Faculty Bibliography

BACKGROUND:The coronavirus disease (COVID)-19 pandemic quickly challenged New York City health care systems. Telemedicine has been suggested to manage acute complaints and divert patients from in-person care. OBJECTIVES/OBJECTIVE:The objective of this study was to describe and assess the impact of a rapidly scaled virtual urgent care platform during the COVID-19 pandemic. METHODS:This was a retrospective cohort study of all patients who presented to a virtual urgent care platform over 1 month during the COVID-19 pandemic surge. We described scaling our telemedicine urgent care capacity, described patient clinical characteristics, assessed for emergency department (ED) referrals, and analyzed postvisit surveys. RESULTS:During the study period, a total of 17,730 patients were seen via virtual urgent care; 454 (2.56%) were referred to an ED. The most frequent diagnoses were COVID-19 related or upper respiratory symptoms. Geospatial analysis indicated a wide catchment area. There were 251 providers onboarded to the platform; at peak, 62 providers supplied 364 h of coverage in 1 day. The average patient satisfaction score was 4.4/5. There were 2668 patients (15.05%) who responded to the postvisit survey; 1236 (49.35%) would have sought care in an ED (11.86%) or in-person urgent care (37.49%). CONCLUSIONS:A virtual urgent care platform was scaled to manage a volume of more than 800 patients a day across a large catchment area during the pandemic surge. About half of the patients would otherwise have presented to an ED or urgent care in person. Virtual urgent care is an option for appropriate patients while minimizing in-person visits during the COVID-19 pandemic.

INTRODUCTION/BACKGROUND:It is difficult to determine illness severity for coronavirus disease 2019 (COVID-19) patients, especially among stable-appearing emergency department (ED) patients. We evaluated patient outcomes among ED patients with a documented ambulatory oxygen saturation measurement. METHODS:This was a retrospective chart review of ED patients seen at New York University Langone Health during the peak of the COVID-19 pandemic in New York City. We identified ED patients who had a documented ambulatory oxygen saturation. We studied the outcomes of high oxygen requirement (defined as >4 liters per minute) and mechanical ventilation among admitted patients and bounceback admissions among discharged patients. We also performed logistic regression and compared the performance of different ambulatory oxygen saturation cutoffs in predicting these outcomes. RESULTS:Between March 15-April 14, 2020, 6194 patients presented with fever, cough, or shortness of breath at our EDs. Of these patients, 648 (11%) had a documented ambulatory oxygen saturation, of which 165 (24%) were admitted. Notably, admitted and discharged patients had similar initial vital signs. However, the average ambulatory oxygen saturation among admitted patients was significantly lower at 89% compared to 96% among discharged patients (p<0.01). Among admitted patients with an ambulatory oxygen saturation, 30% had high oxygen requirements and 8% required mechanical ventilation. These rates were predicted by low ambulatory oxygen saturation (p<0.01). Among discharged patients, 50 (10%) had a subsequent ED visit resulting in admission. Although bounceback admissions were predicted by ambulatory oxygen saturation at the first ED visit (p<0.01), our analysis of cutoffs suggested that this association may not be clinically useful. CONCLUSION/CONCLUSIONS:Measuring ambulatory oxygen saturation can help ED clinicians identify patients who may require high levels of oxygen or mechanical ventilation during admission. However, it is less useful for identifying which patients may deteriorate clinically in the days after ED discharge and require subsequent hospitalization.

Using data from the United States Behavioral Risk Factor Surveillance System (2003-2012; N = 3,397,124 adults), we estimated associations between prevalent diabetes and four county-level exposures (fast food restaurant density, convenience store density, unemployment, active commuting). All associations confirmed our a priori hypotheses in conventional multilevel analyses that pooled across years. In contrast, using a random-effects within-between model, we found weak, ambiguous evidence that within-county changes in exposures were associated with within-county change in odds of diabetes. Decomposition revealed that the pooled associations were largely driven by time-invariant, between-county factors that may be more susceptible to confounding versus within-county associations.

