This course provides an introduction to systems thinking and systems models in public health. Problems in public health and health policy tend to be complex with many actors, institutions and risk factors involved. If an outcome depends on many interacting and adaptive parts and actors the outcome cannot be analyzed or predicted with traditional statistical methods. Systems thinking is a core skill in public health and helps health policymakers build programs and policies that are aware of and prepared for unintended consequences. An important part of systems thinking is the practice to integrate multiple perspectives and synthesize them into a framework or model that can describe and predict the various ways in which a system might react to policy change. Systems thinking and systems models devise strategies to account for real world complexities. This work was coordinated by the Alliance for Health Policy and Systems Research, the World Health Organization, with the aid of a grant from the International Development Research Centre, Ottawa, Canada. Additional support was provided by the Department for International Development (DFID) through a grant (PO5467) to Future Health Systems research consortium. © World Health Organization 2014 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use. Johns Hopkins University Bloomberg School of Public Health has a non-exclusive license to use and reproduce the material.
Lecture 3B: Causal Loop Diagrams: Basic Components
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Systems Thinking In Public Health
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System Conceptualization Using Causal Loop Diagrams
This module will introduce you to the concepts of causal loop diagrams and how they can be used in participatory research. You will also be introduced to the software Vensim that allows you to draw causal loop diagrams.
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David Bishai, MD, PhDProfessor
Population, Family and Reproductive Health
Ligia Paina, PhDAssistant Scientist
International Health
Hello again this is Ligia, and
in this section we'll be discussing the basic components of Causal Loop Diagrams.
So this the Causal Loop Diagram that you might remember from Agnes Rwashana's
article on immunization in Uganda.
And it might seem overwhelming at first, but we'll be going through
each of the singular components of the causal loop diagram, and
we'll be trying to understand it better as this section progresses.
The first element that we'll be discussing in relation to causal loop diagrams is
causality, and causality really refers to the relationship between two variables.
A causes B, as you can see in the top right diagram,
another way to put it, is that changes in B are caused by changes in A.
And to give you a concrete example, we'll start to build our own diagram,
health worker workload causes some changes in the level of health worker motivation.
Same thing with health worker remuneration,
it causes some changes in health worker motivation.
And the direction in which those changes occur is indicated by the polarity.
That's the second element of the Causal Loop Diagram, and
the polarity can be either positive or negative.
So for example, if A and B change in the same direction, whether they increase or
decrease, then the polarity is positive.
And as you can see there's a plus sign indicated in the diagram on the arrow.
If two variables change in the opposite direction, such as C and D,
then the polarity is negative, and
you can see the minus sign on the arrow linking the two variables.
Going back to our initial example, the workload changes health worker motivation.
Increases in workload decrease motivation.
Therefore, the sign is negative.
If we look at the remaining two variables, their remuneration,
as remuneration increases, the motivation also increases.
Because these variables move in the same direction, the polarity is positive.
The central element of a causal loop diagram is the feedback loop.
That is, as you remember from earlier lectures,
one of the main reasons to begin drawing causal loop diagrams.
And what feedback loops denote, is that there is changes in the system
that catalyze a cascading effect through other variables, and
this effect either reinforces or balances the initial change.
On the left hand side you'll see one type of feedback loop, and
on the right hand side you'll see a slightly different one.
In practice, this can look quite messy, as you can see in the bottom diagram
which is also drawn from Agnes's immunization example in Uganda.
But let's go through each of these examples separately
to better understand them.
So the first type of feedback loop is the reinforcing loop.
A reinforcing loop signifies an amplifying effect,
often referred to as an avalanche effect, and, as we know,
there can be vicious cycles and virtuous cycles, so
reinforcing loops don't necessarily denote positive or negative consequences.
But if we look at the relationship between A and B, we see that both of the feedback
loop arrows are positive, which means that it's indeed a reinforcing loop.
And as an example from the immunization example,
we can see the awareness participation loop.
As immunization awareness increases, so does the participation and
immunization services.
And as more families take their children to participate in immunization,
the overall immunization awareness also increases.
So as you can see, this is an awareness loop, and
in this case, a virtuous desirable cycle.
The other type of feedback loop is a balancing loop.
And balancing loops are characterized by dampening effect,
much like a see-saw as you see in this picture.
