Evaluation of Confounding and Selection Bias in Epidemiological Studies of Populations Exposed to Low-Dose, High-Energy Photon Radiation

Adjustment for duration of employment in occupational epidemiology

PURPOSE

In occupational epidemiology, the healthy worker survivor effect can manifest as a time-dependent confounder because healthier workers can accrue greater amounts of exposure over longer periods of employment. For example, in occupational studies of radiation exposure that focus on cumulative annualized radiation dose, workers can accrue greater amounts of cumulative radiation exposure over longer periods of employment, while workers with longer periods of employment can transition into jobs with a reduced potential for annualized radiation exposure. The extent to which confounding arising from the healthy worker survivor effect impacts radiation risk estimates is unknown.

METHODS

We assessed the impact of the healthy worker survivor effect on estimates of radiation risk among nuclear workers in a Million Person Study cohort. In simulation studies, we contrasted the ability of marginal structural Cox models with inverse probability weighting and Cox proportional hazards models to account for time-dependent confounding arising from the healthy worker survivor effect.

RESULTS

Marginal structural Cox models and Cox proportional hazards models with flexible functional forms for duration of employment provided reliable results.

CONCLUSIONS

It is crucial to flexibly adjust for duration of employment to account for confounding arising from the healthy worker survivor effect in occupational epidemiology.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method. The quasi-2DMC+BMA method performs well when a linear model is assumed, but poorly when a linear-quadratic model is assumed. FMA performs as well as quasi-2DMC+BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC+BMA or FMA methods, particularly for the linear-quadratic model. The bias of predicted relative risk at a variety of doses is generally smallest for ERC, and largest for quasi-2DMC+BMA and FMA, with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

In utero exposure to ionizing radiation and metabolic regulation: perspectives for future multi- and trans-generation effects studies

PURPOSE

The radiation protection community has been particularly attentive to the risks of delayed effects on offspring from low dose or low dose-rate exposures to ionizing radiation. Despite this, the current epidemiologic studies and scientific data are still insufficient to provide the necessary evidence for improving risk assessment guidelines. This literature review aims to inform future studies on multigenerational and transgenerational effects. It primarily focuses on animal studies involving in utero exposure and discusses crucial elements for interpreting the results. These elements include in utero exposure scenarios relative to the developmental stages of the embryo/fetus, and the primary biological mechanisms responsible for transmitting heritable or hereditary effects to future generations. The review addresses several issues within the contexts of both multigenerational and transgenerational effects, with a focus on hereditary perspectives.

CONCLUSIONS

Knowledge consolidation in the field of Developmental Origins of Health and Disease (DOHaD) has led us to propose a new study strategy. This strategy aims to address the transgenerational effects of in utero exposure to low dose and low dose-rate radiation. Within this concept, there is a possibility that disruption of epigenetic programming in embryonic and fetal cells may occur. This disruption could lead to metabolic dysfunction, which in turn may cause abnormal responses to future environmental challenges, consequently increasing disease risk. Lastly, we discuss methodological limitations in our studies. These limitations are related to cohort size, follow-up time, model radiosensitivity, and analytical techniques. We propose scientific and analytical strategies for future research in this field.

A generalisation of the method of regression calibration and comparison with the Bayesian 2-dimensional Monte Carlo method

For many cancer sites it is necessary to assess risks from low-dose exposures via extrapolation from groups exposed at moderate and high levels of dose. Measurement error can substantially alter the shape of this relationship and hence the derived population risk estimates. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, much attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. In this paper we test a Bayesian model averaging method, the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, that has been fairly recently proposed against a very newly proposed modification of the regression calibration method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. We also compared both methods against standard regression calibration and Monte Carlo maximum likelihood. The Bayesian 2DMC method performs poorly, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5%, particularly when the magnitudes of classical and Berkson error are both moderate to large (20%-50%). The method also produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. By comparison the extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the Bayesian 2DMC and all other methods. The bias of the predicted relative risk at a variety of doses is generally smallest for extended regression calibration, and largest for the Bayesian 2DMC method (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between the other two methods.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient [Formula: see text] are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient [Formula: see text] are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for [Formula: see text] that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient [Formula: see text] are substantially upwardly biased.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient \(\alpha\) are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient \(\beta\) are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for \(\beta\) that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient \(\beta\) are substantially upwardly biased.

Cancer mortality after low dose exposure to ionising radiation in workers in France, the United Kingdom, and the United States (INWORKS): cohort study

OBJECTIVE

To evaluate the effect of protracted low dose, low dose rate exposure to ionising radiation on the risk of cancer.

DESIGN

Multinational cohort study.

SETTING

Cohorts of workers in the nuclear industry in France, the UK, and the US included in a major update to the International Nuclear Workers Study (INWORKS).

PARTICIPANTS

309 932 workers with individual monitoring data for external exposure to ionising radiation and a total follow-up of 10.7 million person years.

MAIN OUTCOME MEASURES

Estimates of excess relative rate per gray (Gy) of radiation dose for mortality from cancer.

RESULTS

The study included 103 553 deaths, of which 28 089 were due to solid cancers. The estimated rate of mortality due to solid cancer increased with cumulative dose by 52% (90% confidence interval 27% to 77%) per Gy, lagged by 10 years. Restricting the analysis to the low cumulative dose range (0-100 mGy) approximately doubled the estimate of association (and increased the width of its confidence interval), as did restricting the analysis to workers hired in the more recent years of operations when estimates of occupational external penetrating radiation dose were recorded more accurately. Exclusion of deaths from lung cancer and pleural cancer had a modest effect on the estimated magnitude of association, providing indirect evidence that the association was not substantially confounded by smoking or occupational exposure to asbestos.

CONCLUSIONS

This major update to INWORKS provides a direct estimate of the association between protracted low dose exposure to ionising radiation and solid cancer mortality based on some of the world's most informative cohorts of radiation workers. The summary estimate of excess relative rate solid cancer mortality per Gy is larger than estimates currently informing radiation protection, and some evidence suggests a steeper slope for the dose-response association in the low dose range than over the full dose range. These results can help to strengthen radiation protection, especially for low dose exposures that are of primary interest in contemporary medical, occupational, and environmental settings.

Ionizing radiation and solid cancer mortality among US nuclear facility workers

BACKGROUND

The risk of solid cancers from low-level protracted ionizing radiation is not well characterized. Nuclear workers provide valuable information on the effects of ionizing radiation in contemporary exposure scenarios relevant to workers and the public.

METHODS

We evaluated the association between penetrating ionizing radiation exposure and solid cancer mortality among a pooled cohort of nuclear workers in the USA, with extended follow-up to examine cancers with long latencies. This analysis includes 101 363 workers from five nuclear facilities, with 12 069 solid cancer deaths between 1944 and 2016. The association between cumulative equivalent dose measured in sieverts (Sv) and solid cancer subtypes were modelled as the excess relative rate per Sv (ERR Sv-1) using Cox regression.

RESULTS

For the association between ionizing radiation exposure and all solid cancer mortality we observed an elevated rate (ERR Sv-1=0.19; 95% CI: -0.10, 0.52), which was higher among a contemporary sub-cohort of workers first hired in 1960 or later (ERR Sv-1= 2.23; 95% CI: 1.13, 3.49). Similarly, we observed an elevated rate for lung cancer mortality (ERR Sv-1= 0.65; 95% CI: 0.09, 1.30) that was higher among contemporary hires (ERR Sv-1= 2.90; 95% CI: 1.00, 5.26).

CONCLUSIONS

Although concerns remain about confounding, measurement error and precision, this analysis strengthens the evidence base indicating there are radiogenic risks for several solid cancer types.

The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

The linear no-threshold (LNT) model was introduced into the radiological protection system about 60 years ago, but this model and its use in radiation protection are still debated today. This article presents an overview of results on effects of exposure to low linear-energy-transfer radiation in radiobiology and epidemiology accumulated over the last decade and discusses their impact on the use of the LNT model in the assessment of radiation-related cancer risks at low doses. The knowledge acquired over the past 10 years, both in radiobiology and epidemiology, has reinforced scientific knowledge about cancer risks at low doses. In radiobiology, although certain mechanisms do not support linearity, the early stages of carcinogenesis comprised of mutational events, which are assumed to play a key role in carcinogenesis, show linear responses to doses from as low as 10 mGy. The impact of non-mutational mechanisms on the risk of radiation-related cancer at low doses is currently difficult to assess. In epidemiology, the results show excess cancer risks at dose levels of 100 mGy or less. While some recent results indicate non-linear dose relationships for some cancers, overall, the LNT model does not substantially overestimate the risks at low doses. Recent results, in radiobiology or in epidemiology, suggest that a dose threshold, if any, could not be greater than a few tens of mGy. The scientific knowledge currently available does not contradict the use of the LNT model for the assessment of radiation-related cancer risks within the radiological protection system, and no other dose-risk relationship seems more appropriate for radiological protection purposes.

