Oral health, diabetes, and inflammation: Effects of oral hygiene behavior

The aim of this research was to assess the association between inflammation and oral health and diabetes, as well as the mediating role of oral hygiene practice in this association. Data were from the 2009–2010 National Health and Nutrition Examination Survey. The analytical sample consisted of 2,191 respondents aged 50 and older. Poor oral health was clinically defined by significant tooth loss (STL) and periodontal disease (PD). Diabetes mellitus (DM) was determined by glycemic levels. The outcome variable was serum C-reactive protein (CRP) level, dichotomised as ?1 mg/dL (elevated CRP) vs <1 mg/dL (not elevated CRP). Two path models, one using STL and DM as the independent variable, the other using PD and DM as the independent variable, were estimated to assess the direct effects of having poor oral health and DM on elevated CRP and the mediating effects of dental flossing. In path model 1, individuals having both STL and DM (adjusted odds ratio [AOR], 1.92; 95% confidence interval [CI], 1.30–2.82) or having STL alone (AOR, 2.30; 95% CI, 1.68–3.15) were more likely to have elevated CRP than those with neither STL nor DM; dental flossing (AOR, 0.92, 95% CI, 0.88–0.96) was associated with lower risk of elevated CRP. In path model 2, no significant association was found between having both PD and DM and elevated CRP; dental flossing (AOR, 0.91; 95% CI:, 0.86–0.94) was associated with lower risk of elevated CRP. Findings from this study highlight the importance of improving oral health and oral hygiene practice to mitigate inflammation. Further research is needed to assess the longer-term effects of reducing inflammation.ECU Open Access Publishing Support Fun

Bridging the evidence-to-practice gap: a stepped-wedge cluster randomized controlled trial evaluating practice facilitation as a strategy to accelerate translation of a multi-level adherence intervention into safety net practices

BACKGROUND: Poor adherence to antihypertensive medications is a significant contributor to the racial gap in rates of blood pressure (BP) control among Latino adults, as compared to Black and White adults. While multi-level interventions (e.g., those aiming to influence practice, providers, and patients) have been efficacious in improving medication adherence in underserved patients with uncontrolled hypertension, the translation of these interventions into routine practice within real world safety-net primary care settings has been inadequate and slow. This study will fill this evidence-to-practice gap by evaluating the effectiveness of practice facilitation (PF) as a practical and tailored strategy for implementing Advancing Medication Adherence for Latinos with Hypertension through a Team-based Care Approach (ALTA), a multi-level approach to improving medication adherence and BP control in 10 safety-net practices in New York that serve Latino patients. METHODS AND DESIGN: We will conduct this study in two phases: (1) a pre-implementation phase where we will refine the PF strategy, informed by the Consolidated Framework for Implementation Research, to facilitate the implementation of ALTA into routine care at the practices; and (2) an implementation phase during which we will evaluate, in a stepped-wedge cluster randomized controlled trial, the effect of the PF strategy on ALTA implementation fidelity (primary outcome), as well as on clinical outcomes (secondary outcomes) at 12 months. Implementation fidelity will be assessed using a mixed methods approach based on the five core dimensions outlined by Proctor\u27s Implementation Outcomes Framework. Clinical outcome measures include BP control (defined as BP \u3c 130/80 mmHg) and medication adherence (assessed using the proportion of days covered via pharmacy records). DISCUSSION: The study protocol applies rigorous research methods to identify how implementation strategies such as PF may work to expedite the translation process for implementing evidence-based approaches into routine care at safety-net practices to improve health outcomes in Latino patients with hypertension, who suffer disproportionately from poor BP control. By examining the barriers and facilitators that affect implementation, this study will contribute knowledge that will increase the generalizability of its findings to other safety-net practices and guide effective scale-up across primary care practices nationally. TRIAL REGISTRATION: ClinicalTrials.gov NCT03713515, date of registration: October 19, 2018

Effects of the Co-occurrence of Diabetes Mellitus and Tooth Loss on Cognitive Function

