Based on key measures, care quality for Medicare enrollees at safety-net and non-safety-net hospitals was almost equal

Hospital safety-net status and postdischarge outcomes: The impact of socioeconomic status and Medicare post-acute care types

AIM

To examine the impact of socioeconomic status (SES) and postacute care (PAC) locations on the association between hospital safety-net status and 30-day postdischarge outcomes (readmission, hospice use, or death).

METHOD

Medicare Current Beneficiary Survey (MCBS) participants during 2006-2011 who were Medicare Fee-for-Service beneficiaries aged 65.5 years or older were included. The associations between hospital safety-net status and 30-day post-discharge outcomes were evaluated by comparing the models with and without PAC and SES adjustments. Safety-net hospital status was defined as being in the top 20% of hospitals ranked by hospital-level percent of total Medicare patient days. SES was measured using individual-level SES (dual eligibility, income, and education) and the Area Deprivation Index (ADI).

RESULTS

This study identified 13,173 index hospitalizations for 6,825 patients; 1,428 hospitalizations (11.8%) were in safety-net hospitals. The average unadjusted 30-day hospital readmission rate was 22.6% in safety-net hospitals versus 18.8% in nonsafety-net hospitals. Regardless of whether patient SES status was controlled or not, safety-net hospitals had higher estimated probabilities of 30-day readmission (ranging from 0.217 to 0.222 vs. 0.184 to 0.189), and lower probabilities for having neither readmission nor hospice/death (0.750-0.763 vs. 0.780-0.785); for models additionally adjusted for PAC types, safety-net patients had lower rates of hospice use or death (0.019-0.027 vs. 0.030-0.031).

CONCLUSIONS

The results suggested that safety-net hospitals had lower hospice/death rates but higher readmission rates relative to outcomes at nonsafety-net hospitals. Readmission rate differences were similar regardless of patients' SES status. However, the rate of hospice referral or death rate was related to SES, which suggested that the outcomes were affected by SES and PAC types.

Evaluating How Safety-Net Hospitals Are Identified: Systematic Review and Recommendations

Objective:

To systematically review how safety-net hospitals' status is identified and defined, discuss current definitions' limitations, and provide recommendations for a new classification and evaluation framework.

Data Sources:

Safety-net hospital-related studies in the MEDLINE database published before May 16, 2019.

Study Design:

Systematic review of the literature that adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Data Collection/Extraction Methods:

We followed standard selection protocol, whereby studies went through an abstract review followed by a full-text screening for eligibility. For each included study, we extracted information about the identification method itself, including the operational definition, the dimension(s) of disadvantage reflected, study objective, and how safety-net status was evaluated.

Principal Findings:

Our review identified 132 studies investigating safety-net hospitals. Analysis of identification methodologies revealed substantial heterogeneity in the ways disadvantage is defined, measured, and summarized at the hospital level, despite a 4.5-fold increase in studies investigating safety-net hospitals for the past decade. Definitions often exclusively used low-income proxies captured within existing health system data, rarely incorporated external social risk factor measures, and were commonly separated into distinct safety-net status categories when analyzed.

Conclusions:

Consistency in research and improvement in policy both require a standard definition for identifying safety-net hospitals. Yet no standardized definition of safety-net hospitals is endorsed and existing definitions have key limitations. Moving forward, approaches rooted in health equity theory can provide a more holistic framework for evaluating disadvantage at the hospital level. Furthermore, advancements in precision public health technologies make it easier to incorporate detailed neighborhood-level social determinants of health metrics into multidimensional definitions. Other countries, including the United Kingdom and New Zealand, have used similar methods of identifying social need to determine more accurate assessments of hospital performance and the development of policies and targeted programs for improving outcomes.

Contributors to disparities in postpartum readmission rates between safety-net and non-safety-net hospitals: A decomposition analysis

BACKGROUND

Safety-net hospitals (SNHs) treat more maternal patients with risk factors for postpartum readmission.

OBJECTIVE

To assess how patient, hospital, and community characteristics explain the SNH/non-SNH disparity in postpartum readmission rates.

DESIGN

A linear probability model assessed covariates associated with postpartum readmissions. Oaxaca-Blinder decomposition estimates quantified the contribution of covariates to the SNH/non-SNH disparity in postpartum readmission rates.

SETTING

Healthcare Cost and Utilization Project 2016-2018 State Inpatient Databases from 25 states.

PARTICIPANTS

3.5 million maternal delivery stays.

MEASUREMENTS

The outcome was inpatient readmission within 42 days of delivery. SNHs had a share of Medicaid/uninsured stays in the top quartile. A range of patient, hospital, and community characteristics was considered as covariates.

RESULTS

The unadjusted postpartum readmission rate was 4.2 per 1000 index deliveries higher at SNHs than at non-SNHs (19.1 vs. 14.9, p < .001). Adjustment reduced the risk difference to 0.65 per 1000 (95% confidence interval [CI]: -0.14, 1.44). Patient (66%), hospital (14%), and community (4%) characteristics explained 84% of the disparity. The single largest contributors to the disparity were race/ethnicity (20%), hypertension (12%), hospital preterm delivery rate (10%), and preterm delivery (7%). Collectively, patient comorbidities explained 31% of the disparity.

CONCLUSION

Higher postpartum readmission rates at SNHs versus non-SNHs were largely due to differences in the patient mix rather than hospital factors. Hospital initiatives are needed to reduce the risk of postpartum readmissions among SNH patients. Improving factors that contribute to the disparity, including underlying health conditions and health inequities associated with race, requires enduring investments in public health.

The Effect of Hospital Safety Net Status on the Association Between Bundled Payment Participation and Changes in Medical Episode Outcomes

BACKGROUND

Under Medicare's Bundled Payments for Care Improvement (BPCI) program, hospitals have maintained quality and achieved savings for medical conditions. However, safety net hospitals may perform differently owing to financial constraints and organizational challenges.

OBJECTIVE

To evaluate whether hospital safety net status affected the association between bundled payment participation and medical episode outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This observational difference-in-differences analysis was conducted in safety net and non-safety net hospitals participating in BPCI for medical episodes (BPCI hospitals) using data from 2011-2016 Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, pneumonia, congestive heart failure, and chronic obstructive pulmonary disease.

EXPOSURE(S)

Hospital BPCI participation and safety net status.

MAIN OUTCOME(S) AND MEASURE(S)

The primary outcome was postdischarge spending. Secondary outcomes included quality and post-acute care utilization measures.

