Intro to Epidemiology: Crash Course Public Health #6

Summary

This Crash Course Public Health episode explores epidemiology, the study of disease patterns and their causes within populations. It delves into the history of the field, highlighting its evolution from focusing on infectious diseases to encompassing non-communicable diseases and environmental factors. The video explains the importance of both experimental and observational studies in understanding health outcomes. It also discusses the challenges of interpreting data and the use of tools like the Bradford Hill criteria and the Rothman causal pie model to establish causal relationships and risk factors in disease development.

Takeaways

• 🌟 Epidemiology is the study of disease and health patterns within populations, focusing on causes and control methods.
• 📈 The term 'epidemiology' originates from Greek, meaning 'study of what is upon the people', highlighting its broad scope.
• 🔍 Epidemiologists act as detectives, investigating who gets sick, where, and when, to uncover patterns and causes.
• 📚 The field gained prominence in the 19th century, initially focusing on infectious diseases, but has since expanded to include non-communicable diseases and environmental factors.
• 🌍 Health is influenced by a variety of factors beyond germs, including social determinants like neighborhoods and schools.
• 🔬 Epidemiological studies are categorized into experimental and observational, each providing different insights into health outcomes.
• 🚫 Experimental studies are often limited to positive interventions due to ethical concerns about exposing participants to harm.
• 🔄 Observational studies monitor existing exposures within populations to understand effects of factors like viruses or pollution.
• 🔗 The British Doctors Study exemplifies how observational research can establish links between behaviors (like smoking) and health outcomes (like lung cancer).
• 🧠 Epidemiologists use tools like the Bradford Hill criteria and mathematical models to interpret data and establish causal relationships.
• 🍰 The Rothman causal pie model illustrates how multiple component causes can combine to form a sufficient cause for a disease, like tuberculosis.

Q & A

What is the definition of an epidemic according to the script?

An epidemic is defined as a situation where more people in a group develop a particular illness or condition than usual.

How many deaths were associated with the Ebola outbreak in Guinea by the time it was ended?

Over 11,000 deaths were associated with the Ebola outbreak in Guinea.

What is epidemiology and what does it involve?

Epidemiology is the study of the patterns of disease and health conditions within populations, including the causes of these patterns and how they can be controlled. It involves the use of microscopes, data, math, and other scientific methods.

What are the origins of the word 'epidemiology'?

The word 'epidemiology' comes from the Greek words 'epi', 'demos', and 'logos', which mean upon, people, and study, respectively.

What are the two main types of epidemiological studies mentioned in the script?

The two main types of epidemiological studies mentioned are experimental studies and observational studies.

What is the difference between experimental and observational studies in epidemiology?

In experimental studies, investigators expose participants to an intervention or treatment and compare outcomes to a control group. Observational studies involve observing a population already exposed to a treatment or risk factor and comparing their health to a non-exposed group.

What was the hypothesis tested in the British Doctors Study conducted by Richard Doll and Austin Bradford Hill?

The hypothesis tested in the British Doctors Study was that smoking leads to an increase in lung cancer rates.

What is the Bradford Hill criteria used for in epidemiology?

The Bradford Hill criteria are used to establish evidence of a causal relationship between a presumed cause and an observed effect.

What is the Rothman causal pie model and how does it relate to epidemiology?

The Rothman causal pie model is a tool used by epidemiologists to explain how individual risk factors contribute to a disease. It is composed of component causes that, when combined, form a sufficient cause for a health condition.

What is a necessary condition in the context of the causal pie model?

A necessary condition in the causal pie model is a component cause that must be present for a health outcome to occur, such as exposure to mycobacterium tuberculosis in the case of tuberculosis.

How does epidemiology help in understanding the impact of the world on our health?

Epidemiology provides tools and methods to collect and interpret data, which helps in understanding the complex relationships between various factors in the world and their impact on our health.

Outlines

00:00

🌟 Introduction to Epidemiology

The video script introduces the Ebola outbreak in Guinea in 2014 as an example of an epidemic, which is defined as an unusually high number of cases of a particular illness or condition in a population. The script then broadens the discussion to include non-communicable diseases and conditions like nearsightedness, which can also be considered epidemics. The main focus of the video is epidemiology, the scientific study of patterns of health and disease within populations, and its importance in public health. The term 'epidemiology' is derived from Greek words meaning 'study of what is upon the people,' highlighting its broad scope. The video emphasizes the evolution of epidemiology from primarily studying infectious diseases in the 19th century to a broader focus on non-communicable diseases and health determinants in the modern era.

05:03

🔬 Epidemiological Studies: Experimental vs. Observational

This section delves into the two main types of epidemiological studies: experimental and observational. Experimental studies involve intentionally exposing participants to an intervention or treatment to assess its health effects, often introducing positive interventions like vaccines due to ethical considerations. Observational studies, on the other hand, involve monitoring populations that are already exposed to certain treatments or risk factors and comparing them to non-exposed groups. The famous British Doctors Study conducted by Richard Doll and Austin Bradford Hill is highlighted as an example of an observational study that established a link between smoking and lung cancer. The script also touches on the challenges epidemiologists face in interpreting data, emphasizing that correlation does not imply causation and the importance of evidence-based interpretation.

