Observational Epidemiology

by Raywat Deonandan, PhD

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    00:01 one dies anymore from smallpox.

    00:01 So one of the important qualities of epidemiology is through the observational process. We can determine risk factors and likely causes of disease, I say likely causes because I use that word 'cause' very carefully, we'll talk later on about how we define what a causal factor is, but an absence of knowing for sure that something causes something else, we call it a risk factor and we talk about associations, rather than causations. So many times we don't know the cause of a disease, but we can associate it with various exposures. For example, streptococcal infection is often followed by rheumatic fever and sometimes rheumatic heart disease, so we can prevent rheumatic heart disease by preventing streptococcal infection, even if we're not entirely sure what the causal mechanism might be. We know the rheumatic fever is more frequent amongst army recruits than in schoolchildren, so now we know the population to focus on and when an intervention is most likely. Lung cancer and smoking is the classic example.

    01:03 It was fought in courts for many years about whether or not tobacco really was a cause of lung cancer and we're pretty sure that it is, but in absence of solid laboratory evidence, we had mountains of epidemiological observational evidence, showing that people who smoked tended to have lung cancer more so than people who didn't smoke. The power of observation in many cases overcomes the need for solid specific causal information.

    01:32 Observational epidemiology is also useful for understanding morbidity and mortality from diseases in the population as a whole. We can associate lifestyle factors like driving without a seatbelt, or eating too much fat, or having too many calories in our diet, or being too immobile and or not moving enough with other kinds of negative health outcomes, we don't need to know the mechanism in order to be able to control the outcome by controlling lifestyle choices and risk factors.

    02:04 Let's talk about some of the important tasks that epidemiologists are engaged in. Well the first and most important thing that a population epidemiologist cares about is disease surveillance. Most modern countries have several complicated surveillance programs in action all the time, including something called a notifiable disease registry. That's when a list of key diseases are made so that any time a health professional encounters one, they must by law inform the government or whoever's in charge of that surveillance program that they saw a case. In this way we have a solid idea of whether or not our country or population has a particular disease. Some of the key ones include tuberculosis or HIV-AIDS or even Ebola now has made the list in recent months in most countries. Disease surveillance allows us to detect whether or not an epidemic is happening, it allows us to detect whether or not a disease is changing its profile in a way, it allows us to predict or detect any time the population is changing its behavior with respect to certain diseases as well.

    03:10 Epidemiologists are also involved in diagnostic tests, we're going to go into greater detail on diagnostic tests in a further lecture, but know right now that these tests involve computing things like sensitivity and specificity, deciding whether or not we can use this test in this context or whether or not a test is viable as a screening tool to identify individuals who are good candidates for further investigation further on. Epidemiologists are also useful in trend analysis and we will talk a little bit more about trend analysis in a second, this is when we look at the changes in diseases over time, or over populations and try to ascertain some wisdom from looking at the changes in the numbers without actually investigating individual cases. And also one of the important things that clinical epidemiologists and population epidemiologists do is designing studies. So very often a researcher will contract an epidemiologist to go over their study design and their protocols to make sure that everything is methodologically sound.

    04:16 Now let's look at some of the issues in trend analysis. Here's an example from the US data.

    04:20 This is the changes in mortality rates of white women and lung cancer. As you can see from 1973 to 1995, the mortality rates were going up dramatically, this is white women in America dying of lung cancer. If you look at black women, well the rates are kind of the same, so there's no reason to expect white women and black women to be physiologically different, so this is not surprising. If you look at breast cancer, white women the rates have come down slightly from 1973 to 1995, with black women they've gone up. That's interesting.

    04:59 Here's all the data in one side. We see that there are no changes or no differences between white and black women, except with respect to breast cancer, that's very interesting and there could be a host of reasons for this observation, including something we call detection bias. That's when we are looking for more cases, so we find more cases and again we'll talk more about detection bias in a future lecture about biases. But the point I'm trying to make here is that trend analysis allows us to know what questions to ask, allows us to ascertain that there is probably something happening here that I need to investigate further.

    About the Lecture

    The lecture Observational Epidemiology by Raywat Deonandan, PhD is from the course Epidemiology and Biostatistics: Introduction.

    Author of lecture Observational Epidemiology

     Raywat Deonandan, PhD

    Raywat Deonandan, PhD

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