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ABSTRACT: In case-control studies, exposure assessments are almost always error-prone. In the absence of a gold standard, two or more assessment approaches are often used to classify people with respect to exposure. Each imperfect assessment tool may lead to misclassification of exposure assignment; the exposure misclassification may be differential with respect to case status or not; and, the errors in exposure classification under the different approaches may be independent (conditional upon the true exposure status) or not. Although methods have been proposed to study diagnostic accuracy in the absence of a gold standard, these methods are infrequently used in case-control studies to correct exposure misclassification that is simultaneously differential and dependent. In this paper, we proposed a Bayesian method to estimate the measurement-error corrected exposure-disease association, accounting for both differential and dependent misclassification. The performance of the proposed method is investigated using simulations, which show that the proposed approach works well, as well as an application to a case-control study assessing the association between asbestos exposure and mesothelioma. Copyright © 2013 John Wiley & Sons, Ltd.
Statistics in Medicine 05/2013; · 1.88 Impact Factor
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ABSTRACT: PURPOSE: To estimate the association between environmental temperatures and the occurrence of emergency department visits for heat-related illness in North Carolina, a large Southern state with 85 rural and 15 urban counties; approximately half the state's population resides in urban counties. METHODS: County-level daily emergency department visit counts and daily mean temperatures for the period 1/1/2007-12/31/2008 were merged to form a time-series data structure. Incidence rates were calculated by sex, age group, region, day of week, and month. Incidence rate ratios were estimated using categorical and linear spline Poisson regression models and heterogeneity of the temperature-emergency department visit association was assessed using product interaction terms in the Poisson models. RESULTS: In 2007-2008, there were 2539 emergency department visits with heat-related illness as the primary diagnosis. Incidence rates were highest among young adult males (19-44 year age group), in rural counties, and in the Sandhills region. Incidence rates increased exponentially with temperatures over 15.6°C (60°F). The overall incidence rate ratio for each 1°C increase over 15.6°C in daily mean temperature was 1.43 (95%CI: 1.41, 1.45); temperature effects were greater for males than females, for 45-64 year olds, and for residents of rural counties than residents of urban counties. CONCLUSIONS: As heat response plans are developed, they should incorporate findings on climate effects for both mortality and morbidity. While forecast-triggered heat health warning systems are essential to mitigate the effects of extreme heat events, public health preparedness plans should not ignore the effects of more persistently observed high environmental temperatures like those that occur throughout the warm season in North Carolina.
Environmental Research 04/2013; · 3.40 Impact Factor
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ABSTRACT: We employed the parametric G formula to analyze lung cancer mortality in a cohort of textile manufacturing workers who were occupationally exposed to asbestos in South Carolina. A total of 3,002 adults with a median age of 24 years at enrollment (58% male, 81% Caucasian) were followed for 117,471 person-years between 1940 and 2001, and 195 lung cancer deaths were observed. Chrysotile asbestos exposure was measured in fiber-years per milliliter of air, and annual occupational exposures were estimated on the basis of detailed work histories. Sixteen percent of person-years involved exposure to asbestos, with a median exposure of 3.30 fiber-years/mL among those exposed. Lung cancer mortality by age 90 years under the observed asbestos exposure was 9.44%. In comparison with observed asbestos exposure, if the facility had operated under the current Occupational Safety and Health Administration asbestos exposure standard of <0.1 fibers/mL, we estimate that the cohort would have experienced 24% less lung cancer mortality by age 90 years (mortality ratio = 0.76, 95% confidence interval: 0.62, 0.94). A further reduction in asbestos exposure to a standard of <0.05 fibers/mL was estimated to have resulted in a minimal additional reduction in lung cancer mortality by age 90 years (mortality ratio = 0.75, 95% confidence interval: 0.61, 0.92).
American journal of epidemiology 04/2013; · 5.59 Impact Factor
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ABSTRACT: BACKGROUND: Workers employed at the Oak Ridge National Laboratory (ORNL) were potentially exposed to a range of chemical and physical hazards, many of which are poorly characterized. We compared the observed deaths among workers to expectations based upon US mortality rates. METHODS: The cohort included 22,831 workers hired between January 1, 1943 and December 31, 1984. Vital status and cause of death information were ascertained through December 31, 2008. Standardized mortality ratios (SMRs) were computed separately for males and females using US and Tennessee mortality rates; SMRs for men were tabulated separately for monthly-, weekly-, and hourly-paid workers. RESULTS: Hourly-paid males had more deaths due to cancer of the pleura (SMR = 12.09, 95% CI: 4.44, 26.32), cancer of the bladder (SMR = 1.89, 95% CI: 1.26, 2.71), and leukemia (SMR = 1.33, 95% CI: 0.87, 1.93) than expected based on US mortality rates. Female workers also had more deaths than expected from cancer of the bladder (SMR = 2.20, 95% CI: 1.20, 3.69) and leukemia (SMR = 1.64, 95% CI: 1.09, 2.36). The pleural cancer excess has only appeared since the 1980s, approximately 40 years after the start of operations. The bladder cancer excess was larger among workers who also had worked at other Oak Ridge nuclear weapons facilities, while the leukemia excess was among people who had not worked at other DOE facilities. CONCLUSIONS: Occupational hazards including asbestos and ionizing radiation may contribute to these excesses. Am. J. Ind. Med. © 2013 Wiley Periodicals, Inc.
