Faculty Bibliography

BACKGROUND:Financial capability is an understudied social determinant of health (SDoH). Bank account ownership, an indicator of financial capability, has been linked to better health. No research has explored how bank account ownership relates to health behaviors, such as tobacco use. OBJECTIVES/OBJECTIVE:To examine participant characteristics, intervention use, and intervention outcomes among subgroups of unbanked and banked participants enrolled in a randomized controlled trial (RCT) that integrated financial coaching and SDoH referrals into smoking cessation treatment for low-income individuals (N = 257). DESIGN/METHODS:Secondary analysis of an RCT. INTERVENTIONS/METHODS:The parent RCT provided a multi-component intervention (N = 136) that included smoking cessation coaching, nicotine replacement therapy, money management coaching, and referral to financial empowerment services and other SDoH resources. A waitlisted control group (N = 121) received usual care. MEASURES/METHODS:Bivariate analyses compared baseline characteristics and multivariable logistic regression compared intervention use by banking status. Within unbanked and banked subgroups, logistic regression examined treatment group differences (intervention vs. control) in self-reported 7-day abstinence and financial stress at 6 months. RESULTS:At baseline, 36% (n = 92) of participants were unbanked. Unbanked participants had lower income and education, higher unemployment, and greater financial distress (all p < 0.05). Intervention use did not differ by banking status (p > 0.05). At 6 months, unbanked participants had high abstinence rates in the intervention and control groups (ITT 21% vs. 13%, p > 0.05) and no significant treatment group differences in financial stress (p > 0.05). Among banked participants, the intervention group reported higher abstinence than the control group (ITT 19% vs. 6%, p = 0.01) and reduced financial stress across multiple domains (all p = 0.01). CONCLUSIONS:A significant portion of participants in the RCT were unbanked, but being unbanked was not a barrier to smoking cessation. The intervention reduced financial stress among banked participants only. Further research is needed to develop interventions that can support unbanked individuals' health and financial well-being. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov Identifier: NCT03187730.

BACKGROUND:Estimated glomerular filtration rate (eGFR) using creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys) is not sufficiently accurate in many settings, often due to non-glomerular filtration rate (GFR) determinants of the filtration markers. In principle, using a panel of endogenous markers (panel eGFR) could reduce the impact of non-GFR determinants of each marker, improving the accuracy of eGFR. Using global untargeted metabolomics, we previously identified 33 endogenous metabolites that correlate highly with measured GFR. METHODS:A LC-MS/MS measurement procedure was developed to quantify 11 endogenous metabolites from serum and plasma. The assay was evaluated in 99 participants with measured GFR (mGFR) from 2 research studies, including a subgroup of 51 participants with large errors in eGFRcr and large discordance between eGFRcr and eGFRcys. Performance of eGFR models using single metabolites and all metabolites (panel eGFR-11) compared to mGFR was assessed by leave-one-out cross-validated root mean square error (RMSE). RESULTS:Assay CV for single metabolites ranged from 1.1% to 6.3% over the course of 21 days. RMSE of eGFR in single metabolite models ranged from 0.184 to 0.324. RMSEs for panel eGFR-11, eGFRcr, and eGFRcr-cys were 0.195, 0.251, and 0.201, respectively, and 0.155, 0.290, and 0.203, respectively, in the subgroup with large errors and large discordance. CONCLUSIONS:A precise metabolite (LC-MS/MS) measurement procedure shows promise for more accurate GFR estimation when eGFRcr is unreliable, offering a potential new confirmatory test for GFR evaluation.

