Epidemiology
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L2ModelsofCausationandAP12.pptxModels of Causation & Attributable Proportion
Lesson 2
The next few slides may be a review for those of you who’ve had undergraduate epi. These are basic terms for levels of disease in a population that you should know and be able to apply.
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Recall…
The Philosophy of Causation:
If exposure to E and occurrence of D seem to go together, does E cause D?
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Philosophy of Causation: If exposure to E and occurrence of D seem to go together, does E cause D? Example: If Jim has unprotected oral sex and then develops HIV, was it because of the unprotected oral sex?
We would like to answer this question because of the public health implications for preventing disease occurrence in other people.
Many scientists believe that causation cannot be proven by epi studies alone because of scientific reasoning problems and the observational nature of epidemiological studies. In order to characterize or explain a phenomenon such as causation, we must instead set up a model (or a system of a set of rules) to explain the biological phenomenon (people getting sick) that we observe.
Epidemiologic Triangle: Triad of Causation
The epidemiologic triangle, or triad of causation, is the traditional model of infectious disease causation. It has three components: an external agent, a susceptible host, and an environment that brings the host and agent together. In this model, the environment influences the agent, the host, and the route of transmission of the agent from a source to the host.
The epidemiological triad (triad of causation) is one of the first theories of disease causation adopted by modern epidemiology. As depicted by the figure in the slide, there is an interdependence between three constructs: agent, host, and environment.
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The Agent
The agent is the cause of the disease. When studying the epidemiology of most infectious diseases, the agent is a microbe—an organism too small to be seen with the naked eye. Disease-causing microbes are bacteria, virus, fungi, and protozoa (a type of parasite). They are what most people call “germs.” In non-infectious diseases, the agent can be behavioral risk factors…
The Host. Hosts are organisms, usually humans or animals, which are exposed to and harbor a disease. The host can be the organism that gets sick, as well as any animal carrier (including insects and worms) that may or may not get sick. Although the host may or may not know it has the disease or have any outward signs of illness, the disease does take lodging from the host. The “host” heading also includes symptoms of the disease. Different people may have different reactions to the same agent. For example, adults infected with the virus varacella (chickenpox) are more likely than children to develop serious complications
The Environment. The environment is the favorable surroundings and conditions external to the host that cause or allow the disease to be transmitted. Some diseases live best in dirty water. Others survive in human blood. Still others, like E. coli, thrive in warm temperatures but are killed by high heat. Other environment factors include the season of the year (in the U., the peak of the flu season is between November and March, for example). For any given pathogenic organism the range of tolerable environmental conditions may be wide or narrow. Any epidemic model of a specific disease must allow for these variations of the causative organism.
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Triad of Causation
Interaction of Agent-Host-Environment is “cause” of disease
Efforts to prevent/control disease is constantly challenged by this interaction
The interaction of these three factors is the "cause" of disease, so when one changes, it affects the other. For example, an agent may mutate to negate previous immunity to influenza (person). This model would also apply to a situation where flood waters (environment) brought raw sewage (agent) into the water system thus increasing the chance of water-borne disease
Among these factors, there exists a dynamic situation in which the efforts to prevent and/or control disease are constantly challenged:
Populations are highly mobile and tend to live longer, thereby creating circumstances of increased risk of exposure and infection
Urbanization and suburbanization have exerted greater and greater pressures on the environment
Biological agents of disease have shown remarkable adaptability to modern control measures
Non-biological agents are often introduced into the milieu despite precautions of interested groups
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Limitations
Many diseases do not have a single agent
Triad not particularly useful when looking at diseases that require multiple agents
This traditional model was most useful during the era when people died mainly from infectious diseases caused by a single agent. However, the leading causes of death today are non-infectious and not caused by one single factor. Think, for a moment, about heart disease which kills many folks each year. Can you identify a single agent that – on it’s own -- causes heart disease? I’ll give you a few moments…
No?
Well, it’s because of this that the Triad of Causation has limited usefulness for diseases that are brought about by exposure to multiple agents… .