10:04

📊 Tools of Epidemiology: Models and Criteria

The final paragraph introduces tools used by epidemiologists to understand and interpret health data. It discusses the Bradford Hill criteria, a set of principles for establishing causal relationships between potential causes and observed health effects. The Rothman causal pie model is introduced as a way to visualize how various risk factors or component causes contribute to a disease. Using tuberculosis as an example, the video explains how different factors like overcrowding, poor ventilation, and a weakened immune system can combine to create a sufficient cause for the disease. The script concludes by emphasizing the importance of epidemiology in making sense of the complex world of health and disease, and it teases the next topic of discussion: health systems.

Keywords

💡Epidemiology

Epidemiology is the study of the patterns of disease and health conditions within populations, including the causes of these patterns and how they can be controlled. It is central to the video's theme as it provides the framework for understanding and addressing health issues on a population level. The video explains that epidemiologists are like detectives, investigating who gets what diseases, where they get them, and when, which is exemplified by the historical study of the Ebola outbreak in Guinea and the nearsightedness epidemic in East Asian countries.

💡Epidemic

An epidemic is defined as a widespread occurrence of an illness in a community at a particular time. The term is used in the video to describe both the Ebola outbreak, which was a rapid and deadly spread of disease, and the high prevalence of nearsightedness in certain regions. These examples illustrate how epidemics can range from infectious diseases to common health conditions, emphasizing the broad scope of public health concerns.

💡Health Outcomes

Health outcomes refer to the results or consequences of a disease or condition on an individual or population's health status. In the video, health outcomes are discussed in the context of changes in health status due to various factors, such as environmental conditions or public health interventions. The video mentions how epidemiologists seek to understand the causes of health outcomes, which is crucial for developing strategies to prevent or mitigate them.

💡Experimental Studies

Experimental studies in epidemiology involve deliberately introducing an intervention or treatment to a group of participants to observe its effects on health. The video explains that these studies are typically used for positive interventions, such as testing a new vaccine, due to ethical considerations. This concept is contrasted with observational studies, highlighting the different approaches epidemiologists use to investigate health phenomena.

💡Observational Studies

Observational studies are a type of epidemiological research where epidemiologists monitor a population that is already exposed to a particular factor and compare them with a non-exposed group. The video uses the example of the British Doctors Study, which observed the relationship between smoking and lung cancer without manipulating the subjects' behavior. This approach is essential for understanding the effects of factors that are not ethical to introduce experimentally.

💡Correlation vs. Causation

The video emphasizes the distinction between correlation, which is a statistical relationship between two variables, and causation, which implies that one variable causes the other. It humorously illustrates this with the example of cheese consumption and bedsheet fatalities, pointing out that just because two things are related does not mean one causes the other. This concept is critical for epidemiologists to avoid drawing incorrect conclusions from data.

💡Bradford Hill Criteria

The Bradford Hill criteria are a set of guidelines used to assess whether an observed association between two factors implies a causal relationship. The video mentions these criteria as a tool epidemiologists use to interpret data and establish causation. They consider aspects such as the strength of the association, consistency across studies, and temporality, which are all important for determining if one factor causes an effect.

💡Mathematical Models

Mathematical models in epidemiology are used to represent and analyze the relationships between variables that influence health outcomes. The video suggests that these models help epidemiologists identify which factors are significant and which can be disregarded. They are essential tools for making sense of complex data and predicting the spread of diseases or the impact of interventions.

💡Causal Pie Model

The causal pie model, also known as the Rothman causal pie, is a conceptual tool used to explain how various risk factors contribute to a disease. The video describes it as a metaphorical pie made up of component causes, where each 'slice' represents a different risk factor. The model illustrates how a combination of these factors can form a sufficient cause for a health condition, such as tuberculosis. This model helps to visualize the complexity of disease causation.

💡Necessary Condition

A necessary condition is an element that must be present for an outcome to occur. In the context of the video, exposure to Mycobacterium tuberculosis is identified as a necessary condition for the development of tuberculosis. The video explains that while other risk factors may increase the likelihood of TB, the presence of the bacteria is essential for the disease to manifest, highlighting the importance of understanding the components of disease causation.

Highlights

Ebola outbreak in Guinea in 2014, leading to over 11,000 deaths, exemplifies an epidemic.

Epidemiology is the study of disease patterns within populations, their causes, and control methods.

The term 'epidemiology' originates from Greek, meaning 'study of what is upon the people'.

Epidemiologists investigate who gets which diseases, where, and when, like detectives solving mysteries.

Epidemiology evolved in the 19th century, initially focusing on infectious diseases.

Modern epidemiology encompasses a broader view of health, including non-communicable diseases and environmental factors.

Health outcomes are changes in health status due to external factors, which can be positive or negative.

Epidemiological studies are categorized into experimental and observational types.

Experimental studies expose participants to interventions to study health effects.

Observational studies compare health outcomes between groups with and without exposure to a risk factor.

The British Doctors Study by Doll and Bradford Hill linked heavy smoking to lung cancer.

Epidemiologists must interpret data carefully, as correlation does not imply causation.

Bradford Hill criteria help establish causal relationships in epidemiological studies.

Mathematical models assist epidemiologists in identifying relevant variables in disease causation.

The Rothman causal pie model illustrates how individual risk factors combine to cause disease.

Tuberculosis serves as an example where exposure to mycobacterium tuberculosis is a necessary condition.

Disease models are not infallible and rely on human data collection and interpretation.

Epidemiology provides tools to understand the complex world of disease and health impacts.

Transcripts

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 bedsheets.

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 bedsheets 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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