Jan Beyea

In reviewing a video series that they created for the website of the Health Physics Society (HPS), past leaders of the Health Physics Society have treated as authoritative and trustworthy the scientific misconduct theories of University of Massachusetts Professor Edward Calabrese. No mention is made of detailed critiques of Calabrese’s work. I show that Calabrese’s historical work as presented by HPS’s authors is unreliable because it overlooks key historical text and key statistical concepts about the limits of an early atomic bomb genetics study. When these errors are corrected, claims of scientific misconduct on the part of historical figures evaporate. Claims of threshold behavior in early radiation genetic experiments are wrong for atomic bomb data. Calabrese’s unique claims about thresholds in early animal genetic data are not credible for human cancer, given the doses at which they were carried out (>30 R). Recent epidemiological studies of both acute and protracted exposure in humans fail to show dose-rate effects or a dose threshold above 30 R. Such results from human data should be more relevant for most regulators and review committees than Calabrese’s claims about old data on animals. Disclaimers, errata, and links to critiques should be added to the HPS webpage hosting the 22-part video series. Failure to do so can cause damage to reputations and historical accuracy because it erroneously validates Calabrese’s inflammatory claims of scientific misconduct against past scientists, including three Nobel Prize winners, members of the NAS, and presidents of the AAAS.

This study explores the likely prevalence of false indications of dose-response nonlinearity in large epidemiologic cancer radiation cohort studies (A-bomb survivors, INWORKS, Techa River). Reasons: Increasing numbers of tests of nonlinearity are being made in studies. Hypothesized nonlinear dose-response models have been justified to policy makers by analyses that rely in part on isolated findings that could be statistical fluctuations. After removing dose nonlinearity (linearization) by adjusting person-years of observation at each dose category, indications of nonlinearity, necessarily false, were counted in 5,000 randomized replications of six datasets. The average frequency of any false positive for five indicators of nonlinearity tested against a linear null was roughly 25% in Monte Carlo simulations per study, consistent with binomial calculations, increasing to ∼50% within 6 studies assessed. Comparable frequencies were found using Akaike's information criterion (AIC) for model selection or multi-model averaging. False above-zero threshold doses were found more than 50% of the time, averaging to 0.05 Gy, consistent with findings in the 6 studies. Such bias, uncorrected, could distort meta-analyses of multiple studies, because meta-analyses can incorporate high P value findings. AIC-based correction for the extra threshold parameter lowered these false occurrences to 8 to 19%. Given the simulation rates, the possibility of false positives might be noted when isolated findings of nonlinearity are discussed in a regulatory context. When reporting a threshold dose with a P value > 0.05, it would be informative to note the expected high false prevalence rate due to bias.

The objective of this paper is to compare post-2007 epidemiological results for plutonium workers to risk predicted by the software program NIOSH-IREP (IREP for short), which is used to determine the lowest dose for a US veteran to obtain cancer compensation. IREP output and methodology were used to predict excess relative risk per Gy (ERR Gy⁻¹) for lung cancer at the 99^th credibility percentile, which is used for compensation decisions. Also estimated were relative biological effectiveness factors (RBE) predicted for workers using IREP methodology. IREP predictions were compared to results for Mayak and Sellafield plutonium workers, separately and pooled. Indications that IREP might underpredict 99^th-percentile lung cancer plutonium risk came from (1) comparison of worker RBEs and (2) from comparison of Sellafield results separately. When Sellafield and Mayak data were pooled, ERR Gy⁻¹ comparisons at the 99^th percentile roughly matched epidemiological data with regression dose range restricted to < 0.05 Gy, the most relevant region to veterans, but overpredicted for the full dose range. When four plausible distributions for lung cancer risk, including both new and old data, were combined using illustrative weighting factors, compensation cutoff dose for lung cancer matched current IREP values unless regression results below 0.05 were chosen for Sellafield, producing a two-fold reduction. A 1997 claim of a dose threshold in lung cancer dose response was not confirmed in later literature. The benefit of the doubt is given to claimants when the science is unclear. The challenge for NIOSH-IREP custodians is dealing with the Sellafield results, which might best match US claimants.

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Vehicular traffic polycyclic aromatic hydrocarbons (PAHs) have been associated with breast cancer incidence in epidemiologic studies, including our own. Because PAHs damage DNA by forming adducts and oxidative lesions, genetic polymorphisms that alter DNA repair capacity may modify associations between PAH‐related exposures and breast cancer risk. Our goal was to examine the association between vehicular traffic exposure and breast cancer incidence within strata of a panel of nine biologically plausible nucleotide excision repair (NER) and base excision repair (BER) genotypes. Residential histories of 1,508 cases and 1,556 controls were assessed in the Long Island Breast Cancer Study Project between 1996 and 1997 and used to reconstruct residential traffic exposures to benzo[a]pyrene, as a proxy for traffic‐related PAHs. Likelihood ratio tests from adjusted unconditional logistic regression models were used to assess multiplicative interactions. A gene‐traffic interaction was evident (p = 0.04) for ERCC2 (Lys751); when comparing the upper and lower tertiles of 1995 traffic exposure estimates, the odds ratio (95% confidence interval) was 2.09 (1.13, 3.90) among women with homozygous variant alleles. Corresponding odds ratios for 1960–1990 traffic were also elevated nearly 2–3‐fold for XRCC1(Arg194Trp), XRCC1(Arg399Gln) and OGG1(Ser326Cys), but formal multiplicative interaction was not evident. When DNA repair variants for ERCC2, XRCC1 and OGG1 were combined, among women with 4–6 variants, the odds ratios were 2.32 (1.22, 4.49) for 1995 traffic and 2.96 (1.06, 8.21) for 1960–1990 traffic. Our study is first to report positive associations between traffic‐related PAH exposure and breast cancer incidence among women with select biologically plausible DNA repair genotypes.