Faculty Bibliography

BACKGROUND:Osteoporosis is a significant cause of morbidity and mortality in the aging population. Individuals with type 2 diabetes mellitus (T2D) typically have higher bone density yet also a higher rate of fractures. Blacks, meanwhile, have a lower incidence of osteoporosis compared to European Americans. Serum bicarbonate may be a risk factor for bone loss, but studies are conflicting, and little is known about this relationship in T2D or Blacks. METHODS:We examined the longitudinal relationship between serum bicarbonate and change in bone density in 300 participants with T2D in the African American-Diabetes Heart Study (AA-DHS). Serum bicarbonate was measured at baseline, and bone density was assessed using CT volumetric bone mineral density (vBMD) scans of the thoracic and lumbar vertebrae at baseline and after five years of follow-up. Multivariate linear regression models assessed associations between baseline serum bicarbonate and longitudinal change in vBMD, adjusted for multiple confounders. RESULTS:, p < 0.001), without a clear threshold effect or differences by sex. CONCLUSIONS:In this cohort of Blacks with T2D, higher baseline serum bicarbonate levels were associated with improved changes in bone density over time. Further studies are needed to determine if alkali supplementation would ameliorate loss of bone density in this population.

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)-funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

BACKGROUND:Results from national surveys indicate that many older adults reported delayed medical care during the acute phase of the COVID-19 pandemic, yet few studies have used objective data to characterize healthcare utilization among vulnerable older adults in that period. In this study, we characterized healthcare utilization during the acute pandemic phase (March 7-October 6, 2020) and examined risk factors for total disruption of care among older adults with multiple chronic conditions (MCC) in New York City. METHODS:This retrospective cohort study used electronic health record data from NYC patients aged ≥ 50 years with a diagnosis of either hypertension or diabetes and at least one other chronic condition seen within six months prior to pandemic onset and after the acute pandemic period at one of several major academic medical centers contributing to the NYC INSIGHT clinical research network (n=276,383). We characterized patients by baseline (pre-pandemic) health status using cutoffs of systolic blood pressure (SBP) < 140mmHg and hemoglobin A1C (HbA1c) < 8.0% as: controlled (below both cutoffs), moderately uncontrolled (below one), or poorly controlled (above both, SBP > 160, HbA1C > 9.0%). Patients were then assessed for total disruption versus some care during shutdown using recommended care schedules per baseline health status. We identified independent predictors for total disruption using logistic regression, including age, sex, race/ethnicity, baseline health status, neighborhood poverty, COVID infection, number of chronic conditions, and quartile of prior healthcare visits. RESULTS:Among patients, 52.9% were categorized as controlled at baseline, 31.4% moderately uncontrolled, and 15.7% poorly controlled. Patients with poor baseline control were more likely to be older, female, non-white and from higher poverty neighborhoods than controlled patients (P < 0.001). Having fewer pre-pandemic healthcare visits was associated with total disruption during the acute pandemic period (adjusted odds ratio [aOR], 8.61, 95% Confidence Interval [CI], 8.30-8.93, comparing lowest to highest quartile). Other predictors of total disruption included self-reported Asian race, and older age. CONCLUSIONS:This study identified patient groups at elevated risk for care disruption. Targeted outreach strategies during crises using prior healthcare utilization patterns and disease management measures from disease registries may improve care continuity.

OBJECTIVE:To compare hospitalization rates between African American (AA) and European American (EA) deceased-donor (DD) kidney transplant (KT) recipients during over a10-year period. METHOD/METHODS:Data from the Scientific Registry of Transplant Recipients and social determinants of health (SDoH), measured by the Social Deprivation Index, were used. Hospitalization rates were estimated for kidney recipients from AA and EA DDs who had one kidney transplanted into an AA and one into an EA, leading to four donor/recipient pairs (DRPs): AA/AA, AA/EA, EA/AA, and EA/EA. Poisson-Gamma models were fitted to assess post-transplant hospitalizations. RESULT/RESULTS:Unadjusted hospitalization rates (95% confidence interval) were higher among all DRP involving AA, 131.1 (122.5, 140.3), 134.8 (126.3, 143.8), and 102.4 (98.9, 106.0) for AA/AA, AA/EA, and EA/AA, respectively, compared to 97.1 (93.7, 100.6) per 1000 post-transplant person-years for EA/EA pairs. Multivariable analysis showed u-shaped relationships across SDoH levels within each DRP, but findings varied depending on recipients' race, i.e., AA recipients in areas with the worst SDoH had higher hospitalization rates. However, EA recipients in areas with the best SDoH had higher hospitalization rates than their counterparts. CONCLUSIONS:Relationship between healthcare utilization and SDoH depends on DRP, with higher hospitalization rates among AA recipients living in areas with the worst SDoH and among EA recipients in areas with the best SDoH profiles. SDoH plays an important role in driving disparities in hospitalizations after kidney transplantation.

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide and a major global health concern. In the United States (US), individuals of Black or African American racial identity experience disproportionately higher rates of TBI and suffer from worse post-injury outcomes. Contemporary research agendas have largely overlooked or excluded Black populations, resulting in the continued marginalization of Black patient populations in TBI studies, thereby limiting the generalizability of ongoing research to patients in the US and around the world. This review aims to highlight what is currently known, and identify knowledge gaps, in research on molecular biomarkers associated with TBI in Black populations. A PubMed literature search was conducted to identify studies that investigate molecular biomarkers associated with TBI outcomes that include participants of Black racial identity and those of African ancestry. Studies identified for this review investigate biomarkers associated with TBI outcomes through a lens that specifically examines race, ethnicity, or ancestry. Most studies focused on blood- or cerebrospinal fluid-derived protein biomarkers. Studies identified statistical variation in S100ß, ubiquitin C-terminal hydrolase-L1, amyloid-ß, and tau across participant race, either at baseline or following TBI. Additionally, several studies identified genetic polymorphisms associated with TBI outcomes related to apolipoprotein E, ANKK1, and COMT polymorphism and TBI outcome and identified allele frequency variation across population ancestry. The role of race and ancestry on biomarkers associated with TBI outcome remains indeterminate and subsequent work is still required to understand the implications for patients with TBI.