RESULTS

Our sample consisted of 803 safety net and 2263 non-safety net hospitals. Safety net hospitals were larger and located in areas with more low-income individuals than non-safety net hospitals. Among BPCI hospitals, safety net status was not associated with differential postdischarge spending (adjusted difference-in-differences [aDID], $40; 95% CI, -$254 to $335; P = .79) or quality (mortality, readmissions). However, BPCI safety net hospitals had differentially greater discharge to institutional post-acute care (aDID, 1.06 percentage points; 95% CI, 0.37-1.76; P = .003) and lower discharge home with home health (aDID, -1.15 percentage points; 95% CI, -1.73 to -0.58; P < .001) than BPCI non-safety net hospitals.

CONCLUSIONS

Under medical condition bundles, safety net hospitals perform differently from other hospitals in terms of post-acute care utilization, but not spending. Policymakers could support safety net hospitals and consider safety net status when evaluating bundled payment programs.

Processes of care and outcomes for homeless patients hospitalised for cardiovascular conditions at safety-net versus non-safety-net hospitals: cross-sectional study

OBJECTIVES

Evidence suggests that homeless patients experience worse quality of care and poorer health outcomes across a range of medical conditions. It remains unclear, however, whether differences in care delivery at safety-net versus non-safety-net hospitals explain these disparities. We aimed to investigate whether homeless versus non-homeless adults hospitalised for cardiovascular conditions (acute myocardial infarction (AMI) and stroke) experience differences in care delivery and health outcomes at safety-net versus non-safety-net hospitals.

DESIGN

Cross-sectional study.

SETTING

Data including all hospital admissions in four states (Florida, Massachusetts, Maryland, and New York) in 2014.

PARTICIPANTS

We analysed 167 105 adults aged 18 years or older hospitalised for cardiovascular conditions (age mean=64.5 years; 75 361 (45.1%) women; 2123 (1.3%) homeless hospitalisations) discharged from 348 hospitals.

OUTCOME MEASURES

Risk-adjusted diagnostic and therapeutic procedure and in-hospital mortality, after adjusting for patient characteristics and state and quarter fixed effects.

RESULTS

At safety-net hospitals, homeless adults hospitalised for AMI were less likely to receive coronary angiogram (adjusted OR (aOR), 0.42; 95% CI, 0.36 to 0.50; p<0.001), percutaneous coronary intervention (aOR, 0.52; 95% CI, 0.44 to 0.62; p<0.001) and coronary artery bypass graft (aOR, 0.43; 95% CI, 0.26 to 0.71; p<0.01) compared with non-homeless adults. Homeless patients treated for strokes at safety-net hospitals were less likely to receive cerebral arteriography (aOR, 0.23; 95% CI, 0.16 to 0.34; p<0.001), but were as likely to receive thrombolysis therapy. At non-safety-net hospitals, we found no evidence that the probability of receiving these procedures differed between homeless and non-homeless adults hospitalised for AMI or stroke. Finally, there were no differences in in-hospital mortality rates for homeless versus non-homeless patients at either safety-net or non-safety-net hospitals.

CONCLUSION

Disparities in receipt of diagnostic and therapeutic procedures for homeless patients with cardiovascular conditions were observed only at safety-net hospitals. However, we found no evidence that these differences influenced in-hospital mortality markedly.

Medicaid Expansion: Effects On Hospital Finances And Implications For Hospitals Facing COVID-19 Challenges

States' decisions to expand Medicaid may have important implications for their hospitals' financial ability to weather the coronavirus disease 2019 (COVID-19) pandemic. This study estimated the effects of the Affordable Care Act (ACA) Medicaid expansion on hospital finances in 2017 to update earlier findings. The analysis also explored how the ACA Medicaid expansion affects different types of hospitals by size, ownership, rurality, and safety-net status. We found that the early positive financial impact of Medicaid expansion was sustained in fiscal years 2016 and 2017 as hospitals in expansion states continued to experience decreased uncompensated care costs and increased Medicaid revenue and financial margins. The magnitude of these impacts varied by hospital type. As COVID-19 has brought hospitals to a time of great need, findings from this study provide important information on what hospitals in states that have yet to expand Medicaid could gain through expansion and what is at risk should any reversal of Medicaid expansions occur.

Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status

IMPORTANCE

Socioeconomic factors are associated with worse outcomes after hospitalization, but neither the Centers for Medicare & Medicaid Services (CMS) nor the Veterans Affairs (VA) health care system adjust for socioeconomic factors in profiling hospital mortality.

OBJECTIVE

To evaluate changes in Veterans Affairs medical centers' (VAMCs') risk-standardized mortality rates among veterans hospitalized for heart failure and pneumonia after adjusting for socioeconomic factors.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, retrospective data were used to assess 131 VAMCs' risk-standardized 30-day mortality rates with or without adjustment for socioeconomic covariates. The study population included 42 892 veterans hospitalized with heart failure and 39 062 veterans hospitalized with pneumonia from January 1, 2012, to December 31, 2014. Data were analyzed from March 1, 2019, to April 1, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was 30-day mortality after admission. Socioeconomic covariates included neighborhood disadvantage, race/ethnicity, homelessness, rurality, nursing home residence, reason for Medicare eligibility, Medicaid and Medicare dual eligibility, and VA priority.

RESULTS

The study population included 42 892 veterans hospitalized with heart failure (98.2% male; mean [SD] age, 71.9 [11.4] years) and 39 062 veterans hospitalized with pneumonia (96.8% male; mean [SD] age, 71.0 [12.4] years). The addition of socioeconomic factors to the CMS models modestly increased the C statistic from 0.77 (95% CI, 0.77-0.78) to 0.78 (95% CI, 0.78-0.78) for 30-day mortality after heart failure and from 0.73 (95% CI, 0.72-0.73) to 0.74 (95% CI, 0.73-0.74) for 30-day mortality after pneumonia. Mortality rates were highly correlated (Spearman correlations of ≥0.98) in models that included or did not include socioeconomic factors. With the use of the CMS model for heart failure, VAMCs in the lowest quintile had a mean (SD) mortality rate of 6.0% (0.4%), those in the middle 3 quintiles had a mean (SD) mortality rate of 7.2% (0.4%), and those in the highest quintile had a mean (SD) mortality rate of 8.8% (0.6%). After the inclusion of socioeconomic covariates, the adjusted mean (SD) mortality was 6.1% (0.4%) for hospitals in the lowest quintile, 7.2% (0.4%) for those in the middle 3 quintiles, and 8.6% (0.5%) for those in the highest quintile. The mean absolute change in rank after socioeconomic adjustment was 3.0 ranking positions (interquartile range, 1.0-4.0) among hospitals in the highest quintile of mortality after heart failure and 4.4 ranking positions (interquartile range, 1.0-6.0) among VAMCs in the lowest quintile. Similar findings were observed for mortality rankings in pneumonia and after inclusion of clinical covariates.

