Comparison of the Cancer Risk of Methylene Chloride Predicted from Animal Bioassay Data with the Epidemiologic Evidence

Methylene chloride has been shown to be a lung and liver carcinogen in the mouse; yet, the current epidemiologic data show no adverse health effects associated with chronic exposure to this compound. Hearne et al. have compared the results of a large mortality study on occupational exposure to methylene chloride to the human risk predictions based on the rodent bioassay to point out the inconsistency between the animal toxicologic and human epidemiologic data. The maximum number of lung and liver cancers predicted due to methylene chloride exposure based on the rodent bioassay data was 24 compared to 14 deaths from these cancers actually observed in the Hearne et al. epidemiology study. We assess the minimum risk detectable by the human study in order to calculate the upperbound potency of methylene chloride and compare it to the potency derived from the bioassay data. Results from the epidemiology study imply an upperbound potency of 1.5 x 10(-2) per ppm, compared to 1.4 x 10(-2) per ppm calculated using the most conservative analysis of the animal data. We conclude that the negative epidemiology study of Hearne et al. is not sufficiently powerful to show that the risk is inconsistent with the human risk estimated by modeling the rodent bioassay data. Specifically, the doses to which the workers were exposed, the population studied, and the latency period were not adequate to determine that the risks are outside the bounds of the risk estimates predicted by low-dose modeling of the animal data.     

A method for simulating familial disease data with variable age at onset and genetic and environmental effects

The field of genetic epidemiology is growing rapidly with the realization that many important diseases are influenced by both genetic and environmental factors. For this reason, pedigree data are becoming increasingly valuable as a means of studying patterns of disease occurrence. Analysis of pedigree data is complicated by the lack of independence among family members and by the non-random sampling schemes used to ascertain families. An additional complicating factor is the variability in age at disease onset from one person to another. In developing statistical methods for analysing pedigree data, analytic results are often intractable, making simulation studies imperative for assessing the performance of proposed methods and estimators. In this paper, an algorithm is presented for simulating disease data in pedigrees, incorporating variable age at onset and genetic and environmental effects. Computational formulas are developed in the context of a proportional hazards model and assuming single ascertainment of families, but the methods can be easily generalized to alternative models. The algorithm is computationally efficient, making multi-dataset simulation studies feasible. Numerical examples are provided to demonstrate the methods.     

Social epidemiologic comparisons in a European cohort of substance-dependent therapeutic community clients: a case-oriented analysis

A case-oriented analytical approach compared the index national case of France with the typical European outcome, other European cases and American results on social epidemiological indicators. Modal distributions were found to be most similar between the French sample and the total European sample on most social network items in a European therapeutic community cohort ( n =723). The characteristics of the American and our sample were also found to be strikingly similar. Network size varies mainly with the number of substance-dependent contacts reported by the client. Southern European clients have been living with their parents while northern clients have been living alone. Spending most of the time with drug-using friends is not related to developing intimate relationships and a counterbalance to a situation of peer loneliness. The importance of looking at the function of social network relations in risk networks in harmonising European policy is emphasised.     

Peak Exposures in Epidemiologic Studies and Cancer Risks: Considerations for Regulatory Risk Assessment

We review approaches for characterizing “peak” exposures in epidemiologic studies and methods for incorporating peak exposure metrics in dose–response assessments that contribute to risk assessment. The focus was on potential etiologic relations between environmental chemical exposures and cancer risks. We searched the epidemiologic literature on environmental chemicals classified as carcinogens in which cancer risks were described in relation to “peak” exposures. These articles were evaluated to identify some of the challenges associated with defining and describing cancer risks in relation to peak exposures. We found that definitions of peak exposure varied considerably across studies. Of nine chemical agents included in our review of peak exposure, six had epidemiologic data used by the U.S. Environmental Protection Agency (US EPA) in dose–response assessments to derive inhalation unit risk values. These were benzene, formaldehyde, styrene, trichloroethylene, acrylonitrile, and ethylene oxide. All derived unit risks relied on cumulative exposure for dose–response estimation and none, to our knowledge, considered peak exposure metrics. This is not surprising, given the historical linear no‐threshold default model (generally based on cumulative exposure) used in regulatory risk assessments. With newly proposed US EPA rule language, fuller consideration of alternative exposure and dose–response metrics will be supported. “Peak” exposure has not been consistently defined and rarely has been evaluated in epidemiologic studies of cancer risks. We recommend developing uniform definitions of “peak” exposure to facilitate fuller evaluation of dose response for environmental chemicals and cancer risks, especially where mechanistic understanding indicates that the dose response is unlikely linear and that short‐term high‐intensity exposures increase risk.     

