What is Epidemiology?

Summary

This script delves into the realm of epidemiology, the science behind public health decisions. It explains how regulators and health organizations use scientific evidence to establish guidelines, such as smoking bans and dietary recommendations. The script clarifies that direct experimentation on humans is unethical, leading to the reliance on observational studies involving large populations. It also addresses the challenges in interpreting epidemiological data, including the potential for bias, confounding factors, and the importance of considering both statistical significance (p-values) and effect size. The summary emphasizes the importance of epidemiology in shaping public health policies, despite its complexities, for the betterment of societal health and economic benefits.

Takeaways

• 📊 Epidemiology is the science that helps us understand how our behaviors and exposures affect the health of society as a whole.
• 🧪 'In vivo' and 'in vitro' studies are initial scientific methods used to understand health effects, but they have limitations when applied to humans.
• 🚫 Conducting experiments on humans for health research is generally unethical, so epidemiologists rely on observational studies.
• 🔍 Observational studies involve analyzing the health outcomes of large populations to identify patterns and correlations.
• ❓ Correlation does not imply causation; just because two events occur together doesn't mean one causes the other.
• 🤔 Bias and confounding factors can skew epidemiological findings, so researchers must be vigilant in their study design and analysis.
• 📉 A low 'p-value' in epidemiological studies indicates that findings are likely not due to chance, but it doesn't measure the strength of the association.
• 📈 'Effect size' is a crucial metric that indicates the strength of the association between an exposure and a health outcome.
• 👥 Epidemiological studies provide insights into population health but may not always be applicable to individual cases due to varying personal conditions.
• 🌟 Despite the complexities, epidemiology plays a vital role in shaping public health policies and recommendations for healthier communities.

Q & A

What is epidemiology and why is it important for public health decisions?

Epidemiology is the science of understanding how what we're exposed to or what we do may affect the overall health of society. It is important for public health decisions because it provides evidence-based insights into the health effects of various exposures and behaviors on large populations, which helps in making informed decisions about public health policies and recommendations.

What are the limitations of 'in vivo' and 'in vitro' studies in understanding human health?

In vivo studies, which are conducted on animals, and in vitro studies, conducted on cells in test tubes or petri dishes, have limitations because they may not accurately reflect how substances or conditions affect humans. Animals can react differently to substances than humans, and isolated cells in a lab behave differently than those within the complex environment of the human body.

Why is it unethical to conduct certain experiments on humans?

It is unethical to expose humans to potentially harmful substances without their informed consent and without a clear medical benefit, as it could cause unnecessary harm and violate their rights and well-being.

How do epidemiologists study the effects of exposures and behaviors on human health?

Epidemiologists study the effects of exposures and behaviors on human health by observing and measuring what large groups of people are exposed to and analyzing the connections between these exposures and their health outcomes.

What is the difference between correlation and causation in epidemiological studies?

Correlation refers to the occurrence of two events together, but it does not imply that one causes the other. Causation, on the other hand, implies a direct cause-effect relationship. In epidemiological studies, it's important to distinguish between these two to avoid incorrect conclusions about the impact of exposures on health.

What is bias and how can it affect the results of an epidemiological study?

Bias is an error in the design, conduct, or analysis of a study that leads to results that are systematically different from the true values. It can affect the results of an epidemiological study by skewing the data, leading to misleading associations or conclusions.

What is confounding and how does it interfere with the interpretation of epidemiological data?

Confounding occurs when other factors interfere with the observed relationship between an exposure and an outcome, making it difficult to determine the true effect of the exposure. It can lead to incorrect conclusions about causation if these confounding factors are not properly accounted for in the study.

Why do epidemiologists use p-values in their studies and what do they indicate?

Epidemiologists use p-values to determine the probability that the observed results are due to chance rather than a true effect. A p-value of 0.05 or lower is typically considered statistically significant, indicating that there is less than a 5% chance that the results are due to random variation.

What is the significance of 'effect size' in epidemiological studies?

