There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.

Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.

Objective: Determine whether social media platforms can influence article impact as measured by citations. Methods: Cross-sectional study that analyzed articles published in the top 10 otolaryngology journals by Eigenfactor score in January 2015. Total accumulated Twitter mentions and citations were recorded in 2021. The main outcomes examined the difference in citations, tweets, article types, and author counts accumulated over a 5-year period for all articles that were either tweeted or nontweeted. Results: A total of 3094 articles were included for analysis. The average article was cited 11.2 ± 13.2 times and tweeted 2.10 ± 4.0 times. Sixty-four percent of the articles had at least one tweet. Over the study period, there was a statistically significant difference in mean number of citations between tweeted articles (12.1 ± 15.0) versus nontweeted articles (9.6 ± 10.5) citations, representing a 26% difference ( P < .001). Review articles had the highest mean citations (19.4 ± 23.4) while editorials had the lowest mean citations (2.8 ± 6.9). Tweets peaked in the year of publication, but citations continued to rise in the subsequent years. Tweeted articles’ peak citation rate change was +1.27 mean citations per year, compared to +0.99 mean citations per year in nontweeted articles. The mean author count in tweeted articles (5.40 ± 3.1) was not significantly different than the mean author count in nontweeted articles (5.19 ± 2.65, P = .0794). Conclusion: These data suggest a moderate correlation between tweets and article citations, but a clear difference in the number of citations in articles tweeted versus those with no tweets. Thus, dissemination of knowledge may be impacted by social medial platforms such as Twitter.

There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.

Iatrogenic injury to the chorda tympani (CT) is a well recognized, although potentially underestimated, consequence of stapes surgery. This study aims to review the currently available literature to determine the incidence and prognosis of taste disturbances in these patients. Data Sources: PubMed, Embase, and Cochrane Library databases Methods: Databases were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Search terms included (chorda tympani OR gustatory OR taste OR chemosensory OR dysgeusia OR nervus intermedius) AND (ear surgery OR middle ear OR stapes OR stapedectomy OR stapedotomy). Patients with prospective data collection including preoperative data were further divided by methodology into "objective" and "subjective" assessments of taste dysfunction. A systematic review was performed for all included studies, with meta-analysis using a random-effects model was used for those with comparable methodology and patient populations.Results: Initial search yielded 2,959 articles that were screened according to inclusion and exclusion criteria. Once duplicates were removed, seven studies were identified, representing 173 patients with subjective testing (all seven studies) and 146 with objective testing (five studies). Eighty of 173 patients (46.2%) noted a disturbance in taste at early follow-up, whereas as 26 of 173 (15.0%) noted long-term problems. Objective methodology and result reporting were heterogenous and not amenable to pooled meta-analysis for all studies included. Conclusion: Changes in taste occur relatively frequently after stapedectomy. Surgeons should continue to counsel prospective patients as to the risks of both short-and long-term taste disturbances.