Regression calibration for classical exposure measurement error in environmental epidemiology studies using multiple local surrogate exposures

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Heat-mortality relationship in North Carolina: Comparison using different exposure methods

BACKGROUND

Many studies have explored the heat-mortality relationship; however, comparability of results is hindered by the studies' use of different exposure methods.

OBJECTIVE

This study evaluated different methods for estimating exposure to temperature using individual-level data and examined the impacts on the heat-mortality relationship.

METHODS

We calculated different temperature exposures for each individual death by using a modeled, gridded temperature dataset and a monitoring station dataset in North Carolina for 2000-2016. We considered individual-level vs. county-level averages and measured vs. modeled temperature data. A case-crossover analysis was conducted to examine the heat-mortality risk under different exposure methods.

RESULTS

The minimum mortality temperature (MMT) (i.e., the temperature with the lowest mortality rate) for the monitoring station dataset was 23.87 °C and 22.67 °C (individual monitor and county average, respectively), whereas for the modeled temperature dataset the MMT was 19.46 °C and 19.61 °C (individual and county, respectively). We found higher heat-mortality risk while using temperature exposure estimated from monitoring stations compared to risk based on exposure using the modeled temperature dataset. Individual-aggregated monitoring station temperature exposure resulted in higher heat mortality risk (odds ratio (95% CI): 2.24 (95% CI: 2.21, 2.27)) for a relative temperature change comparing the 99th and 90th temperature percentiles, while modeled temperature exposure resulted in lower odds ratio of 1.27 (95% CI: 1.25, 1.29).

SIGNIFICANCE

Our findings indicate that using different temperature exposure methods can result in different temperature-mortality risk. The impact of using various exposure methods should be considered in planning health policies related to high temperatures, including under climate change. IMPACT STATEMENT: (1) We estimated the heat-mortality association using different methods to estimate exposure to temperature. (2) The mean temperature value among different exposure methods were similar although lower for the modeled data, however, use of the monitoring station temperature dataset resulted in higher heat-mortality risk than the modeled temperature dataset. (3) Differences in mortality risk from heat by urbanicity varies depending on the method used to estimate temperature exposure.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Impact of uncertainties in exposure assessment on thyroid cancer risk among cleanup workers in Ukraine exposed due to the Chornobyl accident

A large excess risk of thyroid cancer was observed among Belarusian/Russian/Baltic Chornobyl cleanup workers. A more recent study of Ukraine cleanup workers found more modest excess risks of thyroid cancer. Dose errors in this data are substantial, associated with model uncertainties and questionnaire response. Regression calibration is often used for dose-error adjustment, but may not adequately account for the full error distribution. We aimed to examine the impact of exposure-assessment uncertainties on thyroid cancer among Ukrainian cleanup workers using Monte Carlo maximum likelihood, and compare with results derived using regression calibration. Analyses assessed the sensitivity of results to various components of internal and external dose. Regression calibration yielded an excess odds ratio per Gy (EOR/Gy) of 0.437 (95% CI - 0.042, 1.577, p = 0.100), compared with the EOR/Gy using Monte Carlo maximum likelihood of 0.517 (95% CI - 0.039, 2.035, p = 0.093). Trend risk estimates for follicular morphology tumors exhibited much more extreme effects of full-likelihood adjustment, the EOR/Gy using regression calibration of 3.224 (95% CI - 0.082, 30.615, p = 0.068) becoming ~ 50% larger, 4.708 (95% CI - 0.075, 85.143, p = 0.066) when using Monte Carlo maximum likelihood. Results were sensitive to omission of external components of dose. In summary, use of Monte Carlo maximum likelihood adjustment for dose error led to increases in trend risks, particularly for follicular morphology thyroid cancers, where risks increased by ~ 50%, and were borderline significant. The unexpected finding for follicular tumors needs to be replicated in other exposed groups.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality

BACKGROUND

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.

OBJECTIVES

We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and all-cause mortality.

METHODS

In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders.

RESULTS

We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.

