameters,
they're all mitigating.
Your economy is doing better,
much less likely to be stunted or underweight.
Okay, so moving on the results
from our three papers as you recall from my outline.
First, I'm gonna talk a little bit about diet diversity.
Now, diet diversity is one of those variables
that is collected across a lot of communities.
It is essentially a measure from the previous 24 hours
where you ask the woman that you're interviewing,
'cause you don't interview the children.
You ask the woman you're interviewing,
how many of these food groups did your child eat
in the 24 hours previous, right?
So we have breast milk, grains,
fruit and tubers, legumes, dairy products, flesh foods,
which is otherwise known as meat, eggs,
vitamin A rich foods,
and that is basically things like carrots or orange, squash
and other fruits and vegetables.
Now this is not a measure of quantity.
It is only a measure of items.
So if you have-
if you eat a quarter of a banana,
you get a tick for number eight.
If you eat three bites of your breakfast,
you get a tick for number two.
You know what I mean?
So it's not about how much,
it's just about whether, how many.
And this allows us to compare a number of factors
about how likely it is you're gonna have outcome problems.
So in this little map,
it shows where we had 107,000 children
in 19 low and middle income countries,
across six global regions.
You can see that the very blue areas have the lowest
diet diversity and that's Niger in Nigeria here.
This country here is Nigeria with all that data
and the higher yellow ones of the highest diet diversity,
which are in South America and in Asia.
Okay, so when the results show
that when you throw all of these incredibly diverse things
into one model,
you get the obvious results,
which are wealth matters and age matters, right?
So if the child age, the older the child,
the less likely you're going to have impacts
in diet diversity.
And then this figure,
the elements to the right are mitigating
and the elements to the left are risk factors, right?
So using that concept that we had previously.
So here on the right, the wealthier you are,
the much less likely
that you're gonna have low diet diversity,
but the poorer you are.
So this is like obvious, right?
One of the things we brought in
to this paper were tree cover, livestock density,
distance to urban,
so these are very similar parameters, right.
Now, when you separate out all the regions,
we see a much more complex figure,
but we still see
that the economic variables are very important.
Basically, the mitigating factors
such as child age, education,
improved services, hygiene services,
also poor households have less diet diversity.
But here we begin to see the temperature popping out
'cause remember this is supposed to be about temperature?
And you're like, really?
I haven't heard anything about temperature recently.
So here's temperature.
You can see that the temperature anomalies,
how the current temperature is affecting the results.
The higher the temperature, the greater likelihood
that you're gonna have low diet diversity.
And in the next slide, I just pull out out West Africa,
which is the poorest of the African regions.
And here we see a very high impact
of long term average temperature versus diet diversity.
And this high temperature anomaly outweighs
all the positive relationships between diet diversity
and education, improve toilets,
access to improve water and wealth.
This is really important
because it means that if you live in West Africa
and you're really wealthy,
you're still gonna have hard time getting sufficient,
diverse diet to feed your children, right?
So all of that investment that we've been making
in improving people's incomes are really going to be swamped
by the increasing temperature
and the already observed increases in temperature.
Okay, so this is on diet diversity.
How does it play out when it comes to stunting?
Now here, we see a study by Cooper et al
in the proceedings of the National Academy of Sciences
about this standardized precipitation
and evapotranspiration index.
Here, we combine precipitation and temperature together
to look at 580,000 observations of children
who were stunted.
Now, remember what stunting is.
Stunting is where if you are particularly short
for your age,
which means you've had multiple incidences
of inadequate feeding in the previous life cycle.
And so, and what this figure here shows
that during the 24-month
previous precipitation anomaly
and the residual stunting indicators
during periods of normal rainfall,
which is this part in the middle,
children were typically taller than household
and individual factors would otherwise predict.
In other words, normality is awesome.
It means that people can do what they usually do.
They're able to feed their kids.
Everything is ticking along.
When it's particularly wet out,
we see that severe wetness, children were typically shorter
and severe dryness, particularly shorter.
And the reason why you might get additional stunting
during wet period is because the lack of healthcare
and you get more diseases and more diarrheal events.
And really wet periods,
you get more severe lack of food availability.
