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The relative excess risk due to interaction (RERI) provides a useful metric of departure from additivity of effects on a relative risk scale. In this paper, the authors show that RERI is identical to the product term in a linear odds ratio or a linear relative risk model. SAS and STATA codes are provided for fitting a linear odds ratio model that directly parameterizes RERI. In addition, this paper presents a method for obtaining likelihood-based 95% confidence bound estimates for RERI. The authors show that likelihood-based confidence intervals may differ substantially from the asymptotic confidence interval estimates advocated by previous authors. The approach presented in this paper should facilitate estimation of RERI and associated likelihood-based confidence bounds, by using standard statistical packages.

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 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.

Positive associations between external radiation dose and non-cancer mortality have been found in a number of published studies, primarily of populations exposed to high-dose, high-dose-rate ionizing radiation. The goal of this study was to determine whether external radiation dose was associated with non-cancer mortality in a large pooled cohort of nuclear workers exposed to low-dose radiation accumulated at low dose rates. The cohort comprised 308,297 workers from France, United Kingdom and United States. The average cumulative equivalent dose at a tissue depth of 10 mm [Hp(10)] was 25.2 mSv. In total, 22% of the cohort were deceased by the end of follow-up, with 46,029 deaths attributed to non-cancer outcomes, including 27,848 deaths attributed to circulatory diseases. Poisson regression was used to investigate the relationship between cumulative radiation dose and non-cancer mortality rates. A statistically significant association between radiation dose and all non-cancer causes of death was observed [excess relative risk per sievert (ERR/Sv) = 0.19; 90% CI: 0.07, 0.30]. This was largely driven by the association between radiation dose and mortality due to circulatory diseases (ERR/Sv = 0.22; 90% CI: 0.08, 0.37), with slightly smaller positive, but nonsignificant, point estimates for mortality due to nonmalignant respiratory disease (ERR/Sv = 0.13; 90% CI: −0.17, 0.47) and digestive disease (ERR/Sv = 0.11; 90% CI: −0.36, 0.69). The point estimate for the association between radiation dose and deaths due to external causes of death was nonsignificantly negative (ERR = −0.12; 90% CI: <−0.60, 0.45). Within circulatory disease subtypes, associations with dose were observed for mortality due to cerebrovascular disease (ERR/Sv = 0.50; 90% CI: 0.12, 0.94) and mortality due to ischemic heart disease (ERR/Sv = 0.18; 90% CI: 0.004, 0.36). The estimates of associations between radiation dose and non-cancer mortality are generally consistent with those observed in atomic bomb survivor studies. The findings of this study could be interpreted as providing further evidence that non-cancer disease risks may be increased by external radiation exposure, particularly for ischemic heart disease and cerebrovascular disease. However, heterogeneity in the estimated ERR/Sv was observed, which warrants further investigation. Further follow-up of these cohorts, with the inclusion of internal exposure information and other potential confounders associated with lifestyle factors, may prove informative, as will further work on elucidating the biological mechanisms that might cause these non-cancer effects at low doses.

Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists. The reason for this may in part be that MCMC offers an appealing approach to handling some difficult types of analyses. Additionally, MCMC methods are those most commonly used for Bayesian analysis. However, epidemiologists are still largely unfamiliar with MCMC. They may lack familiarity either with he implementation of MCMC or with interpretation of the resultant output. As with tutorials outlining the calculus behind maximum likelihood in previous decades, a simple description of the machinery of MCMC is needed. We provide an introduction to conducting analyses with MCMC, and show that, given the same data and under certain model specifications, the results of an MCMC simulation match those of methods based on standard maximum-likelihood estimation (MLE). In addition, we highlight examples of instances in which MCMC approaches to data analysis provide a clear advantage over MLE. We hope that this brief tutorial will encourage epidemiologists to consider MCMC approaches as part of their analytic tool-kit.

This study provides further evidence that exposure to chrysotile asbestos in textile manufacturing is associated with increased risk of lung cancer, asbestosis cancer of the pleura and mesothelioma.

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

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