WEBVTT

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Prof: Okay, good morning.

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I'm assuming you guys are all
preparing very hard for the

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mid-term on Wednesday,
are totally on top of all the

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material.

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Just a few logistics of about
that,

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as you know,
the exam will be in this room

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and it will be during the
allotted time for the class from

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9:00 until 10:15 and that should
be plenty of time to finish up

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the exam.

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You'll notice--and you probably
have noticed this already--if

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you look around the room there
some spots in the room that are

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lighter and others that are
darker just because of the way

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the lighting happens to go,
so when you come in for the

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exam you may want to try to find
a seat in an area that's one of

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the better lit areas.

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If you have any questions about
the nature of the exam,

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please see me after class or
talk to one of the teaching

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fellows.

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I wanted to follow up on some
things I meant to get to in the

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last lecture but we ran out of
time, and just go over several

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of those very quickly.

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Then we're going to launch into
the discussion today of public

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health.

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The two things that I didn't
get to in the last lecture were

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two.

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You won't be tested on these
for the exam,

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so that'll give you a little
bit of a break,

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because I didn't have a chance
to go over them in detail in the

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lecture.

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One of the concerns,
one of the issues pertains to

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the use of antibiotics--heavy
use of antibiotics in farm

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animals,
especially animals like

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chickens and things like
hormones that go into farm

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animals like cattle.

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The concern with the
antibiotics is that so

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many--such heavy use gets made
of these,

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especially in animals that are
in close quarters,

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which as you saw from the
discussion of industrial

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farming, is happening more and
more.

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These animals,
clustered together,

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can get sick and spread disease
in a very short period of time.

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The farmers found that you
could give antibiotics to the

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animal's prophylactically,
that is in anticipation of

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getting a disease rather then
waiting for a disease outbreak

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to occur,
and then found almost as an

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aside, that the animals grew
better with the antibiotics.

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The heavy use of antibiotics
allows the chickens to thrive in

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more of a disease-free
environment,

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promotes growth in the animals,
but the environmental concerns

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are that the heavy use of the
antibiotics gets--the

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antibiotics gets into the
groundwater,

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goes from place to place via
the water tables and things like

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this.

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Therefore resistant strains of
bacteria develop.

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There are some studies of
people who are around,

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who live in the vicinity of
some of the places where there's

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a heavy concentration let's say
of poultry farming.

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So for example,
one of the places where this

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happens in greatest
concentration,

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Arkansas, is a big chicken
raising state.

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Also there's a part of Maryland
called the Delmarva Peninsula,

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which is the Delaware,
Maryland, and the Virginia

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confluence,
and Purdue chickens is in that

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area,
but there are others as well

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where researchers from Johns
Hopkins have studied this

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resistant strain of bacteria in
that area,

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and have found what they
believe is very alarming

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findings.

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So of all the different
concerns with modern agriculture

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and the modern raising of
animals,

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the heavy use of antibiotics
and the resistance that people

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can develop to these antibiotics
and--I mean resistant strains of

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bacteria that develop in
response to this have become a

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very real issue.

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Then the other thing that I
mentioned at the end of the

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class is that--and we'll come
back to this later--people are

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interested in food from a lot of
different points of view.

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So if you look at the people
that are interested just in the

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issue of sustainability;
you have people that are

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concerned about the environment;
you have people that are

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concerned about biodiversity;
people concerned about animal

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welfare;
and then of course you got the

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whole public health overlay with
people concerned about the

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effects of food on physical and
mental well being in people.

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All these different
philosophies or conceptual

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approaches are passions that
people have get played out in

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different ways people organize,
so there are different groups

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that represent each of these
different areas.

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One thing that hasn't happened
is that these groups come

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together around a common goal:
the common goal would be better

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food.

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We've talked at The Rudd Center
about possibly convening leaders

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from these various groups to
come together and talk about how

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they each could band together
and form a stronger,

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more united voice if a unifying
theme could be found,

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and that is people need to eat
better food.

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Later in the class,
we'll talk about how those

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groups might come together,
and what they might do.

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Okay, some of you may have seen
yesterday's New York Times

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Sunday Magazine.

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That just so happened,
it contained a number of

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excellent articles on issues
pertinent to the class.

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Michael Pollan had an article,
Mark Bitman,

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a well known food writer had
another one and you see

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the--there are more articles in
the magazine than just this,

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but these are the ones that I
thought were most pertinent to

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the class.

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If you haven't had a chance,
I urge you to get a copy of

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The Times Magazine,
you can read it right online if

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you wish,
you don't have to go find an

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actual copy.

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Some of the articles are really
interesting and have up-to-date

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information on some of the
issues like the potential of a

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Green Revolution in Africa.

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I thought I'd start off today
with something fun.

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Some of you guys will have
listened to one of the NPR shows

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on the weekend where they had
people read crazy tales,

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and the contestants on the show
have to guess which one is true.

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Well I'd like to do a little
thing like that today,

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so I'm going to show you four
things and one of these is true,

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and I'd just like to see a
raise of hands to see if you can

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guess.

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The dairy industry did
something in 2006 in San

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Francisco.

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Option one is that they put a
device in bus shelters that

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released the smell of
fresh-baked cookies,

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in order to encourage people to
drink milk that might be

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associated with cookies;
number two, they tested a

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campaign where children were
given free milk bottle costumes

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and milk mustaches for
Halloween;

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(c) they tested adding subtle
milk flavor to sweet non-milk

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products like soft drinks,
in an attempt to increase

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desire for milk;
or (d) they tested the use of

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electronic signals that
stimulate the part of the brain

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that gets activated during the
act of breastfeeding to increase

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desire for milk.

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All right, so how many of you
believe (a) is the right answer?

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All right, how many (b)?

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All right, (c) and (d)?

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Okay it looks like (a) and (d)
got the most votes in this case.

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The answer is (a).

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They tested a device that
released the smell of

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fresh-baked cookies into bus
shelters to see if this would

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increase desire for milk.

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There was immediate outrage
about this because people were

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feeling there's nowhere you're
safe from marketing,

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but that this was sort of an
obtrusive and sort of guerilla

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way of marketing that we'll come
back and talk about in a

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subsequent class.

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The industry didn't actually do
much of this,

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because the public outcry was
so great,

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and the press got after them,
but it was interesting,

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the different ways that
industry is using to sell food.

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Let's talk about public health.

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Now why am I going to talk
about a profession?

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Everything else in the class
is--or a concept,

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if you will--everything else in
the class is about substantive

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material,
and this is more about a

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conceptual approach in a
profession,

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namely, public health,
and what it means.

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Well it's so important here
because public health--in the

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views that public health
promotes,

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in opposition to the
traditional ways we look at

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things,
are very important in the

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context of changing diet,
and changing the health of the

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population.

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If you look back in history
several hundred years,

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you get some very startling
statistics like this one.

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Then, in this case in France,
the median age of marriage was

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older then the median age of
death,

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and the only way that could
occur of course is if many,

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many people are dying young.

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This has been turned around by
public health.

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This is obviously no longer the
case in any part of the world,

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and the question is,
what's happened,

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and what are the health
victories been that have

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encouraged this sort of thing?

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What changed?

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Things like sanitation changed.

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There are many other things
that changed as well,

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but sanitation would be an
example of a major public health

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victory that has really saved
many, many, many lives.

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Here's the definition of public
health:

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"The science and practice
of protecting and improving the

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health of a community,
as by preventive medicine,

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health education,
control of communicable

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diseases, application of
sanitary measures,

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and monitoring of environmental
hazards."

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Now it's interesting here,
because here the unit to be

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protected isn't the individual
but it's the whole community.

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The community can be construed
broadly as a state,

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a country, or even the whole
world.

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Instead of working with
individuals one at a time,

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the focus is more on large
groups, as in a community.

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The protection of people from
environmental hazards becomes an

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important part of that,
so protection of people from

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water pollution,
or from air pollution,

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would be an example.

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One question one could
reasonably ask is,

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is the food environment toxic?

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And is that an environmental
hazard?

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Not as--like a poison that's in
the water supply;

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but is the environment
sufficiently toxic to produce

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enough disease that people
deserve protection from it?

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And should government get
involved in that process?

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Again, everybody will come down
in a different place with that,

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but to the extent that holds
true, public health becomes very

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important.

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The classic start of modern
health has a very interesting

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history.

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Some of you may have heard this
and I know some of you have a

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background in public health
already,

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so you all have heard this,
but it's a very interesting

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start.

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Certainly, the concept of
public health goes back well

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before this particular event
that I'm going to talk about,

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but this event was pretty
noteworthy,

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and it led to the classic start
of modern epidemiology.

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In August of 1854 there was a
tragic outbreak of cholera in

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London, and many people were
getting sick and losing their

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lives because of it.

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The prevailing theory out there
was called the Miasma Theory,

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and that had to do with
spontaneous generation.

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The idea was that disease came
about from spontaneous life

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forms, from things like swamps
and putrid matter.

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This led mainly to treating the
disease when people had it,

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but there weren't effective
treatments and some theorizing

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about where it might come from,
but not much else.

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There was an alternative that
was embraced by a small number

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of people called The Germ Theory
at the time and the idea here

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was that one didn't just get the
disease as its own entity,

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but there were specific things
that went into the body,

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microorganisms (although they
weren't called that at the time)

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that invaded the body and made
the person sick.

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If there was a way to find the
source of those,

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one might do something about
it;

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but this was very much the
minority view at the time.

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Enter John Snow,
a physician and considered now

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the father of modern
epidemiology,

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but he was a traditionally
trained physician.

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He was an anesthesiologist;
well known in England,

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in fact, ministered to the
Queen and developed something

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called the Chloroform Pump that
was used for anesthesiology,

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so this is what he was known
for.

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During the Cholera break he got
interested in what was going on,

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so what Snow did was he
rejected the Miasma Theory and

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believed there was some toxic
agent that was invading the

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body.

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He wanted to find out where it
came from,

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and so he did things that today
we would consider pretty

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routine,
but were really unknown at the

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time: he tried to trace the
spread of it.

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There was one notable family
that gets discussed in this

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history called Barnes,
where there were a lot of cases

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of this within the same family.

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He did more than that.

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He suspected that the transfer
was through the water supply and

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so he did geographic mapping,
much like pins on a map.

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He tried to find out where the
cases were occurring,

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and where they were clustering.

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He wanted to see whether there
was something common to the

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geographic area that might
explain why the disease was

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occurring in greater
concentrations some places than

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others.

13:35.190 --> 13:38.360
He found that a lot of the
deaths were occurring nearing a

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particular pump called the Broad
Street Pump.

13:40.980 --> 13:44.720
It took him a lot of effort,
and a considerable amount of

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time,
to persuade authorities that

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this might be a water-borne
disease and that this particular

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pump might be one of the main
sources of the disease.

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In September of 1854,
he convinced the community

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leaders to remove the handle of
the Broad Street Pump and the

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Cholera problem stopped
spreading.

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This was a remarkable
conceptual breakthrough,

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nothing remarkable by today's
standards,

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but certainly at the time was a
complete rejection of

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traditional thinking and going
about things in a different way,

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and it led to a really
impressive public health

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victory.

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In fact, this is--this little
cartoon that on the bottom says,

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Death's dispensary,
was an example of how the

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recognition that The Broad
Street Pump was the source--one

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of the sources of disease and
you see the skeleton up there

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pumping the water,
got played out in cartoons.

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There was some developments
later, more traditional medical

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developments,
like more work by Pasteur on

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the germ theory.

14:50.360 --> 14:55.190
Then there was the isolation of
the particular microbe;

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this happened 26 years after
Snow died.

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You can see here that the
public health intervention which

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was to prevent the cases from
beginning in the first place,

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rather then trying to treat
them, was very effective in this

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particular case.

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If you travel to London there's
a John Snow Pub and there's the

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reconstruction of the Broad
Street Pump without the handle,

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in order to commemorate this
particular public health

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advance.

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There's very interesting
background on John Snow.

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In this particular case in this
book,

15:29.090 --> 15:34.220
this booklet created by a
professor at UCLA,

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and I've offered up the website
that you can connect to to

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download this information on
John Snow.

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The book is published by Oxford
University Press.

15:43.279 --> 15:46.519
It's a very interesting
history, but that particular

15:46.524 --> 15:50.344
moment in time really helped
change the way people approached

15:50.341 --> 15:52.641
disease,
and later on this new

15:52.639 --> 15:56.909
conceptual framework to use for
tracking disease and trying to

15:56.914 --> 16:01.334
prevent it onto the traditional
medical kind of an approach.