American Journal of Industrial Medicine 03/2013; · 1.63 Impact Factor
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American journal of epidemiology 02/2013; · 5.59 Impact Factor
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ABSTRACT: The Life Span Study of atomic bomb survivors is an important source of risk estimates used to inform radiation protection and compensation. Interviews with survivors in the 1950s and 1960s provided information needed to estimate radiation doses for survivors proximal to ground zero. Because of a lack of interview or the complexity of shielding, doses are missing for 7,058 of the 68,119 proximal survivors. Recent analyses excluded people with missing doses, and despite the protracted collection of interview information necessary to estimate some survivors' doses, defined start of follow-up as October 1, 1950, for everyone. We describe the prevalence of missing doses and its association with mortality, distance from hypocenter, city, age, and sex. Missing doses were more common among Nagasaki residents than among Hiroshima residents (prevalence ratio = 2.05; 95% confidence interval: 1.96, 2.14), among people who were closer to ground zero than among those who were far from it, among people who were younger at enrollment than among those who were older, and among males than among females (prevalence ratio = 1.22; 95% confidence interval: 1.17, 1.28). Missing dose was associated with all-cancer and leukemia mortality, particularly during the first years of follow-up (all-cancer rate ratio = 2.16, 95% confidence interval: 1.51, 3.08; and leukemia rate ratio = 4.28, 95% confidence interval: 1.72, 10.67). Accounting for missing dose and late entry should reduce bias in estimated dose-mortality associations.
American journal of epidemiology 02/2013; · 5.59 Impact Factor
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ABSTRACT: OBJECTIVES: Standardised mortality ratios (SMRs) play an important role in the epidemiological literature, particularly in evaluations of occupational hazards. While some authors have argued that comparisons of SMRs should be avoided, many investigators find such analyses appealing particularly when data are sparse. For example, calendar period-specific SMRs often are examined to identify emerging problems or to assess whether a hazard that impacted death rates in the past has abated. However, because the distribution of people with respect to age usually changes as calendar time advances, comparisons of SMRs across calendar periods can produce misleading results. METHODS: We propose a random effects model to reduce the potential bias arising from comparisons of SMRs. This approach is illustrated using data from a study of workers employed at the Department of Energy's Oak Ridge National Laboratory. RESULTS: When there is homogeneity across strata of covariates in the ratio of death rates in the target population to that in the reference population, the proposed model yields results equivalent to those obtained by a classical analysis of SMRs. However, as evidence against such homogeneity increases, the model yields a random effects version of SMRs for which patterns will conform better to those obtained from an internal analysis of rate ratios. CONCLUSIONS: The proposed random effects model can reduce potential bias arising in the comparisons of SMRs.
Occupational and environmental medicine 11/2012; · 3.64 Impact Factor
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ABSTRACT: OBJECTIVE:: To determine whether recommended robbery prevention strategies also protect against workplace homicide committed by a perpetrator who has a relationship with either the workplace or an employee (prior-relationship homicide). METHODS:: A case-control study examining the relationship between recommended violence prevention strategies and prior-relationship workplace homicides in North Carolina was conducted. RESULTS:: Workplaces located in an industrial park, employing minorities, reporting a history of violence, open night hours, or open 24 hours were more likely to experience prior-relationship homicide. Keeping entrances to the workplace locked when employees were present (OR = 0.36, 95% CI: 0.13, 0.99) and having at least one security device (OR = 0.28, 95% CI: 0.10, 0.74) decreased the odds of prior-relationship homicide. CONCLUSIONS:: Select strategies recommended to prevent robberies and subsequent violence may also afford protection against prior-relationship homicide.
Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine 11/2012; · 1.88 Impact Factor
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ABSTRACT: Epidemiological studies often include numerous covariates, with a variety of possible approaches to control for confounding of the association of primary interest, as well as a variety of possible models for the exposure-response association of interest. Walsh and Kaiser (Radiat Environ Biophys 50:21-35, 2011) advocate a weighted averaging of the models, where the weights are a function of overall model goodness of fit and degrees of freedom. They apply this method to analyses of radiation-leukemia mortality associations among Japanese A-bomb survivors. We caution against such an approach, noting that the proposed model averaging approach prioritizes the inclusion of covariates that are strong predictors of the outcome, but which may be irrelevant as confounders of the association of interest, and penalizes adjustment for covariates that are confounders of the association of interest, but may contribute little to overall model goodness of fit. We offer a simple illustration of how this approach can lead to biased results. The proposed model averaging approach may also be suboptimal as way to handle competing model forms for an exposure-response association of interest, given adjustment for the same set of confounders; alternative approaches, such as hierarchical regression, may provide a more useful way to stabilize risk estimates in this setting.
Biophysik 03/2012; 51(1):93-5; discussion 97-100. · 1.70 Impact Factor
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ABSTRACT: Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
American journal of epidemiology 03/2012; 175(5):368-75. · 5.59 Impact Factor
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American journal of epidemiology 02/2012; · 5.59 Impact Factor
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Epidemiology (Cambridge, Mass.) 01/2012; 23(1):179. · 5.51 Impact Factor
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ABSTRACT: Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.
Biophysik 12/2011; 51(1):15-22. · 1.70 Impact Factor
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ABSTRACT: Lagging exposure information is often undertaken to allow for a latency period in cumulative exposure-disease analyses. The authors first consider bias and confidence interval coverage when using the standard approaches of fitting models under several lag assumptions and selecting the lag that maximizes either the effect estimate or model goodness of fit. Next, they consider bias that occurs when the assumption that the latency period is a fixed constant does not hold. Expressions were derived for bias due to misspecification of lag assumptions, and simulations were conducted. Finally, the authors describe a method for joint estimation of parameters describing an exposure-response association and the latency distribution. Analyses of associations between cumulative asbestos exposure and lung cancer mortality among textile workers illustrate this approach. Selecting the lag that maximizes the effect estimate may lead to bias away from the null; selecting the lag that maximizes model goodness of fit may lead to confidence intervals that are too narrow. These problems tend to increase as the within-person exposure variation diminishes. Lagging exposure assignment by a constant will lead to bias toward the null if the distribution of latency periods is not a fixed constant. Direct estimation of latency periods can minimize bias and improve confidence interval coverage.
American journal of epidemiology 11/2011; 174(12):1416-22. · 5.59 Impact Factor
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ABSTRACT: This study examines non-battle injuries among U.S. Air Force members deployed during Operations Iraqi and Enduring Freedom. A cohort of 275,843 Active Duty, Guard, and Reserve members were identified for the period September 11, 2001 through October 31, 2006. Data on injuries were obtained from electronic medical records and deployment time was obtained from manpower records. Poisson regression was used to estimate adjusted incidence rate ratios (IRRs). The most common non-battle injuries were sprains and strains (53%) followed by open wounds (27%). Guard and Reserve members tended to have a lower rate of orthopedic non-battle injuries than Active Duty members in crude analyses and after adjustment for age, previous deployment, sex, race/ethnicity, and occupation (IRR = 0.95; 95% CI = 0.89-1.02 and IRR = 0.85; 95% CI = 0.77-0.93). Results from this study are intended to facilitate further research of potential differences between Air Force components to reduce non-battle injuries in a deployed environment.
Military medicine 09/2011; 176(9):1007-14. · 0.92 Impact Factor
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ABSTRACT: In occupational epidemiologic studies, the healthy worker survivor effect refers to a process that leads to bias in the estimates of an association between cumulative exposure and a health outcome. In these settings, work status acts both as an intermediate and confounding variable and may violate the positivity assumption (the presence of exposed and unexposed observations in all strata of the confounder). Using Monte Carlo simulation, we assessed the degree to which crude, work-status adjusted, and weighted (marginal structural) Cox proportional hazards models are biased in the presence of time-varying confounding and nonpositivity. We simulated the data representing time-varying occupational exposure, work status, and mortality. Bias, coverage, and root mean squared error (MSE) were calculated relative to the true marginal exposure effect in a range of scenarios. For a base-case scenario, using crude, adjusted, and weighted Cox models, respectively, the hazard ratio was biased downward 19%, 9%, and 6%; 95% confidence interval coverage was 48%, 85%, and 91%; and root MSE was 0.20, 0.13, and 0.11. Although marginal structural models were less biased in most scenarios studied, neither standard nor marginal structural Cox proportional hazards models fully resolve the bias encountered under conditions of time-varying confounding and nonpositivity.
Epidemiology (Cambridge, Mass.) 09/2011; 22(5):718-23. · 5.51 Impact Factor
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ABSTRACT: Previous studies suggested increased morbidities and mortalities of liver diseases in drivers.
To examine whether driving (monthly driving distance; tenure) is associated with elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), or chronic fatty liver (FL) changes, we performed a cross-sectional, secondary analysis of the Taxi Drivers' Health Study (n = 1,355), adjusting for clinical, demographic, and lifestyle factors.
Prevalence of elevated ALT, elevated AST, and fatty liver changes were 22.0%, 5.1%, and 9.3%, respectively. Driving distance had a positive association with elevated ALT with a prevalence ratio of 1.35 (95% CI: 0.98, 1.89) comparing the highest versus lowest driving quartile. This association differed by alcohol use, with a corresponding prevalence ratio of 2.08 (95% CI: 1.30, 3.33) among "past/current" drinkers but no association among "never" drinkers. Similar patterns were found for AST, but estimates were less stable. We found a curvilinear response pattern for fatty liver changes; prevalence first increased with years as a taxi driver and then receded in the highest ranges of driving tenure, regardless of the alcohol history.
Our results provide evidence that long driving is associated with both short-term and chronic liver insults, although alcohol use appears to modify this putative effect.
American Journal of Industrial Medicine 05/2011; 54(8):618-27. · 1.63 Impact Factor
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ABSTRACT: This study investigates confounding by cigarette smoking of associations between occupational exposure to ionizing radiation and lung cancer mortality among workers at the Savannah River Site (SRS).
Thirteen thousand two hundred sixty-five white males hired at SRS between 1950 and 1986 were followed through 2002 to ascertain causes of death. Estimates of radiation doses from external sources and internal tritium uptakes were derived from dosimetry records. Logistic regression methods were used to derive discrete-time estimates of rate ratios. An indirect approach to control for unmeasured confounding by smoking was employed that involves joint modeling of lung cancer and chronic obstructive pulmonary disease (COPD) mortality.
Prior to indirect adjustment for smoking, there was minimal evidence of association between lung cancer mortality and cumulative radiation dose under a 10-year lag assumption (RR at 100 mSv = 0.90; 90% CI: 0.80-1.01). Subsequent to indirect adjustment for smoking, the association between lung cancer mortality and cumulative radiation dose under a 10-year lag was positive (RR at 100 mSv = 1.33; 90% CI: 1.01-1.77).
In this cohort, there is evidence of negative confounding of radiation dose–lung cancer mortality associations by cigarette smoking.
American Journal of Industrial Medicine 03/2011; 54(6):421-7. · 1.63 Impact Factor
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ABSTRACT: Exposure lagging and exposure-time window analysis are 2 widely used approaches to allow for induction and latency periods in analyses of exposure-disease associations. Exposure lagging implies a strong parametric assumption about the temporal evolution of the exposure-disease association. An exposure-time window analysis allows for a more flexible description of temporal variation in exposure effects but may result in unstable risk estimates that are sensitive to how windows are defined. The authors describe a hierarchical regression approach that combines time window analysis with a parametric latency model. They illustrate this approach using data from 2 occupational cohort studies: studies of lung cancer mortality among 1) asbestos textile workers and 2) uranium miners. For each cohort, an exposure-time window analysis was compared with a hierarchical regression analysis with shrinkage toward a simpler, second-stage parametric latency model. In each cohort analysis, there is substantial stability gained in time window-specific estimates of association by using a hierarchical regression approach. The proposed hierarchical regression model couples a time window analysis with a parametric latency model; this approach provides a way to stabilize risk estimates derived from a time window analysis and a way to reduce bias arising from misspecification of a parametric latency model.
American journal of epidemiology 02/2011; 173(6):695-702. · 5.59 Impact Factor
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ABSTRACT: In April 2010, the U.S. Nuclear Regulatory Commission asked the National Academy of Sciences to update a 1990 study of cancer risks near nuclear facilities. Prior research on this topic has suffered from problems in hypothesis formulation and research design.
We review epidemiologic principles used in studies of generic exposure-response associations and in studies of specific sources of exposure. We then describe logical problems with assumptions, formation of testable hypotheses, and interpretation of evidence in previous research on cancer risks near nuclear facilities.
Advancement of knowledge about cancer risks near nuclear facilities depends on testing specific hypotheses grounded in physical and biological mechanisms of exposure and susceptibility while considering sample size and ability to adequately quantify exposure, ascertain cancer cases, and evaluate plausible confounders.
Next steps in advancing knowledge about cancer risks near nuclear facilities require studies of childhood cancer incidence, focus on in utero and early childhood exposures, use of specific geographic information, and consideration of pathways for transport and uptake of radionuclides. Studies of cancer mortality among adults, cancers with long latencies, large geographic zones, and populations that reside at large distances from nuclear facilities are better suited for public relations than for scientific purposes.
Environmental Health Perspectives 12/2010; 119(4):417-21. · 7.04 Impact Factor
