Readmissions Following Inpatient Treatment for Opioid-Related Conditions.

BACKGROUND: Previous research suggests that relatively few hospitalized patients with opioid-related conditions receive substance use treatment during their inpatient stay. Without treatment, these individuals may be more likely to have subsequent hospitalizations for continued opioid use disorder. OBJECTIVE: To evaluate the relationship between receipt of inpatient drug detoxification and/or rehabilitation services and subsequent opioid-related readmission. METHODS: This study used combined hospital inpatient discharge and emergency department visit data from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Our sample consisted of 329,037 patients from seven states with an opioid-related index hospitalization occurring between March 2010 and September 2013. Multivariate analysis was conducted to examine the relationship between opioid-related readmission and the receipt of inpatient drug detoxification and/or rehabilitation during the index visit. RESULTS: A relatively small percentage (19.4%) of patients with identified opioid-related conditions received treatment for drug use during their hospital inpatient stay. Patients who received drug rehabilitation, but not drug detoxification, during an opioid-related index hospitalization had lower odds of an opioid-related readmission within 90 days of discharge (odds ratio = 0.60, 95% confidence interval = 0.54-0.67) compared with patients with no inpatient drug detoxification or rehabilitation. Conclusions/Importance: A low percentage of patients receive inpatient services for drug use during an index stay involving an opioid-related diagnosis. Our findings indicate that receipt of drug rehabilitation services in acute care hospitals is associated with a lower 90-day readmission rate. Further research is needed to understand factors associated with the receipt of inpatient services and readmissions.

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.

Racial Disparities in Sepsis-Related In-Hospital Mortality: Using a Broad Case Capture Method and Multivariate Controls for Clinical and Hospital Variables, 2004-2013.

OBJECTIVES: As sepsis hospitalizations have increased, in-hospital sepsis deaths have declined. However, reported rates may remain higher among racial/ethnic minorities. Most previous studies have adjusted primarily for age and sex. The effect of other patient and hospital characteristics on disparities in sepsis mortality is not yet well-known. Furthermore, coding practices in claims data may influence findings. The objective of this study was to use a broad method of capturing sepsis cases to estimate 2004-2013 trends in risk-adjusted in-hospital sepsis mortality rates by race/ethnicity to inform efforts to reduce disparities in sepsis deaths. DESIGN: Retrospective, repeated cross-sectional study. SETTING: Acute care hospitals in the Healthcare Cost and Utilization Project State Inpatient Databases for 18 states with consistent race/ethnicity reporting. PATIENTS: Patients diagnosed with septicemia, sepsis, organ dysfunction plus infection, severe sepsis, or septic shock. MEASUREMENTS AND MAIN RESULTS: In-hospital sepsis mortality rates adjusted for patient and hospital factors by race/ethnicity were calculated. From 2004 to 2013, sepsis hospitalizations for all racial/ethnic groups increased, and mortality rates decreased by 5-7% annually. Mortality rates adjusted for patient characteristics were higher for all minority groups than for white patients. After adjusting for hospital characteristics, sepsis mortality rates in 2013 were similar for white (92.0 per 1,000 sepsis hospitalizations), black (94.0), and Hispanic (93.5) patients but remained elevated for Asian/Pacific Islander (106.4) and "other" (104.7; p < 0.001) racial/ethnic patients. CONCLUSIONS: Our results indicate that hospital characteristics contribute to higher rates of sepsis mortality for blacks and Hispanics. These findings underscore the importance of ensuring that improved sepsis identification and management is implemented across all hospitals, especially those serving diverse populations.

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.

Age-related Disparities in Trauma Center Access for Severe Head Injuries Following the Release of the Updated Field Triage Guidelines.

