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

Assessing the quality of public services: For-profits, chains, and concentration in the hospital market

We examine variation in US hospital quality across ownership, chain membership, and market concentration. We propose a new measure of quality derived from penalties imposed on hospitals under the flagship Hospital Readmissions Reduction Program, and use regression models to risk-adjust for hospital characteristics and county demographics. While the overall association between for-profit ownership and quality is negative, there is evidence of substantial heterogeneity. The quality of for-profit relative to non-profit hospitals declines with increasing market concentration. Moreover, the quality gap is primarily driven by for-profit chains. While the competition result mirrors earlier findings in the literature, the chain result appears to be new: it suggests that any potential quality gains afforded by chains are mostly realized by not-for-profit hospitals.

Rates and predictors of 30-day hospital readmissions in adults for drug-induced acute pancreatitis: A retrospective study from the United States National Readmission Database

BACKGROUND AND AIM

Drug-induced acute pancreatitis (DIAP) linked to several medications is a diagnosis of exclusion and is associated with significant morbidity and mortality, contributing to the US healthcare cost burden. Existing studies on DIAP focus on the drug classes that can cause acute pancreatitis. Hence, our retrospective study aims to determine the rates and predictors for 30-day readmissions (30-DR) in patients with index hospitalization for DIAP.

METHODS

From the Nationwide Readmissions Database, we followed adults admitted for DIAP who were discharged alive for 30 days. During 30-DR, we evaluated the rates, predictors, and outcomes of DIAP.

RESULTS

Of the 4457 DIAP patients surviving at discharge, 12.5% were readmitted at 30 days. During readmissions, the predictors of 30-DR for DIAP were young age, the Charlson-Deyo Comorbidity Index of 2 and 3, protein-energy malnutrition, and dyslipidemia. During 30-DR, DIAP had a higher mortality rate (2.4% vs. 0.7%; P < 0.020), extended hospital stays (5.6 days vs. 4 days, 0.000), and higher hospital charges ($12 983.6 vs. $8 255.6; P 0.000).

CONCLUSIONS

DIAP has high 30-DR rates and poorer outcomes.

Independent Associations of Neighborhood Deprivation and Patient-level Social Determinants of Health with Textbook Outcomes after Inpatient Surgery

Objective

Assess associations of Social Determinants of Health (SDoH) using Area Deprivation Index (ADI), race/ethnicity and insurance type with Textbook Outcomes (TO).

Summary Background Data

Individual- and contextual-level SDoH affect health outcomes, but only one SDoH level is usually included.

Methods

Three healthcare system cohort study using National Surgical Quality Improvement Program (2013-2019) linked with ADI risk-adjusted for frailty, case status and operative stress examining TO/TO components (unplanned reoperations, complications, mortality, Emergency Department/Observation Stays and readmissions).

Results

Cohort (34,251 cases) mean age 58.3 [SD=16.0], 54.8% females, 14.1% Hispanics, 11.6% Non-Hispanic Blacks, 21.6% with ADI>85, and 81.8% TO. Racial and ethnic minorities, non-Private insurance, and ADI>85 patients had increased odds of urgent/emergent surgeries (aORs range: 1.17-2.83, all P<.001). Non-Hispanic Black patients, ADI>85 and non-Private insurances had lower TO odds (aORs range: 0.55-0.93, all P<.04), but ADI>85 lost significance after including case status. Urgent/emergent versus elective had lower TO odds (aOR=0.51, P<.001). ADI>85 patients had higher complication and mortality odds. Estimated reduction in TO probability was 9.9% (CI=7.2%-12.6%) for urgent/emergent cases, 7.0% (CI=4.6%-9.3%) for Medicaid, and 1.6% (CI=0.2%-3.0%) for non-Hispanic Black patients. TO probability difference for lowest-risk (White-Private-ADI≤85-elective) to highest-risk (Black-Medicaid-ADI>85-urgent/emergent) was 29.8% for very frail patients.

Conclusion

Multi-level SDoH had independent effects on TO, predominately affecting outcomes through increased rates/odds of urgent/emergent surgeries driving complications and worse outcomes. Lowest-risk versus highest-risk scenarios demonstrated the magnitude of intersecting SDoH variables. Combination of insurance type and ADI should be used to identify high-risk patients to redesign care pathways to improve outcomes. Risk adjustment including contextual neighborhood deprivation and patient-level SDoH could reduce unintended consequences of value-based programs.

Assessing Hospital Quality Scores By Proportion Of Patients Dually Eligible For Medicare And Medicaid

The Centers for Medicare and Medicaid Services has been reporting hospital star ratings since 2016. Some stakeholders have criticized the star ratings methodology for not adjusting for social risk factors. We examined the relationship between 2021 star rating scores and hospitals' proportion of Medicare patients dually eligible for Medicaid. We found that, on average, hospitals caring for a greater proportion of dually eligible patients had lower star ratings, but there was significant overlap in performance among hospitals when we stratified them by quintile of dually eligible patients. Hospitals in the highest quintile (those with the greatest proportion of dually eligible patients) had the best mean mortality scores (0.28) but the worst readmission (-0.44) and patient experience (-0.78) scores. We assigned star ratings after stratifying the readmission measure group by proportion of dually eligible patients and found that a total of 142 hospitals gained a star and 161 hospitals lost a star, of which 126 (89 percent) and 1 (<1 percent) were in the highest quintile, respectively. Adjusting public reporting tools such as star ratings for social risk factors is ultimately a policy decision, and views on the appropriateness of accounting for factors such as proportion of dually eligible patients are mixed, depending on the organization and stakeholder.

Social disparities in unplanned 30-day readmission rates after hospital discharge in patients with chronic health conditions: A retrospective cohort study using patient level hospital administrative data linked to the population census in Switzerland

Unplanned readmissions shortly after discharge from hospital are common in chronic diseases. The risk of readmission has been shown to be related both to hospital care, e.g., medical complications, and to patients’ resources and abilities to manage the chronic disease at home and to make appropriate use of outpatient medical care. Despite a growing body of evidence on social determinants of health and health behaviour, little is known about the impact of social and contextual factors on readmission rates. The objective of this study was to analyse possible effects of educational, financial and social resources of patients with different chronic health conditions on unplanned 30 day-readmission risks. The study made use of nationwide inpatient hospital data that was linked with Swiss census data. The sample included n = 62,109 patients aged 25 and older, hospitalized between 2012 and 2016 for one of 12 selected chronic conditions. Multivariate logistic regressions analysis was performed. Our results point to a significant association between social factors and readmission rates for patients with chronic conditions. Patients with upper secondary education (OR = 1.26, 95% CI: 1.11, 1.44) and compulsory education (OR = 1.51, 95% CI: 1.31, 1.74) had higher readmission rates than those with tertiary education when taking into account demographic, social and health status factors. Having private or semi-private hospital insurance was associated with a lower risk for 30-day readmission compared to patients with mandatory insurance (OR = 0.81, 95% CI: 0.73, 0.90). We did not find a general effect of social resources, measured by living with others in a household, on readmission rates. The risk of readmission for patients with chronic conditions was also strongly predicted by type of chronic condition and by factors related to health status, such as previous hospitalizations before the index hospitalization (+77%), number of comorbidities (+15% higher probability per additional comorbidity) as well as particularly long hospitalizations (+64%). Stratified analysis by type of chronic condition revealed differential effects of social factors on readmissions risks. Compulsory education was most strongly associated with higher odds for readmission among patients with lung cancer (+142%), congestive heart failure (+63%) and back problems (+53%). We assume that low socioeconomic status among patients with chronic conditions increases the risk of unplanned 30-day readmission after hospitalisation due to factors related to their social situation (e.g., low health literacy, material deprivation, high social burden), which may negatively affect cooperation with care providers and adherence to recommended therapies as well as hamper active participation in the medical process and the development of a shared understanding of the disease and its cure. Higher levels of comorbidity in socially disadvantaged patients can also make appropriate self-management and use of outpatient care more difficult. Our findings suggest a need for increased preventive measures for disadvantaged populations groups to promote early detection of diseases and to remove financial or knowledge-based barriers to medical care. Socially disadvantaged patients should also be strengthened more in their individual and social resources for coping with illness.

