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Six Essays Describing Key Concepts In Epidemiology Now Available At The People’s Epidemiology Library

Six essays which constitute a short primer on epidemiology have been posted to the website of the People’s Epidemiology Library, a joint project of Columbia University’s Alfredo Morabia and Leiden University’s Jan Vandenbroucke to bring together documents and essays about the development of epidemiologic methods. These essays were commissioned to help explain epidemiology for laypersons coming to the site for resources. The essays are a work in progress and  both Morabia and Vandenbroucke contributed to them.

The principal author of the essays is McMaster University’s Stephen Walter who was selected for the task in an essay competition run by the Library in 2011. Walter told The Epidemiology Monitor, which helped to sponsor the contest, that writing for the public was not something which academic epidemiologists do very often but which he thought is important. He said he would try to convey his enthusiasm for the discipline and why epidemiology is so important in contemporary society.

The topics for the six essays along with selected excerpts from each one are included below, along with links to the essays. Some of the excerpts have links to references as well.

#1 Introduction to Epidemiology

For a sample of this essay, here is a how Walter presents Snow for the lay public.

In London, some 200 years after Graunt, the epidemiologist John Snow’s studies compared the mortality from cholera between the clients of two companies providing water in London.[Snow, 1855] The study confirmed his hypothesis that getting cholera was somehow linked to contaminated drinking water. The specific inquiry of a cluster of cases which occurred in the vicinity of Broad Street famously led to the removal of the handle of the Broad Street water pump.  This was a controversial move at the time, because several other theories about the cause of cholera existed, including the notion that living at a low altitude would increase the population’s exposure to dangerous vapours.  We know nowadays that Snow was right, and the provision of a clean and secure drinking water supply was one of the main elements of a major movement towards improving public health during the 19th century and subsequent.[Vandenbroucke et al, 1991]ly

#2 How to Count

For a sample of this essay, we have chosen Walter’s paragraph on the distinction between risks and incidence rates

The formal distinction between risks and incidence rates goes back to William Farr’s work between the 1830s and 1850s [Vandenbroucke, 1985]. Farr explained that people were more afraid of cholera than tuberculosis, not because of its ultimate mortality, but because cholera kills more swiftly, i.e. in lesser time: cholera kills in a week, while tuberculosis may take years to kill; thus, the incidence of mortality from  cholera is larger. However, of all people with clinical signs and symptoms of cholera, fewer die of that disease than of all people who develop clinical tuberculosis; thus the ultimate risk of cholera is less than that of tuberculosis.[Farr, 1838] Even today, the distinctions that Farr made - far ahead of his time - continue to give rise to confusion.[Vandenbroucke 2004].

#3 How To Set Up Comparisons

Walter discusses case series, cohort, case-control, randomized trial, and other designs in this essay. Here is Walter’s introduction to case-control design

Recognizing the practical difficulties of carrying out cohort studies, an alternative design which may be more efficient is often used. This alternative is known as the case-control method.[Lane-Claypon, 1926],[Stocks and Karn, 1933],[Lombard and Doering, 1928] In this approach, epidemiologists compare people who have already developed disease (the cases) with other people - known as controls - who do not have the

disease. So, for example, if we were interested to identify risk factors for breast cancer, [Lane-Claypon, 1926] the case-control method would identify a series of cases, such as all newly diagnosed breast cancer who are admitted to a hospital within a certain time period. Additionally, the controls would be selected in some way as a a comparison for the breast cancer cases. These controls might be drawn from people who were admitted to the same hospital for some other reason, or sampled from the general community. The idea is that these control persons should reflect the ‘exposure that the cases would have had, if they had not become diseased’ - i.e., the expected distribution of exposure in the general population.

