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Title: The local geographic distribution of diabetic complications in New York City: Associated population characteristics and differences by type of complication
Citation Type: Journal Article
Publication Year: 2016
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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 4.6 million 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.
Url: http://www.sciencedirect.com/science/article/pii/S0168822716302546
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Authors: Lee, David C; Long, Judith A; Sevick, Mary Ann; Yi, Stella S; Athens, Jessica K; Elbel, Brian; Wall, Stephen P
Periodical (Full): Diabetes Research and Clinical Practice
Issue: 1
Volume: 119
Pages: 88-99
Data Collections: IPUMS NHGIS
Topics: Health
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