Bayesian Methods for the Evaluation of Tritium: Relative Biological Effectiveness and Cancer Risk

Tritium is a radioisotope of hydrogen that releases β- energy, which is a form of ionizing radiation. Tritium is understudied due to a lack of epidemiological data on human exposure, despite the fact that exposure to low energy ionizing radiation is ubiquitous in the environment. In occupational studies of nuclear workers, tritium is usually aggregated with gamma radiation and examined under the assumption that the cancer risk per unit exposure of each is equivalent. However, a recent systematic review of the literature suggests that beta radiation is more biologically effective than gamma radiation. We utilize Bayesian methods to inform estimation of cancer risk for tritium as well as the RBE of tritium compared to gamma radiation using information compiled at the Savannah River Site (SRS) nuclear fuel facility in Aiken, SC. SRS staff maintained detailed records of personnel dosimetry that have been utilized in previous epidemiological studies. Included is information on employment history, radiation exposure, including separate gamma and tritium radiation dosimetry records, as well as vital status information. We calculate the excess relative rate of leukemia and leukemia excluding chronic lymphocytic leukemia (CLL) associated with tritium and gamma radiation. The ERR/10mGy (90% HPD) of leukemia associated with tritium and gamma radiation are 0.282 (0.027, 0.678) and 0.044 (0.000, 0.108), respectively. This yields an estimate of the relative biological effectiveness of tritium relative to gamma radiation (RBE) of 6.24 (1.00, 36.09). With regard to leukemia excluding CLL, the ERR/10mGy associated with tritium and gamma radiation are 0.338 (0.048, 0.805) and 0.087 (0.000, 0.195), respectively. This yields an RBE of 3.88 (1.00, 16.80). The values of the RBE are within the range of plausible values suggested by others. Our results utilize evidence from in vitro and in vivo research to inform estimation of the risk of cancer associated with tritium by incorporating knowledge of the direction and magnitude of tritium's relationship to cancer compared to gamma radiation. This illustrates a simple approach for using Bayesian methods to integrate external knowledge into epidemiological studies without the need to specify estimates of the risk based on research that cannot be easily translated from experimental animal and cellular models into human risk models

Lung Cancer Risk Associated with Regulated and Unregulated Chrysotile Asbestos Fibers

BACKGROUND: Regulation of asbestos fibers in the workplace is partly determined by which fibers can be visually counted. However, a majority of fibers are too short and thin to count this way and are, consequently, not subject to regulation. METHODS: We estimate lung cancer risk associated with asbestos fibers of varying length and width. We apply an order-constrained prior both to leverage external information from toxicological studies of asbestos health effects. This prior assumes that risk from asbestos fibers increases with increasing length and decreases with increasing width. RESULTS: When we apply a shared mean for the effect of all asbestos fiber exposure groups, the rate ratios for each fiber group per unit exposure appear mostly equal. Rate ratio estimates for fibers of diameter 40 μm in the thinnest fiber group are similar in magnitude to estimates of risk associated with long fibers in the regulated fraction of airborne asbestos fibers. Rate ratio estimates for longer fibers are larger than those for shorter fibers, but thicker and thinner fibers do not differ as the toxicologically derived prior had expected. CONCLUSION: Credible intervals for fiber size-specific risk estimates overlap; thus, we cannot conclude that there are substantial differences in effect by fiber size. Nonetheless, our results suggest that some unregulated asbestos fibers may be associated with increased incidence of lung cancer

Sensitivity Analyses for Sparse-Data Problems—Using Weakly Informative Bayesian Priors

Sparse-data problems are common, and approaches are needed to evaluate the sensitivity of parameter estimates based on sparse data. We propose a Bayesian approach that uses weakly informative priors to quantify sensitivity of parameters to sparse data. The weakly informative prior is based on accumulated evidence regarding the expected magnitude of relationships using relative measures of disease association. We illustrate the use of weakly informative priors with an example of the association of lifetime alcohol consumption and head and neck cancer. When data are sparse and the observed information is weak, a weakly informative prior will shrink parameter estimates toward the prior mean. Additionally, the example shows that when data are not sparse and the observed information is not weak, a weakly informative prior is not influential. Advancements in implementation of Markov Chain Monte Carlo simulation make this sensitivity analysis easily accessible to the practicing epidemiologist

Bayesian Posterior Distributions Without Markov Chains

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC

Outdoor Particulate Matter Exposure and Lung Cancer: A Systematic Review and Meta-Analysis

