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Archive for the ‘Epidemiology’ Category

An open population differs from a closed population in that the population at risk is open to new members who did not qualify for the population initially. An example of an open population is the population of a country. People can enter an open population through various mechanisms. Some may be born into it; others may migrate into it. For an open population of people who have attained a specific age, persons can become eligible to enter the population by aging into it. Similarly, persons can exit by dying, aging out of a defined age group, emigrating, or becoming diseased (the latter method of exiting applies only if first bouts of a disease are being studied). Persons may also exit from an open population and then re-enter, for example by emigrating from the geographic area in which the population is located, and later moving back to that area.

The distinction between closed and open populations depends in part on the time axis used to describe the population, as well as on how membership is defined. All persons who ever used a particular drug would constitute a closed population if time is measured from start of their use of the drug. These persons would, however, constitute an open population in calendar time, because new users might accumulate over a period of time. If, as in this example, membership in the population always starts with an event such as initiation of treatment and never ends thereafter, the population is closed along the time axis that marks this event as zero time for each member, because all new members enter only when they experience this event. The same population will, however, be open along most other time axes. If membership can be terminated by later events other than death, the population is an open one along any time axis.

By the above definitions, any study population with loss to follow-up is open. For example, membership in a study population might be defined in part by being under active surveillance for disease; in that case, members who are lost to follow-up have by definition left the population, even if they are still alive and would otherwise be considered eligible for study. It is common practice to analyze such populations using time from start of observation, an axis along which no immigration can occur (by definition, time zero is when the person enters the study). Such populations may be said to be “closed on the left,” and are often called “fixed cohorts,” although the term cohort is often used to refer to a different concept, which we discuss in the following.

A rudimentary understanding of cause and effect seems to be acquired by most people on their own much earlier than it could have been taught to them by someone else. Even before they can speak, many youngsters understand the relation between crying and the appearance of a parent or other adult, and the relation between that appearance and getting held, or fed. A little later, they will develop theories about what happens when a glass containing milk is dropped or turned over, and what happens when a switch on the wall is pushed from one of its resting positions to another. While theories such as these are being formulated, a more general causal theory is also being formed. The more general theory posits that some events or states of nature are causes of specific effects. Without a general theory of causation, there would be no skeleton on which to hang the substance of the many specific causal theories that one needs to survive.
Nonetheless, the concepts of causation that are established early in life are too primitive to serve well as the basis for scientific theories. This shortcoming may be especially true in the health and social sciences, in which typical causes are neither necessary nor sufficient to bring about effects of interest. Hence, as has long been recognized in epidemiology, there is a need to develop a more refined conceptual model that can serve as a starting point in discussions of causation. In particular, such a model should address problems of multifactorial causation, confounding, interdependence of effects, direct and indirect effects, levels of causation, and systems or webs of causation (MacMahon and Pugh, 1967; Susser, 1973). This chapter describes one starting point, the sufficient-component cause model (or sufficient-cause model), which has proven useful in elucidating certain concepts in individual mechanisms of causation.