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

Neighborhood Socioeconomic Disadvantage and Care After Myocardial Infarction in the National Cardiovascular Data Registry

Background:

Patients living in disadvantaged neighborhoods are at high risk for adverse outcomes after acute myocardial infarction (MI). Whether residential socioeconomic status (SES) is associated with quality of in-hospital care among patients presenting with MI is unclear.

Methods and Results:

Multivariable logistic regression was used to examine the relationship between SES, quality of care, and in-hospital cardiovascular outcomes among patients with MI from diverse SES neighborhoods from July 2008 to December 2013, at 586 participating hospitals in the Acute Coronary Treatment and Intervention Outcomes Network Registry–Get With The Guidelines quality improvement program. Patients were categorized according to which SES summary measure group they resided in through linkage with US census block data. Outcomes were in-hospital mortality and major adverse cardiovascular events. Quality of MI care was assessed with the defect-free care measure that delineates the proportion of eligible patients who received all acute and discharge guideline-recommended therapies. Among 390 692 patients, there was a substantially longer median arrival-to-angiography time in lower SES neighborhoods (lowest 8.0 hours, low 5.5 hours, medium 4.8 hours, high 4.5 hours, highest 3.4 hours; P <0.0001), and a higher proportion of ST-segment–elevation myocardial infarction patients treated with fibrinolysis (lowest 23.1%, low 20.2%, medium 18.0%, high 14.2%, highest 5.9%; P <0.0001). However, after adjustment for clinical risk factors, insurance status, and hospital characteristics, socioeconomic disadvantage was not associated with lower rates of guideline-recommended defect-free acute care. Patients presenting from more disadvantaged neighborhoods had a progressively higher independent risk of in-hospital mortality ( P global =0.03) and major bleeding ( P global <0.001), along with lower quality of discharge care.

Conclusions:

In this national registry of MI, patients living in the most disadvantaged neighborhoods received equitable in-hospital care compared with advantaged neighborhoods. However, they experienced substantial delays in receiving angiography. Furthermore, patients living in disadvantaged neighborhoods remain at higher risk of adverse in-hospital outcomes after MI, including mortality. These observations suggest there are further opportunities for improvement in acute and discharge MI care.

When good operations go bad: The additive effect of comorbidity and postoperative complications on readmission after pulmonary lobectomy

BACKGROUND

Hospital readmission after major thoracic surgery has a marked effect on health care delivery, particularly in the era of value-based reimbursement. We sought to investigate the additive impact of comorbidity and postoperative complications on the risk of readmission after thoracic lobectomy.

METHODS

We queried the Nationwide Readmission Database of the Healthcare Cost and Utilization Project between 2010 and 2014 for discharges after pulmonary lobectomy with a primary diagnosis of lung cancer. We compared 90-day all-cause readmission rates across the presence of Elixhauser comorbidities and postoperative complications. Adjusted logistic and linear regression, accounting for patient and hospital factors were used to calculate the mean change in readmission rate by the number of comorbidities and postoperative complications.

RESULTS

A total of 87,894 patients undergoing pulmonary lobectomies were identified during the study period, of whom 15,858 (18.0%) were readmitted for any cause within 90 days of discharge. After adjusting for other factors, each additional comorbidity and postoperative complication were associated with a 2.0% and 2.7% increased probability of readmission, respectively (both P < .0001). Patients with a low burden of low comorbidities were readmitted more frequently for postoperative complications, while those with a high burden of comorbidities were readmitted more frequently for chronic disease.

CONCLUSION

Among patients with the lowest risk profile, there was an 11.7% readmission rate. Adjusting for other factors, each additional comorbidity and complication increased this rate by approximately 2.0% and 2.7%, respectively. These results demonstrate that the avoidance of postoperative complications may represent an effective mechanism for decreasing readmissions after thoracic surgery.

Medicare Program Associated With Narrowing Hospital Readmission Disparities Between Black And White Patients

The Hospital Readmissions Reduction Program has been associated with improvements in readmission rates, yet little is known about its effect on racial disparities. We compared trends in thirty-day readmission rates for congestive heart failure, acute myocardial infarction, and pneumonia among non-Hispanic whites versus non-Hispanic blacks, and among minority-serving hospitals versus others. During the penalty-free implementation period (April 2010-September 2012), readmission rates improved over pre-implementation trends (January 2007-March 2010) for both whites and blacks, with a significantly greater decline among blacks than among whites (-0.45 percent versus -0.36 percent per quarter, respectively). In the period October 2012-December 2014, after penalties began, readmission improvements slowed for both races. Following a similar pattern, minority-serving hospitals saw greater reductions in readmissions than other hospitals did. Despite the narrowing of the two race-based gaps after announcement of the Hospital Readmissions Reduction Program, both persist. It remains to be seen whether new policy efforts will narrow these gaps and reduce the disproportionately high penalties that minority-serving hospitals face.