So there's different mechanisms through which,
but essentially normal weather is better
for children's outcomes,
which is why climate change is such a big threat.
Okay, so very briefly,
because I know you guys are getting tired.
In this paper, we also did this sort of mitigating
and this mitigating on the right
and risk factors on the left.
And we found that mitigating factors improve
diversity of agriculture,
government effectiveness.
This just falls out of that other paper
that we published,
although this one was before that 2020 paper.
But political stability in the absence of violence,
these are all mitigating factors,
which improve the ability of families
to raise healthy children.
On the other side is topographic roughness,
otherwise known as elevation.
Do you live far away and very distant from town?
Percent of bare cover.
Are you near a big desert
or have you really overgrazed?
And then here's the big one here,
average monthly maximum temperature.
Again, temperatures just falls out of this.
And if you have more people,
that temperature is even more-
big risk factor for stunting.
So temperature again, comes out as a very important
and very strong driver of food insecurity.
And then here's a map, which I'm not gonna explain,
'cause I don't wanna talk for too long.
The last study that I'm going to be talking
about here is this Grace et al paper in 2015,
a little bit earlier
and here the dependent variables birth weight.
So this one is in interesting.
It has 66,000 children in it.
It has a much longer timeframe from 1986 through 2010.
And we were interested in looking at birth weight
because of the combined factor
between mother's health and birth weight.
And the livelihood zones here are used
to mediate the outcome.
And there's a lot of results in this paper,
but the ones that I wanted to bring
to your attention were again,
about temperature and precipitation.
What we can see here is that
the impact of mediating factors,
this one, the mitigating is above the zero
and the risk factors are below the zero
instead of left and right,
but we can see that only an increase in education
and an electricity affect birth weight.
When you combine improved education
and improved access to electricity
with temperature increases and precipitation decreases,
you see that these factors totally overwhelm
this investment in development factors.
So again, temperature increases
really have a very large impact on these poor tropical
and marginal communities
that are otherwise experiencing food insecurity.
And we expect that the impact of climate change
on birth weight and low birth weight will depend of course,
on where a mother lives, right?
And along with the mediating impacts such as income,
education and diverse and electricity,
but really after spending 50 years investing in education
and access to electricity,
changes in temperature can overwhelm
those health benefits.
So to conclude, widespread warming is consistently predicted
by climate change models,
far more consistently than precipitation.
Temperature increases may pose the greatest overall threat.
Temperature impacts are affected
by timing of exposure and intensity and duration,
but we really need better predictive models and responses
to changes in temperature and heat waves
than we already,
than we already have now.
So a lot of this work is focused on improving the prediction
because temperature is one of those things
that we can predict
and we can predict changes through time,
much better than rainfall.
And that is I think all I have,
I have one more slide,
but I'm, yeah.
Okay, I'm ready for questions.
(hand thudding) Let me stop sharing, yes.
- Thank you very much, that's a wonderful talk.
I'm amazed at how much you can get
from satellite views. (laughs)
but we are,
there's a lot of talk
about how to reduce the temperature of the earth,
but this is happening already.
- Correct, yeah.
- Do you see changes in people's habits trying
to deal with that in areas that you've been studying?
- Sadly, the places with the biggest impact have the least,
the places that experienced
the biggest health consequences have the least ability
to change the trajectory
because those places have the least use of energy,
they're not flying around,
they're not heating and cooling their houses,
they really have a tiny impact on climate change.
So sadly the haves,
which are Europe, China, India,
and most importantly, us are affecting the have-nots,
which are Africa and Asia.
So it really is up to us to make sure
that those places do not suffer the consequences.
- Thank you. - Uh-huh.
- [Nelson] Dr. Gordon?
- Good morning, Dr. Brown.
I'm Ethel Gordon,
I'm a Biology faculty and a member of the committee.
And I would like to ask you one question relating
to your information you presented on stunting and drought
and you compared wet and dry seasons.
I was wondering if you had actually,
any data or measurements that correspond
to were there more diseases, et cetera,
during either of those periods?
You assumed or you mentioned a correlation,
but I was wondering if there actually were studies saying
what occurred during what seasons
or what occurred during dry seasons
that would affect stunting.
