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Epidemiology is the study of patterns of diseases. And most people might think that means epidemiologists are only studying things like Ebola. But the truth is much more varied. In this episode of Crash Course Public Health, we'll take a look at the different ways Epidemiology is conducted, including the use of...pie? It'll make sense, we promise.

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Introduction: Epidemiology 00:00
Origins of Epidemiology 01:22
Studying Disease 04:04
Interpreting Data 07:27
Bradford Hill Criteria & Mathematical Models 09:34
Rothman Causal Pie 10:23
Review & Credits 13:07

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In 2014, an outbreak of the deadly virus Ebola was spreading across Guinea, a country in West Africa.

The outbreak had started in a small village, but spread rapidly. By the time health officials were able to end the outbreak over two years later there had been more than 11,000 deaths associated with the virus.

The Ebola outbreak is a classic example of an epidemic, when more people in a group than usual develop a particular illness or condition. But epidemics don’t need to be an apocalyptic viral event that dominates headlines. For example, as of 2019, over 80% of school-aged children in China, Singapore, and South Korea were nearsighted, which is more people in that group than we would expect to have that condition.

So, we could say that nearsightedness is an epidemic! But no matter which kind of epidemic we’re studying, we need epidemiology to help us do it. Epidemiology is basically where all the science-y stuff happens in public health.

It uses microscopes, data, math, and…pie. Don’t worry, we’ll get to that. Hi, I’m Vanessa Hill, and this is Crash Course Public Health!

INTRO Epidemiology is the study of the patterns of disease and health conditions within populations. It also studies the patterns’ causes and how they can be controlled. Much like democracy and the Mamma Mia film franchise, we can at least partially give credit for the word “epidemiology” to the Greeks.

It comes from the three Greek words “epi”, "demos", and "logos", which mean upon, people, and study.” So, epidemiology is literally the “study of what is upon the people.” Which is kind of a terrifyingly broad field. Like, “what is upon the people” could describe anything from climate change to whatever Hank Green is plugging on TikTok today - be sure to check out the link in his bio!! But anyway, to specify, an epidemiologist–or someone who studies epidemiology– wants to know who gets what diseases, where they get them, and when.

Epidemiologists are kind of like detectives in a massive game of Clue! (or Cluedo!) Except instead of Professor Plum in the ballroom with a candlestick, it’s more like a bacterial infection on planet Earth that threatens literally everyone. The word and the actual practice of epidemiology didn't gain traction until the 19th century, when it mostly concerned infectious diseases. These are health conditions caused by organisms like viruses, bacteria, and parasites, which are spread between people or picked up from the environment or animals.

But today, we understand epidemiology more broadly. This is partly because we also understand health more broadly. And as we saw in our episodes on the determinants of health, our health is affected by more than just germs.

It’s also impacted by our neighborhoods, schools, and society in general! But we also understand epidemiology differently because, at least in high-income countries, causes of death have changed. As advances in medicine and public health have meant fewer deaths from infectious diseases in these places, non-infectious, also known as non-communicable, causes of death have become a bigger area of focus.

We know - that’s pretty unfair for lower-income countries. We’ll talk about that in Episode 9 when we focus on global health. So today, many epidemiologists put more emphasis on studies of non-communicable diseases like cancer, heart disease, and diabetes, environmental factors like air pollution, and even the health impacts of natural disasters.

They also examine the determinants of health and the inequities in who gets sick. That’s also what we’ll be focusing on today, though over at Crash Course Outbreak Science we spend plenty of time talking about infectious diseases, including how public health tackles them. We know with any big mystery, it’s not enough to know that something happened.

We want a motive! So as epidemiologists solving a health mystery, we want a cause for that health outcome. A health outcome is what happens basically anytime our health status changes because of, well, something happening in the world.

This could be a good outcome, like having lower cancer risk thanks to air pollution laws, or a bad outcome, like having higher cancer risk thanks to factors like only being able to afford to live in an area close to a chemical plant. Epidemiologists begin with a hypothesis about why a health outcome is spreading or occurring in the first place. Then, they conduct a scientific study to evaluate their hypothesis.

In general, there are two kinds of epidemiological studies that we’ll focus on here: experimental studies and observational studies. In an experimental study, investigators expose participants to some kind of intervention or treatment to see how it affects their health. Then they compare the outcomes to a control group that isn’t exposed to the intervention or treatment.

Now, it’s pretty unethical to expose a group of people to something that could harm their health, so experimental studies tend to introduce positive interventions. This might include something like a new vaccine, as opposed to a negative intervention like a virus. In observational studies, epidemiologists observe a population that is already exposed to a particular treatment or risk factor, and compare their health to a non-exposed group.

This is how we go about understanding the effects of things we don’t want to intentionally expose people to, like…viruses. One famous observational study was conducted by a pair of British epidemiologists, Richard Doll and Austin Bradford Hill (no relation to me). In the mid-20th century it was widely known that lung cancer rates were on the rise, but there wasn’t scientific consensus on why.

In 1951, Doll and Bradford Hill began testing a hypothesis for this increase: smoking. The idea that smoking can lead to certain kinds of cancer is pretty common knowledge today. But while some research pointed to an association between smoking and cancer, there wasn’t enough evidence to confirm that one event caused the other.

