Perfluoroalkyl substances (PFASs) are widespread and persistent environmental pollutants. Previous studies, primarily among non-pregnant individuals, suggest positive associations between PFAS levels and certain blood lipids. If there is a causal link between PFAS concentrations and elevated lipids during pregnancy, this may suggest a mechanism by which PFAS exposure leads to certain adverse pregnancy outcomes, including preeclampsia.
This cross-sectional analysis included 891 pregnant women enrolled in the Norwegian Mother and Child (MoBa) Cohort Study in 2003–2004. Non-fasting plasma samples were obtained at mid-pregnancy and analyzed for nineteen PFASs. Total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, and triglycerides were measured in plasma. Linear regression was used to quantify associations between each PFAS exposure and each lipid outcome. A multiple PFAS model was also fitted.
Seven PFASs were quantifiable in >50% of samples. Perfluorooctane sulfonate (PFOS) concentration was associated with total cholesterol, which increased 4.2 mg/dL per interquartile shift (95% CI=0.8, 7.7) in adjusted models. Five of the seven PFASs studied were positively associated with HDL cholesterol, and all seven had elevated HDL associated with the highest quartile of exposure. Perfluoroundecanoic acid showed the strongest association with HDL: HDL increased 3.7 mg/dL per interquartile shift (95% CI=2.5, 4.9).
Plasma concentrations of PFASs were positively associated with HDL cholesterol, and PFOS was positively associated with total cholesterol in this sample of pregnant Norwegian women. While elevated HDL is not an adverse outcome per se, elevated total cholesterol associated with PFASs during pregnancy could be of concern if causal.
The Norwegian Mother and Child Cohort Study; MoBa; perfluoroalkyl substances; perfluorooctanoic acid; perfluorooctane sulfonate
In a recent issue of the Journal, Kirkeleit et al. (Am J Epidemiol. 2013;177(11):1218–1224) provided empirical evidence for the potential of the healthy worker effect in a large cohort of Norwegian workers across a range of occupations. In this commentary, we provide some historical context, define the healthy worker effect by using causal diagrams, and use simulated data to illustrate how structural nested models can be used to estimate exposure effects while accounting for the healthy worker survivor effect in 4 simple steps. We provide technical details and annotated SAS software (SAS Institute, Inc., Cary, North Carolina) code corresponding to the example analysis in the Web Appendices, available at http://aje.oxfordjournals.org/.
causal inference; healthy worker effect; marginal structural models; occupational epidemiology; structural nested models
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.
Case-control study; gold standard; misclassification; dependent; differential
Background: Epidemiologic literature suggests that exposure to air pollutants is associated with fetal development.
Objectives: We investigated maternal exposures to air pollutants during weeks 2–8 of pregnancy and their associations with congenital heart defects.
Methods: Mothers from the National Birth Defects Prevention Study, a nine-state case–control study, were assigned 1-week and 7-week averages of daily maximum concentrations of carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide and 24-hr measurements of fine and coarse particulate matter using the closest air monitor within 50 km to their residence during early pregnancy. Depending on the pollutant, a maximum of 4,632 live-birth controls and 3,328 live-birth, fetal-death, or electively terminated cases had exposure data. Hierarchical regression models, adjusted for maternal demographics and tobacco and alcohol use, were constructed. Principal component analysis was used to assess these relationships in a multipollutant context.
Results: Positive associations were observed between exposure to nitrogen dioxide and coarctation of the aorta and pulmonary valve stenosis. Exposure to fine particulate matter was positively associated with hypoplastic left heart syndrome but inversely associated with atrial septal defects. Examining individual exposure-weeks suggested associations between pollutants and defects that were not observed using the 7-week average. Associations between left ventricular outflow tract obstructions and nitrogen dioxide and between hypoplastic left heart syndrome and particulate matter were supported by findings from the multipollutant analyses, although estimates were attenuated at the highest exposure levels.
Conclusions: Using daily maximum pollutant levels and exploring individual exposure-weeks revealed some positive associations between certain pollutants and defects and suggested potential windows of susceptibility during pregnancy.
