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Weak associations will be increasingly more important in epidemiology as we try to sort out the etiologic pathways of common but multifactorial diseases. The strong risk factors for disease such as smoking or asbestos and lung cancer have been identified through epidemiology and these situations may be less common in the future. This does not mean that some exposures today may not be causing high risks in the population. These factors are yet to be recognised. However, the usual situation is that epidemiology will be challenged to help the clinicians sort out the risk factors for common diseases where multiple etiologies and multiple steps in the pathway to disease exist. Each factor will then demonstrate only a low risk associated with disease. The challenge will be to determine whether these associations are real. Such data may always be subject to the interpretation of the individual investigator. However, it would be helpful to establish some guidelines that will minimise the chances of identifying inappropriate or false associations and clarifying the true associations. Currently, a weak association is often followed by studies which gather similar information on bigger populations or by doing meta-analysis on multiple populations. These procedures could simply root an incorrect observation in the trappings of validity by making it statistically significant. The observation may still be the result of inadequate attention to the possible confounding variables that are associated with the disease.

At present, exposure databases record data primarily for regulatory purposes; they have not focused on serving the needs of epidemiologists or public health. However, the modification of exposure databases could facilitate their use in epidemiology. Characteristics necessary to enhance the use of all databases include easy access by users; documentation of methods, sampling bias, error, and inconsistences; widespread coverage in time and space; and methods and measures for estimating exposure of individuals as well as populations. Also needed are exposure scenarios and models to estimate exposures for geographic areas and time intervals not currently sampled. Multidisciplinary teams are needed to examine current databases, to review strategies for improving data collection, and to suggest and help implement appropriate changes. A long-term goal is to develop and validate data from exposure scenarios and models using data on the relationship of exposure to doses measured in humans.