A model is presented for applying Bayesian statistical techniques to the problem of determining, from the usual limited number of exposure measurements, whether the exposure profile for a similar exposure group can be considered a Category 0, 1, 2, 3, or 4 exposure. The categories were adapted from the AIHA exposure category scheme and refer to (0) negligible or trivial exposure (i.e., the true X 0.95 < or =1%OEL), (1) highly controlled (i.e., X 0.95 < or =10%OEL), (2) well controlled (i.e., X 0.95 < or =50%OEL), (3) controlled (i.e., X 0.95 < or =100%OEL), or (4) poorly controlled (i.e., X0.95 > or =1%OEL) exposures. Unlike conventional statistical methods applied to exposure data, Bayesian statistical techniques can be adapted to explicitly take into account professional judgment or other sources of information. The analysis output consists of a distribution (i.e., set) of decision probabilities: e.g., 1%, 80%, 12%, 5%, and 2% probability that the exposure profile is a Category 0, 1, 2, 3, or 4 exposure. By inspection of these decision probabilities, rather than the often difficult to interpret point estimates (e.g., the sample 95th percentile exposure) and confidence intervals, a risk manager can be better positioned to arrive at an effective (i.e., correct) and efficient decision. Bayesian decision methods are based on the concepts of prior, likelihood, and posterior distributions of decision probabilities. The prior decision distribution represents what an industrial hygienist knows about this type of operation, using professional judgment; company, industry, or trade organization experience; historical or surrogate exposure data; or exposure modeling predictions. The likelihood decision distribution represents the decision probabilities based on an analysis of only the current data. The posterior decision distribution is derived by mathematically combining the functions underlying the prior and likelihood decision distributions, and represents the final decision probabilities. Advantages of Bayesian decision analysis include: (a) decision probabilities are easier to understand by risk managers and employees; (b) prior data, professional judgment, or modeling information can be objectively incorporated into the decision-making process; (c) decisions can be made with greater certainty; (d) the decision analysis can be constrained to a more realistic "parameter space" (i.e., the range of plausible values for the true geometric mean and geometric standard deviation); and (e) fewer measurements are necessary whenever the prior distribution is well defined and the process is fairly stable. Furthermore, Bayesian decision analysis provides an obvious feedback mechanism that can be used by an industrial hygienist to improve professional judgment. For example, if the likelihood decision distribution is inconsistent with the prior decision distribution then it is likely that either a significant process change has occurred or the industrial hygienist's initial judgment was incorrect. In either case, the industrial hyg...

This investigation randomly sampled a fluorochemical manufacturing employee population to determine the distribution of serum fluorochemical levels according to employees' jobs and work areas. Previous analyses of medical surveillance data have not shown significant associations between fluorochemical production employees' clinical chemistry and hematology tests and their serum PFOS and perfluorooctanoate (PFOA, C(7)F(15)COO(-)) concentrations, but may have been subject to nonparticipation bias. A random sample of the on-site film plant employee population, where fluorochemicals are not produced, determined their serum concentrations also. Of the 232 employees randomly selected for serum sampling, 186 (80%) employees participated (n=126 chemical plant; n=60 film plant). Sera samples were extracted using an ion-pairing extraction procedure and were quantitatively analyzed for seven fluorochemicals using high-pressure liquid chromatography electrospray tandem mass spectrometry methods. Geometric means (in parts per million) and 95% confidence intervals (in parentheses) of the random sample of 126 chemical plant employees were: PFOS 0.941 (0.787-1.126); PFOA 0.899 (0.722-1.120); perfluorohexanesulfonate 0.180 (0.145-0.223); N-ethyl perfluorooctanesulfonamidoacetate 0.008 (0.006-0.011); N-methyl perfluorooctanesulfonamidoacetate 0.081 (0.067-0.098); perfluorooctanesulfonamide 0.013 (0.009-0.018); and perfluorooctanesulfonamidoacetate 0.022 (0.018-0.029). These geometric means were approximately one order of magnitude higher than those observed for the film plant employees.

