Faculty Bibliography

PURPOSE/OBJECTIVE:To find out what is known from literature about Long COVID until January 30, 2021. METHODS:We undertook a four-step search with no language restriction. A preliminary search was made to identify the keywords. A search strategy of all electronic databases resulted in 66 eligible studies. A forward and backward search of the references and citations resulted in additional 54 publications. Non-English language articles were translated using Google Translate. We conducted our scoping review based on the PRISMA-ScR Checklist. RESULTS:Of 120 papers, we found only one randomized clinical trial. Of the 67 original studies, 22 were cohort, and 28 were cross-sectional studies. Of the total 120 publications, 49.1% focused on signs and symptoms, 23.3% on management, and 10.8% on pathophysiology. Ten publications focused on imaging studies. The results are also presented extensively in a narrative synthesis in separated sections (nomenclature, diagnosis, pathophysiology, risk factors, signs/symptoms, management). CONCLUSIONS:The controversies in its definition have impaired proper recognition and management. The predominant symptoms were: fatigue, breathlessness, arthralgia, sleep difficulties, and chest pain. Recent reports also point to the risk of long-term sequela with cutaneous, respiratory, cardiovascular, musculoskeletal, mental health, neurologic, and renal involvement in those who survive the acute phase of the illness.

Neighborhood-level social determinants are increasingly recognized as factors shaping mental health in adults. Data-driven informatics methods and geographic information systems (GIS) offer innovative approaches for quantifying neighborhood attributes and studying their influence on mental health. Guided by a modification of Andersen's Behavioral Model of Health Service Use framework, this cross-sectional study examined associations of neighborhood resource groups with psychological distress and depressive symptoms in 1,528 U.S. Veterans. Data came from the Veteran Affairs (VA) Health Services Research and Development Proactive Mental Health trial and publicly available sources. Hierarchical clustering based on the proportions of neighborhood resources within walkable distance was used to identify neighborhood resource groups and generalized estimating equations analyzed the association of identified neighborhood resource groups with mental health outcomes. Few resources were found in walkable areas except alcohol and/or tobacco outlets. In clustering analysis, four meaningful neighborhood groups were identified characterized by alcohol and tobacco outlets. Living in an alcohol-permissive and tobacco-restrictive neighborhood was associated with increased psychological distress but not depressive symptoms. Living in urban or rural areas and access to VA care facilities were not associated with either outcome. These findings can be used in developing community-based mental health-promoting interventions and public health policies such as zoning policies to regulate alcohol outlets in neighborhoods. Augmenting community-based services with Veteran-specialized services in neighborhoods where Veterans live provides opportunities for improving their mental health.

In response to the COVID-19 pandemic, research institutions across the globe have modified their operations in ways that have limited or eliminated the amount of permissible in-person research interaction. In order to prevent the loss of important developmentally-timed data during the pandemic, researchers have quickly pivoted and developed innovative methods for remote assessment of research participants. In this manuscript, we describe methods developed for remote assessment of a parent child cohort with a focus on examining the perinatal environment, behavioral and biological indicators of child neurobehavioral development, parent-child interaction, as well as parent and child mental and physical health. We include recommendations relevant to adapting in-laboratory assessments for remote data collection and conclude with a description of the successful dissemination of the methods to eight research sites across the United States, each of whom are involved in Phase 1 of the HEALthy Brain and Child Development (HBCD) Study. These remote methods were born out of pandemic-related necessity; however, they have much wider applicability and may offer advantages over in-laboratory neurodevelopmental assessments.

BACKGROUND AND AIMS/OBJECTIVE:Obesity is an independent risk factor for atherosclerotic cardiovascular disease (CVD), and platelet hyperactivation in obesity may contribute to this association. Olive oil consumption is associated with lower cardiovascular disease (CVD) risk in the general population. However, little is known for individuals with obesity. We investigated whether olive oil intake is associated with platelet activation in obesity. METHODS AND RESULTS/RESULTS:. Olive oil intake was stratified into <1 time/week (n = 21), 1-3 times/week (n = 18), ≥4 times/week (n = 24). Strata did not differ by age, BMI or platelet count. Unstimulated P-selectin expression did not differ by olive oil consumption. Subjects with more frequent olive oil intake exhibited lower P-selectin expression on submaximal thrombin exposure. CONCLUSIONS:More frequent olive oil intake is associated with reduced thrombin-induced platelet activation in obesity.

In 2018, the U.S. Department of Housing and Urban Development required public housing authorities to implement a smoke-free housing (SFH) policy that included individual apartments. We analyzed the policy implementation process in the New York City Public Housing Authority (NYCHA). From June-November 2019, we conducted 9 focus groups with 64 NYCHA residents (smokers and nonsmokers), 8 key informant interviews with NYCHA staff and resident association leaders, and repeated surveys with a cohort of 130 nonsmoking households pre- and 12-month post policy. One year post policy implementation, participants reported widespread smoking violations and multi-level factors impeding policy implementation. These included the shared belief among residents and staff that the policy overreached by "telling people what to do in their own apartments". This hindered compliance and enforcement efforts. Inconsistent enforcement of illegal marijuana use, staff smoking violations, and a lack of accountability for other pressing housing issues created the perception that smokers were being unfairly targeted, as did the lack of smoking cessation resources. Resident support for the policy remained unchanged but satisfaction with enforcement declined (60.1% vs. 48.8%, p = 0.047). We identified multilevel contextual factors that are influencing SFH policy implementation. Findings can inform the design of strategies to optimize policy implementation.

