Xichen Mou

Community water fluoridation is a key public health intervention, yet ongoing debates about systemic risks call for evidence on fluoride exposure. We analyzed NHANES 2013–2016 biomonitoring data to assess fluoride exposure in children and adolescents aged 0–19 years. We calculated the proportion of children with fluoride levels in relation to guidelines from the United States Environmental Protection Agency (USEPA), World Health Organization (WHO), and Centers for Disease Control and Prevention (CDC). Biomonitoring data was obtained from the National Health and Nutrition Examination Survey (NHANES). Median water fluoride concentration was 0.50 (95% CI: 0.35, 0.64) mg/L for males, 0.49 (95% CI: 0.35, 0.63) mg/L for girls, 0.49 (95% CI: 0.35, 0.63) for the <3 age group, and 0.50 (95% CI: 0.36, 0.64) for the 18–19 age group. Median urine fluoride concentration was 0.56 (95% CI: 0.51, 0.62) mg/L for males, 0.46 (95% CI: 0.41, 0.50) for girls, 0.59 (95% CI: 0.50, 0.62) for the age group 6–8, and 0.51 (95% CI: 0.42, 0.60) for the 18–19 age group. Of the children, 99.96% were below the EPA’s enforceable 4.0 mg/L limit, 99.05% below the EPA’s 2.0 mg/L limit, and 98.6% below the WHO 1.5 mg/L standard. For urinary fluoride, 88.1% were within the CDC reference range of 0.2–<3.2 mg/L. We found that water fluoride levels did not vary across age and sex groups, but urine fluoride levels changed. Results indicate that most children remain within regulatory agencies’ guidelines.

Kernel machine regression is a nonparametric regression method widely applied in biomedical and environmental health research. It employs a kernel function to measure the similarities between sample pairs, effectively identifying significant exposures and assessing their nonlinear impacts on outcomes. This article introduces an enhanced framework, the generalized Bayesian kernel machine regression. In comparison to traditional kernel machine regression, generalized Bayesian kernel machine regression provides substantial flexibility to accommodate a broader array of outcome variables, ranging from continuous to binary and count data. Simulations show generalized Bayesian kernel machine regression can successfully identify the nonlinear relationships between independent variables and outcomes of various types. In the real data analysis, we applied generalized Bayesian kernel machine regression to uncover cytosine phosphate guanine sites linked to health-related conditions such as asthma and smoking. The results identify crucial cytosine phosphate guanine sites and provide insights into their complex, nonlinear relationships with outcome variables.

As adiposity increases in youth, so does the prevalence of cardiometabolic risk factors (CMRFs). The etiology of adiposity-based chronic disease and CMRFs includes ethnoracial disparities that are rarely considered in current treatment approaches. Precision interventions require further characterization of these disparities among high-risk youth. The objective of this study was to characterize differences in CMRF among African American (AA) and Hispanic (H) adolescents with varying levels of adiposity. A cross-sectional analysis of 2284 adolescents aged 12–17 was conducted using 3-year clinical data from Lifedoc Health. CMRF prevalence were compared using χ², with logistic regression models (LRM) applied to explore the relationships between exposures (age, sex, ethnoracial group, adiposity) and CMRF outcomes. Prevalence of CMRF rose with increasing adiposity, which was the strongest determinant of risk overall. However, individual risk profiles differed between the two groups, with H having higher prevalence of metabolic syndrome (MetS), higher triglycerides and liver enzymes, and low high-density lipoprotein cholesterol (HDL-c). Meanwhile, AA had higher prevalence of elevated blood pressure (BP) in the overweight category, prediabetes in overweight to severe obesity, and type 2 diabetes in obesity. LRM showed 3.0-fold greater chance of impaired glucose metabolism in AA than H, who were 1.7, 5.9, and 8.3 times more likely to have low HDL-c, high liver enzymes, and high triglycerides, respectively. Overweight/obesity prevalence was very high among AA and H adolescents. Excess adiposity was associated with an increased prevalence of CMRF, with individual risk factors differing between groups as adiposity increased. Research within routine clinical settings is required to better characterize these discrepancies and ameliorate their adverse impact on health in the transition to adulthood.

