A cautionary tale: The characteristics of two-dimensional distributions and their effects on epidemiological studies employing an ecological design

Toxicological and epidemiological studies on effects of airborne fibers: coherence and public [corrected] health implications

Airborne fibers, when sufficiently biopersistent, can cause chronic pleural diseases, as well as excess pulmonary fibrosis and lung cancers. Mesothelioma and pleural plaques are caused by biopersistent fibers thinner than ∼0.1 μm and longer than ∼5 μm. Excess lung cancer and pulmonary fibrosis are caused by biopersistent fibers that are longer than ∼20 μm. While biopersistence varies with fiber type, all amphibole and erionite fibers are sufficiently biopersistent to cause pathogenic effects, while the greater in vivo solubility of chrysotile fibers makes them somewhat less causal for the lung diseases, and much less causal for the pleural diseases. Most synthetic vitreous fibers are more soluble in vivo than chrysotile, and pose little, if any, health pulmonary or pleural health risk, but some specialty SVFs were sufficiently biopersistent to cause pathogenic effects in animal studies. My conclusions are based on the following: 1) epidemiologic studies that specified the origin of the fibers by type, and especially those that identified their fiber length and diameter distributions; 2) laboratory-based toxicologic studies involving fiber size characterization and/or dissolution rates and long-term observation of biological responses; and 3) the largely coherent findings of the epidemiology and the toxicology. The strong dependence of effects on fiber diameter, length, and biopersistence makes reliable routine quantitative exposure and risk assessment impractical in some cases, since it would require transmission electronic microscopic examination, of representative membrane filter samples, for determining statistically sufficient numbers of fibers longer than 5 and 20 μm, and those thinner than 0.1 μm, based on the fiber types.

Validity of geographically modeled environmental exposure estimates

Geographic modeling is increasingly being used to estimate long-term environmental exposures in epidemiologic studies of chronic disease outcomes. However, without validation against measured environmental concentrations, personal exposure levels, or biologic doses, these models cannot be assumed a priori to be accurate. This article discusses three examples of epidemiologic associations involving exposures estimated using geographic modeling, and identifies important issues that affect geographically modeled exposure assessment in these areas. In air pollution epidemiology, geographic models of fine particulate matter levels have frequently been validated against measured environmental levels, but comparisons between ambient and personal exposure levels have shown only moderate correlations. Estimating exposure to magnetic fields by using geographically modeled distances is problematic because the error is larger at short distances, where field levels can vary substantially. Geographic models of environmental exposure to pesticides, including paraquat, have seldom been validated against environmental or personal levels, and validation studies have yielded inconsistent and typically modest results. In general, the exposure misclassification resulting from geographic models of environmental exposures can be differential and can result in bias away from the null even if non-differential. Therefore, geographic exposure models must be rigorously constructed and validated if they are to be relied upon to produce credible scientific results to inform epidemiologic research. To our knowledge, such models have not yet successfully predicted an association between an environmental exposure and a chronic disease outcome that has eventually been established as causal, and may not be capable of doing so in the absence of thorough validation.

Cancer cluster investigations: review of the past and proposals for the future

Residential clusters of non-communicable diseases are a source of enduring public concern, and at times, controversy. Many clusters reported to public health agencies by concerned citizens are accompanied by expectations that investigations will uncover a cause of disease. While goals, methods and conclusions of cluster studies are debated in the scientific literature and popular press, investigations of reported residential clusters rarely provide definitive answers about disease etiology. Further, it is inherently difficult to study a cluster for diseases with complex etiology and long latency (e.g., most cancers). Regardless, cluster investigations remain an important function of local, state and federal public health agencies. Challenges limiting the ability of cluster investigations to uncover causes for disease include the need to consider long latency, low statistical power of most analyses, uncertain definitions of cluster boundaries and population of interest, and in- and out-migration. A multi-disciplinary Workshop was held to discuss innovative and/or under-explored approaches to investigate cancer clusters. Several potentially fruitful paths forward are described, including modern methods of reconstructing residential history, improved approaches to analyzing spatial data, improved utilization of electronic data sources, advances using biomarkers of carcinogenesis, novel concepts for grouping cases, investigations of infectious etiology of cancer, and "omics" approaches.