Epidemiology Night School: Descriptive Epidemiology
Posted Aug 23 2011 7:53pm
This is an article written by EpiRen. Since his blog has gone offline, I am republishing these articles since I find that they contain some good descriptive information. Here is “Epidemiology Night School: 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?!
When someone calls in an outbreak to the health department where I work, one of the first things we ask for is for a line-list of cases. The line-list is basically a list of people who are sick that includes their name, age, gender, occupation, and other factors of interest. (The traditional first step in an outbreak investigation is to confirm that you indeed have an outbreak going on, but that’s for the outbreak lesson later.) The line-list explains who is being affected by the disease or condition.
From that information we can take a quick look for clues. Are they all males or females? If not, what is the breakdown? What are their ages? Are they all young, old, in between? You might think that this information is trivial, but it isn’t. Suppose you’re investigating cervical cancer. Gender and age surely play a role in the distribution of the disease based on biology alone. (Very few men, if any, have uterine cervices.) I seem to remember a food outbreak where the men in the party were far more likely to be ill than the females. We would later find out that the party attendees were of an ethnic background where men and women celebrated and ate separately.
Another big characteristic of cases that we look at is place. Suppose we’re looking at deaths in car accidents. Are the deaths mostly occurring on a particular road, a particular brand of vehicle, or in one particular State (one without seat belt laws, for example)? In the neighborhood outbreak, we might want to know if the cases are from one particular street or section of your neighborhood.
One of the classic examples of the use of “place” in a public health investigation is John Snow’s mapping of cholera cases in London . John Snow was a physician who was in London during a huge outbreak of cholera. He went from house to house, asking for the characteristics of people in the household who were ill. When he plotted the number and location of those who were ill, he came to the conclusion that one water pump was causing the great majority of cases. He removed the pump handle from the pump in question, and the number of cholera cases dropped precipitously.
Person and place gave Dr. Snow a lot of clues
The third, yet equally important part of descriptive epidemiology is time. In the line-list, we would ideally want to know when the cases had their onset of symptoms, when they were diagnosed, and when their symptoms resolved. Ideally, the exact time when this happened would be included. This is because different diseases have different incubation times (the time from infection to the onset of symptoms). For example, norovirus has a 12-24 hour incubation time. Influenza takes up to 72 hours to appear. Legionnaires’ Disease may appear up to two weeks post-exposure. Likewise, different diseases last for different periods of time. Norovirus clears up in a few hours or couple of days. The flu lasts for days or even a week. Pneumonia can go on for a long time if not treated.
Your symptoms lasted how long?
Time is also important in knowing because it may give us a clue as to what kind of exposure is going on. That part is for our section on outbreaks later in the “course,” so maybe just keep this in mind.
One other thing we can do with person, place, and time is to form a case definition. Case definitions will come in handy when we talk about outbreak investigations and case-control studies. But I’ll tell you right now that case definitions include person, place, and time.
HOW TO GET THE DATA
You could do like Dr. Snow and go from house to house asking if anyone in the household had diarrhea and getting their details. You could also just mail out a survey to all your neighbors. Then again, you could just wait for your neighbors to tell you about their illness. These are all examples of surveillance.
We’ll discuss poor survey techniques later.
Actively going to your neighbors and asking about disease is a form of active surveillance. Waiting for them to tell you, or for someone to tell you, is a form of passive surveillance. We’ll discuss surveillance in a later “lesson,” so make a note of this too.
PRESENTING THE DATA
So you have the scoop on who has diarrhea and who doesn’t. It is essential that you present the data properly in order for your local health department (or you, budding epidemiologist) to do what is needed. There are many ways to present the data, however, and it may take some practice to get it right. So let’s just use some parameters for examples and show you the right and wrong ways to present them.
Let’s say you interviewed or received information from 157 people in your neighborhood. I used a random number generator from random.org to get this dataset of ages:
Totally random, I swear.
Because I used a random number generator, the distribution of ages should be a bell curve (called a “normal distribution”). That is, there will be about an equal number of people in each age group, more or less. Your results will vary. Tip: When averages and medians are about the same, as is the case here, there is a good chance that the data are normally distributed.
With regards to age, I would describe this group in the following way: “The group consisted of 157 people, ages 2 to 100, with an average age of 54 and a median age of 53.” There is a common mistake that a lot of member of the media make, and I think it has more to do with lack of time to present findings than to be malicious. They will usually say or write, “The average person is 54 years old,” or “Most people were 54 years old,” or “Middle-aged people were more likely to get the diease.” Well, no, because you have half of your group older than that, and half of your group will be younger than that. This leads us to describing gender.
Again using a random number generator, I came up with 84 males and 73 females. That is, 54% of the people in your neighborhood are male, and 46% are female. Some will say or write, “Most of the people are men.” While that is true, it doesn’t give the full picture. Giving the percentages is better, and, in my opinion, more honest.
He’s mostly male, 54% or so.
You probably know where I am going with this. Instead of saying, “Most people had an onset of about 12 hours,” you want to say that the onset of symptoms ranges from 6 to 36 hours, with an average incubation of 12 hours.
I could bore you to death even more by showing all the other mistakes done when presenting data gained from descriptive epidemiology. But I won’t. You’re all bright “students,” and you know how all these things can be mixed up to confuse you.
Just some questions for you to ponder about what is going on in your neighborhood
• What was the average incubation period? How would you change your ideas on what happened if the incubation period was shorter or longer?
• What is the average age of a sick person? How would you change your ideas on what the implicated food would be based on that age value?
• Where do most of the cases live? How would you change your ideas on what happened if, for example, they all lived on one single street?
SUMMARY FOR TONIGHT
So tonight we learned that descriptive epidemiology gives us the basic information we need to make educated guesses (hypotheses) of what is going on. We learned that descriptive epidemiology must include details on person, place, and time. And we also learned that there are different ways to get at those data. Hopefully, you now have a better idea of what descriptive epidemiology is. When we talk about public health surveillance, we’ll see how easy or difficult it can be to get those data.
LAST BUT NOT LEAST
“Michael” asked for some tips on what would make a good MPH student. The best answer is that it depends. A lot of my fellow students at George Washington University were not on the Epidemiology/Biostatistics track like I was. They were on the International Health, Community Health, or even the MD/MPH track. They came from a variety of backgrounds, however. Not all of them came form a health background. (Frankly, I don’t remember meeting a fellow medical technologist.)
If your interest is epidemiology, the study of everything and anything that comes upon the people, then you’ll impress the admissions department if you have a good background in biology, mathematics, or any of the sciences that require serious research skills. The biology will come in handy when you have to understand why and how vaccines work, or why and how coffee can’t possibly cause pancreatic cancer. (The former will be discussed in our future “lesson” on clinical trials, and the latter will be discussed in our future “lesson” on confounding and bias.) The math, as you can see, will be handy with biostatistics.
Of course, there are other factors that go into getting admitted to any master’s degree program. I didn’t get admitted when I first submitted an application because my undergrad GPA was awful. I had to talk to the dean of admissions and explain to her that years had passed since I was “just a kid” in college, that I was incredibly interested in understanding how and why things like outbreaks happen, and that my background in the lab would boost my critical thinking skills (not to mention biology). I had to take some courses under “probation,” but even those courses helped me decide that the MPH was the degree for me before diving in completely. I suggest the same… Taking a couple of courses to see if being an epidemiologist (or an MPH in other disciplines) is your cup of tea.