Effect size in epidemiological studies refers to the magnitude of the impact of an exposure on health outcomes. It is important to consider because it provides information on how strong the association is between the exposure and the health effect, beyond just whether an association exists.

How should we interpret the results of epidemiological studies in relation to individual health?

The results of epidemiological studies should be interpreted with the understanding that they apply to the collective health of populations rather than individuals. While they can provide guidance on community health actions, individual health may be influenced by a multitude of unique factors not accounted for in such studies.

Why is it challenging to apply the findings of epidemiological studies to everyone?

It is challenging to apply the findings of epidemiological studies to everyone because individuals have different genetic makeups, lifestyles, and environmental exposures. These factors can influence how individuals respond to various exposures, making it difficult to generalize findings from population studies to specific individuals.

Outlines

00:00

🔬 Understanding Epidemiology: The Science Behind Public Health Decisions

This paragraph introduces the field of epidemiology, which is crucial for making public health decisions. It explains that regulators and health organizations base their decisions on scientific evidence of the effects of various exposures and behaviors on the health of society. The paragraph discusses the limitations of 'in vivo' and 'in vitro' studies, emphasizing the importance of observing real-world data. It highlights the challenges in interpreting epidemiological data, such as distinguishing between correlation and causation, and the potential for bias and confounding factors. The paragraph concludes by noting the complexity of making sense of epidemiological data and the importance of using statistics to determine the significance of findings.

05:00

📊 The Importance of Statistical Analysis in Epidemiology

The second paragraph delves into the statistical aspects of epidemiological studies, focusing on the interpretation of p-values and effect sizes. It explains that a low p-value indicates that study results are likely not due to chance, but also cautions that even low p-values can be misleading if statistics are not handled properly. The paragraph uses the example of smoking and red meat consumption to illustrate the difference between statistical significance and the actual impact on health. It emphasizes the importance of considering the effect size, which measures the strength of the association between an exposure and a health outcome. The paragraph concludes by noting that epidemiological studies provide insights into the collective health of populations rather than individuals, and that they play a vital role in informing public health decisions and improving overall well-being, despite the challenges in conducting and interpreting them.

Keywords

💡Epidemiology

Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control health problems. It is central to public health and helps in understanding how exposures or behaviors may affect the overall health of society. In the video, epidemiology is used to explain how regulators make decisions on public health issues such as smoking in public places or driving certain types of cars.

💡In vivo studies

In vivo studies refer to scientific experiments conducted within a living organism, typically animals. These studies are used to understand biological and physiological functions and the effects of substances on living systems. In the script, it's mentioned as one method to develop evidence for health impacts, but with the caveat that results may not always be directly applicable to humans.

💡In vitro studies

In vitro studies are conducted outside of a living organism, often involving cells or tissues in test tubes or petri dishes. These studies help scientists understand cellular mechanisms and the effects of substances on isolated cells. The video script points out that while in vitro studies can provide valuable information, they have limitations because isolated cells behave differently than those within a living body.

💡Correlation

Correlation refers to a statistical relationship between two variables, indicating that they change together. In the context of the video, correlation is used to illustrate that just because two events occur together, it does not mean one causes the other. For example, eating ice cream and getting sunburned both happen in hot weather, but one does not cause the other.

💡Causation

Causation is the relationship between an action or factor and its effect on an outcome. Unlike correlation, causation implies a direct cause-and-effect relationship. The video emphasizes the importance of distinguishing between correlation and causation in epidemiological studies to avoid incorrect conclusions about what causes health outcomes.

💡Bias

Bias in research refers to systematic errors or prejudices that can lead to incorrect or misleading results. The video script discusses how bias can occur if a study is not designed properly, such as only observing people who eat a lot of ice cream in hot sunny areas, which could falsely strengthen the association between ice cream eating and sunburn.

💡Confounding

Confounding in epidemiology occurs when a factor is associated with both the exposure and the outcome, making it difficult to determine the true effect of the exposure. The video uses the example of coffee drinking and heart disease, where smoking could be a confounder since it is associated with both, potentially misleading the interpretation of the study results.

