Sex-Specific Association of Ambient Temperature With Urine Biomarkers in Southwest Coastal Bangladesh.

Introduction: Men are vulnerable to ambient heat-related kidney disease burden; however, limited evidence exists on how vulnerable women are when exposed to high ambient heat. We evaluated the sex-specific association between ambient temperature and urine electrolytes, and 24-hour urine total protein, and volume. Methods: We pooled a longitudinal 5624 person-visits data of 1175 participants' concentration and 24-hour excretion of urine electrolytes and other biomarkers (24-hour urine total protein and volume) from southwest coastal Bangladesh (Khulna, Satkhira, and Mongla districts) during November 2016 to April 2017. We then spatiotemporally linked ambient temperature data from local weather stations to participants' health outcomes. For evaluating the relationships between average ambient temperature and urine electrolytes and other biomarkers, we plotted confounder-adjusted restricted cubic spline (RCS) plots using participant-level, household-level, and community-level random intercepts. We then used piece-wise linear mixed-effects models for different ambient temperature segments determined by inflection points in RCS plots and reported the maximum likelihood estimates and cluster robust standard errors. By applying interaction terms for sex and ambient temperature, we determined the overall significance using the Wald test. Bonferroni correction was used for multiple comparisons. Results: The RCS plots demonstrated nonlinear associations between ambient heat and urine biomarkers for males and females. Piecewise linear mixed-effects models suggested that sex did not modify the relationship of ambient temperature with any of the urine parameters after Bonferroni correction (P < 0.004). Conclusion: Our findings suggest that women are as susceptible to the effects of high ambient temperature exposure as men.

Disparities in Access to Mental Health Services Among Children Diagnosed with Anxiety and Depression in the United States.

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.

Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 .

Additive partially linear model for pooled biomonitoring data.

Human biomonitoring involves monitoring human health by measuring the accumulation of harmful chemicals, typically in specimens like blood samples. The high cost of chemical analysis has led researchers to adopt a cost-effective approach. This approach physically combines specimens and subsequently analyzes the concentration of toxic substances within the merged pools. Consequently, there arises a need for innovative regression techniques to effectively interpret these aggregated measurements. To address this need, a new regression framework is proposed by extending the additive partially linear model (APLM) to accommodate the pooling context. The APLM is well-known for its versatility in capturing the complex association between outcomes and covariates, which is particularly valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors. Consistent estimators of the APLM are obtained through an iterative process that disaggregates information from the pooled observations. The performance is evaluated through simulations and an environmental health study focused on brominated flame retardants using data from the National Health and Nutrition Examination Survey.

Impact of a Multidisciplinary Approach on Cardiometabolic Risk Reduction in a Multiracial Cohort of Adults: A 1-Year Pilot Study.

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 (ß; 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.

Varying-coefficient regression analysis for pooled biomonitoring.

Human biomonitoring involves measuring the accumulation of contaminants in biological specimens (such as blood or urine) to assess individuals' exposure to environmental contamination. Due to the expensive cost of a single assay, the method of pooling has become increasingly common in environmental studies. The implementation of pooling starts by physically mixing specimens into pools, and then measures pooled specimens for the concentration of contaminants. An important task is to reconstruct individual-level statistical characteristics based on pooled measurements. In this article, we propose to use the varying-coefficient regression model for individual-level biomonitoring and provide methods to estimate the varying coefficients based on different types of pooled data. Asymptotic properties of the estimators are presented. We illustrate our methodology via simulation and with application to pooled biomonitoring of a brominated flame retardant provided by the National Health and Nutrition Examination Survey (NHANES).

Adverse Childhood Experiences (ACEs): Development of an ACEs Knowledge Scale.

BACKGROUND AND PURPOSE: Adverse childhood experiences (ACEs) negatively impacting children's health and later in their lives warrant necessity to educate nursing students about ACEs. The purposes of this study were to evaluate (a) nursing students' understanding of key concepts of ACEs using the ACEs Knowledge Scale (AKS) and (b) psychometric properties of the AKS. METHODS: A survey using AKS was conducted with randomly selected student participants (n = 344) to evaluate students' understanding of ACEs knowledge. Empirical validation of the AKS included content validity using Content Validity Index (CVI), reliability, and construct validity analyses. RESULTS: The results showed students in the Bachelor of Science in Nursing (BSN) program had increased knowledge of ACEs over pre-nursing students, and the BSN graduating students had increased knowledge related to trauma-informed care and building resilience. There were no significant differences in ACEs knowledge between Family Nurse Practitioner (FNP) and BSN students. Evaluation of psychometric properties of AKS revealed S-CVI/Ave=0.912, indicated excellent content validity based on the expert panel's ratings. Cronbach's alpha coefficient = .84 for the overall instrument indicated good reliability. Factor analyses were employed, showing that the 5-factor model gives good fit indexes, supporting the hypothesized factor structure of five key concepts. CONCLUSIONS: The AKS has showed promising implications to future research, nursing education, and nursing practice.

Local polynomial regression for pooled response data.

We propose local polynomial estimators for the conditional mean of a continuous response when only pooled response data are collected under different pooling designs. Asymptotic properties of these estimators are investigated and compared. Extensive simulation studies are carried out to compare finite sample performance of the proposed estimators under various model settings and pooling strategies. We apply the proposed local polynomial regression methods to two real-life applications to illustrate practical implementation and performance of the estimators for the mean function.