Let’s discuss descriptive epidemiology and see if something is going on in the neighborhood, all after the jump…
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| Who? What? When? Where? How? All lead to Why?! |
PERSON
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.
PLACE
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.
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| 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.
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| Your symptoms lasted how long? |
CASE DEFINITION
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.
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| We'll discuss poor survey techniques later. |
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.
AGE
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:
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| Totally random, I swear. |
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.
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.
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