OBJECTIVE: In 2006, the American College of Surgeons' Committee on Trauma and the Centers for Disease Control and Prevention released field triage guidelines with special consideration for older adults. Additional considerations for direct transport to a Level I or II trauma center (TC) were added in 2011, reflecting perceived undertriage to TCs for older adults. We examined whether age-based disparities in TC care for severe head injury decreased following introduction of the 2011 revisions. METHODS: A pre-post design analyzing the 2009 and 2012 Healthcare Cost and Utilization Project State Emergency Department Databases and State Inpatient Databases with multivariable logistic regressions considered changes in 1) the trauma designation of the emergency department where treatment was initiated and 2) transfer to a TC following initial treatment at a non-TC. RESULTS: Compared with adults aged 18 to 44 years, after multivariable adjustment, in both years TC care was less likely for adults aged 45 to 64 years (odds ratio [OR] = 0.76 in 2009 and 0.74 in 2012), aged 65 to 84 years (OR = 0.61 and 0.59), and aged 85+ years (OR = 0.53 and 0.56). Between 2009 and 2012, the likelihood of TC care increased for all age groups, with the largest increase among those aged 85+ years (OR = 1.18), which was statistically different (p = 0.02) from the increase among adults aged 18 to 44 years (OR = 1.12). The analysis of transfers yielded similar results. CONCLUSIONS: Although patterns of increased TC treatment for all groups with severe head trauma indicate improvements, age-based disparities persisted.

Association Between the Opening of Retail Clinics and Low-Acuity Emergency Department Visits.

STUDY OBJECTIVE: We assess whether the opening of retail clinics near emergency departments (ED) is associated with decreased ED utilization for low-acuity conditions. METHODS: We used data from the Healthcare Cost and Utilization Project State Emergency Department Databases for 2,053 EDs in 23 states from 2007 to 2012. We used Poisson regression models to examine the association between retail clinic penetration and the rate of ED visits for 11 low-acuity conditions. Retail clinic "penetration" was measured as the percentage of the ED catchment area that overlapped with the 10-minute drive radius of a retail clinic. Rate ratios were calculated for a 10-percentage-point increase in retail clinic penetration per quarter. During the course of a year, this represents the effect of an increase in retail clinic penetration rate from 0% to 40%, which was approximately the average penetration rate observed in 2012. RESULTS: Among all patients, retail clinic penetration was not associated with a reduced rate of low-acuity ED visits (rate ratio=0.999; 95% confidence interval=0.997 to 1.000). Among patients with private insurance, there was a slight decrease in low-acuity ED visits (rate ratio=0.997; 95% confidence interval=0.994 to 0.999). For the average ED in a given quarter, this would equal a 0.3% reduction (95% confidence interval 0.1% to 0.6%) in low-acuity ED visits among the privately insured if retail clinic penetration rate increased by 10 percentage points per quarter. CONCLUSION: With increased patient demand resulting from the expansion of health insurance coverage, retail clinics may emerge as an important care location, but to date, they have not been associated with a meaningful reduction in low-acuity ED visits.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty.

BACKGROUND: Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospital's control, may not accurately reflect a hospital's performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA. METHODS: We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods. RESULTS: Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals. CONCLUSIONS: Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting. LEVEL OF EVIDENCE: Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

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.

The effects of multiple chronic conditions on hospitalization costs and utilization for ambulatory care sensitive conditions in the United States: a nationally representative cross-sectional study.

BACKGROUND: The presence of multiple chronic conditions (MCCs) complicates inpatient hospital care, leading to higher costs and utilization. Multimorbidity also complicates primary care, increasing the likelihood of hospitalization for ambulatory care sensitive conditions. The purpose of this study was to evaluate how MCCs relate to inpatient hospitalization costs and utilization for ambulatory care sensitive conditions. METHODS: The 2012 Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) provided data to carry out a cross-sectional analysis of 1.43 million claims related to potentially preventable hospitalizations classified by the AHRQ Prevention Quality Indicator (PQI) composites. Categories of MCCs (0-1, 2-3, 4-5, and 6+) were examined in sets of acute, chronic, and overall PQIs. Multivariate models determined associations between categories of MCCs and 1) inpatient costs per stay, 2) inpatient costs per day, and 3) length of inpatient hospitalization. Negative binomial was used to model costs per stay and costs per day. RESULTS: The most common category observed was 2 or 3 chronic conditions (37.8 % of patients), followed by 4 or 5 chronic conditions (30.1 % of patients) and by 6+ chronic conditions (10.1 %). Compared with costs for patients with 0 or 1 chronic condition, hospitalization costs per stay for overall ambulatory care sensitive conditions were 19 % higher for those with 2 or 3 (95 % confidence interval [CI] 1.19-1.20), 32 % higher for those with 4 or 5 (95 % CI 1.31-1.32), and 31 % higher (95 % CI 1.30-3.32) for those with 6+ conditions. Acute condition stays were 11 % longer when 2 or 3 chronic conditions were present (95 % CI 1.11-1.12), 21 % longer when 4 or 5 were present (95 % CI 1.20-1.22), and 27 % longer when 6+ were present (95 % CI 1.26-1.28) compared with those with 0 or 1 chronic condition. Similar results were seen within chronic conditions. Associations between MCCs and total costs were driven by longer stays among those with more chronic conditions rather than by higher costs per day. CONCLUSIONS: The presence of MCCs increased inpatient costs for ambulatory care sensitive conditions via longer hospital stays.