16:01.330 --> 16:05.560
When we think about the major
public health victories,

16:05.557 --> 16:08.477
there are many,
many diseases that used to

16:08.475 --> 16:12.455
ravage the world that just
aren't present at all anymore,

16:12.458 --> 16:14.948
or present in very low numbers.

16:14.950 --> 16:21.680
Many people believe that
medical advances are responsible

16:21.676 --> 16:26.076
for those phenomenon,
and to some extent they are,

16:26.081 --> 16:29.241
the use of antibiotics for
example was a major breakthrough

16:29.235 --> 16:32.605
and there are countless medical
breakthroughs that help explain

16:32.607 --> 16:34.237
the reduction in disease.

16:34.240 --> 16:37.500
But many people believe that
the biggest reductions have

16:37.495 --> 16:40.275
occurred because of public
health interventions,

16:40.275 --> 16:42.935
and have focused primarily on
prevention.

16:42.940 --> 16:48.670
Here would be an example of
this, where a particular public

16:48.669 --> 16:52.129
health intervention is
available.

16:52.129 --> 16:56.969
This shows rates of smallpox
and this line--and this is dated

16:56.974 --> 17:01.984
from the UK--so here are numbers
by date of the number of cases

17:01.980 --> 17:06.260
of smallpox and you see it
really going down to almost

17:06.260 --> 17:07.150
zero.

17:07.150 --> 17:09.740
The black line up here
represents the number

17:09.740 --> 17:11.550
vaccinations that were done.

17:11.548 --> 17:15.658
Here a vaccine was discovered,
many people were immunized,

17:15.655 --> 17:18.535
and smallpox was virtually
eliminated.

17:18.538 --> 17:22.038
This is a pretty clear public
health victory.

17:22.038 --> 17:26.788
Now there are other cases like
with tuberculosis where the

17:26.791 --> 17:31.381
rates went down--excuse me--an
awful lot before specific

17:31.377 --> 17:36.047
treatments got developed,
and so this kind of reduction

17:36.051 --> 17:39.051
is due to public health
interventions,

17:39.048 --> 17:41.698
and to some extent,
medical interventions before

17:41.699 --> 17:43.109
real treatment came in.

17:43.107 --> 17:46.447
There were many examples of
this kind of thing with public

17:46.452 --> 17:46.982
health.

17:46.980 --> 17:50.590
Now an area that's a little
more pertinent to today's

17:50.588 --> 17:54.548
picture would be changing rates
of diseases related to the

17:54.545 --> 17:55.235
heart.

17:55.240 --> 17:58.560
If you look at total
cardiovascular disease;

17:58.557 --> 18:01.907
the blue line on the top,
other diseases of the heart;

18:01.910 --> 18:04.040
the green line,
coronary heart disease--and we

18:04.040 --> 18:06.880
don't need to distinguish these
from one another so much--and

18:06.883 --> 18:10.583
then stroke on the bottom line,
you see that beginning in the

18:10.579 --> 18:14.359
1960s and then proceeding
through the current time there

18:14.357 --> 18:18.617
have been significant reductions
in rates of these diseases and

18:18.615 --> 18:20.055
death from them.

18:20.057 --> 18:23.827
What are some of the things you
guys think these--this might be

18:23.832 --> 18:24.382
due to?

18:24.380 --> 18:25.120
Student:
>

18:25.118 --> 18:29.668


18:29.670 --> 18:31.400
Prof: Okay,
more awareness,

18:31.400 --> 18:34.600
so people may get help earlier
if they experience symptoms.

18:34.598 --> 18:37.728
Any other hands?

18:37.730 --> 18:39.170
There are a lot of
possibilities here.

18:39.170 --> 18:40.770
Yes.

18:40.769 --> 18:44.359
Student:
>

18:44.357 --> 18:46.057
Prof: Okay,
the surgery is better,

18:46.058 --> 18:48.518
so people are more likely to
survive if they've had a heart

18:48.523 --> 18:49.463
attack, let's say.

18:49.460 --> 18:52.390
Student:
>

18:52.390 --> 18:53.930
Prof: Okay,
fewer people smoking

18:53.932 --> 18:55.722
absolutely, one of the major
contributors.

18:55.720 --> 19:00.750
Other things you guys might
guess at?

19:00.750 --> 19:01.440
Yes.

19:01.440 --> 19:04.250
Student:
>

19:04.250 --> 19:06.340
Prof: Well could
be--other diseases are competing

19:06.338 --> 19:08.238
with it like potentially killing
people earlier;

19:08.240 --> 19:12.040
probably the opposite is true
because the diseases--as we

19:12.035 --> 19:16.125
discussed earlier in class,
the sort of diseases that would

19:16.134 --> 19:19.734
kill people early in life
have--some of those have been

19:19.732 --> 19:23.542
eliminated or curtailed a lot,
and so people then get to the

19:23.542 --> 19:26.262
ages in life where they
experience things like heart

19:26.258 --> 19:27.428
disease and cancer.

19:27.430 --> 19:30.530
But your point is a good one,
if there is some other disease

19:30.529 --> 19:33.629
like cancer that might have
stepped in and been explaining a

19:33.630 --> 19:36.290
lot of the deaths,
then fewer people would be able

19:36.287 --> 19:37.547
to die from heart disease.

19:37.548 --> 19:39.558
That's not necessarily true
but--yes.

19:39.557 --> 19:42.757
Student:
>

19:42.759 --> 19:44.289
Prof: Okay,
better screening and earlier

19:44.289 --> 19:44.689
diagnoses.

19:44.690 --> 19:45.750
Yes.

19:45.750 --> 19:49.330
Student:
>

19:49.327 --> 19:50.777
Prof: Okay,
I mean you're--so more

19:50.778 --> 19:52.118
medicines, you're absolutely
right.

19:52.117 --> 19:55.487
Think of all the people that
are on statin drugs or high--for

19:55.490 --> 19:57.570
their cholesterol or their
lipids,

19:57.567 --> 20:00.227
people on high blood pressure
drugs and things like this.

20:00.230 --> 20:01.610
So those are medical advances.

20:01.607 --> 20:05.107
Better emergency response is
another one: we have a much

20:05.106 --> 20:08.666
better emergency response system
then we used to have.

20:08.670 --> 20:12.310
People doing prophylactic
things like taking aspirin as a

20:12.306 --> 20:15.396
preventive measure;
a lot of things are happening

20:15.403 --> 20:16.903
to explain this picture.

20:16.900 --> 20:21.010
What you see is a combination
of things that are medical

20:21.008 --> 20:24.618
interventions,
better emergency response let's

20:24.624 --> 20:25.974
say,
better surgeries,

20:25.973 --> 20:28.013
better treatments,
better medicines,

20:28.012 --> 20:31.142
combined with public health
things like less smoking.

20:31.140 --> 20:34.510
Potentially better diet,
some physical activity gets

20:34.506 --> 20:38.726
mixed in, a lot of things like
that that might be active here.

20:38.730 --> 20:41.940
This is a partial victory,
you'd like to see the numbers

20:41.942 --> 20:45.452
look much better than this,
but it's progress nonetheless.

20:45.450 --> 20:48.060
This can be explained not
totally by medicine,

20:48.056 --> 20:51.356
not totally by public health,
but by their combination.

20:51.357 --> 20:57.177
It's very interesting to look
at tobacco in this regard.

20:57.180 --> 21:01.160
The world's smoking rates you
can see vary a lot and it's--the

21:01.160 --> 21:05.010
type is a little bit small but
you can see the United States

21:05.009 --> 21:06.379
down here at 24%.

21:06.380 --> 21:10.290
Now it wasn't long--that many
years ago that the United States

21:10.289 --> 21:12.599
was up in this sort of
territory,

21:12.597 --> 21:14.847
where about half the people in
the country are smoking.

21:14.847 --> 21:18.077
There are several things that
are hidden--I mean that are

21:18.077 --> 21:20.727
embedded in this slide that are
interesting.

21:20.730 --> 21:23.970
One is we have to ask how the
United States got better from

21:23.970 --> 21:26.150
half the people smoking to a
quarter?

21:26.150 --> 21:29.150
The other thing is,
why do we have such high rates

21:29.148 --> 21:31.658
of smoking in other parts of the
world?

21:31.660 --> 21:34.170
If you look at the countries at
the top of that list,

21:34.167 --> 21:36.337
a lot of them are the
developing countries.

21:36.337 --> 21:40.657
There is a long and sad history
of the exploitation of

21:40.659 --> 21:44.489
developing countries by the
tobacco industry.

21:44.490 --> 21:47.680
Some of it are the big
multi-national tobacco players

21:47.681 --> 21:50.751
that are headquartered outside
these countries,

21:50.750 --> 21:54.040
but in some cases like China,
it's state-owned tobacco

21:54.038 --> 21:56.208
businesses that are driving
this.

21:56.210 --> 21:59.530
The numbers in these countries
are very high,

21:59.529 --> 22:04.309
and this idea of multi-national
companies using the developing

22:04.310 --> 22:08.860
world as an emerging market for
things that may really hurt

22:08.857 --> 22:11.887
people,
is a topic that we'll layer

22:11.894 --> 22:15.314
onto food as we get further into
the class.

22:15.307 --> 22:18.307
The parallels with tobacco
aren't perfect by any means,

22:18.306 --> 22:19.746
but they're interesting.

22:19.750 --> 22:22.010
What happened?

22:22.009 --> 22:26.069
Well, America went down about
half in smoking and a lot of

22:26.070 --> 22:30.280
interesting things happened,
so these are data from New York

22:30.275 --> 22:32.195
City beginning in 1993.

22:32.200 --> 22:37.070
Out here you have a 22% smoking
rate occur, but that's about

22:37.073 --> 22:42.033
half of what it was before,
so a lot of advances occurred.

22:42.029 --> 22:45.669
If you could plot this line
before 1993, it would be up here

22:45.669 --> 22:46.409
somewhere.

22:46.410 --> 22:51.010
This is very interesting,
and what they point out is that

22:51.009 --> 22:54.539
there were specific public
health regulatory

22:54.542 --> 22:59.642
legislative-type interventions
that were made that help explain

22:59.636 --> 23:03.166
the decline from the 22% down to
17%;

23:03.170 --> 23:05.310
and while that doesn't seem
like a big number,

23:05.306 --> 23:08.196
in a city the size of New York
that's actually a lot of people

23:08.201 --> 23:08.821
affected.

23:08.817 --> 23:12.207
If you go back here before this
particular time,

23:12.210 --> 23:16.150
there are things like heavy
taxes because some of the heavy

23:16.152 --> 23:20.392
taxes kicked in around here,
but there was certainly big tax

23:20.387 --> 23:24.437
increases in cigarettes before
surgeon general's reports,

23:24.440 --> 23:27.380
concern about the health
consequences of smoking,

23:27.380 --> 23:31.270
lots of information on
secondhand smoke,

23:31.269 --> 23:31.869
etc.

23:31.867 --> 23:36.307
This is a real public health
victory brought about really

23:36.307 --> 23:39.557
almost not at all by medical
advances,

23:39.557 --> 23:43.097
because the treatment of
diseases that smokers get--with

23:43.095 --> 23:46.685
heart disease being to some
extent an exception but--like

23:46.694 --> 23:49.594
lung cancer are very difficult
to treat,

23:49.587 --> 23:53.457
and so the big advances have
come through public health

23:53.461 --> 23:54.611
interventions.

23:54.607 --> 24:00.407
Public health is very often
important where medicine is

24:00.410 --> 24:01.700
powerless.

24:01.700 --> 24:05.380
If you don't have a specific
disease that can be treated with

24:05.380 --> 24:09.180
a specific medical intervention,
then public health tends to be

24:09.183 --> 24:10.783
the primary way to go.

24:10.778 --> 24:13.068
Cigarette smoking is an example
of that.

24:13.067 --> 24:16.527
90% of lung cancer cases are
due to smoking;

24:16.528 --> 24:20.318
90% of those who are diagnosed
with lung cancer die regardless

24:20.319 --> 24:24.379
of the treatment they receive;
so this is not a very treatable

24:24.378 --> 24:27.318
problem that yields to medical
interventions,

24:27.317 --> 24:30.587
but is one that does yield
nicely to public health

24:30.589 --> 24:31.859
interventions.