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Contemporary Model
Diseases are caused not by exposure to one single agent, but rather by exposure to several different combinations of agents
Sometimes referred to as “Causal Web”
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The contemporary model acknowledges the complexity of disease – that disease can be caused by several difference combinations of agents (aka risk factors). This is sometimes called a CAUSAL WEB and is difficult to identify because of the complexity of contributions of the component causes. This model is complex, but is needed in order to explain our observations of disease.
Terminology
Component Cause
Necessary Component Cause
Sufficient Cause
When looking at the contemporary model, there are some definitions that you need to burn into your memory so that you’ll be able to apply the concepts on your tests, read an epi article correctly, etc.
Component Cause: an agent (E+) that contributes to health outcome (D+), but that by itself will not cause D+
Necessary Component Cause – an exposure (E+) that will not, on its own, cause D+, but one that MUST be present for the health outcome (D+) to occur. Thus, a necessary component cause is present in EVERY case of D+
Sufficient Cause: a collection of component causes that interact to result in D+
Although these are fairly straightforward definitions, many students get tripped up when applying them… So let’s take a close look, shall we?
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A pizza is composed of crust, sauce, cheese, and various toppings – or in the case of the pizza on the slide, pepperoni. Each of these items, individually, is a component cause, and on it’s own will not create a pizza. Collectively the component causes do compose a pizza. Got it?
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Pizzas I like
Pepperoni with red sauce and mozzarella cheese
Salami, feta, onion, white sauce, and mushroom
Bacon, jalepeno, mushroom, and olives with red sauce and mozzarella cheese
Ok, so let’s say I only like the three kinds of pizzas listed on the slide. Each type of pizza is a way to make me happy. Thus, each pizza is a “sufficient cause” of my happiness.
So, in pizza 1, the component causes are crust, red sauce, mozzarella cheese, and pepperoni.
Pizza 2’s component causes are crust, white sauce, salami, onion, mushroom, and feta cheese.
Pizza 3’s component causes are crust, red sauce, bacon, jalepeno, mushroom, olives, and mozzarella cheese.
Would a pizza with crust, red sauce, pepperoni, feta, and mushrooms make me happy? No, because these component causes do not compose one of the three sufficient causes. Would a pizza with crust, white sauce, salami, onions, jalepenos, mushrooms, and feta make me happy? Yes, because all of the component causes for pizza 2 are present. The addition of the jalepenos does not change this.
Because each of the three sufficient causes (pizzas) have a crust as a component cause, crusts are necessary for there to be a sufficient cause. Thus, crusts are a necessary component cause.
Are there any other necessary component causes in this situation? Take a careful look.
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No
No, there are not. While some of the component causes (red sauce, mushroom, and mozzarella cheese) were present in two of the three pizzas, in order to be necessary, they have to be present in all sufficient causes.
Ok, so back to the contemporary model….Most diseases have many sufficient causes. If, as in the case of my pizza happiness, all sufficient causes are known, then we can determine from epi data how much each component cause contributes to causing the disease, and therefore know how much of the disease we could eliminate if we got rid of that component cause.
Let’s look at a different example.
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Crayolaitis
Crayolaitis (not a real disease) has three and only three sufficient causes as shown on the slide above Each circle represents one of the ways you can get crayolaitis. As such, each circle is a sufficient cause, which means that each circle as a whole – in and of itself – is sufficient for causing a disease. I’m repeating myself because this is a really important concept.
Within each circle (aka sufficient cause), each color represents a risk factor – or a component cause – for getting crayolaitis.
Ok, so let’s see if you got it… Pop quiz!
Can I get crayolaitis if I have light green, pink, and light blue?
No – because these three colors are not shown together in one of the sufficient cause circles. Recall, the ONLY way to get crayolaitis is to have one of the sufficient causes.
What about if I have yellow, dark green, pink, purple, and red?
Yes! I can because the yellow, dark green and pink are the colors that make up sufficient cause 3. Having the extra colors is of no consequence…
Now… is there a necessary component cause here?
No… there is no single color that is present in every circle (sufficient cause).
Which color (component cause) contributes the most to getting crayolaitis?
Ok, that’s a trick question… I haven’t taught you how to do that yet. =)
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Attributable Proportion (AP)
Now, after that trick question, I know you are just on the edge of your seat wanting to know how to determine which component cause contributes the most to disease. =) So here’s how you do it… You calculate the attributable proportion (AP). AP is the proportion (percent) of cases of a disease associated with each sufficient cause.