Objective/UNASSIGNED:Given the high mortality and prolonged duration of mechanical ventilation of COVID-19 patients, we evaluated the safety and efficacy of hyperbaric oxygen for COVID-19 patients with respiratory distress. Methods/UNASSIGNED:This is a single-center clinical trial of COVID-19 patients at NYU Winthrop Hospital from March 31 to April 28, 2020. Patients in this trial received hyperbaric oxygen therapy at 2.0 atmospheres of pressure in monoplace hyperbaric chambers for 90 minutes daily for a maximum of five total treatments. Controls were identified using propensity score matching among COVID-19 patients admitted during the same time period. Using competing-risks survival regression, we analyzed our primary outcome of inpatient mortality and secondary outcome of mechanical ventilation. Results/UNASSIGNED:We treated 20 COVID-19 patients with hyperbaric oxygen. Ages ranged from 30 to 79 years with an oxygen requirement ranging from 2 to 15 liters on hospital days 0 to 14. Of these 20 patients, two (10%) were intubated and died, and none remain hospitalized. Among 60 propensity-matched controls based on age, sex, body mass index, coronary artery disease, troponin, D-dimer, hospital day, and oxygen requirement, 18 (30%) were intubated, 13 (22%) have died, and three (5%) remain hospitalized (with one still requiring mechanical ventilation). Assuming no further deaths among controls, we estimate that the adjusted subdistribution hazard ratios were 0.37 for inpatient mortality (p=0.14) and 0.26 for mechanical ventilation (p=0.046). Conclusion/UNASSIGNED:Though limited by its study design, our results demonstrate the safety of hyperbaric oxygen among COVID-19 patients and strongly suggests the need for a well-designed, multicenter randomized control trial.

People who are homeless use more hospital-based care than average, yet little is known about how hospital and shelter use are interrelated. We examined the timing of emergency department (ED) visits and hospitalizations relative to entry into and exit from New York City homeless shelters, using an analysis of linked health care and shelter administrative databases. In the year before shelter entry and the year following shelter exit, 39.3 percent and 43.3 percent, respectively, of first-time adult shelter users had an ED visit or hospitalization. Hospital visits-particularly ED visits-began to increase several months before shelter entry and declined over several months after shelter exit, with spikes in ED visits and hospitalizations in the days immediately before shelter entry and following shelter exit. We recommend cross-system collaborations to better understand and address the co-occurring health and housing needs of vulnerable populations.

INTRODUCTION/BACKGROUND:Although screening for diabetes is recommended at age 45, some populations may be at greater risk at earlier ages. Our objective was to quantify age disparities among patients with type 2 diabetes in New York City. METHODS:Using all-payer hospital claims data for New York City, we performed a cross-sectional analysis of patients with type 2 diabetes identified from emergency department visits during the 5-year period 2011-2015. We estimated type 2 diabetes prevalence at each year of life, the age distribution of patients stratified by decade, and the average age of patients by sex, race/ethnicity, and geographic location. RESULTS:We identified 576,306 unique patients with type 2 diabetes. These patients represented more than half of all people with type 2 diabetes in New York City. Patients in racial/ethnic minority groups were on average 5.5 to 8.4 years younger than non-Hispanic white patients. At age 45, type 2 diabetes prevalence was 10.9% among non-Hispanic black patients and 5.2% among non-Hispanic white patients. In our geospatial analyses, patients with type 2 diabetes were on average 6 years younger in hotspots of diabetes-related emergency department use and inpatient hospitalizations. The average age of patients with type 2 diabetes was also 1 to 2 years younger in hotspots of microvascular diabetic complications. CONCLUSION/CONCLUSIONS:We identified profound age disparities among patients with type 2 diabetes in racial/ethnic minority groups and in neighborhoods with poor health outcomes. The younger age of these patients may be due to earlier onset of diabetes and/or earlier death from diabetic complications. Our findings demonstrate the need for geographically targeted interventions that promote earlier diagnosis and better glycemic control.

