Alexander P. Keil

Parental Autoimmune Diseases Associated With Autism Spectrum Disorders in Offspring

Background: Autism spectrum disorders are often idiopathic. Studies have suggested associations between immune response and these disorders. We explored associations between parental autoimmune disorders and childrens diagnosis of autism by linking Swedish registries. Methods: Data for each participant were linked across 3 Swedish registries. The study includes 1227 cases and 25 matched controls for each case (30,693 controls with parental linkage). Parental diagnoses comprised 19 autoimmune disorders. We estimated odds ratios (ORs) using multivariable conditional logistic regression. Results: Parental autoimmune disorder was weakly associated with autism spectrum disorders in offspring (maternal OR = 1.6 [95% confidence interval = 1.1–2.2]; paternal OR = 1.4 [1.0–2.0]). Several maternal autoimmune diseases were correlated with autism. For both parents, rheumatic fever was associated with autism spectrum disorders. Conclusions: These data support previously reported associations between parental autoimmune disorders and autism spectrum disorders. Parental autoimmune disorders may represent a critical pathway that warrants more detailed investigation.

The parametric g-formula for time-to-event data: intuition and a worked example.

Background: The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied. Methods: We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding. Results: Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula. Conclusions: The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.

Evolving methods for inference in the presence of healthy worker survivor bias

Healthy worker survivor bias may occur in occupational studies due to the tendency for unhealthy individuals to leave work earlier, and consequently accrue less exposure, compared with their healthier counterparts. If occupational data are not analyzed using appropriate methods, this bias can result in attenuation or even reversal of the estimated effects of exposures on health outcomes. Recent advances in computing power, coupled with state-of-the-art statistical methods, have greatly increased the ability of analysts to control healthy worker survivor bias. However, these methods have not been widely adopted by occupational epidemiologists. We update the seminal review by Arrighi and Hertz-Picciotto (Epidemiology.1994; 5: 186-196) of the sources and methods to control healthy worker survivor bias. In our update, we discuss methodologic advances since the publication of that review, notably with a consideration of how directed acyclic graphs can inform the choice of appropriate analytic methods. We summarize and discuss methods for addressing this bias, including recent work applying g-methods to account for employment status as a time-varying covariate affected by prior exposure. In the presence of healthy worker survivor bias, g-methods have advantages for estimating less biased parameters that have direct policy implications and are clearly communicated to decision-makers.

Statistical approaches for estimating sex-specific effects in endocrine disruptors research

Background: When a biologic mechanism of interest is anticipated to operate differentially according to sex, as is often the case in endocrine disruptors research, investigators routinely estimate sex-specific associations. Less attention has been given to potential sexual heterogeneity of confounder associations with outcomes. When relationships of covariates with outcomes differ according to sex, commonly applied statistical approaches for estimating sex-specific endocrine disruptor effects may produce divergent estimates. Objectives: We discuss underlying assumptions and evaluate the performance of two traditional approaches for estimating sex-specific effects, stratification and product terms, and introduce a simple modeling alternative: an augmented product term approach. Methods: We describe the impact of assumptions regarding sexual heterogeneity of confounder relationships on estimates of sex-specific effects of the exposure of interest for three approaches: stratification, traditional product terms, and augmented product terms. Using simulated and applied examples, we demonstrate properties of each approach under a range of scenarios. Results: In simulations, sex-specific exposure effects estimated using the traditional product term approach were biased when confounders had sex-dependent associations with the outcome. Sex-specific estimates from stratification and the augmented product term approach were unbiased but less precise. In the applied example, the three approaches yielded similar estimates, but resulted in some meaningful differences in conclusions based on statistical significance. Conclusions: Investigators should consider sexual heterogeneity of confounder associations when choosing an analytic approach to estimate sex-specific effects of endocrine disruptors on health. In the presence of sex-dependent confounding, our augmented product term approach may be advantageous over stratification when there is prior knowledge available to fit reduced models or when investigators seek an automated test for effect measure modification. https://doi.org/10.1289/EHP334

Negative Control Outcomes and the Analysis of Standardized Mortality Ratios

In occupational cohort mortality studies, epidemiologists often compare the observed number of deaths in the cohort to the expected number obtained by multiplying person-time accrued in the study cohort by the mortality rate in an external reference population. Interpretation of the result may be difficult due to noncomparability of the occupational cohort and reference population with respect to unmeasured risk factors for the outcome of interest. We describe an approach to estimate an adjusted standardized mortality ratio (aSMR) to control for such bias. The approach draws on methods developed for the use of negative control outcomes. Conditions necessary for unbiased estimation are described, as well as looser conditions necessary for bias reduction. The approach is illustrated using data on bladder cancer mortality among male Oak Ridge National Laboratory workers. The SMR for bladder cancer was elevated among hourly-paid males (SMR = 1.9; 95% confidence interval [CI] = 1.3, 2.7) but not among monthly-paid males (SMR = 1.0; 95% CI = 0.67, 1.3). After indirect adjustment using the proposed approach, the mortality ratios were similar in magnitude among hourly- and monthly-paid men (aSMR = 2.2; 95% CI = 1.5, 3.2; and, aSMR = 2.0; 95% CI = 1.4, 2.8, respectively). The proposed adjusted SMR offers a complement to typical SMR analyses.

