Insurance Status and the Transfer of Hospitalized Patients

Context Despite concern for and legislation to prevent insurance status from influencing the transfer of patients between emergency departments, not much is known about interhospital transfer of already-hospitalized patients. Contribution This study of hospitalized patients in the United States found that uninsured persons were less likely to be transferred from one hospital to another than those with private insurance. Women were less likely to be transferred than men. Caution The available data could not indicate clinical variables that might influence decisions about transfer or whether requests were made or denied. Implication Insurance status may be an important factor influencing which hospitalized patients are transferred to other facilities as part of their care. The Editors Hospitals and physicians are generally expected to treat patients in need of emergent medical care without consideration of patients race, ethnicity, sex, or ability to pay for required services. Originally enacted in 1986, the Emergency Medical Treatment and Active Labor Act (EMTALA) (1) stipulates that once a patient enters the emergency department, the hospital (and staff) must provide a medical screening examination and treat and stabilize any patient identified as having an emergency medical condition until the patient is stable for discharge, regardless of his or her ability to pay for services. Notably, this act does not clearly define what constitutes an emergency medical condition, how much care must be provided, and when a patient is stable for discharge (2, 3). Although the application of EMTALA to the emergency department and the decision whether to admit an acutely ill patient is fairly well-defined, the appositeness of EMTALA to the decision to discharge or transfer a patient who has already been admitted remains an active area of debate (4, 5). In the more than 2 decades since the passage of EMTALA, there has been persistent concern that patients are often transferred between hospitals for nonmedical reasons (for example, provider convenience, patient financial status, and race), but the existing data are limited with virtually all studies focusing on the prehospital or emergency department settings. In a landmark study from 1984, Himmelstein and coworkers found evidence that patients transferred from the emergency departments of 14 private hospitals to a public hospital were predominantly uninsured (6). Schiff and colleagues and Kellermann and Hackman (79) also found evidence of patients transferred between emergency departments for economic factors. More recent reports have shown associations between an array of patient characteristics (for example, sex, age, race, or insurance status) and the likelihood of transfer of patients with trauma in the prehospital setting (1013). However, we are unaware of any contemporary analyses that have examined the relationship between insurance coverage and interhospital transfer of patients who have already been admitted. If such evidence were found, it wouldsuggest a new and previously understudied gap in EMTALA. We used 2010 data from the Nationwide Inpatient Sample (NIS) to examine the relationship between patientsinsurance coverage and whether they were transferred between hospitals. In particular, we hypothesized that uninsured patients would be more likely to be transferred by their admitting hospital to another acute care hospital, reflecting the desire of admitting hospitals to relieve themselves of less profitable patients as rapidly as possible. Methods Data Sources We used 2010 discharge data from the NIS, which are available for purchase from the Agency for Health Care Research and Quality (AHRQ) (14). Each year of the NIS contains data from approximately 8 million hospitalizations from 1051 U.S. hospitals. The NIS is generated from states participating in AHRQ's Healthcare Cost and Utilization Project, and the NIS approximates a 20% sample of U.S. hospitals. It does not contain unique patient identifiers, making it impossible to track individual patients across hospitalizations or time. We therefore analyzed discharges and not individual patients. The NIS has extensive preprocessing at AHRQ to reconcile variations in coding by individual states and definitions across them and to ensure consistency of coding and variables in the final data set made available. Excluded from the NIS are short-term rehabilitation hospitals, long-term nonacute care hospitals, psychiatric hospitals, and alcoholism or chemical dependency treatment facilities. The NIS also does not include individuals hospitalized under observation status. Data contain elements summarized from the hospital discharge abstract of universal billing form 92 and are similar to other commonly used administrative data sources, such as Medicare Part A. Data elements include patient demographics; primary and secondary diagnoses as captured by International Classification of Diseases, Ninth Revision, Clinical Modification codes; insurance type (Medicare, Medicaid, private, uninsured, no charge, or other); and discharge disposition (categorized as routine, short-term hospital, skilled-nursing facility, intermediate care facility, another type of facility, home health care, against medical advice, or died). The NIS has been used extensively in previous health services research (15). Weights for the NIS are provided by AHRQ to allow researchers to calculate nationally representative estimates. We identified all discharges of adults aged 18 to 64 years at hospitalization. We excluded all patients aged 65 years and older because nearly all of them are insured by Medicare. Inclusion of this population would not add much to our efforts to evaluate the relationship between variations in insurance coverage and interhospital transfers. We excluded all patients who did not have Medicare, Medicaid, private, or uninsured as their primary payer (categorized as other in the NIS) because this group constitutes a heterogeneous group of patients with varying insurance coverage. We also excluded patients who died or left against medical advice because they did not have the chance to be transferred. We then identified condition-specific cohorts by using each patient's primary admission diagnosis. Diagnoses were identified by using AHRQ's clinical classification software coding (16) (Appendix). These codes amalgamate closely related International Classification of Diseases, Ninth Revision, Clinical Modification codes into clinically meaningful categories. We excluded pregnancy-related, psychiatric, and orthopedic diagnoses because of our focus on general medical conditions. We also excluded diabetes mellitus with complications, device complication, and other complication because in our experience as clinicians we believe these categories to be vague and heterogeneous. After applying these exclusions, we selected the 5 most common diagnoses for further study: biliary tract disease, chest pain, septicemia, skin and subcutaneous tissue infections (skin infection), and pneumonia. After identifying our 5 cohorts, we linked each discharge (and admitting hospital) to the hospital weights file provided by AHRQ. This file includes an array of hospital-level data, such as hospital ownership and financial status (for-profit, not-for-profit, and government-owned), teaching status, and bed size (17). These fields were complete for all but 11 of the 1051 hospitals. Variables Our outcome of interest was each patient's discharge disposition, specifically whether he or she was transferred to another acute care hospital at the end of the observed admission. Patients who were transferred to long-term acute care, inpatient rehabilitation, and skilled-care facilities were not considered to have been transferred for the purposes of our analysis. Our primary independent variable of interest was each patient's insurance status as captured by the primary payer field in the NIS. To make coding uniform across all states contributing to the NIS, AHRQ contractors condense several insurance subcategories into 4 general groups: Medicare includes fee-for-service and managed care Medicare patients; Medicaid includes fee-for-service and managed care Medicaid patients; private insurance includes Blue Cross, commercial carriers, and private HMOs and preferred provider organizations; and uninsured includes self-pay, charity care, and underinsured. We excluded patients whose primary payer was coded as other. Explanatory patient-level variables included each patient's age in years, sex, race/ethnicity (categorized as white [non-Hispanic], black, Hispanic, and other persons), comorbid conditions, and quartile of median household income for the patient's ZIP code of residence. The data set provided by AHRQ includes 29 comorbid conditions identified by using methods developed by Elixhauser and colleagues (18). We calculated the number of comorbid conditions for each patient by summing the total number of conditions at the individual-patient level. We then analyzed this calculated number to present the mean total number of conditions for each diagnosis. Explanatory hospital-level variables included hospital teaching status (major or nonteaching), hospital ownership (for-profit, not-for-profit, or government), location (rural or urban), and bed size. Bed size is a combined variable created by AHRQ that includes the total number of beds, location, and separates teaching status into 3 categories (small, medium, and large) (19). We calculated an additional hospital-level variable for our analysis, which was the percentage of all admissions that were uninsured in each hospital. Statistical Analysis First, we compared the demographic characteristics (age, sex, and race/ethnicity), presence of key comorbid conditions, mean number of comorbid conditions, and insurance coverage of patients who were not transferred to another acute care hospital by using the Rao-Scott chi-square test for a