- Right, okay.
So complicated question.
So what happens is that
these demographic and health survey technicians,
they go and they talk with a family.
So they knock on your door
just sort of like a census worker, right?
They knock on the door of the household
and they record in the survey,
the time, the date and the location of that household.
So then what we do is,
then they measure the height
and the weight and they ask the birthdate
of all the children in the household, right?
So now we have observations of the child
and the time and the date.
So if that child, and then they ask the mother,
has this child experienced diarrhea
in the last (claps) 24 or 48 hours,
depending on the place, right.
And no one knows more than the mom,
whether or not a two-year-old has diarrhea, right?
Because clearly,
so the mom says yes or no.
And so we have those observations
to know whether or not a diarrheal disease is happening.
And then, we can connect the disease to the wasting, right.
Here comes the rub.
Remember I said 108,000 children.
My N is great.
That's only with stunting.
Stunting is a well child who has been malnourished
over their entire lifetime.
Poor height, very skinny, right.
Wasted kid is a perfectly normal kid who has been sick
in the last week who's lost weight, right?
So that means that we have extremely small number
of wasted observations compared to stunted.
So although from a satellite perspective,
I'd much prefer to look at wasted kids
because you have a much more direct connection.
It's raining, it's flooding,
you get contamination of water from up to down.
You have diarrheal, it's like cause and effect.
We got it.
Sadly, (claps)
you only have like 400 kids in the whole country
that you catch right at that point.
Whereas you might have 47,000 children
who are stunted, right?
So this is why a lot of these studies used stunting
because the observations are far more robust,
far more dynamic.
But the answer to that long- is yes.
Yes, we do. (Nelson laughs)
We have way too much information about all,
there's literally 400 questions on any of these surveys.
So we know a lot about,
they also take blood samples and all sorts of other things.
So- - Thank you very much for,
thank you for your answer.
I see we have a number of other questions.
So let's go to this one from our audience.
Would you say natural selection plays a pod in all of this?
- (exhales sharply) That is sadly beyond my expertise,
but in general, humanity is,
how shall we put it?
We're gigantically adaptable.
And because we have culture,
culture plays a big role.
And in general, we do not consider
natural selection to be the primary driver.
I think it's far more structural,
if you know what I mean?
Like I just happened to have been born in Connecticut
and not in Malawi or wherever, right.
So I think my personal answer would be no,
but I'm also not an evolutionary biologist.
So it's really, I'm speculating here.
Very little evidence to support my answer. (laughs)
- Thank you.
I'd like to ask a question.
- Sure.
- Since the extremes of climate change are already
happening right now.
- Yeah. - And the food security
of individuals throughout populations are being affected,
what exactly is being done right now to improve this issue?
And we've had several people ask,
what can individuals do to help?
- Okay, so.
Right,
so what is happening right now is people are investing
in improved agricultural technology use
in these regions.
So this is why I actually am,
I spend a lot of my effort,
I think someone mentioned that I not only am do research,
but I also invest in a company
because I believe that agriculture is primarily a, you know,
no one does farming for their health.
It's really hard work.
So we all try to make a living or make money being farmers
and particularly countries in Africa who are,
80% of the people there actually grow things for a living
or they work in a food related,
like they drive trucks to move food around.
It's not direct,
but far greater percentage
in these food insecure countries, right?
So given that we need to invest in agricultural technology
and agricultural development.
So I think what can people do individually is pay attention
to your carbon footprint basically.
Try not to eat so much meat, red meat.
Think about what you're doing and who you vote for
and whether or not the U.S. is paying attention
to the climate problem.
Carbon, you know, the atmosphere is not a big waste dump.
It's like in 1885,
you could take your garbage and just chuck it out the door.
We don't do that now.
And we're not gonna be dumping our CO2 gas pollution
into the atmosphere any longer
and just a few more years.
So in my opinion, it's being active,
caring about this issue
and moving forward with our climate policy here in the U.S.
And pressuring our brother in abroad to do so as well.
Yeah.
- Thank you for your response.
I see we have other questions.
