Intro to Epidemiology: Crash Course Public Health #6
Summary
TLDRThis 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
- đ Epidemiology is the study of disease and health patterns within populations, focusing on causes and control methods.
- đ The term 'epidemiology' originates from Greek, meaning 'study of what is upon the people', highlighting its broad scope.
- đ Epidemiologists act as detectives, investigating who gets sick, where, and when, to uncover patterns and causes.
- đ 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.
- đ Health is influenced by a variety of factors beyond germs, including social determinants like neighborhoods and schools.
- đŹ Epidemiological studies are categorized into experimental and observational, each providing different insights into health outcomes.
- đ« Experimental studies are often limited to positive interventions due to ethical concerns about exposing participants to harm.
- đ Observational studies monitor existing exposures within populations to understand effects of factors like viruses or pollution.
- đ The British Doctors Study exemplifies how observational research can establish links between behaviors (like smoking) and health outcomes (like lung cancer).
- đ§ Epidemiologists use tools like the Bradford Hill criteria and mathematical models to interpret data and establish causal relationships.
- đ° 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
đ 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.
đŹ 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.
đ 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.
Mindmap
Keywords
đĄEpidemiology
đĄEpidemic
đĄHealth Outcomes
đĄExperimental Studies
đĄObservational Studies
đĄCorrelation vs. Causation
đĄBradford Hill Criteria
đĄMathematical Models
đĄCausal Pie Model
đĄNecessary Condition
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 donât 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 weâre 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, andâŠpie.
Donât worry, weâll get to that.
Hi, Iâm 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 patternsâ 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 âepidemiologyâ to the Greeks.
It comes from the three Greek words âepiâ, "demos", and "logos",
which mean upon, people, and study.â
So, epidemiology is literally the âstudy of what is upon the people.â
Which is kind of a terrifyingly broad field.
Like, âwhat is upon the peopleâ 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 epidemiologistâor someone who studies epidemiologyâ
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,
itâs 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.
Itâs 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 - thatâs pretty unfair for lower-income countries.
Weâll 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.
Thatâs also what weâll 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, itâs 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 weâll 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 isnât exposed to the intervention or treatment.
Now, itâs 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 donât want to intentionally expose people to, likeâŠviruses.
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 wasnât 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 wasnât enough evidence to confirm that one event caused the other.
In fact, smoking wasnât 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 donât 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 trickyâ
because data on its own donât tell a story.
For data to be useful, they need to be interpreted by people.
And data arenât always straightforward!
Like, data have shown that there is a near-perfect relationship between a populationâs 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 doesnât imply causation.
Or, put another way, just because two things seem related, it doesnât 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 wasnât 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 wasnât 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, thatâs the same Bradford Hill from the British Doctor Study âsmall world!
While we wonât 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 onesâŠarenât.
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 causesâ
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 âpieâ.
If we have enough cause-slices to form a whole pie, we have a sufficient causeâ
which means the health condition goes into effect.
Letâs go to the Thought Bubble.
Letâs consider tuberculosis, or TB, a highly infectious disease
ââcaused by the bacteria mycobacterium tuberculosis.
Because TB is an airborne pathogen, itâs 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,
thereâs 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.
ââAnother component of our pie is having a weakened, or compromised, immune system.
Basically, if our body isnât as good at fighting off disease, itâs 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 diseaseâso thatâs 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 thatâs in every causal tuberculosis pie is exposure to ââmycobacterium tuberculosis.
This is because TB canât, 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 âactivateâ the disease.
Now, while the presence of each individual risk factor increases the likelihood of TB,
it doesnât 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 causeâ
and this âenoughnessâ varies from person to person.
Now, disease models arenât completely human-error-proof.
Theyâre 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.
Weâll 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 APHAâs YouTube channel
to watch âThatâs Public Healthâ a series created by APHA and Complexly.
Crash Course was filmed in the Castle Geraghty studio in Indianapolis, IN,
and made with the help of all these smart people.
If you'd like to help keep Crash Course free for everyone forever
please consider joining our community of supporters on Patreon.
Weitere Àhnliche Videos ansehen
5.0 / 5 (0 votes)