Cohort Studies

Hello again! We've arrived at the final lesson of the Epidemiology Night School, where I've done my darn best to bring Epidemiology and Public Health to the people. It started a while back, when I thought to myself, "Hey, why not?" So I told you about overall Epidemiology, descriptive Epidemiology, incidence and prevalence (which are used interchangeably by too many people, some of whom should know better), screening tests, bias, confounding, outbreaks (while poking some fun at Dr. Jay), survey studies (and how they can be used for some very racist purposes), randomness (while poking some fun at Dr. Jay again), and last time we talked about case-control studies in the setting of an outbreak.

I'd like to thank Dr. Jay for being a good sport and not suing me into oblivion. The man didn't even threat to sue, unlike that other guy... Alright, enough of that. Thanks, Dr. Jay, for being a good sport.

Tonight we're going to discuss cohort studies and why we have come to the understanding that there are certain lifestyle choices that we know are bad for your health, and why we know that vaccines do certain things. Seriously, we don't pull public health recommendations out of a magic hat. We're not wizards. So let's look at cohort studies, their design, their analysis, and why they are the strongest when it comes to studies... All after the jump.

We're down the stretch!

Case-Control Studies

Yes. So. We're back in session. Good. Last time we met, we talked about design of survey studies and about randomness. No, not randomness in that we had a random conversation. We talked about randomness in the context that a branch falling from a tree and hitting you is not, as Dr. Jay would think, just as random as an outbreak of measles or some such. We also talked about outbreaks a little bit. The main gist there was to define an outbreak in terms of person, place, and time.

It's okay if you head over to the other lessons to refresh your memory.

Alright, then... Tonight we're going to talk about case-control studies that are done in response to an outbreak and in order to get an idea of what is causing - or what caused - the outbreak. We'll also touch on why sometimes some outbreaks are just not conclusive in their findings, even with some pretty good theories on what happened. We'll do all this, of course, after the jump...

Random and Non-Random Events

I have not forgotten about the two final topics in the Epi Night School™, Design of Studies: Case-Control Studies and Design of Studies: Controlled Randomized Trials. I'm working on them. They'll probably be two-parters. However, today I'd like to talk about randomness. Thanks to pediatrician-to-the-stars, Dr. Jay Gordon, for suggesting this "lesson". See, Dr. Gordon equated a tree limb falling on a child and killing said child with the recent outbreaks of measles. (They ARE outbreaks, Dr. Gordon. That's not up for discussion.)

So let's see what is random and what is not... after the jump...

It looks organized, but they chose the shape at random.

Design of Surveys

Whew! What a crapstorm that article and subsequent post caused, didn't it? Anyway, let's back away from issues on which some really wish to disagree (and are even downright nasty about) and talk about something much more tame by comparison. First, however, I need to clarify something that my friend Reuben brought up. The term "white-hot stupid" is not a derogatory term against White people, not any more than "black hole" is a racist epithet. "White-hot stupid" refers to stupid that burns so hot it's white in color. "Burning stupid" comes from, among other things, this cartoon:

As you can see, someone told the cartoon man something so stupid that his head caught on fire. If the comment were to be more stupid than most, it would burn "white-hot" because of the way incandescence works. It's science. Some of the people threatening to go to the town hall meeting to attack me personally plan to bring up that comment about "white-hot stupid" as an example of me being racist. (Seriously, anything to turn the tables.) I look forward to all of us having a good laugh.

This brings me straight into surveys because surveys seem to guide a lot of policy in the United States in particular and the world in general. Surveys can be very misleading in their questions and give exactly the answers that the party or parties that commissioned the survey want to hear. (For example, this survey on immigration. Check it out. The pictures are photoshop gold.) Other surveys have to stick to a high standard of imparciality and be subject to review by an Institutional Review Board (IRB). (For example, this survey also on immigration.) And current events greatly sway the results of surveys from one moment to another. Let's look at what makes a good survey and what doesn't, all after the jump...

2 out of 3 kids like water AND trampolines

Intro to Outbreaks

If you've been reading me for a while, you know that I absolutely detest having to attack someone personally. Sure, I may point out the stupidity in some statements by people like Christina England, some homeopath here and there, and even Ms. Jennings was a subject of several postings. But I try not to inject my personal opinions about a person (much) because discussions of science should leave personal feelings out of it. (Sorry it was too late for you, Galileo.) Doing this avoids all that background noise. Know what I mean?