Hospitals serving nursing home residents disproportionately penalized under hospital readmissions reduction program

BACKGROUND

Risk factors common to nursing home (NH) residents are potentially not fully captured by the Hospital Readmissions Reduction Program (HRRP). The unique challenges faced by hospitals that disproportionately serve NH residents who are at greater risk of readmissions have not been studied.

METHODS

Using 100% Medicare Provider Analysis and Review File and the Minimum Data Set from 2010-2013, we constructed a measure of hospital share of NH-originating hospitalizations (NOHs). We defined hospital share of NOHs as the proportion of inpatient stays by patients aged 65 or older who were directly admitted from NHs. To evaluate the impact of the share of NOHs on readmission penalties, we categorized hospitals into quartiles according to their share of NOHs and estimated the differences in the adjusted penalties across hospital quartiles after accounting for hospital characteristics, market characteristics and state fixed effects. We repeated the analyses for the penalties incurred in each year between 2015 and 2019.

RESULTS

Hospitals varied substantially in the share of NOHs (median [interquartile range], 11.3% [8.2%-15.1%]), with limited variation over time. In 2015, hospitals in the highest quartile of NOH received on average 0.58% Medicare payment reduction compared to 0.44% reduction among those in the lowest quartile (32.9% higher penalties, p < 0.001). The increase in penalties continued to grow in 2017 and 2018 when the HRRP expanded to include additional target conditions (47.3% and 66.7%, respectively, p < 0.001 for both). Although the effect diminished in 2019 following the additional adjustment for hospital's dual-eligible share, hospitals in the highest quartile of NOH still incurred 43.0% (p < 0.001) higher penalties than those in the lowest quartile.

CONCLUSIONS

Hospitals varied considerably in their share of NOHs. Hospitals having a higher share of NOHs were disproportionately penalized for excess readmissions, even under the revised policy that adjusts for the share of dual-eligible admissions.

Social Risk Adjustment In The Hospital Readmissions Reduction Program: A Systematic Review And Implications For Policy

Value-based payment programs adjust payments to providers based on spending, quality, or health outcomes. Concern that these programs penalize providers disproportionately serving vulnerable patients prompted calls to adjust performance measures for social risk factors. We reviewed fourteen studies of social risk adjustment in Medicare's Hospital Readmissions Reduction Program (HRRP), a value-based payment model that initially did not adjust for social risk factors but subsequently began to do so. Seven studies found that adding social risk factors to the program's base risk-adjustment model (which adjusts only for age, sex, and comorbidities) reduced differences in risk-adjusted readmissions and penalties between safety-net hospitals and other hospitals. Three studies found that peer grouping, the HRRP's current approach to social risk adjustment, reduced penalties among safety-net hospitals. Two studies found that differences in risk-adjusted readmissions and penalties were further narrowed when augmentation of the base model was combined with peer grouping. Two studies showed that it is possible to adjust for social risk factors without obscuring quality differences between hospitals. These findings support the use of social risk adjustment to improve provider payment equity and highlight opportunities to enhance social risk adjustment in value-based payment programs.

Measuring Stroke Quality: Methodological Considerations in Selecting, Defining, and Analyzing Quality Measures

Knowledge about stroke and its management is growing rapidly and stroke systems of care must adapt to deliver evidence-based care. Quality improvement initiatives are essential for translating knowledge from clinical trials and recommendations in guidelines into routine clinical practice. This review focuses on issues central to the measurement of the quality of stroke care, including selection and definition of quality measures, identification of the eligible patient cohorts, optimization of data quality, and considerations for data analysis and interpretation.

Neighborhood Socioeconomic Disadvantage and Mortality Among Medicare Beneficiaries Hospitalized for Acute Myocardial Infarction, Heart Failure, and Pneumonia

BACKGROUND

The Centers for Medicare and Medicaid Services' Hospital Value-Based Purchasing program uses 30-day mortality rates for acute myocardial infarction, heart failure, and pneumonia to evaluate US hospitals, but does not account for neighborhood socioeconomic disadvantage when comparing their performance.

OBJECTIVE

To determine if neighborhood socioeconomic disadvantage is associated with worse 30-day mortality rates after a hospitalization for acute myocardial infarction (AMI), heart failure (HF), or pneumonia in the USA, as well as within the subset of counties with a high proportion of Black individuals.

DESIGN AND PARTICIPANTS

This retrospective, population-based study included all Medicare fee-for-service beneficiaries aged 65 years or older hospitalized for acute myocardial infarction, heart failure, or pneumonia between 2012 and 2015.

EXPOSURE

Residence in most socioeconomically disadvantaged vs. less socioeconomically disadvantaged neighborhoods as measured by the area deprivation index (ADI).

MAIN MEASURE(S)

All-cause mortality within 30 days of admission.

KEY RESULTS

The study included 3,471,592 Medicare patients. Of these patients, 333,472 resided in most disadvantaged neighborhoods and 3,138,120 in less disadvantaged neighborhoods. Patients living in the most disadvantaged neighborhoods were younger (78.4 vs. 80.0 years) and more likely to be Black adults (24.6% vs. 7.5%) and dually enrolled in Medicaid (39.4% vs. 21.8%). After adjustment for demographics (age, sex, race/ethnicity), poverty, and clinical comorbidities, 30-day mortality was higher among beneficiaries residing in most disadvantaged neighborhoods for AMI (adjusted odds ratio 1.08, 95% CI 1.06-1.11) and pneumonia (aOR 1.05, 1.03-1.07), but not for HF (aOR 1.02, 1.00-1.04). These patterns were similar within the subset of US counties with a high proportion of Black adults (AMI, aOR 1.07, 1.03-1.11; HF 1.02, 0.99-1.05; pneumonia 1.03, 1.00-1.07).

CONCLUSIONS

Neighborhood socioeconomic disadvantage is associated with higher 30-day mortality for some conditions targeted by value-based programs, even after accounting for individual-level demographics, clinical comorbidities, and poverty. These findings may have implications as policymakers weigh strategies to advance health equity under value-based programs.

Readmission After ACS: Burden, Epidemiology, and Mitigation

PURPOSE OF REVIEW

The past decade has brought increased efforts to better understand causes for ACS readmissions and strategies to minimize them. This review seeks to provide a critical appraisal of this rapidly growing body of literature.

RECENT FINDINGS

Prior to 2010, readmission rates for patients suffering from ACS remained relatively constant. More recently, several strategies have been implemented to mitigate this including improved risk assessment models, transition care bundles, and development of targeted programs by federal organizations and professional societies. These strategies have been associated with a significant reduction in ACS readmission rates in more recent years. With this, improvements in 30-day post-discharge mortality rates are also being appreciated. As we continue to expand our knowledge on independent risk factors for ACS readmissions, further strategies targeting at-risk populations may further decrease the rate of readmissions. Efforts to understand and reduce 30-day ACS readmission rates have resulted in overall improved quality of care for patients.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Adjusting for Social Risk Factors in Pediatric Quality Measures: Adding to the Evidence Base

BACKGROUND

Outcome and utilization quality measures are adjusted for patient case-mix including demographic characteristics and comorbid conditions to allow for comparisons between hospitals and health plans. However, controversy exists around whether and how to adjust for social risk factors.

OBJECTIVE

To assess an approach to incorporating social risk variables into a pediatric measure of utilization from the Pediatric Quality Measures Program (PQMP).