Pattern and Adherence to Maintenance Medication Use in Medicare Beneficiaries with Chronic Obstructive Pulmonary Disease: 2008-2013

Background: Maintenance medications provide symptomatic relief, improve lung function and reduce the risk of exacerbations in patients with chronic obstructive pulmonary disease (COPD). Despite their proven benefits, limited information exists on maintenance medication use and adherence among users. Objective: We examined the patterns and factors associated with the receipt of and adherence to maintenance medication in individuals with COPD. Methods: A retrospective cross-sectional study of 5% of Medicare beneficiaries enrolled in Parts A, B and D with COPD who received maintenance medication from 2008 to 2013 was conducted. Maintenance medication includes: inhaled corticosteroids (ICSs), long-acting beta2- agonists (LABAs) and long-acting muscarinic antagonists (LAMAs) alone or in combination. We examined the proportion of beneficiaries with COPD who had at least one prescription filled for maintenance medication. Among users of maintenance medications, we also examined adherence, defined as proportion of days covered (PDC) ≥80% over the year from the first maintenance medication prescription fill date. Results: Overall, maintenance medication (LAMAs, LABAs, ICSs and/or LABA/ICS) use increased from 67.8% in 2008 to 72.1% in 2013. The increase is related to increases in use of LABA/ICS, which rose from 41.1% in 2008 to 49.6% in 2013. Factors associated with receipt of maintenance medication include female gender, recent COPD hospitalization (odds ratio [OR] 1.63; 95% confidence interval [CI] 1.54-1.73), oxygen therapy (OR 1.74 95% CI, 1.68-1.81), dual eligibility status (OR 1.45; 95% CI 1.39-1.51), higher education level and evaluation by a pulmonary provider (OR 1.88; 95% CI 1.81-1.96). The overall adherence among maintenance medication users remained flat. The most important factor associated with adherence was dual eligibility status (OR, 1.67; 95% CI: 1.59-1.75). Conclusions: Receipt of maintenance medications increased during the study period and was higher in those with dual eligibility. Overall, adherence to maintenance medications was suboptimal and remained unchanged.

Area Deprivation Index Predicts Readmission Risk at an Urban Teaching Hospital

A growing body of evidence has shown that neighborhood characteristics have significant effects on quality metrics that evaluate health plans or health care providers. Using a data set of an urban teaching hospital patient discharges, this study aimed to determine whether a significant effect of neighborhood characteristics, measured by the Area Deprivation Index, could be observed on patients' readmission risk, independent of patient-level clinical and demographic factors. This study found that patients residing in more disadvantaged neighborhoods had significantly higher 30-day readmission risks compared to those living in less disadvantaged neighborhoods, even after accounting for individual-level factors. Those who lived in the most extremely socioeconomically challenged neighborhoods were 70% more likely to be readmitted than their counterparts who lived in less disadvantaged neighborhoods. These findings suggest that neighborhood-level factors should be considered along with individual-level factors in future work on adjustment of quality metrics for social risk factors.

Excess Readmission-Based Penalty: Is Arthroplasty Different From the Other Outcomes?

Whether factors not under a hospital's control affect readmissions remains intensely debated in the context of the Centers for Medicare & Medicaid Services' Hospital Readmission Reduction Program. This study aimed to evaluate the potential effects of poverty, race, and hospital volume on excess readmissions, with >3000 hospitals participating in "Hospital Compare." Correlations between excess readmission ratios for five eligible outcomes (including hip and knee arthroplasty) were assessed with the three area and hospital-level factors: poverty, race (percent of black population), and hospital volume (number of discharges). Correlation coefficients of the ratios with race were approximately r = 0.2, consistently larger than those with poverty (r = 0-0.1), and those with volume were r = 0 to -0.5. Hip and knee arthroplasty had unique findings: null correlation with poverty (r ≈ 0), largest variability, and strong monotonicity with volume (r ≈ -0.5). The percent of Hispanic population showed negligible correlations in secondary analysis. Penalty assessment and hospital profiling should consider areas with high percentages of black population and a small volume of hospitals and providers of hip and knee surgery. (Journal of Surgical Orthopaedic Advances 27(4):286-294, 2018).