ObjectiveTo evaluate mortality and cancer incidence in a cohort of ammonium perfluorooctanoate (APFO) exposed workers.MethodsWe linked a combined cohort (n=9027) of employees from APFO and non-APFO production facilities in Minnesota to the National Death Index and to cancer registries of Minnesota and Wisconsin. Industrial hygiene data and expert evaluation were used to create a task-based job exposure matrix to estimate APFO exposure. Standardised mortality ratios were estimated using Minnesota population rates. HRs and 95% CIs for time-dependent cumulative APFO exposure were estimated with an extended Cox model. A priori outcomes of interest included cancers of the liver, pancreas, testes, kidney, prostate and breast, and mortality from cardiovascular, cerebrovascular and chronic renal diseases.ResultsMortality rates in the APFO-exposed cohort were at or below the expected, compared with Minnesota. The HR for dying from the cancer and non-cancer outcomes of interest did not show an association with APFO exposure. Similarly, there was little evidence that the incident cancers were associated with APFO exposure. Compared to the non-exposed population, modestly elevated, but quite imprecise HRs were observed in the higher-exposure quartiles for bladder cancer (HR=1.66, 95% CI 0.86 to 3.18) and pancreatic cancer (HR=1.36, 95% CI 0.59 to 3.11). No association was observed between APFO exposure and kidney, prostate or breast cancers.ConclusionsThis analysis did not support an association between occupational APFO exposure and the evaluated health endpoints, however, the study had limited power to evaluate some conditions of interest.

The mortality of 2650 employees (93.4% males) in the mine and mill production of roofing granules at four plants was examined between 1945 and 2004. Hypotheses focused on diseases associated with exposure to silica: nonmalignant respiratory disease, lung cancer, and nonmalignant renal disease. Study eligibility required ≥ 1 year of employment by 2000. Work history and vital status were followed through 2004 with < 1% lost to follow-up. Industrial hygiene sampling data (1871 sampling measurements over a 32-year period) and professional judgment were used to construct 15 respirable crystalline silica exposure categories. A category was assigned to all plant-, department-, and time-dependent standard job titles. Cumulative respirable crystalline silica exposure (mg/m(3)-years) was calculated as the sum of the product of time spent and the average exposure for each plant-, department-, job-, and calendar-year combination. The cohort geometric mean was 0.17 mg/m(3)-years (geometric standard deviation 4.01) and differed by plant. Expected deaths were calculated using U.S. (entire cohort) and regional (each plant) mortality rates. Poisson regression was used for internal comparisons. For the entire cohort, 772 deaths (97.4% males) were identified (standardized mortality ratio 0.95, 95% CI 0.88-1.02). There were 50 deaths from nonmalignant respiratory diseases (1.14, 95% CI 0.85-1.51). Lagging exposure 15 years among the male cohort, the relative risks for nonmalignant respiratory disease were 1.00 (reference), 0.80, 1.94, and 2.03 (p value trend = 0.03) when cumulative exposure was categorized < 0.1, 0.1- < 0.5, 0.5- < 1.0, and ≥ 1.0 mg/m(3)-years, respectively. There was a total of 77 lung cancer deaths (1.11, 95% CI 0.88-1.39). Lagging exposure 15 years, the relative risks for males were 1.00 (reference), 1.83, 1.83, and 1.05 (p value trend = 0.9). There were 16 deaths from nonmalignant renal disease (1.76, 95% CI 1.01-2.86). This exposure-response trend was suggestive but imprecise. The study results are consistent with other cohorts with similar levels of exposure to respirable crystalline silica.

This investigation randomly sampled a fluorochemical manufacturing employee population to determine the distribution of serum fluorochemical levels according to employees' jobs and work areas. Previous analyses of medical surveillance data have not shown significant associations between fluorochemical production employees' clinical chemistry and hematology tests and their serum PFOS and perfluorooctanoate (PFOA, C(7)F(15)COO(-)) concentrations, but may have been subject to nonparticipation bias. A random sample of the on-site film plant employee population, where fluorochemicals are not produced, determined their serum concentrations also. Of the 232 employees randomly selected for serum sampling, 186 (80%) employees participated (n=126 chemical plant; n=60 film plant). Sera samples were extracted using an ion-pairing extraction procedure and were quantitatively analyzed for seven fluorochemicals using high-pressure liquid chromatography electrospray tandem mass spectrometry methods. Geometric means (in parts per million) and 95% confidence intervals (in parentheses) of the random sample of 126 chemical plant employees were: PFOS 0.941 (0.787-1.126); PFOA 0.899 (0.722-1.120); perfluorohexanesulfonate 0.180 (0.145-0.223); N-ethyl perfluorooctanesulfonamidoacetate 0.008 (0.006-0.011); N-methyl perfluorooctanesulfonamidoacetate 0.081 (0.067-0.098); perfluorooctanesulfonamide 0.013 (0.009-0.018); and perfluorooctanesulfonamidoacetate 0.022 (0.018-0.029). These geometric means were approximately one order of magnitude higher than those observed for the film plant employees.