Faculty Bibliography

BACKGROUND:Between 2022 and 2023, overdose mortality decreased among non-Hispanic (NH) white people but stayed the same or increased among people of colour in the USA. County racialised economic segregation may contribute to overdose mortality. METHODS:measures, one for higher-income NH white and lower-income black residents and another for higher-income NH white and lower-income Hispanic residents. Models included random effects for county, year and county-year interaction, and fixed effects for proportion male, proportion aged 25-44, land area, state and year. We estimated relative risk (RR) by quintile (least vs most privileged) and the difference in overdose mortality per 100 000 (RD) had all counties shifted to the risk of the most advantaged counties (Q5). RESULTS:Counties with the highest proportion of lower-income racially minoritised residents (Q1) had an increased RR of overdose deaths compared with Q5 counties, both overall (aRRs 1.64 (1.51-1.78); 1.40 (1.29-1.52)), and among subgroups. Had all counties experienced the risk of Q5 counties, we estimated an average reduction in overdose deaths overall (RDs per 100 000: -7.20 (-8.25 to -6.10); -6.37 (-7.38 to -5.25)) and among subgroups. CONCLUSION/CONCLUSIONS:County racialised economic segregation was associated with overdose mortality risk in 2018-2022. Investment in evidence-based strategies to reduce overdose risk in places experiencing harms related to racialised economic segregation is critical.

BACKGROUND:Wearable accelerometers, which continuously record physical activity metrics, are commonly used in mobile health-enabled cardiac rehabilitation (mHealth-CR). The association between adherence to accelerometer use during mHealth-CR and improvement in clinical outcomes, such as functional capacity, is understudied. The emergence of artificial intelligence (AI) technology provides novel opportunities to investigate accelerometry use patterns in relation to mHealth-CR outcomes. OBJECTIVE:In this study, we sought to use an AI clustering framework to identify distinct behavioral phenotypes of adherence to accelerometer use. We then aimed to quantify the association of these adherence phenotypes with functional capacity improvements in older adults undergoing mHealth-CR. METHODS:We analyzed data from the RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized clinical study to date comparing mHealth-CR versus usual care in older adults (aged ≥65 years). Intervention arm participants were instructed to wear a Fitbit accelerometer for the 3-month study duration. Adherence to accelerometer use was quantified as overall adherence (percentage of days worn) via k-means clustering AI-derived measures and compared with changes in 6-minute walk distance (6-MWD), adjusted for demographic and clinical covariates. RESULTS:Among 271 participants with a mean age of 71 years (SD 8), of whom 198 (73%) were male, accelerometers were worn for an average of 76 days (95% confidence limits 73,78) over 3 months. Adjusted analyses showed a weak association between days of wear and improvement in 6-MWD, with every 30 additional days associated with an 11-meter improvement (P=.08). Our k-means clustering framework identified adherence phenotypes at two resolutions: low resolution (k=2 clusters) and high resolution (k=8 clusters). The consistently high adherence cluster trended toward a 24.6-meter improvement in 6-MWD compared to the low and declining adherence clusters (n=39; 95% CI 0.7-49.9; P=.06). The 8-cluster phenotyping revealed a richer set of adherence patterns, with the consistently high adherence cluster in this analysis having a 38.5-meter (95% CI 2.2-74.7; P=.04) improvement in 6-MWD than the low adherence cluster, as well as greater average daily steps over the 3-month intervention (mean 7518, SD 3415 vs mean 4800, SD 2920 steps; P=.008). CONCLUSIONS:A time-series AI clustering framework identified a range of behavioral phenotypes representing different degrees of adherence to accelerometer use. Regression analysis identified a weak association between the higher adherence phenotype and functional capacity improvement in older adults undergoing mHealth-CR. Our AI-derived accelerometry adherence phenotypes may offer a new approach to tailor mHealth-CR regimens to individual patients, potentially leading to better outcomes in this high-risk population. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03978130; https://clinicaltrials.gov/study/NCT03978130. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:RR2-10.2196/32163.

BACKGROUND:The COVID-19 pandemic had a significant impact on healthcare delivery. Older adults with multimorbidities were at risk of healthcare disruptions for the management of their chronic conditions. OBJECTIVE:To characterize healthcare disruptions during the COVID-19 healthcare shutdown and recovery period (March 7, 2020-October 6, 2020) and their effects on disease management among older adults with multimorbidities who were patients of NYC Health + Hospitals (H + H), the largest municipal safety-net system in the United States. DESIGN/METHODS:Observational. PATIENTS/METHODS:Patients aged 50 + with hypertension or diabetes and at least one other comorbidity, at least one H + H ambulatory visit in the six months before COVID-19 pandemic onset (March 6, 2020), and at least one visit in the post-acute shutdown period (October 7, 2020 to December 31, 2023). MAIN MEASURES/METHODS:We characterized disruption in care (defined as no ambulatory or telehealth visits during the acute shutdown) and estimated the effect of disruption on blood pressure control, hemoglobin A1c (HbA1c), and low-density lipoprotein (LDL) cholesterol using difference-in-differences models. KEY RESULTS/RESULTS:Out of 73,889 individuals in the study population, 12.5% (n = 9,202) received no ambulatory or telehealth care at H + H during the acute shutdown. Low pre-pandemic healthcare utilization, Medicaid insurance, and self-pay were independent predictors of care disruption. In adjusted analyses, the disruption group had a 3.0-percentage point (95% CI: 1.2-4.8) greater decrease in blood pressure control compared to those who received care. Disruption did not have a significant impact on mean HbA1c or LDL. CONCLUSIONS:Care disruption was associated with declines in blood pressure control, which while clinically modest, could impact risk of cardiovascular outcomes if sustained. Disruption did not affect HbA1c or LDL. Telehealth mitigated impacts of the pandemic on care disruption and subsequent disease management. Targeted outreach to those at risk of care disruption is needed during future crises.

Homework is a potential contributor to treatment response in cognitive-behavioral therapy (CBT) for anxiety, but less is known regarding the importance of yoga homework for generalized anxiety disorder (GAD). This study examined the impact of homework engagement on treatment response within a randomized controlled trial (RCT) of 12 weeks of group CBT, Kundalini Yoga (KY), or stress education (SE) in a subsample of 190 adults with GAD (71% female, Mean age = 33 ± 13) who attended ≥2 sessions and submitted ≥1 homework log. Participants in CBT and KY showed greater overall homework engagement than those in SE (ps < .05). Across treatment arms, staff-rated homework compliance (p = .002, OR = 1.74), but not participant-reported days per week engaged in homework (p = .108), predicted clinical response at post-treatment ("response"). Greater staff-rated homework compliance was related to a greater response for those in CBT (p = .005, OR = 2.49) and KY (p = .049, OR = 1.66), but not SE. Greater participant-reported homework days per week was only marginally related to response to CBT (p = .054, OR = 1.71), and was not related to response to KY or SE. These findings highlight the importance of homework engagement in CBT for GAD. More research is needed to further elucidate the role of homework engagement in yoga for GAD.TRIAL REGISTRATION: clinicaltrials.gov: NCT01912287; https://clinicaltrials.gov/ct2/show/NCT01912287.

PURPOSE/OBJECTIVE:Chemotherapy-induced alopecia (CIA) affects approximately 65% of patients receiving chemotherapy and has a negative impact on quality of life (QoL). Scalp cooling (SC) is the only FDA-cleared intervention for CIA. This systematic review and meta-analysis evaluated SC adverse events (AEs), reasons for discontinuation, and scalp metastasis incidence. METHODS:Meta-analyses using random-effects models estimated pooled prevalences of SC AEs, SC discontinuation, and reasons for discontinuation. A generalized linear mixed model was used to estimate the incidence of scalp metastasis. RESULTS:Sixty-seven studies met the inclusion criteria. The most common AEs were generalized chills (42%, 95% confidence interval (CI) 26-58%), cap heaviness (35%, 95% CI 18-52%), and headache (30%, 95% CI 21-39%). The SC discontinuation rate was 18% (95% CI 13-23%). The most common reasons for discontinuation were progressive alopecia (15%, 95% CI 10-20%) and reasons unrelated to SC (9%, 95% CI 5-13%). The most frequent AEs leading to SC discontinuation were headache (4%, 95% CI 2-6%), cold intolerance (4%, 95% CI 3-5%), and general discomfort (4%, 95% CI 2-7%). Secondary analysis of scalp metastases yielded an incidence of 0.15% (95% CI 0.05-0.47%). Analysis of FDA Manufacturer and User Facility Device Experience (MAUDE) database medical device reports revealed that user error contributed to cold thermal injuries. Prevalence estimates were limited by significant heterogeneity between studies, reflecting variations in study methodology and real-world SC practices. CONCLUSION/CONCLUSIONS:SC is generally well tolerated with minimal safety concerns. Clinical comfort strategies like supportive medications and improved patient education could enhance SC tolerability and support its implementation.

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.