24:31.857 --> 24:35.757
The differences between the
traditional medical model and

24:35.761 --> 24:38.901
the public health model are
quite profound.

24:38.900 --> 24:43.090
Now by the medical model,
what we mean is that you deal

24:43.090 --> 24:47.510
with people who have a disease,
and the hope is that you apply

24:47.505 --> 24:50.865
some treatment to the disease,
the disease goes away,

24:50.866 --> 24:53.466
and everybody is happy in the
process.

24:53.470 --> 24:56.770
There are lots of examples of
this.

24:56.769 --> 24:59.949
Somebody--a child gets an ear
infection,

24:59.950 --> 25:02.190
the parents take the child to
the doctor,

25:02.190 --> 25:04.150
the doctor treats it with an
antibiotic,

25:04.150 --> 25:07.470
the disease goes away,
so that would be wait for the

25:07.474 --> 25:09.764
disease to occur,
intervene with it,

25:09.758 --> 25:12.858
hopefully have an effective
intervention and you have some

25:12.861 --> 25:13.461
success.

25:13.460 --> 25:17.230
Hopefully that intervention can
be used on a broad scale in a

25:17.233 --> 25:19.913
cost effective way,
so that all parts of the

25:19.913 --> 25:22.833
population can get exposure to
the helpful treatment.

25:22.827 --> 25:25.767
The public health model is
different.

25:25.769 --> 25:28.439
Here's how they differ
fundamentally from each other:

25:28.442 --> 25:31.172
the medical model really
focuses on the individual.

25:31.170 --> 25:35.930
What's causing the disease in a
person--not in the population,

25:35.929 --> 25:37.489
but in the person?

25:37.490 --> 25:40.320
What are the consequences for
the individual,

25:40.315 --> 25:44.035
and what are the remedies that
will help the individual?

25:44.038 --> 25:46.968
The public health model looks
at the population,

25:46.973 --> 25:47.853
in contrast.

25:47.847 --> 25:51.797
What are the causes of the
disease in the population?

25:51.798 --> 25:56.528
What are the consequences of
the disease to the population?

25:56.529 --> 25:58.349
How many people have the
disease?

25:58.348 --> 25:59.558
How serious is it?

25:59.557 --> 26:01.537
What's the overall health care
burden of this?

26:01.538 --> 26:06.588
The remedies are delivered to
populations, not necessarily the

26:06.589 --> 26:07.749
individuals.

26:07.750 --> 26:11.350
As you can imagine,
what these are leading toward,

26:11.351 --> 26:16.131
is a focus on treatment in one
case, and prevention in another.

26:16.130 --> 26:20.530
Now there are many,
many cases where treatment is

26:20.529 --> 26:24.289
absolutely appropriate and life
saving.

26:24.288 --> 26:28.598
There are other cases where the
treatments aren't so effective

26:28.603 --> 26:33.133
or can't be applied on a broad
scale, but there is prevention.

26:33.130 --> 26:35.480
The issue is,
if you want to change the

26:35.481 --> 26:38.021
world's diet,
and lead to a healthier food

26:38.019 --> 26:41.359
environment around the world,
which of these two models

26:41.363 --> 26:42.233
applies?

26:42.230 --> 26:45.190
They both apply to some extent.

26:45.190 --> 26:48.310
For example,
you can have the treatment over

26:48.308 --> 26:52.078
here on the left apply when
people have been eating a

26:52.080 --> 26:55.130
terrible diet,
they get heart disease,

26:55.126 --> 26:58.296
they need quadruple bypass,
you intervene with the

26:58.297 --> 26:59.217
expensive surgery.

26:59.220 --> 27:02.340
That would be a case where
treatment could be life saving

27:02.336 --> 27:05.226
and potentially helpful,
but you can imagine the cost

27:05.229 --> 27:07.399
and you're only helping one
person.

27:07.400 --> 27:10.160
You could take a public health
point of view and focus on

27:10.159 --> 27:13.459
prevention,
and potentially with this same

27:13.457 --> 27:18.327
amount of money that gets spent
to help one person with a

27:18.326 --> 27:22.216
medical intervention,
you could intervene with many

27:22.217 --> 27:24.347
more people at a preventive
level,

27:24.347 --> 27:26.977
and potentially have greater
impact.

27:26.980 --> 27:30.260
We have to ask ourselves,
what's the potential for the

27:30.262 --> 27:33.982
traditional medical model versus
the public health model with

27:33.980 --> 27:34.540
diet?

27:34.538 --> 27:38.858
Now the one metaphor that gets
used a lot in public health that

27:38.856 --> 27:42.056
is memorable and meaningful in
this case is the

27:42.059 --> 27:44.359
upstream/downstream metaphor.

27:44.357 --> 27:48.327
Let's say you start with a nice
clean mountain stream like this,

27:48.327 --> 27:50.977
but then it goes through
farmland, it goes through

27:50.978 --> 27:53.208
cities,
it goes through a number of

27:53.214 --> 27:56.464
polluting opportunities,
and you end up with a polluted

27:56.464 --> 27:58.034
stream that looks like this.

27:58.029 --> 28:02.119
Medicine really works here,
you wait until there's disease

28:02.116 --> 28:06.486
that exists in an individual or
in a body, and then you try to

28:06.491 --> 28:07.641
clean it up.

28:07.640 --> 28:10.130
The public health works over
here,

28:10.130 --> 28:13.800
where you try to find out
what's happening between the

28:13.798 --> 28:17.258
upstream and the downstream and
then intervene,

28:17.259 --> 28:20.579
so you never get what you see
on the right hand side here.

28:20.577 --> 28:24.987
The focus on prevention in
public health leads to three

28:24.986 --> 28:29.146
different terms that get
described--that get used to

28:29.148 --> 28:31.108
describe prevention.

28:31.107 --> 28:35.587
One is primary prevention,
and the object here is to avoid

28:35.593 --> 28:39.373
the development of disease in
the first place.

28:39.367 --> 28:42.897
The second is secondary
prevention, which focuses on

28:42.903 --> 28:45.193
early detection of the disease.

28:45.190 --> 28:48.350
So you're not preventing the
disease, but you're catching

28:48.349 --> 28:52.019
people who have it and then you
try to prevent its progression;

28:52.019 --> 28:56.769
and then tertiary prevention is
try to reduce the impact and the

28:56.770 --> 28:58.810
complications of disease.

28:58.807 --> 29:01.527
Now as much possible,
you'd like to see primary

29:01.528 --> 29:02.888
prevention occurring.

29:02.890 --> 29:05.370
That's not really always
possible.

29:05.367 --> 29:09.147
But one thing you could do for
example with traditional

29:09.147 --> 29:12.817
infectious diseases,
is you can take people who have

29:12.817 --> 29:15.407
the disease and then quarantine
them,

29:15.410 --> 29:18.170
or give them medicines that
stop the spread and that would

29:18.174 --> 29:21.044
be not primary prevention,
but more secondary prevention.

29:21.038 --> 29:25.848
All these kinds of
prevention--types of prevention

29:25.846 --> 29:27.706
have their place.

29:27.710 --> 29:32.440
One thing that the folks in
public health talk a lot about

29:32.441 --> 29:34.851
is the epidemiologic triad.

29:34.847 --> 29:38.067
What they say is that the
disease--when people or

29:38.067 --> 29:40.877
populations get disease,
it's a combination of an

29:40.877 --> 29:42.877
interaction of three factors:
there is the agent,

29:42.875 --> 29:44.245
the host, and the environment.

29:44.250 --> 29:49.800
The agent is what is it that's
causing the disease.

29:49.798 --> 29:53.268
What's toxic in the environment
that causes the disease in the

29:53.270 --> 29:54.070
first place?

29:54.067 --> 29:57.847
The host is the--whoever gets
the disease or the population

29:57.845 --> 30:01.295
that gets disease and they would
have their own set of

30:01.296 --> 30:02.596
vulnerabilities.

30:02.597 --> 30:06.957
Then in the environment of
course, those are the location,

30:06.960 --> 30:10.710
or the sources of the
potentially toxic agents.

30:10.710 --> 30:14.180
The philosophy in public health
is you really need to understand

30:14.178 --> 30:17.588
all these things in order to get
a full grip on what's going on

30:17.590 --> 30:20.290
with disease and finding a way
to prevent it.

30:20.288 --> 30:24.108
Just as an example of this,
the agent,

30:24.107 --> 30:27.607
you could have a specific toxic
agent like bacteria,

30:27.607 --> 30:31.817
a virus, whatever it happens to
be that's causing the disease.

30:31.817 --> 30:35.717
The host, some groups are more
vulnerable to the disease than

30:35.722 --> 30:36.312
others.

30:36.307 --> 30:38.887
For example,
earlier in class we talked

30:38.888 --> 30:42.758
about how people of Asian
descent have much greater-- have

30:42.759 --> 30:46.149
many greater--more severe
metabolic consequences of

30:46.154 --> 30:48.744
increasing weight,
than people of other

30:48.736 --> 30:50.226
nationalities.

30:50.230 --> 30:52.690
So at a given level of
overweight, Asian people are

30:52.692 --> 30:55.452
more likely to get some of the
metabolic consequences.

30:55.450 --> 31:00.090
That particular group would
form a vulnerability group,

31:00.087 --> 31:02.637
and they would be a host that
would be particularly

31:02.642 --> 31:05.402
susceptible to these kinds of
food-related problems.

31:05.400 --> 31:09.780
You could find groups that are
vulnerable by virtue of living

31:09.779 --> 31:13.109
in poverty;
or there are groups that are

31:13.108 --> 31:17.018
especially exploited I guess you
could call,

31:17.019 --> 31:20.789
are targeted by industry that
might be selling some harmful

31:20.788 --> 31:24.428
product that could fall into a
host vulnerability kind of

31:24.429 --> 31:25.079
group.

31:25.077 --> 31:28.057
Of course we've been talking a
lot, and we'll talk even more

31:28.057 --> 31:30.477
about environmental issues
pertaining to food.

31:30.480 --> 31:34.360
I don't just mean the food
environment,

31:34.357 --> 31:36.757
pesticides, and herbicides,
and pollution,

31:36.759 --> 31:38.739
and things like that we've been
talking about,

31:38.740 --> 31:40.750
but a lot of the factors that
we'll be discussing later in

31:40.747 --> 31:41.027
class.

31:41.029 --> 31:42.769
What about portion sizes?

31:42.769 --> 31:47.349
What about heavy marketing of
unhealthy food to children?

31:47.347 --> 31:51.507
These are all factors that--and
we'll talk about how many

31:51.507 --> 31:55.217
calories people are consuming in
liquid form today,

31:55.220 --> 31:58.340
compared to what was occurring
before.

31:58.338 --> 32:01.778
We'll talk about fast food;
we'll talk about economics;

32:01.778 --> 32:05.018
we'll talk about a lot of these
factors that focus on the bottom

32:05.019 --> 32:06.819
right hand side of that
triangle.

32:06.817 --> 32:09.637
What are the environmental
factors that are contributing?

32:09.640 --> 32:11.630
The problems of what might be
done about them.

32:11.630 --> 32:15.340
This little chart from The
World Health Organization will

32:15.339 --> 32:18.979
give you a sense of how public
health might proceed with

32:18.982 --> 32:21.372
dealing with different problems.

32:21.367 --> 32:24.827
There are these four boxes
where you end up with

32:24.828 --> 32:27.478
relationships that look like
this.

32:27.480 --> 32:30.090
The first box is surveillance.

32:30.087 --> 32:33.827
You need to find out where the
problem is occurring,

32:33.829 --> 32:36.759
what is the problem,
and some definition,

32:36.763 --> 32:40.293
some description of its
severity, its prevalence,

32:40.285 --> 32:41.015
etc.

32:41.019 --> 32:45.819
Just to layer in some examples
with diet, you might assess

32:45.824 --> 32:49.624
diseases related to diet in the
population.

32:49.617 --> 32:52.787
How much cancer is happening,
how much heart disease,

32:52.785 --> 32:55.095
how much of this is related to
diet?

32:55.097 --> 32:57.627
That would be an example of
surveillance where you're just

32:57.634 --> 32:58.574
tracking something.

32:58.567 --> 33:02.697
The next step is to identify
causes of whatever the diseases

33:02.699 --> 33:06.619
are, and to identify both risk
and protective factors.

