BACKGROUND: High levels of attention-deficit/hyperactivity disorder (ADHD) traits are associated with various outcomes, including depressive symptoms, functional impairment, and low self-esteem. Additionally, individuals with high levels of ADHD traits are reported to be more adversely affected by fear of coronavirus disease 2019 (COVID-19). The current study aimed to examine whether the association between ADHD traits and outcomes was partially mediated by fear of COVID-19 using mediation analysis. METHODS: A cross-sectional study was conducted in a sample of university students in medical-related faculties (n = 1,166). ADHD traits, fear of COVID-19, depressive symptoms, functional impairment, and self-esteem were assessed using the adult ADHD Self-Report Scale, Fear of COVID-19 Scale, K6 Scale, Sheehan Disability Scale, and Rosenberg Self-Esteem Scale, respectively. We used linear regression analysis and the Paramed command in Stata to analyze whether fear of COVID-19 mediated the association between ADHD traits and outcomes. RESULTS: ADHD traits were significantly associated with outcomes. Regarding the impact of fear of COVID-19, the results revealed a significant association between ADHD traits and fear of COVID-19, and between fear of COVID-19 and outcomes. The results of the mediation analyses showed that the association between ADHD traits and outcomes was partially mediated by the fear of COVID-19 (depressive symptoms: direct effect B = 1.029, 95% confidence interval [CI] 0.878, 1.181, indirect effect B = 0.021, 95% CI 0.002, 0.040; functional impairment: direct effect B = 0.786, 95% CI 0.593, 0.979, indirect effect B = 0.033, 95% CI 0.005, 0.060; self-esteem: direct effect B = -1.052, 95% CI -1.226, -0.878, indirect effect B = -0.024, 95% CI -0.046, -0.002). CONCLUSIONS Developing preventive measures against the adverse impacts of pandemics like COVID-19 will be particularly important for individuals with high levels of ADHD traits in future.
Humans ; COVID-19/psychology* ; Attention Deficit Disorder with Hyperactivity/epidemiology* ; Cross-Sectional Studies ; Self Concept ; Male ; Female ; Depression/epidemiology* ; Fear/psychology* ; Young Adult ; Universities ; Students/psychology* ; Adult ; SARS-CoV-2 ; Mediation Analysis ; Adolescent

BACKGROUND: Recent studies have provided compelling evidence that exposure to brominated flame retardants (BFRs) can adversely affect human health. We aim to explore the potential impact of BFRs on adiposity and central obesity. METHODS: Data from the National Health and Nutrition Examination Surveys (NHANES) cycles conducted between 2009 and 2014 was used to study the connections between variables. After filtering, we analyzed a sample of 4,110 adults aged 20 years and above. Our goal was to examine the potential association between BFRs and consequences and investigate the part played by oxidative stress and inflammatory markers as intermediaries. To achieve this, we used advanced statistical methods such as weighted quantile sum (WQS) regression, quantile-based g-computation (QGC), and the Bayesian kernel machine regression (BKMR). RESULTS: The findings showed that among the examined chemicals, exposure to PBDE85 (weight: 41%), PBDE100 (24%), and PBB153 (23%) may be the dominant contributors to general obesity risk. Upon controlling for all variables that could impact the results, it was found that the QGC outcomes indicated a positive correlation between exposure to mixtures of brominated flame retardants and the occurrence of abdominal obesity (OR = 1.187, 95% CI: 1.056-1.334, p = 0.004). Significant contributions were made by PBDE85 (52%), PBB153 (27%), and PBDE100 (21%). Mediation analysis shows that lymphatic cells (LC) and albumin (ALB) partially mediate the link between brominated flame retardants and obesity. The results of BKMR are generally consistent with those of WQS and QGC. CONCLUSION At a population level, our research has revealed a noteworthy correlation between BFRs and obesity. However, further investigation is required through prospective cohort studies and in-depth mechanistic exploratory studies.
Humans ; Flame Retardants/adverse effects* ; Oxidative Stress/drug effects* ; Adult ; Male ; Female ; Middle Aged ; Inflammation/epidemiology* ; Obesity/chemically induced* ; Biomarkers/blood* ; Nutrition Surveys ; Mediation Analysis ; Young Adult ; United States/epidemiology* ; Environmental Exposure/adverse effects* ; Aged ; Environmental Pollutants/adverse effects* ; Halogenated Diphenyl Ethers/adverse effects*

Humans ; Male ; Semen ; Sperm Motility ; Mediation Analysis ; East Asian People ; Microcystins/toxicity* ; Spermatozoa ; Sperm Count ; Infertility, Male

Objective: To introduce and compare four analysis methods of multiple parallel mediation model, including pure regression method, method based on inverse probability weighting, extended natural effect model method and weight-based imputation strategies. Methods: For the multiple parallel mediation model, the simulation experiments of three scenarios were carried out to compare the performance of different methods in estimating direct and indirect effects in different situations. Dataset from UK Biobank was then analyzed by using the four methods. Results: The estimation biases of the regression method and the inverse probability weighting method were relatively small, followed by the extended natural effect model method, and the estimation results of the weight-based imputation strategies were quite different from the other three methods. Conclusions: Different multiple parallel mediation analysis methods have different application situations and their own advantages and disadvantages. The regression method is more suitable for continuous mediator, and the inverse probability weighting method is more suitable for binary mediator. The extended natural effect model method has better performances when the residuals of two parallel mediators are positively correlated and the correlation degree is small. The weight-based imputation strategies might not be appropriate for parallel mediation analysis. Therefore, appropriate methods should be selected according to the specific situation in practice.
Bias ; Computer Simulation ; Humans ; Mediation Analysis ; Models, Statistical ; Probability ; Regression Analysis ; Research Design