Shifts in Medicaid and Uninsured Payer Mix at Safety-Net and Non-Safety-Net Hospitals During the Great Recession.

EXECUTIVE SUMMARY: There has been ongoing concern regarding the viability of safety-net hospitals (SNHs), which care for vulnerable populations. The authors examined payer mix at SNHs and non-SNHs during a period covering the Great Recession using data from the 2006 to 2012 Healthcare Cost and Utilization Project State Inpatient Databases from 38 states. The number of privately insured stays decreased at both SNHs and non-SNHs. Non-SNHs increasingly served Medicaid-enrolled and uninsured patients; in SNHs, the number of Medicaid stays decreased and uninsured stays remained stable. These study findings suggest that SNHs were losing Medicaid-enrolled patients relative to non-SNHs before the Medicaid expansion under the Affordable Care Act (ACA). Postexpansion, Medicaid stays will likely increase for both SNHs and non-SNHs, but the increase at SNHs may not be as large as expected if competition increases. Because hospital stays with private insurance and Medicaid help SNHs offset uncompensated care, a lower-than-expected increase could affect SNHs' ability to care for the remaining uninsured population. Continued monitoring is needed once post-ACA data become available.

Differences in Use of High-quality and Low-quality Hospitals Among Working-age Individuals by Insurance Type.

BACKGROUND: Research suggests that individuals with Medicaid or no insurance receive fewer evidence-based treatments and have worse outcomes than those with private insurance for a broad range of conditions. These differences may be due to patients' receiving care in hospitals of different quality. RESEARCH DESIGN: We used the Healthcare Cost and Utilization Project State Inpatient Databases 2009-2010 data to identify patients aged 18-64 years with private insurance, Medicaid, or no insurance who were hospitalized with acute myocardial infarction, heart failure, pneumonia, stroke, or gastrointestinal hemorrhage. Multinomial logit regressions estimated the probability of admissions to hospitals classified as high, medium, or low quality on the basis of risk-adjusted, in-hospital mortality. RESULTS: Compared with patients who have private insurance, those with Medicaid or no insurance were more likely to be minorities and to reside in areas with low-socioeconomic status. The probability of admission to high-quality hospitals was similar for patients with Medicaid (23.3%) and private insurance (23.0%) but was significantly lower for patients without insurance (19.8%, P<0.01) compared with the other 2 insurance groups. Accounting for demographic, socioeconomic, and clinical characteristics did not influence the results. CONCLUSIONS: Previously noted disparities in hospital quality of care for Medicaid recipients are not explained by differences in the quality of hospitals they use. Patients without insurance have lower use of high-quality hospitals, a finding that needs exploration with data after 2013 in light of the Affordable Care Act, which is designed to improve access to medical care for patients without insurance.

Understanding Racial and Ethnic Disparities in Postsurgical Complications Occurring in U.S. Hospitals.

OBJECTIVE: To examine the role of patient, hospital, and community characteristics on racial and ethnic disparities in in-hospital postsurgical complications. DATA SOURCES: Healthcare Cost and Utilization Project, 2011 State Inpatient Databases; American Hospital Association Annual Survey of Hospitals; Area Health Resources Files; Centers for Medicare & Medicaid Services Hospital Compare database. METHODS: Nonlinear hierarchical modeling was conducted to examine the odds of patients experiencing any in-hospital postsurgical complication, as defined by Agency for Healthcare Research and Quality Patient Safety Indicators. PRINCIPAL FINDINGS: A total of 5,474,067 inpatient surgical discharges were assessed using multivariable logistic regression. Clinical risk, payer coverage, and community-level characteristics (especially income) completely attenuated the effect of race on the odds of postsurgical complications. Patients without private insurance were 30 to 50 percent more likely to have a complication; patients from low-income communities were nearly 12 percent more likely to experience a complication. Private, not-for-profit hospitals in small metropolitan or micropolitan areas and higher nurse-to-patient ratios led to fewer postsurgical complications. CONCLUSIONS: Race does not appear to be an important determinant of in-hospital postsurgical complications, but insurance and community characteristics have an effect. A population-based approach that includes improving the socioeconomic context may help reduce disparities in these outcomes.

Differences in severity at admission for heart failure between rural and urban patients: the value of adding laboratory results to administrative data.

BACKGROUND: Rural/urban variations in admissions for heart failure may be influenced by severity at hospital presentation and local practice patterns. Laboratory data reflect clinical severity and guide hospital admission decisions and treatment for heart failure, a costly chronic illness and a leading cause of hospitalization among the elderly. Our main objective was to examine the role of laboratory test results in measuring disease severity at the time of admission for inpatients who reside in rural and urban areas. METHODS: We retrospectively analyzed discharge data on 13,998 hospital discharges for heart failure from three states, Hawai'i, Minnesota, and Virginia. Hospital discharge records from 2008 to 2012 were derived from the State Inpatient Databases of the Healthcare Cost and Utilization Project, and were merged with results of laboratory tests performed on the admission day or up to two days before admission. Regression models evaluated the relationship between clinical severity at admission and patient urban/rural residence. Models were estimated with and without use of laboratory data. RESULTS: Patients residing in rural areas were more likely to have missing laboratory data on admission and less likely to have abnormal or severely abnormal tests. Rural patients were also less likely to be admitted with high levels of severity as measured by the All Patient Refined Diagnosis Related Groups (APR-DRG) severity subclass, derivable from discharge data. Adding laboratory data to discharge data improved model fit. Also, in models without laboratory data, the association between urban compared to rural residence and APR-DRG severity subclass was significant for major and extreme levels of severity (OR 1.22, 95% CI 1.03-1.43 and 1.55, 95% CI 1.26-1.92, respectively). After adding laboratory data, this association became non-significant for major severity and was attenuated for extreme severity (OR 1.12, 95% CI 0.94-1.32 and 1.43, 95% CI 1.15-1.78, respectively). CONCLUSION: Heart failure patients from rural areas are hospitalized at lower severity levels than their urban counterparts. Laboratory test data provide insight on clinical severity and practice patterns beyond what is available in administrative discharge data.

Length of stay in EDs: variation across classifications of clinical condition and patient discharge disposition.

STUDY OBJECTIVE: Duration of a stay in an emergency department (ED) is considered a measure of quality, but current measures average lengths of stay across all conditions. Previous research on ED length of stay has been limited to a single condition or a few hospitals. We use a census of one state's data to measure length of ED stays by patients' conditions and dispositions and explore differences between means and medians as quality metrics. METHODS: The data source was the Healthcare Cost and Utilization Project 2011 State Emergency Department Databases and State Inpatient Databases for Florida. Florida is unique in collecting ED length of stay for both released and admitted patients. Clinical Classifications Software was used to group visits based on first-listed International Classification of Disease, Ninth Edition, Clinical Modification, diagnoses. RESULTS: For the 10 most common diagnoses, patients with relatively minor injuries typically required the shortest mean stay (3 hours or less); conditions resulting in admission or transfer tended to be more serious, resulting in longer stays. Patients requiring the longest stays, by disposition, had discharge diagnoses of nonspecific chest pain (mean 7.4 hours among discharged patients), urinary tract infections (4.8 hours among admissions), and schizophrenia (9.6 hours among transfers) among the top 10 diagnoses. CONCLUSION: Emergency department length of stay as a measure of ED quality should take into account the considerable variation by condition and disposition of the patient. Emergency department length of stay measurement could be improved in the United States by standardizing its definition; distinguishing visits involving treatment, observation, and boarding; and incorporating more distributional information.

Admissions after discharge from an emergency department for chest symptoms.

BACKGROUND: Often patients who present to the emergency department (ED) with chest symptoms return to the hospital within 30 days with the same or closely related symptoms and are admitted, raising questions about quality of care, timeliness of diagnosis, and patient safety. This study examined the frequency of and patient characteristics associated with subsequent inpatient admissions for related symptoms after discharge from an ED for chest symptoms. METHODS: We used data from the 2012 and 2013 Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) and State Emergency Department Databases (SEDD) from eight states to identify over 1.8 million ED discharges for chest symptoms. RESULTS: Approximately 3% of ED discharges experienced potentially related subsequent admissions within 30 days - 0.2% for acute myocardial infarction (AMI), 1.7% for other cardiovascular conditions, 0.5% for respiratory conditions, and 0.6% for mental disorders. Logistic regression results showed higher odds of subsequent admission for older patients and those residing in low-income areas, and lower odds for females and non White racial/ethnic groups. Privately insured patients had lower odds of subsequent admission than did those who were uninsured or covered by other programs. CONCLUSIONS: Because we included multiple diagnostic categories of subsequent admissions, our results show a more complete picture of patients presenting to the ED with chest symptoms compared with previous studies. In particular, we show a lower rate of subsequent admission for AMI versus other diagnoses. ED physicians and administrators can use the results to identify characteristics associated with increased odds of subsequent admission to target at-risk populations.

Disparities in rates of inpatient mortality and adverse events: race/ethnicity and language as independent contributors.

