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.

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.

Evidence examining specific effects of a multidisciplinary team (MDT) on cardiometabolic risk factors (CMRFs) among multi-ethnic patients in real-world clinical settings is lacking. This one-year retrospective chart review (2018) analyzed 598 adults (African American 59%, Hispanic 35%, and Caucasian 6%) with mean age of 43.8 ± 14.0 years. Qualifying patients with primary inclusion criteria of having body mass indices and blood pressure (BP) measurements in the first and last quarter of the study period were treated under an MDT protocol and compared to those qualifying for MDT but treated solely by a primary care provider (PCP). MDT included endocrinologist-directed visits, lifestyle counseling, and shared medical appointments. MDT patients experienced a greater reduction (β; 95% CI) in weight (−4.29 kg; −7.62, −0.97), BMI (−1.43 kg/m²; −2.68, −0.18), systolic BP (−2.18 mmHg; −4.09, −0.26), and diastolic BP (−1.97 mmHg; −3.34, −0.60). Additionally, MDT patients had 77%, 83%, and 59% higher odds of reducing ≥5% of initial weight, 1 BMI point, and ≥2 mmHg DBP, respectively. Improvements in hemoglobin A1C measurements were observed in the MDT group (insufficient data to compare with the PCP group). Compared to PCP only, MDT co-management improves CMRF related to adiposity and hypertension in a multiethnic adult cohort in real-world clinical settings. Patient access to best practices in cardiometabolic care is a priority, including the incorporation of culturally adapted evidence-based recommendations translated within a multi-disciplinary infrastructure, where competing co-morbidities are better managed, and associated research and education programs can promote operational sustainability.

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.