BACKGROUND:Data on patient-centered outcomes of mobile health cardiac rehabilitation (mHealth-CR) for older adults with ischemic heart disease are limited. The RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized study of mHealth-CR in this population, found no significant improvements in functional capacity, health status, angina, or disability compared with usual care. OBJECTIVES/OBJECTIVE:The purpose of this study was to evaluate whether mHealth-CR affects personalized goal attainment-a prespecified secondary endpoint of RESILIENT-using goal attainment scaling (GAS). METHODS:A total of 400 patients (≥65 years) with ischemic heart disease were randomized to mHealth-CR or usual care. Participants specified goals for CR at baseline using the five-category goal attainment scale: much-less-than-expected (-2), less-than-expected (-1), expected (0), better-than-expected (+1), and much-better-than-expected (+2). Goal attainment was assessed at 3 months. RESULTS:Of 400 patients (median age, 71.0 years [range 65.0-91.0]; 72.8% male; 65.2% prefrail/frail) randomized to mHealth-CR (n = 298) or usual care (n = 102), 353 (88.3%) completed GAS. Most goals addressed physical activity (54.0% mHealth-CR vs 59.0% usual care), health care behaviors (14.4% vs 11.9%), or symptom management (13.1% vs 9.0%). Rates of attaining or exceeding goals (GAS ≥0) were similar between groups (80.5% vs 77.6%; P = 0.492). However, in the intervention arm, there was a higher rate of exceeding expected level of goal attainment (GAS +1, +2) compared with usual care (52.6% vs 34.2%; P = 0.006). CONCLUSIONS:In a trial that did not demonstrate differences on traditional endpoints, those receiving mHealth-CR were more likely to exceed personalized CR goals. These findings suggest the intervention facilitated greater progress toward individualized goals and underscore the importance of patient-centered outcomes in CR.

INTRODUCTION/BACKGROUND:The United States is facing an opioid use disorder (OUD) epidemic, marked by unprecedented overdose death rates. In New York State, synthetic opioids significantly contribute to the increasing overdose deaths, disproportionately impacting Black and Latinx communities. There is an urgent need to address issues related to equitable access to and the quality of care provided by substance use disorder (SUD) treatment programs. In light of this, the Quality Measurement and Management Research Center (QM2-RC) brought together an academic-government partnership to develop a person-centered quality measurement system and to assess its impact on a statewide treatment system that serves approximately 180,000 individuals per year. METHODS AND ANALYSIS/METHODS:The QM2-RC encompasses three interconnected projects (Project 1, 2, and 3) aimed at developing a quality management strategy and evaluating its impact on system performance across New York State. This report specifically focuses on Project 3, which involves a stepped-wedge trial with 35 clinics receiving a quality management intervention that includes performance coaching. This intervention will be compared to a treatment-as-usual (TAU) condition for clinics not participating in the trial. Administrative data will be utilized to monitor outcomes over four years. The coaching intervention, guided by the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) model, emphasizes interpreting quality measures and applying insights to enhance care. Coaches will provide support on data utilization, patient-centered care, harm reduction strategies, and the use of patient monitoring tools. The trial aims to evaluate clinic staff and leadership attitudes, experiences, and behaviors through surveys, semi-structured interviews, and external facilitator notes. Primary clinic outcomes will be assessed through adverse events, decreased clinic rates of substance use related emergency department visits and hospitalizations as well as mortality among patients within the first 12 months after admission to treatment after adjusting for individual and community level characteristics. This study is being developed over a multi-year period and will be informed by a mixed-methods approach incorporating multiple data sources, qualitative interviews, patient and clinic surveys. The study is being conducted in partnership with New York State Office of Addiction Services and Supports (OASAS) and will be informed by input from patient, providers, health insurers, family members and local governing units. DISCUSSION/CONCLUSIONS:Project 3 of the QM2 study specifically targets key barriers in measuring the quality of SUD treatment, including technological limitations, unvalidated measures, workforce data literacy, and concerns about fairness in assessing clinical complexity. Through the implementation of a stepped-wedge trial involving 35 clinics, the project aims to develop new quality measures, offer performance feedback, and engage clinic leadership and staff in efforts to improve practices. The ultimate goal of Project 3 is to overcome these barriers, promote person-centered care, and improve SUD treatment practices across New York State.

Prior studies suggest that diabetes is associated with severe outcomes following COVID-19. However, most research has focused on early phases of the COVID-19 pandemic, and less is known about changing diabetes-associated risks over time. We constructed a retrospective cohort of U.S. Veterans with documented COVID-19 between March 2020 and August 2023 (N = 426,170). We used Poisson regression models to estimate relative risks of 60-day mortality following COVID-19 among Veterans with and without diabetes, incorporating demographic and clinical covariates, as well as weights to address unequal probabilities of selection into the sample. We then incorporated interaction terms representing six-month time windows and plotted predicted mortality risks over time. To contextualize risk estimates, we repeated the analysis among a cohort of Veterans without documented COVID-19. Diabetes was associated with overall higher risk of 60-day mortality following COVID-19 (RR = 1.21, 95% CI = 1.17-1.26). Mortality risks attenuated over time and converged with risks observed among Veterans without COVID-19 by March-August 2022. Results suggest that post-COVID-19 mortality risks associated with diabetes may have attenuated over time. Mechanisms underlying the attenuation of mortality risks were beyond the scope of the paper, however, future studies can potentially shed light on the contributions of population immunity (driven by previous infection or vaccination status), changing treatment patterns, and other factors to time-varying mortality risks following COVID-19 among individuals with diabetes.