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Ann Emerg Med ; 63(5): 561-571.e8, 2014 May.
Article in English | MEDLINE | ID: mdl-24342815

ABSTRACT

STUDY OBJECTIVE: We study the association of payer status with odds of transfer compared with admission from the emergency department (ED) for multiple diagnoses with a high percentage of transfers. METHODS: This was a retrospective study of adult ED encounters using the Healthcare Cost and Utilization Project 2010 Nationwide Emergency Department Sample. We used the Clinical Classification Software to identify disease categories with 5% or more encounters resulting in transfer (27 categories; 3.7 million encounters based on survey weights). We sorted encounters by condition into 12 groups according to expected medical or surgical specialist needs. We used logistic regression to assess the role of payer status on odds of transfer compared with admission and report adjusted odds ratios (ORs). RESULTS: Among high-transfer conditions in 2010, uninsured patients had double the odds of transfer compared with privately insured patients (OR 2.12; 95% confidence interval [CI] 1.72 to 2.62). Medicaid patients were also more likely to be transferred (OR 1.2; 95% CI 1.04 to 1.38). Uninsured patients had higher odds of transfer in all specialist categories (significant in 9 of 12). The categories with the highest odds of transfer for the uninsured included nephrology (OR 2.44; 95% CI 1.07 to 5.55), psychiatry (OR 2.26; 95% CI 1.65 to 3.25), and hematology-oncology (OR 2.21; 95% CI 1.50 to 3.25); the highest for Medicaid were general surgery (OR 1.61; 95% CI 1.09 to 1.83), hematology-oncology (OR 1.55; 95% CI 1.05 to 2.30), and vascular surgery (OR 1.55; 95% CI 1.02 to 2.28). CONCLUSION: Insurance status appears to play a role in ED disposition (transfer versus admission) for many high-transfer conditions.


Subject(s)
Emergency Service, Hospital/statistics & numerical data , Insurance, Health/statistics & numerical data , Patient Admission/statistics & numerical data , Patient Transfer/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Insurance Coverage/statistics & numerical data , Logistic Models , Male , Medicaid/statistics & numerical data , Medically Uninsured/statistics & numerical data , Middle Aged , Retrospective Studies , United States/epidemiology , Young Adult
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