DISCUSSION

Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

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.

Quantitative Bias Analysis of the Association between Occupational Radiation Exposure and Ischemic Heart Disease Mortality in UK Nuclear Workers

The scientific question of whether protracted low-dose or low-dose-rate exposure to external radiation is causally related to the risk of circulatory disease continues to be an important issue for radiation protection. Previous analyses of a matched case-control dataset nested in a large cohort of UK nuclear fuel cycle workers indicated that there was little evidence that observed associations between external radiation dose and ischemic heart disease (IHD) mortality risk [OR = 1.35 (95% CI: 0.99-184) for 15-year-lagged exposure] could alternatively be explained by confounding from pre-employment tobacco smoking, BMI or blood pressure, or from socioeconomic status or occupational exposure to excessive noise or shiftwork. To improve causal inference about the observed external radiation dose and IHD mortality association, we estimated the potential magnitude and direction of non-random errors, incorporated sensitivity analyses and simulated bias effects under plausible scenarios. We conducted quantitative bias analyses of plausible scenarios based on 1,000 Monte Carlo samples to explore the impact of exposure measurement error, missing information on tobacco smoking, and unmeasured confounding, and assessed whether observed associations were reliant on the inclusion of specific matched pairs using bootstrapping with 10% of matched pairs randomly excluded in 1,000 samples. We further explored the plausibility that having been monitored for internal exposure, which was an important confounding factor in the case-control analysis for which models were adjusted, was indeed a confounding factor or whether it might have been the result of some form of selection bias. Consistent with the broader epidemiological evidence-base, these analyses provide further evidence that the dose-response association between cumulative external radiation exposure and IHD mortality is non-linear in that it has a linear shape plateauing at an excess risk of 43% (95% CI: 7-92%) on reaching 390 mSv. Analyses of plausible scenarios of patterns of missing data for tobacco smoking at start of employment indicated that this resulted in relatively little bias towards the null in the original analysis. An unmeasured confounder would have had to have been highly correlated (rp > 0.60) with cumulative external radiation dose to importantly bias observed associations. The confounding effect of "having been monitored for internal dose" was unlikely to have been a true confounder in a biological sense, but instead may have been some unknown factor related to differences over time and between sites in selection criteria for internal monitoring, possibly resulting in collider bias. Plausible patterns of exposure measurement error negatively biased associations regardless of the modeled scenario, but did not importantly change the shape of the observed dose-response associations. These analyses provide additional support for the hypothesis that the observed association between external radiation exposure and IHD mortality may be causal.

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.

Maternal cumulative exposure to extremely low frequency electromagnetic fields, prematurity and small for gestational age: a pooled analysis of two birth cohorts

Background

Data on the effects of extremely low frequency electromagnetic fields (ELF-EMF) on pregnancy outcomes are inconclusive.

Objective

To study the relation between maternal cumulative exposure to ELF-EMF during pregnancy and the risk of prematurity or small for gestational age (SGA) in a pooled analysis of two French birth cohorts.

Methods

Elfe and Epipage2 are both population-based birth cohorts initiated in 2011 and included 18 329 and 8400 births, respectively. Health data and household, mother and child characteristics were obtained from medical records and questionnaires at maternity and during follow-up. A job exposure matrix was used to assess cumulative exposure to ELF-EMF during three periods: (1) until 15 weeks of gestation, (2) until 28 weeks of gestation and (3) until 32 weeks of gestation. Analyses were restricted to single live births in mainland France and to mothers with documented jobs (N=19 894). Adjusted logistic regression models were used.

Results

According to the period studied, 3.2%–4% of mothers were classified as highly exposed. Results were heterogeneous. Increased risks of prematurity were found among low exposed mothers for the three periods, and no association was observed among the most exposed (OR1=0.92 (95% CI 0.74 to 1.15); OR2=0.98 (95% CI 0.80 to 1.21); OR3=1.14 (95% CI 0.92 to 1.41)). For SGA, no association was observed with the exception of increased risk among the low exposed mothers in period 2 and the most exposed in period 3 (OR=1.25 (95% CI 1.02 to 1.53)).