Cancer risk associated with the use of valsartan in Korea: A nationwide cohort study

BACKGROUND

Despite valsartan's widespread use, few studies have explored its potential carcinogenicity. We evaluated the association between valsartan and cancer.

METHODS

We conducted a retrospective cohort study using data from 2002 to 2015 gathered from the National Health Insurance database. Patients with hypertension aged ≥ 30 who used valsartan or other angiotensin II receptor blockers (ARBs) were included. Eligible patients were those with no prior history of the use of any ARBs, diagnosis of cancer, or organ transplantation in the 4 years predating their first use of the drugs of interest. The primary and secondary outcomes included the occurrence of all cancers and site-specific solid cancers, respectively. After applying propensity score (PS) matching, Cox regression was used to calculate the hazard ratios (HRs) and 95 % confidence intervals (CIs).

RESULTS

A total of 1,550,734 individuals were identified as new users of valsartan or other ARBs. Of the 153,047 valsartan users, 16,047 were diagnosed with cancer. No increased risk of overall cancer was observed in valsartan users as compared to other ARB users (aHR = 1.00; 95 % CI, 0.98-1.02). Valsartan was, however, associated with a slightly elevated risk of liver (aHR = 1.09; 95 % CI, 1.01-1.16) and kidney cancer (aHR = 1.11; 95 % CI, 1.02-1.22).

CONCLUSION

Compared with other ARBs, valsartan did not increase the risk of overall cancer. A slightly increased risk for some solid cancers was associated with valsartan use, though the absolute rate difference was small.

Age effects on radiation response: summary of a recent symposium and future perspectives

One of the principal uncertainties when estimating population risk of late effects from epidemiological data is that few radiation-exposed cohorts have been followed up to extinction. Therefore, the relative risk model has often been used to estimate radiation-associated risk and to extrapolate risk to the end of life. Epidemiological studies provide evidence that children are generally at higher risk of cancer induction than adults for a given radiation dose. However, the strength of evidence varies by cancer site and questions remain about site-specific age at exposure patterns. For solid cancers, there is a large body of evidence that excess relative risk (ERR) diminishes with increasing age at exposure. This pattern of risk is observed in the Life Span Study (LSS) as well as in other radiation-exposed populations for overall solid cancer incidence and mortality and for most site-specific solid cancers. However, there are some disparities by endpoint in the degree of variation of ERR with exposure age, with some sites (e.g., colon, lung) in the LSS incidence data showing no variation, or even increasing ERR with increasing age at exposure. The pattern of variation of excess absolute risk (EAR) with age at exposure is often similar, with EAR for solid cancers or solid cancer mortality decreasing with increasing age at exposure in the LSS. We shall review the human data from the Japanese LSS cohort, and a variety of other epidemiological data sets, including a review of types of medical diagnostic exposures, also some radiobiological animal data, all bearing on the issue of variations of radiation late-effects risk with age at exposure and with attained age. The paper includes a summary of several oral presentations given in a Symposium on "Age effects on radiation response" as part of the 67th Annual Meeting of the Radiation Research Society, held virtually on 3-6 October 2021.

Comparison of All Solid Cancer Mortality and Incidence Dose-Response in the Life Span Study of Atomic Bomb Survivors, 1958-2009

