Faculty Bibliography

BACKGROUND:It is estimated that up to 40% of Alzheimer's Disease and Related Dementias cases can be prevented or delayed by addressing modifiable factors including those that influence vascular risk (hypertension, obesity, smoking, physical activity, diabetes). Prevention may be particularly important in the vascular dementia subtypes. Despite the supporting evidence, the rates of medical therapy to reduce vascular risk are not well described. METHOD/METHODS:We assessed the utilization of statins, aspirin, and blood pressure (BP) medications in adults age ≥65 years cared for at NYU Langone Health, as recorded in the electronic health record. We included two cohorts: cohort 1 included patients who were diagnosed with vascular dementia (VaD) at NYU Langone Barlow Center for Memory Evaluation between January 1, 2015 and June 24, 2019. Cohort 2 extended the inclusion to seniors with VD diagnosis by any NYU Langone physician. Definitions for vascular dementia, the covariates assessed, and medications that we included in each category are shown in Tables 1-3. RESULT/RESULTS:We included 419 and 3745 patients in cohort 1 and cohort 2, respectively. Table 4 shows the characteristics and medication adherence in cohorts 1 and 2. In cohort 1, the prescription rates for statins, aspirin, and BP medications were 66%, 66%, 70%. In cohort 2, the rates for statin, aspirin, and BP medications were 56%, 46%, and 65%, respectively. The differences between prescription rates in cohort 1 and 2 for the three medication groups were statistically significant (p<0.05). CONCLUSION/CONCLUSIONS:Our analysis of the utilization of cardiovascular medications among patients with vascular dementia illuminates potential gaps both among patients who receive care at specialty clinics, as well as the overall population with vascular dementia. The rates of medication utilization are higher for patients under the care of cognitive neurologists. Electronic health records can help identify large cohorts of patients who may benefit from improved access to preventative measures including cardiovascular medications.

OBJECTIVE:Our objectives were to assess in perinatal women: the most effective methods used to meet social support needs during COVID-19, the impact of COVID-19 on self-reported social support levels, and how perceived change in social support related to distress, depression, and mental health. DESIGN/METHODS:One-time survey administered from April to August 2020 SETTING: Online PARTICIPANTS: Pregnant and postpartum women with infants less than 6 months of age MEASUREMENT AND FINDINGS: Participants indicated the methods they used to meet social support needs during COVID-19. They self-rated their social support level pre- and during pandemic and their distress, depressive symptoms, and mental health changes on a Likert scale. Out of 1142 participants, the most effective methods for obtaining social support during the pandemic were virtual means (e.g. video call) and interaction with friends. There was a significant difference in distribution of self-reported levels of social support before and during the pandemic, with more respondents reporting a decrease in support. Decreases in social support were associated with higher distress levels, higher levels of depressive symptoms, and poorer mental health. KEY CONCLUSIONS/CONCLUSIONS:Perinatal women reported decreased social support during the COVID-19 pandemic which was associated with poorer mental health. Using virtual means of social support and support provided by friends had the largest positive effect on perceived social support levels. IMPLICATIONS FOR PRACTICE/CONCLUSIONS:Interventions using virtual support means from friends may be helpful to improve social support and mental health in this population.

In eSwatini and across sub-Saharan Africa, adolescent girls and young women (AGYW) are at significantly higher risk of HIV infection and poorer sexual and reproductive health (SRH) than their male counterparts. AGYW demonstrate low demand for SRH services, further contributing to poor outcomes. Strategic marketing approaches, including those used by multinational corporations, have potential to support demand creation for SRH services among AGYW, but there is limited empirical evidence on the direct application of private-sector strategic marketing approaches in this context. Therefore, we examined how Project Last Mile worked with eSwatini's Ministry of Heath to translate strategic marketing approaches from the Coca-Cola system to attract AGYW to SRH services. We present qualitative market research using the ZMET® methodology with 12 young Swazi women (ages 15-24), which informed development of a highly branded communication strategy consistent with other successful gain-framing approaches. Qualitative in-depth interviews with 19 stakeholders revealed receptivity to the market research findings, and highlighted local ownership over the strategic marketing process and brand. These results can inform similar efforts to translate strategic marketing to support demand generation in pursuit of public health goals to reduce HIV risk and improve SRH.

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.

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.

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: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.