Conclusion

Some heterogeneous associations between ELF-EMF exposure and prematurity and SGA were observed. However, due to heterogeneity (ie, their independence regarding the level of exposure), associations cannot be definitely explained by ELF-EMF exposure.

Long-term effect of fine particulate matter on hospitalization with dementia

BACKGROUND

New evidence suggests that particulate matter less than 2.5 μm in diameter (PM_2.5) is associated with late-onset dementia (LOD). However, epidemiological studies for the entire population are lacking.

METHODS

We analyzed approximately 94 million follow-up records from fee-for-service Medicare records for 13 million Medicare beneficiaries residing in the southeastern United States (U.S.) from 2000 to 2013. We used spatially and temporally continuous PM_2.5 exposure data. To account for time-varying PM_2.5 levels, we applied an Andersen-Gill counting process proportional hazard model; we stratified our analyses by subtype of dementia and level of urbanization of residence.

RESULTS

During a median follow-up of 6 years, 1,409,599 hospitalizations with dementia occurred. The adjusted hazard ratio (HR) of hospitalization with dementia was 1.049 (95% confidence interval [CI], 1.048 to 1.051) per 1 μg/m³ increase in annual PM_2.5. The hazard ratio for vascular dementia was higher (HR, 1.086; 95% CI, 1.082 to 1.090). In large, the magnitude of the effect grew as the level of urbanization increased (HR, 1.036; 95% CI, 1.031 to 1.041 in rural areas versus HR, 1.052; 95% CI, 1.050 to 1.054 in metropolitan areas).

CONCLUSIONS

Long-term exposure to higher PM_2.5 was associated with increased hospitalizations with dementia.

The association between prenatal bisphenol A exposure and birth weight: a meta-analysis

The association between prenatal BPA exposure and birth weight is controversial. Here, a meta-analysis was performed to estimate the association between prenatal BPA exposure and birth weight. We searched literature addressing the association of interest in relevant databases. Data were independently extracted and analyzed using partial regression coefficient (β) and/or odds ratio (OR) and their 95% confidence intervals (CIs). We identified 140 references and included 8 studies. Based on the results of meta-analysis, the association between prenatal BPA exposure and continuous birth weight was estimated to be 4.42 g (95% CI: -8.83 to 17.67 g) when comparing the highest vs. the lowest BPA concentration. Findings from this study indicated that prenatal BPA exposure was not statistically associated with continuous birth weight. However, more evidence, based on large prospective cohort studies, is required to provide conclusive evidence on whether or not prenatal BPA exposure is associated with birth weight.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses

PURPOSE OF REVIEW

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data.

RECENT FINDINGS

We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the INTEROCC study

To estimate occupational exposures to electromagnetic fields (EMF) for the INTEROCC study, a database of source-based measurements extracted from published and unpublished literature resources had been previously constructed. The aim of the current work was to summarize these measurements into a source-exposure matrix (SEM), accounting for their quality and relevance. A novel methodology for combining available measurements was developed, based on order statistics and log-normal distribution characteristics. Arithmetic and geometric means, and estimates of variability and maximum exposure were calculated by EMF source, frequency band and dosimetry type. The mean estimates were weighted by our confidence in the pooled measurements. The SEM contains confidence-weighted mean and maximum estimates for 312 EMF exposure sources (from 0 Hz to 300 GHz). Operator position geometric mean electric field levels for radiofrequency (RF) sources ranged between 0.8 V/m (plasma etcher) and 320 V/m (RF sealer), while magnetic fields ranged from 0.02 A/m (speed radar) to 0.6 A/m (microwave heating). For extremely low frequency sources, electric fields ranged between 0.2 V/m (electric forklift) and 11,700 V/m (high-voltage transmission line-hotsticks), whereas magnetic fields ranged between 0.14 μT (visual display terminals) and 17 μT (tungsten inert gas welding). The methodology developed allowed the construction of the first EMF-SEM and may be used to summarize similar exposure data for other physical or chemical agents.

Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies

Supplemental Digital Content is available in the text.

For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high.

Evaluation of a two-part regression calibration to adjust for dietary exposure measurement error in the Cox proportional hazards model: A simulation study

Dietary questionnaires are prone to measurement error, which bias the perceived association between dietary intake and risk of disease. Short-term measurements are required to adjust for the bias in the association. For foods that are not consumed daily, the short-term measurements are often characterized by excess zeroes. Via a simulation study, the performance of a two-part calibration model that was developed for a single-replicate study design was assessed by mimicking leafy vegetable intake reports from the multicenter European Prospective Investigation into Cancer and Nutrition (EPIC) study. In part I of the fitted two-part calibration model, a logistic distribution was assumed; in part II, a gamma distribution was assumed. The model was assessed with respect to the magnitude of the correlation between the consumption probability and the consumed amount (hereafter, cross-part correlation), the number and form of covariates in the calibration model, the percentage of zero response values, and the magnitude of the measurement error in the dietary intake. From the simulation study results, transforming the dietary variable in the regression calibration to an appropriate scale was found to be the most important factor for the model performance. Reducing the number of covariates in the model could be beneficial, but was not critical in large-sample studies. The performance was remarkably robust when fitting a one-part rather than a two-part model. The model performance was minimally affected by the cross-part correlation.

Impact of Uncertainties in Exposure Assessment on Thyroid Cancer Risk among Persons in Belarus Exposed as Children or Adolescents Due to the Chernobyl Accident

Background

The excess incidence of thyroid cancer in Ukraine and Belarus observed a few years after the Chernobyl accident is considered to be largely the result of ¹³¹I released from the reactor. Although the Belarus thyroid cancer prevalence data has been previously analyzed, no account was taken of dose measurement error.

Methods

We examined dose-response patterns in a thyroid screening prevalence cohort of 11,732 persons aged under 18 at the time of the accident, diagnosed during 1996–2004, who had direct thyroid ¹³¹I activity measurement, and were resident in the most radio-actively contaminated regions of Belarus. Three methods of dose-error correction (regression calibration, Monte Carlo maximum likelihood, Bayesian Markov Chain Monte Carlo) were applied.

Results

There was a statistically significant (p<0.001) increasing dose-response for prevalent thyroid cancer, irrespective of regression-adjustment method used. Without adjustment for dose errors the excess odds ratio was 1.51 Gy⁻ (95% CI 0.53, 3.86), which was reduced by 13% when regression-calibration adjustment was used, 1.31 Gy⁻ (95% CI 0.47, 3.31). A Monte Carlo maximum likelihood method yielded an excess odds ratio of 1.48 Gy⁻ (95% CI 0.53, 3.87), about 2% lower than the unadjusted analysis. The Bayesian method yielded a maximum posterior excess odds ratio of 1.16 Gy⁻ (95% BCI 0.20, 4.32), 23% lower than the unadjusted analysis. There were borderline significant (p = 0.053–0.078) indications of downward curvature in the dose response, depending on the adjustment methods used. There were also borderline significant (p = 0.102) modifying effects of gender on the radiation dose trend, but no significant modifying effects of age at time of accident, or age at screening as modifiers of dose response (p>0.2).

Conclusions

In summary, the relatively small contribution of unshared classical dose error in the current study results in comparatively modest effects on the regression parameters.

Prenatal exposure to phenols and growth in boys

BACKGROUND

Phenols interact with nuclear receptors implicated in growth and adipogenesis regulation. Only a few studies have explored their effects on growth in humans.

OBJECTIVES

We studied the associations of maternal exposure to phenols during pregnancy with prenatal and postnatal growth of male newborns.

METHODS

Within a cohort of women recruited during pregnancy, we selected 520 mother-son pairs and quantified 9 phenols in spot urine samples collected during pregnancy. We used ultrasonography during pregnancy, together with birth measurements, to assess fetal growth. We modeled individual postnatal growth trajectories from repeated measures of weight and height in the first 3 years of life.

