Excess Relative Risk Models Research Articles

A note on potential gains in precision of radiation risk estimates from joint analysis.

In estimating radiation-related risk of cancer and other diseases based on the RERF Life Span Study (LSS), joint analyses can be performed where multiple health outcome endpoints are combined in the same model, allowing some parameters to be estimated in common among all endpoints with possible increase in precision of radiation risk and other model parameter estimates. Using as a basis excess relative risk (ERR) and excess absolute risk (EAR) models of the type commonly used in analysis of LSS data at RERF, we use maximum likelihood theory to compute the asymptotic relative standard error of endpoint-specific radiation effect and other parameter estimates using joint analyses as compared to traditional independent analysis. We show that some gains in precision of endpoint-specific radiation risk parameter estimates can be achieved by sharing effect modifier and other model parameters, but only small or negligible gains in precision are achieved for endpoint-specific background modifying or effect modifying parameters when other model parameters are shared. The magnitude of the precision gain for radiation risk estimates depends on the number of endpoints, the baseline incidence rate of the endpoint, and the type of model being used.

Risk of death from hematological malignancies in the South Urals Population Exposed to Radiation cohort

The results of studies of long-term effects of population exposure in the South Urals in the 1950s were based for a long time on studies of the effects of exposure in the Techa River cohort and later in the East Urals Radioactive Trace cohort. After the creation of the South Urals Population Exposed to Radiation cohort, combining all persons exposed in the South Urals in the period from January 1, 1950, to December 31, 1960 the size of the cohort doubled, follow-up period reached 71 years, and the number of person-years at risk increased to 1,964,333. The average dose to red bone marrow for all cohort members was 231mGy. Regression analysis using a simple parametric excess relative risk model was performed using the EPICURE statistical package. The analysis resulted in confirmation of a statistically significant (p <0.001) linear doseresponse relationship of mortality for all hemoblastoses, and leukemia. The excess relative risk and 95% confidence intervals of death at 2-year latency period from all hemoblastoses were 0.71/Gy (0.28;1.31); from all leukemias - 1.28/Gy (0.55-2.39) and from leukemias excluding chronic lymphocytic leukemia -1.52/Gy (0.64-2.94). The quadratic model also significantly described the dose dependence; (the differences between the models were not significant). Increasing the follow-up period to 71 years resulted in a slight decrease in risk estimates. The width of the confidence intervals of the risk estimates decreased by more than a factor of 3 compared to earlier studies in the Techa River cohort, which indicates a decrease in uncertainties of risk estimates. Increasing the size of the cohort made it possible to obtain significant risk values for individual population groups (by sex, age, etc.). Analysis of risk values modification did not reveal significant differences in dose dependence on the studied factors, including attained age and age at exposure. The study will be continued to investigate in more detail the influence of time-dependent factors on dose dependence, as well as to assess the risk of death from different cellular forms of leukemia.

Solid cancer mortality risk in the cohort of Urals population exposed to radiation for up the age of 20

The objective of the current study is the direct assessment of the excess relative risk of solid cancer mortality in people affected by low dose-rate chronic exposure mainly in low-to medium dose range (up to 1 Gy) at the age of <20. The study cohort consists of the population exposed at the age of <20 as a result of two large radiation accidents at the “Mayak” Production Association. The size of the cohort is 32,762 persons, follow-up period – 71 years (from 1950 through 2020), the number of person-years at risk is – 1 218 855. For this cohort the mortality catchment area includes Chelyabinsk and Kurgan Oblasts. Over a 71-year follow-up period 1,788 solid cancer deaths were registered in the cohort. Mean dose accumulated in the stomach walls over the whole follow-up period was 0.04 Gy, maximum dose was 1.1 Gy. Statistical processing of data was performed using Amfit and Datab software packages (Epicure statistics software package). Solid cancer mortality risk was analyzed with the Poisson regression using simple parametric excess relative risk model. Statistical significance of the results was estimated using the maximum likelihood method with 95% probability. For the first time the direct assessments of the excess relative risk of solid cancer mortality have been obtained in the Urals Cohort of population exposed at age <20 for a 71-year period (Excess Relative Risk was 0.84/Gy, 95% confidence interval: 0.24-1.55 for 10-years minimal latency period and 0.83 (0.24-1.55) for 5-year minimal latency period). Statistically significant estimates of the excess relative risk have been obtained for specific groups within the cohort selected according to the sex, age at the onset of exposure, and Oblast of residence at the time of exposure.

