Estimating Lung Cancer Mortality from Residential Radon Using Data for Low Exposures of Miners

Lubin, Jay H.; Edling, Christer; Hornung, Richard; Howe, Geoffrey R.; Kunz, E; Kusiak, Robert A.; Morrison, Howard; Radford, Edward P.; Samet, Jonathan M.; Tirmarche, M.; Woodward, Alistair; Yao, Shu Xiang; Tomášek, Ladislav

doi:10.2307/3579412

Cited by 99 publications

(39 citation statements)

References 25 publications

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“…An advantage of this approach is that estimates of the independent effect of exposure during each window may be obtained (Ha et al 2007; Lubin et al 1997). However, the approach is not always feasible because of collinearity issues (e.g., inflated standard errors, unstable regression coefficients).…”

Section: Methodsmentioning

confidence: 99%

Statistical Methods to Study Timing of Vulnerability with Sparsely Sampled Data on Environmental Toxicants

Sánchez¹,

Litman

et al. 2011

Environ Health Perspect

178

123

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BackgroundIdentifying windows of vulnerability to environmental toxicants is an important area in children’s health research.ObjectiveWe compared and contrasted statistical approaches that may help identify windows of vulnerability by formally testing differences in exposure effects across time of exposure, incorporating continuous time metrics for timing of exposure, and efficiently incorporating incomplete cases.MethodsWe considered four methods: 1) window-specific and simultaneously adjusted regression; 2) multiple informant models; 3) using features of individual exposure patterns to predict outcomes; and 4) models of population exposure patterns depending on the outcome. We illustrate them using a study of prenatal vulnerability to lead in relation to Bayley’s Mental Development Index at 24 months of age (MDI24).ResultsThe estimated change in MDI24 score with a 1-loge-unit increase in blood lead during the first trimester was −2.74 [95% confidence interval (CI), −5.78 to 0.29] based on a window-specific regression. The corresponding change in MDI24 was −4.13 (95% CI, −7.54 to −0.72) based on a multiple informant model; estimated effects were similar across trimesters (p = 0.23). Results from method 3 suggested that blood lead levels in early pregnancy were significantly associated with reduced MDI24, but decreasing blood leads over the course of pregnancy were not. Method 4 results indicated that blood lead levels before 17 weeks of gestation were lower among children with MDI24 scores in the 90th versus the 10th percentile (p = 0.08).ConclusionsMethod 2 is preferred over method 1 because it enables formal testing of differences in effects across a priori–defined windows (e.g., trimesters of pregnancy). Methods 3 and 4 are preferred over method 2 when there is large variability in the timing of exposure assessments among participants. Methods 3 and 4 yielded smaller p-values for tests of the hypothesis that not only level but also timing of lead exposure are relevant predictors of MDI24; systematic power comparisons are warranted.

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Section: Methodsmentioning

confidence: 99%

Statistical Methods to Study Timing of Vulnerability with Sparsely Sampled Data on Environmental Toxicants

Sánchez¹,

Litman

et al. 2011

Environ Health Perspect

178

123

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“…In most cases linear extrapolation and excess relative risk (ERR) per unit exposure derived from the miners' data have been used to estimate risks at low doses [2,3]. However, in practice large differences in ERR (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…lung cancer) has not yet been satisfactorily defined and continues to be the subject of discussion and analysis. In spite of the different radon exposure conditions in homes and mines, studies of lung cancer incidence among uranium miners still form the most important source of information for estimating the risk of lung cancer in the population from indoor radon exposure [1][2][3][4]. The studies of different cohorts of uranium miners [1,2,5] have clearly shown that high levels of radon exposure can cause lung cancer and evidence has been provided that even when data from exposures as low as 50 and 100 WLM (Working Level Months, a measure of the exposure to radon used for the miners) are considered, increased levels of lung cancer have been observed [4,5].…”

Section: Introductionmentioning

confidence: 99%

Radon-induced lung cancer in smokers and non-smokers: risk implications using a two-mutation carcinogenesis model