Patients with limited English proficiency have known limitations accessing health care, but differences in hospital outcomes once access is obtained are unknown. We investigate inpatient mortality rates and obstetric trauma for self-reported speakers of English, Spanish, and languages of Asia and the Pacific Islands (API) and compare quality of care by language with patterns by race/ethnicity. Data were from the United States Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, 2009 State Inpatient Databases for California. There were 3,757,218 records. Speaking a non-English principal language and having a non-White race/ethnicity did not place patients at higher risk for inpatient mortality; the exception was significantly higher stroke mortality for Japanese-speaking patients. Patients who spoke API languages or had API race/ethnicity had higher risk for obstetric trauma than English-speaking White patients. Spanish-speaking Hispanic patients had more obstetric trauma than English-speaking Hispanic patients. The influence of language on obstetric trauma and the potential effects of interpretation services on inpatient care are discussed. The broader context of policy implications for collection and reporting of language data is also presented. Results from other countries with and without English as a primary language are needed for the broadest interpretation and generalization of outcomes.

The value of linking hospital discharge and mortality data for comparative effectiveness research.

BACKGROUND: Linkage of US state hospital discharge records to state death certificate records offers the possibility of tracking long-term mortality outcomes across large, diverse patient populations, which may be useful for comparative effective analyses. AIM: To demonstrate the value of linking state community hospital discharge data to vital statistics death files for research by conducting a comparative effectiveness analysis. METHODS: Linked Patient Discharge Data and Vital Statistics Death Files from the California Office of Statewide Health Planning and Development were used to compare survival rates for patients with an elective repair for abdominal aortic aneurysm who received open aneurysm repair (OAR) versus endovascular aneurysm repair (EVAR). The sample consisted of 13,652 hospitalized patients who underwent an OAR or EVAR for abdominal aortic aneurysm between 1 July 2000 and 31 January 2006. Patients were matched using propensity scores (8966 patients in the matched sample). In-hospital, 30-day, 1-year and 5-year mortality rates were compared between the OAR and EVAR populations, before and after propensity score matching. RESULTS: We found a few data anomalies (92 out of 13,652), primarily in patients' sex and date of death. The analysis revealed that in the matched cohort, in-hospital and 30-day postdischarge mortality rates were significantly lower following EVAR than OAR; however, consistent with previous clinical trials, differences in the 1- and 5-year rates were not statistically significant. CONCLUSION: The study demonstrates that linked US state discharge and mortality data can be a valuable resource for comparative effectiveness analyses. In particular, this approach may be useful when generally available data sets such as Medicare claims data limit the generalizability of findings. Policy-makers and others should consider greater investments in these data.

Federal spending on behavioral health accelerated during recession as individuals lost employer insurance.

The 2007-09 recession had a dramatic effect on behavioral health spending, with the effect most prominent for private, state, and local payers. During the recession behavioral health spending increased at a 4.6 percent average annual rate, down from 6.1 percent in 2004-07. Average annual growth in private behavioral health spending during the recession slowed to 2.7 percent from 7.2 percent in 2004-07. State and local behavioral health spending showed negative average annual growth, -1.2 percent, during the recession, compared with 3.7 percent increases in 2004-07. In contrast, federal behavioral health spending growth accelerated to 11.1 percent during the recession, up from 7.2 percent in 2004-07. These behavioral health spending trends were driven largely by increased federal spending in Medicaid, declining private insurance enrollment, and severe state budget constraints. An increased federal Medicaid match reduced the state share of Medicaid spending, which prevented more drastic cuts in state-funded behavioral health programs during the recession. Federal Medicaid served as a critical safety net for people with behavioral health treatment needs during the recession.

Current and future funding sources for specialty mental health and substance abuse treatment providers.

OBJECTIVES: Goals were to describe funding for specialty behavioral health providers in 1986 and 2005 and examine how the recession, parity law, and Affordable Care Act (ACA) may affect future funding. METHODS: Numerous public data sets and actuarial methods were used to estimate spending for services from specialty behavioral health providers (general hospital specialty units; specialty hospitals; psychiatrists; other behavioral health professionals; and specialty mental health and substance abuse treatment centers). RESULTS: Between 1986 and 2005, hospitals-which had received the largest share of behavioral health spending-declined in importance, and spending shares trended away from specialty hospitals that were largely funded by state and local governments. Hospitals' share of funding from private insurance decreased from 25% in 1986 to 12% in 2005, and the Medicaid share increased from 11% to 23%. Office-based specialty providers continued to be largely dependent on private insurance and out-of-pocket payments, with psychiatrists receiving increased Medicaid funding. Specialty centers received increased funding shares from Medicaid (from 11% to 29%), and shares from other state and local government sources fell (from 64% to 46%). CONCLUSIONS: With ACA's full implementation, spending on behavioral health will likely increase under private insurance and Medicaid. Parity in private plans will also push a larger share of payments for office-based professionals from out-of-pocket payments to private insurance. As ACA provides insurance for formerly uninsured individuals, funding by state behavioral health authorities of center-based treatment will likely refocus on recovery and support services. Federal Medicaid rules will increase in importance as more people needing behavioral health treatment become covered.

