Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models

Advancing environmental epidemiologic methods to confront the cancer burden

Even though many environmental carcinogens have been identified, studying their effects on specific cancers has been challenging in nonoccupational settings, where exposures may be chronic but at lower levels. Although exposure measurement methods have improved considerably, along with key opportunities to integrate multi-omic platforms, there remain challenges that need to be considered, particularly around the design of studies. Cancer studies typically exclude individuals with prior cancers and start recruitment in midlife. This translates into a failure to capture individuals who may have been most susceptible because of both germline susceptibility and higher early-life exposures that lead to premature mortality from cancer and/or other environmentally caused diseases like lung diseases. Using the example of breast cancer, we demonstrate how integration of susceptibility, both for cancer risk and for exposure windows, may provide a more complete picture regarding the harm of many different environmental exposures. Choice of study design is critical to examining the effects of environmental exposures, and it will not be enough to just rely on the availability of existing cohorts and samples within these cohorts. In contrast, new, diverse, early-onset case-control studies may provide many benefits to understanding the impact of environmental exposures on cancer risk and mortality. This article is part of a Special Collection on Environmental Epidemiology.

Mathematical characterization of dose uncertainty effects on functions summarizing findings of community noise attitudinal surveys

Previous Monte Carlo simulations have quantified the extent to which dose (sound level) uncertainty in community noise dose-response surveys can bias the shape of inferred dose-response functions. The present work extends the prior findings to create a mathematical model of the biasing effect. The exact effect on any particular data set depends on additional attributes (situational variables) beyond dose uncertainty itself. Several variables and their interaction effects are accounted for in the model. The model produced identical results to the prior Monte Carlo simulations and thereby demonstrated the same slope reduction effect. This model was further exercised to demonstrate the nature and extent of situational variable interaction effects related to the range of doses employed and their distribution across the range. One manifestation was a false asymptotic behavior in the observed dose-response relationship. The mathematical model provides a means to not only predict dose uncertainty effects but also to serve as a foundation for correcting for such effects in regression analyses of transportation noise dose-response relationships.

Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework

BACKGROUND

The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand the relative contribution of different factors towards generating variable results, including low sample sizes, researchers' flexibility in model choices, and measurement error in variables of interest and adjustment variables.

METHODS

We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework. In a practical application, we examine these types of uncertainty in a Cox model using data from the National Health and Nutrition Examination Survey. In addition, we analyse the behaviour of sampling, model and measurement uncertainty for varying sample sizes in a simulation study.

RESULTS

All types of uncertainty are associated with a potentially large variability in effect estimates. Measurement error in the variable of interest attenuates the true effect in most cases, but can occasionally lead to overestimation. When we consider measurement error in both the variable of interest and adjustment variables, the vibration of effects are even less predictable as both systematic under- and over-estimation of the true effect can be observed. The results on simulated data show that measurement and model vibration remain non-negligible even for large sample sizes.

CONCLUSION

Sampling, model and measurement uncertainty can have important consequences for the stability of observational associations. We recommend systematically studying and reporting these types of uncertainty, and comparing them in a common framework.

Occupational Radiation Exposure and Validity of National Dosimetry Registry among Korean Interventional Radiologists

The national dose registry (NDR) contains essential information to help protect radiation workers from radiation-related health risks and to facilitate epidemiological studies. However, direct validation of the reported doses has not been considered. We investigated the validity of the NDR with a personal dosimeter monitoring conducted among Korean interventional radiologists. Among the 56 interventional radiologists, NDR quarterly doses were compared with actively monitored personal thermoluminescent dosimeter (TLD) doses as standard measures of validation. We conducted analyses with participants categorized according to compliance with TLD badge-wearing policies. A correlation between actively monitored doses and NDR doses was low (Spearman ρ = 0.06), and the mean actively monitored dose was significantly higher than the mean NDR dose (mean difference 0.98 mSv) in all participants. However, interventional radiologists who wore badges irregularly showed a large difference between actively monitored doses and NDR doses (mean difference 2.39 mSv), and participants who wore badges regularly showed no apparent difference between actively monitored doses and NDR doses (mean difference 0.26 mSv). This study indicated that NDR data underestimate the actual occupational radiation exposure, and the validity of these data varies according to compliance with badge-wearing policies. Considerable attention is required to interpret and utilize NDR data based on radiation workers’ compliance with badge-wearing policies.