Recent analysis of all solid cancer incidence (1958-2009) in the Life Span Study (LSS) revealed evidence of upward curvature in the radiation dose response among males but not females. Upward curvature in sex-averaged excess relative risk (ERR) for all solid cancer mortality (1950-2003) was also observed in the 0-2 Gy dose range. As reasons for non-linearity in the LSS are not completely understood, we conducted dose-response analyses for all solid cancer mortality and incidence applying similar methods [1958-2009 follow-up, DS02R1 doses, including subjects not-in-city (NIC) at the time of the bombing] and statistical models. Incident cancers were ascertained from Hiroshima and Nagasaki cancer registries, while cause of death was ascertained from death certificates throughout Japan. The study included 105,444 LSS subjects who were alive and not known to have cancer before January 1, 1958 (80,205 with dose estimates and 25,239 NIC subjects). Between 1958 and 2009, there were 3.1 million person-years (PY) and 22,538 solid cancers for incidence analysis and 3.8 million PY and 15,419 solid cancer deaths for mortality analysis. We fitted sex-specific ERR models adjusted for smoking to both types of data. Over the entire range of doses, solid cancer mortality dose-response exhibited a borderline significant upward curvature among males (P = 0.062) and significant upward curvature among females (P = 0.010); for solid cancer incidence, as before, we found a significant upward curvature among males (P = 0.001) but not among females (P = 0.624). The sex difference in magnitude of dose-response curvature was statistically significant for cancer incidence (P = 0.017) but not for cancer mortality (P = 0.781). The results of analyses in the 0-2 Gy range and restricted lower dose ranges generally supported inferences made about the sex-specific dose-response shape over the entire range of doses for each outcome. Patterns of sex-specific curvature by calendar period (1958-1987 vs. 1988-2009) and age at exposure (0-19 vs. 20-83) varied between mortality and incidence data, particularly among females, although for each outcome there was an indication of curvature among 0-19-year-old male survivors in both calendar periods and among 0-19-year-old female survivors in the recent period. Collectively, our findings indicate that the upward curvature in all solid cancer dose response in the LSS is neither specific to males nor to incidence data; its evidence appears to depend on the composition of sites comprising all solid cancer group and age at exposure or time. Further follow up and site-specific analyses of cancer mortality and incidence will be important to confirm the emerging trend in dose-response curvature among young survivors and unveil the contributing factors and sites.

Association Between Radioactive Iodine Treatment for Pediatric and Young Adulthood Differentiated Thyroid Cancer and Risk of Second Primary Malignancies

PURPOSE

Since the 1980s, both the incidence of differentiated thyroid cancer (DTC) and use of radioactive iodine (RAI) treatment increased markedly. RAI has been associated with an increased risk of leukemia, but risks of second solid malignancies remain unclear. We aimed to quantify risks of second malignancies associated with RAI treatment for DTC in children and young adults, who are more susceptible than older adults to the late effects of radiation.

METHODS

Using nine US SEER cancer registries (1975-2017), we estimated relative risks (RRs) for solid and hematologic malignancies associated with RAI (yes v no or unknown) using Poisson regression among ≥ 5- and ≥ 2-year survivors of nonmetastatic DTC diagnosed before age 45 years, respectively.

RESULTS

Among 27,050 ≥ 5-year survivors (median follow-up = 15 years), RAI treatment (45%) was associated with increased risk of solid malignancies (RR = 1.23; 95% CI, 1.11 to 1.37). Risks were increased for uterine cancer (RR = 1.55; 95% CI, 1.03 to 2.32) and nonsignificantly for cancers of the salivary gland (RR = 2.15; 95% CI, 0.91 to 5.08), stomach (RR = 1.61; 95% CI, 0.70 to 3.69), lung (RR = 1.42; 95% CI, 0.97 to 2.08), and female breast (RR = 1.18; 95% CI, 0.99 to 1.40). Risks of total solid and female breast cancer, the most common cancer type, were highest among ≥ 20-year DTC survivors (RR_solid = 1.47; 95% CI, 1.24 to 1.74; RR_breast = 1.46; 95% CI, 1.10 to 1.95). Among 32,171 ≥ 2-year survivors, RAI was associated with increased risk of hematologic malignancies (RR = 1.51; 95% CI, 1.08 to 2.01), including leukemia (RR = 1.92; 95% CI, 1.04 to 3.56). We estimated that 6% of solid and 14% of hematologic malignancies in pediatric and young adult DTC survivors may be attributable to RAI.

CONCLUSION

In addition to leukemia, RAI treatment for childhood and young-adulthood DTC was associated with increased risks of several solid cancers, particularly more than 20 years after exposure, supporting the need for long-term surveillance of these patients.

Review of the risk of cancer following low and moderate doses of sparsely ionising radiation received in early life in groups with individually estimated doses

BACKGROUND

The detrimental health effects associated with the receipt of moderate (0.1-1 Gy) and high (>1 Gy) acute doses of sparsely ionising radiation are well established from human epidemiological studies. There is accumulating direct evidence of excess risk of cancer in a number of populations exposed at lower acute doses or doses received over a protracted period. There is evidence that relative risks are generally higher after radiation exposures in utero or in childhood.