METHODS

We used data from California Medicaid claims (2015-16) and Massachusetts All Payer Claims Database (2014-2015) to calculate health plan performance using measure specifications from the Pediatric Asthma Emergency Department Use measure. Health plan performance categories were assessed using mixed effect negative binomial models with and without adjustment for social risk factors, with both models adjusting for age, gender and chronic condition category. Mixed effects linear models were then used to compare patient social risk for health plans that changed performance categories to patient social risk for health plans that did not.

RESULTS

Of 133 health plans, serving 404,649 pediatric patients with asthma, 7% to 13% changed performance categories after social risk adjustment. Health plans that moved to higher performance categories cared for lower socioeconomic status (SES) patients whereas those that moved to lower performance categories cared for higher SES patients.

CONCLUSIONS

Adjustment for social risk factors changed performance rankings on the PQMP Pediatric Asthma Emergency Department Use measure for a substantial number of health plans. Some health plans caring for higher risk patients performed more poorly when social risk factors were not included in risk adjustment models. In light of this, social risk factors are incorporated into the National Quality Forum-endorsed measure; whether to incorporate social risk factors into pediatric quality measures will differ depending on the use case.

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.

Impact of Acute Self-Care Indicators and Social Factors on Medicare Inpatient Readmission Risk

IMPORTANCE

Readmissions are costly for Medicare and are associated with poor patient outcomes.

OBJECTIVE

To determine whether two domains relevant to acute occupational therapy practice-self-care status and social factors-were associated with readmissions for Medicare patients in the Medicare Hospital Readmissions Reduction Program (HRRP).

DESIGN

Cross-sectional, retrospective study.

SETTING

Single academic medical center.

PARTICIPANTS

Medicare inpatients with a diagnosis included in the HRRP (N = 17,618). Outcomes and Measures: Three logistic regression models were estimated to examine the associations among (1) self-care status and 30-day readmission, (2) social support and 30-day readmission, and (3) housing situation and 30-day readmission. Subgroup analyses were conducted for the individual HRRP diagnoses.

RESULTS

No associations were found between acute self-care status, social support, or housing situation and 30-day readmission when all HRRP diagnoses were examined together. However, higher levels of independence with self-care were significantly associated with reduced odds of readmission for patients with pneumonia.

CONCLUSIONS AND RELEVANCE

The findings for patients with pneumonia are consistent with those of other studies done in the acute care setting. Deficiencies in acute occupational therapy documentation may have affected the findings for the other HRRP diagnoses. What This Article Adds: This study is the first to examine the association between acute self-care status (as documented by acute care occupational therapy practitioners) and readmission.

Socio-demographic and -economic factors associated with 30-day readmission for conditions targeted by the hospital readmissions reduction program: a population-based study

Background

Early hospital readmissions remain common in patients with conditions targeted by the CMS Hospital Readmission Reduction Program (HRRP). There is still no consensus on whether readmission measures should be adjusted based on social factors, and there are few population studies within the U.S. examining how social characteristics influence readmissions for HRRP-targeted conditions. The objective of this study was to determine if specific socio-demographic and -economic factors are associated with 30-day readmissions in HRRP-targeted conditions: acute exacerbation of chronic obstructive pulmonary disease, pneumonia, acute myocardial infarction, and heart failure.

Methods

The Nationwide Readmissions Database was used to identify patients admitted with HRRP-targeted conditions between January 1, 2010 and September 30, 2015. Stroke was included as a control condition because it is not included in the HRRP. Multivariate models were used to assess the relationship between three social and economic characteristics (gender, urban/rural hospital designation, and estimated median household income within the patient’s zip code) and 30-day readmission rates using a hierarchical two-level logistic model. Age-adjusted models were used to assess relationship differences between Medicare vs. non-Medicare populations.

Results

There were 19,253,997 weighted index hospital admissions for all diagnoses and 3,613,488 30-day readmissions between 2010 and 2015. Patients in the lowest income quartile (≤$37,999) had an increased odds of 30-day readmission across all conditions (P < 0.0001). Female gender and rural hospital designation were associated with a decreased odds of 30-day readmission for most targeted conditions (P < 0.05). Similar findings were also seen in patients ≥65 years old.

Conclusions

Socio-demographic and -economic factors are associated with 30-day readmission rates and should be incorporated into tools or interventions to improve discharge planning and mitigate against readmission.

Adjusting Quality Measures For Social Risk Factors Can Promote Equity In Health Care

Risk adjustment of quality measures using clinical risk factors is widely accepted; risk adjustment using social risk factors remains controversial. We argue here that social risk adjustment is appropriate and necessary in defined circumstances and that social risk adjustment should be the default option when there are valid empirical arguments for and against adjustment for a given measure. Social risk adjustment is an important way to avoid exacerbating inequity in the health care system.

Adding a Social Risk Adjustment Into the Estimation of Efficiency: The Case of Chilean Hospitals

BACKGROUND AND OBJECTIVES

There is much interest in adding social variables to hospital performance assessments. Many of the existing analyses, however, already include patients' diagnosis data, and it is not clear that adding a social adjustment variable would improve the quality of the results: the growing literature on this issue provides mixed results. The purpose in this study was to add evidence from a developing country into this discussion.

METHODS

We estimate the efficiency of hospitals controlling for casemix, with and without adjusting the hospital's casemix for the patients' sociodemographic variables. The magnitude of the adjustment is based on the observed impact of age, sex, and income on length of stay, conditional on the diagnosis related group (DRG). We use a data envelopment analysis (DEA) to assess the efficiency of 50 Chilean hospitals' discharges, including 780 DRGs and covering about 60% of total discharges in Chile from 2013 to 2015.

RESULTS

We found that the sociodemographic adjustment introduces very small changes in the DEA estimation of efficiency. The underlying reason is the relatively low influence of sociodemographics on hospital costs, conditional on DRG, and the changing pattern of sociodemographics across DRGs for any given hospital.

CONCLUSION

We conclude that the casemix-adjusted estimation of hospital efficiency is robust to the heterogeneity of patients' sociodemographic heterogeneity across hospitals. These results confirm, in a developing country, what has been observed in developed countries. For management purposes, then, the processing costs of adding social variables into hospitals' performance assessments might not be justified.

Readmission within three months after inpatient geriatric care-Incidence, diagnosis and associated factors in a Swedish cohort

INTRODUCTION

Readmissions are very costly, in monetary terms but also for the individual patient's safety and health. Only by understanding the reasons and drivers of readmissions, it is possible to ensure quality of care and improve the situation. The aim of this study was to assess inpatient readmissions during the first three months after discharge from geriatric inpatient care regarding main diagnosis and frequency of readmission. Furthermore, the aim was to analyze association between readmission and patient characteristics including demography and socioeconomics, morbidity, physical function, risk screening and care process respectively.

METHODS

The study includes all individuals admitted for inpatient care at three geriatric departments operated by the Stockholm region during 2016. Readmission after discharge was studied within three different time intervals; readmission within 10 days after discharge, within 11-30 days and within 31-90 days, respectively. Main diagnosis at readmission was assessed.

RESULTS

One fourth of the individuals discharged from inpatient geriatric care was readmitted during the first three months after discharge. The most common main diagnoses for readmission were heart failure, chronic obstructive pulmonary disease and pneumonia. Statistically significant risk factors for readmission included age, sex, number of diagnoses at discharge, and to some extent polypharmacy and destination of discharge.

CONCLUSIONS

Several clinical risk factors relating to physical performance and vulnerability were associated with risk of readmission. Socioeconomic information did not add to the predictability. To enable reductions in readmission rates, proactive monitoring of frail individuals afflicted with chronic conditions is necessary, and an integrated perspective including all stakeholders involved is crucial.