#4 Errors In Measurements and Comparisons

The sample here is Walter’s conclusion for the essay

In designing and executing research studies, epidemiologists must be aware of the many errors in measurements, comparisons and analysis that might affect their results. As we have seen, random errors of measurement tend to obscure the picture in the data, and consequently study results will tend to be imprecise. Systematic errors of measurement may cause bias in the distribution of observed data values, and may also lead to biases when making comparisons between study groups. Other general categories of bias exist, such as selection bias,[ Hernan et al, 2004] which can occur if the groups of people being compared differ in some systematic way that affects the study outcome. For instance, in evaluating the risks of diseases or death in an occupational group, it would be

inappropriate to compare people working in a certain industry with the population at large; the reason for this is the so-called “healthy worker effect”, which is the fact that individuals who are working in any occupation are generally more healthy than those not working. Epidemiologists respond to these challenges by trying to design measurement instruments that have good measurement properties, so that they are easily understood and interpretable, and give reliable and accurate answers. They also pay considerable attention to making sure that data analyses involving group comparisons do not suffer from the bias problems we have identified. Finally, they try to ensure that study sample sizes are sufficiently large that the problem of statistical uncertainty in the results is reduced. However, despite these best efforts, it remains true that many factors studied by epidemiologists are inherently hard to measure, and furthermore their associations with health outcomes may be only moderate or weak. This ultimately can lead to continuing uncertainty about the study findings. In such cases the epidemiologist must try to determine what additional evidence would be required to eliminate the uncertainty and arrive at more definitive conclusions.

#5 What Do Epidemiologists See As Causes?

In this sample, Walter’s discusses intuitive ideas about causality in the first two paragraphs, and a second excerpt presents the final paragraph of this section

Most people carry with them a relatively simple and intuitive interpretation of causality, but nevertheless one which is quite useful in practice. When they observe that an outcome follows some particular exposure or event, and they believe that the outcome would not have occurred without the prior exposure or event, they conclude that the outcome was “because of” the exposure, or that the exposure “caused” the outcome. For instance, if there is a head-on traffic traffic collision and all the passengers are found dead at the scene, one would probably conclude that the accident “caused” the deaths of those individuals. Implicitly, we are saying that had the accident not occurred those people would not have died.

This kind of logic is known as counterfactual causation. We observe that A (the accident) preceded B (the deaths) and we imagine that failure to observe A would also have led to a failure to observe B. Unfortunately, simple conclusions like this may not be adequate. In the traffic accident example, suppose that the drivers had been drinking, or that the road surface was icy. Suppose one car was full of noisy teenagers playing the radio at high volume and distracting the driver, while the driver of the other car was trying to locate his cell phone in his pocket and answer it. While initially we thought that the accident caused the deaths, now we are not so sure. Perhaps it is a combination of risk factors that might be thought of as responsible for the accident itself and hence for the deaths. We will discuss the problems of separating the effects of several possible causes in the final essay of this series.

…As we have seen, epidemiologists are usually not able to adopt the ideal experimental approach to causality, nor even a very approximate experiment, because they cannot control or dictate who is or is not exposed to risk. And even if randomisation of exposure is possible, the results of randomised studies may still not provide adequate evidence of causality. Instead, epidemiologists must observe people in their day-to-day lives, but without artificially exposing them to risk. While this considerably considerably complicates the causal interpretation of epidemiologic data, such a challenge is also one of epidemiology’s greatest strengths. By studying these issues in the the real world outside the laboratory, epidemiologists are confronting health problems in the most relevant way for human populations.

#6 How To Deal With Multiple Causes

This short excerpt explains confounding and gives the Latin root of the term.

A further major difficulty that confronts epidemiologists in many cases is that the exposure to various risk factors may be correlated in the population. For instance, persons who smoke may be less likely to engage in regular activity. Both smoking and the lack of physical activity are risk factors for heart disease, so it becomes a difficult task to determine which of the two factors might be more or less responsible for cases of disease. This phenomenon of correlated exposures is known as confounding. “Confounding” come from the Medieval Latin word “con-fundere” which meant “pour together.” In a certain sense, the two potential causes are ‘poured together’ and it becomes difficult which is the real agent and which is not. This leads often to confusion, which, amusingly, in the English language is linked to the other meaning of the word “confounding”, like in “Confound Thy Enemies”, which means, bring your enemies in a state of utter confusion.


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