INTRODUCTION/BACKGROUND:Similar to other non-communicable diseases (NCDs), people who develop cardiovascular disease (CVD) typically have more than one risk factor. The clustering of cardiovascular risk factors begins in youth, early adulthood, and middle age. The presence of multiple risk factors simultaneously has been shown to increase the risk for atherosclerosis development in young and middle-aged adults and risk of CVD in middle age. OBJECTIVE:This study aimed to address the interrelationship of CVD risk factors and their accumulation in a large sample of young adults in the United Arab Emirates (UAE). METHODS:Baseline data was drawn from the UAE Healthy Future Study (UAEHFS), a volunteer-based multicenter study that recruits Emirati nationals. Data of participants aged 18 to 40 years was used for cross-sectional analysis. Demographic and health information was collected through self-reported questionnaires. Anthropometric data and blood pressure were measured, and blood samples were collected. RESULTS:A total of 5126 participants were included in the analysis. Comorbidity analyses showed that dyslipidemia and obesity co-existed with other cardiometabolic risk factors (CRFs) more than 70% and 50% of the time, respectively. Multivariate logistic regression analysis of the risk factors with age and gender showed that all risk factors were highly associated with each other. The strongest relationship was found with obesity; it was associated with four-fold increase in the odds of having central obesity [adjusted OR 4.70 (95% CI (4.04-5.46)], and almost three-fold increase odds of having abnormal glycemic status [AOR 2.98 (95% (CI 2.49-3.55))], hypertension (AOR 3.03 (95% CI (2.61-3.52))] and dyslipidemia [AOR 2.71 (95% CI (2.32-3.15)]. Forty percent of the population accumulated more than 2 risk factors, and the burden increased with age. CONCLUSION/CONCLUSIONS:In this young population, cardiometabolic risk factors are highly prevalent and are associated with each other, therefore creating a heavy burden of risk factors. This forecasts an increase in the burden of CVD in the UAE. The robust longitudinal design of the UAEHFS will enable researchers to understand how risk factors cluster before disease develops. This knowledge will offer a novel approach to design group-specific preventive measures for CVD development.

(1) Background: The King-Devick (KD) rapid number naming test is sensitive for concussion diagnosis, with increased test time from baseline as the outcome measure. Eye tracking during KD performance in concussed individuals shows an association between inter-saccadic interval (ISI) (the time between saccades) prolongation and prolonged testing time. This pilot study retrospectively assesses the relation between ISI prolongation during KD testing and cognitive performance in persistently-symptomatic individuals post-concussion. (2) Results: Fourteen participants (median age 34 years; 6 women) with prior neuropsychological assessment and KD testing with eye tracking were included. KD test times (72.6 ± 20.7 s) and median ISI (379.1 ± 199.1 msec) were prolonged compared to published normative values. Greater ISI prolongation was associated with lower scores for processing speed (WAIS-IV Coding, r = 0.72, p = 0.0017), attention/working memory (Trails Making A, r = -0.65, p = 0.006) (Digit Span Forward, r = 0.57, p = -0.017) (Digit Span Backward, r= -0.55, p = 0.021) (Digit Span Total, r = -0.74, p = 0.001), and executive function (Stroop Color Word Interference, r = -0.8, p = 0.0003). (3) Conclusions: This pilot study provides preliminary evidence suggesting that cognitive dysfunction may be associated with prolonged ISI and KD test times in concussion.

BACKGROUND:Primary care practices have remained on the frontline of health care service delivery throughout the COVID-19 pandemic. The purpose of our study was to understand the early pandemic experience of primary care practices, how they adapted care processes for chronic disease management and preventive care, and the future potential of these practices' service delivery adaptations. METHODS:We interviewed 44 providers and staff at 22 high-performing primary care practices located throughout the United States between March and May 2020. Interviews were transcribed and coded using a modified rapid assessment process due to the time-sensitive nature of the study. RESULTS:Practices reported employing a variety of adaptations to care during the COVID-19 pandemic including maintaining safe and socially distanced access through increased use of telehealth visits, using disease registries to identify and proactively outreach to patients, providing remote patient education, and incorporating more home-based monitoring into care. Routine screening and testing slowed considerably, resulting in concerns about delayed detection. Patients with fewer resources, lower health literacy, and older adults were the most difficult to reach and manage during this time. CONCLUSION:Our findings indicate that primary care structures and processes developed for remote chronic disease management and preventive care are evolving rapidly. Emerging adapted care processes, most notably remote provision of care, are promising and may endure beyond the pandemic, but issues of equity must be addressed (e.g., through payment reform) to ensure vulnerable populations receive the same benefit.

OBJECTIVE:Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. MATERIALS AND METHODS:Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. RESULTS:Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. DISCUSSION:DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. CONCLUSION:DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

OBJECTIVE:We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research. MATERIALS AND METHODS/METHODS:Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements. RESULTS:We received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data. DISCUSSION/CONCLUSIONS:Based on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries. CONCLUSION/CONCLUSIONS:We are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.