Citation: Stingone JA, Luben TJ, Daniels JL, Fuentes M, Richardson DB, Aylsworth AS, Herring AH, Anderka M, Botto L, Correa A, Gilboa SM, Langlois PH, Mosley B, Shaw GM, Siffel C, Olshan AF, National Birth Defects Prevention Study. 2014. Maternal exposure to criteria air pollutants and congenital heart defects in offspring: results from the National Birth Defects Prevention Study. Environ Health Perspect 122:863–872; http://dx.doi.org/10.1289/ehp.1307289
Distinct strains of methicillin resistant Staphylococcus aureus (MRSA) have been identified on livestock and livestock workers. Industrial food animal production may be an important environmental reservoir for human carriage of these pathogenic bacteria. The objective of this study was to investigate environmental and occupational exposures associated with nasal carriage of MRSA in patients hospitalized at Vidant Medical Center, a tertiary hospital serving a region with intensive livestock production in eastern North Carolina.
MRSA nasal carriage was identified via nasal swabs collected within 24 hours of hospital admission. MRSA carriers (cases) were gender and age matched to non-carriers (controls). Participants were interviewed about recent environmental and occupational exposures. Home addresses were geocoded and publicly available data were used to estimate the density of swine in residential census block groups of residence. Conditional logistic regression models were used to derive odds ratio (OR) estimates and 95% confidence intervals (CI). Presence of the scn gene in MRSA isolates was assessed. In addition, multi locus sequence typing (MLST) of the MRSA isolates was performed, and the Diversilab® system was used to match the isolates to USA pulsed field gel electrophoresis types.
From July - December 2011, 117 cases and 119 controls were enrolled. A higher proportion of controls than cases were current workforce members (41.2% vs. 31.6%) Cases had a higher odds of living in census block groups with medium densities of swine (OR: 4.76, 95% CI: 1.36-16.69) and of reporting the ability to smell odor from a farm with animals when they were home (OR: 1.51, 95% CI: 0.80-2.86). Of 49 culture positive MRSA isolates, all were scn positive. Twenty-two isolates belonged to clonal complex 5.
Absence of livestock workers in this study precluded evaluation of occupational exposures. Higher odds of MRSA in medium swine density areas could reflect environmental exposure to swine or poultry.
Methicillin resistant Staphylococcus aureus; Livestock; Bacterial antibiotic resistance; Concentrated animal feeding operations; North Carolina
In contrast to other types of leukemia, chronic lymphocytic leukemia (CLL) has long been regarded as non-radiogenic, i.e. not caused by ionizing radiation. However, the justification for this view has been challenged. We therefore report on the relationship between CLL mortality and external ionizing radiation dose within the 15-country nuclear workers cohort study. The analyses included, in seven countries with CLL deaths, a total of 295,963 workers with more than 4.5 million person-years of follow-up and an average cumulative bone marrow dose of 15 mSv; there were 65 CLL deaths in this cohort. The relative risk (RR) at an occupational dose of 100 mSv compared to 0 mSv was 0.84 (95% CI 0.39, 1.48) under the assumption of a 10-year exposure lag. Analyses of longer lag periods showed little variation in the RR, but they included very small numbers of cases with relatively high doses. In conclusion, the largest nuclear workers cohort study to date finds little evidence for an association between low doses of external ionizing radiation and CLL mortality. This study had little power due to low doses, short follow-up periods, and uncertainties in CLL ascertainment from death certificates; an extended follow-up of the cohorts is merited and would ideally include incident cancer cases.
The healthy worker survivor bias is well-recognized in occupational epidemiology. Three component associations are necessary for this bias to occur: i) prior exposure and employment status; ii) employment status and subsequent exposure; and iii) employment status and mortality. Together, these associations result in time-varying confounding affected by prior exposure. We illustrate how these associations can be assessed using standard regression methods.
We use data from 2975 asbestos textile factory workers hired between January 1940 and December 1965 and followed for lung cancer mortality through December 2001.
At entry, median age was 24 years, with 42% female and 19% non-Caucasian. Over follow-up, 21% and 17% of person-years were classified as at work and exposed to any asbestos, respectively. For a 100 fiber-year/mL increase in cumulative asbestos, the covariate-adjusted hazard of leaving work decreased by 52% (95% confidence interval [CI], 46–58). The association between employment status and subsequent asbestos exposure was strong due to nonpositivity: 88.3% of person-years at work (95% CI, 87.0–89.5) were classified as exposed to any asbestos; no person-years were classified as exposed to asbestos after leaving work. Finally, leaving active employment was associated with a 48% (95% CI, 9–71) decrease in the covariate-adjusted hazard of lung cancer mortality.
We found strong associations for the components of the healthy worker survivor bias in these data. Standard methods, which fail to properly account for time-varying confounding affected by prior exposure, may provide biased estimates of the effect of asbestos on lung cancer mortality under these conditions.
