Faculty Bibliography

Black Americans (AA) face a confluence of challenges when seeking care including unaffordable costs, negative experiences with providers, racism, and distrust in the healthcare system. This study utilized linear regressions and mediation analysis to explore the interconnectedness of these challenges within a community-based sample of 313 AA women aged 45 and older. Approximately 23% of participants reported affordability problems, while 44% had a negative experience with a provider. In the initial linear regression model excluding perceived racism, higher levels of distrust were observed among women reporting affordability problems (β = 2.66; p = 0.003) or negative experiences with a healthcare provider (β = 3.02; p = <0.001). However, upon including perceived racism in the model, it emerged as a significant predictor of distrust (β = 0.81; p = < 0.001), attenuating the relationships between affordability and distrust (β = 1.74; p = 0.030) and negative experience with a provider and distrust (β = 1.79; p = 0.009). Mediation analysis indicated that perceived racism mediated approximately 35% and 41% of the relationships between affordability and distrust and negative experience with a provider and distrust, respectively. These findings underscore the critical imperative of addressing racism in the efforts to mitigate racial disparities in healthcare. Future research should explore the applicability of these findings to other marginalized populations.

While a broad consensus about the first successful migration modern humans out of Africa seems established, the peopling of Arabia remains somewhat enigmatic. Identifying the ancestral populations that contributed to the gene pool of the current populations inhabiting Arabia and the impact of their contributions remains a challenging task. We investigate the genetic makeup of the current Yemeni population using 46 whole genomes and 169 genotype arrays derived from Yemeni individuals from all geographic regions across Yemen and 351 genotype arrays derived from neighboring populations providing regional context. Principal Component Analysis shows stratification between Yemen districts but also with respect to nearby populations: Yemeni, other Arabian and Bedouin samples form a continuum towards the populations of the Levant, whereas East Africa and India appear strongly differentiated. This finding is further supported by higher Principal Components, admixture and haplogroup analyses, and F-statistics. Moreover, two-reference linkage disequilibrium decay estimates are most significant for Yemeni admixture from an ancient northern influx (up to 5220BP from Palestine) and East Africa (750BP). We show that the initial gene flow into the Yemeni populations of today came from the rest of Arabia and the Levant, and a less substantial and more recent genetic impact into coastal Yemen from East Africa, particularly.

BACKGROUND/UNASSIGNED:Older adults treated in emergency departments (EDs) are at higher risk for adverse outcomes. Using multiple facilities can worsen this issue through service duplication and poor care transitions. Veterans with dual insurance coverage can access both Veterans Health Administration (VHA) and non-VHA EDs. This study aimed to identify factors associated with non-VHA ED use among veterans. METHODS/UNASSIGNED:We conducted a retrospective observational study of patients aged ≥ 65 who had primary care at the James J Peters VA Medical Center and at least one VHA or non-VHA ED visit between October 2017 and February 2020. Data were collected from the Veterans Affairs Corporate Data Warehouse and the Bronx Regional Health Information Exchange Organization. Generalized linear mixed models were used to examine factors influencing non-VHA ED use. RESULTS/UNASSIGNED:The study sample consisted of 3,897 veterans and a total of 13,312 ED visits. Compared to VHA-exclusive ED users, non-VHA ED users were more likely to live farther away (OR 1.04, CI 1.02 - 1.06) and seek care outside regular hours, including mornings (OR 1.61, CI 1.39 - 1.87), nights (OR 1.49, CI 1.33 - 1.66), weekends (OR 1.28, CI 1.16 - 1.42), and holidays (OR 1.32, CI 1.04 - 1.68). They were also more likely to present with emergency care sensitive conditions (OR 2.13, CI 1.90 - 2.37) and recent inpatient hospitalizations (OR 1.22, CI 1.05 - 1.41). CONCLUSION/UNASSIGNED:These findings suggested that distance and acuity are important predictors of non-VHA ED use in urban areas such as the Bronx, NY. Identifying veterans with key risk factors could improve care coordination and potentially reduce non-VHA ED use.

INTRODUCTION/UNASSIGNED:Despite the effectiveness of Diabetes Prevention Programmes (DPP) in reducing diabetes risk, primary care provider (PCP) referrals remain low, especially among men and racial/ethnic minorities, exacerbating their under-representation in DPPs. Understanding PCPs' perspectives on referrals is crucial for enhancing the intervention's reach and effectiveness. RESEARCH DESIGN AND METHODS/UNASSIGNED:We conducted a qualitative study to explore PCPs' experiences, perspectives and engagement with DPP referrals, focusing on factors influencing variations in referral rates. Based on electronic record data, high and low-referring PCPs from a large integrated health system in the Bronx, NY, participated in interviews conducted between February and September 2023. Interviews were conducted and recorded on Zoom, anonymised, transcribed and analysed using the constant comparative method. RESULTS/UNASSIGNED:From 22 PCP interviews, 4 themes emerged representing factors that influenced referrals: (1) perceived barriers to the patient engagement with the DPP, including infrastructure gaps, programme accessibility issues and unmet social needs, particularly affecting low-referring PCPs; (2) perceived effectiveness of the DPP, with concerns raised about its efficacy, especially for male and socioeconomically disadvantaged patients; (3) perceived self-efficacy in referring patients, driven by knowledge gaps and limited opportunities, especially among low-referrers and (4) recommendations to facilitate and strengthen referrals, highlighting areas for PCP and patient support. The perspectives of high-referring/low-referring PCPs often differed across these themes and associated subthemes. CONCLUSIONS/UNASSIGNED:Our research illuminates the challenges PCPs face in treating prediabetic patients and factors influencing DPP referrals in underserved populations. This understanding can guide interventions to enhance equitable DPP referrals and engagement, thereby reducing diabetes risk in vulnerable populations.