METHODS AND FINDINGS

We reviewed and summarised evidence from 60 studies of cancer or benign neoplasms following low- or moderate-level exposure in utero or in childhood from medical and environmental sources. In most of the populations studied the exposure was predominantly to sparsely ionising radiation, such as X-rays and gamma-rays. There were significant (p < 0.001) excess risks for all cancers, and particularly large excess relative risks were observed for brain/CNS tumours, thyroid cancer (including nodules) and leukaemia.

CONCLUSIONS

Overall, the totality of this large body of data relating to in utero and childhood exposure provides support for the existence of excess cancer and benign neoplasm risk associated with radiation doses < 0.1 Gy, and for certain groups exposed to natural background radiation, to fallout and medical X-rays in utero, at about 0.02 Gy.

Cancer risk following low doses of ionising radiation - Current epidemiological evidence and implications for radiological protection

Recent studies suggest that every year worldwide about a million patients might be exposed to doses of the order of 100 mGy of low-LET radiation, due to recurrent application of radioimaging procedures. This paper presents a synthesis of recent epidemiological evidence on radiation-related cancer risks from low-LET radiation doses of this magnitude. Evidence from pooled analyses and meta-analyses also involving epidemiological studies that, individually, do not find statistically significant radiation-related cancer risks is reviewed, and evidence from additional and more recent epidemiological studies of radiation exposures indicating excess cancer risks is also summarized. Cohorts discussed in the present paper include Japanese atomic bomb survivors, nuclear workers, patients exposed for medical purposes, and populations exposed environmentally to natural background radiation or radioactive contamination. Taken together, the overall evidence summarized here is based on studies including several million individuals, many of them followed-up for more than half a century. In summary, substantial evidence was found from epidemiological studies of exposed groups of humans that ionizing radiation causes cancer at acute and protracted doses above 100 mGy, and growing evidence for doses below 100 mGy. The significant radiation-related solid cancer risks observed at doses of several 100 mGy of protracted exposures (observed, for example, among nuclear workers) demonstrate that doses accumulated over many years at low dose rates do cause stochastic health effects. On this basis, it can be concluded that doses of the order of 100 mGy from recurrent application of medical imaging procedures involving ionizing radiation are of concern, from the viewpoint of radiological protection.

Epidemiological studies of CT scans and cancer risk: the state of the science

20 years ago, 3 manuscripts describing doses and potential cancer risks from CT scans in children raised awareness of a growing public health problem. We reviewed the epidemiological studies that were initiated in response to these concerns that assessed cancer risks from CT scans using medical record linkage. We evaluated the study methodology and findings and provide recommendations for optimal study design for new efforts. We identified 17 eligible studies; 13 with published risk estimates, and 4 in progress. There was wide variability in the study methodology, however, which made comparison of findings challenging. Key differences included whether the study focused on childhood or adulthood exposure, radiosensitive outcomes (e.g. leukemia, brain tumors) or all cancers, the exposure metrics (e.g. organ doses, effective dose or number of CTs) and control for biases (e.g. latency and exclusion periods and confounding by indication). We were able to compare results for the subset of studies that evaluated leukemia or brain tumors. There were eight studies of leukemia risk in relation to red bone marrow (RBM) dose, effective dose or number of CTs; seven reported a positive dose-response, which was statistically significant (p < 0.05) in four studies. Six of the seven studies of brain tumors also found a positive dose-response and in five, this was statistically significant. Mean RBM dose ranged from 6 to 12 mGy and mean brain dose from 18 to 43 mGy. In a meta-analysis of the studies of childhood exposure the summary ERR/100 mGy was 1.78 (95%CI: 0.01-3.53) for leukemia/myelodisplastic syndrome (n = 5 studies) and 0.80 (95%CI: 0.48-1.12) for brain tumors (n = 4 studies) (p-heterogeneity >0.4). Confounding by cancer pre-disposing conditions was unlikely in these five studies of leukemia. The summary risk estimate for brain tumors could be over estimated, however, due to reverse causation. In conclusion, there is growing evidence from epidemiological data that CT scans can cause cancer. The absolute risks to individual patients are, however, likely to be small. Ongoing large multicenter cohorts and future pooling efforts will provide more precise risk quantification.

