Each year, approximately 4 million US patients are exposed to low-dose ionising radiation through medical diagnostic procedures.1 Moreover, according to estimates by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), over 11 million workers worldwide are exposed to ionising radiation each year.2 Epidemiological evidence unequivocally establishes ionising radiation as a human carcinogen3 4; thus, safeguards have been established to mitigate cancer risks in exposed populations. For over 50 years, risk models forming the basis of radiation protection standards have been based almost exclusively on the Life Span Study (LSS) cohort of Japanese atomic bomb survivors. It is unclear whether cancer risks estimated from this population reflect actual risks in populations exposed to low-dose, protracted ionising radiation.

Leukaemia, excluding the chronic lymphocytic subtype (CLL), is considered to be among the cancers most susceptible to induction by ionising radiation exposure5; thus, leukaemia risk in environmentally and occupationally exposed populations has been studied extensively. Therefore, our goal was to synthesise available information from observational studies of these populations to obtain an aggregate estimate of the excess RR for persons receiving a whole-body absorbed dose of 100 mGy.

Aggregate risks are typically estimated using pooled or meta-analytical approaches. In the former case, individual-level data are combined across studies to estimate the effect size. In the latter, risk estimates from individual studies are compared and then combined into an aggregate estimate. While both methods can provide an efficient summary estimate, it is difficult to control for differences in data quality across subpopulations in pooled studies. Moreover, obtaining raw data from multiple studies, each with its own set of data protection requirements, can be daunting. Thus, we conducted a meta-analysis of leukaemia risk from ionising radiation using published data.

The study population was restricted to adults exposed to low-dose ionising radiation in occupational and environmental settings. Studies of medical therapy patients and childhood leukaemias in populations near nuclear facilities were excluded. We also limited our synthesis to populations exposed primarily to low linear energy transfer (LET) radiations, and thus studies of radon-exposed populations, uranium miners and millers, and aircraft crews were excluded.

Ionising radiation is a known and extensively studied human carcinogen. However, its effects at doses encountered in occupational and diagnostic medical settings remain a matter of much debate.43 44 In part, the disagreement stems from the difficulties in quantifying small risks using observational data. It is impractical in most settings to conduct a single epidemiological study that is sufficiently large to directly obtain precise estimates of risks at low doses.45 Furthermore, although the RR may be statistically significant, the absolute risk in the low-dose range is small and may be influenced by a host of external factors. Nevertheless, systematically combining the available information can reduce the effects of random error and provide a precise aggregate estimate.

We used a simple statistical approach to pool available information on the dose–response relationship between leukaemia and ionising radiation in a manner that is both quantitative and reproducible. Our estimates of the ERR at 100 mGy ranged from 0.15 (95% CI 0.07 to 0.23) for full model I adjusted for publication bias to 0.37 (95% CI 0.03 to 0.81) for model IId restricted to non-influential mortality studies also with bias adjustment. These estimates were in reasonable agreement with the leukaemia risk observed among males exposed as adults in the LSS cohort (ERR at 100 mGy=0.15; 95% CI −0.11 to 0.53) using the linear term in a linear-quadratic dose–response model.46 Unlike the LSS which reports risks from acute exposure to a wide range of doses, our models provided a precise and quantitative summary of the direct estimates of excess risk from studies of protracted occupational and environmental radiation exposures.

A possible explanation for the overall lack of between-study variance is that most studies were occupationally based and examined workers with similar jobs and eras of employment. Measurement data from personal dosimetry systems are often available for study participants from nuclear industries, leading to reduced uncertainties in exposures and avoidance of exposure misclassification that may bias results. Finally, leukaemia is an endpoint that has been shown to be relatively resistant to confounding or effect modification by exposures to other leukaemogens in epidemiological studies, as it has few known risk factors.23 42

Sensitivity analyses revealed that study no. 2 particularly influenced the aggregate estimate because of its small standard error compared to other studies in the analysis. Study no. 2 evaluated the leukaemia mortality risks associated with protracted exposures in Russian nuclear workers (n=21 557) employed at the Mayak complex between 1948 and 1972. The large study size and wide dose distribution increased the precision of risk estimates. The average cumulative external dose among the Mayak workers (810 mGy) was about 20-fold higher than most other studies under observation. Recent improvements in Mayak exposure estimates47 show a shift in the external dose distribution away from the higher exposures found in earlier studies,48 suggesting confirmation of exposure misclassification that would bias risk estimates toward the null. Other studies have made similar observations resulting in additional explanations for risk attenuation with moderate to high exposures: a healthy worker survivor effect, a depletion of the number of susceptible people in the population at high exposure levels, confounding by other risk factors that vary across levels of the main exposure, and saturation in the exposure–response relationship for biological processes involved in disease development.23 49–51 Regardless of the cause, risk attenuation at high doses may explain the reduction in the slope of the linear dose response observed in the Mayak cohort compared to cohorts with lower average cumulative exposures.

The next likely influential study was study no. 1. Hwang et al19 examined leukaemia incidence in a cohort of Taiwanese residents (n=6242) exposed between 1983 and 1992 from52 Co-contaminated reinforcing steel used to construct their dwellings.19 Only six incident leukaemias occurred in a cohort whose average excess cumulative exposure was less than 50 mGy (range: <1–2363). The precision of this study is remarkable given its size; studies of similar size and exposure characteristics were much less precise. This population-based study differs from most occupational studies in that the cohort is relatively young (mean age at first exposure was 16.9±16.5 years) and individual exposures were estimated without the availability of personal dosimetry measurements. However, these differences are unlikely to fully explain the observed disparity in study precision. The overall effect of choosing whether to exclude one or both outlying studies was small, suggesting that aggregate results are robust.

Meta-analyses of cancer risk among low-dose radiation studies must consider sources and magnitude of bias within each contributing study. Each study possesses limitations in methods and available data that may contribute to bias. The major sources of bias for leukaemia risk in low-dose observational studies are (1) dose error and uncertainty and (2) exposure to other leukaemogens that is differentially distributed with respect to ionising radiation. With regard to the former, most studies of nuclear workers involve individual badge dosimetry, the magnitude of which has been quantified in recent studies.53 These errors have generally been found to be small ‘classical’ errors which would be more likely to cause bias toward the null.54 Environmental studies, by contrast, frequently involve exposures that are Berksonian in error and can have uncertain effects on risk estimates.54 Regarding the latter source, exposures to benzene, 1,3-butadiene, carbon tetrachloride, ethylene oxide, formaldehyde, and trichloroethylene are known (ie, benzene) or suspected to be linked to leukaemia.55 56 Of these chemicals, benzene is the only known leukaemogen likely to be associated with radiation exposures, and then only among worker-based studies. A previous pooled study found that inclusion of benzene slightly reduced radiation–leukaemia risk estimates.23 The risk estimate for this study (within this meta-analysis) is benzene-adjusted. Other worker-based studies, particularly those based on more recent employment, have found little evidence of potential benzene exposures, as this substance was never used or has been phased out of many workplaces.29 40 Unlike solid cancers, leukaemia is advantageous for meta-analysis because it is not strongly associated with lifestyle factors, although there is some evidence of a moderate association (RR=1.4–2.0) between smoking and some leukaemias.57–59 Given the small magnitude of the observed association, occupational radiation exposure and smoking would need to be highly correlated to account for even a modest effect on the dose–response trend for leukaemia.60 Nonetheless, the effects of smoking and concomitant leukaemogen exposures are largely unaccounted for in most studies, which is a limitation in our meta-analysis.52

Studies suitable for inclusion were limited to populations primarily exposed to low-LET radiations; however, concomitant exposures to neutrons and internally deposited alpha-emitters were likely in many situations. Some studies included high-LET exposures so that the effects due solely to low-LET exposures could not be examined.27 29 Thus, there is a potential for bias from limitations in adjusting for dose heterogeneity. However, recent studies of the relationship between low-LET irradiation and leukaemia have shown little effect from adjusting for plutonium exposures 20 23 and have also shown exposures to neutrons and alpha-emitting radionuclides are typically small relative to low-LET exposures.23 24 27 29 32–34 37 38 42 Therefore, a substantial bias from dose heterogeneity in selected studies is unlikely.