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.

Missed diagnoses of acute myocardial infarction in the emergency department: variation by patient and facility characteristics.

BACKGROUND: An estimated 1.2 million people in the US have an acute myocardial infarction (AMI) each year. An estimated 7% of AMI hospitalizations result in death. Most patients experiencing acute coronary symptoms, such as unstable angina, visit an emergency department (ED). Some patients hospitalized with AMI after a treat-and-release ED visit likely represent missed opportunities for correct diagnosis and treatment. The purpose of the present study is to estimate the frequency of missed AMI or its precursors in the ED by examining use of EDs prior to hospitalization for AMI. METHODS: We estimated the rate of probable missed diagnoses in EDs in the week before hospitalization for AMI and examined associated factors. We used Healthcare Cost and Utilization Project State Inpatient Databases and State Emergency Department Databases for 2007 to evaluate missed diagnoses in 111,973 admitted patients aged 18 years and older. RESULTS: We identified missed diagnoses in the ED for 993 of 112,000 patients (0.9% of all AMI admissions). These patients had visited an ED with chest pain or cardiac conditions, were released, and were subsequently admitted for AMI within 7 days. Higher odds of having missed diagnoses were associated with being younger and of Black race. Hospital teaching status, availability of cardiac catheterization, high ED admission rates, high inpatient occupancy rates, and urban location were associated with lower odds of missed diagnoses. CONCLUSIONS: Administrative data provide robust information that may help EDs identify populations at risk of experiencing a missed diagnosis, address disparities, and reduce diagnostic errors.

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.

Missed diagnosis of stroke in the emergency department: a cross-sectional analysis of a large population-based sample.

BACKGROUND: Some cerebrovascular events are not diagnosed promptly, potentially resulting in death or disability from missed treatments. We sought to estimate the frequency of missed stroke and examine associations with patient, emergency department (ED), and hospital characteristics. METHODS: Cross-sectional analysis using linked inpatient discharge and ED visit records from the 2009 Healthcare Cost and Utilization Project State Inpatient Databases and 2008-2009 State ED Databases across nine US states. We identified adult patients admitted for stroke with a treat-and-release ED visit in the prior 30 days, considering those given a non-cerebrovascular diagnosis as probable (benign headache or dizziness diagnosis) or potential (any other diagnosis) missed strokes. RESULTS: There were 23,809 potential and 2243 probable missed strokes representing 12.7% and 1.2% of stroke admissions, respectively. Missed hemorrhages (n = 406) were linked to headache while missed ischemic strokes (n = 1435) and transient ischemic attacks (n = 402) were linked to headache or dizziness. Odds of a probable misdiagnosis were lower among men (OR 0.75), older individuals (18-44 years [base]; 45-64:OR 0.43; 65-74:OR 0.28; ≥ 75:OR 0.19), and Medicare (OR 0.66) or Medicaid (OR 0.70) recipients compared to privately insured patients. Odds were higher among Blacks (OR 1.18), Asian/Pacific Islanders (OR 1.29), and Hispanics (OR 1.30). Odds were higher in non-teaching hospitals (OR 1.45) and low-volume hospitals (OR 1.57). CONCLUSIONS: We estimate 15,000-165,000 misdiagnosed cerebrovascular events annually in US EDs, disproportionately presenting with headache or dizziness. Physicians evaluating these symptoms should be particularly attuned to the possibility of stroke in younger, female, and non-White patients.

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.