In this case if we look at the top right corner, the relationship between C and
D is positive in one direction.
This relationship between C and D is negative in the other direction.
Because there's not this amplifying effect that we were describing earlier,
we call this a balancing loop, and as an example,
I picked out the immunization awareness loop from the article in Uganda.
And as you can see, one key trick to figuring out whether this is a balancing
or a reinforcing loop, is whether there is an even number of minuses or not.
So if there is an uneven number of negative polarities,
then the loop is balancing.
So that is a shortcut to understanding whether its balancing or reinforcing.
Moving on to the next element of the causal diagram,
we have delays, and the delays signify the effect that
A causes on B is delayed by some time element.
In a causal loop diagram this is symbolized through these parallel lines
that you see cutting through the arrow between A and B, and it is often the case
that balancing loops are self-regulating, and therefore experience delays.
And I wanted to give you an example from the causal loop diagram we've been
examining, and as you can see, there's a delay between the effect of
health service delivery and the parent's level of trust in the health system.
And if we think about this conceptually, the relationship between health service
delivery and the parent's level of trust really takes time to develop, and
therefore there can be a delay of several years before poor health services,
for example, erode at the parent's level of trust in the health system.
Alternatively it can be many years before a parent that has had a bad
experience can regain the level of trust in the health system that is needed to,
again, increase participation, increase immunizations awareness, and such.
One final element of the causal loop diagram,
are these clockwise and counterclockwise loops and
bows that you see surrounding these B's and R's in the diagram.
And these are a guide for
reading the loops, especially when the diagrams can get messy.
So I'm showing you just the central feature of the causal diagrams
from the Rwashana article, yet you can see that there are many loops,
signified by many colors, and it can get quite confusing to read.
The clockwise and counterclockwise symbols help you,
as the reader, understand in which direction you should follow the loop.
So for example, the epidemic control is a counterclockwise loop.
You start following the red arrows around.
And B5, the trust in the health system loop, is a clockwise loop.
So you follow the purple arrows in a clockwise direction.
To summarize, I wanted to illustrate the different components that we talked about,
and their definition.
And, as you see, the first component is the variables.
So you first have to identify the items of interest.
Then in a causal loop diagram, you have to determine how the variables are related,
and that is defined by both the causality, direction, and also the polarity.
The central feature of the casual loop diagram is the feedback loops, and
these can be reinforcing or balancing, and
reinforcing loops can be vicious or virtuous.
Oftentimes, the relationship between variables is characterized by delays, so
A can affect B, but it might take some element of time before that is happening,
and that can be also symbolized in a diagram.
And finally the loop symbols are an aid for
reading the CLDs especially as diagrams become more complicated.
And in conclusion, I wanted to bring up the earlier example of
the balancing loop again, and I wanted to take some time to
go through it, so that we understand how to actually read such a diagram.
So you might want to take a couple of minutes and think about it yourselves.
For example, by starting with the relationship between program funding and
campaigns, and then, I'll also go through them in detail.
If we start with program funding, we see in the top right corner of the diagram,
we see that the program funding for immunization is the more funding there is,
it causes an increase in that campaign's immobilization of communities for
immunization.
And this can be seen through the counterclockwise direction if we move from
program funding, to campaigns, to mobilization.
The more campaigns and
mobilization lead to greater level of immunization awareness.
And the more families and communities are aware of immunization,
there will be a higher level of participation in immunization services,
which directly leads to an increase in the number of children immunized, and
a decrease in the gap that is between the actual number of children immunized and
the target number of children immunized.
And in one instance, one hypothesis one could make is that the smaller the gap
that exists, the more funding would become allocated to higher priority
services and therefore, perhaps there would be less focus on campaigns.
So this is assuming that the burden decreases and
therefore there would be a reprioritization of program funding.
So this is one example of a balancing loop, and
you can read more about this loop and also the larger causal
loop diagram in the Rwashana article that is found in your readings.
In this section, we had an introduction to the basic components of the causal loop
diagrams, and we also gave you an opportunity to understand how to
read a causal loop diagram.
In the next section, we'll discuss where the data comes from to actually
populate a causal loop diagram, and we'll do that through some recent articles, and
discuss the strengths and weaknesses of this tool for systems thinking.
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