💡P-value

The p-value is a statistical measure used to determine whether the results of a study are likely to be true or due to chance. A lower p-value suggests that the findings are less likely to be a result of random variation. The video explains that epidemiologists often consider a p-value of 0.05 or lower as statistically significant, indicating a less than 5% chance of the results being due to random variations.

💡Effect size

Effect size is a measure of the strength of the relationship between an exposure and an outcome in a study. It helps to understand the magnitude of the impact of a particular exposure. The video script contrasts the effect size of smoking, which greatly increases cancer risk, with that of red and processed meat consumption, which has a much smaller effect on cancer risk.

💡Public health decisions

Public health decisions are choices made by regulatory bodies or health organizations to improve the health of the population. These decisions are often based on epidemiological evidence and can include recommendations for healthy behaviors or policies to reduce health risks. The video emphasizes the importance of epidemiology in informing these decisions to ensure healthier lifestyles and reduce healthcare costs.

💡Statistical significance

Statistical significance in a study indicates that the results are unlikely to have occurred by chance alone. The video script mentions that a p-value of 0.05 or lower is often used as a threshold for determining statistical significance, which helps to establish whether an observed effect is real or due to random variation.

Highlights

Public health decisions are often based on epidemiology, the study of how exposures and behaviors affect society's health.

To determine if something is harmful, science must develop evidence showing negative health effects.

In vivo studies on animals and in vitro studies on cells can provide limited insights into human health.

Direct experiments on humans are generally unethical, leading to the reliance on observational studies.

Observational studies measure exposures and health outcomes in large populations to identify potential connections.

Correlation does not imply causation, as seen with ice-cream consumption and sunburns.

Epidemiological data interpretation is complex and requires careful consideration to avoid incorrect conclusions.

Bias can skew study results due to errors in study design or selection of participants.

Confounding factors can obscure the true relationship between exposure and health outcomes.

Epidemiologists use statistics, including p-values, to assess the likelihood that study findings are not due to chance.

A p-value of 0.05 or lower is often considered statistically significant in epidemiological studies.

P-values alone do not indicate the strength of an association or the importance of health implications.

Effect size is a crucial measure to understand the magnitude of an exposure's impact on health.

Epidemiological studies focus on collective health, making individual applications challenging.

Epidemiological studies may not be representative of all populations, affecting their relevance to certain individuals.

Despite complexities, epidemiology is vital for making public health decisions and improving overall well-being.

Epidemiology contributes to healthier lifestyle recommendations, reduced healthcare costs, and increased productivity.

Transcripts

How do regulators make decisions on what were not allowed to do, like smoking in

public places, or driving gas-guzzling, air-polluting cars?

And how do health organizations decided what healthy eating recommendations to make?

Like having five servings of fruit and vegetables a day?

These are examples of public health decisions based on epidemiology - the science of understanding

how what we're exposed to, or what we do, may affect the overall health of society.

But to make sense of epidemiology, we need to dig a little deeper.

Let's start by going back to basics and ask: How do we work out whether something we do,

or are exposed to, is harmful; and how harmful is it?

The only reliable way is to use science to develop evidence that shows something

is bad for health.

But this isn't as it sounds.

One way is to do research on animals, called "in vivo" studies.

Or on cells in test tubes and petri dishes.

These are called "in vitro" studies.

But such research can only tell us so much.

For example, many substances can affect us differently than they affect animals.

So while chocolate is harmful to dogs, and aspirin is toxic to cats, both are safe for

humans, when used appropriately.

As for in vitro studies, cells that are isolated in the lab behave differently than cells inside the body.

So we have to be very careful applying the results of such studies directly to human health.

Of course, the most straight forward approach is to do experiments on real people.

But with a few exceptions, this is highly unethical!!

You can't go around exposing people to potentially harmful substances, just to see what happens.

This leaves us with observing the real world - measuring what a lot of people are exposed

to - and by a lot, we mean tens and hundreds of thousands of people - and trying to work

out what the connections are between these exposures and their health.