Association of Socioeconomic Area Deprivation Index with Hospital Readmissions After Colon and Rectal Surgery

BACKGROUND

Risk adjustment for reimbursement and quality measures omits social risk factors despite adversely affecting health outcomes. Social risk factors are not usually available in electronic health records (EHR) or administrative data. Socioeconomic status can be assessed by using US Census data. Distressed Communities Index (DCI) is based upon zip codes, and the Area Deprivation Index (ADI) provides more granular estimates at the block group level. We examined the association of neighborhood disadvantage using the ADI, DCI, and patient-level insurance status on 30-day readmission risk after colorectal surgery.

METHODS

Our 677 patient cohort was derived from the 2013-2017 National Surgical Quality Improvement Program at a safety net hospital augmented with EHR data to determine insurance status and 30-day readmissions. Patients' home addresses were linked to the ADI and DCI.

RESULTS

Our cohort consisted of 53.9% males and 63.8% Hispanics with a 22.9% 30-day readmission rate from the date of discharge; > 50% lived in highly deprived neighborhoods. Controlling for medical comorbidities and complications, ADI was associated with increased risk of 30 days from the date of discharge readmissions among patients living in medium (OR = 2.15, p = .02) or high (OR = 1.88, p = .03) deprived areas compared to less-deprived neighborhoods, but not insurance status or DCI.

CONCLUSIONS

The ADI identified patients living in deprived communities with increased readmission risk. Our results show that block-group level ADI can potentially be used in risk adjustment, to identify high-risk patients and to design better care pathways that improve health outcomes.

Determining the feasibility of an index of the social determinants of health using data from public sources

Examining the feasibility of developing an index measure for the social determinants of health using public data is needed. We examined these characteristics at the ZIP code in California and New York using public data extracted from the US Census, American Community Survey, the USDA Food Research Access Atlas, and the Dartmouth Atlas. We conducted a retrospective study from 2000 to 2017. The main outcome was a novel index measure representing six domains (economic stability, neighborhood and physical environment, education, community and social context, food access, and health care) and encompassing 13 items. The index measure at the ZIP code was created using principal component analysis, normalized to "0" worse and "1" better in California (ZIP codes n = 1,447 to 1,515) and New York (ZIP codes n = 1,211 to 1,298). We assessed the reliability and conducted a nonparametric comparison to the Robert Wood Johnson Foundation County Health Rankings, Area Deprivation Index, Social Deprivation Index, and GINI Index. These measures shared similarities and differences with the novel measure. Mapping of this novel measure showed regional variation. As a result, developing a universal social determinants of health measure is feasible and more research is needed to link it to health outcomes.

Social determinants of health and hospital readmissions: can the HOSPITAL risk score be improved by the inclusion of social factors?

Background

The HOSPITAL Risk Score (HRS) predicts 30-day hospital readmissions and is internationally validated. Social determinants of health (SDOH) such as low socioeconomic status (SES) affect health outcomes and have been postulated to affect readmission rates. We hypothesized that adding SDOH to the HRS could improve its predictive accuracy.

Methods

Records of 37,105 inpatient admissions at the University of Chicago Medical Center were reviewed. HRS was calculated for each patient. Census tract-level SDOH then were combined with the HRS and the performance of the resultant “Social HRS” was compared against the HRS. Patients then were assigned to 1 of 7 typologies defined by their SDOH and a balanced dataset of 14,235 admissions was sampled from the larger dataset to avoid over-representation by any 1 sociodemographic group. Principal component analysis and multivariable linear regression then were performed to determine the effect of SDOH on the HRS.

Results

The c-statistic for the HRS predicting 30-day readmission was 0.74, consistent with published values. However, the addition of SDOH to the HRS did not improve the c-statistic (0.71). Patients with unfavorable SDOH (no high-school, limited English, crowded housing, disabilities, and age > 65 yrs) had significantly higher HRS (p < 0.05 for all). Overall, SDOH explained 0.2% of the HRS.

Conclusion

At an urban tertiary care center, the addition of census tract-level SDOH to the HRS did not improve its predictive power. Rather, the effects of SDOH are already reflected in the HRS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-020-05989-7.

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.

Are Improvements Still Needed to the Modified Hospital Readmissions Reduction Program: a Health and Retirement Study (2000-2014)?

BACKGROUND

To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown.

OBJECTIVE

Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors.

DESIGN

Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status.

PATIENTS

20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations MAIN MEASURES: RSRRs KEY RESULTS: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized.

CONCLUSIONS

Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk.

Do Socioeconomic Disparities Exist in Postoperative Opioid Prescription and Consumption?

BACKGROUND

Since 1999, >200 000 people in the United States have died from a prescription opioid overdose. Lower socioeconomic status (SES) is one important risk factor. This study investigates socioeconomic disparities in postoperative opioid prescription and consumption.

METHODS

September 2018-April 2019, 128 patients were surveyed postoperatively regarding opioid consumption. The neighborhood disadvantage was calculated using area deprivation index (ADI). The top 3 quartiles were "high SES" and the bottom quartile "low SES."

RESULTS

The study population included 96 high SES patients, median ADI 6 (2-12.3) and 32 low SES, median ADI 94.5 (81.3-97.3). For both, median Oxycodone 5 mg prescribed was 20 pills. 29.2% of high SES consumed 0 pills, 40.6% consumed 1-9 pills, and 27.1% consumed 10+ pills. 25.0% of low SES consumed 0 pills, 46.9% consumed 1-9 pills, and 18.8% consumed 10+ pills. No significant difference in opioid prescription (P = .792) or consumption (P = .508) between SES groups.

DISCUSSION

Patients of all SES are prescribed and consumed opioids in similar patterns with no significant difference in postoperative pain following ambulatory surgery.

U.S. hospital performance methodologies: a scoping review to identify opportunities for crossing the quality chasm

Background

Hospital performance quality assessments inform patients, providers, payers, and purchasers in making healthcare decisions. These assessments have been developed by government, private and non-profit organizations, and academic institutions. Given the number and variability in available assessments, a knowledge gap exists regarding what assessments are available and how each assessment measures quality to identify top performing hospitals. This study aims to: (a) comprehensively identify current hospital performance assessments, (b) compare quality measures from each methodology in the context of the Institute of Medicine’s (IOM) six domains of STEEEP (safety, timeliness, effectiveness, efficiency, equitable, and patient-centeredness), and (c) formulate policy recommendations that improve value-based, patient-centered care to address identified gaps.

Methods

A scoping review was conducted using a systematic search of MEDLINE and the grey literature along with handsearching to identify studies that provide assessments of US-based hospital performance whereby the study cohort examined a minimum of 250 hospitals in the last two years (2017–2019).

Results

From 3058 unique records screened, 19 hospital performance assessments met inclusion criteria. Methodologies were analyzed across each assessment and measures were mapped to STEEEP. While safety and effectiveness were commonly identified measures across assessments, efficiency, and patient-centeredness were less frequently represented. Equity measures were also limited to risk- and severity-adjustment methods to balance patient characteristics across populations, rather than stand-alone indicators to evaluate health disparities that may contribute to community-level inequities.

Conclusions

To further improve health and healthcare value-based decision-making, there remains a need for methodological transparency across assessments and the standardization of consensus-based measures that reflect the IOM’s quality framework. Additionally, a large opportunity exists to improve the assessment of health equity in the communities that hospitals serve.

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure—standardized readmission ratio—and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).