Congestive heart failure: who is likely to be readmitted?

Readmission for congestive heart failure (CHF) is the most common reason for readmission among Medicare fee-for-service patients. Yet CHF readmissions are not just a Medicare problem. This study examined who is likely to be readmitted for CHF, using all-payer hospital discharges from 14 of the states participating in the Healthcare Cost and Utilization Project. Patients with the strongest positive association with readmission were discharged against medical advice, covered by Medicaid, and had more severe loss of function and certain comorbidities such as drug abuse, renal failure, or psychoses. Weak negative relationship between readmission and cost of index admission provides some evidence that hospitals with higher readmission rates do not systematically use fewer resources in treating patients in initial encounters. High readmission rate for Medicaid patients suggests that state and federal governments should target Medicaid populations and drug abuse treatment for better care coordination to reduce readmissions and health care costs.

Changes In US spending on Mental Health And Substance Abuse Treatment, 1986-2005, and implications for policy.

The United States invests a sizable amount of money on treatments for mental health and substance abuse: $135 billion in 2005, or 1.07 percent of the gross domestic product. We provide treatment spending estimates from the period 1986-2005 to build understanding of past trends and consider future possibilities. We find that the growth rate in spending on mental health medications-a major driver of mental health expenditures in prior years-declined dramatically. As a result, mental health and substance abuse spending grew at a slightly slower rate than gross domestic product in 2004 and 2005, and it continued to shrink as a share of all health spending. Of note, we also find that Medicaid's share of total spending on mental health grew from 17 percent in 1986 to 27 percent in 2002 to 28 percent in 2005. The recent recession, the full implementation of federal parity law, and such health reform-related actions as the planned expansion of Medicaid all have the potential to improve access to mental health and substance abuse treatment and to alter spending patterns further. Our spending estimates provide an important context for evaluating the effect of those policies.

Psychiatric discharges in community hospitals with and without psychiatric units: how many and for whom?

OBJECTIVE: This study sought to describe the extent to which community hospitals, in a sample of states, are caring for patients with psychiatric disorders in medical-surgical beds (scatter beds) and to compare the characteristics of patients treated in scatter beds with those of patients treated in psychiatric units in community hospitals. METHODS: Information on hospital discharges in 12 states for patients with a principal psychiatric diagnosis was gathered from the Healthcare Cost and Utilization Project State Inpatient Databases. Discharges of patients who were treated in community hospital psychiatric units (N=370,984) were compared with those of patients who were treated in scatter beds (N=26,969). RESULTS: Overall, only 6.8% of discharges were from scatter beds. The rate of total psychiatric discharges per 10,000 total state population ranged from a high of 62.3 in one study state to a low of 9.6 in another. The average rate of scatter bed discharges per 10,000 state population ranged from 1.6 to 5.8, whereas the average rate of psychiatric unit discharges ranged from 7.4 to 58.9. A comparison of discharges of patients treated in scatter beds with discharges of patients treated in psychiatric units indicated that patients in scatter beds were more likely to have somatic conditions and were half as likely to have an accompanying substance use disorder. Discharge codes indicated that almost 40% of patients from scatter beds had a diagnosis of schizophrenia, episodic mood disorder, or depression; about two-thirds were admitted from emergency rooms; and about one-fifth were transferred to another facility. CONCLUSIONS: More research is needed to determine the optimal supply of psychiatric unit beds across regions and whether and how scatter beds should be used to address the lack of psychiatric beds.

Evidence for substance abuse services and policy research: a systematic review of national databases.

We reviewed 39 national government- and nongovernment-sponsored data sets related to substance addiction policy. These data sets describe patients with substance use disorders (SUDs), treatment providers and the services they offer, and/or expenditures on treatment. Findings indicate the availability of reliable data on the prevalence of SUD and the characteristics of specialty treatment facilities, but meager data on financing and services. Gaps in information might be filled through agency collaboration to redesign, coordinate, and augment existing substance abuse and general health surveys. Despite noted gaps, these data sets represent an unusually rich set of resources for health services and policy research.

Future funding for mental health and substance abuse: increasing burdens for the public sector.

