Faculty Bibliography

OBJECTIVE:We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS/METHODS:In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS:Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION/CONCLUSIONS:Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION/CONCLUSIONS:Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

INTRODUCTION/BACKGROUND:The coronavirus disease 2019 (COVID-19) pandemic caused unprecedented disruptions to healthcare systems worldwide, significantly affecting patients with chronic kidney disease (CKD). In this study, we evaluated the impact of the pandemic on healthcare-seeking behavior and CKD progression among patients in New York City. METHODS:Using electronic health records from PCORnet's INSIGHT Clinical Research Network, we conducted a retrospective cohort study focused on 84,062 patients with CKD aged 50 years or older with multiple chronic conditions seen between 2017 and 2022. Patients were identified using pre-pandemic CKD diagnostic codes, and confirmed by estimated glomerular filtration rate (eGFR) measurements. Care disruption was defined as receiving fewer visits than recommended by Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We used linear mixed-effects models to estimate annual eGFR changes and analyze trends in care visits stratified by CKD stage and care disruption. RESULTS:. Care visits declined sharply in 2020 across patients at all but the end stage, with incomplete recovery by 2022. Patients with adequate pre-pandemic care maintained their visits above KDIGO levels, while those with inadequate care increased visits during the pandemic. Pronounced eGFR decline occurred in 2020 (10.6%), with slower declines observed thereafter. CONCLUSION/CONCLUSIONS:The COVID-19 pandemic disrupted CKD care, potentially leading to reduced healthcare-seeking behavior and accelerated kidney function decline in 2020. Slower decline post-2020 may reflect improved healthcare utilization, better medication adherence, and new therapies, and other factors.

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.

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: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.

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.

During infectious disease epidemics, accurate diagnostic testing is key to rapidly identify and treat cases, and mitigate transmission. When a novel pathogen is involved, building testing capacity and scaling testing services at the local level can present major challenges to healthcare systems, public health agencies, and laboratories. This mixed methods study examined lessons learned from the scale-up of SARS-CoV-2 testing services in New York City (NYC), as a core part of NYC's Test & Trace program. Using quantitative and geospatial analyses, the authors assessed program success at maximizing reach, equity, and timeliness of SARS-CoV-2 diagnostic testing services across NYC neighborhoods. Qualitative analysis of key informant interviews elucidated key decisions, facilitators, and barriers involved in the scale-up of SARS-CoV-2 testing services. A major early facilitator was the ability to establish working relationships with private sector vendors and contractors to rapidly procure and manufacture necessary supplies locally. NYC residents were, on average, less than 25 min away from free SARS-CoV-2 diagnostic testing services by public transport, and services were successfully directed to most neighborhoods with the highest transmission rates, with only one notable exception. A key feature was to direct mobile testing vans and rapid antigen testing services to areas based on real-time neighborhood transmission data. Municipal leaders should prioritize fortifying supply chains, establish cross-sectoral partnerships to support and extend testing services, plan for continuous testing and validation of assays, ensure open communication feedback loops with CBO partners, and maintain infrastructure to support mobile services during infectious disease emergencies.

As part of a program evaluation of the New York City Test & Trace program (T2)-one of the largest such programs in the USA-we conducted a study to assess how implementing organizations (NYC Health + Hospitals, government agencies, CBOs) communicated information about the T2 program on Twitter. Study aims were as follows: (1) quantify user engagement of posts ("tweets") about T2 by NYC organizations on Twitter and (2) examine the emotional tone of social media users' T2-related tweets in our sample of 1987 T2-related tweets. Celebrities and CBOs generated more user engagement (0.26% and 0.07%, respectively) compared to government agencies (e.g., Mayor's Office, 0.0019%), reinforcing the value of collaborating with celebrities and CBOs in social media public health campaigns. Sentiment analysis revealed that positive tweets (46.5%) had higher user engagement than negative tweets (number of likes: R² = .095, p < .01), underscoring the importance of positively framing messages for effective public health campaigns.

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.