Trends and Factors Associated with Nebulized Therapy Prescription in Older Adults with Chronic Obstructive Pulmonary Disease from 2008 to 2015

Background: Medical management of patients with chronic obstructive pulmonary disease (COPD) includes nebulized therapy as an option for inhalational drug delivery. A broad variety of short- and long-acting bronchodilators and inhaled corticosteroids in the nebulized form are available. Despite this, limited information exists on the pattern and predictors of nebulized prescription. We examined the trend and factors associated with prescription of nebulized therapy among Medicare beneficiaries with COPD. Methods: A retrospective cross-sectional study of 5% Medicare beneficiaries with COPD (n = 66,032) who were enrolled in parts A, B, and D and received nebulized prescription from 2008 to 2015 was conducted. This sample has shown to be representative of the entire fee-for-service Medicare population. The primary outcome was a prescription of nebulized medications. Reliever nebulized medications included short-acting beta agonist (SABA), short-acting muscarinic agents (SAMAs), and a combination of SABA and SAMA, while maintenance nebulized medications included long-acting beta agonists, long-acting muscarinic agents, and corticosteroid solutions as well as combinations of these agents. The secondary outcome was prescription of other inhaler respiratory medications not administered with a nebulizer. Results: Overall, 38.9% patients were prescribed nebulized medication and their prescription significantly declined from 42.4% in 2008 to 35.1% in 2015, majority of which was related to decreased prescriptions of nebulized relievers. Factors associated with the prescription of nebulized medications include female gender (odds ratio [OR] = 1.06; 95% confidence interval [CI] = 1.02-1.09), dual eligibility or low-income subsidy beneficiaries (OR = 1.49; CI = 1.44-1.53), hospitalization for COPD in the previous year (OR = 1.29; CI = 1.25-1.34), home oxygen therapy (OR = 2.29; CI = 2.23-2.36), pulmonary specialist visit (OR = 1.24; CI = 1.20-1.27), and moderate (OR = 1.61; CI = 1.57-1.65) or high (OR = 1.52; CI = 1.46-1.59) severity of COPD. Conclusion: Between 2008 and 2015, prescriptions for nebulized therapy for COPD declined among Medicare beneficiaries, probably related to increase in use of maintenance non-nebulized medications.

Measuring hospital-specific disparities by dual eligibility and race to reduce health inequities

Objective

To propose and evaluate a metric for quantifying hospital‐specific disparities in health outcomes that can be used by patients and hospitals.

Data Sources/Study Setting

Inpatient admissions for Medicare patients with acute myocardial infarction, heart failure, or pneumonia to all non‐federal, short‐term, acute care hospitals during 2012‐2015.

Study Design

Building on the current Centers for Medicare and Medicaid Services methodology for calculating risk‐standardized readmission rates, we developed models that include a hospital‐specific random coefficient for either patient dual eligibility status or African American race. These coefficients quantify the difference in risk‐standardized outcomes by dual eligibility and race at a given hospital after accounting for the hospital's patient case mix and proportion of dual eligible or African American patients. We demonstrate this approach and report variation and performance in hospital‐specific disparities.

Principal Findings

Dual eligibility and African American race were associated with higher readmission rates within hospitals for all three conditions. However, this disparity effect varied substantially across hospitals.

Conclusion

Our models isolate a hospital‐specific disparity effect and demonstrate variation in quality of care for different groups of patients across conditions and hospitals. Illuminating within‐hospital disparities can incentivize hospitals to reduce inequities in health care quality.

Relative Effects of the Hospital Readmissions Reduction Program on Hospitals That Serve Poorer Patients

IMPORTANCE

Hospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP).

OBJECTIVE

To compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals.

DESIGN

Retrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served.

SETTING

Acute care hospitals within the United States.

PARTICIPANTS

Medicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia.

MAIN OUTCOME AND MEASURE

Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend.

RESULTS

For AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P<0.0001 for all).

CONCLUSIONS AND RELEVANCE

For all hospitals, differences in pre-post trends in RSRRs varied with disease conditions. However, for the highest-penalized hospitals, the pre-post decline in RSRRs was greater for low than high dual-eligible hospitals for all penalized conditions. These results suggest that high penalty, high dual-eligible hospitals may be less able to improve performance on readmission metrics.

Impact of the Medicare hospital readmissions reduction program on vulnerable populations

BACKGROUND

The Hospital Readmissions Reduction Program (HRRP) was established by the 2010 Patient Protection and Affordable Care Act (ACA) in an effort to reduce excess hospital readmissions, lower health care costs, and improve patient safety and outcomes. Although studies have examined the policy's overall impacts and differences by hospital types, research is limited on its effects for different types of vulnerable populations. The aim of this study was to analyze the impact of the HRRP on readmissions for three targeted conditions (acute myocardial infarction, heart failure, and pneumonia) among four types of vulnerable populations, including low-income patients, patients served by hospitals that serve a high percentage of low-income or Medicaid patients, and high-risk patients at the highest quartile of the Elixhauser comorbidity index score.

METHODS

Data on patient and hospital information came from the Nationwide Readmission Database (NRD), which contained all discharges from community hospitals in 27 states during 2010-2014. Using difference-in-difference (DD) models, linear probability regressions were conducted for the entire sample and sub-samples of patients and hospitals in order to isolate the effect of the HRRP on vulnerable populations. Multiple combinations of treatment and control groups and triple difference (DDD) methods were used for testing the robustness of the results. All models controlled for the patient and hospital characteristics.

RESULTS

There have been statistically significant reductions in readmission rates overall as well as for vulnerable populations, especially for acute myocardial infarction patients in hospitals serving the largest percentage of low-income patients and high-risk patients. There is also evidence of spillover effects for non-targeted conditions among Medicare patients compared to privately insured patients.

CONCLUSIONS

The HRRP appears to have created the right incentives for reducing readmissions not only overall but also for vulnerable populations, accruing societal benefits in addition to previously found reductions in costs. As the reduction in the rate of readmissions is not consistent across patient and hospital groups, there could be benefits to adjusting the policy according to the socioeconomic status of a hospital's patients and neighborhood.

Inaugural Readmission Penalties for Total Hip and Total Knee Arthroplasty Procedures Under the Hospital Readmissions Reduction Program

IMPORTANCE

The Hospital Readmissions Reduction Program (HRRP) is a Centers for Medicare and Medicaid Services policy that levies hospital reimbursement penalties based on excess readmissions of patients with 4 medical conditions and 3 surgical procedures. A greater understanding of factors associated with the 3 surgical reimbursement penalties is needed for clinicians in surgical practice.

OBJECTIVE

To investigate the first year of HRRP readmission penalties applied to 2 surgical procedures-elective total hip arthroplasty (THA) and total knee arthroplasty (TKA)-in the context of hospital and patient characteristics.

DESIGN, SETTING, AND PARTICIPANTS

Fiscal year 2015 HRRP penalization data from Hospital Compare were linked with the American Hospital Association Annual Survey and with the Healthcare Cost and Utilization Project State Inpatient Database for hospitals in the state of Florida. By using a case-control framework, those hospitals were separated based on HRRP penalty severity, as measured with the HRRP THA and TKA excess readmission ratio, and compared according to orthopedic volume as well as hospital-level and patient-level characteristics. The first year of HRRP readmission penalties applied to surgery in Florida Medicare subsection (d) hospitals was examined, identifying 60 663 Medicare patients who underwent elective THA or TKA in 143 Florida hospitals. The data analysis was conducted from February 2016 to January 2017.

EXPOSURES

Annual hospital THA and TKA volume, other hospital-level characteristics, and patient factors used in HRRP risk adjustment.

MAIN OUTCOMES AND MEASURES

The HRRP penalties with HRRP excess readmission ratios were measured, and their association with annual THA and TKA volume, a common measure of surgical quality, was evaluated. The HRRP penalties for surgical care according to hospital and readmitted patient characteristics were then examined.

RESULTS

Among 143 Florida hospitals, 2991 of 60 663 Medicare patients (4.9%) who underwent THA or TKA were readmitted within 30 days. Annual hospital arthroplasty volume seemed to follow an inverse association with both unadjusted readmission rates (r = -0.16, P = .06) and HRRP risk-adjusted readmission penalties (r = -0.12, P = .14), but these associations were not statistically significant. Other hospital characteristics and readmitted patient characteristics were similar across HRRP orthopedic penalty severity.

CONCLUSIONS AND RELEVANCE

This study's findings suggest that higher-volume hospitals had less severe, but not significantly different, rates of readmission and HRRP penalties, without systematic differences across readmitted patients.

Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices

Background

Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients.

Objectives

We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model.

Methods

We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time.

Results

In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses.

Conclusions

Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Reliability of the American Community Survey Estimates of Risk-Adjusted Readmission Rankings for Hospitals Before and After Peer Group Stratification

IMPORTANCE

Since the transition to the American Community Survey, data uncertainty has complicated its use for policy making and research, despite the ongoing need to identify disparities in health care outcomes. The US Centers for Medicare & Medicaid Services' new, stratified payment adjustment method for its Hospital Readmissions Reduction Program may be able to reduce the reliance on data linkages to socioeconomic survey estimates.

OBJECTIVE

To determine whether there are differences in the reliability of socioeconomically risk-adjusted hospital readmission rates among hospitals that serve a disproportionate share of low-income populations after stratifying hospitals into peer group-based classification groups.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study uses data from the 2014 New York State Health Cost and Utilization Project State Inpatient Database for 96 278 hospital admissions for acute myocardial infarction, pneumonia, and congestive heart failure. The analysis included patients aged 18 years and older who were not transferred to another hospital, who were discharged alive, who did not leave the hospital against medical advice, and who were discharged before December 2014.

MAIN OUTCOMES AND MEASURES

The main outcomes were 30-day hospital readmissions after acute myocardial infarction, pneumonia, and congestive heart failure assessed using hierarchical logistic regression.

RESULTS

The mean (SD) age of the patients was 69.6 (16.0) years for the safety-net hospitals and 74.9 (14.7) years for the non-safety-net hospitals; 9382 (48.8%) and 7003 (48.5%) patients, respectively, were female. For safety net designations, 20% (3 of 15) of all evaluations concealed and distorted differences in risk, with factors such as poverty failing to identify similar risk of acute myocardial infarction readmission until unreliable estimates were excluded from the analysis (OR, 1.23 [95% CI, 1.00-1.52], P = .02; vs OR, 1.17 [95% CI, 0.94-1.46], P = .15). By comparison, 2 of the 60 models (3%) for the peer group-based classification altered the association between socioeconomic status and readmission risk, concealing similarities in congestive heart failure readmission when adjusted using high school completion rates (OR, 1.27 [95% CI 1.02-1.58], P = .04; vs OR, 1.23 [95% CI, 0.98-1.53], P = .06) and distorting similarities in pneumonia readmissions when accounting for the proportion of lone-parent families (OR, 1.27 [95% CI, 0.98-1.66], P = .07; vs OR, 1.35 [95% CI, 1.02-1.80], P = .04) between the lowest and highest socioeconomic status hospitals in quartile 1.

CONCLUSIONS AND RELEVANCE

There was greater precision in socioeconomic adjusted readmission estimates when hospitals were stratified into the new payment adjustment criteria compared with safety net designations. A contributing factor for improved reliability of American Community Survey estimates under the new payment criteria was the merging of patients from low-income neighborhoods with greater homogeneity in survey estimates into groupings similar to those for higher-income patients, whose neighborhoods often exhibit greater estimate variability. Additional efforts are needed to explore the effect of measurement error on American Community Survey-adjusted readmissions using the new peer group-based classification methods.

State variation in the characteristics of Medicare-Medicaid dual enrollees: Implications for risk adjustment

OBJECTIVE

To examine between-state differences in the socioeconomic and health characteristics of Medicare beneficiaries dually enrolled in Medicaid, focusing on characteristics not observable to or used by policy makers for risk adjustment.

DATA SOURCE

2010-2013 Medicare Current Beneficiary Survey.

STUDY DESIGN

Retrospective analyses of survey-reported health and socioeconomic status (SES) measures among low-income Medicare beneficiaries and low-income dual enrollees. We used hierarchical linear regression models with state random effects to estimate the between-state variation in respondent characteristics and linear models to compare the characteristics of dual enrollees by state Medicaid policies.

PRINCIPAL FINDINGS

Between-state differences in health and socioeconomic risk among low-income Medicare beneficiaries, as measured by the coefficient of variation, ranged from 17.5 percent for an index of socioeconomic risk to 20.3 percent for an index of health risk. Between-state differences were comparable among the subset of low-income beneficiaries dually enrolled in Medicare and Medicaid. Dual enrollees with incomes below the Federal Poverty Level were in better health and had higher SES in states that offered Medicaid to individuals with relatively higher incomes. Duals' average incomes were higher in states with Medically Needy programs.

CONCLUSIONS

Characteristics of dual enrollees differ substantially across states, reflecting differences in states' low-income Medicare populations and Medicaid policies. Risk-adjustment methods using dual enrollment to proxy for poor health and low SES should account for this state-level heterogeneity.

Impact Of Risk Adjustment For Socioeconomic Status On Medicare Advantage Plan Quality Rankings

Sociodemographically disadvantaged patients have worse outcomes on some quality measures that inform Medicare Advantage plan ratings. Performance measurement that does not adjust for sociodemographic factors may penalize plans that disproportionately serve disadvantaged populations. We assessed the impact of adjusting for socioeconomic and demographic factors (sex, race/ethnicity, dual eligibility, disability, rurality, and neighborhood disadvantage) on Medicare Advantage plan rankings for blood pressure, diabetes, and cholesterol control. After adjustment, 20.3 percent, 19.5 percent, and 11.4 percent of Medicare Advantage plans improved by one or more quintiles in rank on the diabetes, cholesterol, and blood pressure measures, respectively. Plans that improved in ranking after adjustment enrolled higher proportions of disadvantaged enrollees. Adjusting quality measures for socioeconomic factors is important for equitable payment and quality reporting. Our study suggests that plans serving disadvantaged populations would have improved relative rankings for three important outcome measures if socioeconomic factors were included in risk-adjustment models.

Safety-Net Hospitals, Neighborhood Disadvantage, and Readmissions Under Maryland's All-Payer Program: An Observational Study

BACKGROUND

Safety-net hospitals have higher-than-expected readmission rates. The relative roles of the mean disadvantage of neighborhoods the hospitals serve and the disadvantage of individual patients in predicting a patient's readmission are unclear.

OBJECTIVE

To examine the independent contributions of the patient's neighborhood and the hospital's service area to risk for 30-day readmission.

DESIGN

Retrospective observational study.

SETTING

Maryland.

PARTICIPANTS

All Maryland residents discharged from a Maryland hospital in 2015.

MEASUREMENTS

Predictors included the disadvantage of neighborhoods for each Maryland resident (area disadvantage index) and the mean disadvantage of each hospital's discharged patients (safety-net index). The primary outcome was unplanned 30-day hospital readmission. Generalized estimating equations and marginal modeling were used to estimate readmission rates. Results were adjusted for clinical readmission risk.

RESULTS

13.4% of discharged patients were readmitted within 30 days. Patients living in neighborhoods at the 90th percentile of disadvantage had a readmission rate of 14.1% (95% CI, 13.6% to 14.5%) compared with 12.5% (CI, 11.8% to 13.2%) for similar patients living in neighborhoods at the 10th percentile. Patients discharged from hospitals at the 90th percentile of safety-net status had a readmission rate of 14.8% (CI, 13.4% to 16.1%) compared with 11.6% (CI, 10.5% to 12.7%) for similar patients discharged from hospitals at the 10th percentile of safety-net status. The association of readmission risk with the hospital's safety-net index was approximately twice the observed association with the patient's neighborhood disadvantage status.