CONCLUSIONS AND RELEVANCE

This study suggests that adjustments for socioeconomic factors did not meaningfully change VAMCs' risk-adjusted 30-day mortality rates for veterans hospitalized for heart failure and pneumonia. The implications of such adjustments should be examined for other quality measures and health systems.

Treatment of Acute Cholecystitis at Safety-Net Hospitals: Analysis of the National Inpatient Sample

Evidence supports index cholecystectomy as the quality of care for patients admitted with acute cholecystitis. We sought to examine the role of hospital safety-net status on whether patients received appropriate index procedures for cholecystitis. The National Inpatient Sample was queried for patients with acute cholecystitis. Proportion of Medicaid and uninsured discharges were used to define safety-net hospitals (SNHs). Multivariate logistic regression was used to calculate associations between the frequency of index cholecystectomy and prolonged length of stay (LOS), and the effect of SNH designation. SNHs and non-SNHs had similar rates of index cholecystectomy in all geographic regions, except in the northeast, where the likelihood of having an index cholecystectomy was lower at SNHs. Patients at SNHs had longer LOS for acute cholecystitis, regardless of index or delayed cholecystectomy. When controlling for insurance status, patients at SNHs had longer LOS than those at non-SNHs. There was also increased LOS in SNHs in the Midwest, in urban hospitals, and in large hospitals. Our data showed no difference in the frequency of index cholecystectomy overall between SNHs and non-SNHs, except in the northeast. The variability and increased LOS at SNHs highlight potential opportunities to improve quality and decrease cost of care at our most vulnerable hospitals.

Increased 30-day readmission rate after craniotomy for tumor resection at safety net hospitals in small metropolitan areas

PURPOSE

Unplanned readmission of post-operative brain tumor patients is often attributed to hospital and patient characteristics and is associated with higher mortality and cost. Previous studies demonstrate multiple patient outcome disparities in safety net hospitals (SNHs) when compared to non-SNHs. This study uses the Nationwide Readmissions Database (NRD) to determine if initial brain tumor resection at SNHs is associated with increased 30-day non-elective readmission rates.

METHODS

Patients with benign or malignant primary or metastatic brain tumor undergoing craniotomy for surgical resection were retrospectively identified in the NRD from 2010 to 2014. SNHs were defined as hospitals with Medicaid and uninsured patient burden in the top quartile. Descriptive and multivariate analyses employing survey-adjusted logistic regression evaluated patient and hospital level factors influencing 30-day readmissions.

RESULTS

During the study period, 83,367 patients met inclusion criteria. 44.7% of patients had a benign tumor, and 55.3% had a malignant tumor. Secondary CNS neoplasm (5.99%), post-operative infection (5.96%), and septicemia (4.26%) caused most readmissions within 30 days. Patients had increased unplanned readmission rates if they underwent craniotomy for tumor resection at a SNH in a small metropolitan area (OR 1.11, 95% CI 1.02-1.21, p = 0.01), but not at a SNH in a large metropolitan area (OR 0.99, 95% CI 0.93-1.05, p = 0.73).

CONCLUSION

This finding may reflect differences in access to care and disparities in neurosurgical resources between small and large metropolitan areas. Inequities in expertise and capacity are relevant as surgical volume was also related to readmission rates. Further studies may be warranted to address such disparities.

Evidence of the Linkage Between Hospital-based Care Coordination Strategies and Hospital Overall (Star) Ratings

BACKGROUND

In the new era of value-based payment models and pay for performance, hospitals are in search of the silver bullet strategy or bundle of strategies capable of improving their performance on quality measures.

OBJECTIVES

To determine whether there is an association between adoption of hospital-based care coordination strategies and Centers for Medicare and Medicaid Services overall hospital quality (star) ratings and readmission rates.

RESEARCH DESIGN

We used survey data from the American Hospital Association (AHA) and categorized respondents by the number of care coordination strategies that they reported having widely implemented. We used multiple logistic regression models to examine the association between the number of strategies and hospital overall rating performance and disease-specific 30-day excess readmission ratios, while controlling for hospital and county characteristics and state-fixed effects.

SUBJECTS

A total of 710 general acute care noncritical access hospitals that received star ratings and responded to the 2015 AHA Care Systems and Payment Survey.

MEASURES

Centers for Medicare and Medicaid Services overall hospital ratings, 30-day excess readmission ratios.

RESULTS

As compared with hospitals with 0-2 strategies, hospitals with 3 to 4 strategies (P=0.007), 5-7 strategies (P=0.002), or 8-12 strategies (P=0.002) had approximately 2.5× the odds of receiving a top rating (4 or 5 stars). Care coordination strategies were positively associated with lower 30-day readmission ratios for patients with chronic medical conditions, but not for surgical patients. Medication reconciliation, visit summaries, outreach after discharge, discharge care plans, and disease management programs were each individually associated with top ratings.

CONCLUSIONS

Care coordination strategies are associated with high overall hospital ratings.

Comparison of 3 Safety-Net Hospital Definitions and Association With Hospital Characteristics

IMPORTANCE

No consensus exists on how to define safety-net hospitals (SNHs) for research or policy decision-making. Identifying which types of hospitals are classified as SNHs under different definitions is key to assessing policies that affect SNH funding.

OBJECTIVE

To examine characteristics of SNHs as classified under 3 common definitions.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional analysis includes noncritical-access hospitals in the Healthcare Cost and Utilization Project State Inpatient Databases from 47 US states for fiscal year 2015, linked to the Centers for Medicare & Medicaid Services Hospital Cost Reports and to the American Hospital Association Annual Survey. Data were analyzed from March 1 through September 30, 2018.

EXPOSURES

Hospital characteristics including organizational characteristics, scope of services provided, and financial attributes.

MAIN OUTCOMES AND MEASURES

Definitions of SNH based on Medicaid and Medicare Supplemental Security Income inpatient days historically used to determine Medicare Disproportionate Share Hospital (DSH) payments; Medicaid and uninsured caseload; and uncompensated care costs. For each measure, SNHs were defined as those within the top quartile for each state.