33:06.617 --> 33:10.687
Again, you can see how risk and
protective factors might be

33:10.686 --> 33:14.686
different for the population
than it might be for specific

33:14.685 --> 33:15.805
individuals.

33:15.807 --> 33:21.447
Here you'd look at what parts
of the diet raise risk,

33:21.450 --> 33:24.480
who's vulnerable,
and why they're vulnerable,

33:24.480 --> 33:29.410
and are there protective
factors that might be relevant?

33:29.410 --> 33:32.240
The next is to develop and
evaluate interventions,

33:32.240 --> 33:34.730
try to find out what works and
what doesn't,

33:34.727 --> 33:37.037
so you can intervene with a
problem.

33:37.038 --> 33:41.268
Here you could create programs,
do small clinical trials with

33:41.270 --> 33:45.300
individuals or larger systems,
and test cost effectiveness so

33:45.298 --> 33:48.528
you know what might get rolled
out to the broader world.

33:48.529 --> 33:50.839
Then the last step,
as you might imagine,

33:50.842 --> 33:54.252
is scaling up with policies and
programs, so you could apply

33:54.253 --> 33:57.093
effective interventions to many,
many people.

33:57.087 --> 34:00.407
Here you'd make social,
economic, policy,

34:00.410 --> 34:05.560
public opinion changes in order
to maximize the benefit to cost

34:05.558 --> 34:06.388
ratio.

34:06.390 --> 34:10.200
I'll give you some examples of
really stunning public health

34:10.204 --> 34:13.964
victories where this sort of
thing has been played out with

34:13.956 --> 34:15.376
specific problems.

34:15.380 --> 34:19.340
Now, if you look at the way
medicine, or even my native

34:19.344 --> 34:22.574
profession of psychology deals
with problems,

34:22.574 --> 34:26.104
they tend not to look at the
world this way.

34:26.099 --> 34:29.029
For example,
instead of looking at what are

34:29.034 --> 34:33.304
risk factors and vulnerability
factors for the population,

34:33.300 --> 34:35.740
they're looking at what
constitutes risk and

34:35.740 --> 34:37.560
vulnerability for individuals.

34:37.559 --> 34:40.179
Now, here's an example of where
this might play out,

34:40.182 --> 34:42.552
and I think I mentioned this
example before.

34:42.550 --> 34:47.590
There are many people out there
in the health professions who

34:47.592 --> 34:51.462
are looking for the gene--for
obesity genes.

34:51.460 --> 34:54.390
What they want to find out is
when obesity occurs;

34:54.389 --> 34:57.289
are there genetic reasons for
it occurring;

34:57.289 --> 34:59.299
and who might be most
vulnerable;

34:59.300 --> 35:03.170
and what are the conditions
that create this vulnerability?

35:03.170 --> 35:06.520
Now there's great excitement
about this, a lot of funding for

35:06.521 --> 35:09.761
it, and there's some really
quite exciting science going on

35:09.762 --> 35:10.882
in this context.

35:10.880 --> 35:15.320
One point of view that would
say that it's--that's really not

35:15.324 --> 35:19.584
going to be very helpful,
because why do we think that

35:19.577 --> 35:22.767
genes are going to explain this
problem?

35:22.768 --> 35:26.448
I mean you have rampant obesity
in the U.S.,

35:26.449 --> 35:30.419
Mexico, other countries,
and you have almost none--I

35:30.416 --> 35:33.106
mean not none,
but you have very little in

35:33.110 --> 35:34.990
countries like Somalia and
Ethiopia.

35:34.989 --> 35:37.669
Is that--can you explain that
by genetics?

35:37.670 --> 35:41.080
Are we genetically different
from people in those countries

35:41.083 --> 35:42.263
that explain that?

35:42.260 --> 35:44.350
Well no, absolutely not.

35:44.349 --> 35:45.449
How do you know?

35:45.449 --> 35:48.739
Because studies show that if
people move from those kinds of

35:48.744 --> 35:50.034
countries to the U.S.

35:50.030 --> 35:52.670
they gain weight;
people from the U.S.

35:52.670 --> 35:54.650
move to those kinds of
countries, they lose weight.

35:54.650 --> 35:57.700
If you take the sort of typical
American diet,

35:57.702 --> 36:00.962
feed it to laboratory animals
you get obesity.

36:00.960 --> 36:02.660
We've shown you examples of
that.

36:02.659 --> 36:05.109
There are many reasons to
believe that this is an

36:05.105 --> 36:07.855
environmental problem,
and that you're not going to

36:07.856 --> 36:10.596
learn an awful lot through the
genetic discoveries.

36:10.599 --> 36:14.489
Now maybe, maybe the genetic
discoveries will led to some

36:14.490 --> 36:18.940
drug that can override the toxic
influence of the environment.

36:18.940 --> 36:21.910
There's nothing on the horizon
that looks very promising in

36:21.907 --> 36:23.697
that respect,
but you never know.

36:23.699 --> 36:26.359
If something like that came
about it would be a great

36:26.356 --> 36:26.866
advance.

36:26.869 --> 36:30.039
The traditional medical
approach would be to look for

36:30.038 --> 36:33.818
the gene because you really want
to know why individual A has a

36:33.818 --> 36:34.548
problem.

36:34.550 --> 36:37.130
From a public health point of
view,

36:37.130 --> 36:40.570
you're not paying attention so
much to whether individual A has

36:40.568 --> 36:43.538
the problem,
but how many individuals in the

36:43.539 --> 36:46.229
population fall victim to the
problem;

36:46.230 --> 36:51.040
and then that leads you to a
sense of what the causes are.

36:51.039 --> 36:56.709
You can see with this process
here how you'd start with

36:56.711 --> 36:59.241
surveillance;
end up with broad scale

36:59.244 --> 36:59.874
interventions.

36:59.869 --> 37:03.419
Now the other thing that very
often happens in my field,

37:03.420 --> 37:09.590
and in medicine,
is that people work up in this

37:09.588 --> 37:15.488
area and they might find out for
example,

37:15.489 --> 37:19.969
that people with high
cholesterol are at risk for

37:19.974 --> 37:22.934
heart disease;
or people that eat too much

37:22.925 --> 37:25.235
saturated fat are at risk for
heart disease,

37:25.244 --> 37:27.624
and those would be
vulnerability factors.

37:27.619 --> 37:30.269
So they published their papers
and that's their job,

37:30.268 --> 37:33.978
that's their part of the world
and they do a good job at that,

37:33.980 --> 37:37.620
and then they hope that there's
uptake of that information by

37:37.619 --> 37:41.139
other people in ways that will
make a social difference.

37:41.139 --> 37:44.189
Some people work over here,
and develop programs;

37:44.190 --> 37:48.260
so for example,
there are plenty of people in

37:48.262 --> 37:53.082
my field who do intervention
programs in the schools,

37:53.077 --> 37:54.557
for example.

37:54.559 --> 37:57.139
You go in the schools,
you try to get rid of the soft

37:57.141 --> 37:59.201
drinks,
you teach nutrition education,

37:59.202 --> 38:01.342
you put in a physical activity
program,

38:01.340 --> 38:05.940
etc., and so they publish their
work in the journals and then

38:05.943 --> 38:09.093
they hope that there's uptake
out here.

38:09.090 --> 38:12.330
But very often there's not
because the people that are

38:12.331 --> 38:16.191
out--responsible for doing this
very often don't know about this

38:16.186 --> 38:18.806
kind of work,
and the people who are doing

38:18.811 --> 38:21.531
this don't communicate very well
with this group.

38:21.530 --> 38:26.200
So there's a real breakdown
between various segments of the

38:26.199 --> 38:27.729
scientific world.

38:27.730 --> 38:31.070
The hope in public health is
that all these things come

38:31.068 --> 38:34.218
together, and you've got
somebody watching the whole

38:34.222 --> 38:34.782
ship.

38:34.780 --> 38:37.090
Now that doesn't always happen
because there are these

38:37.092 --> 38:39.802
segmented parts of public health
as well, but there's more hope

38:39.795 --> 38:41.275
for it in that kind of regard.

38:41.280 --> 38:45.220
The job isn't done if you
accomplish something here or

38:45.215 --> 38:50.115
here, but only when these things
happen, is the job really done.

38:50.119 --> 38:53.219
Again, I'll give you some very
interesting examples of that.

38:53.219 --> 38:58.929
Now, we've talked a lot in this
class about science.

38:58.929 --> 39:03.639
I've shown you slide after
slide, after slide of scientific

39:03.641 --> 39:04.211
data.

39:04.210 --> 39:07.650
Why is that?

39:07.650 --> 39:11.920
I mean we could--this could be
a course on focusing on the

39:11.920 --> 39:16.490
history of food and food systems
without respect to scientific

39:16.490 --> 39:17.690
information.

39:17.690 --> 39:22.140
We could be talking about the
anthropology of food,

39:22.139 --> 39:24.359
we could be talking about food
and literature,

39:24.360 --> 39:27.150
we could be doing a lot of
things that don't really have

39:27.148 --> 39:28.568
science as the background.

39:28.570 --> 39:30.450
Why do we focus on science?

39:30.449 --> 39:34.029
It's terribly important in this
arena, and here's why.

39:34.030 --> 39:36.920
First of all,
you have to have agreed on

39:36.918 --> 39:38.548
standards for truth.

39:38.550 --> 39:43.340
I'll give you an example of--in
a later class of a study that my

39:43.340 --> 39:48.130
colleagues and I did looking at
the impact--in fact I think it's

39:48.130 --> 39:53.000
in your readings--looking at the
impact of soft drink consumption

39:52.998 --> 39:54.898
on health outcomes.

39:54.900 --> 39:58.520
As you might imagine,
this study found that the more

39:58.523 --> 40:00.713
soft drinks,
sugared beverages people

40:00.706 --> 40:03.266
consume, the higher their
likelihood is of eating a poor

40:03.268 --> 40:05.038
diet in general,
developing obesity and

40:05.038 --> 40:08.018
diabetes;
and the stronger the methods

40:08.018 --> 40:13.008
get within this literature the
more likely that finding is to

40:13.010 --> 40:14.260
be the case.

40:14.260 --> 40:17.000
We published that in the
scientific literature.

40:17.000 --> 40:21.630
The food industry,
the trade association for the

40:21.625 --> 40:26.155
soft drink industry,
then funds a scientist who has

40:26.161 --> 40:30.781
a very well-known reputation for
publishing things friendly to

40:30.782 --> 40:34.372
the industry,
goes out and finds the studies,

40:34.373 --> 40:37.723
the university of studies that
we reviewed,

40:37.719 --> 40:40.859
reviews them himself--there was
a group of people involved in

40:40.862 --> 40:44.112
this--and then publishes a paper
that says the opposite of what

40:44.112 --> 40:44.692
we did.

40:44.690 --> 40:47.140
No, that sugar beverages really
aren't bad after all.

40:47.139 --> 40:49.469
Well, what's the truth here?

40:49.469 --> 40:52.629
What should people pay
attention to?

40:52.630 --> 40:56.790
Is ours more credible or less
credible then the other one?

40:56.789 --> 40:59.519
Well, we'll sort that through
and we'll talk about this

40:59.521 --> 41:01.851
interesting issue of conflicts
of interest,

41:01.849 --> 41:06.339
and how science gets used in
many interesting ways.

41:06.340 --> 41:08.990
We need some standard of truth.

41:08.989 --> 41:11.499
If you're going to change your
diet,

41:11.500 --> 41:14.310
take some supplement,
start exercising because you

41:14.311 --> 41:18.031
think it's good for your health,
you darn well want to make sure

41:18.032 --> 41:21.022
that it's reliable information
that you're making those

41:21.016 --> 41:21.896
decisions on.

41:21.900 --> 41:24.790
What if you decide to take fish
oil supplements,

41:24.793 --> 41:27.383
for example,
because you hear in some place

41:27.378 --> 41:29.778
that fish oil supplements are
good?

41:29.780 --> 41:31.250
What if they're not good?

41:31.250 --> 41:34.380
What if that was crummy science?

41:34.380 --> 41:37.910
What if it was industry funded
stuff that would suggest that

41:37.911 --> 41:39.891
fish oil is good when it's not?

41:39.889 --> 41:42.739
That would be an example of you
making the decision that could

41:42.735 --> 41:44.785
really affect your health and
well being,

41:44.789 --> 41:47.379
and then there are lots of you
out there so it affects

41:47.375 --> 41:49.895
population,
health, and wellbeing when the

41:49.902 --> 41:52.362
standard for truth is being
undermined.

