title: Admit or transfer? The role of insurance in high-transfer-rate medical conditions in the emergency department.
abstract: 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).
  
  
  CONCLUSIONS
  Insurance status appears to play a role in ED disposition (transfer versus admission) for many high-transfer conditions.
authors.affiliation: Department of Emergency Medicine, George Washington University Hospital, Washington, DC. Electronic address: danakindermann@gmail.com.
authors.forename: Dana R
authors.initials: DR
authors.lastname: Kindermann
authors.affiliation: Agency for Healthcare Research and Quality, Rockville, MD.
authors.forename: Ryan L
authors.initials: RL
authors.lastname: Mutter
authors.forename: Lara
authors.initials: L
authors.lastname: Cartwright-Smith
authors.affiliation: Department of Health Policy, George Washington University School of Public Health and Health Services, Washington, DC.
authors.forename: Lara
authors.initials: L
authors.lastname: Cartright-Smith
authors.affiliation: Department of Health Policy, George Washington University School of Public Health and Health Services, Washington, DC.
authors.forename: Sara
authors.initials: S
authors.lastname: Rosenbaum
authors.affiliation: Departments of Health Policy and Emergency Medicine, George Washington University, Washington, DC.
authors.forename: Jesse M
authors.initials: JM
authors.lastname: Pines
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data; D007348:Insurance, Health / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D017057:Medically Uninsured / Q000706:statistics & numerical data; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D010360:Patient Transfer / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology; D055815:Young Adult