Background: Particulate matter (PM) in outdoor air pollution was recently designated a Group I carcinogen by the International Agency for Research on Cancer (IARC). This determination was based on the evidence regarding the relationship of PM2.5 and PM10 to lung cancer risk; however, the IARC evaluation did not include a quantitative summary of the evidence. Objective: Our goal was to provide a systematic review and quantitative summary of the evidence regarding the relationship between PM and lung cancer. Methods: We conducted meta-analyses of studies examining the relationship of exposure to PM2.5 and PM10 with lung cancer incidence and mortality. In total, 18 studies met our inclusion criteria and provided the information necessary to estimate the change in lung cancer risk per 10-μg/m3 increase in exposure to PM. We used random-effects analyses to allow between-study variability to contribute to meta-estimates. Results: The meta-relative risk for lung cancer associated with PM2.5 was 1.09 (95% CI: 1.04, 1.14). The meta-relative risk of lung cancer associated with PM10 was similar, but less precise: 1.08 (95% CI: 1.00, 1.17). Estimates were robust to restriction to studies that considered potential confounders, as well as subanalyses by exposure assessment method. Analyses by smoking status showed that lung cancer risk associated with PM2.5 was greatest for former smokers [1.44 (95% CI: 1.04, 1.22)], followed by never-smokers [1.18 (95% CI: 1.00, 1.39)], and then current smokers [1.06 (95% CI: 0.97, 1.15)]. In addition, meta-estimates for adenocarcinoma associated with PM2.5 and PM10 were 1.40 (95% CI: 1.07, 1.83) and 1.29 (95% CI: 1.02, 1.63), respectively. Conclusion: The results of these analyses, and the decision of the IARC Working Group to classify PM and outdoor air pollution as carcinogenic (Group 1), further justify efforts to reduce exposures to air pollutants that can arise from many sources. Citation: Hamra GB, Guha N, Cohen A, Laden F, Raaschou-Nielsen O, Samet JM, Vineis P, Forastiere F, Saldiva P, Yorifuji T, Loomis D. 2014. Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environ Health Perspect 122:906–911; http://dx.doi.org/10.1289/ehp.140809

Ionising radiation and risk of death from leukaemia and lymphoma in radiation-monitored workers (INWORKS): an international cohort study

SummaryBackgroundThere is much uncertainty about the risks of leukaemia and lymphoma after repeated or protracted low-dose radiation exposure typical of occupational, environmental, and diagnostic medical settings. We quantified associations between protracted low-dose radiation exposures and leukaemia, lymphoma, and multiple myeloma mortality among radiation-monitored adults employed in France, the UK, and the USA.MethodsWe assembled a cohort of 308 297 radiation-monitored workers employed for at least 1 year by the Atomic Energy Commission, AREVA Nuclear Cycle, or the National Electricity Company in France, the Departments of Energy and Defence in the USA, and nuclear industry employers included in the National Registry for Radiation Workers in the UK. The cohort was followed up for a total of 8·22 million person-years. We ascertained deaths caused by leukaemia, lymphoma, and multiple myeloma. We used Poisson regression to quantify associations between estimated red bone marrow absorbed dose and leukaemia and lymphoma mortality.FindingsDoses were accrued at very low rates (mean 1·1 mGy per year, SD 2·6). The excess relative risk of leukaemia mortality (excluding chronic lymphocytic leukaemia) was 2·96 per Gy (90% CI 1·17–5·21; lagged 2 years), most notably because of an association between radiation dose and mortality from chronic myeloid leukaemia (excess relative risk per Gy 10·45, 90% CI 4·48–19·65).InterpretationThis study provides strong evidence of positive associations between protracted low-dose radiation exposure and leukaemia.FundingCenters for Disease Control and Prevention, Ministry of Health, Labour and Welfare of Japan, Institut de Radioprotection et de Sûreté Nucléaire, AREVA, Electricité de France, National Institute for Occupational Safety and Health, US Department of Energy, US Department of Health and Human Services, University of North Carolina, Public Health England

Risk of cancer from occupational exposure to ionising radiation: retrospective cohort study of workers in France, the United Kingdom, and the United States (INWORKS)