RESULTS

The 2066 hospitals in this study were distributed across the Northeast (340 [16.5%]), Midwest (587 [28.4%]), South (790 [38.2%]), and West (349 [16.9%]). Concordance between definitions was low; 269 hospitals (13.0%) or fewer were identified as SNHs under any 2 definitions. Uncompensated care captured smaller (200 of 523 [38.2%]) and more rural (65 of 523 [12.4%]) SNHs, whereas DSH index and Medicaid and uncompensated caseload identified SNHs that were larger (264 of 518 [51.0%] and 158 of 487 [32.4%], respectively) and teaching facilities (337 of 518 [65.1%] and 229 of 487 [47.0%], respectively) that provided more essential services than non-SNHs. Uncompensated care also distinguished remarkable financial differences between SNHs and non-SNHs. Under the uncompensated care definition, median (interquartile range [IQR]) bad debt ($27.1 [$15.5-$44.3] vs $12.8 [$6.7-$21.6] per $1000 of operating expenses; P < .001) and charity care ($19.9 [$9.3-$34.1] vs $9.1 [$4.0-$18.7] per $1000 of operating expenses) were twice as high and median (IQR) unreimbursed costs ($32.6 [$12.4-$55.4] vs $23.6 [$9.0-$42.7] per $1000 of operating expenses; P < .001) were 38% higher for SNHs than for non-SNHs. Safety-net hospitals defined by uncompensated care burden had lower median (IQR) total (4.7% [0%-9.9%] vs 5.8% [1.2%-11.2%]; P = .003) and operating (0.3% [-8.0% to 7.2%] vs 2.3% [-3.9% to 8.9%]; P < .001) margins than their non-SNH counterparts, whereas differences between SNH and non-SNH profit margins generally were not statistically significant under the other 2 definitions.

CONCLUSIONS AND RELEVANCE

Different SNH definitions identify hospitals with different characteristics and financial conditions. The new DSH formula, which accounts for uncompensated care, may lead to redistributed payments across hospitals. Our results may inform which types of hospitals will experience funding changes as DSH payment policies evolve.

Early predictors of outcomes of hospitalization for cirrhosis and assessment of the impact of race and ethnicity at safety-net hospitals

Background

Safety-net hospitals provide care for racially/ethnically diverse and disadvantaged urban populations. Their hospitalized patients with cirrhosis are relatively understudied and may be vulnerable to poor outcomes and racial/ethnic disparities.

Aims

To examine the outcomes of patients with cirrhosis hospitalized at regionally diverse safety-net hospitals and the impact of race/ethnicity.

Methods

A study of patients with cirrhosis hospitalized at 4 safety-net hospitals in 2012 was conducted. Demographic, clinical factors, and outcomes were compared between centers and racial/ethnic groups. Study endpoints included mortality and 30-day readmission.

Results

In 2012, 733 of 1,212 patients with cirrhosis were hospitalized for liver-related indications (median age 55 years, 65% male). The cohort was racially diverse (43% White, 25% black, 22% Hispanic, 3% Asian) with cirrhosis related to alcohol and viral hepatitis in 635 (87%) patients. Patients were hospitalized mainly for ascites (35%), hepatic encephalopathy (20%) and gastrointestinal bleeding (GIB) (17%). Fifty-four (7%) patients died during hospitalization and 145 (21%) survivors were readmitted within 30 days. Mortality rates ranged from 4 to 15% by center (p = .007) and from 3 to 10% by race/ethnicity (p = .03), but 30-day readmission rates were similar. Mortality was associated with Model for End-stage Liver Disease (MELD), acute-on-chronic liver failure, hepatocellular carcinoma, sodium and white blood cell count. Thirty-day readmission was associated with MELD and Charlson Comorbidity Index >4, with lower risk for GIB. We did not observe geographic or racial/ethnic differences in hospital outcomes in the risk-adjusted analysis.

Conclusions

Hospital mortality and 30-day readmission in patients with cirrhosis at safety-net hospitals are associated with disease severity and comorbidities, with lower readmissions in patients admitted for GIB. Despite geographic and racial/ethnic differences in hospital mortality, these factors were not independently associated with mortality.

Assessing the Quality of Microvascular Breast Reconstruction Performed in the Urban Safety-Net Setting: A Doubly Robust Regression Analysis

BACKGROUND

Safety-net hospitals serve vulnerable populations; however, care delivery may be of lower quality. Microvascular immediate breast reconstruction, relative to other breast reconstruction subtypes, is sensitive to the performance of safety-net hospitals and an important quality marker. The authors' aim was to assess the quality of care associated with safety-net hospital setting.

METHODS

The 2012 to 2014 National Inpatient Sample was used to identify patients who underwent microvascular immediate breast reconstruction after mastectomy. Primary outcomes of interest were rates of medical complications, surgical inpatient complications, and prolonged length of stay. A doubly-robust approach (i.e., propensity score and multivariate regression) was used to analyze the impact of patient and hospital-level characteristics on outcomes.

RESULTS

A total of 858 patients constituted our analytic cohort following propensity matching. There were no significant differences in the odds of surgical and medical inpatient complications among safety-net hospital patients relative to their matched counterparts. Black (OR, 2.95; p < 0.001) and uninsured patients (OR, 2.623; p = 0.032) had higher odds of surgical inpatient complications. Safety-net hospitals (OR, 1.745; p = 0.005), large bedsize hospitals (OR, 2.170; p = 0.023), and Medicaid patients (OR, 1.973; p = 0.008) had higher odds of prolonged length of stay.

CONCLUSIONS

Safety-net hospitals had comparable odds of adverse clinical outcomes but higher odds of prolonged length of stay, relative to non-safety-net hospitals. Institution-level deficiencies in staffing and clinical processes of care might underpin the latter. Ongoing financial support of these institutions will ensure delivery of needed breast cancer care to economically disadvantaged patients.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, III.

Changes to Racial Disparities in Readmission Rates After Medicare's Hospital Readmissions Reduction Program Within Safety-Net and Non-Safety-Net Hospitals

Importance

Although readmission rates are declining under Medicare's Hospital Readmissions Reduction Program (HRRP), concerns remain that the HRRP will harm quality at safety-net hospitals because they are penalized more often. Disparities between white and black patients might widen because more black patients receive care at safety-net hospitals. Disparities may be particularly worse for clinical conditions not targeted by the HRRP because hospitals might reallocate resources toward targeted conditions (acute myocardial infarction, pneumonia, and heart failure) at the expense of nontargeted conditions.

Objective

To examine disparities in readmission rates between white and black patients discharged from safety-net or non-safety-net hospitals after the HRRP began, evaluating discharges for any clinical condition and the subsets of targeted and nontargeted conditions.

Design, Setting, and Participants

Cohort study conducting quasi-experimental analyses of patient hospital discharges for any clinical condition among fee-for-service Medicare beneficiaries from 2007 to 2015 after controlling for patient and hospital characteristics. Changes in disparities were measured within safety-net and non-safety-net hospitals after the HRRP penalties were enforced and compared with prior trends. These analyses were then stratified by targeted and nontargeted conditions. Analyses were conducted from October 1, 2017, through August 31, 2018.

Main Outcomes and Measures

Trends in 30-day readmission rates among white and black patients by quarter and differences in trends across periods.