Let's say we know that 50% of the people with crayolaitis got it through Sufficient Cause 1, and that another 30% got it through Sufficient Cause 2. Given that there are three and only three sufficient causes for crayolaitis, this would mean that Sufficient Cause 3 is responsible for 20% of the cases…
In epi, we would state this as such: AP I = 50% ; APII = 30%; and APIII = 20%
Given that there are three and only three sufficient causes, AP I + APII + APIII = 100% of cases of crayolaitis. The sum of all sufficient causes will always = 100%
Still with me?
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AP of Component Cause
The impact of various component causes in "manufacturing" cases of a disease can be estimated if we know enough about the disease to construct a model of how -- or why -- the disease occurs. That is, if you know the AP of each sufficient cause, you can calculate the contribution of each component cause simply by giving each component cause the same AP as it’s sufficient cause.
Attributable Proportion (AP) is the proportion (percent) of cases of a disease associated with each sufficient cause
When a component cause appears in more than one sufficient cause, its AP is the sum of the sufficient cause APs that contains it.
For example, yellow is in sufficient cause 1 (AP = 50%) and sufficient cause 3 (AP = 20%). So, the AP for yellow is 70% (50% + 20%). So, APyellow= 70%, which you would interpret to say yellow is present in 70% of the cases of crayolaitis. Therefore, eliminating yellow would eliminate 70% of cases of crayolaitis.
Now, lets suppose that sufficient cause 2 also had yellow… That would make it a (insert correct answer here) and the AP would be (insert correct answer here). If the correct answers you inserted were “necessary component cause” and “100%,” you would be correct! And, you would interpret the , APyellow= 100%, as “Yellow was present in 100% of the cases of crayolaitis”. Now, to drive another point home about necessary causes, if we were to get rid of yellow, no one would ever get crayolaitis.
Got it?
NOTE: You’ve probably figured out that the sum of APs for component causes may exceed 100%. This is because the elimination of ANY component cause within a sufficient cause will render that sufficient cause inoperable…
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In-Class Example
From 1999 to 2012 there were 150,000 cases of lung cancer in the US. According to the several studies all patients with lung cancer had one of these profiles:
Profile 1 (22,500 patients): Smoking, occupational exposure to PAH, obesity, presence of other cancers
Profile 2 (60,000 patients): Smoking, radon, particle pollution exposure, male
Profile 3 (37,500 patients); Particle pollution exposure, Smoking, geographical area of living, male
Profile 4 (30,000 patients); Smoking, radon, presence the other cancers, particle pollution exposure, age > 50
From 1999 to 2012 there were 150,000 cases of lung cancer in the US. According to the several studies all patients with lung cancer had one of these profiles:
Profile 1 (22,500 patients): Smoking, occupational exposure to poly-aromatic hydrocarbons (PAH), obesity, presence of other cancers
Profile 2 (60,000 patients): Smoking, radon, particle pollution exposure, male
Profile 3 (37,500 patients): Particle pollution exposure, Smoking, geographical area of living, male
Profile 4 (30,000 patients): Smoking, radon, presence the other cancers, particle pollution exposure, age > 50
Questions you’ll be answering:
1. How many sufficient causes are there for lung cancer?
2. Determine the AP for each sufficient cause.
3. How many component causes are there for lung cancer?
4. Calculate the AP for each component cause.
5. What is/are the necessary component cause(s), if any, for lung cancer?
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1. How many sufficient causes are there for lung cancer?
From 1999 to 2012 there were 150,000 cases of lung cancer in the US. According to the several studies all patients with lung cancer had one of these profiles:
Profile 1 (22,500 patients): Smoking, occupational exposure to PAH, obesity, presence of other cancers
Profile 2 (60,000 patients): Smoking, radon, particle pollution exposure, male
Profile 3 (37,500 patients); Particle pollution exposure, Smoking, geographical area of living, male
Profile 4 (30,000 patients); Smoking, radon, presence the other cancers, particle pollution exposure, age > 50
How many sufficient causes are there for lung cancer? Four. We know this because it states that “According to the several studies all patients with lung cancer had one of these profiles” and then four profiles were listed.