41:52.360 --> 41:55.510
Now in--I don't want to give
the wrong impression about fish

41:55.505 --> 41:57.125
oil,
in fact, fish oil is good and

41:57.128 --> 41:59.278
there's a lot of research on
that kind of stuff,

41:59.280 --> 42:02.670
but that would be an example,
just a hypothetical example of

42:02.672 --> 42:05.032
how you might find something
like that.

42:05.030 --> 42:09.550
You need to have some commonly
agreed upon rules about what

42:09.550 --> 42:11.110
constitutes proof.

42:11.110 --> 42:14.750
How many studies constitute
reliable evidence that something

42:14.746 --> 42:16.656
is related to something else?

42:16.659 --> 42:19.329
How good do those studies have
to be?

42:19.329 --> 42:21.639
How many subjects do they have
to have, etc.?

42:21.639 --> 42:25.269
The science is one of the ways,
although it gets undermined by

42:25.271 --> 42:29.081
the kind of thing I talked about
a moment ago with the soft drink

42:29.083 --> 42:29.743
papers.

42:29.739 --> 42:34.669
It erects barriers to lying,
and you hope that it helps

42:34.670 --> 42:40.060
erase the self interest that
some parties have in things.

42:40.059 --> 42:44.719
As another example,
the cereal companies have been

42:44.722 --> 42:49.772
funding a lot of research by
various investigators.

42:49.768 --> 42:52.748
The money tends to go to
certain investigators that

42:52.748 --> 42:55.908
reliably produce results
favorable to industry showing

42:55.907 --> 42:58.527
that people that eat
ready-to-eat cereals for

42:58.529 --> 43:01.029
breakfast do better,
they're healthier,

43:01.032 --> 43:03.862
they do better on cognitive
tasks and things like that,

43:03.860 --> 43:06.960
than people that don't eat any
breakfast at all.

43:06.960 --> 43:11.380
What they--in that--what they
conclude from that is that any

43:11.380 --> 43:15.580
breakfast cereal is good for you
and is going to help.

43:15.579 --> 43:19.699
Of course, the comparison here
shouldn't be breakfast cereals

43:19.699 --> 43:23.409
as a group compared to not
eating breakfast at all,

43:23.409 --> 43:25.989
because we already know that
it's a good idea to eat

43:25.990 --> 43:26.600
breakfast.

43:26.599 --> 43:29.859
The most relevant comparison
would be crummy breakfast

43:29.858 --> 43:33.608
cereals and good breakfast
cereals compared to one another.

43:33.610 --> 43:36.290
By crummy I mean high in sugar
or high in fat,

43:36.289 --> 43:37.779
or even high in sodium.

43:37.780 --> 43:41.260
The industry sets up the
research where they're going to

43:41.257 --> 43:42.837
get a positive outcome.

43:42.840 --> 43:45.590
They know in advance which
scientists are going to give

43:45.592 --> 43:48.422
them the positive outcome,
and as a consequence,

43:48.423 --> 43:52.103
you've got self-interest in
there and you've got distorted

43:52.099 --> 43:53.259
biased science.

43:53.260 --> 43:56.680
The fact that you use science
helps control for that to some

43:56.684 --> 43:59.244
extent, but doesn't eliminate it
entirely.

43:59.239 --> 44:03.109
This was interesting quote from
a very well known marine biology

44:03.112 --> 44:06.742
scientist that says science is
nothing more then a system of

44:06.740 --> 44:09.570
rules to keep us from lying to
each other.

44:09.570 --> 44:12.290
Well, why would we lie to each
other?

44:12.289 --> 44:15.619
Well, you've got a lot of
interested parties with a big

44:15.623 --> 44:18.763
stake in the game,
and so it's easy to see why

44:18.762 --> 44:21.682
lying would occur in science,
to some extent,

44:21.681 --> 44:23.181
as protection against that.

44:23.179 --> 44:25.549
We're going to talk about
science,

44:25.550 --> 44:28.900
and the question really becomes
who's the referee here when you

44:28.898 --> 44:32.408
got self interest involved,
when you've got the soft drink

44:32.413 --> 44:35.533
industry saying,
and science supporting it by

44:35.530 --> 44:38.150
their own funded scientists
saying,

44:38.150 --> 44:39.820
well soft drinks really aren't
bad,

44:39.820 --> 44:42.210
it doesn't hurt anybody to
drink sugared beverages,

44:42.210 --> 44:44.410
and then you have other groups
of scientists saying,

44:44.409 --> 44:46.869
well hold on there's an awful
lot of science on this that

44:46.873 --> 44:47.933
suggests the opposite.

44:47.929 --> 44:49.689
Who's the referee?

44:49.690 --> 44:51.080
Who's going to decide?

44:51.079 --> 44:54.309
Well, that's where you need an
informed public and you need

44:54.313 --> 44:57.723
people to understand as much as
possible about what's going on

44:57.717 --> 45:00.497
in science,
and that's what I'm going to

45:00.496 --> 45:02.246
try to help foster today.

45:02.250 --> 45:06.050
There are a number of very
interesting issues pertaining to

45:06.054 --> 45:08.224
the science of diet and health.

45:08.219 --> 45:11.799
One of the kinds of studies
that get done,

45:11.795 --> 45:15.455
so for example,
observing large populations

45:15.458 --> 45:18.598
versus doing studies in the lab.

45:18.599 --> 45:21.579
I'll talk about correlation and
causation.

45:21.579 --> 45:24.909
It's very important in studies
to control for confounding

45:24.914 --> 45:25.514
factors.

45:25.510 --> 45:27.820
We'll talk about
cross-sectional versus

45:27.820 --> 45:29.220
longitudinal studies.

45:29.219 --> 45:33.929
There are three common methods
that get used in public health

45:33.931 --> 45:38.961
to test the relationship between
certain things and diseases,

45:38.960 --> 45:41.410
and then to test whether you
can turn the picture around.

45:41.409 --> 45:45.019
There are cross-sectional
studies, and one of the types of

45:45.018 --> 45:47.488
those is called a case control
study.

45:47.489 --> 45:49.639
I won't talk about that in
particular, but I will about

45:49.635 --> 45:50.665
cross-sectional studies.

45:50.670 --> 45:53.440
There are longitudinal studies,
and experimental studies,

45:53.436 --> 45:54.816
so let's break those down.

45:54.820 --> 45:58.200
Cross-sectional studies might
look like this,

45:58.199 --> 46:02.949
where you're taking a snapshot
at one point in time of a group

46:02.947 --> 46:07.097
of individuals and you might,
if you have enough subjects,

46:07.099 --> 46:09.589
be able to compare groups of
individuals.

46:09.590 --> 46:13.600
Let's say this is a
hypothetical example that looks

46:13.601 --> 46:17.771
at cholesterol levels,
smoking and heart disease in a

46:17.773 --> 46:20.263
group of males and females.

46:20.260 --> 46:24.490
You do this one shot in time
assessment, so let's say you get

46:24.492 --> 46:26.542
5,000 males, 5,000 females.

46:26.539 --> 46:29.839
You do a big study on this and
you get collect data where each

46:29.835 --> 46:31.075
of these X's appears.

46:31.079 --> 46:34.699
What can you do with that
information?

46:34.699 --> 46:38.139
Well, you can look to see how,
let's say cholesterol and

46:38.144 --> 46:40.844
smoking are related to risk for
disease,

46:40.840 --> 46:44.560
you could compare what happens
with males and females,

46:44.559 --> 46:48.419
so a study like this will take
you partway down the road to

46:48.418 --> 46:50.878
establishing scientific
certainty,

46:50.880 --> 46:52.690
but leaves a lot of things
unanswered.

46:52.690 --> 46:56.880
One of the problems here,
is that let's say you find the

46:56.878 --> 47:00.838
relationship in the females
between smoking and heart

47:00.838 --> 47:04.878
disease but not in the males,
and you wouldn't--that's not

47:04.878 --> 47:08.038
what you would find but let's
just say you did--then does that

47:08.036 --> 47:11.196
prove that smoking is causing
heart disease in females but not

47:11.195 --> 47:11.915
in males?

47:11.920 --> 47:15.140
Well no, it doesn't really
prove that because you've just

47:15.139 --> 47:18.279
got this one snapshot in time,
and so it could be that there

47:18.277 --> 47:20.837
are things related to smoking
and risk for heart disease that

47:20.840 --> 47:23.020
you haven't measured,
so let's just take stress.

47:23.018 --> 47:29.178
Let's say women are under
higher stress then men,

47:29.179 --> 47:31.699
women smoke more because of the
stress,

47:31.699 --> 47:35.069
and they're dying of heart
disease because of the stress

47:35.065 --> 47:37.655
not the smoking,
the smoking is just in there as

47:37.661 --> 47:38.771
an incidental variable.

47:38.768 --> 47:41.498
Now that's not the case:
smoking is a very strong

47:41.500 --> 47:44.900
predictor of heart disease;
but you can get a sense of the

47:44.896 --> 47:47.046
weakness of cross-sectional
designs.

47:47.050 --> 47:50.340
These are the quickest way,
and the most inexpensive way

47:50.338 --> 47:53.268
(although not inexpensive in an
absolute way),

47:53.268 --> 47:56.618
to start to get a sense of
what's going on with risk

47:56.623 --> 47:58.073
factors and disease.

47:58.070 --> 48:03.140
You can also do cross-sections
over time, so it might look like

48:03.137 --> 48:03.707
this.

48:03.710 --> 48:09.120
Let's say that a series of
scientists collect data in

48:09.123 --> 48:14.013
1978,1988, 1998,
and 2008, and that they do have

48:14.014 --> 48:17.144
data on females and males.

48:17.139 --> 48:21.709
In 1978, they get a group of
females, they also get a group

48:21.710 --> 48:26.440
of males, but of course they're
different from each other.

48:26.440 --> 48:30.480
Then in 1988 you get yet
another group of females,

48:30.480 --> 48:34.450
and another group of males,
now this is cross-sectional

48:34.445 --> 48:38.185
over time because the females
let's say up here,

48:38.190 --> 48:41.080
this group of females is
different from that group of

48:41.081 --> 48:41.641
females.

48:41.639 --> 48:44.279
If they were the same,
and you were following the same

48:44.275 --> 48:47.315
group of people over time,
then you'd have a longitudinal

48:47.318 --> 48:49.878
or a cohort study that I'll get
to in a minute.

48:49.880 --> 48:53.260
If you're just taking this
snapshot in time in 1978,

48:53.260 --> 48:56.110
then you get another group,
let's say a random group of

48:56.106 --> 48:59.156
people from the population then
you've got another group of

48:59.163 --> 49:00.723
females,
another group of males,

49:00.719 --> 49:02.539
but they're different from the
original groups.

49:02.539 --> 49:05.739
Then you could fill in these
other groups over here,

49:05.739 --> 49:09.739
and then you could look at
trends over time and you could

49:09.740 --> 49:14.030
see what's happening with say
rates of disease or things,

49:14.030 --> 49:17.170
and you could put in data
points, so up at Group A you'd

49:17.166 --> 49:20.066
get a data point there,
with Group B you'd get a data

49:20.072 --> 49:22.242
point there,
and then you get all the other

49:22.235 --> 49:24.985
data points and then you can
compare these data points in

49:24.992 --> 49:25.782
various ways.

49:25.780 --> 49:28.860
For example,
you could compare the males and

49:28.860 --> 49:31.440
the females at one slice in
time,

49:31.440 --> 49:33.250
that's what we talked about
before,

49:33.250 --> 49:36.990
but you could do sequential
cross-sectional studies and so

49:36.994 --> 49:39.234
some kind of tracking over time.

49:39.230 --> 49:41.930
But there are some weaknesses
in this method as well,

49:41.931 --> 49:44.791
but it's better then just
having one snapshot in time of

49:44.788 --> 49:45.358
course.

49:45.360 --> 49:48.230
This would be an example of a
cross-sectional study,

49:48.226 --> 49:51.086
but you're just taking
different cross-sections over

49:51.092 --> 49:51.602
time.

49:51.599 --> 49:56.129
Here would be an example of a
cross-sectional study,

49:56.126 --> 49:59.676
and I've showed you this graph
before.

49:59.679 --> 50:02.589
This is the graph that shows
body mass index,

50:02.590 --> 50:05.190
level of weight increasing on
this axis,

50:05.190 --> 50:07.730
and then mortality rate on this
axis,

50:07.730 --> 50:10.010
and you get a curve that looks
like this.