One is, have you done any research on the colder climates
and birth growth or success,
such as Greenland and how do they compare? (chuckles)
- Right.
So I would love to do that.
The tricky part,
okay, so there's several tricky parts.
First, Greenland and United States
and all of Europe actually have very poor data
on child health outcomes
because of issues having to do with privacy.
I would love to study, for example,
United States, compare Alaska to Washington state,
but it's very, very hard to do that
because the data is difficult to get.
It's in the private domain
because our healthcare system is private, sorry.
And I cannot study Greenland or Iceland
or any of those countries
because you need to be local in order to access the data.
So no data.
However, there are a number of DHS surveys
in places like Kazakhstan, Tajikistan,
Turkmenistan, the -stans,
which are in the Middle East,
between Russia and Europe and north of Iran and Iraq.
So they are cold, wet, winter,
dry, hot, summer places.
Also Nepal has great data from this.
And so I've written a few papers about Nepal.
And the bottom line is this is where all this elevation.
Remember that parameter you saw in all pop out, elevation,
you're like really?
Where does elevation come into it?
Not a lot of elevation, right,
in Africa, it's pretty flat place.
It all comes from Nepal and India
and those -stans.
So essentially if you live in town in a place like Nepal,
you're much better off than if you're growing food
and you live far from town
because it's all about access to markets, et cetera.
So I can send you a paper if you email me
by Shively et al about that Nepal thing.
Nepal is a great place to study elevation.
(computer dings)
in cold. - I'm really,
could I ask a question, Nelson?
- [Nelson] Absolutely.
- Hi,
the diet diversity on the survey, Molly.
That 24-hour look
at just the past 24 hours. - Yes.
- Does the survey include like 48 hours too?
Or is it,
or is that, not just the 24-hour window?
- Yes, so they ask us 24 hours.
So if you know anything about these kinds of surveys,
there's been a huge,
we would love, what I would really want,
if I could just design myself my own survey,
I would like to know exactly what all these kids ate
and what they cost and whether their mother got it,
did they grow it or did they buy it or where they got it
and where they help, you know,
what are all the different,
sadly, no one can remember.
So this is called consumption surveys.
They're very, very inaccurate. - Okay.
- Because it's just damn hard to remember all this stuff.
So they asked the mother about the child
over the past 24 hours.
And by not asking two weeks or 48 or 36 hours ago,
you don't have to remember it all that much.
It's just yesterday, right?
And then, they also do not ask quantities
for the same reason.
They find the data is much more comparable
and replicatable if you ask only
like types of food
and essentially, because none of us eat a lot of like,
I can remember whether or not I had a banana,
but I can't remember how many bananas
or if I also had an orange.
And I can tell you whether or not I typically keep
those things in the house as a mother
and then whether or not my kid would be eating it,
basically we don't have chips,
so we don't eat chips.
So it's that kind of thing, right?
You remember it.
So it's all about the consumption.
Humans are terrible at data and they're very bad at recall,
but if you just ask what type of food,
then you can easily answer
according to what you typically have in the pantry.
- Okay, thank you. - So that, yeah.
So it's all about data accuracy.
- Right.
- Another the question from the chat, the question answer,
is it possible to reverse wasting, stunting,
and other food insecurity-induced problems?
What is the window of time to respond
with a reasonable likelihood of mitigation?
- Very excellent question.
So this is why we are really into anticipatory.
I have a paper about out anticipating all of these things,
which came out last year,
but essentially, once you are stunted,
you cannot reverse.
You cannot catch up,
however, wasting you can catch up,
which is why our-
we really have been focused on wasting.
And that 2020 review paper was all about wasting
and not about stunting really.
And basically stunting is multiple times
of when you lose weight,
but you can't catch up
'cause there's not enough food available,
right? - Uh-huh.
- And the other thing is,
if you're interested in this question,
you can Google a thousand days,
"First Thousand Days"
that is a new thing in The Lancet.
There's a great paper about the first thousand days of life.
If you are undernourished in that first thousand days,
you have cognitive and psychological
and physical delays and you cannot catch up
and then it gets much worse.
Actually, it's sad.