Still, there are those times when someone just somehow manages to get under my skin with comments so outrageous (in my opinion) that I am forced to think ill of them. (Not wish them ill, though. Even I am not that big of a bastard so as to wish others ill.) So I'm going to weave in some comments from the twitter feed of one Dr. Jay Gordon, just to show you how someone who doesn't understand epidemiology can come off as crass and uncaring (in the opinion of many). All, of course, after the jump...

You see a monkey. I see flying Ebola.

Confounding

I know what you're thinking. You're thinking that this post has something to do with the logic behind anti-vaccine groups. Confounding doesn't even begin to describe what they're up to. No, this post is about a concept in research studies and epidemiology called "confounding". Simply defined, confounding is what happens when you can't see the forest for the trees... When you assume that A and B are related without knowing that A and C are related and C and B are related, but A and B are not really related. Confusing? Confounding? Let's try to clarify it after the jump...

It's not the fall that kills you. It's the sudden stop.

Bias, Part Three

Last time we met to talk about bias, we talked about measurement bias and how choosing the wrong instrument, the wrong measurement, or measuring the wrong thing can make all the difference in the outcome of your study. We also talked about recall and attention bias, where the study subjects' expectations can have an effect on your outcomes as well. Today we're going to talk about Exposure/Intervention Bias, where the experimental group goes awry. All after the jump...

He would eventually get treated for exposure.

Bias, Part Two

Last time we met, there had not been a catastrophic earthquake in Japan like the one that struck on Friday. Even at such a distance, the events there managed to take me away from writing the next "lesson" in the night school. But here I am, and we're going to talk some more about bias. This time we'll look at measurement bias, and how that can have an effect on your findings. So let's look at why it's incredibly important to calibrate your thermometers and mind your decimal places after the jump...

I hope someone calibrated the bungee cord...

Bias, Part One

Look, we all make mistakes. We’re human. If we stick our fingers into an electrical socket, and we get shocked, it will more than likely be the only time we do it. I know I only did it once. It didn’t matter if the electricity was on or off anymore after that. The one shock was enough for me.

Researchers are also human, so they will also make errors. In research, there are two main types of errors. One type is random errors, those that happen “just because” and are more related to the inherent nature of what you’re testing or who you’re sampling. While there is nothing you can do about them, you can minimize them with things like randomization of subjects or stratifying the results. The second type of error is more insidious and can be easily overlooked. So let’s talk about it… Let’s talk about systematic errors, also known as bias, after the jump.

There are biases and bi-asses, get it?

Common Terms and Concepts in Epidemiology

Okay, so over the last few weeks, we looked at incidence and prevalence, and we also looked at how prevalence relates to screening tests. Hopefully, you now know why prevalence of a disease under control can continue to rise (because people are surviving). You also know why incidence will continue to rise even if you have the exact same number of new cases year after year (because the number of people at risk declines). And you hopefully know why you can’t go screening everyone in the population at random for diseases using screening tests that are less than perfect (because you’ll have a low prevalence and high number of false-positives).

For today’s lesson, I decided to keep it a little bit simpler and just go through some common terms used in epidemiology and define them with simple examples. So let’s do that after the jump…

If she hits ten three-pointers in a row, is it statistically significant?

Screening Tests, Part Two

Last time we talked about screening tests, we talked about the definition of concepts such as sensitivity, specificity, positive predictive value, and negative predictive value. I told you at the end about an interesting effect observed in the relationship between positive predictive value (PPV) and prevalence. The effect is that as prevalence rises, so does the PPV. However, as prevalence rises, the negative predictive value (NPV) comes down. Let's analyze that and look at some examples... Ready for it? After the jump...

Hilarity ensues

Screening Tests, Part One

One of the most common tests I performed at the lab was the "Beta Strep Test," a throat swab test for Group A Streptococcus (GAS), the causative agent of "Scarlet Fever" or "Strep throat." It is a simple test really. You swab a person's throat, put the swab in some solution, take the swab out after a minute, drop in a stick, then read the stick for positive or negative. It's so simple that anyone can do it, like a pregnancy test. However, like all screening tests, it is subject to the rules of sensitivity and specificity. That is, you'll sometimes get positives that are not really positives (false-positives) and negatives that are not really negative (false-negatives). False positives are not really a big deal with this test because the patient just gets prescribed simple antibiotics and is on his/her way. False negatives are a problem because, if left untreated, GAS infections can cause some damage down the road.

