00:01
This presentation is all about
an epidemiological framework
called The Web of Causation.
00:07
This framework is used to
understand the causes of disease.
00:12
We use this in order to
understand all of the multiple factors
that can cause disease to occur.
00:17
This framework can be used
for communicable diseases,
chronic diseases,
as well as health outcomes.
00:23
Now, a web of causation
looks different depending on
the specific disease or health
outcome that you're mapping out.
00:31
Some webs are as simple as
just linking together a couple points,
but others contain complex links
between several different factors
that all contribute to the
same disease process.
00:43
Developing a web of causation
requires that you ask questions
about the root causes
of a disease process.
00:50
So we ask ourselves why,
why, why, over and over again,
until we get to that
root cause of disease.
00:58
A communicable disease that
has a clearly identified agent
can be diagrammed based on factors
such as the availability of treatments,
or the availability of preventative
medications or vaccines,
public awareness
about the disease.
01:14
Any of these factors
could greatly influence
the progression of the
disease within a community.
01:19
So let's take a
look at an example.
01:21
We're going to use the
communicable disease of tuberculosis.
01:26
So anytime we start to
build a web of causation,
we start at the ending
point and move backwards.
01:32
So here, we're going to start with
an individual who has tuberculosis,
and ask herself
why that happened.
01:39
So here's our
individual who has TB.
01:42
Why do they have TB?
Well,
it's because they've been infected.
01:47
Well, why was this person
infected with tuberculosis?
It's because of exposure
to the mycobacterium.
01:54
Now that's the agent, the agent that
caused infection in the susceptible host.
02:00
So here we are, again,
we have to ask ourselves
why that exposure
happened in the first place.
02:06
Here are a few reasons.
02:08
It could be related to overcrowding,
or malnutrition,
or they could be susceptible
based on genetics,
or susceptible because they
did not get the TB vaccine.
02:19
And then you could take
this even a step further
and ask why these
conditions exist.
02:24
Why is the population living
in overcrowded conditions?
Based on your knowledge
about the population,
you might even add poverty
to this as an additional layer.
02:34
By starting with the outcome
here that's tuberculosis,
and asking ourselves why,
over and over again,
we've created a web of causation
for a communicable disease.
02:46
Now, let's develop a web of
causation for chronic disease.
02:50
Again, we start with the outcome,
and we move backwards by asking ourselves
why, why did this disease
form in the first place?
We're going to use heart
disease as our example here.
03:03
So this is the very beginning of a web
for an individual who has heart disease.
03:09
What I've done here
is presented factors
related to heart disease
for this individual.
03:14
As you can see, we have smoking,
alcohol consumption,
arterial stiffness, stress.
03:21
Now the next step here is
to connect all of the dots.
03:26
We know that there's a
direct link between smoking,
high cholesterol,
high blood pressure, and diabetes.
03:34
We also see that high
cholesterol is linked to
alcohol consumption
and high blood pressure.
03:40
High blood pressure is
linked to arterial stiffness.
03:44
Arterial stiffness is
linked to diabetes,
which is also linked back to smoking
and poor diet and high cholesterol.
03:52
As you can see,
we continue to connect the dots.
03:55
And as we do so what we
have is a comprehensive
web of causation for chronic
condition for one individual.
04:03
These are all the things that
contribute to heart disease for a person.
04:09
Now, let's create a web of
causation for a health outcome.
04:13
Again,
we start with the outcome.
04:15
Here, we're going to use infant
mortality as our health outcome.
04:19
And we ask ourselves,
why, why, why.
04:23
So let's start by
asking ourselves,
why could an infant die
in their first year of life?
Here we see some answers.
04:32
We see the why's.
04:34
Low birth weight,
birth injuries, SIDS, accidents.
04:39
So of course,
we're going to ask ourselves, why again.
04:43
Here are some of the why's.
04:44
Maternal age, marital status,
access to prenatal care.
04:49
So now we need to ask ourselves,
why is it that only some people
get access to prenatal care?
Well,
here are some contributors.
04:58
Race and ethnicity,
socioeconomic and educational status.
05:03
Now let's ask why one more time.
05:05
We'll take it a step
further and ask,
why is race a factor
that contributes
to everything else that
we see on this web so far?
So let's take a look at our
web one more time, all together.
05:17
As a reminder,
we started with the outcome.
05:19
We started with
infant mortality.
05:22
We asked ourselves why.
05:24
Once we came up with those answers,
we asked why again,
then why again, and why again.
05:29
And finally,
we got to the very top where we see
structural racism,
and discriminatory policies.
05:35
Those are root causes
of infant mortality.
05:39
So clearly, the use of this
model allows us to understand
all of the factors that
influence disease.
05:45
In addition, this model gives
us the information that we need
in order to break the web
and stop the disease onset.
05:53
A public health nurse could focus on any
specific factor that's included in the web,
and work to eliminate that factor as
a way to decrease the risk of disease.
06:03
And I'm not suggesting
that all factors
have to be eliminated
in order to stop disease.
06:08
However, the more factors
addressed through an intervention,
the higher chance
of preventing disease.
06:16
So let's take a
look at an example.
06:18
Let's go back to our web of
causation for heart disease,
we'll focus on smoking.
06:23
We could put together
strategies for decreasing smoking
with individuals or
in the community.
06:29
Here, we see that there's a
direct link between smoking,
diabetes,
cholesterol and high blood pressure.
06:36
So by eliminating
smoking as a risk factor,
we begin to decrease the impact of all
other factors that are related to smoking.
06:44
And doing so we begin to
break up the web of causation
and decrease the chances
of the onset of heart disease.