Faculty Bibliography

Teacher interactions around instructional practices have been a topic of study for a long time. Previous studies concerning such interactions have focused on questions pertaining to cross-sectional networks. In fact, very few studies have considered longitudinal networks and still fewer have employed longitudinal network models to study changes in such interactions. We analyse teachers"™ advice-seeking networks, observed annually between 2010 and 2013, in schools within a district where several initiatives were implemented starting in 2011. We assess whether formal structures, teaching assignment and leadership position, and teacher characteristics, gender and experience, are associated with advice-seeking ties, and the extent to which these associations change over time. To analyse the advice-seeking networks, we implement a Bayesian longitudinal latent space network model with covariates and random sender-receiver effects. Within the Bayesian framework, we address practical aspects of a principled network analysis such as missing ties and yearly immigration and emigration of teachers. Goodness of model fit assessment is conducted using posterior predictive checks. Our results demonstrate that while some of the associations between observed covariates and teachers"™ interactions varied in 2011, most were otherwise stable. In 2011, we found decreases in the associations with same grade assignment, leadership position, and teaching in the same school.

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/CONCLUSIONS: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.

Importance/UNASSIGNED:Black and Hispanic populations have higher rates of coronavirus disease 2019 (COVID-19) hospitalization and mortality than White populations but lower in-hospital case-fatality rates. The extent to which neighborhood characteristics and comorbidity explain these disparities is unclear. Outcomes in Asian American populations have not been explored. Objective/UNASSIGNED:To compare COVID-19 outcomes based on race and ethnicity and assess the association of any disparities with comorbidity and neighborhood characteristics. Design, Setting, and Participants/UNASSIGNED:This retrospective cohort study was conducted within the New York University Langone Health system, which includes over 260 outpatient practices and 4 acute care hospitals. All patients within the system's integrated health record who were tested for severe acute respiratory syndrome coronavirus 2 between March 1, 2020, and April 8, 2020, were identified and followed up through May 13, 2020. Data were analyzed in June 2020. Among 11 547 patients tested, outcomes were compared by race and ethnicity and examined against differences by age, sex, body mass index, comorbidity, insurance type, and neighborhood socioeconomic status. Exposures/UNASSIGNED:Race and ethnicity categorized using self-reported electronic health record data (ie, non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and multiracial/other patients). Main Outcomes and Measures/UNASSIGNED:The likelihood of receiving a positive test, hospitalization, and critical illness (defined as a composite of care in the intensive care unit, use of mechanical ventilation, discharge to hospice, or death). Results/UNASSIGNED:Among 9722 patients (mean [SD] age, 50.7 [17.5] years; 58.8% women), 4843 (49.8%) were positive for COVID-19; 2623 (54.2%) of those were admitted for hospitalization (1047 [39.9%] White, 375 [14.3%] Black, 715 [27.3%] Hispanic, 180 [6.9%] Asian, 207 [7.9%] multiracial/other). In fully adjusted models, Black patients (odds ratio [OR], 1.3; 95% CI, 1.2-1.6) and Hispanic patients (OR, 1.5; 95% CI, 1.3-1.7) were more likely than White patients to test positive. Among those who tested positive, odds of hospitalization were similar among White, Hispanic, and Black patients, but higher among Asian (OR, 1.6, 95% CI, 1.1-2.3) and multiracial patients (OR, 1.4; 95% CI, 1.0-1.9) compared with White patients. Among those hospitalized, Black patients were less likely than White patients to have severe illness (OR, 0.6; 95% CI, 0.4-0.8) and to die or be discharged to hospice (hazard ratio, 0.7; 95% CI, 0.6-0.9). Conclusions and Relevance/UNASSIGNED:In this cohort study of patients in a large health system in New York City, Black and Hispanic patients were more likely, and Asian patients less likely, than White patients to test positive; once hospitalized, Black patients were less likely than White patients to have critical illness or die after adjustment for comorbidity and neighborhood characteristics. This supports the assertion that existing structural determinants pervasive in Black and Hispanic communities may explain the disproportionately higher out-of-hospital deaths due to COVID-19 infections in these populations.