Excess pancreatic cancer risk due to smoking and modifying effect of quitting smoking: The Multiethnic Cohort Study

PurposeRisk factors for pancreatic cancer include racial/ethnic disparities and smoking. However, risk trajectories by smoking history and race/ethnicity are unknown. We examined the association of smoking with pancreatic cancer by race/ethnicity to generate age-specific incidence estimates by smoking history.MethodsWe modeled pancreatic cancer incidence by race/ethnicity, age, pack-years, and years-quit using an excess relative risk model for 182,011 Multiethnic Cohort participants. We tested heterogeneity of smoking variables and pancreatic cancer by race/ethnicity and predicted incidence by smoking history.ResultsWe identified 1,831 incident pancreatic cancer cases over an average 19.3 years of follow-up. Associations of pack-years (p interaction by race/ethnicity = 0.41) and years-quit (p interaction = 0.83) with pancreatic cancer did not differ by race/ethnicity. Fifty pack-years smoked was associated with 91% increased risk (95% CI 54%, 127%) relative to never smokers in the combined sample. Every year quit corresponded to 9% decreased excess risk (95% CI 2%, 15%) from pack-years smoked. Differences in baseline pancreatic cancer risk across racial/ethnic groups (p < 0.001) translated to large differences in risk for smokers at older ages across racial/ethnic groups (65–122 cases per 100,000 at age 70).ConclusionSmoking pack-years were positively associated with elevated pancreatic cancer risk. Predicted risk trajectories showed a high impact of smoking cessation at < 65 years. Although we did not identify significant heterogeneity in the association of pack-years or years quit with pancreatic cancer risk, current smoker risk varied greatly by race/ethnicity in later life due to large differences in baseline risk.

Abstract 6461: Excess risk due to smoking and modifying effect of quitting on bladder cancer incidence: the multiethnic cohort

Abstract Background: Smoking is an established risk factor for urothelial cancer of the bladder (BC), attributed to over 50% of cases among men, however associations outside of European-descent populations are infrequently reported. Further, absolute risk of BC by smoking history has not yet been reported. Characterizing these associations will identify value of smoking cessation across non-European ancestry populations. Methods: We estimated the association between pack-years, years quit, and BC among 181,231 Multiethnic Cohort Study participants from five major racial/ethnic groups (African Americans [AA], European Americans [EA], Japanese Americans [JA], Latinos [LA], Native Hawaiians [NH]) 45-75 years old at enrollment (1993-1996). Smoking history was assessed by self-report at cohort entry and on a follow-up survey (2003-2008). Associations were estimated using an excess relative risk model to distinguish between variables that affect baseline risk and variables (e.g. years quit) that only affect excess risk due to smoking, while adjusting for sex, age, race/ethnicity, diabetes, body mass index, and cigarettes smoked per day. BC cases were identified through SEER cancer registries to 2017. We tested for heterogeneity of smoking variables by race/ethnicity and estimated incidence across age, race-ethnicity, pack-years, and years quit. Results: Over a mean 19 years of follow-up, 1,914 BC cases were identified. Age-standardized incidence rates of BC were highest among EA (51/100,000) and lowest among LA (24/100,000). Never smokers were most common among JA and LA (51%) and least common in other racial/ethnic groups (39-40%). In the full study sample, 50 pack-years smoked was associated with BC (HR=3.2 95% CI 2.7, 3.6). The association was heterogeneous by race/ethnicity (p=0.02) and statistically significant within each racial/ethnic group (p&amp;lt;0.05), except NH (p=0.07). Associations were strongest among EA (HR=4.0), followed by AA (HR=3.6), JA (HR=2.7), LA (HR=2.6), and NH (HR=2.4). There was no statistically significant heterogeneity of smoking cessation (p=0.08) or cigarettes smoked per day (p=0.82) by race/ethnicity. Each year quit resulted in a less than 1% decrease in the excess relative risk from smoking (p=0.12). The absolute risk models showed highest incidence at age 70 among EA current smokers (176/100,000) and lowest among AA never smokers (27/100,000). At age 70, current smokers across all ethnic groups had lower risk than EA who quit at age 45. Conclusion: Pack-years smoked is significantly associated with BC risk. EA and AA groups experience the greatest excess relative risk from smoking and NH the lowest. Reduction in excess relative risk following smoking cessation is weak, meaning the cumulative effect of pack-years smoked on BC risk cannot easily be attenuated with quitting. This contrasts with the risk reduction seen for lung and pancreatic cancer. Citation Format: David Bogumil, Victoria Cortessis, Daniel Stram, Veronica W. Setiawan, Christopher Haiman, Loic Le Marchand, Gertraud Maskarinec. Excess risk due to smoking and modifying effect of quitting on bladder cancer incidence: the multiethnic cohort. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6461.

