Bertram Price

Time trend of mesothelioma incidence in the United States and projection of future cases: an update based on SEER data for 1973 through 2005.

The time trend of mesothelioma incidence and projections of future cases provide useful information for analyzing proposed public health interventions where asbestos exposure may be an issue, evaluating regulatory proposals, and estimating the remaining potential costs of programs to compensate individuals with asbestos-related diseases. We used the April 2008 release of Surveillance, Epidemiology, and End Results (SEER) data, which covers 1973 through 2005, to analyze the time trends in age-adjusted mesothelioma incidence and to estimate an age and birth-cohort model to project the number of future mesothelioma cases. The increase in the number of SEER cancer registries from 13 to 17 in 2000 had little effect on the time pattern of age-adjusted mesothelioma incidence, and the pattern over time of pleural mesothelioma was indistinguishable from the pattern for total mesothelioma defined as sum of pleural and peritoneal cases. Our analysis suggests that the SEER registries viewed as a sample of the U.S. population over-represents high mesothelioma incidence, a fact that we accounted for in our projections. For 2008 we estimate approximately 2,400 cases, with asbestos the likely cause in 58%. We project that asbestos will no longer be a factor in mesothelioma cases after the year 2042. For 2008 through 2042, we estimate slightly more than 68,000 total cases, with asbestos the likely cause in 34%.

Mesothelioma : Risk apportionment among asbestos exposure sources

The mesothelioma epidemic in the United States, which peaked during the 2000-2004 period, can be traced to high-level asbestos exposures experienced by males in occupational settings prior to the full recognition of the disease-causing potential of asbestos and the establishment of enforceable asbestos exposure limits by the Occupational Safety and Health Administration (OSHA) in 1971. Many individuals diagnosed with mesothelioma where asbestos has been identified as a contributing cause of the disease have filed claims seeking compensation from asbestos settlement trusts or through the court system. An individual with mesothelioma typically has been exposed to asbestos in more than one setting and from more than one asbestos product. Apportioning risk for mesothelioma among contributing factors is an ongoing problem faced by occupational disease compensation boards, juries, parties responsible for paying damages, and currently by the U.S. Senate in its efforts to formulate a bill establishing an asbestos settlement trust. In this article we address the following question: If an individual with mesothelioma where asbestos has been identified as a contributing cause were to be compensated for his or her disease, how should that compensation be apportioned among those responsible for the asbestos exposures? For the purposes of apportionment, we assume that asbestos is the only cause of mesothelioma and that every asbestos exposure contributes, albeit differentially, to the risk. We use an extension of the mesothelioma risk model initially proposed in the early 1980s to quantify the contribution to risk of each exposure as a percentage of the total risk. The percentage for each specific discrete asbestos exposure depends on the start and end dates, the intensity, and the asbestos fiber type for the exposure. We provide justification for the use of the mesothelioma risk model for apportioning risk and discuss how to assess uncertainty associated with its application.

Industrial-grade talc exposure and the risk of mesothelioma

Industrial-grade talc deposits are complex mixtures of mineral particles and may vary substantially in composition across small geographical areas. Typical industrial-grade talc includes amphibole cleavage fragments, platy talc, serpentine minerals, talc in fibrous form, and a minor presence of transitional fibers. Industrial-grade talc was erroneously determined to be an asbestos-containing material due to an unintended consequence of Occupational Health and Safety Administration’s (OSHA’s) method for measuring airborne asbestos mandated in 1972. This error was repeated, most notably, by the National Institute for Occupational Safety and Health (NIOSH) in, 1980 for talc mined in northern New York State (NYS) by RT Vanderbilt Company (RTV). Subsequent exposure studies of northern NYS talc conducted through the, 1980s and one study published after, 2000 relied on the conclusion that talc was an asbestos-containing material to infer a causal relationship between talc and mesothelioma. The present review included (1) publications concerning talc’s cancer-causing potential issued by organizations concerned with occupational and public health; (2) talc exposure studies and animal and cellular studies of RTV talc; (3) mesothelioma rates in northern NYS; and (4) mesothelioma mortality among RTV mining employees. The review indicated that failure to correctly identify the mineral characteristics of talc resulted in misleading reports concerning the carcinogenic potential of talc. However, the collective data from animal and cellular studies, mesothelioma rates in northern NYS, exposure studies, and a mortality analysis of RTV mining employees do not support a causal relationship between RTV talc and mesothelioma. This conclusion is applicable to all mineral components in RTV talc and to other industrial-grade talcs and mineral aggregates with the same components.

Classification Tree Method for Bacterial Source Tracking with Antibiotic Resistance Analysis Data

ABSTRACT Various statistical classification methods, including discriminant analysis, logistic regression, and cluster analysis, have been used with antibiotic resistance analysis (ARA) data to construct models for bacterial source tracking (BST). We applied the statistical method known as classification trees to build a model for BST for the Anacostia Watershed in Maryland. Classification trees have more flexibility than other statistical classification approaches based on standard statistical methods to accommodate complex interactions among ARA variables. This article describes the use of classification trees for BST and includes discussion of its principal parameters and features. Anacostia Watershed ARA data are used to illustrate the application of classification trees, and we report the BST results for the watershed.

Multiple Interacting Risk Factors: On Methods for Allocating Risk Factor Interactions

A persistent problem in health risk analysis where it is known that a disease may occur as a consequence of multiple risk factors with interactions is allocating the total risk of the disease among the individual risk factors. This problem, referred to here as risk apportionment, arises in various venues, including: (i) public health management, (ii) government programs for compensating injured individuals, and (iii) litigation. Two methods have been described in the risk analysis and epidemiology literature for allocating total risk among individual risk factors. One method uses weights to allocate interactions among the individual risk factors. The other method is based on risk accounting axioms and finding an optimal and unique allocation that satisfies the axioms using a procedure borrowed from game theory. Where relative risk or attributable risk is the risk measure, we find that the game-theory-determined allocation is the same as the allocation where risk factor interactions are apportioned to individual risk factors using equal weights. Therefore, the apportionment problem becomes one of selecting a meaningful set of weights for allocating interactions among the individual risk factors. Equal weights and weights proportional to the risks of the individual risk factors are discussed.

Alternative estimate of source distribution in microbial source tracking using posterior probabilities.

Microbial source tracking (MST) is a procedure used to determine the relative contributions of humans and animals to fecal microbial contamination of surface waters in a given watershed. Studies of MST methodology have focused on optimizing sampling, laboratory, and statistical analysis methods in order to improve the reliability of determining which sources contributed most to surface water fecal contaminant. The usual approach for estimating a source distribution of microbial contamination is to classify water sample microbial isolates into discrete source categories and calculate the proportion of these isolates in each source category. The set of proportions is an estimate of the contaminant source distribution. In this paper we propose and compare an alternative method for estimating a source distribution-averaging posterior probabilities of source identity across isolates. We conducted a Monte Carlo simulation covering a wide variety of watershed scenarios to compare the two methods. The results show that averaging source posterior probabilities across isolates leads to more accurate source distribution estimates than proportions that follow classification.