Study question Is protracted exposure to low doses of ionising radiation associated with an increased risk of solid cancer?Methods In this cohort study, 308 297 workers in the nuclear industry from France, the United Kingdom, and the United States with detailed monitoring data for external exposure to ionising radiation were linked to death registries. Excess relative rate per Gy of radiation dose for mortality from cancer was estimated. Follow-up encompassed 8.2 million person years. Of 66 632 known deaths by the end of follow-up, 17 957 were due to solid cancers.Study answer and limitations Results suggest a linear increase in the rate of cancer with increasing radiation exposure. The average cumulative colon dose estimated among exposed workers was 20.9 mGy (median 4.1 mGy). The estimated rate of mortality from all cancers excluding leukaemia increased with cumulative dose by 48% per Gy (90% confidence interval 20% to 79%), lagged by 10 years. Similar associations were seen for mortality from all solid cancers (47% (18% to 79%)), and within each country. The estimated association over the dose range of 0-100 mGy was similar in magnitude to that obtained over the entire dose range but less precise. Smoking and occupational asbestos exposure are potential confounders; however, exclusion of deaths from lung cancer and pleural cancer did not affect the estimated association. Despite substantial efforts to characterise the performance of the radiation dosimeters used, the possibility of measurement error remains. What this study adds The study provides a direct estimate of the association between protracted low dose exposure to ionising radiation and solid cancer mortality. Although high dose rate exposures are thought to be more dangerous than low dose rate exposures, the risk per unit of radiation dose for cancer among radiation workers was similar to estimates derived from studies of Japanese atomic bomb survivors. Quantifying the cancer risks associated with protracted radiation exposures can help strengthen the foundation for radiation protection standards. Funding, competing interests, data sharing Support from the US Centers for Disease Control and Prevention; Ministry of Health, Labour and Welfare of Japan; Institut de Radioprotection et de Sûreté Nucléaire; AREVA; Electricité de France; US National Institute for Occupational Safety and Health; US Department of Energy; and Public Health England. Data are maintained and kept at the International Agency for Research on Cancer

Model Averaging for Improving Inference from Causal Diagrams

Model selection is an integral, yet contentious, component of epidemiologic research. Unfortunately, there remains no consensus on how to identify a single, best model among multiple candidate models. Researchers may be prone to selecting the model that best supports their a priori, preferred result; a phenomenon referred to as “wish bias”. Directed acyclic graphs (DAGs), based on background causal and substantive knowledge, are a useful tool for specifying a subset of adjustment variables to obtain a causal effect estimate. In many cases, however, a DAG will support multiple, sufficient or minimally-sufficient adjustment sets. Even though all of these may theoretically produce unbiased effect estimates they may, in practice, yield somewhat distinct values, and the need to select between these models once again makes the research enterprise vulnerable to wish bias. In this work, we suggest combining adjustment sets with model averaging techniques to obtain causal estimates based on multiple, theoretically-unbiased models. We use three techniques for averaging the results among multiple candidate models: information criteria weighting, inverse variance weighting, and bootstrapping. We illustrate these approaches with an example from the Pregnancy, Infection, and Nutrition (PIN) study. We show that each averaging technique returns similar, model averaged causal estimates. An a priori strategy of model averaging provides a means of integrating uncertainty in selection among candidate, causal models, while also avoiding the temptation to report the most attractive estimate from a suite of equally valid alternatives

Lung cancer and exposure to nitrogen dioxide and traffic : a systematic review and meta-analysis

Background and objective: Exposure to traffic-related air pollutants is an important public health issue. Here, we present a systematic review and meta-analysis of research examining the relationship of measures of nitrogen oxides and of various measures of traffic related air pollution exposure with lung cancer. Methods: We conducted random effects meta-analyses of studies examining exposure to NO₂ and NOx exposure and lung cancer. We identified 20 studies that met inclusion criteria and provided information necessary to estimate the change in lung cancer per 10 μg/m³ increase in exposure to measured NO₂. Further, we qualitatively assess the evidence of association between distance to roadways and traffic volume associated with lung cancer. Results: The meta-estimate for the change in lung cancer associated with a 10 μg/m³ increase in exposure to NO₂ was 4% (95% CI: 1%, 8%). The meta-estimate for change in lung cancer associated with a 10 μg/m³ increase in NOx was similar and slightly more precise, 3% (95% CI: 1%, 5%). The NO₂ meta-estimate was robust to different confounding adjustment sets as well as the exposure assessment techniques utilized. Trim-and-fill analyses suggest that if publication bias exists the overall meta-estimate is biased away from the null. Forest plots for measures of traffic volume and distance to roadways largely suggest a modest increase in lung cancer risk. Conclusion: We found consistent evidence of a relationship between NO₂ and NOx, as proxies for traffic sourced air pollution exposure, with lung cancer. Studies of lung cancer related to residential proximity to roadways also suggest increased risk, which may be due in part to air pollution exposure. The International Agency for Research on Cancer recently classified outdoor air pollution and particulate matter as carcinogenic (Group 1). These meta-analyses support this conclusion, drawing particular attention to traffic sourced air pollution.Population and Public Health (SPPH), School ofNon UBCMedicine, Faculty ofReviewedFacult