Results

The study sample included 58 237 056 patient discharges (black patients, 9.8%; female, 57.7%; mean age [SD] age, 78.8 [7.9] years; nontargeted conditions, 50 372 806 [86.5%]). Within safety-net hospitals, disparities in readmission rates for all clinical conditions widened between black and white patients by 0.04 percentage point per quarter in the HRRP penalty period (95% CI, 0.01 to 0.07; P = .01). This widening was driven by nontargeted conditions (0.05 percentage point per quarter [95% CI, 0.01 to 0.08]; P = .006), whereas disparities for the HRRP-targeted conditions did not change (with an increase of 0.01 percentage point per quarter [95% CI, -0.07 to 0.10]; P = .74). Within non-safety-net hospitals, racial disparities remained stable in the HRRP penalty period across all conditions, whether the conditions were HRRP-targeted or nontargeted.

Conclusions and Relevance

Findings from this study suggest that disparities are widening within safety-net hospitals, specifically for non-HRRP-targeted conditions. Although increases in racial disparities for nontargeted conditions were modest, they represent 6 times more discharges in our cohort than targeted conditions.

Association Between Postdischarge Emergency Department Visitation and Readmission Rates

BACKGROUND

Hospital readmission rates are publicly reported by the Centers for Medicare & Medicaid Services (CMS); however, the implications of emergency department (ED) visits following hospital discharge on readmissions are uncertain. We describe the frequency, diagnoses, and hospital-level variation in ED visitation following hospital discharge, including the relationship between risk-standardized ED visitation and readmission rates.

METHODS

This is a cross-sectional analysis of Medicare beneficiaries hospitalized for acute myocardial infarction (AMI), heart failure, and pneumonia between July 2011 and June 2012. We used Medicare Standard Analytic Files to identify admissions, readmissions, and ED visits consistent with CMS measures. Postdischarge ED visits were defined as treat-and-discharge ED services within 30 days of hospitalization without readmission. We utilized hierarchical generalized linear models to calculate hospital risk-standardized postdischarge ED visit rates and readmission rates.

RESULTS

We included 157,035 patients hospitalized at 1656 hospitals for AMI, 391,209 at 3044 hospitals for heart failure, and 342,376 at 3484 hospitals for pneumonia. After hospitalization for AMI, heart failure, and pneumonia, there were 14,714 (9%), 31,621 (8%), and 26,681 (8%) ED visits, respectively. Hospital-level variation in postdischarge ED visit rates was substantial: AMI (median: 8.3%; 5th and 95th percentile: 2.8%-14.3%), heart failure (median: 7.3%; 5th and 95th percentile: 3.0%-13.3%), and pneumonia (median: 7.1%; 5th and 95th percentile: 2.4%-13.2%). There was statistically significant inverse correlation between postdischarge ED visit rates and readmission rates: AMI (-0.23), heart failure (-0.29), and pneumonia (-0.18).

CONCLUSIONS

Following hospital discharge, ED treatand- discharge visits are half as common as readmissions for Medicare beneficiaries. There is wide hospital-level variation in postdischarge ED visitation, and hospitals with higher ED visitation rates demonstrated lower readmission rates.

Patterns of Hospital Performance on the Hospital-Wide 30-Day Readmission Metric: Is the Playing Field Level?

BACKGROUND

Hospital performance on the 30-day hospital-wide readmission (HWR) metric as calculated by the Centers for Medicare and Medicaid Services (CMS) is currently reported as a quality measure. Focusing on patient-level factors may provide an incomplete picture of readmission risk at the hospital level to explain variations in hospital readmission rates.

OBJECTIVE

To evaluate and quantify hospital-level characteristics that track with hospital performance on the current HWR metric.

DESIGN

Retrospective cohort study.

SETTING/PATIENTS

A total of 4785 US hospitals.

METRICS

We linked publically available data on individual hospitals published by CMS on patient-level adjusted 30-day HWR rates from July 1, 2011, through June 30, 2014, to the 2014 American Hospital Association annual survey. Primary outcome was performance in the worst CMS-calculated HWR quartile. Primary hospital-level exposure variables were defined as: size (total number of beds), safety net status (top quartile of disproportionate share), academic status [member of the Association of American Medical Colleges (AAMC)], National Cancer Institute Comprehensive Cancer Center (NCI-CCC) status, and hospital services offered (e.g., transplant, hospice, emergency department). Multilevel regression was used to evaluate the association between 30-day HWR and the hospital-level factors.

RESULTS

Hospital-level characteristics significantly associated with performing in the worst CMS-calculated HWR quartile included: safety net status [adjusted odds ratio (aOR) 1.99, 95% confidence interval (95% CI) 1.61-2.45, p < 0.001], large size (> 400 beds, aOR 1.42, 95% CI 1.07-1.90, p = 0.016), AAMC alone status (aOR 1.95, 95% CI 1.35-2.83, p < 0.001), and AAMC plus NCI-CCC status (aOR 5.16, 95% CI 2.58-10.31, p < 0.001). Hospitals with more critical care beds (aOR 1.26, 95% CI 1.02-1.56, p = 0.033), those with transplant services (aOR 2.80, 95% CI 1.48-5.31,p = 0.001), and those with emergency room services (aOR 3.37, 95% CI 1.12-10.15, p = 0.031) demonstrated significantly worse HWR performance. Hospice service (aOR 0.64, 95% CI 0.50-0.82, p < 0.001) and having a higher proportion of total discharges being surgical cases (aOR 0.62, 95% CI 0.50-0.76, p < 0.001) were associated with better performance.

LIMITATION

The study approach was not intended to be an alternate readmission metric to compete with the existing CMS metric, which would require a re-examination of patient-level data combined with hospital-level data.

CONCLUSION

A number of hospital-level characteristics (such as academic tertiary care center status) were significantly associated with worse performance on the CMS-calculated HWR metric, which may have important health policy implications. Until the reasons for readmission variability can be addressed, reporting the current HWR metric as an indicator of hospital quality should be reevaluated.

Outcomes and Costs of Managing Appendicitis at Safety-Net Hospitals

IMPORTANCE

Safety-net hospitals serve vulnerable populations with limited resources. Although complex, elective operations performed at safety-net hospitals have been associated with inferior outcomes and higher costs, it is unclear whether a similar association has been seen with common emergency general surgery performed at safety-net hospitals.

OBJECTIVE

To evaluate the association of safety-net burden with the outcomes of appendectomy.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective review was conducted of all nonfederally funded hospitals in the California state inpatient database that performed appendectomies from January 1, 2005, to December 31, 2011. A total of 349 hospitals performing 274 405 nonincidental appendectomies were stratified based on safety-net burden; low-burden hospitals had the lowest quartile of patients who either had Medicaid or were uninsured (0%-14%), medium-burden hospitals had the middle 2 quartiles (15%-41%), and high-burden hospitals had the highest quartile (>42%). Data analysis was performed from August 27 to September 8, 2016.