Modifiers of radiation effects on breast cancer incidence revealed by a reanalysis of archival data of rat experiments.

Cancer risk after exposure to ionizing radiation can vary between individuals and populations, but the impact of factors governing those variations is not well understood. We previously conducted a series of carcinogenesis experiments using a rat model of breast cancer, in which 1654 rats born in 2002-2012 were exposed to γ rays at various doses and ages with or without non-radiation factors including high-fat diet, parity and chemical carcinogens. We herein reanalyze the incidence data from these archival experiments to clarify the effect of age at exposure, attained age, radiation dose and non-radiation factors (i.e. fat, parity, chemicals and birth cohorts) on radiation-related mammary cancer incidence. The analysis used excess relative risk (ERR) and excess absolute risk (EAR) models as well as generalized interaction models. Age-at-exposure dependence displayed a peak of susceptibility at puberty in both the ERR and EAR models. Attained age decreased ERR and increased EAR per unit radiation dose. The dose response was concordant with a linear model. Dietary fat exhibited a supra-multiplicative interaction, chemicals represented a multiplicative interaction, and parity and birth cohorts displayed interactions that did not significantly depart from additivity or multiplicativity. Treated as one entity, the four non-radiation factors gave a multiplicative interaction, but separation of the four factors significantly improved the fit of the model. Thus, the present study supports age and dose dependence observed in epidemiology, indicates heterogenous interactions between radiation and various non-radiation factors, and suggests the potential use of more flexible interaction modeling in radiological protection.

Flying without a Net: Space Radiation Cancer Risk Predictions without a Gamma-ray Basis.

The biological effects of high linear energy transfer (LET) radiation show both a qualitative and quantitative difference when compared to low-LET radiation. However, models used to estimate risks ignore qualitative differences and involve extensive use of gamma-ray data, including low-LET radiation epidemiology, quality factors (QF), and dose and dose-rate effectiveness factors (DDREF). We consider a risk prediction that avoids gamma-ray data by formulating a track structure model of excess relative risk (ERR) with parameters estimated from animal studies using high-LET radiation. The ERR model is applied with U.S. population cancer data to predict lifetime risks to astronauts. Results for male liver and female breast cancer risk show that the ERR model agrees fairly well with estimates of a QF model on non-targeted effects (NTE) and is about 2-fold higher than the QF model that ignores NTE. For male or female lung cancer risk, the ERR model predicts about a 3-fold and more than 7-fold lower risk compared to the QF models with or without NTE, respectively. We suggest a relative risk approach coupled with improved models of tissue-specific cancers should be pursued to reduce uncertainties in space radiation risk projections. This approach would avoid low-LET uncertainties, while including qualitive effects specific to high-LET radiation.