Conditional Gaussian mixture modelling for dietary pattern analysis

Summary.  Free-living individuals have multifaceted diets and consume foods in numerous combinations. In epidemiological studies it is desirable to characterize individual diets not only in terms of the quantity of individual dietary components but also in terms of dietary patterns. We describe the conditional Gaussian mixture model for dietary pattern analysis and show how it can be adapted to take account of important characteristics of self-reported dietary data. We illustrate this approach with an analysis of the 2000–2001 National Diet and Nutrition Survey of adults. The results strongly favoured a mixture model solution allowing clusters to vary in shape and size, over the standard approach that has been used previously to find dietary patterns.     

Complementary Approach of Data Analysis and Modeling to Estimate the Pattern of the BSE Epidemic: The Example of France

Clinical surveillance was the only way to detect bovine spongiform encephalopathy (BSE) until July 2000 in France. From the 103 cases identified as such between 1991 and June 2000, we used a back-calculation method to reconstruct the longitudinal trend of BSE infections. Between July 1987 and June 1997, an estimated 51,300 (CI =[24,300-84,700]) cattle were infected in France. The comprehensive surveillance of BSE with rapid tests, set up in France since 2001 at the abattoir and fallen plant, allowed study of the relative exposure of the successive birth cohorts with nonconditional logistic regression models adjusted for possible confounding variables. The results were in agreement with those of the back-calculation model, estimating a decrease of the BSE exposure from the birth cohort July 1995-June 1996 that matched with the decrease of the infection after June 1996. In view of the long incubation period of BSE, it is not possible to precisely assess the impact of any control measure before several years. Modeling was therefore used to estimate prospectively the efficiency of the ban of meat and bone meal extended to all farm species in November 2000. Using parameters about age at infection and incubation time estimated earlier, we assessed the minimum time to first detection if infections still occurred. We have waited up to June 2007 to know if less than 100 infections occurred among French cattle during the 6 months following January 2001.     

BAYESIAN BETA REGRESSION: APPLICATIONS TO HOUSEHOLD EXPENDITURE DATA AND GENETIC DISTANCE BETWEEN FOOT-AND-MOUTH DISEASE VIRUSES

There is considerable interest in understanding how factors such as time and geographic distance between isolates might influence the evolutionary direction of foot‐and‐mouth disease. Genetic differences between viruses can be measured as the proportion of nucleotides that differ for a given sequence or gene. We present a Bayesian hierarchical regression model for the statistical analysis of continuous data with sample space restricted to the interval (0, 1). The data are modelled using beta distributions with means that depend on covariates through a link function. We discuss methodology for: (i) the incorporation of informative prior information into an analysis; (ii) fitting the model using Markov chain Monte Carlo sampling; (iii) model selection using Bayes factors; and (iv) semiparametric beta regression using penalized splines. The model was applied to two different datasets.     

Methods to Explore Uncertainty and Bias Introduced by Job Exposure Matrices

Job exposure matrices (JEMs) are used to measure exposures based on information about particular jobs and tasks. JEMs are especially useful when individual exposure data cannot be obtained. Nonetheless, there may be other workplace exposures associated with the study disease that are not measured in available JEMs. When these exposures are also associated with the exposures measured in the JEM, biases due to uncontrolled confounding will be introduced. Furthermore, individual exposures differ from JEM measurements due to differences in job conditions and worker practices. Uncertainty may also be present at the assessor level since exposure information for each job may be imprecise or incomplete. Assigning individuals a fixed exposure determined by the JEM ignores these uncertainty sources. We examine the uncertainty displayed by bias analyses in a study of occupational electric shocks, occupational magnetic fields, and amyotrophic lateral sclerosis.     

Poster Abstracts

Objectives. To analyse the relation between results of the Aging Males' Symptoms (AMS) questionnaire for aging males, and of quality of life (QOL) questionnaire SF-12 and cardiovascular risk factors.

Methods. 1,927 men aged 55–85 years were interviewed by 56 general practitioners. During the interview the men were asked to fill in the AMS scale and the QOL questionnaire SF-12.

Results. Of 1,927 men 1,806 men filled correctly the AMS questionnaire. The mean SF-12 mental index was respectively 55.9 in men with a total AMS score indicating no impairment, 50.9 mild, 42.8 moderate, and 32.8 severe impairment. The corresponding values for the physical index were 51.2, 46.7, 40.8 and 32.3.

A history of diabetes was associated with an increased risk of reporting moderate/severe impairment: in relation to the total AMS score the odds ratio, (OR), of moderate/severe impairment in comparison with no impairment was 1.6 (95%CI 1.2–2.1). A history of myocardial infarction and hypertension increased the risk (respectively OR 1.4 (95%CI 1.1–18) and 1.7 (95%CI 1.2–2.4)).

Conclusions. This study shows that higher AMS scores are associated with lower SF-12 indices and suggests that elevated values of the AMS score are associated with cardiovascular risk factors or diseases.