In fact, smoking wasn’t even formally recognized as a public health issue in the United States at the time. They sent out surveys to almost 60,000 British doctors asking about their smoking status in a study that experts have so ingeniously dubbed the British Doctors Study. And after getting over 40,000 responses, Doll and Bradford Hill found a strong association between heavy smoking and lung cancer.

And repeated follow-up studies of the same doctors over the next 50 years confirmed the originally reported relationship of smoking to several different kinds of cancers, including lung and mouth cancer. Unfortunately, when it comes to identifying the cause of a particular health effect, we don’t always have the luxury of 50 years of research and 40,000 British doctors on hand. Epidemiologists often have to work quickly to assess and respond to a health emergency.

Regardless of the timeline involved, interpreting data is where epidemiology gets tricky– because data on its own don’t tell a story. For data to be useful, they need to be interpreted by people. And data aren’t always straightforward!

Like, data have shown that there is a near-perfect relationship between a population’s level of cheese consumption and the number of people in that population who die from getting tangled in their bed sheets. This is a very strange but true example of a cliche that scientists have been muttering in their sleep for centuries: correlation doesn’t imply causation. Or, put another way, just because two things seem related, it doesn’t mean that one caused the other.

So while the data appear to say that cheese consumption and bed sheet murder both increase at the same time, we need someone - like an epidemiologist - to examine and interpret the data to tell us whether that actually means anything, or if cheese consumption just happens at a similar rate to a lot of other things. To successfully interpret data, epidemiologists need to rely on several pieces of evidence. Like, while the British Doctors Study was one of the first population-level studies that linked smoking to lung cancer, this wasn’t a new idea at the time.

As Doll and Bradford Hill conducted their studies, they were following in the footsteps of animal-based studies and chemical analyses of tobacco that had been happening as far back as the 1900s. Like a lot of science, their conclusion wasn’t the product of two dudes doing a thing. It took decades of collaboration between a bunch of people in different disciplines, each following their own unique path of evidence to the same conclusion.

One useful tool epidemiologists use to understand the cause of an observed effect is the Bradford Hill criteria. And yes, that’s the same Bradford Hill from the British Doctor Study –small world! While we won’t get into all of them here, Bradford Hill proposed nine principles for establishing evidence of a causal relationship between a presumed cause and an observed effect, like whether the effect happened after the cause or if the effect could be reproduced.

Another tool epidemiologists use to better understand the sometimes messy relationship between cause and effect is mathematical models. These models tell epidemiologists which variables are worth paying attention to, and which ones…aren’t. Which brings us, at long last, to pie!

The Rothman causal pie is a model that helps epidemiologists explain how individual risk factors contribute to a disease. And, like any good pie, a causal pie needs a few different ingredients. Except instead of sugar and rhubarb, this pie is made up of component causes– which probably settles the debate about the worst pie flavor ever.

Component causes are basically the different risk factors that work together to produce a certain health effect “pie”. If we have enough cause-slices to form a whole pie, we have a sufficient cause– which means the health condition goes into effect. Let’s go to the Thought Bubble.

Let’s consider tuberculosis, or TB, a highly infectious disease ​​caused by the bacteria mycobacterium tuberculosis. Because TB is an airborne pathogen, it’s generally spread through a pretty basic human action: breathing. And when you have too many people breathing in the same space, and one of them is sick, there’s a higher risk for transmission.

So, we might consider overcrowded homes and communities to be one slice of the causal pie. Similarly, buildings with poor ventilation will be worse at getting rid of airborne bacteria, making poor ventilation another notable component cause. ​​Another component of our pie is having a weakened, or compromised, immune system. Basically, if our body isn’t as good at fighting off disease, it’s also more likely to get sick from exposure to the bacterium.

Similarly, lack of access to a tuberculosis vaccine means our bodies will be more likely to contract the disease–so that’s also a pretty big part of the causal pie. Just like how everyone cuts a pie differently, everyone will have a different combination of component causes that combine to produce a sufficient cause for TB. But one slice that’s in every causal tuberculosis pie is exposure to ​​mycobacterium tuberculosis.

This is because TB can’t, like, spontaneously generate in our bodies. It needs a source. This is why exposure to mycobacterium tuberculosis is a necessary condition of TB.

Even if all the other risk factors of the pie are in place, mycobacterium tuberculosis is needed to “activate” the disease. Now, while the presence of each individual risk factor increases the likelihood of TB, it doesn’t guarantee it. But if we fill up our causal pie with a combination of components that produces a sufficient cause, we get our health outcome: tuberculosis.

Thanks, Thought Bubble. Not every disease has an easily identifiable necessary condition like TB does. Like, the causal pies for high blood pressure might not necessarily share a slice in common.

Instead, enough component causes can combine in different ways to form a sufficient cause– and this “enoughness” varies from person to person. Now, disease models aren’t completely human-error-proof. They’re still invented by people and rely on data collected by people.

But the more data we collect and interpret, the better our health models get! And in a big, messy world where the causes of disease are often invisible, where cheese and murderous bed sheets go hand-in-hand, epidemiology gives us the tools we need to make a little bit more sense of the world and how it impacts our health. We’ll continue our examination of this big messy world next time, when we talk about Health Systems!

See you then! Thanks for watching this episode of Crash Course Public Health, which was produced by Complexly in partnership with the American Public Health Association. If you want to learn even more about Public Health, head over to APHA’s YouTube channel to watch “That’s Public Health” a series created by APHA and Complexly.

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