But the reality is if you are stunted,
you have a much lower potential for making money
when you're 45
and your society,
the community that you're in has a much lower probability
of growing faster,
and your children are more likely to be stunted and wasted
if you have been stunted and wasted
or you may be obese.
So normality is very hard to recapture
once you lose it, (hand thuds) basically.
And so it's really very,
what we want to do is move towards anticipation
and prevent prevention.
Just like once you contaminate a lake,
it will cost a thousand times more to clean up that lake
than to keep it clean in the first place.
(Nelson laughs) Same with people.
Don't screw it up,
try to keep it,
provide healthcare, provide feeding centers,
do whatever you can,
because it will be much more expensive for the household,
the community and the country
if you don't prevent that malnutrition
than to try to mitigate it after the fact. (hand thuds)
Yeah, so there's tons of work on that,
tons of literature,
easy to find.
- [Nelson] Cool.
- I think we have four minutes folks.
(John laughs)
- There's lots- - I'm gonna-
- [Nelson] Go ahead.
- I'm gonna ask one question from one of our colleagues.
We rely heavily on very small number of staple crops
and the genetic diversity of these crops tends
to be quite low.
- Right. - What concerns might you have
this lack of diversity of crops
with regard to food security?
- Yeah, that's a really very great question.
Thank you so much for that question.
This is where the,
you might have noticed in that 2020 paper, review paper,
crop diversity, and also in the Niles et al,
we had crop diversity.
It is not very well connected to diet diversity,
it turns out,
but the question that you have is extremely important
and continues to be very,
a big worry.
I would also say that in general,
there's this huge push towards
natural-based climate solutions,
where we need to move away from mono crop
into much more diverse pattern
so that we're less vulnerable.
And the long and the short of it is that
if we have multiple regions experiencing climate shocks,
either too wet or too dry,
we could easily have a huge spike of prices,
which then would have very negative effects
on food security of these communities.
So although, we have a global food system,
it's connected by the market
and the market will be very sensitive to having,
think about it, a huge wheat production decline
in both Europe and North America and Canada
at the same time,
then you have a huge spike of wheat prices,
which then cascades into corn prices.
So that would be bad for food security.
But essentially when you're thinking
about that crop diversity question,
you need to remember the complexity of the food system
and how all these elements are connected
and how the economy is the mediating factors,
not just one-to-one.
In other words, food security is not food production,
it's production plus income and livelihood
and trade and roads and all sorts of other things.
And then, your own personal food security outcome
if you see what I'm saying, so that-
There is a great paper.
I did a study from the USDA
in 2015 called "Climate Change,
Food Security and the U.S. Food System,"
have a look at that paper.
We addressed this crop,
this diversity in the major crops extensively in that paper,
in that report.
2015 USDA.
- Thank you.
- You're welcome. - Another question
from a student is do you perceive
population growth of humans,
which is rapidly increasing
with every passing year on our planet
and then a demand for food resources becoming an issue
in your research?
If so, any suggestions on what can be done to resolve this?
So basically asking about population growth.
- Yeah. (exhales sharply)
I think that the answer is no,
I don't really think
population growth is a huge issue basically.
It's a very complex issue,
but I do not think that solving the problem by telling women
that you shouldn't have babies is a good idea
because people, it's really a poverty problem, essentially.
- That's right. - So think about it.
If you are a woman who lives in these year,
you're gonna want 10 kids
because eight of them might die of starvation
or you know, or an infection
'cause you have no access to healthcare.
And somebody has to take care of you when you're old.
(hand thuds) If you live in Singapore,
you might have one or zero kids
because you don't need children.
What the heck your children for?
They're a pain, right?
(John laughs) So essentially,
what we need is better development,
we need better economic,
we need better migration,
more open societies.
(hand thuds) It's not a population problem.
It's a development problem.
It's an economy problem.
It's a structural problem,
not a population problem,
but call me up and we'll argue about it more. (laughs)
(Ethel and John laughing)
- I wish we could continue this for another half an hour.
We have many more questions than we have time to ask,
but I think we need to respect the time limits
that everybody's expecting.
And so thank you so much for your time
and wonderful presentation.
We really,
really appreciate it. - Okay.