I started working at a medical laboratory when I was 14 as an apprentice. That’s how you start a lot of professions in Mexico. For two years, I showed up on my days off and practiced drawing blood, spinning it down, and preparing the serum or plasma for tests. I also learned how to do Gram stains and read urine analysis strips. It was great fun, and it came in handy when I applied to the medical technology program in El Paso.
A lot of the clients in that little lab came straight from their healthcare provider to have a test done. The most common one was fasting blood sugar, so a lot of our blood drawing was done early in the day. Because of the equipment we used, it would take most of the rest of the morning to get the tests done. (Remember, this was early 1990’s Mexico. Having a glucometer was a luxury.) Complicated tests would get shipped off to a larger lab across the border, and they could cost the person an insane amount of money once you adjust for wages in Mexico.
One of those tests that we shipped off was the HIV antibody test. There were no at-home kits to test for HIV at that time. If the healthcare provider thought the person was at risk for HIV, the person would come in, get their blood drawn, and wait for about a week or so to get the results. We would give them the results in a sealed envelope with a disclaimer that they should give the unopened results to their provider since the provider would be the most qualified person to interpret the results. Surely, none of us in the lab were there to tell people that they had tested positive for what, at the time, was a sure death sentence. And none of us wanted to deal with a false positive, where the person tested positive when they were really not infected. Or even a false negative, where the person tested negative when they were really infected.
Why the false results? Because screening tests are not 100% accurate. By their very nature, screening tests are subject to error. Theoretically, the only tests that are not subject to error, called “gold standard” tests, are those that are 100% accurate all the time. In reality, even those tests are subject to error, but the error doesn’t appear to be bound to variants in the population being tested. In screening tests, the number of false results is bound to variants in the population. Continue reading Get Yourself Tested: Screening Tests and Their Implications→
Do you drink coffee? I drink coffee. I don’t drink a lot of coffee, but I do drink coffee, and I love it. I like the “Donut Shop” coffee from the Keurig more than any other coffee, even the expensive stuff they sell at Starbucks. But did you know that there was a time when coffee was thought to cause pancreatic cancer?
It’s hard to write just one post discussing bias, confounding, and effect modification. Like in a brick-and-mortar school, it takes multiple lessons to really grasp the concepts. So we’ll take it one at a time over this and the next two “lessons.” For this lesson, we’ll talk about bias.
You probably have heard about bias in a sociological way, meaning that someone who has a bias has a preconceived notion about something. In epidemiology, a bias is a form of error in the design and implementation of a study. It’s something that affects the results of the study, and something that you need to take into consideration when designing your study, when carrying out your study, and when interpreting what you’re seeing in the study. Continue reading Bias, Confounding, and Effect Modification: Part One→
Incidence and prevalence are two concepts that people get confused all the time, even epidemiologists with years of experience. They’ll give you one number instead of the other, or interpret a number as incidence when it’s actually prevalence. The two are not the same, and it is very important to know what each number means and how to interpret them when you see them together.
The example I like to use the most is that of a local politician who was at a town hall meeting I attended. He criticized anti-HIV programs because, according to him, “The prevalence of HIV continues to increase, no matter what the federal government does.” He was right, the prevalence was increasing, but it wasn’t because anti-HIV programs and interventions were failing. If anything, they were working just fine, and the prevalence was the best evidence of it.
Person. Place. Time. Person, place, and time. Person and place and time. I am going to emphasize this as much as I can because, if I have to hear it from my professors, then so will you. When describing a population, and what is going on in that population, you need to include person, place, and time in your description. This will help to know what is going, where it is happening, to whom it is happening, when it is happening, and how much of it is happening. Hopefully, knowing all these things will help you come up with a good hypothesis on how to stop what is going on.
If you’re not familiar with technical jargon, it can be very easy for you to get lost in epidemiology. I know that even I can get some terms mixed up and end up getting a question or two wrong on an exam. (“Cumulative incidence” vs. “Incidence rate”, know what I mean?) Instead of just giving you the terms and then a dull definition, I’m going to try to put those terms in context. Also, I’m not giving you all the terms you need to know, just the terms you need to be familiar with for reading a research article, an outbreak report, or any of the number of documents that come out of the Centers for Disease Control and the World Health Organization. (Or, if you’re in British Columbia, the BC CDC. China? China CDC.)
We are observant beings. We see that clouds are gathering, and we know that rain is coming. We see the leaves turning, and we know that fall is coming. We observe and we associate one thing with the other. Many times, too many times, we get it wrong and say that X causes Y when X is only associated with Y. There’ll be more on that later when we discuss causality. All you need to know right now is that we humans like to observe and describe, and learn from what we observe and what is described to us.
Every good project needs a plan. The old Epi Night School had about 16 lessons and I wrote them about every two to three weeks. I think that this should be enough for the introductory part of this blog. Later, as time goes on and I have time from school, we’ll cover more complex epidemiological concepts like genetic epidemiology, clinical trial design, etc. It will help me refresh what I know and put down as notes of sorts what I’m learning right now.
So here’s the plan for the “intro” sessions:
Intro to Epidemiology
Common Terminology in Epidemiology and Biostatistics
Bias and Confounding (which may take up more than one blog post)
What do you think? Sound like a plan? We start sometime later next week. And, if you haven’t done so already, check out the Facebook page for this blog. I’ll be posting news and articles related to epidemiology and public health there. Oh, and follow me on Twitter.
A couple of years ago, I had this little project called the “Epi Night School“. It was a series of blog posts on epidemiology that attempted to clarify some of the concepts we hear about almost every day (but ignore or just plain don’t understand). We hear about studies and their findings, and how those findings have an effect on policy and our lives. It is because of a series of epidemiological studies that you cannot smoke indoors and that you have to be vaccinated to participate in school (in some places). Go read the “About” section for more on why I’m writing this blog again.