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The epidemiologist is interested in relationships between variables, chiefly exposure and outcome variables. Typically, epidemiologists want to ascertain whether the occurrence of disease is related to the presence of a particular agent (exposure) in the population. The ways in which these relationships are studied may vary considerably. One can identify all persons who are exposed to that agent and follow them up to measure the incidence of disease, comparing such incidence with disease occurrence in a suitable unexposed population. Alternatively, one can simply sample from among the exposed and unexposed, without having a complete enumeration of them. Or, as a third alternative, one can identify all people who develop a disease of interest in a defined time period (“cases”) and a suitable group of disease-free individuals (a sample of the source population of cases), and ascertain whether the patterns of exposure differ between the two groups. Follow-up of study participants is one option (in so-called longitudinal studies): in this situation, a time lag exists between the occurrence of exposure and disease onset. One alternative option is a cross-section of the population, where both exposure and disease are measured at the same point in time.

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Epidemiology is recognized both as the science basic to preventive medicine and one that informs the public health policy process. Several operational definitions of epidemiology have been suggested. The simplest is that epidemiology is the study of the occurrence of disease or other health-related characteristics in human and in animal populations. Epidemiologists study not only the frequency of disease, but whether the frequency differs across groups of people; i.e., they study the cause-effect relationship between exposure and illness. Diseases do not occur at random; they have causes—quite often man-made causes—which are avoidable. Thus, many diseases could be prevented if the causes were known. The methods of epidemiology have been crucial to identifying many causative factors which, in turn, have led to health policies designed to prevent disease, injury and premature death.

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Among the early observations of occupational diseases was the increased occurrence of lung cancer among Schneeberg miners (Harting and Hesse 1879). It is noteworthy (and tragic) that a recent case study shows that the epidemic of lung cancer in Schneeberg is still a huge public health problem, more than a century after the first observation in 1879. An approach to identify an “increase” in disease and even to quantify it had been present in the history of occupational medicine. For example, as Axelson (1994) has pointed out, W.A. Guy in 1843 studied “pulmonary consumption” in letter press printers and found a higher risk among compositors than among pressmen; this was done by applying a design similar to the case-control approach (Lilienfeld and Lilienfeld 1979). Nevertheless, it was not until perhaps the early 1950s that modern occupational epidemiology and its methodology began to develop. Major contributions marking this development were the studies on bladder cancer in dye workers (Case and Hosker 1954) and lung cancer among gas workers (Doll 1952).

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Two groups are defined at the start of the study: an exposed group and an unexposed group. Problems of diagnostic bias will arise if the search for cases differs between these two groups. For example, consider a cohort of people exposed to an accidental release of dioxin in a given industry. For the highly exposed group, an active follow-up system is set up with medical examinations and biological monitoring at regular intervals, whereas the rest of the working population receives only routine care. It is highly likely that more disease will be identified in the group under close surveillance, which would lead to a potential over-estimation of risk.

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The reverse mechanism to that described in the preceding paragraph may occur in retrospective cohort studies. In these studies, the usual way of proceeding is to start with the files of all the people who have been employed in a given industry in the past, and to assess disease or mortality subsequent to employment. Unfortunately, in almost all studies files are incomplete, and the fact that a person is missing may be related either to exposure status or to disease status or to both. For example, in a recent study conducted in the chemical industry in workers exposed to aromatic amines, eight tumours were found in a group of 777 workers who had undergone cytological screening for urinary tumours. Altogether, only 34 records were found missing, corresponding to a 4.4% loss from the exposure assessment file, but for bladder cancer cases, exposure data were missing for two cases out of eight, or 25%. This shows that the files of people who became cases were more likely to become lost than the files of other workers. This may occur because of more frequent job changes within the company (which may be linked to exposure effects), resignation, dismissal or mere chance.