RESULTS

Triclosan concentration was negatively associated with growth parameters measured at the third ultrasound examination but not earlier in pregnancy. At birth, this phenol tended to be negatively associated with head circumference (-1.2 mm for an interquartile range [IQR] increase in ln-transformed triclosan concentration [95% confidence interval = -2.6 to 0.3]) but not with weight or height. Parabens were positively associated with weight at birth. This positive association remained for 3 years for methylparaben (β = 193 g [-4 to 389]) for an IQR increase in ln-transformed concentrations.

CONCLUSION

We relied on only 1 spot urine sample to assess exposure; because of the high variability in phenol urinary concentrations reported during pregnancy, using only 1 sample may result in exposure misclassification, in particular for bisphenol A. Our study suggested associations between prenatal exposure to parabens and triclosan and prenatal or early postnatal growth.

Biomarker-based calibration of retrospective exposure predictions of perfluorooctanoic acid

Estimated historical exposures and serum concentrations of perfluorooctanoic acid (PFOA) have been extensively used in epidemiologic studies that examined associations between PFOA exposures and adverse health outcomes among residents in highly exposed areas in the Mid-Ohio Valley. Using measured serum PFOA levels in 2005-2006, we applied two calibration methods to these retrospective exposure predictions: (1) multiplicative calibration and (2) Bayesian pharmacokinetic calibration with larger adjustments to more recent exposure estimates and smaller adjustments to exposure estimates for years farther in the past. We conducted simulation studies of various hypothetical exposure scenarios and compared hypothetical true historical intake rates with estimates based on mis-specified baseline exposure and pharmacokinetic models to find the method with the least bias. The Bayesian method outperformed the multiplicative method if a change to bottled water consumption was not reported or if the half-life of PFOA was mis-specified. On the other hand, the multiplicative method outperformed the Bayesian method if actual tap water consumption rates were systematically overestimated. If tap water consumption rates gradually decreased over time because of substitution with bottled water or other liquids, neither method clearly outperformed another. Calibration of retrospective exposure estimates using recently collected biomarkers may help reduce uncertainties in environmental epidemiologic studies.

Increased ultrafine particles and carbon monoxide concentrations are associated with asthma exacerbation among urban children

OBJECTIVES

Increased air pollutant concentrations have been linked to several asthma-related outcomes in children, including respiratory symptoms, medication use, and hospital visits. However, few studies have examined effects of ultrafine particles in a pediatric population. Our primary objective was to examine the effects of ambient concentrations of ultrafine particles on asthma exacerbation among urban children and determine whether consistent treatment with inhaled corticosteroids could attenuate these effects. We also explored the relationship between asthma exacerbation and ambient concentrations of accumulation mode particles, fine particles (≤2.5 micrograms [μm]; PM2.5), carbon monoxide, sulfur dioxide, and ozone. We hypothesized that increased 1-7 day concentrations of ultrafine particles and other pollutants would be associated with increases in the relative odds of an asthma exacerbation, but that this increase in risk would be attenuated among children receiving school-based corticosteroid therapy.

METHODS

We conducted a pilot study using data from 3 to 10 year-old children participating in the School-Based Asthma Therapy trial. Using a time-stratified case-crossover design and conditional logistic regression, we estimated the relative odds of a pediatric asthma visit treated with prednisone (n=96 visits among 74 children) associated with increased pollutant concentrations in the previous 7 days. We re-ran these analyses separately for children receiving medications through the school-based intervention and children in a usual care control group.

RESULTS

Interquartile range increases in ultrafine particles and carbon monoxide concentrations in the previous 7 days were associated with increases in the relative odds of a pediatric asthma visit, with the largest increases observed for 4-day mean ultrafine particles (interquartile range=2088p/cm(3); OR=1.27; 95% CI=0.90-1.79) and 7-day mean carbon monoxide (interquartile range=0.17ppm; OR=1.63; 95% CI=1.03-2.59). Relative odds estimates were larger among children receiving school-based inhaled corticosteroid treatment. We observed no such associations with accumulation mode particles, black carbon, fine particles (≤2.5μm), or sulfur dioxide. Ozone concentrations were inversely associated with the relative odds of a pediatric asthma visit.