Healthy Worker Survivor Bias in the Colorado Plateau Uranium Miners Cohort

Cohort mortality studies of underground miners have been used to estimate the number of lung cancer deaths attributable to radon exposure. However, previous studies of the radon-lung cancer association among underground miners may have been subject to healthy worker survivor bias, a type of time-varying confounding by employment status. We examined radon-mortality associations in a study of 4,124 male uranium miners from the Colorado Plateau who were followed from 1950 through 2005. We estimated the time ratio (relative change in median survival time) per 100 working level months (radon exposure averaging 130,000 mega-electron volts of potential α energy per liter of air, per working month) using G-estimation of structural nested models. After controlling for healthy worker survivor bias, the time ratio for lung cancer per 100 working level months was 1.168 (95% confidence interval: 1.152, 1.174). In an unadjusted model, the estimate was 1.102 (95% confidence interval: 1.099, 1.112)-39% lower. Controlling for this bias, we estimated that among 617 lung cancer deaths, 6,071 person-years of life were lost due to occupational radon exposure during follow-up. Our analysis suggests that healthy worker survivor bias in miner cohort studies can be substantial, warranting reexamination of current estimates of radons estimated impact on lung cancer mortality.

Mortality Among Workers at Oak Ridge National Laboratory

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.

Reassessing the link between airborne arsenic exposure among anaconda copper smelter workers and multiple causes of death using the parametric g-formula

Background: Prior studies have indicated associations between ingestion of inorganic arsenic and ischemic heart disease, nonmalignant respiratory disease, and lung, skin, bladder, and kidney cancers. In contrast, inhaled arsenic has been consistently associated only with lung cancer. Evidence for health effects of inhaled arsenic derives mainly from occupational studies that are subject to unique biases that may attenuate or obscure such associations. Objectives: We estimated the excess mortality from respiratory cancers, heart disease, and other causes resulting from occupational arsenic exposure while controlling for confounding using the parametric g-formula. Methods: Using a cohort of 8,014 male copper smelter workers who were hired between 1938 and 1955 and followed through 1990, we estimated the impacts of hypothetical workplace interventions on arsenic exposure on the risk of mortality from all causes, heart disease, and lung cancer using the parametric g-formula. Results: We estimate that eliminating arsenic exposure at work would have prevented 22 deaths by age 70 per 1,000 workers [95% confidence interval (CI): 10, 35]. Of those 22 excess deaths, we estimate that 7.2 (95% CI: –1.2, 15) would be due to heart disease, 4.0 (95% CI: –0.8, 8.2) due to respiratory cancers, and 11 (95% CI: 0.0, 23) due to other causes. Conclusions: Our analyses suggest that the excess deaths from causes other than respiratory cancers comprise the majority of the excess deaths caused by inhaled arsenic exposure. Healthy worker survivor bias may have masked such associations in previous analyses. These results emphasize the need for consideration of all exposure routes for upcoming risk assessment by the U.S. Environmental Protection Agency. Citation: Keil AP, Richardson DB. 2017. Reassessing the link between airborne arsenic exposure among Anaconda copper smelter workers and multiple causes of death using the parametric g-formula. Environ Health Perspect 125:608–614; http://dx.doi.org/10.1289/EHP438

Left Truncation Bias to Explain the Protective Effect of Smoking on Preeclampsia: Potential, but How Plausible?

Background: An inverse association between maternal smoking and preeclampsia has been frequently observed in epidemiologic studies for several decades. In the May 2015 issue of this journal, Lisonkova and Joseph described a simulation study suggesting that bias from left truncation might explain the inverse association. The simulations were based on strong assumptions regarding the underlying mechanisms through which bias might occur. Methods: To examine the sensitivity of the previous authors’ conclusions to these assumptions, we constructed a new Monte Carlo simulation using published estimates to frame our data-generating parameters. We estimated the association between smoking and preeclampsia across a range of scenarios that incorporated abnormal placentation and early pregnancy loss. Results: Our results confirmed that the previous authors’ findings are highly dependent on assumptions regarding the strength of association between abnormal placentation and preeclampsia. Thus, the bias they described may be less pronounced than was suggested. Conclusions: Under empirically derived constraints of these critical assumptions, left truncation does not appear to fully explain the inverse association between smoking and preeclampsia. Furthermore, when considering processes in which left truncation may result from the exposure, it is important to precisely describe the target population and parameter of interest before assessing potential bias. We comment on the specification of a meaningful target population when assessing maternal smoking and preeclampsia as a public health issue. We describe considerations for defining a target population in studies of perinatal exposures when those exposures cause competing events (e.g., early pregnancy loss) for primary outcomes of interest.

A Bayesian approach to the g-formula

Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4–9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally.