Faculty Bibliography

Given the inequalities in the distribution of disease burden, geographically detailed methods of disease surveillance are needed to identify local hot spots of chronic disease. However, few data sources include the patient-level addresses needed to perform these studies. Given that individual hospitals would have access to this geographically granular data, this study assessed the reliability of estimating chronic disease prevalence using emergency department surveillance at specific hospitals. Neighborhood-level diabetes, hypertension, and asthma prevalence were estimated using emergency claims data from each individual hospital in New York City from 2009-2012. Estimates were compared to prevalence obtained from a traditional health survey. A multivariable analysis also was performed to identify which individual hospitals were more accurate at estimating citywide disease prevalence. Among 52 hospitals, variation was found in the accuracy of disease prevalence estimates using emergency department surveillance. Estimates at some hospitals, such as NYU Langone Medical Center, had strong correlations for all diseases studied (diabetes: 0.81, hypertension: 0.84, and asthma: 0.84). Hospitals with patient populations geographically distributed throughout New York City had better accuracy in estimating citywide disease prevalence. For diabetes and hypertension, hospitals with racial/ethnic patient distributions similar to Census estimates and higher fidelity of diagnosis coding also had more accurate prevalence estimates. This study demonstrated how citywide chronic disease surveillance can be performed using emergency data from specific sentinel hospitals. The findings may provide an alternative means of mapping chronic disease burden by using existing data, which may be critical in regions without resources for geographically detailed health surveillance.

OBJECTIVE: Injury related morbidity and mortality is an important emergency medicine and public health challenge in the United States (US). Here we describe the epidemiology of traumatic injury presenting to US emergency departments, define changes in types and causes of injury among the elderly and the young, characterize the role of trauma centers and teaching hospitals in providing emergency trauma care, and estimate the overall economic burden of treating such injuries. METHODS: We conducted a secondary retrospective, repeated cross-sectional study of the Nationwide Emergency Department Data Sample (NEDS), the largest all-payer emergency department survey database in the US. Main outcomes and measures were survey-adjusted counts, proportions, means, and rates with associated standard errors, and 95% confidence intervals. We plotted annual age-stratified emergency department discharge rates for traumatic injury and present tables of proportions of common injuries and external causes. We modeled the association of Level 1 or 2 trauma center care with injury fatality using a multi-variable survey-adjusted logistic regression analysis that controlled for age, gender, injury severity, comorbid diagnoses, and teaching hospital status. RESULTS: There were 181,194,431 (standard error, se = 4234) traumatic injury discharges from US emergency departments between 2006 and 2012. There was an average year-to-year decrease of 143 (95% CI -184.3, -68.5) visits per 100,000 US population during the study period. The all-age, all-cause case-fatality rate for traumatic injuries across US emergency departments during the study period was 0.17% (se = 0.001). The case-fatality rate for the most severely injured averaged 4.8% (se = 0.001), and severely injured patients were nearly four times as likely to be seen in Level 1 or 2 trauma centers (relative risk = 3.9 (95% CI 3.7, 4.1)). The unadjusted risk ratio, based on group counts, for the association of Level 1 or 2 trauma centers with mortality was RR = 4.9 (95% CI 4.5, 5.3), however, after accounting for gender, age, injury severity and comorbidities, Level 1 or 2 trauma centers were not associated with an increased risk of fatality (odds ratio = 0.96 (0.79, 1.18)). There were notable changes at the extremes of age in types and causes of emergency department discharges for traumatic injury between 2009 and 2012. Age-stratified rates of diagnoses of traumatic brain injury increased 29.5% (se = 2.6) for adults older than 85, and increased 44.9% (se = 1.3) for children younger than 18. Firearm related injuries increased 31.7% (se = 0.2) in children five years and younger. The total inflation-adjusted cost of emergency department injury care in the US between 2006 and 2012 was $99.75 billion (se = 0.03). CONCLUSIONS: Emergency departments are a sensitive barometer of the continuing impact of traumatic injury as an important cause of morbidity and mortality in the US. Level 1 or 2 trauma centers remain a bulwark against the tide of severe trauma in the US. But, the types and causes of traumatic injury in the US are changing in consequential ways, particularly at the extremes of age, with traumatic brain injuries and firearm-related trauma presenting increased challenges

OBJECTIVE: To use novel geographic methods and large-scale claims data to identify the local distribution of pediatric chronic diseases in New York City. METHODS: Using a 2009 all-payer emergency claims database, we identified the proportion of unique children aged 0 to 17 with diagnosis codes for specific medical and psychiatric conditions. As a proof of concept, we compared these prevalence estimates to traditional health surveys and registry data using the most geographically granular data available. In addition, we used home addresses to map local variation in pediatric disease burden. RESULTS: We identified 549,547 New York City children who visited an emergency department at least once in 2009. Though our sample included more publicly insured and uninsured children, we found moderate to strong correlations of prevalence estimates when compared to health surveys and registry data at prespecified geographic levels. Strongest correlations were found for asthma and mental health conditions by county among younger children (0.88, P = .05 and 0.99, P < .01, respectively). Moderate correlations by neighborhood were identified for obesity and cancer (0.53 and 0.54, P < .01). Among adolescents, correlations by health districts were strong for obesity (0.95, P = .05), and depression estimates had a nonsignificant, but strong negative correlation with suicide attempts (-0.88, P = .12). Using SaTScan, we also identified local hot spots of pediatric chronic disease. CONCLUSIONS: For conditions easily identified in claims data, emergency department surveillance may help estimate pediatric chronic disease prevalence with higher geographic resolution. More studies are needed to investigate limitations of these methods and assess reliability of local disease estimates.

AIMS: To identify population characteristics associated with local variation in the prevalence of diabetic complications and compare the geographic distribution of different types of complications in New York City. METHODS: Using an all-payer database of emergency visits, we identified the proportion of unique adults with diabetes who also had cardiac, neurologic, renal and lower extremity complications. We performed multivariable regression to identify associations of demographic and socioeconomic factors, and diabetes-specific emergency department use with the prevalence of diabetic complications by Census tract. We also used geospatial analysis to compare local hotspots of diabetic complications. RESULTS: We identified 4.6million unique New York City adults, of which 10.5% had diabetes. Adjusting for demographic and socioeconomic factors, diabetes-specific emergency department use was associated with severe microvascular renal and lower extremity complications (p-values<0.001), but not with severe macrovascular cardiac or neurologic complications (p-values of 0.39 and 0.29). Our hotspot analysis demonstrated significant geographic heterogeneity in the prevalence of diabetic complications depending on the type of complication. Notably, the geographic distribution of hotspots of myocardial infarction were inversely correlated with hotspots of end-stage renal disease and lower extremity amputations (coefficients: -0.28 and -0.28). CONCLUSIONS: We found differences in the local geographic distribution of diabetic complications, which highlight the contrasting risk factors for developing macrovascular versus microvascular diabetic complications. Based on our analysis, we also found that high diabetes-specific emergency department use was correlated with poor diabetic outcomes. Emergency department utilization data can help identify the location of specific populations with poor glycemic control.

BACKGROUND: Geographic variation in healthcare has been traditionally studied in large areas such as hospital referral regions or service areas. These analyses are limited by variation that exists within local communities. MATERIALS AND METHODS: Using a New York claims database, we analyzed variation in emergency department use using 35 million visits from 2008 to 2012 among 4797 Census tracts, a smaller unit than usually studied. Using multivariate analysis, we studied associations between population characteristics and proximity to healthcare with rates of emergency department use. We analyzed how factors associated with emergency department utilization differed among urban, suburban, and rural regions. RESULTS: We found significant geographic variation in emergency department use among Census tracts. Public insurance and uninsurance were correlated with high emergency department utilization across all types of regions. We found that race, ethnicity, and poverty were only associated with high emergency department use in urban regions. In suburban and rural regions, a lower proportion of elderly residents and shorter distances to the nearest ED were correlated with high emergency department use. CONCLUSIONS: Significant variation in emergency department use exists locally when studied within small geographic areas. Insurance type is significantly associated with variation in emergency department use across urban, suburban, and rural regions, whereas the significance of other factors depended on urbanicity. IMPLICATIONS: Studying geographic variation at a more granular level can lead to better understanding of local population health, drivers of healthcare utilization, and inform targeted interventions. Given heterogeneity in emergency department use by Census tract, policies directed at shaping acute care utilization must consider these local geographic differences.

OBJECTIVE: We aimed to characterize the geographic distribution of post-Hurricane Sandy emergency department use in administrative flood evacuation zones of New York City. METHODS: Using emergency claims data, we identified significant deviations in emergency department use after Hurricane Sandy. Using time-series analysis, we analyzed the frequency of visits for specific conditions and comorbidities to identify medically vulnerable populations who developed acute postdisaster medical needs. RESULTS: We found statistically significant decreases in overall post-Sandy emergency department use in New York City but increased utilization in the most vulnerable evacuation zone. In addition to dialysis- and ventilator-dependent patients, we identified that patients who were elderly or homeless or who had diabetes, dementia, cardiac conditions, limitations in mobility, or drug dependence were more likely to visit emergency departments after Hurricane Sandy. Furthermore, patients were more likely to develop drug-resistant infections, require isolation, and present for hypothermia, environmental exposures, or administrative reasons. CONCLUSIONS: Our study identified high-risk populations who developed acute medical and social needs in specific geographic areas after Hurricane Sandy. Our findings can inform coherent and targeted responses to disasters. Early identification of medically vulnerable populations can help to map "hot spots" requiring additional medical and social attention and prioritize resources for areas most impacted by disasters. (Disaster Med Public Health Preparedness. 2016;page 1 of 11).

OBJECTIVE: We aimed to evaluate emergency medical services (EMS) data as disaster metrics and to assess stress in surrounding hospitals and a municipal network after the closure of Bellevue Hospital during Hurricane Sandy in 2012. METHODS: We retrospectively reviewed EMS activity and call types within New York City's 911 computer-assisted dispatch database from January 1, 2011, to December 31, 2013. We evaluated EMS ambulance transports to individual hospitals during Bellevue's closure and incremental recovery from urgent care capacity, to freestanding emergency department (ED) capability, freestanding ED with 911-receiving designation, and return of inpatient services. RESULTS: A total of 2,877,087 patient transports were available for analysis; a total of 707,593 involved Manhattan hospitals. The 911 ambulance transports disproportionately increased at the 3 closest hospitals by 63.6%, 60.7%, and 37.2%. When Bellevue closed, transports to specific hospitals increased by 45% or more for the following call types: blunt traumatic injury, drugs and alcohol, cardiac conditions, difficulty breathing, "pedestrian struck," unconsciousness, altered mental status, and emotionally disturbed persons. CONCLUSIONS: EMS data identified hospitals with disproportionately increased patient loads after Hurricane Sandy. Loss of Bellevue, a public, safety net medical center, produced statistically significant increases in specific types of medical and trauma transports at surrounding hospitals. Focused redeployment of human, economic, and social capital across hospital systems may be required to expedite regional health care systems recovery. (Disaster Med Public Health Preparedness. 2016;0:1-11).

We conducted individual and ecologic analyses of prospectively collected data from 839 injured bicyclists who collided with motorized vehicles and presented to Bellevue Hospital, an urban Level-1 trauma center in New York City, from December 2008 to August 2014. Variables included demographics, scene information, rider behaviors, bicycle route availability, and whether the collision occurred before the road segment was converted to a bicycle route. We used negative binomial modeling to assess the risk of injury occurrence following bicycle path or lane implementation. We dichotomized U.S. National Trauma Data Bank Injury Severity Scores (ISS) into none/mild (0-8) versus moderate, severe, or critical (>8) and used adjusted multivariable logistic regression to model the association of ISS with collision proximity to sharrows (i.e., bicycle lanes designated for sharing with cars), painted bicycle lanes, or physically protected paths. Negative binomial modeling of monthly counts, while adjusting for pedestrian activity, revealed that physically protected paths were associated with 23% fewer injuries. Painted bicycle lanes reduced injury risk by nearly 90% (IDR 0.09, 95% CI 0.02-0.33). Holding all else equal, compared to no bicycle route, a bicycle injury nearby sharrows was nearly twice as likely to be moderate, severe, or critical (adjusted odds ratio 1.94; 95% confidence interval (CI) 0.91-4.15). Painted bicycle lanes and physically protected paths were 1.52 (95% CI 0.85-2.71) and 1.66 (95% CI 0.85-3.22) times as likely to be associated with more than mild injury respectively.

Emergency visits are rising nationally, whereas the number of emergency departments is shrinking. However, volume has not increased uniformly at all emergency departments. It is unclear what factors account for this variability in emergency volume growth rates. The objective of this study was to test the association of hospital and population characteristics and the effect of hospital closures with increases in emergency department volume. The study team analyzed emergency department volume at New York State hospitals from 2004 to 2010 using data from cost reports and administrative databases. Multivariate regression was used to evaluate characteristics associated with emergency volume growth. Spatial analytics and distances between hospitals were used in calculating the predicted impact of hospital closures on emergency department use. Among the 192 New York hospitals open from 2004 to 2010, the mean annual increase in emergency department visits was 2.7%, but the range was wide (-5.5% to 11.3%). Emergency volume increased nearly twice as fast at tertiary referral centers (4.8%) and nonurban hospitals (3.7% versus urban at 2.1%) after adjusting for other characteristics. The effect of hospital closures also strongly predicted variation in growth. Emergency volume is increasing faster at specific hospitals: tertiary referral centers, nonurban hospitals, and those near hospital closures. This study provides an understanding of how emergency volume varies among hospitals and predicts the effect of hospital closures in a statewide region. Understanding the impact of these factors on emergency department use is essential to ensure that these populations have access to critical emergency services. (Population Health Management 2015;xx:xxx-xxx).

Study Objectives: New York City (NYC) recently expanded its bicycle routes by demarcating bike lanes with paint and constructing 30 miles of protected paths that physically separate automobile traffic from bicyclists to address increases in bicyclist fatalities. Our objective was to determine whether bike lanes and protected paths result in safety benefits for bicyclists in NYC. We hypothesized that bike lanes would be associated with reduced injury severity among bicyclists colliding with motor vehicles and that protected paths would be associated with an even greater safety benefit. Methods: We performed a secondary analysis of bicyclist injury data collected from December 2008 to August 2014 at a Level 1 Trauma Center in NYC. We evaluated the association of protected paths and bike lanes on injury severity while controlling for potential confounders including patient demographics, scene-related and environmental information, helmet use, traffic law compliance, street characteristics, and injury mechanism. Data were obtained from patient interviews and medical records. Injury severity score (ISS) was categorized according to National Trauma Data Bank (NT