CONCLUSIONS

These findings suggest a response to markers of traffic pollution among urban asthmatic children. Effects were strongest among children receiving preventive medications through school, suggesting that this group of children was particularly sensitive to environmental triggers. Medication adherence alone may be insufficient to protect the most vulnerable from environmental asthma triggers. However, further research is necessary to confirm this finding.

Impact of uncertainties in exposure assessment on estimates of thyroid cancer risk among Ukrainian children and adolescents exposed from the Chernobyl accident

The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history, and excess thyroid cancers, particularly among those exposed to releases of iodine-131 remain the best-documented sequelae. Failure to take dose-measurement error into account can lead to bias in assessments of dose-response slope. Although risks in the Ukrainian-US thyroid screening study have been previously evaluated, errors in dose assessments have not been addressed hitherto. Dose-response patterns were examined in a thyroid screening prevalence cohort of 13,127 persons aged <18 at the time of the accident who were resident in the most radioactively contaminated regions of Ukraine. We extended earlier analyses in this cohort by adjusting for dose error in the recently developed TD-10 dosimetry. Three methods of statistical correction, via two types of regression calibration, and Monte Carlo maximum-likelihood, were applied to the doses that can be derived from the ratio of thyroid activity to thyroid mass. The two components that make up this ratio have different types of error, Berkson error for thyroid mass and classical error for thyroid activity. The first regression-calibration method yielded estimates of excess odds ratio of 5.78 Gy⁻¹ (95% CI 1.92, 27.04), about 7% higher than estimates unadjusted for dose error. The second regression-calibration method gave an excess odds ratio of 4.78 Gy⁻¹ (95% CI 1.64, 19.69), about 11% lower than unadjusted analysis. The Monte Carlo maximum-likelihood method produced an excess odds ratio of 4.93 Gy⁻¹ (95% CI 1.67, 19.90), about 8% lower than unadjusted analysis. There are borderline-significant (p = 0.101–0.112) indications of downward curvature in the dose response, allowing for which nearly doubled the low-dose linear coefficient. In conclusion, dose-error adjustment has comparatively modest effects on regression parameters, a consequence of the relatively small errors, of a mixture of Berkson and classical form, associated with thyroid dose assessment.

Short-term impact of atmospheric pollution on fecundability

BACKGROUND

Epidemiologic studies have reported associations between air pollution levels and semen characteristics, which might in turn affect a couple's ability to achieve a live birth. Our aim was to characterize short-term effects of atmospheric pollutants on fecundability (the month-specific probability of pregnancy among noncontracepting couples).

METHODS

For a cohort of births between 1994 and 1999 in Teplice (Czech Republic), we averaged fine particulate matter (PM2.5), carcinogenic polycyclic aromatic hydrocarbons, ozone, nitrogen dioxide (NO2), and sulfur dioxide levels estimated from a central measurement site over the 60-day period before the end of the first month of unprotected intercourse. We estimated changes in the probability of occurrence of a pregnancy during the first month of unprotected intercourse associated with exposure, using binomial regression and adjusting for maternal behaviors and time trends.

RESULTS

Among the 1,916 recruited couples, 486 (25%) conceived during the first month of unprotected intercourse. Each increase of 10 µg/m in PM2.5 levels was associated with an adjusted decrease in fecundability of 22% (95% confidence interval = 6%-35%). NO2 levels were also associated with decreased fecundability. There was no evidence of adverse effects with the other pollutants considered. Biases related to pregnancy planning or temporal trends in air pollution were unlikely to explain the observed associations.

CONCLUSIONS

In this polluted area, we highlighted short-term decreases in a couple's ability to conceive in association with PM2.5 and NO2 levels assessed in a central monitoring station.