Epidemiologic methods; Occupational health; Healthy worker effect; Bias; Lung cancer; Mortality
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).
asbestos; bias (epidemiology); epidemiologic methods; healthy worker effect; occupations
Outcome misclassification is widespread in epidemiology, but methods to account for it are rarely used. We describe the use of multiple imputation to reduce bias when validation data are available for a subgroup of study participants. This approach is illustrated using data from 308 participants in the multicenter Herpetic Eye Disease Study between 1992 and 1998 (48% female; 85% white; median age, 49 years). The odds ratio comparing the acyclovir group with the placebo group on the gold-standard outcome (physician-diagnosed herpes simplex virus recurrence) was 0.62 (95% confidence interval (CI): 0.35, 1.09). We masked ourselves to physician diagnosis except for a 30% validation subgroup used to compare methods. Multiple imputation (odds ratio (OR) = 0.60; 95% CI: 0.24, 1.51) was compared with naive analysis using self-reported outcomes (OR = 0.90; 95% CI: 0.47, 1.73), analysis restricted to the validation subgroup (OR = 0.57; 95% CI: 0.20, 1.59), and direct maximum likelihood (OR = 0.62; 95% CI: 0.26, 1.53). In simulations, multiple imputation and direct maximum likelihood had greater statistical power than did analysis restricted to the validation subgroup, yet all 3 provided unbiased estimates of the odds ratio. The multiple-imputation approach was extended to estimate risk ratios using log-binomial regression. Multiple imputation has advantages regarding flexibility and ease of implementation for epidemiologists familiar with missing data methods.
bias(epidemiology); logistic regression; Monte Carlo method; sensitivity and specificity
Background: Air pollution epidemiologic studies, often conducted in large metropolitan areas because of proximity to regulatory monitors, are limited in their ability to examine potential associations between air pollution exposures and health effects in rural locations.
Methods: Using a time-stratified case-crossover framework, we examined associations between asthma emergency department (ED) visits in North Carolina (2006–2008), collected by a surveillance system, and short-term ozone (O3) exposures using predicted concentrations from the Community Multiscale Air Quality (CMAQ) model. We estimated associations by county groupings based on four urbanicity classifications (representative of county size and urban proximity) and county health.
Results: O3 was associated with asthma ED visits in all-year and warm season (April–October) analyses [odds ratio (OR) = 1.019; 95% CI: 0.998, 1.040; OR = 1.020; 95% CI: 0.997, 1.044, respectively, for a 20-ppb increase in lag 0–2 days O3]. The association was strongest in Less Urbanized counties, with no evidence of a positive association in Rural counties. Associations were similar when adjusted for fine particulate matter in copollutant models. Associations were stronger for children (5–17 years of age) compared with other age groups, and for individuals living in counties identified with poorer health status compared with counties that had the highest health rankings, although estimated associations for these subgroups had larger uncertainty.
Conclusions: Associations between short-term O3 exposures and asthma ED visits differed by overall county health and urbanicity, with stronger associations in Less Urbanized counties, and no positive association in Rural counties. Results also suggest that children are at increased risk of O3-related respiratory effects.
Citation: Sacks JD, Rappold AG, Davis JA Jr, Richardson DB, Waller AE, Luben TJ. 2014. Influence of urbanicity and county characteristics on the association between ozone and asthma emergency department visits in North Carolina. Environ Health Perspect 122:506–512; http://dx.doi.org/10.1289/ehp.1306940
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.
atomic bombs; cohort studies; ionizing radiation; missing data; mortality; nuclear weapons
Methicillin resistant Staphylococcus aureus (MRSA) poses a threat to patient safety and public health. Understanding how MRSA is acquired is important for prevention efforts. This study investigates risk factors for MRSA nasal carriage among patients at an eastern North Carolina hospital in 2011.
Using a case-control design, hospitalized patients ages 18 – 65 years were enrolled between July 25, 2011 and December 15, 2011 at Vidant Medical Center, a tertiary care hospital that screens all admitted patients for nasal MRSA carriage. Cases, defined as MRSA nasal carriers, were age and gender matched to controls, non-MRSA carriers. In-hospital interviews were conducted, and medical records were reviewed to obtain information on medical and household exposures. Multivariable conditional logistic regression was used to derive odds ratio (OR) estimates of association between MRSA carriage and medical and household exposures.
In total, 117 cases and 119 controls were recruited to participate. Risk factors for MRSA carriage included having household members who took antibiotics or were hospitalized (OR: 3.27; 95% Confidence Interval (CI): 1.24–8.57) and prior hospitalization with a positive MRSA screen (OR: 3.21; 95% CI: 1.12–9.23). A lower proportion of cases than controls were previously hospitalized without a past positive MRSA screen (OR: 0.40; 95% CI: 0.19–0.87).