50:10.010 --> 50:13.100
As weight goes up,
risk goes way up as well,

50:13.103 --> 50:17.203
and we talked in this about
this part of the graph-- let's

50:17.204 --> 50:19.584
see if we can get it up here.

50:19.579 --> 50:23.359


50:23.360 --> 50:25.900
First, this is an association,
it's a correlation,

50:25.902 --> 50:27.982
but it doesn't really prove
causation.

50:27.980 --> 50:31.390
So as weight goes up risk goes
up, but you don't know if it's

50:31.387 --> 50:33.147
the weight that's causing it.

50:33.150 --> 50:38.740
Let's say heavier people are
more likely to be in poverty

50:38.742 --> 50:42.132
which is the case,
and it could be that the

50:42.126 --> 50:45.606
poverty is causing the mortality
rather than the weight itself,

50:45.610 --> 50:48.500
weight is just a marker of it
or a correlate of it.

50:48.500 --> 50:51.340
That would be an example of a
correlation and one of the

50:51.335 --> 50:53.395
weaknesses in cross-sectional
studies.

50:53.400 --> 50:56.930
I also mentioned this up curve
over here that we--as we

50:56.925 --> 50:59.665
discussed in class might be
attributed,

50:59.670 --> 51:03.020
in fact, is from the way the
studies go to cigarette smoking.

51:03.018 --> 51:06.468
Now, if all you were looking at
is correlations and you didn't

51:06.469 --> 51:08.959
have data on whether people were
smoking,

51:08.960 --> 51:12.210
you would assume that there was
something bad about being

51:12.213 --> 51:15.883
underweight or being in the low
weight range that was leading to

51:15.875 --> 51:16.975
increased risk.

51:16.980 --> 51:20.020
In fact, it's not the weight
itself, it's just that the lower

51:20.021 --> 51:22.911
weights are associated with
smoking, and smoking is what's

51:22.909 --> 51:24.379
driving up the mortality.

51:24.380 --> 51:28.330
That's why you need--in these
studies you need to look at

51:28.327 --> 51:31.217
causation rather than just
correlation.

51:31.219 --> 51:35.829
The solution to that particular
problem was to compare smokers

51:35.827 --> 51:38.847
to non-smokers in a sample like
this,

51:38.849 --> 51:41.859
or you can take statistics and
control from the impact of

51:41.862 --> 51:45.152
smoking and find out how much
it's contributing to that little

51:45.146 --> 51:46.436
J part of the curve.

51:46.440 --> 51:49.590
This is interesting,
and we can learn things from

51:49.585 --> 51:51.285
cross-sectional studies.

51:51.289 --> 51:54.099
Longitudinal studies are the
strongest way.

51:54.099 --> 51:57.259
They're more expensive,
more difficult to pull of for

51:57.264 --> 52:00.314
reasons I'll explain,
but they're the strongest way

52:00.307 --> 52:03.227
to nail down cause and effect
relationships.

52:03.230 --> 52:06.580
What we would have here would
be a group of people studied

52:06.583 --> 52:10.293
over time, and each of the dots
would represent a data point.

52:10.289 --> 52:12.679
Let's say Time 1,
Time 2, Time 3,

52:12.684 --> 52:16.124
Time 4 separated by five years,
for example,

52:16.117 --> 52:19.957
and instead of studying
different people at different

52:19.963 --> 52:23.093
points in time,
you're taking a single group of

52:23.094 --> 52:25.384
individuals and tracking them
over time.

52:25.380 --> 52:29.380
They are called a cohort,
and the study is a longitudinal

52:29.380 --> 52:29.810
one.

52:29.809 --> 52:35.079


52:35.079 --> 52:37.969
In this case,
you've got a group of females,

52:37.969 --> 52:40.629
so a group of females is
identified out here,

52:40.630 --> 52:43.320
those same people are studied
at this time point,

52:43.320 --> 52:45.450
that time point,
and that time point and the

52:45.452 --> 52:46.942
same might be true of males.

52:46.940 --> 52:50.170
Now the nice thing about a
study like this-- well first of

52:50.170 --> 52:53.740
all it's difficult doing studies
like this because one thing you

52:53.744 --> 52:56.804
need if you're doing a
scientific study is to keep your

52:56.804 --> 52:58.284
subject pool intact.

52:58.280 --> 53:01.730
If you start off with 100% of
subjects and you end up with 20%

53:01.733 --> 53:05.193
you've got a study that's not
terribly valid because you don't

53:05.186 --> 53:08.016
know what happened to all those
80% of people.

53:08.018 --> 53:12.898
It's pretty hard to keep a
cohort intact like this over a

53:12.902 --> 53:17.522
sufficient period of time,
but some investigators have

53:17.523 --> 53:18.573
done it.

53:18.570 --> 53:20.910
You remember I talked about the
Framingham Heart Study.

53:20.909 --> 53:24.249
That would be a perfect example
of a longitudinal cohort study

53:24.246 --> 53:26.486
where a group of people in
Framingham,

53:26.489 --> 53:29.859
Mass were followed over a
period of time for risk factors

53:29.856 --> 53:31.476
related to heart disease.

53:31.480 --> 53:35.290
Now the nice thing about these
particular studies is that

53:35.288 --> 53:38.488
you're controlling,
because people are their own

53:38.485 --> 53:39.705
controls here.

53:39.710 --> 53:43.180
You're controlling for the
factors that might otherwise

53:43.181 --> 53:47.171
complicate interpretations that
you have of associating certain

53:47.168 --> 53:49.098
risk factors with disease.

53:49.099 --> 53:55.449
Let's just say family history
is one predictor of disease.

53:55.449 --> 53:59.239
Well, if you've got a bunch of
people in a cross-sectional

53:59.244 --> 54:02.684
study studied one point in time;
they're all going to have

54:02.677 --> 54:05.337
different family histories and
you've got to measure that,

54:05.340 --> 54:06.790
control for it,
or whatever.

54:06.789 --> 54:10.419
If you're making--using people
as their own controls over time.

54:10.420 --> 54:13.840
The individual's family history
isn't changing as they go from

54:13.836 --> 54:15.396
Time one,
to two, to three,

54:15.402 --> 54:17.192
to four but it remains
constant,

54:17.190 --> 54:20.260
and it provides a stronger
method for looking at the

54:20.260 --> 54:23.210
association of certain risk
factors to disease.

54:23.210 --> 54:27.400
A great example of this would
be let's say this is a dietary

54:27.398 --> 54:31.278
intervention study,
and you get baseline data out

54:31.284 --> 54:34.834
here at Time One,
at Time Two you intervene with

54:34.831 --> 54:37.371
some diet intervention,
let's say have people lose

54:37.365 --> 54:39.185
weight, eat more fruits and
vegetables, take supplements,

54:39.193 --> 54:39.783
whatever it is.

54:39.780 --> 54:44.260
Then you follow people--that
same group of people over time

54:44.255 --> 54:48.955
and you can get a pretty strong
cause and effect inference out

54:48.961 --> 54:49.891
of that.

54:49.889 --> 54:54.149
The reason you can draw a
stronger inference is in part is

54:54.152 --> 54:57.252
because the people,
as I said, are their own

54:57.253 --> 55:00.463
controls, and so variability
that gets introduced into

55:00.463 --> 55:04.223
studies because of differences
between people don't exist using

55:04.217 --> 55:06.787
this method,
because the same group of

55:06.791 --> 55:08.631
people are followed over time.

55:08.630 --> 55:11.740
You'll see longitudinal
studies, cross-sectional studies

55:11.739 --> 55:15.019
mixed in to the sort of papers
that I'm having you read.

55:15.018 --> 55:18.748
It's interesting for you to
think about what you--what it

55:18.753 --> 55:22.293
means when it--in the abstract
of a paper it says,

55:22.289 --> 55:25.619
this is a longitudinal cohort
study versus a cross-sectional

55:25.617 --> 55:26.067
study.

55:26.070 --> 55:27.860
Here's an example.

55:27.860 --> 55:30.550
Let's take a study and
interpret this.

55:30.550 --> 55:34.380
2006 there was a paper
published in The Journal of

55:34.376 --> 55:38.196
the American Medical
Association looking at green

55:38.204 --> 55:41.054
tea consumption,
the mortality due to

55:41.048 --> 55:44.948
cardiovascular disease in a
Japanese sample of individuals.

55:44.949 --> 55:48.139
I'm going to talk about certain
things from the abstract of this

55:48.141 --> 55:50.831
particular paper to point out
some of these issues.

55:50.829 --> 55:55.489
First, the context of this is
there's some research suggesting

55:55.489 --> 55:59.309
that green tea may have some
protective benefit.

55:59.309 --> 56:02.409
What they did was they did a
population based,

56:02.413 --> 56:06.283
which means that it's more or
less a random sample of the

56:06.275 --> 56:09.515
population, and a prospective
cohort study.

56:09.518 --> 56:11.858
Prospective and longitudinal
mean the same thing:

56:11.858 --> 56:15.028
you're taking the same people
and following them through time,

56:15.030 --> 56:18.580
the word cohort means a group
of people were identified and

56:18.579 --> 56:20.539
followed over multiple points.

56:20.539 --> 56:24.469
They looked at all cause
mortality here,

56:24.467 --> 56:28.597
so that would be death from any
reason.

56:28.599 --> 56:30.989
Now here are the results,
and this is going to be very

56:30.985 --> 56:33.545
difficult to sort through but
let me try to help guide you

56:33.554 --> 56:34.234
through it.

56:34.230 --> 56:37.920
What they do in studies like
this is they'll say,

56:37.918 --> 56:42.298
look at people at five levels
of green tea consumption.

56:42.300 --> 56:44.770
They'll take the bottom fifth,
a second fifth,

56:44.766 --> 56:48.216
on up until the people that are
consuming the most green tea.

56:48.219 --> 56:52.149
They'll take the group that--at
one of the extremes,

56:52.150 --> 56:55.430
usually the people with the
lowest level in this case and

56:55.425 --> 56:58.935
say okay whatever risks those
people have we're going to call

56:58.936 --> 57:01.566
it 1, we're going to establish
that as 1.

57:01.570 --> 57:04.650
They've taken the people with
the lowest green tea consumption

57:04.646 --> 57:06.006
and said their risk is 1.

57:06.010 --> 57:09.730
Let's see what happens as green
tea consumption goes up in those

57:09.733 --> 57:13.343
other four groups (because they
broke them into quintiles,

57:13.340 --> 57:15.730
or five groups),
and see what happens to risk as

57:15.728 --> 57:17.758
a function of 1 for the
referenced group,

57:17.760 --> 57:19.590
and that's called relative
risk.

57:19.590 --> 57:24.860
You see the terms relative risk
used a lot in these epidemiology

57:24.862 --> 57:25.702
studies.

57:25.699 --> 57:30.329
I'm going to point out this
particular part here.

57:30.329 --> 57:35.069
Now what this says,
in women, the hazard ratios--we

57:35.065 --> 57:39.705
don't need to talk about what
that is so much,

57:39.710 --> 57:43.130
of cardiovascular disease,
mortality across increasing

57:43.128 --> 57:45.838
green tea consumption categories
were 1.

57:45.840 --> 57:49.220
Okay, so that's the lowest
group of green tea consumption.

57:49.219 --> 57:54.269
Then it goes to .69;
.69 for the next group.

57:54.268 --> 57:58.318
Now let's see where we are.

57:58.320 --> 58:01.180
Okay.

58:01.179 --> 58:08.279
.69 is the highest group;
1.0 is the lowest green tea

58:08.280 --> 58:09.240
consumption.

58:09.239 --> 58:13.949
It looks like 1.84,
and then it goes down to the

58:13.947 --> 58:14.547
.69.

58:14.550 --> 58:16.190
Okay.

58:16.190 --> 58:20.360
That would say that if you are
in the highest level of green

58:20.356 --> 58:24.516
tea consumption you have 69% of
the risk that people have if

58:24.523 --> 58:28.623
they're at the lowest level of
green tea consumption,

58:28.619 --> 58:32.469
so this would represent a 31%
reduction in risk.

58:32.469 --> 58:34.589
That's what relative risk means.

58:34.590 --> 58:39.090
This study sounds pretty good,
high green tea consumption,

58:39.090 --> 58:42.410
31% reduction in cardiovascular
disease.