Studies like this form a big part of epidemiology.

And it is a powerful way to understand how exposures and behaviors potentially affect

large groups of people.

But it's also sometimes difficult to make sense of.

To start with, just because someone was exposed to something, and they got sick, doesn't mean

the two events are related.

The exposure may not have caused the sickness.

For instance, lots of people eat ice-cream when it's hot.

And lots of people get sunburned when it's hot.

But ice-cream clearly does not cause sunburn.

These events are instead correlated, meaning that they often happen together.

But they're not causative, meaning that eating ice-cream doesn't directly cause sunburn.

And while this example is pretty obvious, making sense of epidemiology data is often

really hard!

And care needs to be taken that we don't jump to the wrong conclusions.

When examining relationships between exposures and health outcomes, there are a number of

reasons why we might see an association.

These include an actual cause; pure chance; bias; and what epidemiologists call "confounding"

- other things interfering with what we observe.

Bias can come about because of errors in how a study is designed.

For example, if we observe only people who eat a lot of ice-cream, and live in really

hot sunny areas, and don't include anyone else who doesn't eat so much, or lives elsewhere,

the association between ice-cream eating and getting sunburned, will seem very strong.

In other words, the result will be misleading; there will be bias toward a particular - and

in this case, wrong - conclusion.

Confounding on the other hand is where other factors confuse our interpretation

of exposure and outcome.

For example, imagine a study that suggests people who drink more coffee are more likely

to develop heart disease.

I would be tempting to conclude that coffee causes heart disease.

But people who drink coffee also tend to smoke.

And in this case, smoking is a confounder.

Since it is associated with both drinking coffee and heart disease, it can make it seem

that coffee causes the condition, if we don't take smoking into account.

In real life, there are many confounders - some more obvious than others.

Because of this, epidemiological investigations take quite a bit of detective work to figure

out what exposures really lead to the health outcomes, and which ones only appear to.

To tease out what is relevant, and what is not, epidemiologists use statistics.

One standard practice in analyzing data is to look at the probability, or "p" value,

to determine if the findings are likely to be true, or are simply due to chance.

The lower the p-value, the more likely it is that the results of the study represent

reality, and did not just happen because of chance or random variation.

Usually epidemiologists consider a p-value of 0.05 or lower as indicating that the study

results are statistically significant; which is just a fancy way of saying that there's

less than 5% chance of these results being due to random variations.

However, the p-value only helps you get a sense of whether study outcomes are due to

chance or not.

It does not help us examine how strong the association is, or how important the health

implications are.

And if statistics aren't done well, even low p-values can be misleading.

Consider cancer risk.

Epidemiological work shows that both smoking, and processed and red meat, have a statistically

significant association with increased cancer risk - meaning they both have p-values below 0.05.

But, while smoking increases your chances of getting cancer by around 20x, or 2,000%,

eating red and processed meats increases it by only 20%, or 0.2x.

This is extremely low when you consider all the other things you're exposed to that potentially

impact your health - especially if the chances of getting cancer aren't high to start with.

And this is why looking at the p-value on its own is not enough.

And researchers also need to consider how large of an effect an exposure has - called

"effect size" - not just whether there's likely to be an effect or not.

Lastly, when making sense of epidemiological studies, it's important to remember that this

science deals with the collective health of thousands and millions of people - and not individuals.

Because we're all different, and live under different conditions, it's very hard to apply

broad conclusions from such studies to single people.

But the are good at indicating what whole communities should do to stay healthy.

And just to complicate things further, epidemiology studies may not include people like you; meaning

that they may be less important to you than to others.

The bottom line is that it takes a lot of work to conduct epidemiology studies well,

and it takes a lot of work to interpret them correctly.

Despite these difficulties, epidemiology is crucial to making public health decisions,

and improving the well-being of people - so that, on balance, we all live healthier lives.

These decisions do not only mean better healthy lifestyle recommendations and programs; they're

also important for reducing health care bills, and increasing productivity over

tens of millions of people

Which is why, even though it's complicated, epidemiology is so important.