and individuals are then followed, have a potential for more-accurate measurements but may suffer from loss of subjects to followup or bias in ascertainment of end points. Also, it may be necessary to wait for many years or even for the time of followup to exceed the latent period between exposure and effect or for sufficient outcome events to occur.
Cohort studies can utilize questionnaires or laboratory tests to measure both exposure and outcome. One advantage over case-control studies is that multiple outcomes can be evaluated simultaneously in relation to the exposure data. However, the power to test associations will depend on the frequencies of the different outcomes considered, which in turn depend on the number of persons followed (see discussion below on power considerations).
One type of cohort study seeks to correlate time trends in outcome measures and environmental exposures. Such studies can be divided into 3 broad classes: those in which the outcome is estimated or measured relatively few times, those in which outcome variables are linked to episodic variations in exposure, and those in which long-term time trends in measures or estimates of health outcomes are linked with variations in monitored or estimated exposures. The first class is seen in some cardiovascular studies in which determinations of health status are made annually. Outcome measures are often continuous, as well as dichotomous. Other examples are those that correlate the development of chronic bronchitis with exposure to air pollution and prospective cohort studies that follow children's lead exposure and cognitive development from conception or birth. The second broad class examines changes in response to exposures that are episodic or of short duration. Studies that link peaks in air pollution to patterns of asthma fall into this category. The third broad class is similar to time-series studies often conducted in the social sciences. In such studies, both exposure and outcome measures are collected, perhaps on a daily basis, for periods of months or even years. Short-term fluctuations in those outcomes are correlated with short-term variations in environmental exposures. For instance, studies of changes in peak respiratory flow, respiratory symptoms, hospital admission, and daily mortality can be linked to changes in environmental air pollution. In most of these studies, the multifactorial nature of the outcome means that the explanatory power of each environmental variable is generally small. This has necessitated relatively large samples and careful modeling to avoid potential confounding.
These studies are similar to ordinary cohort studies except that only a sample of controls (persons free of the disease) are studied in detail. They