MAIN OUTCOMES AND MEASURES

Rates of laparoscopy, perforation, negative appendectomy, morbidity, length of stay, and cost.

RESULTS

Among the 349 hospitals in the study, high-burden hospitals treated a larger proportion of black patients than did medium- and low-burden hospitals (4.5% vs 2.4% vs 2.9%; P = .01), as well as Hispanic patients (64.8% vs 27.0% vs 22.0%; P < .001) and patients with perforated appendicitis (27.6% vs 23.6% vs 23.6%; P = .005). High-burden hospitals were less likely than medium- or low-burden hospitals to use laparoscopy (51.6% vs 60.7% vs 71.9%; P < .001). There were no differences in morbidity, length of stay, or cost. Multivariable regression analysis confirmed that high-burden hospitals were more likely than low-burden hospitals to treat perforated appendicitis (log %, 0.07; 95% CI, 0.03-0.12; P = .04) and less likely to use laparoscopy (-16.9% difference; 95% CI, -26.1% to -7.6%; P < .001), while achieving similar complication rates. Multivariable analysis also confirmed that high-burden hospitals have similar costs, despite being associated with longer length of stay (relative risk, 1.17; 95% CI, 1.09-1.26; P < .001).

CONCLUSIONS AND RELEVANCE

Safety-net hospitals treat a disproportionate number of patients with advanced appendicitis while falling behind in the use of laparoscopy. Nonetheless, safety-net hospitals treat this common surgical emergency with morbidity and cost similar to that seen at other hospitals. Additional research is needed to evaluate how these outcomes are achieved to improve all surgical outcomes at underresourced hospitals.

Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects

BACKGROUND

To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year.

METHODS

We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles.

RESULTS

In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001).

CONCLUSIONS

When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.).

Trends in readmission rates for safety net hospitals and non-safety net hospitals in the era of the US Hospital Readmission Reduction Program: a retrospective time series analysis using Medicare administrative claims data from 2008 to 2015

OBJECTIVE

To compare trends in readmission rates among safety net and non-safety net hospitals under the US Hospital Readmission Reduction Program (HRRP).

DESIGN

A retrospective time series analysis using Medicare administrative claims data from January 2008 to June 2015.

SETTING

We examined 3254 US hospitals eligible for penalties under the HRRP, categorised as safety net or non-safety net hospitals based on the hospital's proportion of patients with low socioeconomic status.

PARTICIPANTS

Admissions for Medicare fee-for-service patients, age ≥65 years, discharged alive, who had a valid five-digit zip code and did not have a principal discharge diagnosis of cancer or psychiatric illness were included, for a total of 52 516 213 index admissions.

PRIMARY AND SECONDARY OUTCOME MEASURES

Mean hospital-level, all-condition, 30-day risk-adjusted standardised unplanned readmission rate, measured quarterly, along with quarterly rate of change, and an interrupted time series examining: April-June 2010, after HRRP was passed, and October-December 2012, after HRRP penalties were implemented.

RESULTS

58.0% (SD 15.3) of safety net hospitals and 17.1% (SD 10.4) of non-safety net hospitals' patients were in the lowest quartile of socioeconomic status. The mean safety net hospital standardised readmission rate declined from 17.0% (SD 3.7) to 13.6% (SD 3.6), whereas the mean non-safety net hospital declined from 15.4% (SD 3.0) to 12.7% (SD 2.5). The absolute difference in rates between safety net and non-safety net hospitals declined from 1.6% (95% CI 1.3 to 1.9) to 0.9% (0.7 to 1.2). The quarterly decline in standardised readmission rates was 0.03 percentage points (95% CI 0.03 to 0.02, p<0.001) greater among safety net hospitals over the entire study period, and no differential change among safety net and non-safety net hospitals was found after either HRRP was passed or penalties enacted.

CONCLUSIONS

Since HRRP was passed and penalties implemented, readmission rates for safety net hospitals have decreased more rapidly than those for non-safety net hospitals.

Hospital Use of Observation Stays: Cross-sectional Study of the Impact on Readmission Rates

BACKGROUND

The Centers for Medicare and Medicaid Services publicly reports hospital risk-standardized readmission rates (RSRRs) as a measure of quality and performance; mischaracterizations may occur because observation stays are not captured by current measures.

OBJECTIVES

To describe variation in hospital use of observation stays, the relationship between hospitals observation stay use and RSRRs.

MATERIALS AND METHODS

Cross-sectional analysis of Medicare fee-for-service beneficiaries discharged after acute myocardial infarction (AMI), heart failure, or pneumonia between July 2011 and June 2012. We calculated 3 hospital-specific 30-day outcomes: (1) observation rate, the proportion of all discharges followed by an observation stay without a readmission; (2) observation proportion, the proportion of observation stays among all patients with an observation stay or readmission; and (3) RSRR.

RESULTS

For all 3 conditions, hospitals' observation rates were <2.5% and observation proportions were <12%, although there was variation across hospitals, including 28% of hospital with no observation stay use for AMI, 31% for heart failure, and 43% for pneumonia. There were statistically significant, but minimal, correlations between hospital observation rates and RSRRs: AMI (r=-0.02), heart failure (r=-0.11), and pneumonia (r=-0.02) (P<0.001). There were modest inverse correlations between hospital observation proportion and RSRR: AMI (r=-0.34), heart failure (r=-0.26), and pneumonia (r=-0.21) (P<0.001). If observation stays were included in readmission measures, <4% of top performing hospitals would be recategorized as having average performance.

CONCLUSIONS

Hospitals' observation stay use in the postdischarge period is low, but varies widely. Despite modest correlation between the observation proportion and RSRR, counting observation stays in readmission measures would minimally impact public reporting of performance.

Demographic and socioeconomic factors influencing disparities in prevalence of alcohol-related injury among underserved trauma patients in a safety-net hospital

BACKGROUND

Alcohol-related trauma remains high among underserved patients despite ongoing preventive measures. Geographic variability in prevalence of alcohol-related injury has prompted reexamination of this burden across different regions. We sought to elucidate demographic and socioeconomic factors influencing the prevalence of alcohol-related trauma among underserved patients and determine alcohol effects on selected outcomes.

METHODS

A retrospective analysis examined whether patients admitted to a suburban trauma center differed according to their blood alcohol concentration (BAC) on admission. Patients were stratified based on their BAC into four categories (undetectable BAC, BAC 1-99mg/dL, BAC 100-199mg/dL, and BAC ≥ 200mg/dL). T-tests and X2 tests were used to detect differences between BAC categories in terms of patient demographics and clinical outcomes. Multivariate linear and logistic regressions were used to investigate the association between patient variables and selected outcomes while controlling for confounders.

