In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case–control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

BackgroundThere are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one).ObjectivesWe determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk.MethodsWe conducted a population-based case–control study of NHL in four National Cancer Institute–Surveillance, Epidemiology, and End Results centers—Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington—from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL.ResultsThe WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa.ConclusionsAn index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk.CitationCzarnota J, Gennings C, Colt JS, De Roos AJ, Cerhan JR, Severson RK, Hartge P, Ward MH, Wheeler DC. 2015. Analysis of environmental chemical mixtures and non-Hodgkin lymphoma risk in the NCI-SEER NHL Study. Environ Health Perspect 123:965–970; http://dx.doi.org/10.1289/ehp.1408630

Objective To determine the factor structure of persistent post-concussive syndrome (PPCS) symptoms in a blast-exposed military sample and validate factors against objective and symptom measures. Setting Veterans Affairs medical center and military bases. Participants One hundred eighty-one service members and veterans with at least one significant exposure to blast during deployment within the two years prior to study enrollment. Design Confirmatory and exploratory factor analysis of the Rivermead Post-concussion Questionnaire (RPQ). Main Measures RPQ, PTSD Symptom Checklist-Civilian, Center for Epidemiologic Studies Depression inventory, Sensory Organization Test, Paced Auditory Serial Addition Test, California Verbal Learning Test, Delis-Kaplan Executive Function System subtests. Results The three-factor structure of PPCS was not confirmed. A four-factor structure was extracted, and factors were interpreted as reflecting emotional, cognitive, visual, and vestibular functions. All factors were associated with scores on psychological symptom inventories; visual and vestibular factors were also associated with balance performance. There was no significant association between the cognitive factor and neuropsychological performance, nor between a history of mTBI and factor scores. Conclusion Persistent post-concussive symptoms observed months after blast exposure seem to be related to four distinct forms of distress, but not to mTBI per se, with vestibular and visual factors possibly related to injury of sensory organs by blast.

Socioeconomic status (SES) is often considered a risk factor for health outcomes. SES is typically measured using individual variables of educational attainment, income, housing, and employment variables or a composite of these variables. Approaches to building the composite variable include using equal weights for each variable or estimating the weights with principal components analysis or factor analysis. However, these methods do not consider the relationship between the outcome and the SES variables when constructing the index. In this project, we used weighted quantile sum (WQS) regression to estimate an area-level SES index and its effect in a model of colonoscopy screening adherence in the Minnesota–Wisconsin Metropolitan Statistical Area. We considered several specifications of the SES index including using different spatial scales (e.g., census block group-level, tract-level) for the SES variables. We found a significant positive association (odds ratio = 1.17, 95% CI: 1.15–1.19) between the SES index and colonoscopy adherence in the best fitting model. The model with the best goodness-of-fit included a multi-scale SES index with 10 variables at the block group-level and one at the tract-level, with home ownership, race, and income among the most important variables. Contrary to previous index construction, our results were not consistent with an assumption of equal importance of variables in the SES index when explaining colonoscopy screening adherence. Our approach is applicable in any study where an SES index is considered as a variable in a regression model and the weights for the SES variables are not known in advance.