Spending on mental health (MH) and substance abuse (SA) treatment is expected to double between 2003 and 2014, to $239 billion, and is anticipated to continue falling as a share of all health spending. By 2014, our projections of SA spending show increasing responsibility for state and local governments (45 percent); deteriorating shares financed by private insurance (7 percent); and 42 percent of SA spending going to specialty SA centers. For MH, Medicaid is forecasted to fund an increasingly larger share of treatment costs (27 percent), and prescription medications are expected to capture 30 percent of MH spending by 2014.

Transforming mental health and substance abuse data systems in the United States.

State efforts to improve mental health and substance abuse service systems cannot overlook the fragmented data systems that reinforce the historical separateness of systems of care. These separate systems have discrete approaches to treatment, and there are distinct funding streams for state mental health, substance abuse, and Medicaid agencies. Transforming mental health and substance abuse services in the United States depends on resolving issues that underlie separate treatment systems--access barriers, uneven quality, disjointed coordination, and information silos across agencies and providers. This article discusses one aspect of transformation--the need for interoperable information systems. It describes current federal and state initiatives for improving data interoperability and the special issue of confidentiality associated with mental health and substance abuse treatment data. Some achievable steps for states to consider in reforming their behavioral health data systems are outlined. The steps include collecting encounter-level data; using coding that is compliant with the Health Insurance Portability and Accountability Act, including national provider identifiers; forging linkages with other state data systems and developing unique client identifiers among systems; investing in flexible and adaptable data systems and business processes; and finding innovative solutions to the difficult confidentiality restrictions on use of behavioral health data. Changing data systems will not in itself transform the delivery of care; however, it will enable agencies to exchange information about shared clients, to understand coordination problems better, and to track successes and failures of policy decisions.

Mental health treatment expenditure trends, 1986-2003.

OBJECTIVE: This study determined spending on mental health treatment in the United States over time by provider and payer relative to all health spending. METHODS: Estimates were developed to be consistent with the National Health Expenditure Accounts. Numerous public data sources were used. RESULTS: Mental health treatment expenditures grew from $33 billion in 1986 to $100 billion in 2003. In real 2003 dollars, spending per capita on mental health treatment rose from $205 to $345. The average annual nominal total mental health growth rate was 6.7%. In comparison, total health care expenditures increased by 8.0%. As a result of the slower growth rate of mental health expenditures compared with all health spending, mental health fell from 8% of all health expenditures in 1986 to 6% in 2003. Total national health spending increased by approximately $1.175 trillion from 1986 to 2003; of this, 6% is attributed to an increase in mental health spending. The mix of services has changed, with more care being provided through prescription drugs and in outpatient settings and less in inpatient settings. Payer mix has also shifted, with Medicaid taking a more prominent role. CONCLUSIONS: Spending on mental health treatment has increased over the past decade, reflecting increases in the number of individuals receiving mental health treatment, particularly prescription drugs and outpatient treatment. Changes in payer and provider mix raise new challenges for ensuring quality and access.

Trends in spending for substance abuse treatment, 1986-2003.

Since 1987, public and private investment in substance abuse (SA) treatment has not kept pace with other health spending. SA treatment spending in the United States grew from $9.3 billion in 1986 to $20.7 billion in 2003. The average annual total growth rate was 4.8 percent. In comparison, total U.S. health care spending grew by 8.0 percent. As a result of the slower growth of SA spending compared to that for all health care, SA spending fell as a share of all health spending from 2.1 percent in 1986 to 1.3 percent in 2003.

Substance abuse benefits: still limited after all these years.

Using data from a special supplement to the 2006 Kaiser/HRET Employer Health Benefits Survey, this study examines the state of employer-sponsored insurance substance abuse benefits in 2006 and how benefits compare to coverage for medical-surgical services. In 2006, 88 percent of insured workers had some coverage for substance abuse services. Current substance abuse benefits, however, do not provide the same protection afforded under medical-surgical benefits. Instead, substance abuse benefits are characterized by higher cost sharing and annual limits and lifetime limits on inpatient and outpatient care. These limits generally do not exist for other medical conditions and have increased since 1990.

U.S. spending for mental health and substance abuse treatment, 1991-2001.

Spending for mental health and substance abuse (MHSA) treatment in the United States totaled dollar 104 billion in 2001, representing 7.6 percent of all health care spending. The nominal MHSA annual spending growth rate from 1991 to 2001 was 5.6 percent, almost one percentage point below the growth rate for all health care (6.5 percent). During this period, Medicaid has increased to be the largest payer of mental health care, with prescription drugs the fastest-growing spending component. Private insurance payment for substance abuse actually dropped in real dollars, increasing the public share of substance abuse spending.