While the emergency department (ED) is an important setting for identifying youth with psychiatric symptoms and connecting them to services, the demands of the ED make efficient and accurate measurement essential in the implementation of mental health screening. The Kiddie-Computerized Adaptive Test (K-CAT) scales, a new electronically administered measure that offers quick and comprehensive assessment across several mental health domains, may be particularly useful in this setting. Given current recommendations for youth suicide risk screening in EDs, this study compared the K-CAT-Suicide Scale (K-CAT-SS) and the Ask Suicide-screening Questions (ASQ), a widely used measure in EDs, in a sample of participants presenting to a pediatric, psychiatric emergency setting. The measures agreed on the presence of suicide risk in over 85% of cases (κ=0.59), and the characteristics of youth who screened at risk on both were similar. Cases of disagreement were more often male and more often had educational accommodations. They had lower symptom levels of and were less often diagnosed with internalizing disorders and were less often identified as high risk by ED psychiatrists and psychologists. Examination of item endorsement patterns in cases of disagreement revealed important areas of future study, including the role of caregiver report in suicide risk screening, item comprehension concerns, and the validity of assessing youth with neurodevelopmental disabilities. While additional research would be beneficial into its psychometrics when deployed in real-world settings, the K-CAT-SS should be considered a viable alternative for suicide risk screening in EDs.

In this policy brief, we explore several potential drivers of heterogeneity in policy outcomes that can be examined in tobacco control policy evaluations, expanding the evidence base to contribute to continued, equitable progress in reducing tobacco-related health outcomes. We discuss these factors in the context of a hypothetical evaluation of the impact of smoke-free laws on current smoking and quit attempts in the Tobacco Nation. Despite a similar policy environment within the Tobacco Nation, there is variation in the strength of smoke-free law coverage across states. This commentary considers how policy design and other contextual factors, including co-occurring policies, and differential impacts across subgroups, may influence policy-attributable outcomes across time and space.

Image-guided biopsy is an integral step in the diagnosis and management of suspicious image-detected breast or axillary lesions, allowing for accurate diagnosis and, if indicated, treatment planning. Tissue sampling can be performed under guidance of a full spectrum of breast imaging modalities, including stereotactic, tomosynthesis, sonographic, and MRI, each with its own set of advantages and limitations. Procedural planning, which includes consideration of technical, patient, and lesion factors, is vital for diagnostic accuracy and limitation of complications. The purpose of this paper is to review and provide guidance for breast imaging radiologists in selecting the best procedural approach for the individual patient to ensure accurate diagnosis and optimal patient outcomes. Common patient and lesion factors that may affect successful sampling and contribute to postbiopsy complications are reviewed and include obesity, limited patient mobility, patient motion, patients prone to vasovagal reactions, history of anticoagulation, and lesion location, such as proximity to vital structures or breast implant.

INTRODUCTION:As the utility of genomic sequencing increases, its use in healthcare will continue to expand beyond expert clinics toward nonspecialist practices such as primary care. At the same time, discordance in genetic variant identification and classification between laboratories remains a concern for the field. This research assesses how clinicians with and without genetics expertise understand and trust genetic test results, underscoring how variation in the handling of genetic test results can have real impact on patient care. METHODS:We conducted 40 interviews with genetics experts, including clinical geneticists and genetic counselors, and nonexpert clinicians including primary care providers and cardiologists. RESULTS:Clinical geneticists and genetic counselors reported spending significant time assessing the validity of results from genetic testing laboratories, conversing with laboratories about those results, and potentially reinterpreting results. Conversely, primary care providers and cardiologists without specific genetics expertise reported high levels of trust in laboratory accuracy and variant interpretation, and did not reassess results. CONCLUSION:We find significant variation in how genetics experts and nonexperts understand the trustworthiness of genetic laboratory reports. This variation could lead to differences in patient care between clinical settings and requires additional guidance for clinicians regarding the handling of genetic test results.

BACKGROUND AND OBJECTIVES/UNASSIGNED:Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting financial outcomes. Traditional surgical scheduling often results in suboptimal OR use. We aim to build a machine learning (ML) model to predict incision times for robotic-assisted hysterectomies, enhancing scheduling accuracy and hospital finances. METHODS/UNASSIGNED:A retrospective study was conducted using data from robotic-assisted hysterectomy cases performed between January 2017 and April 2021 across 3 hospitals within a large academic health system. Cases were filtered for surgeries performed by high-volume surgeons and those with an incision time of under 3 hours (n = 2,702). Features influencing incision time were extracted from electronic medical records and used to train 5 ML models (linear ridge regression, random forest, XGBoost, CatBoost, and explainable boosting machine [EBM]). Model performance was evaluated using a dynamic monthly update process and novel metrics such as wait-time blocks and excess-time blocks. RESULTS/UNASSIGNED: < .001, 95% CI [-329 to -89]), translating to approximately 52-hours over the 51-month study period. The model predicted more surgeries within a 15% range of the true incision time compared to traditional methods. Influential features included surgeon experience, number of additional procedures, body mass index (BMI), and uterine size. CONCLUSION/UNASSIGNED:The ML model enhanced the prediction of incision times for robotic-assisted hysterectomies, providing a potential solution to reduce OR underutilization and increase surgical throughput and hospital revenue.