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2. Determine AP for each sufficient cause.
Step 1: Calculate the total number of patients
Step 2: Divide number of patients in each profile by total number of patients
2. Determine the AP for each sufficient cause.
Step 1: The first thing we need to do is calculate our denominator, which we do by adding the number of patients in each of the profiles together:
Profile 1: 22,500 patients
Profile 2: 60,000 patients
Profile 3: 37,500 patients
Profile 4: 30,000 patients
Thus, 22,500 + 60,000 + 37,500 + 30,000 = 150,000 total patients
Step 2: Next, for each profile, we divide the number of patients in that profile by the total number of patients from all four profiles.
AP Profile 1 = 22,500 / 150,000 = 15%
AP Profile 2 = 60,000 / 150,000 = 40%
AP Profile 3 = 37,500 / 150,000 = 25%
AP Profile 4 = 30,000 / 150,000 = 20%
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3. How many component causes are there for lung cancer
Smoking
Occupational exposure to PAH
Obesity
Presence of other cancers
Radon
Particle pollution exposure
Male
Geographic area
Age > 50
3. How many component causes are there for lung cancer? Nine. To answer this, you simply count the number of unique risk factors from each of the profiles. Here’s the list of nine unique component causes listed in the four profiles:
Smoking
Occupational exposure to PAH
Obesity
Presence of other cancers
Radon
Particle pollution exposure
Male
Geographic area
Age > 50
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4. Calculate the AP for each component cause
| Component Cause | SC 1 AP=15% | SC 2 AP=40% | SC 3 AP=25% | SC 4 AP=20% | AP Total |
| Smoking | 15% | 40% | 25% | 20% | 100% |
| Occupational exp. to PAH | 15% | 15% | |||
| Obesity | 15% | 15% | |||
| Presence of other cancers | 15% | 20% | 35% | ||
| Radon | 40% | 20% | 60% | ||
| Particle pollution exposure | 40% | 25% | 20% | 85% | |
| Male | 40% | 25% | 65% | ||
| Geographic area | 25% | 25% | |||
| Age > 50 | 20% | 20% |
4. Calculate the AP for each component cause.
To answer this question, you’ll need to sum the AP% of each sufficient cause in which the component cause appears. To stay organized, I suggest creating a table such as the one shown on the slide, where each row is a component cause and the columns are sufficient causes (profiles) and their related APs. The total column adds the APs present in that row, and thus is the AP for that component cause.
Let’s take a look at the “male” component cause. Male is present in sufficient cause 2, which has an AP of 40%, and sufficient cause 3, which has an AP of 25%. Thus the AP for male is the 65% (40% + 20%).
IMPORTANT: The total column represents the amount of the disease that would be reduced if that component cause were eliminated.
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5. What is/are the necessary component cause(s), if any, for lung cancer?
| Component Cause | SC 1 AP=15% | SC 2 AP=40% | SC 3 AP=25% | SC 4 AP=20% | AP Total |
| Smoking | 15% | 40% | 25% | 20% | 100% |
| Occupational exp. to PAH | 15% | 15% | |||
| Obesity | 15% | 15% | |||
| Presence of other cancers | 15% | 20% | 35% | ||
| Radon | 40% | 20% | 60% | ||
| Particle pollution exposure | 40% | 25% | 20% | 85% | |
| Male | 40% | 25% | 65% | ||
| Geographic area | 25% | 25% | |||
| Age > 50 | 20% | 20% |
5. What is/are the necessary component cause(s), if any, for lung cancer?
Smoking, because it appears in all of the sufficient causes. Thus, is we could eliminate smoking, we would eliminate lung cancer.
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The End. =)
So, I could hear some of you on the last slide saying “But Dr. G, there are people who get lung cancer who don’t smoke, so eliminating smoking really won’t eliminate lung cancer entirely.” And that’s correct. Further, in real life, it is quite unlikely that we will know all of the sufficient causes of a disease, or all of the component causes of the known sufficient causes. Thus, there is always a presumption that while based on the known data, our answers are accurate, there are unknowns that could make them inaccurate. Epi (and research in general) is messy, like life, but it’s the best tool set we have. =)
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