Let's talk about sensitivity and specificity, throw in some prevalence, then look at positive and negative predictive value... I hope you have your thinking caps on for this one... All after the jump...

He'll be jumping off the "screen." Get it?

Incidence and Prevalence, Part Two

So last time we looked at the definitions of incidence and prevalence, and I asked you to think in your heads about water going into a bucket (incident cases) and the water that was already in it (prevalent cases). If the spigot on the bucket were opened, the water would go out, but there would still be the same rate of water coming in. (Prevalent cases remained constant, if the spigot let out as much water as the water that came in.) If the spigot is closed, then the prevalent cases continue to rise and rise. That's the case if those infected never recover (or die) from the disease.

Anyway, enough with the mental exercises. Let's look at some examples, all after the jump...

Looks like an incidence curve

Incidence and Prevalence, Part One

I was telling my wife the other day that I was really angry at a man on the radio who criticized investment in overseas HIV/AIDS relief because, according to him, "the prevalence of AIDS continues to increase." My wife answered, "Of course it is. People are still getting infected." Now, unless you completely understand the meaning of "incidence" and "prevalence," you might have (maybe) agreed with the man, like my wife did. (She agreed prevalence was increasing because of new cases, not on the de-funding part.) And that's the thing, folks. People who know better (and some who don't) are using whatever epidemiological statistics fit their agendas. So let's clear up the confusion after the jump...

I'm confused. Is he catching the cat?

Descriptive Epidemiology

Let’s say that you have been told by several of your neighbors that they became ill after the neighborhood mixer over at the fire hall the other day. You’ve heard from enough people to make you a little worried that the food at the mixer (some of which you made yourself) may be involved. Descriptive epidemiology helps us form theories about what, if anything, is going on. What is descriptive epidemiology? Simply stated, it’s looking at the location and characteristics of the cases (and non-cases) and letting the evidence guide your decisions.

Let’s discuss descriptive epidemiology and see if something is going on in the neighborhood, all after the jump…

Who? What? When? Where? How? All lead to Why?!

Intro to Epidemiology

Before becoming an epidemiologist, I was a medical technologist. A medical technologist is someone who works in the clinical laboratory, performing tests on human samples like urine, blood, etcetera. My work in the lab exposed me to a lot of very interesting medical cases at the hospitals where I worked. Many of the health care providers I worked with would call the lab to ask about what tests would be the best to diagnose a given condition. As time went by, I did a lot of research into diseases and conditions and how to diagnose them. After a few years, my colleagues suggested that I study epidemiology. So I did, and I learned a lot more than I could ever have learned just googling things left and right. There is something to be said about a formal education, you know? Now I wish to share that knowledge, in my own special way.


Let me give you an introduction to epidemiology, as always, after the jump…

Bring with you the curiosity of a child

Epidemiology Night School Project

I'm seriously thinking of writing a series of posts about epidemiology, making the most complex concepts as clear as I can. I would call this project "Epidemiology Night School." I would offer no college credit for it, though. And I would not say that it would replace any class you can get in a formal, accredited pubic health program. But I will say that it might make it a little easier to understand the myriad of studies and health-related news that you see in the media. After the series of posts, I hope that you will be able to answer the following:

• What is epidemiology?
• What tables, graphs, and measures are used to describe disease trends? And what rules do you need to follow to present the data in the most honest and open way?
  
• When do you use an "average"? When do you use a "median"? And how do you interpret these?
• What is public health surveillance? And what are its limitations?
• How do you investigate an outbreak?

I'll be using a lot of my own experience in addressing these and other questions. If you need some text to follow along, I recommend CDC's Principles of Epidemiology (PDF).
Of course, there are some prerequisites. You can't just walk in off the street and understand epidemiology, though I will aim to do that. The main prerequisite will be an understanding of mathematics (adding, dividing, multiplying, and subtracting) and an open mind (because some stuff will blow your mind).
I plan on starting this project this coming weekend, maybe sooner than that or maybe later than that. We'll see how it goes.

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