Worldwide, over half of all individuals with dementia are undiagnosed. In the United States, racial, ethnic, and economic inequities mirror global findings, with higher rates of missed and delayed diagnosis and poorer diagnostic quality among minoritized and disadvantaged groups. For example, delayed diagnosis is more prevalent among people identifying as non-Hispanic Black or Latino than non-Hispanic White. Systematic efforts to improve detection can increase diagnosis rates; there is broad consensus that earlier detection and initiation of focused care and support services benefit both affected individuals and their loved ones. Systemic under-detection and its contributions to persistent population-level suffering underscore the importance of early detection of dementia as a key public health issue. Improving early detection calls for comprehensive, coordinated responses from local, regional, and national public health systems in partnership with health care delivery systems and community-based organizations. The Public Health Center of Excellence on Early Detection of Dementia (PHCOE on EDD), funded by the Centers for Disease Control and Prevention (CDC), is a national resource to promote understanding and implementation of evidence-based and evidence-informed public health strategy for early detection of dementia. We, together with the PHCOEs on Dementia Risk Reduction and Dementia Caregiving, and nearly four dozen state and local initiatives, seek to operationalize the priorities of the Building Our Largest Dementia Infrastructure for Alzheimer's Act and National Healthy Brain Initiative, established by federal legislation in 2018 and 2024. Our efforts support the CDC's mandate to build a national public health infrastructure for brain health and dementia.

BACKGROUND:Chronic stress in African American individuals is multilayered amid the context of experiences of racism and discrimination. Cortisol dynamic range (CDR) may be an indicator of chronic stress, but CDR is understudied in African American populations compared with White populations, and is hypothesized to differ by sex. OBJECTIVE:Using a community-engaged approach within the Fostering African-American Improvement in Total Health! (FAITH!) Heart Health+ ancillary study, we assessed the feasibility of participant-centric CDR collection, and its association with measures for individual, interpersonal, and structural stress and exposure to racism in medically underserved African American women and men. METHODS:Participants residing in the Minneapolis-St Paul and Rochester, Minnesota areas provided survey data (everyday discrimination, perceived stress, mood, sleep quality, and high effort coping measures), and saliva samples (morning and afternoon) via at-home, self-collection kits for cortisol measurement. CDR was calculated as a difference in log cortisol levels (ie, log of the cortisol diurnal peak-to-nadir ratio). Geospatial Area Deprivation Index and the distance lived from George Floyd Square in Minneapolis were calculated. Linear regression examined the association between CDR and outcome variables. RESULTS:Of consented participants (n=53), 70% (37/53) provided cortisol samples. The final analytic sample included 32 participants with complete and physiological diurnal cortisol curves (mean age 57.5 years, 62.5% [20/32] women). Lower (less dynamic) CDR in women (n=20) was associated with greater perceived stress (β=-0.07, P=.01), greater anxiety (β=-0.06, P=.01), higher Superwoman Schema score (β=-0.02, P=.04), and greater distance from George Floyd Square (β=-0.02, P=.01). No associations were observed in men (P>.05). CONCLUSIONS:The current results suggest that CDR from participant-led saliva collection is feasible and may serve as a biomarker of chronic and physiological stress in African American women, particularly those residing in underresourced areas.

IMPORTANCE/UNASSIGNED:Residence in a disadvantaged neighborhood is a key driver of racial and ethnic disparities in the diagnosis and management of chronic diseases; however, its impact on disparities in access to waitlisting and kidney transplantation (KT) is unclear. OBJECTIVE/UNASSIGNED:To examine the association between neighborhood disadvantage and access to waitlisting and KT. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This retrospective cohort study (January 1, 2015, to December 31, 2021) used a US national registry to assess adults (aged ≥18 years) with end-stage kidney disease (ESKD) and adult KT candidates. Statistical analysis was performed in March 2025. EXPOSURE/UNASSIGNED:Residential neighborhood disadvantage score (built environment disadvantage, criminal injustice, education disadvantage, unemployment, housing instability, poverty, social fragmentation, transportation barrier, and wealth inequality) ascertained by American Community Survey and other public data sources. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The adjusted hazard ratios (AHRs) of waitlisting and KT (any KT, live-donor KT [LDKT], and preemptive KT) were assessed across tertiles of the neighborhood disadvantage score using cause-specific hazard models. Interaction terms were used to quantify these aforementioned associations by race and ethnicity. RESULTS/UNASSIGNED:The study included 501 444 adults with ESKD initiating dialysis (mean [SD] age, 63.9 [14.6] years; 293 937 [58.6%] male; 25 790 [5.1%] Asian [Asian American, Native Hawaiian, and Pacific Islander], 133 923 [26.7%] Black, 66 323 [13.2%] Hispanic, and 275 408 [54.9%] White) and 95 068 KT candidates on the waitlist (mean [SD] age, 53.7 [13.0] years; 60 328 [63.5%] male; 6956 [7.3%] Asian, 25 215 [26.5%] Black, 15 685 [16.5%] Hispanic, and 47 212 [49.7%] White). A total of 173 880 adults with ESKD (34.7%) and 26 718 KT candidates (28.1%) resided in high-disadvantage neighborhoods. After adjustment, adults residing in high-disadvantage neighborhoods were less likely to be waitlisted (AHR, 0.71; 95% CI, 0.69-0.72) compared with those in low-disadvantage neighborhoods. Specifically, Asian (AHR, 0.87; 95% CI, 0.80-0.95), Black (AHR, 0.68; 95% CI, 0.66-0.70), Hispanic (AHR, 0.89; 95% CI, 0.86-0.92), and White (AHR, 0.68; 95% CI, 0.66-0.71) adults in high-disadvantage neighborhoods were less likely to be waitlisted compared with White adults in low-disadvantage neighborhoods. Overall, candidates residing in high-disadvantage neighborhoods were less likely to receive any KT (AHR, 0.89; 95% CI, 0.87-0.92), LDKT (AHR, 0.65; 95% CI, 0.62-0.69), and preemptive KT (AHR, 0.62; 95% CI, 0.58-0.67). Notably, Black candidates residing in high-disadvantage neighborhoods were less likely to receive KT (AHR, 0.60; 95% CI, 0.58-0.62), LDKT (AHR, 0.23; 95% CI, 0.21-0.25), and preemptive KT (AHR, 0.22; 95% CI, 0.20-0.25) compared with White candidates in low-disadvantage neighborhoods. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study of adults with ESKD and KT candidates, residence in high-disadvantage neighborhoods was associated with reduced access to waitlisting and KT; it also was associated with persistent racial and ethnic disparities in LDKT and preemptive KT. These results suggest that to support equitable access, clinicians and transplant programs should work with social workers and community advocates to implement initiatives (eg, outreach and financial support) that address structural barriers and direct resources to affected neighborhoods.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

OBJECTIVE:Early childhood advanced primary care models are promising ways of addressing child and family needs, but there is limited evidence to support short-term sustainability within current Medicaid payment structures. We evaluate claims-based outcomes associated with 3-2-1 IMPACT (IMPACT), an early childhood advanced primary care model, compared with the standard of care. METHODS:Using New York State Medicaid claims, we identified and matched children aged 1 to 35 months receiving care at 3 IMPACT sites and 3 comparison sites within a large public hospital system. Regression models were used to analyze use, expenditure, enrollment, and quality outcomes between groups. RESULTS:There were 6045 children at the treatment sites and 4832 matched children from the comparison sites. IMPACT was associated with a significant increase in 6 or more well-child visits and a decrease in emergency department visit rates. There was also a significant increase in 6 more well-child visits specifically for Black and Hispanic children seeking care at IMPACT sites compared with comparison sites. There were no significant differences in expenditures, other use types, or Medicaid enrollment across groups. CONCLUSION/CONCLUSIONS:An early childhood advanced primary care model that incorporates multiple evidence-based programs can show short-term, positive effects on preventative and acute care use and quality within Medicaid. These results highlight short-term strategies for sustainability while awaiting the long-term, cross-sector benefits expected from models like IMPACT. Future studies addressing additive model component effects and longer-term outcomes across mother-child dyadic and social-emotional outcomes are warranted.