A review of studies of childhood cancer and natural background radiation

PURPOSE

The projected existence and magnitude of carcinogenic effects of ionizing radiation at low doses and low-dose rates is perhaps the most important issue in radiation protection today. Studies of childhood cancer and natural background radiation have the potential to throw direct light on this question, into a dose range below a few tens of mSv. This paper describes the studies that have been undertaken and their context, discusses some problems that arise and summarizes the present position.

CONCLUSIONS

Many such studies have been undertaken, but most were too small to have a realistic chance of detecting the small effects expected from such low doses, based on risk projections from higher exposures. Case-control or cohort studies are to be preferred methodologically to ecological studies but can be prone to problems of registration/participation bias. Interview-based studies of the requisite size would be prohibitively expensive and would undoubtedly also run into problems of participation bias. Register-based studies can be very large and are free of participation bias. However, they need to estimate the radiation exposure of study subjects using models rather than individual measurements in the homes of those concerned. At present, no firm conclusions can be drawn from the studies that have been published to date. Further data and perhaps pooled studies offer a way forward.

Risk of Bias Assessments and Evidence Syntheses for Observational Epidemiologic Studies of Environmental and Occupational Exposures: Strengths and Limitations

BACKGROUND

Increasingly, risk of bias tools are used to evaluate epidemiologic studies as part of evidence synthesis (evidence integration), often involving meta-analyses. Some of these tools consider hypothetical randomized controlled trials (RCTs) as gold standards.

METHODS

We review the strengths and limitations of risk of bias assessments, in particular, for reviews of observational studies of environmental exposures, and we also comment more generally on methods of evidence synthesis.

RESULTS

Although RCTs may provide a useful starting point to think about bias, they do not provide a gold standard for environmental studies. Observational studies should not be considered inherently biased vs. a hypothetical RCT. Rather than a checklist approach when evaluating individual studies using risk of bias tools, we call for identifying and quantifying possible biases, their direction, and their impacts on parameter estimates. As is recognized in many guidelines, evidence synthesis requires a broader approach than simply evaluating risk of bias in individual studies followed by synthesis of studies judged unbiased, or with studies given more weight if judged less biased. It should include the use of classical considerations for judging causality in human studies, as well as triangulation and integration of animal and mechanistic data.

CONCLUSIONS

Bias assessments are important in evidence synthesis, but we argue they can and should be improved to address the concerns we raise here. Simplistic, mechanical approaches to risk of bias assessments, which may particularly occur when these tools are used by nonexperts, can result in erroneous conclusions and sometimes may be used to dismiss important evidence. Evidence synthesis requires a broad approach that goes beyond assessing bias in individual human studies and then including a narrow range of human studies judged to be unbiased in evidence synthesis. https://doi.org/10.1289/EHP6980.

Epidemiological Studies of Low-Dose Ionizing Radiation and Cancer: Summary Bias Assessment and Meta-Analysis

BACKGROUND

Ionizing radiation is an established carcinogen, but risks from low-dose exposures are controversial. Since the Biological Effects of Ionizing Radiation VII review of the epidemiological data in 2006, many subsequent publications have reported excess cancer risks from low-dose exposures. Our aim was to systematically review these studies to assess the magnitude of the risk and whether the positive findings could be explained by biases.

METHODS

Eligible studies had mean cumulative doses of less than 100 mGy, individualized dose estimates, risk estimates, and confidence intervals (CI) for the dose-response and were published in 2006-2017. We summarized the evidence for bias (dose error, confounding, outcome ascertainment) and its likely direction for each study. We tested whether the median excess relative risk (ERR) per unit dose equals zero and assessed the impact of excluding positive studies with potential bias away from the null. We performed a meta-analysis to quantify the ERR and assess consistency across studies for all solid cancers and leukemia.

RESULTS

Of the 26 eligible studies, 8 concerned environmental, 4 medical, and 14 occupational exposure. For solid cancers, 16 of 22 studies reported positive ERRs per unit dose, and we rejected the hypothesis that the median ERR equals zero (P = .03). After exclusion of 4 positive studies with potential positive bias, 12 of 18 studies reported positive ERRs per unit dose (P  = .12). For leukemia, 17 of 20 studies were positive, and we rejected the hypothesis that the median ERR per unit dose equals zero (P  = .001), also after exclusion of 5 positive studies with potential positive bias (P  = .02). For adulthood exposure, the meta-ERR at 100 mGy was 0.029 (95% CI = 0.011 to 0.047) for solid cancers and 0.16 (95% CI = 0.07 to 0.25) for leukemia. For childhood exposure, the meta-ERR at 100 mGy for leukemia was 2.84 (95% CI = 0.37 to 5.32); there were only two eligible studies of all solid cancers.