Three primary studies (13%) reported results from log-linear dose–response modelling.19 24 38 We assumed that risk estimates from log-linear and linear models using these data would be similar at low doses (ie, <100 mGy) because dose ranges did not appear great. However, the uncertainty between these models may substantially differ. Our sensitivity analyses revealed little evidence that the log-linear model results had a strong effect on the aggregate estimates as shown by models If and IIf. Thus, our estimates of ERR at 100 mGy as a risk per 100 mGy may be reasonable at exposures below this level, but caution should be used when extrapolating above this level.

Obtaining sufficient information to estimate study precision was problematic. It was common for studies to report uncertainty using confidence intervals calculated from profile likelihood methods, and the distribution of the likelihood function was unknown. Reported confidence intervals were often asymmetric and were sometimes reported as inestimable due to parameter space constraints of the model. We were able to obtain the original estimates of standard errors for seven studies. Comparing our estimates to reported standard errors suggested that our methods slightly overestimated standard errors. Replacing our estimates with reported values or adjusting to account for an observed bias did not noticeably change the aggregate estimate (data not shown). Thus, there is little evidence of a significant bias due to inadequate estimation of the standard errors. However, more work is needed to examine the coverage of confidence intervals for the aggregate estimates.

Although we observed little change in estimates following adjustment, there was evidence of publication bias away from the null. It is likely that smaller and earlier studies lacked the statistical power needed to perform informative dose–response analyses. Of 15 studies that were superseded by studies with improved follow-up, seven (47%) reported negative ERR values. Moreover, some larger studies included dose–response modelling only if an elevation was observed in other comparisons (eg, standardised mortality ratio (SMR), standardised rate ratio (SRR)).61 Our analysis suggests that model II results were the least affected by publication bias. We propose this bias was minimised because the large pooled studies used in model II included many small cohorts that likely had null or negative values, which may not have been published individually with dose–response estimates. We reported results following an adjustment for publication bias in some models, although publication bias cannot be confirmed without uncovering the actual ‘negative’ unpublished studies. We advise cautious interpretation of tests of heterogeneity and bias in our meta-analysis as there is no statistical test that can confirm or exclude bias with certainty.

We excluded CLL from our analysis because information on CLL risks was sparse among the studies examined. However, we note that CLL radiogenicity is the subject of recent enquiry.62–66 The majority of information obtained for our analysis resulted from mortality studies that may underestimate risks due to improved survival in leukaemia patients. Furthermore, several studies acknowledged difficulty in case ascertainment, most notably among incident and environmental studies. Thus, a potential bias may exist from underascertainment of incidence cases.

Occupational and environmental epidemiological studies are, in principle, preferred over studies of high-dose exposures for estimating the effects of low-dose protracted ionising radiation exposures. Unfortunately, these studies have individually lacked the precision necessary for the projection of population-based risks. Using a simple meta-analytical approach to synthesise the available information, we found that leukaemia (excluding CLL) is significantly associated with exposure to protracted, low-level ionising radiation. Our preferred estimate of leukaemia risk at 100 mGy is 0.19 (95% CI 0.07 to 0.32), achieved by combining information from existing pooled analyses and non-overlapping studies, excluding one influential study, and adjusting for publication bias.

We found between-study effects to be minimal, which suggests that differences in study populations and epidemiological methods were essentially unimportant. Although publication bias was indicated, subsequent adjustment did not markedly change aggregate estimates. Likewise, excluding influential studies resulted in only moderate effects on risk estimates. In fact, all model results were in reasonable agreement with the leukaemia risk observed in atomic bomb survivors, which is regarded by most as the gold standard. Thus, our results appear robust and strengthen our confidence in the current understanding of leukaemia risk among populations exposed to ionising radiation.