BACKGROUND:Lifesaving medications for opioid use disorder (MOUD) exist; however, most people with opioid use disorder (OUD) do not receive treatment. Hospitalization is one important opportunity to engage people with OUD and offer treatment, including MOUD. Between 2018 and 2020, 6 public hospitals in New York City launched the "Consult for Addiction Treatment and Care in Hospitals" (CATCH) program to provide interprofessional addiction consult services to hospitalized patients. METHODS:This qualitative study aims to add perspectives from 30 racially and ethnically diverse people with opioid-related diagnoses who were hospitalized at a CATCH hospital between October 2019 and April 2021. We used purposive sampling to recruit demographically diverse individuals who accepted or declined aspects of CATCH services. Interviews were audio-recorded, transcribed, and coded for emergent themes using grounded theory techniques. The framework of structural vulnerability was utilized to highlight how social context impacts patients' experiences of healthcare, and in turn affects their addiction trajectories. RESULTS:Participants overwhelmingly accepted MOUD to manage withdrawal symptoms during hospitalization, and many planned to continue MOUD after discharge. Participants appreciated the interprofessional support of CATCH teams which included medical providers, social workers, addiction counselors, and peers. While participants felt that CATCH made holistic addiction treatment including MOUD more accessible, structural issues created barriers to continuing treatment long term. Some participants still felt stigmatized or "punished" for their drug use by non-CATCH providers. CONCLUSION/CONCLUSIONS:CATCH met an urgent need for nonjudgmental care and medical management of opioid withdrawal. Additional interventions that address broader needs, including housing and social supports, as well as trust-building healthcare encounters for patients who have been historically marginalized, are needed to meet the public health goal of preventing overdose and reducing drug-related morbidity for this population.

OBJECTIVE:While timely interventions can improve medication adherence, it is challenging to identify which patients are at risk of nonadherence at point-of-care. We aim to develop and validate flexible machine learning (ML) models to predict a continuous measure of adherence to guideline-directed medication therapies (GDMTs) for heart failure (HF). MATERIALS AND METHODS/METHODS:We utilized a large electronic health record (EHR) cohort of 34,697 HF patients seen at NYU Langone Health with an active prescription for ≥1 GDMT between April 01, 2021 and October 31, 2022. The outcome was adherence to GDMT measured as proportion of days covered (PDC) at 6 months following a clinical encounter. Over 120 predictors included patient-, therapy-, healthcare-, and neighborhood-level factors guided by the World Health Organization's model of barriers to adherence. We compared performance of several ML models and their ensemble (superlearner) for predicting PDC with traditional regression model (OLS) using mean absolute error (MAE) averaged across 10-fold cross-validation, % increase in MAE relative to superlearner, and predictive-difference across deciles of predicted PDC. RESULTS:Superlearner, a flexible nonparametric prediction approach, demonstrated superior prediction performance. Superlearner and quantile random forest had the lowest MAE (mean [95% CI] = 18.9% [18.7%-19.1%] for both), followed by MAEs for quantile neural network (19.5% [19.3%-19.7%]) and kernel support vector regression (19.8% [19.6%-20.0%]). Gradient boosted trees and OLS were the 2 worst performing models with 17% and 14% higher MAEs, respectively, relative to superlearner. Superlearner demonstrated improved predictive difference. CONCLUSION/CONCLUSIONS:This development phase study suggests potential of linked EHR-pharmacy data and ML to identify HF patients who will benefit from medication adherence interventions. DISCUSSION/CONCLUSIONS:Fairness evaluation and external validation are needed prior to clinical integration.

According to the family stress model (FSM), economic stressors undermine optimal child development through negative impacts on parent psychological well-being and family relationships, which in turn disrupt positive parenting. However, few studies have examined the role of interparental conflict among these pathways and the resilience factors that buffer the FSM processes. Understanding risk and resilience is especially relevant for families in Flint, MI, for whom poverty resulting from structural racism and chronic disinvestment has coincided with public health crises. Using 199 families from low socioeconomic backgrounds in an ongoing parenting intervention in Flint, this study examined whether parent psychological distress and interparental conflict mediated the association between economic pressure at baseline (around birth) and cognitive stimulation at 9 months, and whether parenting self-efficacy and social support moderated the sequential mediation. Data were collected through parent interviews at both time points. We found that the negative association between economic pressure at baseline and cognitive stimulation at 9 months was sequentially mediated by parent psychological distress and interparental conflict. Furthermore, this negative sequential mediation was reduced and became nonsignificant when parents reported higher levels of parenting self-efficacy and social support. These findings suggest that improving interparental relationships in addition to parent mental health may promote positive parenting in at-risk two-parent families and that strength-based interventions are needed to reinforce parenting self-efficacy and facilitate parents' social networks and connections with the community to foster positive parenting. Programs should address these issues during infancy to build a strong foundation for long-term healthy development. (PsycInfo Database Record (c) 2025 APA, all rights reserved).