Ischaemic heart and cerebrovascular disease mortality in uranium enrichment workers

OBJECTIVE

Linear and non-linear dose-response relationships between radiation absorbed dose to the lung from internally deposited uranium and external sources and circulatory system disease (CSD) mortality were examined in a cohort of 23 731 male and 5552 female US uranium enrichment workers.

METHODS

Rate ratios (RRs) for categories of lung dose and linear excess relative rates (ERRs) per unit lung dose were estimated to evaluate the associations between lung absorbed dose and death from ischaemic heart disease (IHD) and cerebrovascular disease.

RESULTS

There was a suggestion of modestly increased IHD risk in workers with internal uranium lung dose above 1 milligray (mGy) (RR=1.4, 95% CI 0.76 to 2.3) and a statistically significantly increased IHD risk with external dose exceeding 150 mGy (RR=1.3, 95% CI 1.1 to 1.6) compared with the lowest exposed groups. ERRs per milligray were positive for IHD and uranium internal dose and for both outcomes per gray external dose, although the CIs generally included the null.

CONCLUSIONS

Non-linear dose-response models using restricted cubic splines revealed sublinear responses at lower internal doses, suggesting that linear models that are common in radioepidemiological cancer studies may poorly describe the association between uranium internal dose and CSD mortality.

A Bayesian hierarchical approach to account for left-censored and missing radiation doses prone to classical measurement error when analyzing lung cancer mortality due to γ-ray exposure in the French cohort of uranium miners

Epidemiological data on cohorts of occupationally exposed uranium miners are currently used to assess health risks associated with chronic exposure to low doses of ionizing radiation. Nevertheless, exposure uncertainty is ubiquitous and questions the validity of statistical inference in these cohorts. This paper highlights the flexibility and relevance of the Bayesian hierarchical approach to account for both missing and left-censored (i.e. only known to be lower than a fixed detection limit) radiation doses that are prone to measurement error, when estimating radiation-related risks. Up to the authors' knowledge, this is the first time these three sources of uncertainty are dealt with simultaneously in radiation epidemiology. To illustrate the issue, this paper focuses on the specific problem of accounting for these three sources of uncertainty when estimating the association between occupational exposure to low levels of γ-radiation and lung cancer mortality in the post-55 sub-cohort of French uranium miners. The impact of these three sources of dose uncertainty is of marginal importance when estimating the risk of death by lung cancer among French uranium miners. The corrected excess hazard ratio (EHR) is 0.81 per 100 mSv (95% credible interval: [0.28; 1.75]). Interestingly, even if the 95% credible interval of the corrected EHR is wider than the uncorrected one, a statistically significant positive association remains between γ-ray exposure and the risk of death by lung cancer, after accounting for dose uncertainty. Sensitivity analyses show that the results obtained are robust to different assumptions. Because of its flexible and modular nature, the Bayesian hierarchical models proposed in this work could be easily extended to account for high proportions of missing and left-censored dose values or exposure data, prone to more complex patterns of measurement error.

Analysis of Cataract in Relationship to Occupational Radiation Dose Accounting for Dosimetric Uncertainties in a Cohort of U.S. Radiologic Technologists

Cataract is one of the major morbidities in the U.S. population and it has long been appreciated that high and acutely delivered radiation doses of 1 Gy or more can induce cataract. Some more recent studies, in particular those of the U.S. Radiologic Technologists, have suggested that cataract may be induced by much lower, chronically delivered doses of ionizing radiation. It is well recognized that dosimetric measurement error can substantially alter the shape of the radiation dose-response relationship and thus, the derived study risk estimates, and can also inflate the variance of the estimates. In the current study, we evaluate the impact of uncertainties in eye-lens absorbed doses on the estimated risk of cataract in the U.S. Radiologic Technologists' Monte Carlo Dosimetry System, using both absolute and relative risk models. Among 11,345 cases we show that the inflation in the standard error for the excess relative risk (ERR) is generally modest, at most approximately 20% of the unadjusted standard error, depending on the model used for the baseline risk. The largest adjustment results from use of relative risk models, so that the ERR/Gy and its 95% confidence intervals change from 1.085 (0.645, 1.525) to 1.085 (0.558, 1.612) after adjustment. However, the inflation in the standard error of the excess absolute risk (EAR) coefficient is generally minimal, at most approximately 0.04% of the standard error.