Leenhouts

1999

Radiation and Environmental Biophysics

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Three sets of data (population statistics in non-smokers, data from an investigation of the smoking habits of British doctors and a study of Colorado uranium miners) were used to analyse lung cancer in humans as a function of exposure to radon and smoking. One of the aims was to derive implications for radon risk estimates. The data were analysed using a two-mutation radiation carcinogenesis model and a stepwise determination of the model parameters. The basic model parameters for lung cancer were derived from the age dependence fit of the spontaneous lung cancer incidence in non-smokers. The effect of smoking was described by two additional parameters and, subsequently, the effect of radon by three other parameters; these five parameters define the dependence of the two mutation steps on smoking and exposure to radon. Using this approach, a consistent fit and comprehensive description of the three sets of data have been achieved, and the parameters could, at least partly, be related to cellular radiobiological data. The model results explain the different effect of radon on non-smokers and smokers as seen in epidemiological data. Although the analysis was only applied to a limited number of populations, lung cancer incidence as a result of radon exposure is estimated to be about ten times higher for people exposed at the age of about 15 than at about 50, although this effect is masked (especially for smokers) by the high lung cancer incidence from smoking. Using the model to calculate the lung cancer risks from lifetime exposure to radon, as is the case for indoor radon, higher risks were estimated than previously derived from epidemiological studies of the miners' data. The excess absolute risk per unit exposure of radon is about 1.7 times higher for smokers of 30 cigarettes per day than for non-smokers, even though, as a result of the low spontaneous tumour incidence in the non-smokers, the excess relative risk per unit exposure for the smokers is about 20 times lower than for the non-smokers. This prediction could have serious consequences for the transfer of risk estimates between populations. Although the solution of the model presented here is not unique but dependent on the model assumptions, the predictions and risk implications are sufficiently supported to justify a thorough investigation of the applicability of the model to other radon data sets.

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“…Radon further decays into a series of radon daughters that emit alpha particles. Underground miners exposed to high levels of radon gas in the past have been shown to be at increased risk of lung cancer (Lubin et al, 1995(Lubin et al, , 1997. These epidemiological observations have resulted in the designation of radon as a risk factor for lung cancer in humans by the International Agency for Research on Cancer (1988).…”

Section: Introductionmentioning

confidence: 99%

Modeling seasonal variation in indoor radon concentrations

Krewski

Mallick

Zielinski

et al. 2004

J Expo Sci Environ Epidemiol

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Radon, a well-established risk factor for human lung cancer, is present at low concentrations in most homes. Consequently, many countries have established national guidelines for residential radon concentrations. In this article, we evaluate two models for describing seasonal variation in residential radon concentrations based on the data from a large case-control study conducted in Winnipeg, Canada. In this study, radon levels in homes were monitored during two successive 6-month periods, with integrated annual radon concentrations obtained using CR-39 a-track detectors. Significant differences were noted among measurements taken during different seasons of the year. Using the model introduced by Pinel et al. (1995) to describe temporal variation in residential radon levels in southwest England using seasonal adjustment factors, reasonable predictions of annual average radon concentrations were obtained from the 6-month integrated radon measurements. However, a simple multiplicative model was found to provide better predictions than the seasonal adjustment model. Although model coefficients vary somewhat from one geographic location to another, the concordance with respect discriminating between results above and below 150 Bq/m 3 in Winnipeg was in the range 85-90% using seasonal adjustment models with coefficients derived from data in either Winnipeg or southwest England.

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Estimating Lung Cancer Mortality from Residential Radon Using Data for Low Exposures of Miners

Cited by 99 publications

References 25 publications

Statistical Methods to Study Timing of Vulnerability with Sparsely Sampled Data on Environmental Toxicants

Statistical Methods to Study Timing of Vulnerability with Sparsely Sampled Data on Environmental Toxicants

Radon-induced lung cancer in smokers and non-smokers: risk implications using a two-mutation carcinogenesis model

Modeling seasonal variation in indoor radon concentrations

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