Antidepressants are among the most prescribed drugs in the US, but the current treatment patterns and modalities among older adults are unclear. This study assessed the patterns of discontinuation of antidepressants and the risk of medication resumption among community-dwelling older adults with depression. Using Medicare Current Beneficiary Survey (MCBS) data from 2015–2019, we identified 1084 beneficiaries with depression who newly initiated serotonergic antidepressants. The risk of medication resumption was explored using survival analysis. The median duration of continuous medication was 90 days. However, about 30% of patients had a treatment duration of 30 days or shorter, 26% for 31–90 days, 15% for 91–180 days, and 30% for 181 days or more. After adjusting for all covariates, patients with less than 30 days of continuous medication were half as likely to resume the medications compared to those with 91–180 days (HR: 0.49 (95% CI: 0.37, 0.65)). Nearly one-third of older adults used an antidepressant medication for a short duration with a lower risk of medication resumption. A shorter treatment duration without resumption might suggest over-prescription of antidepressants among community-dwelling older adults.

Child and adolescent mental health are major public health concerns in the US. Overall, 20% of US children have a reported mental health condition, while an estimated 40% will be diagnosed with one by age 18. Despite these concerns, little is known about factors associated with access to mental health services among children and adolescents. We analyzed data from a sample of 6655 children (aged 6 to 17 years) with either anxiety and/or depression drawn from the 2020–2021 National Survey of Children’s Health (NSCH). A multivariable logistic regression model was fit to investigate predisposing, enabling, and need factors associated with caregiver’s (i.e., parent or other guardian) perceived access to mental health services for their children. Approximately 50.8% of caregivers perceived obtaining mental health services for their children to be somewhat difficult, very difficult, or impossible. Children meeting criteria for having a medical home had lower odds of experiencing such difficulties (adjusted [a]OR = 0.38; 95% CI: 0.30–0.49). Further, compared to children who sometimes or never had health insurance coverage for mental or behavioral health needs, children who were always insured (aOR: 0.19; 95% CI 0.14, 0.25) and those who usually had coverage (aOR: 0.38; 95% CI 0.28, 0.51) had lower odds of experiencing perceived difficulties in obtaining care. The results indicate several enabling and need predictors of perceived access to mental health services––highlighting potential structural barriers to care access. Efforts to address access challenges should adopt a multifaceted approach and be tailored to families living in poverty, those with limited health coverage, and minoritized children with less than optimal general health.

DNA methylation is a biochemical process in which a methyl group is added to the cytosine-phosphate-guanine (CpG) site on DNA molecules without altering the DNA sequence. Multiple CpG sites in a certain genome region can be differentially methylated across phenotypes. Identifying these differentially methylated CpG regions (DMRs) associated with the phenotypes contributes to disease prediction and precision medicine development. We propose a novel DMR detection algorithm, gbdmr. In contrast to existing methods under a linear regression framework, gbdmr assumes that DNA methylation levels follow a generalized beta distribution. We compare gbdmr to alternative approaches via simulations and real data analyses, including dmrff, a new DMR detection approach that shows promising performance among competitors, and the traditional EWAS that focuses on single CpG sites. Our simulations demonstrate that gbdmr is superior to the other two when the correlation between neighboring CpG sites is strong, while dmrff shows a higher power when the correlation is weak. We provide an explanation of these phenomena from a theoretical perspective. We further applied the three methods to multiple real DNA methylation datasets. One is from a birth cohort study undertaken on the Isle of Wight, United Kingdom, and the other two are from the Gene Expression Omnibus database repository. Overall, gbdmr identifies more DMR CpGs linked to phenotypes than dmrff, and the simulated results support the findings. Gbdmr is an innovative method for detecting DMRs based on generalized beta regression. It demonstrated notable advantages over dmrff and traditional EWAS, particularly when adjacent CpGs exhibited moderate to strong correlations. Our real data analyses and simulated findings highlight the reliability of gbdmr as a robust DMR detection tool. The gbdmr approach is accessible and implemented by R on GitHub: https://github.com/chengzhouwu/gbdmr .