Journal of General Internal Medicine

, Volume 33, Issue 12, pp 2078–2084 | Cite as

Insurance Coverage Predicts Mortality in Patients Transferred Between Hospitals: a Cross-Sectional Study

  • Michael G. UsherEmail author
  • Christine Fanning
  • Vivian W. Fang
  • Madeline Carroll
  • Amay Parikh
  • Anne Joseph
  • Dana Herrigel
Original Research



Patients transferred between hospitals are at high risk of adverse events and mortality. The relationship between insurance status, transfer practices, and outcomes has not been definitively characterized.


To identify the association between insurance coverage and mortality of patients transferred between hospitals.


We conducted a single-institution observational study, and validated results using a national administrative database of inter-hospital transfers.


Three ICUs at an academic tertiary care center validated by a nationally representative sample of inter-hospital transfers.


The single-institution analysis included 652 consecutive patients transferred from 57 hospitals between 2011 and 2012. The administrative database included 353,018 patients transferred between 437 hospitals.


Adjusted inpatient mortality and 24-h mortality, stratified by insurance status.


Of 652 consecutive transfers to three ICUs, we observed that uninsured patients had higher adjusted inpatient mortality (OR 2.67, p = 0.021) when controlling for age, race, gender, Apache-II, and whether the patient was transferred from an ED. Uninsured were more likely to be transferred from ED (OR 2.3, p = 0.026), and earlier in their hospital course (3.9 vs 2.0 days, p = 0.002). Using an administrative dataset, we validated these observations, finding that the uninsured had higher adjusted inpatient mortality (OR 1.24, 95% CI 1.13–1.36, p < 0.001) and higher mortality within 24 h (OR 1.33 95% CI 1.11–1.60, p < 0.002). The increase in mortality was independent of patient demographics, referral patterns, or diagnoses.


This is an observational study where transfer appropriateness cannot be directly assessed.


Uninsured patients are more likely to be transferred from an ED and have higher mortality. These data suggest factors that drive inter-hospital transfer of uninsured patients have the potential to exacerbate outcome disparities.


inter-hospital transfers health disparities insurance hospital ownership 



We would like to thank Anne Marie Webber-Main PhD for her critical review of a draft of this paper and writing clarification.

Prior Presentation

This paper was presented at the Society of General Internal Medicine National Meeting in May 2016.


Funding support for this study was provided by the NIH Clinical and Translational Science Award at the University of Minnesota: 8UL1TR000114-02.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have do not have a conflict of interest.

Supplementary material

11606_2018_4687_MOESM1_ESM.docx (17 kb)
ESM 1 (DOCX 16 kb)