CONCLUSIONS

Our systematic assessments in this monograph showed that these new epidemiological studies are characterized by several limitations, but only a few positive studies were potentially biased away from the null. After exclusion of these studies, the majority of studies still reported positive risk estimates. We therefore conclude that these new epidemiological studies directly support excess cancer risks from low-dose ionizing radiation. Furthermore, the magnitude of the cancer risks from these low-dose radiation exposures was statistically compatible with the radiation dose-related cancer risks of the atomic bomb survivors.

Strengths and Weaknesses of Dosimetry Used in Studies of Low-Dose Radiation Exposure and Cancer

BACKGROUND

A monograph systematically evaluating recent evidence on the dose-response relationship between low-dose ionizing radiation exposure and cancer risk required a critical appraisal of dosimetry methods in 26 potentially informative studies.

METHODS

The relevant literature included studies published in 2006-2017. Studies comprised case-control and cohort designs examining populations predominantly exposed to sparsely ionizing radiation, mostly from external sources, resulting in average doses of no more than 100 mGy. At least two dosimetrists reviewed each study and appraised the strengths and weaknesses of the dosimetry systems used, including assessment of sources and effects of dose estimation error. An overarching concern was whether dose error might cause the spurious appearance of a dose-response where none was present.

RESULTS

The review included 8 environmental, 4 medical, and 14 occupational studies that varied in properties relative to evaluation criteria. Treatment of dose estimation error also varied among studies, although few conducted a comprehensive evaluation. Six studies appeared to have known or suspected biases in dose estimates. The potential for these biases to cause a spurious dose-response association was constrained to three case-control studies that relied extensively on information gathered in interviews conducted after case ascertainment.

CONCLUSIONS

The potential for spurious dose-response associations from dose information appeared limited to case-control studies vulnerable to recall errors that may be differential by case status. Otherwise, risk estimates appeared reasonably free of a substantial bias from dose estimation error. Future studies would benefit from a comprehensive evaluation of dose estimation errors, including methods accounting for their potential effects on dose-response associations.

Issues in Interpreting Epidemiologic Studies of Populations Exposed to Low-Dose, High-Energy Photon Radiation

This article addresses issues relevant to interpreting findings from 26 epidemiologic studies of persons exposed to low-dose radiation. We review the extensive data from both epidemiologic studies of persons exposed at moderate or high doses and from radiobiology that together have firmly established radiation as carcinogenic. We then discuss the use of the linear relative risk model that has been used to describe data from both low- and moderate- or high-dose studies. We consider the effects of dose measurement errors; these can reduce statistical power and lead to underestimation of risks but are very unlikely to bring about a spurious dose response. We estimate statistical power for the low-dose studies under the assumption that true risks of radiation-related cancers are those expected from studies of Japanese atomic bomb survivors. Finally, we discuss the interpretation of confidence intervals and statistical tests and the applicability of the Bradford Hill principles for a causal relationship.

Epidemiological Studies of Low-Dose Ionizing Radiation and Cancer: Rationale and Framework for the Monograph and Overview of Eligible Studies

Whether low-dose ionizing radiation can cause cancer is a critical and long-debated question in radiation protection. Since the Biological Effects of Ionizing Radiation report by the National Academies in 2006, new publications from large, well-powered epidemiological studies of low doses have reported positive dose-response relationships. It has been suggested, however, that biases could explain these findings. We conducted a systematic review of epidemiological studies with mean doses less than 100 mGy published 2006-2017. We required individualized doses and dose-response estimates with confidence intervals. We identified 26 eligible studies (eight environmental, four medical, and 14 occupational), including 91 000 solid cancers and 13 000 leukemias. Mean doses ranged from 0.1 to 82 mGy. The excess relative risk at 100 mGy was positive for 16 of 22 solid cancer studies and 17 of 20 leukemia studies. The aim of this monograph was to systematically review the potential biases in these studies (including dose uncertainty, confounding, and outcome misclassification) and to assess whether the subset of minimally biased studies provides evidence for cancer risks from low-dose radiation. Here, we describe the framework for the systematic bias review and provide an overview of the eligible studies.