Faculty Bibliography

OBJECTIVE:To examine the associations between patient out-of-pocket (OOP) costs and nonadherence to glucagon-like peptide 1 receptor agonists (GLP-1a), and the consequent impact on adverse outcomes, including hospitalizations and emergency department (ED) visits. RESEARCH DESIGN AND METHODS/METHODS:This retrospective cohort study used MarketScan Commercial data (2016-2021). The cohort included nonpregnant adults aged 18-64 years with type 2 diabetes who initiated GLP-1a therapy. Participants were continuously enrolled in the same private insurance plan for 6 months before the prescription date and 1 year thereafter. Exposures included average first 30-day OOP costs for GLP-1a, categorized into quartiles (lowest [Q1] to highest [Q4]). Primary outcomes were the annual proportion of days covered (PDC) for GLP-1a and nonadherence, defined as PDC <0.8. Secondary outcomes included diabetes-related and all-cause hospitalizations and ED visits 1 year post-GLP-1a initiation. RESULTS:Among 61,907 adults who initiated GLP-1a, higher 30-day OOP costs were associated with decreased adherence. Patients in the highest OOP cost quartile (Q4: $80-$3,375) had significantly higher odds of nonadherence (odds ratio [OR]1.25; 95% CI 1.19-1.31) compared with those in Q1 ($0-$21). Nonadherence was linked to increased incidence rates of diabetes-related hospitalizations or ED visits (incidence rate ratio [IRR] 1.86; 95% CI 1.43-2.42), cumulative length of hospitalization (IRR 1.56; 95% CI 1.41-1.72), all-cause ED visits (IRR 1.38; 95% CI 1.32-1.45), and increased ED-related costs ($69.81, 95% CI $53.54-$86.08). CONCLUSIONS:Higher OOP costs for GLP-1a were associated with reduced adherence and increased rates of adverse outcomes among type 2 diabetes patients.

OBJECTIVE:The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes. RESEARCH DESIGN AND METHODS/METHODS:We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype. RESULTS:The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics. CONCLUSIONS:The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.

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.

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.

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:Disruption of ambulatory healthcare in New York City (NYC) during the COVID-19 pandemic was common, but the impact on the cardiometabolic health of vulnerable patient groups is unknown. Therefore, we estimated the effect of total care disruption (TCD) on blood pressure (BP) control among older NYC residents with hypertension and at least one other chronic condition, and examined whether neighborhood poverty moderated this impact. METHODS:From the INSIGHT Clinical Research Network, we identified NYC residents ≥50 years of age with hypertension and at least one other chronic condition. TCD was defined as no ambulatory or telehealth visit during the pandemic. We contrasted the change in prevalence of controlled BP (BP <140/90) before and after the pandemic among those with and without TCD via an inverse probability weighted (IPW) difference-in-difference regression model. RESULTS:Among 212,673 eligible individuals, mean age was 69.5 years (SD: 10.2 years) and 15.1% experienced TCD. BP control declined from 52.4% to 45.9% among those with TCD and from 53.6% to 48.9% among those without TCD. After IPW adjustment, a larger decline in BP control was noted among those with TCD (adjusted difference-in-difference = 1.13 percentage points (95% CI 0.32-1.94, p-value=0.0058)). There was no consistent difference in the relationship between TCD and post-pandemic BP control across neighborhood poverty levels. CONCLUSION/CONCLUSIONS:COVID-19-related TCD was associated with a modest decline in BP control among older adults with hypertension in NYC; this was not moderated by neighborhood poverty level.

BACKGROUND:Incidence estimates of post-acute sequelae of SARS-CoV-2 infection, also known as long-COVID, have varied across studies and changed over time. We estimated long-COVID incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). METHODS:This EHR-based retrospective cohort study included adult and pediatric patients with documented acute SARS-CoV-2 infection and two control groups-- contemporary COVID-19 negative and historical patients (2019). We examined the proportion of individuals identified as having symptoms or conditions consistent with probable long-COVID within 30-180 days after COVID-19 infection (incidence proportion). Each network (the National COVID Cohort Collaborative (N3C), National Patient-Centered Clinical Research Network (PCORnet), and PEDSnet) implemented its own long-COVID definition. We introduced a harmonized definition for adults in a supplementary analysis. RESULTS:Overall, 4% of children and 10-26% of adults developed long-COVID, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 1.5% in children and ranged from 5-6% among adults, representing a lower-bound incidence estimation based on our control groups. Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. CONCLUSION/CONCLUSIONS:Our findings indicate that preventing and mitigating long-COVID remains a public health priority. Examining temporal patterns and risk factors of long-COVID incidence informs our understanding of etiology and can improve prevention and management.

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.