Traditional methods of health surveillance often under-represent racial and ethnic minorities. Our objective was to use geospatial analysis and emergency claims data to estimate local chronic disease prevalence separately for specific racial and ethnic groups. We also performed a regression analysis to identify associations between median household income and local disease prevalence among Black, Hispanic, Asian, and White adults in New York City. The study population included individuals who visited an emergency department at least once from 2009 to 2013. Our main outcomes were geospatial estimates of diabetes, hypertension, and asthma prevalence by Census tract as stratified by race and ethnicity. Using emergency claims data, we identified 4.9 million unique New York City adults with 28.5% of identifying as Black, 25.2% Hispanic, and 6.1% Asian. Age-adjusted disease prevalence was highest among Black and Hispanic adults for diabetes (13.4 and 13.1%), hypertension (28.7 and 24.1%), and asthma (9.9 and 10.1%). Correlation between disease prevalence maps demonstrated moderate overlap between Black and Hispanic adults for diabetes (0.49), hypertension (0.57), and asthma (0.58). In our regression analysis, we found that the association between low income and high disease prevalence was strongest for Hispanic adults, whereas increases in income had more modest reductions in disease prevalence for Black adults, especially for diabetes. Our geographically detailed maps of disease prevalence generate actionable evidence that can help direct health interventions to those communities with the highest health disparities. Using these novel geographic approaches, we reveal the underlying epidemiology of chronic disease for a racially and culturally diverse population.

OBJECTIVE:Focusing health interventions in places with suboptimal glycemic control can help direct resources to neighborhoods with poor diabetes-related outcomes, but finding these areas can be difficult. Our objective was to use indirect measures versus a gold standard, population-based A1C registry to identify areas of poor glycemic control. RESEARCH DESIGN AND METHODS/METHODS:Census tracts in New York City were characterized by race, ethnicity, income, poverty, education, diabetes-related emergency visits, inpatient hospitalizations, and proportion of adults with diabetes having poor glycemic control, based on A1C >9.0% (75 mmol/mol). Hot spot analyses were then performed, using the Getis-Ord Gi* statistic for all measures. We then calculated the sensitivity, specificity, positive and negative predictive values, and accuracy of using the indirect measures to identify hot spots of poor glycemic control found using the A1C Registry data. RESULTS:Using A1C Registry data, we identified hot spots in 42.8% of 2,085 NYC census tracts analyzed. Hot spots of diabetes-specific inpatient hospitalizations, diabetes-specific emergency visits, and age-adjusted diabetes prevalence estimated from emergency department data, respectively, had 88.9, 89.6, and 89.5% accuracy for identifying the same hot spots of poor glycemic control found using A1C Registry data. No other indirect measure tested had accuracy >80% except for the proportion of minority residents, which was 86.2%. CONCLUSIONS:Compared with demographic and socioeconomic factors, health care utilization measures more accurately identified hot spots of poor glycemic control. In places without a population-based A1C Registry, mapping diabetes-specific health care utilization may provide actionable evidence for targeting health interventions in areas with the highest burden of uncontrolled diabetes.

OBJECTIVE:Older adults are a potentially medically vulnerable population with increased mortality rates during and after disasters. To evaluate the impact of a natural disaster on this population, we performed a temporal and geospatial analysis of emergency department (ED) use by adults aged 65 years and older in New York City (NYC) following Hurricane Sandy's landfall. METHODS:We used an all-payer claims database to analyze demographics, insurance status, geographic distribution, and health conditions for post-disaster ED visits among older adults. We compared ED patterns of use in the weeks before and after Hurricane Sandy throughout NYC and the most afflicted evacuation zones. RESULTS:We found significant increases in ED utilization by older adults (and disproportionately higher in those aged ≥85 years) in the 3 weeks after Hurricane Sandy, especially in NYC evacuation zone one. Primary diagnoses with notable increases included dialysis, electrolyte disorders, and prescription refills. Secondary diagnoses highlighted homelessness and care access issues. CONCLUSIONS:Older adults display heightened risk for worse health outcomes with increased ED visits after a disaster. Our findings suggest the need for dedicated resources and planning for older adults following a natural disaster by ensuring access to medical facilities, prescriptions, dialysis, and safe housing and by optimizing health care delivery needs to reduce the burden of chronic disease. (Disaster Med Public Health Preparedness. 2018;12:184-193).