RESULTS

One third of 738 patients analyzed were BAC-positive, mean (SD) BAC was 211.4 (118.9) mg/dL, 80% of BAC-positive patients had levels ≥ 100mg/dL. After risk adjustments, the following patient characteristics were predictive of having highly elevated BAC (≥200mg/dL) upon admission to the Trauma Center; Hispanic patients (adjusted odds ratio (OR)=1.91, 95% confidence interval (CI): 1.14-3.21), unemployment (OR=1.74, 95% CI: 1.09-2.78), Medicaid beneficiaries (OR=3.59, 95% CI: 1.96-6.59), and uninsured patients (OR=2.86, 95% CI: 1.60-5.13). Patients with BAC of 100-199mg/dL were likely to be more severely injured (P=0.016) compared to undetectable-BAC patients. There was no association between being intoxicated, and being ICU-admitted or having differences in length of ICU or hospital stay.

CONCLUSION

Demographic and socioeconomic factors underlie disparities in the prevalence of alcohol-related trauma among underserved patients. These findings may guide targeted interventions toward specific populations to help reduce the burden of alcohol-related injury.

Accounting For Patients' Socioeconomic Status Does Not Change Hospital Readmission Rates

There is an active public debate about whether patients' socioeconomic status should be included in the readmission measures used to determine penalties in Medicare's Hospital Readmissions Reduction Program (HRRP). Using the current Centers for Medicare and Medicaid Services methodology, we compared risk-standardized readmission rates for hospitals caring for high and low proportions of patients of low socioeconomic status (as defined by their Medicaid status or neighborhood income). We then calculated risk-standardized readmission rates after additionally adjusting for patients' socioeconomic status. Our results demonstrate that hospitals caring for large proportions of patients of low socioeconomic status have readmission rates similar to those of other hospitals. Moreover, readmission rates calculated with and without adjustment for patients' socioeconomic status are highly correlated. Readmission rates of hospitals caring for patients of low socioeconomic status changed by approximately 0.1 percent with adjustment for patients' socioeconomic status, and only 3-4 percent fewer such hospitals reached the threshold for payment penalty in Medicare's HRRP. Overall, adjustment for socioeconomic status does not change hospital results in meaningful ways.

Impact of Risk Adjustment for Socioeconomic Status on Risk-adjusted Surgical Readmission Rates

OBJECTIVE

To assess whether differences in readmission rates between safety-net hospitals (SNH) and non-SNHs are due to differences in hospital quality, and to compare the results of hospital profiling with and without SES adjustment.

BACKGROUND

In response to concerns that quality measures unfairly penalizes SNH, NQF recently recommended that performance measures adjust for socioeconomic status (SES) when SES is a risk factor for poor patient outcomes.

METHODS

Multivariate regression was used to examine the association between SNH status and 30-day readmission after major surgery. The results of hospital profiling with and without SES adjustment were compared using the CMS Hospital Compare and the Hospital Readmissions Reduction Program (HRRP) methodologies.

RESULTS

Adjusting for patient risk and SES, patients admitted to SNHs were not more likely to be readmitted compared with patients in in non-SNHs (AOR 1.08; 95% CI:0.95-1.23; P = 0.23). The results of hospital profiling based on Hospital Compare were nearly identical with and without SES adjustment (ICC 0.99, κ 0.96). Using the HRRP threshold approach, 61% of SNHs were assigned to the penalty group versus 50% of non-SNHs. After adjusting for SES, 51% of SNHs were assigned to the penalty group.

CONCLUSIONS

Differences in surgery readmissions between SNHs and non-SNHs are due to differences in the patient case mix of low-SES patients, and not due to differences in quality. Adjusting readmission measures for SES leads to changes in hospital ranking using the HRRP threshold approach, but not using the CMS Hospital Compare methodology. CMS should consider either adjusting for the effects of SES when calculating readmission thresholds for HRRP, or replace it with the approach used in Hospital Compare.

Current State of Value-Based Purchasing Programs

The US healthcare system is rapidly moving toward rewarding value. Recent legislation, such as the Affordable Care Act and the Medicare Access and CHIP Reauthorization Act, solidified the role of value-based payment in Medicare. Many private insurers are following Medicare's lead. Much of the policy attention has been on programs such as accountable care organizations and bundled payments; yet, value-based purchasing (VBP) or pay-for-performance, defined as providers being paid fee-for-service with payment adjustments up or down based on value metrics, remains a core element of value payment in Medicare Access and CHIP Reauthorization Act and will likely remain so for the foreseeable future. This review article summarizes the current state of VBP programs and provides analysis of the strengths, weaknesses, and opportunities for the future. Multiple inpatient and outpatient VBP programs have been implemented and evaluated; the impact of those programs has been marginal. Opportunities to enhance the performance of VBP programs include improving the quality measurement science, strengthening both the size and design of incentives, reducing health disparities, establishing broad outcome measurement, choosing appropriate comparison targets, and determining the optimal role of VBP relative to alternative payment models. VBP programs will play a significant role in healthcare delivery for years to come, and they serve as an opportunity for providers to build the infrastructure needed for value-oriented care.

Association of Admission to Veterans Affairs Hospitals vs Non-Veterans Affairs Hospitals With Mortality and Readmission Rates Among Older Men Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia

IMPORTANCE

Little contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.

OBJECTIVE

To assess and compare mortality and readmission rates among men in VA and non-VA hospitals.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional analysis involving male Medicare fee-for-service beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).

EXPOSURES

Hospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital.

MAIN OUTCOMES AND MEASURES

For each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed.

RESULTS

We studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; ≥65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; -0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; -0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, -0.22; 95% CI, -0.40 to -0.04) and HF (-0.63; 95% CI, -0.95 to -0.31), and mortality rates for pneumonia were not significantly different (-0.03; 95% CI, -0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).

CONCLUSIONS AND RELEVANCE

Among older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small.

Race/Ethnicity, Primary Language, and Income Are Not Demographic Drivers of Mortality in Breast Cancer Patients at a Diverse Safety Net Academic Medical Center

Objective. To examine the impact of patient demographics on mortality in breast cancer patients receiving care at a safety net academic medical center. Patients and Methods. 1128 patients were diagnosed with breast cancer at our institution between August 2004 and October 2011. Patient demographics were determined as follows: race/ethnicity, primary language, insurance type, age at diagnosis, marital status, income (determined by zip code), and AJCC tumor stage. Multivariate logistic regression analysis was performed to identify factors related to mortality at the end of follow-up in March 2012. Results. There was no significant difference in mortality by race/ethnicity, primary language, insurance type, or income in the multivariate adjusted model. An increased mortality was observed in patients who were single (OR = 2.36, CI = 1.28–4.37, p = 0.006), age > 70 years (OR = 3.88, CI = 1.13–11.48, p = 0.014), and AJCC stage IV (OR = 171.81, CI = 59.99–492.06, p < 0.0001). Conclusions. In this retrospective study, breast cancer patients who were single, presented at a later stage, or were older had increased incidence of mortality. Unlike other large-scale studies, non-White race, non-English primary language, low income, or Medicaid insurance did not result in worse outcomes.