OBJECTIVE:To characterize the hospitalization and death rates among patients with inflammatory arthritis affected by COVID-19 and to analyze the associations between comorbidities and immunomodulatory medications and infection outcomes. METHODS:Clinical, demographic, maintenance treatment, and disease course data and outcomes of individuals with inflammatory arthritis (IA; rheumatoid arthritis and spondylarthritis) with symptomatic COVID-19 infection were prospectively assessed via web-based questionnaire followed by individual phone calls and electronic medical record review. Baseline characteristics and medication use were summarized for hospitalized and ambulatory patients, and outcomes were compared for each medication class using multivariable logistic regression. RESULTS:A total of 103 patients with IA were included in the study (n=80 confirmed and n=23 highly suspicious for COVID-19). Twenty-six percent of participants required hospitalization, and 4% died. Patients who warranted hospitalization were significantly more likely to be older (P<0.001) and have comorbid hypertension (P=0.001) and chronic obstructive pulmonary disease (P=0.022). IA patients taking oral glucocorticoids had a higher likelihood of being admitted for COVID-19 (P<0.001) while those on maintenance anti-cytokine biologic therapies did not. CONCLUSION/CONCLUSIONS:In patients with underlying IA, COVID-19 outcomes were worse in those receiving glucocorticoids but not in patients on maintenance anti-cytokine therapy. Further work is needed to understand whether immunomodulatory therapies affect COVID-19 incidence.

OBJECTIVE:Scalable, accessible forms of behavioral therapy for migraine prevention are needed. We assessed the feasibility and acceptability of progressive muscle relaxation (PMR) delivered by a smartphone application (app) in the Primary Care setting. METHODS:This pilot study was a non-blinded, randomized, parallel-arm controlled trial of adults with migraine and 4+ headache days/month. Eligible participants spoke English and owned a smartphone. All participants were given the RELAXaHEAD app which includes an electronic headache diary. Participants were randomized to receive 1 of the 2 versions of the app-one with PMR and the other without PMR. The primary outcomes were measures of feasibility (adherence to the intervention and diary entries during the 90-day interval) and acceptability (satisfaction levels). We conducted exploratory analyses to determine whether there was a change in Migraine Disability Assessment Scale (MIDAS) scores or a change in headache days. RESULTS:Of 139 participants (77 PMR, 62 control), 116 (83%) were female, mean age was 41.7 ± 12.8 years. Most patients 108/139 (78%) had moderate-severe disability. Using a 1-5 Likert scale, participants found the app easy to use (mean 4.2 ± 0.7) and stated that they would be happy to engage in the PMR intervention again (mean 4.3 ± 0.6). For the first 6 weeks, participants practiced PMR 2-4 days/week. Mean per session duration was 11.1 ± 8.3 minutes. Relative to the diary-only group, the PMR group showed a greater non-significant decline in mean MIDAS scores (-8.7 vs -22.7, P = .100) corresponding to a small-moderate mean effect size (Cohen's d = 0.38). CONCLUSION/CONCLUSIONS:Smartphone-delivered PMR may be an acceptable, accessible form of therapy for migraine. Mean effects show a small-moderate mean effect size in disability scores.

To determine the impact of tocilizumab, a monoclonal antibody against the interleukin 6 receptor, on survival in patients with coronavirus disease 2019.

Latent variable network models that accommodate edge correlations implicitly, by assuming an underlying latent factor, are increasing in popularity. Although, these models are examples of what is a growing body of research, much of the research is focused on proposing new models or extending others. There has been very little work on unifying the models in a single framework. In this paper, we present a complete framework that organizes existing latent variable network models within an integrative generalized additive model. Our framework is called Conditionally Independent Dyad (CID) models, and includes existing network models that assume dyad (or edge) independence conditional on latent variables and other components in the model. We further discuss practical aspects of model fitting such as posterior parameter estimation via MCMC, identifiability of parameters, approaches to handle missing data and model selection via cross-validation, for the proposed additive CID models. Finally, by presenting several data examples, we illustrate the utility of the proposed framework and provide advice on selecting components for building new CID models.