These findings suggest that household exposures are important determinants of MRSA nasal carriage in hospitalized patients screened at admission.
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.
cohort studies; hierarchical model; latency; neoplasms; regression
Though toxicological experiments demonstrate the teratogenicity of organic solvents in animal models, epidemiologic studies have reported inconsistent results. Using data from the population-based National Birth Defects Prevention Study, we examined the relation between maternal occupational exposure to aromatic solvents, chlorinated solvents and Stoddard solvent during early pregnancy and neural tube defects (NTDs) and orofacial clefts (OFCs).
Cases of NTDs (anencephaly, spina bifida and encephalocele) and OFCs (cleft lip ± cleft palate and cleft palate alone) delivered between 1997 and 2002 were identified by birth defect surveillance registries in 8 states; non-malformed control infants were selected using birth certificates or hospital records. Maternal solvent exposure was estimated by industrial hygienist review of self-reported occupational histories in combination with a literature-derived exposure database. Odds ratios (OR) and 95% confidence intervals (CI) for the association between solvent class and each birth defect group and component phenotype were estimated using multivariable logistic regression, adjusting for maternal age, race/ethnicity, education, pre-pregnancy body mass index, folic acid supplement use and smoking.
The prevalence of exposure to any solvent among mothers of NTD cases (n=511), OFC cases (n=1163) and controls (n=2977) was 13.1%, 9.6% and 8.2%, respectively. Exposure to chlorinated solvents was associated with increased odds of NTDs (OR=1.96; CI=1.34, 2.87), especially spina bifida (OR=2.26; CI=1.44, 3.53). No solvent class was strongly associated with OFCs in these data.
Our findings suggest that maternal occupational exposure to chlorinated solvents during early pregnancy is positively associated with the prevalence of NTDs in offspring.
congenital abnormalities; occupational exposure; solvents
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.
Bayes theorem; epidemiologic methods; inference; Monte Carlo method; posterior distribution; simulation
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.
Cohort studies; Poisson regression; Ionizing radiation; Survival analysis
Changes in the workforce during the civil rights movement may have impacted occupational exposures in the United States. We examined Savannah River Site (SRS) employee records (1951–1999) for changes in radiation doses and monitoring practices, by race and sex. Segregation of jobs by race and sex diminished but remained pronounced in recent years. Female workers were less likely than males to be monitored for occupational radiation exposure [odds of being unmonitored = 3.11; 95% CI: (2.79, 3.47)] even after controlling for job and decade of employment. Black workers were more likely than non-black workers to have a detectable radiation dose [OR = 1.36 (95% CI: 1.28, 1.43)]. Female workers have incomplete dose histories that would hinder compensation for illnesses related to occupational exposures. The persistence of job segregation and excess radiation exposures of black workers shows the need for further action to address disparities in occupational opportunities and hazardous exposures in the U.S. South.
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.
asbestos; cohort studies; latency; neoplasms; survival analysis
The authors investigated the relation between ionizing radiation and lymphoma mortality in 2 cohorts: 1) 20,940 men in the Life Span Study, a study of Japanese atomic bomb survivors who were aged 15–64 years at the time of the bombings of Hiroshima and Nagasaki, and 2) 15,264 male nuclear weapons workers who were hired at the Savannah River Site in South Carolina between 1950 and 1986. Radiation dose-mortality trends were evaluated for all malignant lymphomas and for non-Hodgkin's lymphoma. Positive associations between lymphoma mortality and radiation dose under a 5-year lag assumption were observed in both cohorts (excess relative rates per sievert were 0.79 (90% confidence interval: 0.10, 1.88) and 6.99 (90% confidence interval: 0.96, 18.39), respectively). Exclusion of deaths due to Hodgkin's disease led to small changes in the estimates of association. In each cohort, evidence of a dose-response association was primarily observed more than 35 years after irradiation. These findings suggest a protracted induction and latency period for radiation-induced lymphoma mortality.
lymphoma; mortality; nuclear weapons; radiation, ionizing
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 assess 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 simulate 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.
The objective of this study is to characterize the effect of temperature on emergency department visits for asthma and modification of this association by season. This association is of interest in its own right, and also important to understand because temperature may be an important confounder in analyses of associations between other environmental exposures and asthma. For example, the case-crossover study design is commonly used to investigate associations between air pollution and respiratory outcomes, such as asthma. This approach controls for confounding by month and season by design, and permits adjustment for potential confounding by temperature through regression modeling. However, such models may fail to adequately control for confounding if temperature effects are seasonal, since case-crossover analyses rarely account for interactions between matching factors (such as calendar month) and temperature.