The Financial Effect of Value-Based Purchasing and the Hospital Readmissions Reduction Program on Safety-Net Hospitals in 2014: A Cohort Study

BACKGROUND

Medicare's value-based purchasing (VBP) and the Hospital Readmissions Reduction Program (HRRP) could disproportionately affect safety-net hospitals.

OBJECTIVE

To determine whether safety-net hospitals incur larger financial penalties than other hospitals under VBP and HRRP.

DESIGN

Cross-sectional analysis.

SETTING

United States in 2014.

PARTICIPANTS

3022 acute care hospitals participating in VBP and the HRRP.

MEASUREMENTS

Safety-net hospitals were defined as being in the top quartile of the Medicare disproportionate share hospital (DSH) patient percentage and Medicare uncompensated care (UCC) payments per bed. The differences in penalties in both total dollars and dollars per bed between safety-net hospitals and other hospitals were estimated with the use of bivariate and graphical regression methods.

RESULTS

Safety-net hospitals in the top quartile of each measure were more likely to be penalized under VBP than other hospitals (62.9% vs. 51.0% under the DSH definition and 60.3% vs. 51.5% under the UCC per-bed definition). This was also the case under the HRRP (80.8% vs. 69.0% and 81.9% vs. 68.7%, respectively). Safety-net hospitals also had larger payment penalties ($115 900 vs. $66 600 and $150 100 vs. $54 900, respectively). On a per-bed basis, this translated to $436 versus $332 and $491 versus $314, respectively. Sensitivity analysis setting the cutoff at the top decile rather than the top quartile decile led to similar conclusions with somewhat larger differences between safety-net and other hospitals. The quadratic fit of the data indicated that the larger effect of these penalties is in the middle of the distribution of the DSH and UCC measures.

LIMITATION

Only 2 measures of safety-net status were included in the analyses.

CONCLUSION

Safety-net hospitals were disproportionately likely to be affected under VBP and the HRRP, but most incurred relatively small payment penalties in 2014.

PRIMARY FUNDING SOURCE

Patient-Centered Outcomes Research Institute.

Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study

BACKGROUND

Measures of socioeconomic disadvantage may enable improved targeting of programs to prevent rehospitalizations, but obtaining such information directly from patients can be difficult. Measures of U.S. neighborhood socioeconomic disadvantage are more readily available but are rarely used clinically.

OBJECTIVE

To evaluate the association between neighborhood socioeconomic disadvantage at the census block group level, as measured by the Singh validated area deprivation index (ADI), and 30-day rehospitalization.

DESIGN

Retrospective cohort study.

SETTING

United States.

PATIENTS

Random 5% national sample of Medicare patients discharged with congestive heart failure, pneumonia, or myocardial infarction between 2004 and 2009 (n = 255,744).

MEASUREMENTS

Medicare data were linked to 2000 census data to construct an ADI for each patient's census block group, which were then sorted into percentiles by increasing ADI. Relationships between neighborhood ADI grouping and 30-day rehospitalization were evaluated using multivariate logistic regression models, controlling for patient sociodemographic characteristics, comorbid conditions and severity, and index hospital characteristics.

RESULTS

The 30-day rehospitalization rate did not vary significantly across the least disadvantaged 85% of neighborhoods, which had an average rehospitalization rate of 21%. However, within the most disadvantaged 15% of neighborhoods, rehospitalization rates increased from 22% to 27% with worsening ADI. This relationship persisted after full adjustment, with the most disadvantaged neighborhoods having a rehospitalization risk (adjusted risk ratio, 1.09 [95% CI, 1.05 to 1.12]) similar to that of chronic pulmonary disease (adjusted risk ratio, 1.06 [CI, 1.04 to 1.08]) and greater than that of uncomplicated diabetes (adjusted risk ratio, 0.95 [CI, 0.94 to 0.97]).

LIMITATION

No direct markers of care quality or access.

CONCLUSION

Residence within a disadvantaged U.S. neighborhood is a rehospitalization predictor of magnitude similar to chronic pulmonary disease. Measures of neighborhood disadvantage, such as the ADI, could potentially be used to inform policy and care after hospital discharge.

PRIMARY FUNDING SOURCE

National Institute on Aging and University of Wisconsin School of Medicine and Public Health's Institute for Clinical and Translational Research and Health Innovation Program.

Strategies to Reduce 30-Day Readmissions in Older Patients Hospitalized with Heart Failure and Acute Myocardial Infarction

Readmission within 30 days after hospital discharge for common cardiovascular conditions such as heart failure and acute myocardial infarction is extremely common among older persons. To incentivize investment in reducing preventable rehospitalizations, the United States federal government has directed increasing financial penalties to hospitals with higher-than-expected 30-day readmission rates. Uncertainty exists, however, regarding the best approaches to reducing these adverse outcomes. In this review, we summarize the literature on predictors of 30-day readmission, the utility of risk prediction models, and strategies to reduce short-term readmission after hospitalization for heart failure and acute myocardial infarction. We report that few variables have been found to consistently predict the occurrence of 30-day readmission and that risk prediction models lack strong discriminative ability. We additionally report that the literature on interventions to reduce 30-day rehospitalization has significant limitations due to heterogeneity, susceptibility to bias, and lack of reporting on important contextual factors and details of program implementation. New information is characterizing the period after hospitalization as a time of high generalized risk, which has been termed the post-hospital syndrome. This framework for characterizing inherent post-discharge instability suggests new approaches to reducing readmissions.

Hospital readmission performance and patterns of readmission: retrospective cohort study of Medicare admissions

OBJECTIVES

To determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.

DESIGN

Retrospective cohort study.

SETTING

Medicare beneficiaries in the United States.

PARTICIPANTS

Patients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09.

MAIN OUTCOME MEASURES

Readmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services' condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥ 95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.

RESULTS

For readmissions in the 30 days after the index admission, there were 320,003 after 1,291,211 admissions for heart failure (4041 hospitals), 102,536 after 517,827 admissions for acute myocardial infarction (2378 hospitals), and 208,438 after 1,135,932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.

CONCLUSIONS

High performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.