Lifetime Risk Assessment of Lung Cancer Incidence for Nonsmokers in Japan Considering the Joint Effect of Radiation and Smoking Based on the Life Span Study of Atomic Bomb Survivors

Background: The lifetime risk of lung cancer incidence due to radiation for nonsmokers is overestimated because of the use of the average cancer baseline risk among a mixed population, including smokers. In recent years, the generalized multiplicative (GM)-excess relative risk (ERR) model has been developed in the life span study of atomic bomb survivors to consider the joint effect of radiation and smoking. Based on this background, this paper discusses the issues of radiation risk assessment considering smoking in two parts.Materials and Methods: In Part 1, we proposed a simple method of estimating the baseline risk for nonsmokers using current smoking data. We performed sensitivity analysis on baseline risk estimation to discuss the birth cohort effects. In Part 2, we applied the GM-ERR model for Japanese smokers to calculate lifetime attributable risk (LAR). We also performed a sensitivity analysis using other ERR models (e.g., simple additive (SA)-ERR model).Results and Discussion: In Part 1, the lifetime baseline risk from mixed population including smokers to nonsmokers decreased by 54% (44%–60%) for males and 24% (18%–29%) for females. In Part 2, comparison of LAR between SA- and GM-ERR models showed that if the radiation dose was ≤200 mGy or less, the difference between these ERR models was within the standard deviation of LAR due to the uncertainty of smoking information.Conclusion: The use of mixed population for baseline risk assessment overestimates the risk for lung cancer due to low-dose radiation exposure in Japanese males.

Bias correction for estimates from linear excess relative risk models in small case-control studies.

Epidemiologic studies conducted to quantify the impact of radiation dose d on an outcome typically model the hazard ratio (HR) for association using a linear term, , via a linear excess relative risk (ERR) model, based on biological considerations. To study associations of risk of a second cancer with radiation treatment for a first cancer, several nested case-control designs to estimate have been proposed that use refined doses received by different locations in the organ of interest. Here we first evaluated the small sample bias in maximum likelihood estimates of for the linear ERR model using location-specific radiation doses in simulations. As we found substantial upward bias for studies of realistic sample sizes (more than 50% relative bias for studies with 75 cases), we also proposed and investigated several approaches to correct this bias. We studied first and second order jackknife bias corrections and we derived a modified set of score functions under retrospective case-control sampling, from which we directly obtained bias-corrected estimates. In simulations based on doses from a study of stomach cancer among testicular cancer survivors and synthetically generated data, neither the first nor second order jackknife bias correction performed well. Estimates based on the modified score equations corrected the bias much better, albeit not completely, and were numerically much more stable.

Radiogenic risk of solid cancer incidence in persons exposed to radiation in childhood in the Southern Urals

The aim of this work is to assess the radiogenic risk of solid cancers incidence in the members of the Urals Childhood Exposure Cohort. The cohort includes people exposed under 20 years of age as a result of two radiation accidents at the Mayak Production Association in the Southern Urals (discharges of radioactive waste into the Techa River and the formation of the East Ural radioactive trace). The number of the cohort for solid cancer incidence analysis is 31,578 individuals. All the members were postnatally exposed and some of them – in-utero. Some of their parents were exposed before conception. 2,018 solid cancers were registered on the incidence catchment area during the period 1956-2018, the total amount of person years was 818,083. The analysis was carried out by the Poisson regression method with a simple parametric excess relative risk model. 95% confidence intervals were estimated with maximum likelihood approach. Only a postnatal dose was used in the first solid cancer incidence analysis of this cohort members with due account for preconception exposure of parents. TRDS-2016 mean postnatal dose accumulated over the entire follow-up period in the stomach of cohort members was 0.047 Gy. The analysis showed linear dependence of solid cancer incidence excess relative risk on postnatal dose. Excess relative risk was 0.66/Gy, р=0.006 with a five-year latency period. While estimating excess relative risk in different age groups at the beginning of exposure, a significant risk was present only in the age group under 1 year and amounted to 2.16/Gy; р&lt;0.02 at the onset of exposure. The present results are in agreement with the results of the solid cancer incidence risk analysis both in the Techa River Cohort of exposed In-Utero where a statistically significant excess relative risk from a postnatal dose was revealed, and with the results of risk analysis in the Japanese cohort of people exposed in-utero and in early childhood.

Lung Cancer in the Mayak Workers Cohort: Risk Estimation and Uncertainty Analysis.