Reconsidering Hospital Readmission Measures

Current hospital readmission measures are part of the Centers for Medicare & Medicaid Services Five-Star Quality Rating System but are inadequate for reporting hospital quality. We review potential biases in the readmission measures and offer policy recommendations to address these biases. Hospital readmission rates are influenced by multiple sources of variation (eg, mix of patients served, bias in the performance measure); true differences in quality of care are often a much smaller source of this variation. Thus, variation from caring for large proportions of socioeconomically disadvantaged or tertiary-care patients will bias a hospital's ratings. Ratings aside, readmission measures may indirectly harm patients because low readmission rates do not correlate with reduced mortality, yet the Five-Star Quality Rating System weighs readmission equally with mortality. We propose that hospital quality rankings not use readmission measures as currently constructed.

Area-level poverty, race/ethnicity & dialysis star ratings

The Centers for Medicare and Medicaid Services recently released a five star rating system as part of ‘Dialysis Facility Compare’ to help patients identify and choose high performing clinics in the US. Eight dialysis-related measures determine ratings. Little is known about the association between surrounding community sociodemographic characteristics and star ratings. Using data from the U.S. Census and over 6000 dialysis clinics across the country, we examined the association between dialysis clinic star ratings and characteristics of the local population: 1) proportion of population below the federal poverty level (FPL); 2) proportion of black individuals; and 3) proportion of Hispanic individuals, by correlation and regression analyses. Secondary analyses with Quality Incentive Program (QIP) scores and population characteristics were also performed. We observed a negligible correlation between star ratings and the proportion of local individuals below FPL; Spearman coefficient, R = -0.09 (p<0.0001), and a stronger correlation between star ratings and the proportion of black individuals; R = -0.21 (p<0.0001). Ordered logistic regression analyses yielded adjusted odds ratio of 0.91 (95% confidence interval [0.80–1.30], p = 0.12) and 0.55 ([0.48–0.63], p<0.0001) for high vs. low level of proportion below FPL and proportion of black individuals, respectively. In contrast, a near-zero correlation was observed between star ratings and the proportion of Hispanic individuals. Correlations varied substantially by country region, clinic profit status and clinic size. Analyses using clinic QIP scores provided similar results. Sociodemographic characteristics of the surrounding community, factors typically outside of providers’ direct control, have varying levels of association with clinic dialysis star ratings.

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.).

Risk factors and costs associated with nationwide nonelective readmission after trauma

BACKGROUND

Most prior studies of readmission after trauma have been limited to single institutions, whereas multi-institutional studies have been limited to single states and an inability to distinguish between elective and nonelective readmissions. The purpose of this study was to identify the risk factors and costs associated with nonelective readmission after trauma across the United States.

METHODS

The Nationwide Readmission Database was queried for all patients with nonelective admissions in 2013 and 2014 with a primary diagnosis of trauma. Univariate and multivariate logistic regression identified risk factors for 30-day nonelective same- and different-hospital readmission. The diagnosis groups on readmission were evaluated, and the total cost of readmissions was calculated.

RESULTS

There were 1,180,144 patients admitted for trauma, the 30-day readmission rate was 9.4%, and 26.4% of readmissions occurred at a different hospital. The median readmission cost for patients readmitted to the same hospital was $8,298 (interquartile range, $4,899-$14,911), whereas the median readmission cost for patients readmitted to a different hospital was $8,568 (interquartile range, $4,935-$16,078; p < 0.01). Multivariate regression revealed that patients discharged against medical advice were at increased risk of readmission (odds ratio, 2.79; p < 0.01) and readmission to a different facility (odds ratio, 1.58; p < 0.01). Home health care was associated with a decreased risk of readmission to a different hospital (odds ratio, 0.74; p < 0.01). Septicemia and disseminated infections were the most common diagnoses on readmission (8.4%) and readmission to a different hospital (8.6%).

CONCLUSIONS

A significant portion of US readmissions occur at different hospitals with implications for continuity of care, quality metrics, cost, and resource allocation. Home health care reduces the likelihood of nonelective readmission to a different hospital. Infection was the most common reason for readmission, with ramifications for outcomes research and quality improvement.

LEVEL OF EVIDENCE

Care management/epidimeological, level IV.

Reductions in Readmission Rates Are Associated With Modest Improvements in Patient-reported Health Gains Following Hip and Knee Replacement in England

Supplemental Digital Content is available in the text.

Background:

Although many hospital readmission reduction initiatives have been introduced globally, health care systems ultimately aim to improve patients’ health and well-being. We examined whether the hospitals that report greater success in reducing readmissions also see greater improvements in patient-reported outcomes.