LIMITATIONS

Generalizability outside Maryland is unknown. Confounding may be present.

CONCLUSION

In Maryland, residing in a disadvantaged neighborhood and being discharged from a hospital serving a large proportion of disadvantaged neighborhoods are independently associated with increased risk for readmission.

PRIMARY FUNDING SOURCE

National Institute on Minority Health and Health Disparities and Maryland Health Services Cost Review Commission.

Association of Stratification by Dual Enrollment Status With Financial Penalties in the Hospital Readmissions Reduction Program

IMPORTANCE

Beginning in fiscal year 2019, Medicare's Hospital Readmissions Reduction Program (HRRP) stratifies hospitals into 5 peer groups based on the proportion of each hospital's patient population that is dually enrolled in Medicare and Medicaid. The effect of this policy change is largely unknown.

OBJECTIVE

To identify hospital and state characteristics associated with changes in HRRP-related performance and penalties after stratification.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional analysis was performed of all 3049 hospitals participating in the HRRP in fiscal years 2018 and 2019, using publicly available data on hospital penalties, merged with information on hospital characteristics and state Medicaid eligibility cutoffs.

EXPOSURES

The HRRP, under the 2018 traditional method and the 2019 stratification method.

MAIN OUTCOMES AND MEASURES

Performance on readmissions, as measured by the excess readmissions ratio, and penalties under the HRRP both in relative percentage change and in absolute dollars.

RESULTS

The study sample included 3049 hospitals. The mean proportion of dually enrolled beneficiaries ranged from 9.5% in the lowest quintile to 44.7% in the highest quintile. At the hospital level, changes in penalties ranged from an increase of $225 000 to a decrease of more than $436 000 after stratification. In total, hospitals in the lowest quintile of dual enrollment saw an increase of $12 330 157 in penalties, while those in the highest quintile of dual enrollment saw a decrease of $22 445 644. Teaching hospitals (odds ratio [OR], 2.13; 95% CI, 1.76-2.57; P < .001) and large hospitals (OR, 1.51; 95% CI, 1.22-1.86; P < .001) had higher odds of receiving a reduced penalty. Not-for-profit hospitals (OR, 0.64; 95% CI, 0.52-0.80; P < .001) were less likely to have a penalty reduction than for-profit hospitals, and hospitals in the Midwest (OR, 0.44; 95% CI, 0.34-0.57; P < .001) and South (OR, 0.42; 95% CI, 0.30-0.57; P < .001) were less likely to do so than hospitals in the Northeast. Hospitals with patients from the most disadvantaged neighborhoods (OR, 2.62; 95% CI, 2.03-3.38; P < .001) and those with the highest proportion of beneficiaries with disabilities (OR, 3.12; 95% CI, 2.50-3.90; P < .001) were markedly more likely to see a reduction in penalties, as were hospitals in states with the highest Medicaid eligibility cutoffs (OR, 1.79; 95% CI, 1.50-2.14; P < .001).

CONCLUSIONS AND RELEVANCE

Stratification of the hospitals under the HRRP was associated with a significant shift in penalties for excess readmissions. Policymakers should monitor the association of this change with readmission rates as well as hospital financial performance as the policy is fully implemented.

Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program

OBJECTIVE

Medicare's Hospital Readmissions Reduction Program (HRRP) does not account for social risk factors in risk adjustment, and this may lead the program to unfairly penalize safety-net hospitals. Our objective was to determine the impact of adjusting for social risk factors on HRRP penalties.

STUDY DESIGN

Retrospective cohort study.

DATA SOURCES/STUDY SETTING

Claims data for 2 952 605 fee-for-service Medicare beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF) or pneumonia from December 2012 to November 2015.

PRINCIPAL FINDINGS

Poverty, disability, housing instability, residence in a disadvantaged neighborhood, and hospital population from a disadvantaged neighborhood were associated with higher readmission rates. Under current program specifications, safety-net hospitals had higher readmission ratios (AMI, 1.020 vs 0.986 for the most affluent hospitals; pneumonia, 1.031 vs 0.984; and CHF, 1.037 vs 0.977). Adding social factors to risk adjustment cut these differences in half. Over half the safety-net hospitals saw their penalty decline; 4-7.5 percent went from having a penalty to having no penalty. These changes translated into a $17 million reduction in penalties to safety-net hospitals.

CONCLUSIONS

Accounting for social risk can have a major financial impact on safety-net hospitals. Adjustment for these factors could reduce negative unintended consequences of the HRRP.

Social Risk Adjustment of Quality Measures for Diabetes and Cardiovascular Disease in a Commercially Insured US Population

IMPORTANCE

Patients' social risk factors may be associated with physician group performance on quality measures.

OBJECTIVE

To examine the association of social risk with change in physician group performance on diabetes and cardiovascular disease (CVD) quality measures in a commercially insured population.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study using claims data from 2010 to 2014 from a US national health insurance plan, the performance of 1400 physician groups (physicians billing under the same tax identification number) was estimated. After base adjustments for age and sex, changes in variation across groups and reordering of rankings resulting from additional adjustments for clinical, social, or both clinical and social risk factors were analyzed. In all models, only within-group associations were adjusted to distinguish the association of patients' social risk factors with outcomes while excluding physician groups' distinct characteristics that could also change observed performance. Data analysis was conducted between April and July 2018.

MAIN OUTCOMES AND MEASURES

Process measures (hemoglobin A1c [HbA1c] testing, low-density lipoprotein cholesterol [LDL-C] testing, and statin use), disease control measures (HbA1c and LDL-C level control), and use-based outcome measures (hospitalizations for ambulatory-sensitive conditions) were calculated with base adjustment (age and sex), clinical adjustment, social risk factor adjustment, and both clinical and social adjustments. Quality variance in physician group performance and changes in rankings following these adjustments were measured.

RESULTS

This study identified 1 684 167 enrollees (859 618 [51%] men) aged 18 to 65 years (mean [SD] age, 50 [10.7] years) with diabetes or CVD. Performance rates were high for HbA1c and LDL-C level testing (mean ranged from 79.5% to 87.2%) but lower for statin use (54.7% for diabetes cohort and 44.2% for CVD cohort) and disease control measures (57.9% on LDL-C control for diabetes cohort and 40.0% for CVD cohort). On average, only 8.8% of enrollees with diabetes and 1.0% of enrollees with CVD in a group were hospitalized. The addition of clinical and social risk factors to base adjustment reduced variance across physician groups for most measures (percentage change in SD ranged from -13.9% to 1.6%). Although overall agreement between performance scores with base vs full adjustment was high, there was still substantial reordering for some measures. For example, social risk adjustment resulted in reordering for disease control in the diabetes cohort. Of the 1400 physician groups, 330 (23.6%) had performance rankings for HbA1c control that increased or decreased by at least 10 percentile points after adding social risk factors to age and sex. Both clinical and social risk adjustment affected rankings on hospital admissions.

CONCLUSIONS AND RELEVANCE

Accounting for social risk may be important to mitigate adverse consequences of performance-based payments for physician groups serving socially vulnerable populations.

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.

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates: Implications for Pay for Performance

IMPORTANCE

In several pay-for-performance programs, Medicare ties payments to readmission rates but accounts only for a limited set of patient characteristics-and no measures of social risk-when assessing performance of health care providers (clinicians, practices, hospitals, or other organizations). Debate continues over whether accounting for social risk would mitigate inappropriate penalties or would establish lower standards of care for disadvantaged patients if they are served by lower-quality providers.

OBJECTIVES

To assess changes in hospital performance on readmission rates after adjusting for additional clinical and social patient characteristics by using methods that distinguish the association between patient characteristics and readmission from between-hospital differences in quality.