The workers at the Mayak nuclear facility near Ozyorsk, Russia are a primary source of information about exposure to radiation at low-dose rates, since they were subject to protracted exposures to external gamma rays and to internal exposures from plutonium inhalation. Here we re-examine lung cancer mortality rates and assess the effects of external gamma and internal plutonium exposures using recently developed Monte Carlo dosimetry systems. Using individual lagged mean annual lung doses computed from the dose realizations, we fit excess relative risk (ERR) models to the lung cancer mortality data for the Mayak Workers Cohort using risk-modeling software. We then used the corrected-information matrix (CIM) approach to widen the confidence intervals of ERR by taking into account the uncertainty in doses represented by multiple realizations from the Monte Carlo dosimetry systems. Findings of this work revealed that there were 930 lung cancer deaths during follow-up. Plutonium lung doses (but not gamma doses) were generally higher in the new dosimetry systems than those used in the previous analysis. This led to a reduction in the risk per unit dose compared to prior estimates. The estimated ERR/Gy for external gamma-ray exposure was 0.19 (95% CI: 0.07 to 0.31) for both sexes combined, while the ERR/Gy for internal exposures based on mean plutonium doses were 3.5 (95% CI: 2.3 to 4.6) and 8.9 (95% CI: 3.4 to 14) for males and females at attained age 60. Accounting for uncertainty in dose had little effect on the confidence intervals for the ERR associated with gamma-ray exposure, but had a marked impact on confidence intervals, particularly the upper bounds, for the effect of plutonium exposure [adjusted 95% CIs: 1.5 to 8.9 for males and 2.7 to 28 for females]. In conclusion, lung cancer rates increased significantly with both external gamma-ray and internal plutonium exposures. Accounting for the effects of dose uncertainty markedly increased the width of the confidence intervals for the plutonium dose response but had little impact on the external gamma dose effect estimate. Adjusting risk estimate confidence intervals using CIM provides a solution to the important problem of dose uncertainty. This work demonstrates, for the first time, that it is possible and practical to use our recently developed CIM method to make such adjustments in a large cohort study.

Cancer incidence risks above and below 1 Gy for radiation protection in space

The risk assessment quantities called lifetime attributable risk (LAR) and risk of exposure-induced cancer (REIC) are used to calculate the cumulative cancer incidence risks for astronauts, attributable to radiation exposure accumulated during long term lunar and Mars missions. These risk quantities are based on the most recently published epidemiological data on the Life Span Study (LSS) of Japanese A-bomb survivors, who were exposed to γ-rays and neutrons. In order to analyze the impact of a different neutron RBE on the risk quantities, a model for the neutron relative biological effectiveness (RBE) relative to gammas in the LSS is developed based on an older dataset with less follow-up time. Since both risk quantities are based on uncertain quantities, such as survival curves, and REIC includes deterministic radiation induced non-cancer mortality risks, modelled with data based on the general population, the risks for astronauts may not be optimally estimated. The suitability of these risk assessment measures for the use of cancer risk calculation for astronauts is discussed. The work presented here shows that the use of a higher neutron RBE than the value of 10, traditionally used in the LSS risk models, can reduce the risks up to almost 50%. Additionally, including an excess absolute risk (EAR) baseline scaling also increases the risks by between 0.4% and 8.1% for the space missions considered in this study. Using just an EAR model instead of an equally weighted EAR and excess relative risk (ERR) model can decrease the cumulative risks for the considered missions by between 0.4% and 4.1% if no EAR baseline scaling is applied. If EAR baseline scaling is included, the calculated risks with the EAR- and the mixed model, as well as the risks calculated with just the ERR model are almost identical and only small differences in the uncertainties are visible.

Risk of Leukemia Associated with Protracted Low-Dose Radiation Exposure: Updated Results from the National Registry for Radiation Workers Study.