OBJECTIVE: Both diabetes mellitus (DM) and poor oral health are common chronic conditions and risk factors of Alzheimer’s disease and related dementia among older adults. This study assessed the effects of DM and complete tooth loss (TL) on cognitive function, accounting for their interactions. METHODS: Longitudinal data were obtained from the 2006, 2012, and 2018 waves of the Health and Retirement Study. This cohort study included 7,805 respondents aged 65 years or older with 18,331 person-year observations. DM and complete TL were self-reported. Cognitive function was measured by the Telephone Interview for Cognitive Status. Random-effect regressions were used to test the associations, overall and stratified by sex. RESULTS: Compared with older adults without neither DM nor complete TL, those with both conditions (b = −1.35, 95% confidence interval [CI]: −1.68, −1.02), with complete TL alone (b = −0.67, 95% CI: −0.88, −0.45), or with DM alone (b = −0.40, 95% CI: −0.59, −0.22), had lower cognitive scores. The impact of having both conditions was significantly greater than that of having DM alone (p < .001) or complete TL alone (p = 0.001). Sex-stratified analyses showed the effects were similar in males and females, except having DM alone was not significant in males. CONCLUSION: The co-occurrence of DM and complete TL poses an additive risk for cognition. Healthcare and family-care providers should pay attention to the cognitive health of patients with both DM and complete TL. Continued efforts are needed to improve older adults’ access to dental care, especially for individuals with DM

A hierarchical latent space network model for mediation

For interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019) introduced a statistical model for social networks as mediators in network-level interventions. We build on their approach and introduce a new model in which the network is a mediator using a latent space approach. We investigate our model through a simulation study and a real-world analysis of teacher advice-seeking networks.https://doi.org/10.1017/nws.2022.1

Applied machine learning to identify differential risk groups underlying externalizing and internalizing problem behaviors trajectories: A case study using a cohort of Asian American children.

BackgroundInternalizing and externalizing problems account for over 75% of the mental health burden in children and adolescents in the US, with higher burden among minority children. While complex interactions of multilevel factors are associated with these outcomes and may enable early identification of children in higher risk, prior research has been limited by data and application of traditional analysis methods. In this case example focused on Asian American children, we address the gap by applying data-driven statistical and machine learning methods to study clusters of mental health trajectories among children, investigate optimal predictions of children at high-risk cluster, and identify key early predictors.MethodsData from the US Early Childhood Longitudinal Study 2010-2011 were used. Multilevel information provided by children, families, teachers, schools, and care-providers were considered as predictors. Unsupervised machine learning algorithm was applied to identify groups of internalizing and externalizing problems trajectories. For prediction of high-risk group, ensemble algorithm, Superlearner, was implemented by combining several supervised machine learning algorithms. Performance of Superlearner and candidate algorithms, including logistic regression, was assessed using discrimination and calibration metrics via crossvalidation. Variable importance measures along with partial dependence plots were utilized to rank and visualize key predictors.FindingsWe found two clusters suggesting high- and low-risk groups for both externalizing and internalizing problems trajectories. While Superlearner had overall best discrimination performance, logistic regression had comparable performance for externalizing problems but worse for internalizing problems. Predictions from logistic regression were not well calibrated compared to those from Superlearner, however they were still better than few candidate algorithms. Important predictors identified were combination of test scores, child factors, teacher rated scores, and contextual factors, which showed non-linear associations with predicted probabilities.ConclusionsWe demonstrated the application of data-driven analytical approach to predict mental health outcomes among Asian American children. Findings from the cluster analysis can inform critical age for early intervention, while prediction analysis has potential to inform intervention programing prioritization decisions. However, to better understand external validity, replicability, and value of machine learning in broader mental health research, more studies applying similar analytical approach is needed

Disparities in routine healthcare utilization disruptions during COVID-19 pandemic among veterans with type 2 diabetes

Abstract Background While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. Methods We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018–2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. Results In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110–140) to 50 (95%CI,30–80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640–2040) to 810 (95%CI,710–930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020–2021 from 330 (95%CI,310–350) to 770 (95%CI,720–820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. Conclusions Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity