David C Lee1, Judith A Long2, Mary Ann Sevick3, Stella S Yi3, Jessica K Athens3, Brian Elbel4, Stephen P Wall5. 1. Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 560 First Avenue, New York, NY 10016, United States; Department of Population Health, New York University School of Medicine, 227 East 30th Street, New York, NY 10016, United States. Electronic address: david.lee@nyumc.org. 2. Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, United States; Center for Health Equity Research, Corporal Michael J. Crescenz Veterans Affairs Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104, United States. 3. Department of Population Health, New York University School of Medicine, 227 East 30th Street, New York, NY 10016, United States. 4. Department of Population Health, New York University School of Medicine, 227 East 30th Street, New York, NY 10016, United States; Wagner Graduate School of Public Service, New York University, 295 Lafayette Street, New York, NY 10012, United States. 5. Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 560 First Avenue, New York, NY 10016, United States.
Abstract
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.
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.
Authors: David C Lee; Qun Jiang; Bahman P Tabaei; Brian Elbel; Christian A Koziatek; Kevin J Konty; Winfred Y Wu Journal: Diabetes Care Date: 2018-04-24 Impact factor: 19.112
Authors: Marcela Osorio; Joseph E Ravenell; Mary A Sevick; Yonathan Ararso; Ta'Loria Young; Stephen P Wall; David C Lee Journal: JAMA Intern Med Date: 2020-04-01 Impact factor: 21.873
Authors: David C Lee; Stella S Yi; Jessica K Athens; Andrew J Vinson; Stephen P Wall; Joseph E Ravenell Journal: J Racial Ethn Health Disparities Date: 2017-08-08
Authors: David C Lee; Nancy A McGraw; Kelly M Doran; Amanda K Mengotto; Sara L Wiener; Andrew J Vinson; Lorna E Thorpe Journal: Emerg Themes Epidemiol Date: 2020-11-23
Authors: David C Lee; Ta'Loria Young; Christian A Koziatek; Christopher J Shim; Marcela Osorio; Andrew J Vinson; Joseph E Ravenell; Stephen P Wall Journal: Prev Chronic Dis Date: 2019-08-01 Impact factor: 2.830