  1. 1.
    Chen SI, Wang Y, Dreyer R, Strait KM, Spatz ES, Xu X, et al. Insurance and Prehospital Delay in Patients </=55 Years With Acute Myocardial Infarction. Am J Cardiol. 2015;116(12):1827–32.CrossRefGoogle Scholar
  2. 2.
    Christopher AS, McCormick D, Woolhandler S, Himmelstein DU, Bor DH, Wilper AP. Access to Care and Chronic Disease Outcomes Among Medicaid-Insured Persons Versus the Uninsured. Am J Public Health. 2016;106(1):63–9.CrossRefGoogle Scholar
  3. 3.
    Kangovi S, Barg FK, Carter T, Levy K, Sellman J, Long JA, et al. Challenges faced by patients with low socioeconomic status during the post-hospital transition. J Gen Intern Med. 2014;29(2):283–9.CrossRefGoogle Scholar
  4. 4.
    Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Occurrence and outcomes of sepsis: influence of race. Crit Care Med. 2007;35(3):763–8.CrossRefGoogle Scholar
  5. 5.
    Fowler RA, Noyahr LA, Thornton JD, Pinto R, Kahn JM, Adhikari NK, et al. An official American Thoracic Society systematic review: the association between health insurance status and access, care delivery, and outcomes for patients who are critically ill. Am J Respir Crit Care Med. 2010;181(9):1003–11.CrossRefGoogle Scholar
  6. 6.
    Guadagnolo BA, Liao KP, Giordano SH, Elting LS, Shih YC. Variation in intensity and costs of care by payer and race for patients dying of cancer in Texas: an analysis of registry-linked medicaid, medicare, and dually eligible claims data. Med Care. 2015;53(7):591–8.CrossRefGoogle Scholar
  7. 7.
    Kumar G, Taneja A, Majumdar T, Jacobs ER, Whittle J, Nanchal R, et al. The association of lacking insurance with outcomes of severe sepsis: retrospective analysis of an administrative database*. Crit Care Med. 2014;42(3):583–91.CrossRefGoogle Scholar
  8. 8.
    Shah AA, Haider AH, Zogg CK, Schwartz DA, Haut ER, Zafar SN, et al. National estimates of predictors of outcomes for emergency general surgery. J Trauma Acute Care Surg. 2015;78(3):482–90; discussion 90-1.CrossRefGoogle Scholar
  9. 9.
    Woolhandler S, Himmelstein DU. The Relationship of Health Insurance and Mortality: Is Lack of Insurance Deadly?, Ann Intern Med. 2017.Google Scholar
  10. 10.
    Lochner P. Hill-Burton enforcement: a proposed remedy to cure hospital inertia. J Legis. 1987;14(1):5.Google Scholar
  11. 11.
    Hyman DA, Studdert DM. Emergency Medical Treatment and Labor Act: what every physician should know about the federal antidumping law. Chest. 2015;147(6):1691–6.CrossRefGoogle Scholar
  12. 12.
    Obama B. United States health care reform: progress to date and next steps. JAMA. 2016;316(5):525–32.CrossRefGoogle Scholar
  13. 13.
    Schiff RL, Ansell DA, Schlosser JE, Idris AH, Morrison A, Whitman S. Transfers to a public hospital. A prospective study of 467 patients. N Engl J Med. 1986;314(9):552–7.CrossRefGoogle Scholar
  14. 14.
    Cheng TC, Haisken-DeNew JP, Yong J. Cream skimming and hospital transfers in a mixed public-private system. Soc Sci Med. 2015;132:156–64.CrossRefGoogle Scholar
  15. 15.
    Bayindir EE. Hospital ownership type and treatment choices. J Health Econ. 2012;31(2):359–70.CrossRefGoogle Scholar
  16. 16.
    Cook SF, Visscher WA, Hobbs CL, Williams RL, Project ICIC. Project IMPACT: results from a pilot validity study of a new observational database. Crit Care Med. 2002;30(12):2765–70.CrossRefGoogle Scholar
  17. 17.
    HCUP National Inpatient Sample (NIS). Agency for Healthcare Research and Quality, Rockville, MD. at: Healthcare Cost and Utilizaltion Project (HCUP). 2012.
  18. 18.
    Usher MG, Fanning C, Wu D, Muglia C, Balonze K, Kim D, et al. Information handoff and outcomes of critically ill patients transferred between hospitals. J Crit Care. 2016;36:240–5.CrossRefGoogle Scholar
  19. 19.
    Sokol-Hessner L, White AA, Davis KF, Herzig SJ, Hohmann SF. Interhospital transfer patients discharged by academic hospitalists and general internists: Characteristics and outcomes. J Hosp Med. 2016;11(4):245–50.CrossRefGoogle Scholar
  20. 20.
    Hanmer J, Lu X, Rosenthal GE, Cram P. Insurance status and the transfer of hospitalized patients: an observational study. Ann Intern Med. 2014;160(2):81–90.CrossRefGoogle Scholar
  21. 21.
    Kindermann DR, Mutter RL, Cartwright-Smith L, Rosenbaum S, Pines JM. Admit or transfer? The role of insurance in high-transfer-rate medical conditions in the emergency department. Ann Emerg Med. 2014;63(5):561–71 e8.CrossRefGoogle Scholar
  22. 22.
    Kindermann DR, Mutter RL, Houchens RL, Barrett ML, Pines JM. Emergency department transfers and transfer relationships in United States hospitals. Acad Emerg Med. 2015;22(2):157–65.CrossRefGoogle Scholar
  23. 23.
    Spain DA, Bellino M, Kopelman A, Chang J, Park J, Gregg DL, et al. Requests for 692 transfers to an academic level I trauma center: implications of the emergency medical treatment and active labor act. J Trauma. 2007;62(1):63–7; discussion 7-8.CrossRefGoogle Scholar
  24. 24.
    HCUP State Inpatient Satabase (SID) and State Emergency Department Database (SEDD). Agency for Healthcare Research and Quality, Rockville, MD. at: Healthcare Cost and Utilizaltion Project (HCUP). 2011-2013.
  25. 25.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818–29.CrossRefGoogle Scholar
  26. 26.
    Encinosa WE, Bernard DM. Hospital finances and patient safety outcomes. Inquiry. 2005;42(1):60–72.CrossRefGoogle Scholar
  27. 27.
    Kao DP, Martin MH, Das AK, Ruoss SJ. Consequences of federal patient transfer regulations: effect of the 2003 EMTALA revision on a tertiary referral center and evidence of possible misuse. Arch Intern Med. 2012;172(11):891–2.CrossRefGoogle Scholar
  28. 28.
    Dong GN. Performing well in financial management and quality of care: evidence from hospital process measures for treatment of cardiovascular disease. BMC Health Serv Res. 2015;15:45.CrossRefGoogle Scholar
  29. 29.
    Waters TM, Daniels MJ, Bazzoli GJ, Perencevich E, Dunton N, Staggs VS, et al. Effect of Medicare's nonpayment for Hospital-Acquired Conditions: lessons for future policy. JAMA Intern Med. 2015;175(3):347–54.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • Michael G. Usher
    • 1
    Email author
  • Christine Fanning
    • 2
  • Vivian W. Fang
    • 3
  • Madeline Carroll
    • 2
  • Amay Parikh
    • 4
  • Anne Joseph
    • 1
  • Dana Herrigel
    • 5
  1. 1.Department of Medicine, Division of General Internal Medicine University of Minnesota Medical SchoolMinneapolisUSA
  2. 2.Department of Medicine, Division of General Internal MedicineRutgers, Robert Wood Johnson Medical SchoolNew BrunswickUSA
  3. 3.Department of Accounting, Carlson School of ManagementUniversity of MinnesotaMinneapolisUSA
  4. 4.Department of Medicine, Divisions of Nephrology and Critical CareRutgers, Robert Wood Johnson Medical SchoolNew BrunswickUSA
  5. 5.Department of Hospital Internal MedicineMayo Clinic FloridaJacksonvilleUSA

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