BACKGROUND/UNASSIGNED:Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations. OBJECTIVE/UNASSIGNED:In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults. METHODS/UNASSIGNED:We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems. RESULTS/UNASSIGNED:Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95% CI 2.86-3.18 vs ORBRFSS 1.23, 95% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50% (ORMissingData 1.79, 95% CI 1.67-1.92 and ORCausal 1.42, 95% CI 1.34-1.51). CONCLUSIONS/UNASSIGNED:Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates.

OBJECTIVE:To examine the pattern of health services access and utilization that may contribute to racial/ethnic disparities in receiving continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA). METHODS:This cross-sectional study used a national sample from the All of Us Research Program, which included over 80 % of participants from underrepresented populations in biomedical research. Study participants included adults aged 18 years and older diagnosed with OSA (N = 8518). Diagnosis of OSA and CPAP treatment were ascertained by diagnostic and procedural codes from the electronic health records. Sociodemographic characteristics and health service utilization factors were identified using self-reported survey data. RESULTS:With this national survey, the overall diagnosed prevalence of OSA was 8.8 %, with rates of 8.12 % in non-Hispanic (NH) Black adults, 5.99 % in Hispanic adults, and 10.35 % in NH White adults. When comparing to NH White adults, Hispanic adults were less likely to receive CPAP treatment for OSA after adjusting for socioeconomic and demographic characteristics, access to and utilization of health services, and comorbidities such as obesity and having multiple chronic conditions (OR = 0.73, 95 % CI = 0.59,0.90), p < 0.01. CONCLUSIONS:The rates of CPAP treatment among OSA patients are not consistent across racial and ethnic groups. Unequal access to health services based on residence may contribute to these differences. Interventions that target disparities in OSA diagnosis, access to treatment, and barriers in insurance coverage could potentially help reduce racial and ethnic differences in OSA diagnosis and management.

BACKGROUND:This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice. METHODS:We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high-risk agent (insulin or insulin secretagogue). RESULTS:The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five-year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP-1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT-2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), "other" ([aOR] 0.72) or "unknown" ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98). CONCLUSIONS:We found an encouraging trend toward less use of high-risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population.

BACKGROUND:Medicare supplement insurance, or Medigap, covers 21% of Medicare beneficiaries. Despite offsetting some out-of-pocket (OOP) expenses, remaining OOP costs may pose a barrier to medication adherence. This study aims to evaluate how OOP costs and insurance plan types influence medication adherence among beneficiaries covered by Medicare supplement plans. METHODS:We conducted a retrospective analysis of the Merative MarketScan Medicare Supplement Database (2017-2019) in Medigap enrollees (≥65 years) with hypertension. The proportion of days covered (PDC) was a continuous measure of medication adherence and was also dichotomized (PDC ≥0.8) to quantify adequate adherence. Beta-binomial and logistic regression models were used to estimate associations between these outcomes and insurance plan type and log-transformed OOP costs, adjusting for patient characteristics. RESULTS:Among 27,407 patients with hypertension, the average PDC was 0.68 ± 0.31; 47.5% achieved adequate adherence. A mean $1 higher in 30-day OOP costs were associated with a 0.06 (95% confidence intervals [CIs]: -0.09 to -0.03) lower probability of adequate adherence, or a 5% (95% CI: 4%-7%) decrease in PDC. Compared with comprehensive plan enrollees, the odds of adequate adherence were lower among those with point-of-service plans (odds ratio [OR]: 0.69, 95% CI: 0.62-0.77), but higher among those with preferred provider organization (PPO) plans (OR: 1.08, 95% CI: 1.01-1.15). Moreover, the association between OOP costs and PDC was significantly greater for PPO enrollees. CONCLUSIONS:While Medicare supplement insurance alleviates some OOP costs, different insurance plans and remaining OOP costs influence medication adherence. Reducing patient cost-sharing may improve medication adherence.

In this study we examined the association between payor type, a proxy for health-care affordability, and presenting COVID-19 disease severity among 2108 polymerase chain reaction-positive nonelderly patients admitted to an acute-care hospital between March 1 and June 30, 2020. The adjacent-category logit model was used to fit pairwise odds of individuals' having (1) an asymptomatic-to-mild modified sequential organ failure assessment (mSOFA) score (0-3) versus a moderate-to-severe mSOFA score (4-7) and (2) a moderate-to-severe mSOFA score (4-7) versus a critical mSOFA score (>7). Despite representing the smallest population, Medicare recipients experienced the highest in-hospital death rate (19%), a rate twice that of the privately insured. The uninsured had the highest rate of critical mSOFA score on admission and had twice the odds of presenting with a critical illness when compared with the privately insured (odds ratio = 2.08, P =.03). Because payor type was statistically related to the most severe presentations of COVID-19, we question whether policy changes affecting health-care affordability might have prevented deaths and rationing of scarce resources, such as intensive care unit beds and ventilators.