Exposure Measurement Error in Air Pollution Studies: The Impact of Shared, Multiplicative Measurement Error on Epidemiological Health Risk Estimates

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

PUMA - pooled uranium miners analysis: cohort profile

OBJECTIVES

Epidemiological studies of underground miners have provided clear evidence that inhalation of radon decay products causes lung cancer. Moreover, these studies have served as a quantitative basis for estimation of radon-associated excess lung cancer risk. However, questions remain regarding the effects of exposure to the low levels of radon decay products typically encountered in contemporary occupational and environmental settings on the risk of lung cancer and other diseases, and on the modifiers of these associations. These issues are of central importance for estimation of risks associated with residential and occupational radon exposures.

METHODS

The Pooled Uranium Miner Analysis (PUMA) assembles information on cohorts of uranium miners in North America and Europe. Data available include individual annual estimates of exposure to radon decay products, demographic and employment history information on each worker and information on vital status, date of death and cause of death. Some, but not all, cohorts also have individual information on cigarette smoking, external gamma radiation exposure and non-radiological occupational exposures.

RESULTS

The PUMA study represents the largest study of uranium miners conducted to date, encompassing 124 507 miners, 4.51 million person-years at risk and 54 462 deaths, including 7825 deaths due to lung cancer. Planned research topics include analyses of associations between radon exposure and mortality due to lung cancer, cancers other than lung, non-malignant disease, modifiers of these associations and characterisation of overall relative mortality excesses and lifetime risks.

CONCLUSION

PUMA provides opportunities to evaluate new research questions and to conduct analyses to assess potential health risks associated with uranium mining that have greater statistical power than can be achieved with any single cohort.

Methods to account for uncertainties in exposure assessment in studies of environmental exposures

BACKGROUND

Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies.

MAIN TEXT

We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates.

CONCLUSIONS

In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Exposure measurement error in air pollution studies: A framework for assessing shared, multiplicative measurement error in ensemble learning estimates of nitrogen oxides

BACKGROUND

Increasingly ensemble learning-based spatiotemporal models are being used to estimate residential air pollution exposures in epidemiological studies. While these machine learning models typically have improved performance, they suffer from exposure measurement error that is inherent in all models. Our objective is to develop a framework to formally assess shared, multiplicative measurement error (SMME) in our previously published three-stage, ensemble learning-based nitrogen oxides (NO_x) model to identify its spatial and temporal patterns and predictors.

METHODS

By treating the ensembles as an external dosimetry system, we quantified shared and unshared, multiplicative and additive (SUMA) measurement error components in our exposure model. We used generalized additive models (GAMs) with a smooth term for location to identify geographic locations with significantly elevated SMME and explain their spatial and temporal determinants.

RESULTS

We found evidence of significant shared and unshared multiplicative error (p < 0.0001) in our ensemble-learning based spatiotemporal NO_x model predictions. Unshared multiplicative error was 26 times larger than SMME. We observed significant geographic (p < 0.0001) and temporal variation in SMME with the majority (43%) of predictions with elevated SMME occurring in the earliest time-period (1992-2000). Densely populated urban prediction regions with complex air pollution sources generally exhibited highest odds of elevated SMME.

CONCLUSIONS

We developed a novel statistical framework to formally evaluate the magnitude and drivers of SMME in ensemble learning-based exposure models. Our framework can be used to inform building future improved exposure models.

A cautionary comment on the generation of Berkson error in epidemiological studies

Exposure measurement error can be seen as one of the most important sources of uncertainty in studies in epidemiology. When the aim is to assess the effects of measurement error on statistical inference or to compare the performance of several methods for measurement error correction, it is indispensable to be able to generate different types of measurement error. This paper compares two approaches for the generation of Berkson error, which have recently been applied in radiation epidemiology, in their ability to generate exposure data that satisfy the properties of the Berkson model. In particular, it is shown that the use of one of the methods produces results that are not in accordance with two important properties of Berkson error.