While the link between risk of leukemia and acute radiation exposure is well established for large doses received acutely, uncertainty remains around the translation of these risk estimates to occupational exposure scenarios where the doses are low and accumulated over time, possibly over many years. We present leukemia incidence and mortality radiation risk estimates derived from the National Registry for Radiation Workers, which is a large cohort of occupationally exposed workers from the United Kingdom (UK). The cohort comprised 173,081 workers from the UK who were monitored for occupational exposure to radiation. The cohort was followed for a total of 5.3 million person-years and the incidence and mortality due to leukemia was identified through to the end of follow-up in 2011. Poisson regression was used to investigate the relationship between cumulative radiation dose and leukemia mortality and incidence rates using excess relative risk (ERR) and excess additive risk (EAR) models. The results of this work showed a collective dose of 4,414 person-Sv accumulated by the cohort with an average cumulative dose of 25.5 mSv. Among male workers both the ERR and EAR models showed evidence of increased leukemia risk (excluding chronic lymphatic leukemia) associated with increasing cumulative dose. The ERR was 1.38 per Sv (90% CI: 0.04; 3.24) and EAR was 1.33 per 10,000 person-year-Sv (90% CI: 0.04; 2.89) when a linear model was used. These excess risks were driven by increased risks for chronic myeloid leukemia [ERR/Sv = 6.77 (90% CI: 2.14; 15.44)]. In conclusion, this study provides further evidence that leukemia risks may be increased by low-dose and protracted external radiation exposure. The risks are generally consistent with those observed in the atomic bomb survivor studies, as well as with risk coefficients on which international radiation safety standards, including the dose limits and constraints used to control exposures, are based.

Assessing the Relative Biological Effectiveness of Neutrons across Organs of Varying Depth among the Atomic Bomb Survivors.

When assessing radiation-related risk among the atomic bomb survivors, choices in modeling approach can have an important impact on the results, which are then used to inform radiation protection standards throughout the world. The atomic bombings of Hiroshima and Nagasaki produced a mixed-field radiation exposure from two sources: neutrons and gamma rays. Neutrons are more densely ionizing and cause greater biological damage per unit absorbed dose, resulting in greater relative biological effectiveness (RBE) than gamma rays. To account for this, a combined weighted dose is typically calculated as the sum of the gamma-ray dose and 10 times the neutron dose in the Radiation Effects Research Foundation's reports of mortality, solid cancer incidence and other outcomes. In addition, the colon, which is often chosen as the whole-body representative organ in these analyses, is relatively deep in the body and therefore its dose calculation involves heavy body shielding of neutrons and a low neutron/gamma-ray ratio. With added follow-up and recently updated doses, we used a data-driven approach to determine the best-fitting neutron RBE for a range of organs of varying depth. Aggregated person-year tables of solid cancer incidence (1958-2009) from the Life Span Study were created with separate neutron and gamma-ray DS02R1 doses for several organs including breast, brain, thyroid, bone marrow, lung, liver and colon. Typical excess relative risk models estimating the linear effect of radiation dose were fitted using a range of neutron weights (1-250) to calculate combined dose for each organ, and model deviances were compared to assess fit. Furthermore, models using separate terms for gamma-ray and neutron dose were also examined, wherein the ratio of the neutron/gamma-ray linear terms indicated the best estimate of the RBE. The best-fitting RBE value for the traditional weighted colon dose was 80 [95% confidence interval (CI): 20-190], while the RBEs for other organs using weighted doses ranged from 25 to 60, with the best-fitting weights and confidence interval widths both incrementally increasing with greater depth of organ. Models using separate neutron- and gamma-ray-dose terms gave similar results to weighted linear combinations, with a neutron/gamma-ray term ratio of 79.9 (95% CI: 18.8-192.3) for colon. These results indicated that the traditionally modeled RBE of 10 may underestimate the effect of neutrons across the full dose range, although these updated estimates still have fairly wide confidence bounds. Furthermore, the colon is among the deepest of organs and may not be the best choice as a single surrogate organ dose, as it may minimize the role of the neutrons. Future work with more refined organ doses could shed more light on RBE-related information available in the Life Span Study data.

Effects of Omitting Non-confounding Predictors From General Relative-Risk Models for Binary Outcomes.