Research Design:

We examined hospital groups (Trusts) that provided hip replacement or knee replacement surgery in England between April 2010 and February 2013. For each Trust, we calculated risk-adjusted 30-day readmission rates from administrative datasets. We also obtained changes in patient-reported health between presurgical assessment and 6-month follow-up, using general health EuroQuol five dimensions questionaire (EQ-5D) and EuroQuol visual analogue scales (EQ-VAS) and procedure-specific (Oxford Hip and Knee Scores) measures. Panel models were used to assess whether changes over time in risk-adjusted readmission rates were associated with changes over time in risk-adjusted health gains.

Results:

Each percentage point reduction in the risk-adjusted readmission rate for hip replacement was associated with an additional health gain of 0.004 for EQ-5D [95% confidence interval (CI), 0.002–0.006], 0.39 for EQ-VAS (95% CI, 0.26–0.52), and 0.32 for Oxford Hip Score (95% CI, 0.15–0.27). Corresponding figures for knee replacement were 0.003 for EQ-5D (95% CI, 0.001–0.004), 0.21 for EQ-VAS (95% CI, 0.12–0.30), and 0.14 in the Oxford Knee Score (95% CI, 0.09–0.20).

Conclusions:

Reductions in readmission rates were associated with modest improvements in patients’ sense of their health and well-being at the hospital group level. In particular, fears that efforts to reduce readmission rates have had unintended consequences for patients appear to be unfounded.

Sepsis-Associated 30-Day Risk-Standardized Readmissions: Analysis of a Nationwide Medicare Sample

OBJECTIVES

To determine national readmission rates among sepsis survivors, variations in rates between hospitals, and determine whether measures of quality correlate with performance on sepsis readmissions.

DESIGN

Cross-sectional study of sepsis readmissions between 2008 and 2011 in the Medicare fee-for-service database.

SETTING

Acute care, Medicare participating hospitals from 2008 to 2011.

PATIENTS

Septic patients as identified by International Classification of Diseases, Ninth Revision codes using the Angus method.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We generated hospital-level, risk-standardized, 30-day readmission rates among survivors of sepsis and compared rates across region, ownership, teaching status, sepsis volume, hospital size, and proportion of underserved patients. We examined the relationship between risk-standardized readmission rates and hospital-level composite measures of quality and mortality. From 633,407 hospitalizations among 3,315 hospitals from 2008 to 2011, median risk-standardized readmission rates was 28.7% (interquartile range, 26.1-31.9). There were differences in risk-standardized readmission rates by region (Northeast, 30.4%; South, 29.6%; Midwest, 28.8%; and West, 27.7%; p < 0.001), teaching versus nonteaching status (31.1% vs 29.0%; p < 0.001), and hospitals serving the highest proportion of underserved patients (30.6% vs 28.7%; p < 0.001). The best performing hospitals on a composite quality measure had highest risk-standardized readmission rates compared with the lowest (32.0% vs 27.5%; p < 0.001). Risk-standardized readmission rates was lower in the highest mortality hospitals compared with those in the lowest (28.7% vs 30.7%; p < 0.001).

CONCLUSIONS

One third of sepsis survivors were readmitted and wide variation exists between hospitals. Several demographic and structural factors are associated with this variation. Measures of higher quality in-hospital care were correlated with higher readmission rates. Several potential explanations are possible including poor risk standardization, more research is needed.

Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program

OBJECTIVE

To assess the reliability of risk-standardized readmission rates (RSRRs) for medical conditions and surgical procedures used in the Hospital Readmission Reduction Program (HRRP).

DATA SOURCES

State Inpatient Databases for six states from 2011 to 2013 were used to identify patient cohorts for the six conditions used in the HRRP, which was augmented with hospital characteristic and HRRP penalty data.

STUDY DESIGN

Hierarchical logistic regression models estimated hospital-level RSRRs for each condition, the reliability of each RSRR, and the extent to which socioeconomic and hospital factors further explain RSRR variation. We used publicly available data to estimate payments for excess readmissions in hospitals with reliable and unreliable RSRRs.

PRINCIPAL FINDINGS

Only RSRRs for surgical procedures exceeded the reliability benchmark for most hospitals, whereas RSRRs for medical conditions were typically below the benchmark. Additional adjustment for socioeconomic and hospital factors modestly explained variation in RSRRs. Approximately 25 percent of payments for excess readmissions were tied to unreliable RSRRs.

CONCLUSIONS

Many of the RSRRs employed by the HRRP are unreliable, and one quarter of payments for excess readmissions are associated with unreliable RSRRs. Unreliable measures blur the connection between hospital performance and incentives, and threaten the success of the HRRP.