58:42.409 --> 58:45.959
Now let's skip that.

58:45.960 --> 58:48.740
Now, here are the numbers of
how it works out in a study like

58:48.744 --> 58:49.074
this.

58:49.070 --> 58:51.760
I mean who wouldn't do
something like drink green tea

58:51.760 --> 58:54.810
if you're going to get a 31%
reduction in risk for something

58:54.813 --> 58:56.473
as serious as heart disease?

58:56.469 --> 59:01.359
Even the big studies become
interesting in this regard.

59:01.360 --> 59:04.600
In this particular study,
there were 40,000 subjects that

59:04.601 --> 59:07.821
started initially;
86% of those completed,

59:07.817 --> 59:10.587
so 34,000 completed the study.

59:10.590 --> 59:15.450
The number of people who died
of heart disease in that

59:15.445 --> 59:21.025
particular study--cardiovascular
disease is the CVD--is 892 of

59:21.034 --> 59:25.684
that 34,000;
the 31% reduction of that would

59:25.684 --> 59:31.644
mean that 615 people would die
of heart disease out of that

59:31.641 --> 59:32.671
34,000.

59:32.670 --> 59:38.560
If you subtract the 615 from
the 892 there are 277 people who

59:38.556 --> 59:42.576
would have benefited out of the
34,000.

59:42.579 --> 59:47.459
What does that mean in terms of
your own personal behavior and

59:47.458 --> 59:48.978
your own choices?

59:48.980 --> 59:51.970
Well, if you're one of those
277 you're golden,

59:51.965 --> 59:55.595
and you've got benefit from
being in that particular high

59:55.601 --> 59:57.291
tea consumption group.

59:57.289 --> 1:00:00.919
But the chances are that you're
not going to get heart disease

1:00:00.923 --> 1:00:04.443
if you're living in Japan and in
this particular sample,

1:00:04.440 --> 1:00:06.420
at least during the time of the
study,

1:00:06.420 --> 1:00:09.060
and even if you get heart
disease, there's not a big

1:00:09.059 --> 1:00:12.159
chance that you're one of the
ones who would benefit from the

1:00:12.164 --> 1:00:12.894
green tea.

1:00:12.889 --> 1:00:14.509
Do you take the green tea?

1:00:14.510 --> 1:00:17.830
Well personal choice of course;
but that's how these numbers

1:00:17.831 --> 1:00:19.341
were work out in these big
studies.

1:00:19.340 --> 1:00:25.140
Here would be an extreme
hypothetical example of that.

1:00:25.139 --> 1:00:30.679
Let's say that somebody has
proposed that there is a 33%

1:00:30.682 --> 1:00:36.432
reduction in the--33% drop in
some disease if you eat some

1:00:36.427 --> 1:00:38.947
incredibly icky food.

1:00:38.949 --> 1:00:41.309
Remember that little NPR clip
we showed about that fruit--I

1:00:41.311 --> 1:00:43.801
forget what it was called--but
people were making all kinds of

1:00:43.795 --> 1:00:46.235
faces and eating the fruit,
so that would be an example.

1:00:46.239 --> 1:00:50.329
Let's say this is fruit that
you just can't tolerate very

1:00:50.331 --> 1:00:54.061
well, but it leads to a 33%
reduction of disease;

1:00:54.059 --> 1:00:55.459
that sounds pretty good.

1:00:55.460 --> 1:00:58.540
Let's say somebody did an
enormous study,

1:00:58.539 --> 1:01:00.299
nothing like this has ever been
done,

1:01:00.300 --> 1:01:02.710
but did a study with ten
million people and of the people

1:01:02.706 --> 1:01:04.596
who don't eat that,
three get the disease.

1:01:04.599 --> 1:01:08.469
Of the people who do eat it two
get the disease,

1:01:08.465 --> 1:01:11.915
and of course that's your 33%
reduction.

1:01:11.920 --> 1:01:16.450
The chances that you're going
to be helped by eating that bad

1:01:16.445 --> 1:01:18.855
tasting fruit isn't very high.

1:01:18.860 --> 1:01:23.060
So that's why the distilling
science into terms that the

1:01:23.057 --> 1:01:26.567
public can make sense of,
is very important.

1:01:26.570 --> 1:01:30.210
Now there are experimental
studies that are interesting in

1:01:30.213 --> 1:01:31.113
this regard.

1:01:31.110 --> 1:01:35.010
Experimental studies are when
you manipulate some variable.

1:01:35.010 --> 1:01:37.320
You're actually doing an
experiment where you're

1:01:37.322 --> 1:01:38.802
manipulating some variables.

1:01:38.800 --> 1:01:44.080
Randomized control trials,
RCTs, are the gold standard for

1:01:44.083 --> 1:01:45.293
doing this.

1:01:45.289 --> 1:01:47.829
Where you randomly assign
people to groups getting an

1:01:47.831 --> 1:01:50.621
intervention or something that's
not the intervention,

1:01:50.619 --> 1:01:53.139
and then you typically have
control groups.

1:01:53.139 --> 1:01:57.259
In these, you want to control
for as many confounds as

1:01:57.255 --> 1:01:58.105
possible.

1:01:58.110 --> 1:02:01.140
I found this funny little
figure in a psychology journal

1:02:01.139 --> 1:02:04.329
once that talked about the need
for control groups in these

1:02:04.333 --> 1:02:06.373
studies,
but of course the need for out

1:02:06.369 --> 1:02:07.559
of control groups as well.

1:02:07.559 --> 1:02:13.159
Here would be an example of an
experimental study.

1:02:13.159 --> 1:02:17.039
Let's say you get access to
schools,

1:02:17.039 --> 1:02:20.239
and you hypothesize that a
nutrition intervention would

1:02:20.242 --> 1:02:23.742
improve what kids eat in schools
and at home, and ultimately

1:02:23.740 --> 1:02:24.750
their health.

1:02:24.750 --> 1:02:29.360
Let's say you have four schools
available to do this kind of

1:02:29.355 --> 1:02:29.975
thing.

1:02:29.980 --> 1:02:31.630
How would you design the study?

1:02:31.630 --> 1:02:35.470
Trying to be as scientifically
sound as possible,

1:02:35.469 --> 1:02:39.089
you have an intervention,
you hypothesize that it's going

1:02:39.094 --> 1:02:41.764
to help kids,
and you want to find out

1:02:41.760 --> 1:02:45.050
whether it really does,
and you're lucky enough to get

1:02:45.052 --> 1:02:47.922
four schools to work with:
how would you design a study

1:02:47.918 --> 1:02:48.608
like this?

1:02:48.610 --> 1:02:50.640
Anybody take a shot at it?

1:02:50.639 --> 1:02:56.769


1:02:56.768 --> 1:02:59.458
There's a pretty
straightforward initial answer,

1:02:59.463 --> 1:02:59.813
yes.

1:02:59.809 --> 1:03:00.399
Student:
>

1:03:00.400 --> 1:03:04.010


1:03:04.010 --> 1:03:05.380
Prof: Okay,
so that would be the typical

1:03:05.382 --> 1:03:06.762
approach,
is you take two schools and

1:03:06.759 --> 1:03:09.529
give them the intervention,
and you take two schools and

1:03:09.532 --> 1:03:11.392
not give them any intervention.

1:03:11.389 --> 1:03:15.179
If you were being--if you were
going a good job at controlling

1:03:15.177 --> 1:03:17.507
things,
you would randomly decide which

1:03:17.512 --> 1:03:20.552
schools got the intervention and
which schools didn't.

1:03:20.550 --> 1:03:23.010
That leaves out at least one
source of bias.

1:03:23.010 --> 1:03:24.950
Let's just say you're the
investigator.

1:03:24.949 --> 1:03:27.479
You really want to find that
your intervention works,

1:03:27.478 --> 1:03:30.198
and you know that the staff in
one school is particularly

1:03:30.202 --> 1:03:31.762
motivated to carry this out.

1:03:31.760 --> 1:03:35.650
If you put the intervention in
that school and don't randomize

1:03:35.648 --> 1:03:38.578
then you're likely to get bias
in your results,

1:03:38.581 --> 1:03:40.941
so randomization helps with
that.

1:03:40.940 --> 1:03:44.500
The unit of randomization
becomes interesting.

1:03:44.500 --> 1:03:47.790
You could randomize by school
and that would be the obvious

1:03:47.786 --> 1:03:49.256
way to start doing that.

1:03:49.260 --> 1:03:52.880
You'd have school 1,2,
3, and 4 and just say by random

1:03:52.880 --> 1:03:55.750
assignment you get something
like this,

1:03:55.750 --> 1:03:57.670
you get your intervention and
control, two in each condition.

1:03:57.670 --> 1:04:01.290
Now the problem with an
approach like this,

1:04:01.289 --> 1:04:06.379
is that you've only got-- you
basically got four subjects in

1:04:06.376 --> 1:04:07.666
this study.

1:04:07.670 --> 1:04:10.400
You've got a lot of kids within
each school, but only four

1:04:10.396 --> 1:04:13.026
subjects when it comes down to
doing your statistics.

1:04:13.030 --> 1:04:16.070
If there's something that makes
some of these schools different

1:04:16.065 --> 1:04:18.305
from others,
it's going to be very hard to

1:04:18.306 --> 1:04:21.216
figure out whether any results
you get are because of the

1:04:21.215 --> 1:04:23.085
intervention,
or because of these

1:04:23.085 --> 1:04:26.245
predisposing factors that might
have led one set of schools to

1:04:26.253 --> 1:04:27.763
do better than the others.

1:04:27.760 --> 1:04:31.780
Another thing--another way to
randomize would be to do it

1:04:31.780 --> 1:04:34.080
within classes across schools.

1:04:34.079 --> 1:04:37.129
In School 1,
let's just say you're going to

1:04:37.126 --> 1:04:41.696
take the sixth graders in School
1 and there are two sixth grade

1:04:41.699 --> 1:04:43.659
classes in that school.

1:04:43.659 --> 1:04:47.079
You do a random assignment to
intervention or control.

1:04:47.079 --> 1:04:52.239
Then you go to seventh grade
classes in that school and do

1:04:52.244 --> 1:04:55.694
random assignment for Class 1
and 2.

1:04:55.690 --> 1:04:57.860
Then you go to the eighth grade
and do the same thing;

1:04:57.860 --> 1:05:00.160
and then you'd repeat that with
School 2,

1:05:00.159 --> 1:05:04.269
3, and 4 and then instead of
four subjects you have many more

1:05:04.268 --> 1:05:08.378
subjects because your unit of
randomization is classes within

1:05:08.376 --> 1:05:09.196
schools.

1:05:09.199 --> 1:05:12.219
This would be preferable
because then if there are any

1:05:12.219 --> 1:05:15.409
things going on that are
specific to a school--like let's

1:05:15.411 --> 1:05:18.891
just say School 1 is one of the
school's with highly motivated

1:05:18.889 --> 1:05:21.619
staff,
that's going to occur evenly

1:05:21.619 --> 1:05:23.609
across all your conditions.

1:05:23.610 --> 1:05:26.200
So there are equal numbers of
kids in the highly motivated

1:05:26.197 --> 1:05:28.147
school who got the control
intervention,

1:05:28.150 --> 1:05:32.230
and ones who got the control
versus the intervention.

1:05:32.230 --> 1:05:35.340
That helps rule out one
potential source of bias.

1:05:35.340 --> 1:05:38.080
This would be an example of an
intervention trial,

1:05:38.079 --> 1:05:40.759
but there are lots and lots of
intervention trials that we'll

1:05:40.764 --> 1:05:42.694
discuss some of those later in
the class.

1:05:42.690 --> 1:05:46.080
Ultimately, if you want to find
out that some change in

1:05:46.081 --> 1:05:49.661
something leads to benefits,
you're going to be doing some

1:05:49.661 --> 1:05:51.421
kind of a control trial.

1:05:51.420 --> 1:05:56.570
Let's try to pull all this
together into some overriding

1:05:56.574 --> 1:05:58.454
conceptual scheme.

1:05:58.449 --> 1:06:02.359
If we look at public health,
how does it approach the world

1:06:02.360 --> 1:06:06.680
in a unique way compared to the
way traditional medicine does?

1:06:06.679 --> 1:06:10.239
What does that lead us to in
terms of trying to help people?

1:06:10.239 --> 1:06:13.469
The traditional approach,
as I mentioned with medicine,

1:06:13.472 --> 1:06:15.392
is to focus on the individual.

