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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: This study aims to determine whether social media platforms (SMPs) can influence article impact as measured by citations. Methods: This is a cross-sectional study that analyzed articles published in the top ten otolaryngology journals by the 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 non-tweeted. Results: 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) vs non-tweeted 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 +.99 mean citations per year in non-tweeted articles. The mean author count in tweeted articles (5.40 ± 3.1) was not significantly different than mean author count in non-tweeted 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 vs those with no tweets. Thus, dissemination of knowledge may be impacted by social media platforms such as Twitter. [ABSTRACT FROM AUTHOR]

Our primary objectives are to empirically identify distinct childhood groups based on trajectories of waist circumference (WC) and waist circumference index measurements, and then to estimate associations between these groups and adult diabetes incidence, as well as other outcomes, including blood pressure, body size, body composition, and hemoglobin levels. Childhood WC and height measurements as well as various adult measurements are taken from participants in the Fels Longitudinal Study. Childhood groups are identified using group-based trajectory modeling. Associations between the resulting group probabilities and adult outcomes are examined using mixed models. Our results show that distinct childhood groups are identifiable for both waist size measurements, with growth curves exhibited by these groups becoming distinguishably separate at around 4 years of age. Higher probabilities for groups exhibiting the larger waist size for either measurement were estimated to have higher risks of developing diabetes in adulthood. Associations were also observed between group probabilities and systolic blood pressure, diastolic blood pressure, and various anthropomorphic measurements, with most associations consistently occurring in early adulthood. These findings expand upon the existing literature, showing that childhood trends in waist size, distinguishable at ages as early as 4 years, are associated with adult Type-2 diabetes. [ABSTRACT FROM AUTHOR]

Objective: This study aims to determine whether social media platforms (SMPs) can influence article impact as measured by citations., Methods: This is a cross-sectional study that analyzed articles published in the top ten otolaryngology journals by the 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 non-tweeted., Results: 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) vs non-tweeted 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 +.99 mean citations per year in non-tweeted articles. The mean author count in tweeted articles (5.40 ± 3.1) was not significantly different than mean author count in non-tweeted 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 vs those with no tweets. Thus, dissemination of knowledge may be impacted by social media platforms such as Twitter., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.