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Environmental scientists play a key role in society's responses to environmental problems, and many of the studies they perform are intended ultimately to affect policy. The precautionary principle, proposed as a new guideline in environmental decision making, has four central components: taking preventive action in the face of uncertainty; shifting the burden of proof to the proponents of an activity; exploring a wide range of alternatives to possibly harmful actions; and increasing public participation in decision making. In this paper we examine the implications of the precautionary principle for environmental scientists, whose work often involves studying highly complex, poorly understood systems, while at the same time facing conflicting pressures from those who seek to balance economic growth and environmental protection. In this complicated and contested terrain, it is useful to examine the methodologies of science and to consider ways that, without compromising integrity and objectivity, research can be more or less helpful to those who would act with precaution. We argue that a shift to more precautionary policies creates opportunities and challenges for scientists to think differently about the ways they conduct studies and communicate results. There is a complicated feedback relation between the discoveries of science and the setting of policy. While maintaining their objectivity and focus on understanding the world, environmental scientists should be aware of the policy uses of their work and of their social responsibility to do science that protects human health and the environment. The precautionary principle highlights this tight, challenging linkage between science and policy.

A lung cancer case-control study was conducted in a Slovenian asbestos-cement factory for which unusually good records of asbestos exposures were available. The cohort consisted of all 6714 workers employed at the Salonit Anhovo factory after 31 December 1946 who worked there for at least one day between 1964 and 1994. Fifty-eight histologically confirmed cases of primary lung cancer and 290 controls were selected from the cohort. Working life exposure histories to amphibole and chrysotile forms of asbestos were estimated separately. Airborne asbestos concentrations were low. For example, the arithmetic mean exposure to all forms of asbestos in the highest exposure period (1947–1971) was 1.2 f/cm3. Chrysotile asbestos made up about 90% of this exposure (mean 1.1 f/cm3), whereas amphibole accounted for 10% (0.1 f/cm3). Comparing those above and below the 90 percentile of cumulative exposure, the odds ratios for all asbestos, chrysotile and amphibole were 1.5, 1.6 and 2.0, respectively, but confidence intervals were wide. There are only a few asbestos-lung cancer studies with high-quality exposure data and exposures in this low range. Though imprecise, the findings are important to the ongoing debate about asbestos risks. [ABSTRACT FROM PUBLISHER]

PURPOSE: To determine whether cumulative daily average wet-bulb globe temperature (WBGT) index, over one or two preceding days, is a better measure for predicting cases of exertional heat illness (EHI) than current daily average WBGT, which is the standard heat index used by the Marine Corps; and to identify the most accurate index of heat stress to prevent and predict future cases of EHI. METHODS: A case-crossover study was conducted in male and female Marine Corps recruits in basic training at Marine Corps Recruit Depot, Parris Island, SC. Weather measurements were obtained for 2069 cases of EHI during 1979-1997 and for randomly selected control periods before and after each EHI episode. RESULTS: The risk of EHI increased with WBGT (OR = 1.11 degrees F(-1); 95% CI, 1.10-1.13). EHI risk was associated not only with the WBGT at the time of the event (OR = 1.10 degrees F(-1); 95% CI, 1.08-1.11) but with the previous day's average WBGT as well (OR = 1.03 degrees F(-1); 95% CI, 1.02-1.05). Alternative combinations of WBGT components were identified that better predicted EHI risk. CONCLUSION: Our results provide evidence for a cumulative effect of previous day's heat exposure on EHI risk in these Marine Corps recruits. A simple index for use in predicting EHI risk is proposed that includes the dry-bulb temperature and the relative humidity. [ABSTRACT FROM AUTHOR]

Abstract The eruption of genetic research presents a tremendous opportunity to epidemiologists to improve our ability to identify causes of ill health. Epidemiologists have enthusiastically embraced the new tools of genomics and proteomics to investigate gene-environment interactions. We argue that neither the full import nor limitations of such studies can be appreciated without clarifying underlying theoretical models of interaction, etiologic fraction, and the fundamental concept of causality. We therefore explore different models of causality in the epidemiology of disease arising out of genes, environments, and the interplay between environments and genes. We begin from Rothman's "pie" model of necessary and sufficient causes, and then discuss newer approaches, which provide additional insights into multifactorial causal processes. These include directed acyclic graphs and structural equation models. Caution is urged in the application of two essential and closely related concepts found in many studies: interaction (effect modification) and the etiologic or attributable fraction. We review these concepts and present four important limitations. 1. Interaction is a fundamental characteristic of any causal process involving a series of probabilistic steps, and not a second-order phenomenon identified after first accounting for "main effects". 2. Standard methods of assessing interaction do not adequately consider the life course, and the temporal dynamics through which an individual's sufficient cause is completed. Different individuals may be at different stages of development along the path to disease, but this is not usually measurable. Thus, for example, acquired susceptibility in children can be an important source of variation. 3. A distinction must be made between individual-based and population-level models. Most epidemiologic discussions of causality fail to make this distinction. 4. At the population level, there is additional uncertainty in quantifying interaction and assigning etiologic fractions to different necessary causes because of ignorance about the components of the sufficient cause.