BackgroundThe effects, in terms of bias and precision, of omitting non-confounding predictive covariates from generalized linear models have been well studied, and it is known that such omission results in attenuation bias but increased precision with logistic regression. However, many epidemiologic risk analyses utilize alternative models that are not based on a linear predictor, and the effect of omitting non-confounding predictive covariates from such models has not been characterized.MethodsWe employed simulation to study the effects on risk estimation of omitting non-confounding predictive covariates from an excess relative risk (ERR) model and a general additive-multiplicative relative-risk mixture model for binary outcome data in a case-control setting. We also compared the results to the effects with ordinary logistic regression.ResultsFor these commonly employed alternative relative-risk models, the bias was similar to that with logistic regression when the risk was small. More generally, the bias and standard error of the risk-parameter estimates demonstrated patterns that are similar to those with logistic regression, but with greater magnitude depending on the true value of the risk. The magnitude of bias and standard error had little relation to study size or underlying disease prevalence.ConclusionsPrior conclusions regarding omitted covariates in logistic regression models can be qualitatively applied to the ERR and the general additive-multiplicative relative-risk mixture model without substantial change. Quantitatively, however, these alternative models may have slightly greater omitted-covariate bias, depending on the magnitude of the true risk being estimated.

Radiation Risks of Uterine Cancer in Atomic Bomb Survivors: 1958-2009.

BackgroundIonizing radiation is known to be capable of causing cancer of many organs, but its relationship with uterine cancer has not been well characterized.MethodsWe studied incidence of uterine cancer during 1958–2009 among 62 534 female atomic bomb survivors. Using Poisson regression analysis, we fitted excess relative risk (ERR) models to uterine cancer rates adjusted for several lifestyle and reproductive factors. Person-years at risk were also adjusted for the probability of prior hysterectomy, because it could affect the subsequent risk of uterine cancer. We assessed the modifying effect of age and other factors on the radiation risk. For analysis of the modifying effect of age at radiation exposure around menarche, we compared the radiation risk for several exposure-age categories as well as using parametric models.ResultsThere were 224 uterine corpus cancers and 982 cervical cancers. We found a significant association between radiation dose and risk of corpus cancer (ERR per Gray [ERR/Gy] = 0.73, 95% confidence interval [CI] = 0.03 to 1.87) but not for cervical cancer (ERR/Gy = 0.00, 95% CI = −0.22 to 0.31). For corpus cancer, we found statistically significant heterogeneity in ERR/Gy by age (Pheterogeneity = .001) with elevated risk for women exposed to radiation between ages 11 and 15 years (ERR/Gy = 4.10, 95% CI = 1.47 to 8.42) and no indication of a radiation effect for exposures before or after this exposure-age range.ConclusionsThe current data suggest that uterine corpus is especially sensitive to the carcinogenic effect of radiation exposure occurring during the mid-pubertal period preceding menarche. There is little evidence for a radiation effect on cervical cancer risk.

Influence of Dosimetric Considerations in Evaluating Second Cancer Risks in Prostate Cancer

Influence of dosimetric considerations in evaluating second cancer risks in prostate cancer. Material and methods: Fifteen patients in this study suffering from early stage of prostate cancer, each patient underwent three plans: 1) Three-dimensional conformal radiation therapy (3DCRT), 2) Rotation therapy (Arc therapy), and 3) intensity-modulated radiation therapy (IMRT) plan. Estimate secondary metastasis risk models: Excess Relative Risk (ERR) and Excess absolute risk (EAR) based on age of exposure by taking dosimetry data from Dose Volume Histograms (DVHs) to calculate risk models. Result: The second cancer risk models (ERR and EAR) for organs at risk OARs decrease with increasing age of exposure for 3D-CRT, ARC and IMRT and there is no significant difference for ERR and EAR model for developing second cancer risk in 3D-CRT, ARC and IMRT.

Mortality in the French cohort of nuclear workers

Populations of nuclear workers are particularly relevant to study health effects of protracted exposures to low doses at low dose-rates of ionizing radiation. In France, a cohort of nuclear workers employed by the Commissariat a l'energie atomique et aux energies alternatives (CEA), AREVA Nuclear Cycle (AREVA NC), or Electricite de France (EDF), and badge-monitored for external radiation exposure, has been followed-up for several decades. Annual exposure to penetrating photons was reconstructed for each worker. Standardized mortality ratios were calculated using national mortality rates as the reference. Estimates of radiation dose-mortality associations were obtained using linear excess relative risk models. Mortality of 59 004 workers was followed-up between 1968 and 2004, for an average of 25 years. The mean cumulative photon Hp (10) dose was 18.4 mSv in the whole cohort (median value: 2.1 mSv) and 25.7 mSv among exposed workers. At the end of the follow-up, workers were 56 years old on average and 6310 workers had died. A strong healthy worker effect was observed. Estimated dose-risk relationships were consistent with those from other worker studies for all solid cancers and leukaemia excluding chronic lymphocytic leukaemia, but remained associated to a large uncertainty. This cohort is the most informative study ever conducted in France among nuclear workers.

Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors.

In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures. While the effects of independent measurement errors on parameter estimation and methods to correct these effects have been studied comprehensively in the epidemiological literature, the literature on the effects of correlated errors, and associated correction methods is much more sparse. In this paper, we implement a novel method that calculates corrected confidence intervals based on the approximate asymptotic distribution of parameter estimates in linear excess relative risk (ERR) models. These models are widely used in survival analysis, particularly in radiation epidemiology. Specifically, for the dose effect estimate of interest (increase in relative risk per unit dose), a mixture distribution consisting of a normal and a lognormal component is applied. This choice of asymptotic approximation guarantees that corrected confidence intervals will always be bounded, a result which does not hold under a normal approximation. A simulation study was conducted to evaluate the proposed method in survival analysis using a realistic ERR model. We used both simulated Monte Carlo dosimetry systems (MCDS) and actual dose histories from the Mayak Worker Dosimetry System 2013, a MCDS for plutonium exposures in the Mayak Worker Cohort. Results show our proposed methods provide much improved coverage probabilities for the dose effect parameter, and noticeable improvements for other model parameters.

Solid Cancer Incidence among the Life Span Study of Atomic Bomb Survivors: 1958-2009.

This is the third analysis of solid cancer incidence among the Life Span Study (LSS) cohort of atomic bomb survivors in Hiroshima and Nagasaki, adding eleven years of follow-up data since the previously reported analysis. For this analysis, several changes and improvements were implemented, including updated dose estimates (DS02R1) and adjustment for smoking. Here, we focus on all solid cancers in aggregate. The eligible cohort included 105,444 subjects who were alive and had no known history of cancer at the start of follow-up. A total of 80,205 subjects had individual dose estimates and 25,239 were not in either city at the time of the bombings. The follow-up period was 1958-2009, providing 3,079,484 person-years of follow-up. Cases were identified by linkage with population-based Hiroshima and Nagasaki Cancer Registries. Poisson regression methods were used to elucidate the nature of the radiation-associated risks per Gy of weighted absorbed colon dose using both excess relative risk (ERR) and excess absolute risk (EAR) models adjusted for smoking. Risk estimates were reported for a person exposed at age 30 years with attained age of 70 years. In this study, 22,538 incident first primary solid cancer cases were identified, of which 992 were associated with radiation exposure. There were 5,918 cases (26%) that occurred in the 11 years (1999-2009) since the previously reported study. For females, the dose response was consistent with linearity with an estimated ERR of 0.64 per Gy (95% CI: 0.52 to 0.77). For males, significant upward curvature over the full dose range as well as restricted dose ranges was observed and therefore, a linear-quadratic model was used, which resulted in an ERR of 0.20 (95% CI: 0.12 to 0.28) at 1 Gy and an ERR of 0.010 (95% CI: -0.0003 to 0.021) at 0.1 Gy. The shape of the ERR dose response was significantly different among males and females (P = 0.02). While there was a significant decrease in the ERR with increasing attained age, this decrease was more rapid in males compared to females. The lowest dose range that showed a statistically significant dose response using the sex-averaged, linear ERR model was 0-100 mGy (P = 0.038). In conclusion, this analysis demonstrates that solid cancer risks remain elevated more than 60 years after exposure. Sex-averaged upward curvature was observed in the dose response independent of adjustment for smoking. Findings from the current analysis regarding the dose-response shape were not fully consistent with those previously reported, raising unresolved questions. At this time, uncertainties in the shape of the dose response preclude definitive conclusions to confidently guide radiation protection policies. Upcoming results from a series of analyses focusing on the radiation risks for specific organs or organ families, as well as continued follow-up are needed to fully understand the nature of radiation-related cancer risk and its public health significance. Data and analysis scripts are available for download at: http://www.rerf.or.jp .