1:06:15.389 --> 1:06:18.109
You hope that by focusing on
the individual,

1:06:18.110 --> 1:06:21.720
you get a good outcome:
improved health and well being.

1:06:21.719 --> 1:06:25.119
In order to do this you try to
motivate the person to make a

1:06:25.121 --> 1:06:27.291
change,
or you give them knowledge,

1:06:27.293 --> 1:06:30.723
or in the case of medicine of
course you can intervene with

1:06:30.717 --> 1:06:32.487
drugs and things like that.

1:06:32.489 --> 1:06:35.799
The idea here is that you apply
something to the individual,

1:06:35.798 --> 1:06:38.938
the individual gets better and
their health improves as a

1:06:38.942 --> 1:06:39.842
consequence.

1:06:39.840 --> 1:06:42.450
Here you educate,
you implore,

1:06:42.447 --> 1:06:46.217
and you hope that the world
works this way,

1:06:46.224 --> 1:06:50.994
so all these things fit
together into this neat little

1:06:50.990 --> 1:06:52.250
picture.

1:06:52.250 --> 1:06:54.370
Cigarette smoking,
for example,

1:06:54.373 --> 1:06:57.423
you tell people smoking is bad
for them,

1:06:57.420 --> 1:07:00.800
you tell kids that sugared
cereals aren't good for them,

1:07:00.800 --> 1:07:03.010
that soft drinks aren't good
for them,

1:07:03.010 --> 1:07:04.610
that fast food's not good for
them,

1:07:04.610 --> 1:07:07.280
and they should eat these
things in small amounts--that

1:07:07.277 --> 1:07:10.287
would be educating and imploring
people and you hope you get a

1:07:10.289 --> 1:07:11.129
good outcome.

1:07:11.130 --> 1:07:14.700
Now in the case--there are
advantages to this kind of

1:07:14.699 --> 1:07:15.249
thing.

1:07:15.250 --> 1:07:18.570
Government doesn't have to get
so involved necessarily with

1:07:18.565 --> 1:07:19.705
changing policies.

1:07:19.710 --> 1:07:23.040
There are disadvantages of this
as well.

1:07:23.039 --> 1:07:25.459
One is that it tends not to
work very well.

1:07:25.460 --> 1:07:29.430
But we have to ask when this
will work, or are there enough

1:07:29.431 --> 1:07:31.351
resources to make it work?

1:07:31.349 --> 1:07:34.099
You'll see some examples of
that in just a minute.

1:07:34.099 --> 1:07:36.729
Another approach,
a different conceptual approach

1:07:36.731 --> 1:07:39.641
is to change conditions that
affect the individual.

1:07:39.639 --> 1:07:43.549
You'd back these things out,
and instead focus on things

1:07:43.552 --> 1:07:47.042
that occur before the individual
gets involved.

1:07:47.039 --> 1:07:50.279
You're changing the conditions
that affect the individual.

1:07:50.280 --> 1:07:53.230
So you might change economic
conditions, so maybe the

1:07:53.230 --> 1:07:56.580
fundamental cost of food would
be an example using taxes.

1:07:56.579 --> 1:07:59.279
You'd use legislation to
intervene here,

1:07:59.279 --> 1:08:03.359
maybe legislation that would
prohibit marketing of unhealthy

1:08:03.364 --> 1:08:04.894
foods to children.

1:08:04.889 --> 1:08:07.719
The environment could get
involved by making healthier

1:08:07.724 --> 1:08:10.674
conditions, and there are a
million examples of that;

1:08:10.670 --> 1:08:13.530
and government can use its
regulatory authority,

1:08:13.528 --> 1:08:15.538
for example,
banning Trans fats in

1:08:15.536 --> 1:08:16.506
restaurants.

1:08:16.510 --> 1:08:21.190
To create what economists call
optimal defaults.

1:08:21.189 --> 1:08:23.679
This is a key concept,
optimal defaults.

1:08:23.680 --> 1:08:26.810
The idea here is that you want
to set up environmental

1:08:26.805 --> 1:08:30.105
conditions where the optimal
behavior becomes the default

1:08:30.108 --> 1:08:33.468
rather then suboptimal behavior
becoming the default.

1:08:33.470 --> 1:08:36.890
Then that, in turn,
affects the individual and then

1:08:36.890 --> 1:08:40.040
you get the increased health and
well being.

1:08:40.037 --> 1:08:41.997
Here's an example drawn from
economics.

1:08:42.000 --> 1:08:46.170
There's a group of economists
who have studied enrollment in

1:08:46.168 --> 1:08:47.298
pension plans.

1:08:47.300 --> 1:08:51.170
Some employers automatically
enroll people in pension plans,

1:08:51.170 --> 1:08:53.440
which are a good idea by the
way, because then if you're

1:08:53.435 --> 1:08:55.285
investing money throughout your
work life,

1:08:55.287 --> 1:08:57.817
you're less likely to be
dependent on the state later in

1:08:57.818 --> 1:08:58.138
life.

1:08:58.140 --> 1:09:03.370
Some employers make you--enroll
you by default others--but

1:09:03.368 --> 1:09:06.488
you're allowed to opt out of it.

1:09:06.488 --> 1:09:09.878
Others don't enroll you but
you're allowed to opt in,

1:09:09.877 --> 1:09:13.917
so the same choices but whether
the default is in or out varies

1:09:13.920 --> 1:09:15.290
across employers.

1:09:15.287 --> 1:09:18.527
The number of people who join
pension plans varies a lot

1:09:18.528 --> 1:09:20.118
depending on the default.

1:09:20.118 --> 1:09:23.008
Same choices but just different
defaults.

1:09:23.010 --> 1:09:27.390
Here's another stunning
example, people who become organ

1:09:27.390 --> 1:09:28.110
donors.

1:09:28.109 --> 1:09:32.159
These are data from European
countries that break down about

1:09:32.164 --> 1:09:34.714
60%/40% into ones that use the
U.S.

1:09:34.710 --> 1:09:37.450
model where you're not an organ
donor by default:

1:09:37.453 --> 1:09:40.773
you can choose to be an organ
donor, but you'd have to take

1:09:40.766 --> 1:09:42.536
active steps to become one.

1:09:42.537 --> 1:09:45.857
Other countries you're an organ
donor by default,

1:09:45.856 --> 1:09:48.896
but you can opt not to be one
if you wish.

1:09:48.899 --> 1:09:49.959
Here are the rates.

1:09:49.960 --> 1:09:52.730
Using the U.S. Model,
Denmark, The Netherlands,

1:09:52.726 --> 1:09:56.156
the UK, and Germany have this
percentage of people who are

1:09:56.155 --> 1:09:57.295
organ donors.

1:09:57.300 --> 1:09:59.270
The other countries are like
this.

1:09:59.270 --> 1:10:06.210
Now that is breathtaking,
that difference.

1:10:06.210 --> 1:10:11.120
Imagine if you wanted to
produce this effect from this

1:10:11.118 --> 1:10:15.938
baseline with education,
motivation, imploring people

1:10:15.935 --> 1:10:17.505
and the like.

1:10:17.510 --> 1:10:20.950
You could never get an effect
like this and it would cost an

1:10:20.948 --> 1:10:23.978
absolute fortune to do it,
or you can just change the

1:10:23.979 --> 1:10:24.679
default.

1:10:24.680 --> 1:10:28.930
Just changing the default
becomes an important theme that

1:10:28.934 --> 1:10:32.434
will play through all the rest
of the class.

1:10:32.430 --> 1:10:36.800
We hope to create optimal
defaults in ways that change

1:10:36.798 --> 1:10:38.118
public health.

1:10:38.118 --> 1:10:42.158
There are some classic examples
of this in the environmental

1:10:42.157 --> 1:10:45.507
area like chlorinating water,
using immunizations,

1:10:45.511 --> 1:10:48.321
and you see some of the numbers
here.

1:10:48.319 --> 1:10:50.199
These are classic examples.

1:10:50.198 --> 1:10:55.408
I'm going to skip over this
because I want to make a point

1:10:55.409 --> 1:10:56.049
here.

1:10:56.050 --> 1:10:59.640
You remember when we talked
about malnutrition,

1:10:59.644 --> 1:11:04.184
and some of the diseases and
maladies of the body that fall

1:11:04.176 --> 1:11:05.266
from that.

1:11:05.270 --> 1:11:08.210
One in particular was Vitamin A
deficiency.

1:11:08.210 --> 1:11:12.900
Scientists discovered that
Vitamin A deficiency provoked by

1:11:12.900 --> 1:11:16.460
malnutrition leads to a number
of bad things,

1:11:16.460 --> 1:11:19.130
and you see them listed here.

1:11:19.130 --> 1:11:22.020
Here's a case study of public
health in action,

1:11:22.015 --> 1:11:23.955
a very successful case study.

1:11:23.960 --> 1:11:27.680
There's a particular scientist
at Johns Hopkins in the School

1:11:27.675 --> 1:11:30.085
of Public Health named Alfred
Sommer.

1:11:30.090 --> 1:11:33.720
He was an ophthalmologist by
training, but also a public

1:11:33.721 --> 1:11:34.781
health expert.

1:11:34.779 --> 1:11:37.889
He was one of the ones who
initially did some of the

1:11:37.886 --> 1:11:41.176
documentation that Vitamin A
deficiency was linked to a

1:11:41.176 --> 1:11:44.346
number of diseases that were
killing many millions of

1:11:44.345 --> 1:11:45.255
children.

1:11:45.260 --> 1:11:48.040
He then conducted small trials.

1:11:48.037 --> 1:11:53.067
Remember the WHO slide with the
surveillance becoming the first

1:11:53.069 --> 1:11:55.439
step,
and then you establish the risk

1:11:55.435 --> 1:11:57.745
factors,
and then you do small studies

1:11:57.753 --> 1:12:00.863
to see if you can correct what
the risk factor is?

1:12:00.859 --> 1:12:03.219
Here's all that happening by
one person.

1:12:03.220 --> 1:12:06.760
Here's the risk factor Vitamin
A deficiency;

1:12:06.760 --> 1:12:09.890
leads to these;
then he conducted small trials

1:12:09.889 --> 1:12:13.329
of supplementing by doing
Vitamin A supplementation for

1:12:13.328 --> 1:12:15.238
children in poor countries.

1:12:15.238 --> 1:12:19.148
Here's a picture of Sommer
doing those kind of studies.

1:12:19.149 --> 1:12:22.559
What's most impressive here is
that he connected science with

1:12:22.564 --> 1:12:24.904
public policy in a very
impressive way.

1:12:24.899 --> 1:12:29.549
He now, throughout the world,
there is wide scale Vitamin A

1:12:29.554 --> 1:12:34.214
supplementation due mainly to
the scientific and the public

1:12:34.210 --> 1:12:36.780
health efforts of Al Sommer.

1:12:36.779 --> 1:12:39.579
You can see from the results
here that huge,

1:12:39.583 --> 1:12:43.373
huge changes have come about
and you can see that it's very

1:12:43.368 --> 1:12:46.238
cost effective to do this kind
of thing.

1:12:46.238 --> 1:12:49.908
This would be an example of a
startling public health victory

1:12:49.908 --> 1:12:53.638
where the science gets connected
to the public policy in a way

1:12:53.640 --> 1:12:55.720
that really affects the world.

1:12:55.720 --> 1:12:58.480
There are many examples of
this, but this is one of the

1:12:58.478 --> 1:13:00.878
most startling involving the
nutrition arena.

1:13:00.880 --> 1:13:04.640
If you're interested in reading
more about public health I've

1:13:04.641 --> 1:13:06.651
listed a couple of books here.

1:13:06.649 --> 1:13:09.679
Those of you who might be
interested in pursuing a career

1:13:09.677 --> 1:13:13.247
in public health,
the website down here,

1:13:13.246 --> 1:13:17.876
The American Public Health
Association,

1:13:17.880 --> 1:13:21.830
is a good one to go to for
information on different degrees

1:13:21.833 --> 1:13:22.793
one can get.

1:13:22.787 --> 1:13:26.067
That's all listed there,
and of course this will on the

1:13:26.067 --> 1:13:27.097
course outline.

1:13:27.100 --> 1:13:32.000
The creed here is that instead
of saving lives one at a time

1:13:31.997 --> 1:13:35.567
you try to save lives millions
at a time.

1:13:35.569 --> 1:13:40.999