Faculty Bibliography

OBJECTIVE:To increase the rate of low dose aspirin (LDA) counseling and treatment in patients with 2 or more moderate risk factors of preeclampsia(PMRF) from 9% to 50% within a four-month period after implementation of interventions. STUDY DESIGN/METHODS:A single-institution quality improvement initiative aimed at LDA screening and counseling of those with PMRF. Two groups were evaluated: pre-intervention (January - April 2022) and post-intervention (January - April 2023). This initiative focused on identifying PMRF and monitoring rates of LDA counseling and treatment. Rates were assessed at two-week intervals and presented on a run chart to visualize trends and measure progress over time. Providers underwent education utilizing preeclampsia (PEC) screening flowsheets and integrated a clinical decision-making (CDM) tool in initial prenatal visit documentation using a smart-tool. Patients were provided with educational flyers. RESULTS:In the pre-intervention group (n=126), 8.7% of patients received counseling on PMRF risk factors and LDA use, 7.9% were treated with LDA. In the post-intervention group (n=112) 52.7% of patients received counseling on PMRF risk factors and LDA use, and 35.7% were treated with LDA. There was an 83.5% increase in the percentage of patients counseled following intervention implementation. A progressive increase was noted in counseling rates within the 18 weeks post-intervention. CONCLUSION/CONCLUSIONS:Integrating PEC screening flowsheets, clinical decision-making tools, and patient education flyers effectively enhances LDA counseling for patients with ≥2 PMRF with additional benefits seen in high-risk patients. These interventions offer a replicable model to enhance guideline adherence and reduce preeclampsia risk in vulnerable populations.

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.

BACKGROUND:Studies of type 1 diabetes in sub-Saharan Africa have suggested that the clinical phenotype might differ from phenotypes reported elsewhere. We aimed to establish whether type 1 diabetes diagnosed in children and young adults in three countries across sub-Saharan Africa is of autoimmune origin. METHODS:In this observational, cross-sectional study, we identified participants without obesity from outpatient clinics in government and private hospitals in Cameroon, Uganda, and South Africa who were of self-reported Black African ethnicity with young-onset (age <30 years), insulin-treated, clinically diagnosed type 1 diabetes. We measured islet autoantibodies to GADA, IA-2A, and ZnT8A, and calculated a genetic risk score (GRS) for type 1 diabetes, which we compared with control populations without diabetes derived from the Uganda Genome Resource databank and other studies. Endogenous insulin secretion was assessed using plasma C-peptide. We compared findings with those for participants with self-reported Black (n=429) and White (n=2602) ancestry with type 1 diabetes from the SEARCH for Diabetes in Youth (SEARCH) study in the USA. FINDINGS/RESULTS:(19·5-24·1). Only 312 (34·9%) of 894 participants were positive for islet autoantibodies; these participants had classic features of type 1 diabetes, including 225 (82·7%) of 272 with plasma C-peptide <200 pmol/L, and high type 1 diabetes GRS. Those without islet autoantibodies (582 [65·1%] of 894) had significantly lower median type 1 diabetes GRS than those with autoantibodies (9·66 [IQR 7·77-11·33] vs 11·76 [10·49-12·91]; p<0·0001), suggesting a subgroup with a non-autoimmune diabetes subtype, with clinical features and C-peptide concentrations not consistent with type 2 diabetes. Among participants diagnosed younger than 20 years, autoantibody-negative diabetes was also observed in 65 (15·1%) of 429 participants with Black ancestry in SEARCH (although less frequently than in sub-Saharan Africa [59 (55·1%) of 107]), and these participants also had a low type 1 diabetes GRS (median 10·41 [IQR 8·65-12·22] in autoantibody-negative subgroup). No such pattern was observed in White participants in SEARCH: 241 (9·3%) of 2602 were autoantibody negative and median GRS for type 1 diabetes was similar in autoantibody-negative and autoantibody-positive participants (median 13·42 [IQR 11·80-14·61] vs 13·49 [12·29-14·58]). INTERPRETATION/CONCLUSIONS:In sub-Saharan Africa, clinically diagnosed type 1 diabetes is heterogeneous, comprising classic autoimmune type 1 diabetes and a novel, non-autoimmune, insulin-deficient diabetes subtype. There is evidence of this subtype in Black but not White individuals in the USA. Therefore, alternative causes must be considered in this group of individuals, and understanding the drivers of this subtype might offer new insights into prevention and treatment. FUNDING/BACKGROUND:UK National Institute of Health and Care Research. TRANSLATION/UNASSIGNED:For the French translation of the abstract see Supplementary Materials section.

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: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.

OBJECTIVE:To compare anonymized and non-anonymized approaches for imputing race and ethnicity in descriptive studies of chronic disease burden using electronic health record (EHR)-based datasets. STUDY SETTING AND DESIGN/METHODS:In this New York City-based study, we first conducted simulation analyses under different missing data mechanisms to assess the performance of Bayesian Improved Surname Geocoding (BISG), single imputation using neighborhood majority information, random forest imputation, and multiple imputation with chained equations (MICE). Imputation performance was measured using sensitivity, precision, and overall accuracy; agreement with self-reported race and ethnicity was measured with Cohen's kappa (κ). We then applied these methods to impute race and ethnicity in two EHR-based data sources and compared chronic disease burden (95% CIs) by race and ethnicity across imputation approaches. DATA SOURCES AND ANALYTIC SAMPLE/UNASSIGNED:Our data sources included EHR data from NYU Langone Health and the INSIGHT Clinical Research Network from 3/6/2016 to 3/7/2020 extracted for a parent study on older adults in NYC with multiple chronic conditions. PRINCIPAL FINDINGS/RESULTS: = 0.33). When these methods were applied to the NYU and INSIGHT cohorts, however, racial and ethnic distributions and chronic disease burden were consistent across all imputation methods. Slight improvements in the precision of estimates were observed under all imputation approaches compared to a complete case analysis. CONCLUSIONS:BISG imputation may provide a more accurate racial and ethnic classification than single or multiple imputation using anonymized covariates, particularly if the missing data mechanism is MNAR. Descriptive studies of disease burden may not be sensitive to methods for imputing missing data.

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: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.