We conducted a case-crossover study to determine whether the association between temperature and emergency department visits for asthma varies by season or month. Asthma emergency department visits among North Carolina adults during 2007–2008 were identified using a statewide surveillance system. Marginal as well as season- and month-specific associations between asthma visits and temperature were estimated with conditional logistic regression.
The association between temperature and adult emergency department visits for asthma is near null when the overall association is examined [odds ratio (OR) per 5 degrees Celsius = 1.01, 95% confidence interval (CI): 1.00, 1.02]. However, significant variation in temperature-asthma associations was observed by season (chi-square = 18.94, 3 degrees of freedom, p <0.001) and by month of the year (chi-square = 45.46, 11 degrees of freedom, p <0.001). ORs per 5 degrees Celsius were increased in February (OR = 1.06, 95% CI: 1.02, 1.10), July (OR = 1.16, 95% CI: 1.04, 1.29), and December (OR = 1.04, 95% CI: 1.01, 1.07) and decreased in September (OR = 0.92, 95% CI: 0.87, 0.97).
Our empirical example suggests that there is significant seasonal variation in temperature-asthma associations. Epidemiological studies rarely account for interactions between ambient temperature and temporal matching factors (such as month of year) in the case-crossover design. These findings suggest that greater attention should be given to seasonal modification of associations between temperature and respiratory outcomes in case-crossover analyses of other environmental asthma triggers.
Asthma; Temperature; Season; Case-crossover
Urinary 1,6-hexamethylene diamine (HDA) may serve as a biomarker for systemic exposure to 1,6-hexamethylene diisocyanate (HDI) in occupationally exposed populations. However, the quantitative relationships between dermal and inhalation exposure to HDI and urine HDA levels have not been established. We measured acid-hydrolyzed urine HDA levels along with dermal and breathing-zone levels of HDI in 48 automotive spray painters. These measurements were conducted over the course of an entire workday for up to three separate workdays that were spaced approximately 1 month apart. One urine sample was collected before the start of work with HDI-containing paints and subsequent samples were collected during the workday. HDA levels varied throughout the day and ranged from nondetectable to 65.9 μg l−1 with a geometric mean and geometric standard deviation of 0.10 μg l−1 ± 6.68. Dermal exposure and inhalation exposure levels, adjusted for the type of respirator worn, were both significant predictors of urine HDA levels in the linear mixed models. Creatinine was a significant covariate when used as an independent variable along with dermal and respirator-adjusted inhalation exposure. Consequently, exposure assessment models must account for the water content of a urine sample. These findings indicate that HDA exhibits a biphasic elimination pattern, with a half-life of 2.9 h for the fast elimination phase. Our results also indicate that urine HDA level is significantly associated with systemic HDI exposure through both the skin and the lungs. We conclude that urinary HDA may be used as a biomarker of exposure to HDI, but biological monitoring should be tailored to reliably capture the intermittent exposure pattern typical in this industry.
biomarkers; creatinine; dermal exposure; 1,6-hexamethylene diamine; 1,6-hexamethylene diisocyanate; inhalation exposure; urine analysis
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.
childhood cancer; environmental epidemiology; ionizing radiation; methodology; nuclear power
Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods for fitting non-log-linear Cox and conditional logistic regression models. The authors use data from a study of lung cancer mortality among Colorado Plateau uranium miners (1950–1982) to illustrate these methods for fitting general relative risk models to matched case-control control data, countermatched data with weights, d:m matching, and full cohort Cox regression using the SAS statistical package (SAS Institute Inc., Cary, North Carolina).
algorithms; cohort studies; conditional likelihood; dose-response function; linear trend; logistic models; models, statistical; software
The effect of an increment of exposure on disease risk may vary with time since exposure. If the pattern of temporal variation is simple (e.g., a peak then decline in excess risk of disease) then this may be modeled efficiently via a parametric latency function. Estimation of the parameters for such a model can be difficult because the parameters are not a function of a simple summary of the exposure history. Typically such parameters are estimated via an iterative search that requires calculating a different time-weighted exposure for each combination of the latency function parameters. This paper describes a simple approach to fitting logistic regression models that include a parametric latency function. This approach is illustrated using data from a study of the association between radon exposure and lung cancer mortality among underground uranium miners. This approach should facilitate fitting models to assess variation with time since exposure in the effect of a protracted environmental or occupational exposure.