DESIGN, SETTING, AND PARTICIPANTS

Using Medicare claims for admissions in 2013 through 2014 and linked US Census data, we assessed several clinical and social characteristics of patients that are not currently used for risk adjustment in the Hospital Readmission Reduction Program. We compared hospital readmission rates with and without adjustment for these additional characteristics, using only the average within-hospital associations between patient characteristics and readmission as the basis for adjustment, thereby appropriately excluding hospitals' distinct contributions to readmission from the adjustment.

MAIN OUTCOMES AND MEASURES

All-cause readmission within 30 days of discharge.

RESULTS

The study sample consisted of 1 169 014 index admissions among 1 003 664 unique Medicare beneficiaries (41.5% men; mean [SD] age, 79.9 [8.3] years) in 2215 hospitals. Compared with adjustment for patient characteristics currently implemented by Medicare, adjustment for the additional characteristics reduced overall variation in hospital readmission rates by 9.6%, changed rates upward or downward by 0.37 to 0.72 percentage points for the 10% of hospitals most affected by the additional adjustments (±30.3% to ±58.9% of the hospital-level standard deviation), and would be expected to reduce penalties (in relative terms) by 52%, 46%, and 41% for hospitals with the largest 1%, 5%, and 10% of penalty reductions, respectively. The additional adjustments reduced the mean difference in readmission rates between hospitals in the top and bottom quintiles of high-risk patients by 0.53 percentage points (95% CI, 0.50-0.55; P < .001), or 54% of the difference estimated with CMS adjustments alone. Both clinical and social characteristics contributed to these reductions, and these reductions were considerably greater for conditions targeted by the Hospital Readmission Reduction Program. Adjustment for social characteristics resulted in greater changes in rates of readmission or death than in rates of readmission alone.

CONCLUSIONS AND RELEVANCE

Hospitals serving higher-risk patients may be penalized substantially because of the patients they serve rather than their quality of care. Adjusting solely for within-hospital associations may allow adjustment for additional patient characteristics to mitigate unintended consequences of pay for performance without holding hospitals to different standards because of the patients they serve.

Differential Impact of Hospital and Community Factors on Medicare Readmission Penalties

OBJECTIVE

To identify hospital/county characteristics and sources of regional heterogeneity associated with readmission penalties.

DATA SOURCES/STUDY SETTING

Acute care hospitals under the Hospital Readmissions Reduction Program from fiscal years 2013 to 2018 were linked to data from the Annual Hospital Association, Centers for Medicare and Medicaid Services, Medicare claims, Hospital Compare, Nursing Home Compare, Area Resource File, Health Inequity Project, and Long-term Care Focus. The final sample contained 3,156 hospitals in 1,504 counties.

DATA COLLECTION/EXTRACTION METHODS

Data sources were combined using Medicare hospital identifiers or Federal Information Processing Standard codes.

STUDY DESIGN

A two-level hierarchical model with correlated random effects, also known as the Mundlak correction, was employed with hospitals nested within counties.

PRINCIPAL FINDINGS

Over a third of the variation in readmission penalties was attributed to the county level. Patient sociodemographics and the surrounding access to and quality of care were significantly associated with penalties. Hospital measures of Medicare volume, percentage dual-eligible and Black patients, and patient experience were correlated with unobserved area-level factors that also impact penalties.

CONCLUSIONS

As the readmission risk adjustment does not include any community-level characteristics or geographic controls, the resulting endogeneity bias has the potential to disparately penalize certain hospitals.

Effect of Hospital Readmission Reduction on Patients at Low, Medium, and High Risk of Readmission in the Medicare Population

BACKGROUND

Hospitalization and readmission rates have decreased in recent years, with the possible consequence that hospitals are increasingly filled with high-risk patients.

OBJECTIVE

We studied whether readmission reduction has affected the risk profile of hospitalized patients and whether readmission reduction was similarly realized among hospitalizations with low, medium, and high risk of readmissions.

DESIGN

Retrospective study of hospitalizations between January 2009 and June 2015.

PATIENTS

Hospitalized fee-for-service Medicare beneficiaries, categorized into 1 of 5 specialty cohorts used for the publicly reported hospital-wide readmission measure.

MEASUREMENTS

Each hospitalization was assigned a predicted risk of 30-day, unplanned readmission using a risk-adjusted model similar to publicly reported measures. Trends in monthly mean predicted risk for each cohort and trends in monthly observed to expected readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of risk of readmission were assessed using time series models.

RESULTS

Of 47,288,961 hospitalizations, 16.2% (n = 7,642,161) were followed by an unplanned readmission within 30 days. We found that predicted risk of readmission increased by 0.24% (P = .03) and 0.13% (P = .004) per year for hospitalizations in the surgery/ gynecology and neurology cohorts, respectively. We found no significant increase in predicted risk for hospitalizations in the medicine (0.12%, P = .12), cardiovascular (0.32%, P = .07), or cardiorespiratory (0.03%, P = .55) cohorts. In each cohort, observed to expected readmission rates steadily declined, and at similar rates for patients at low, medium, and high risk of readmission.

CONCLUSIONS

Hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. The risk of readmission for hospitalized patients has increased for 2 of 5 clinical cohorts.

Data concordance between ESRD Medical Evidence Report and Medicare claims: is there any improvement?

BACKGROUND

Medicare is one of the world's largest health insurance programs. It provides health insurance to nearly 44 million beneficiaries whose entitlements are based on age, disability, or end-stage renal disease (ESRD). Data of these ESRD beneficiaries are collected in the US Renal Data System (USRDS), which includes comorbidity information entered at the time of dialysis initiation (medical evidence data), and are used to shape health care policy. One limitation of USRDS data is the lack of validation of these medical evidence comorbidities against other comorbidity data sources, such as medical claims data.

METHODS

We examined the potential for discordance between USRDS Medical Evidence and medical claims data for 11 comorbid conditions amongst Medicare beneficiaries in 2011-2013 via sensitivity, specificity, kappa and hierarchical logistic regression.

RESULTS

Among 61,280 patients, most comorbid conditions recorded on the Medical Evidence forms showed high specificity (>0.9), compared to prior medical claims as reference standard. However, both sensitivity and kappa statistics varied greatly and tended to be low (most <0.5). Only diabetes appeared accurate, whereas tobacco use and drug dependence showed the poorest quality (sensitivity and kappa <0.1). Institutionalization and patient region of residency were associated with data discordance for six and five comorbidities out of 11, respectively, after conservative adjustment of multiple testing. Discordance appeared to be non-informative for congestive heart failure but was most varied for drug dependence.

CONCLUSIONS

We conclude that there is no improvement in comorbidity data quality in incident ESRD patients over the last two decades. Since these data are used in case-mix adjustment for outcome and quality of care metrics, the findings in this study should press regulators to implement measures to improve the accuracy of comorbidity data collection.

Risk Adjustment May Lessen Penalties On Hospitals Treating Complex Cardiac Patients Under Medicare's Bundled Payments

To reduce variation in spending, Medicare has considered implementing a cardiac bundled payment program for acute myocardial infarction and coronary artery bypass graft. Because the proposed program does not account for patient risk factors when calculating hospital penalties or rewards ("reconciliation payments"), it might unfairly penalize certain hospitals. We estimated the impact of adjusting for patients' medical complexity and social risk on reconciliation payments for Medicare beneficiaries hospitalized for the two conditions in the period 2011-13. Average spending per episode was $29,394. Accounting for medical complexity substantially narrowed the gap in reconciliation payments between hospitals with high medical severity (from a penalty of $1,809 to one of $820, or a net reduction of $989), safety-net hospitals (from a penalty of $217 to one of $87, a reduction of $130), and minority-serving hospitals (from a penalty of $70 to a reward of $56, an improvement of $126) and their counterparts. Accounting for social risk alone narrowed these gaps but had minimal incremental effects after medical complexity was accounted for. Risk adjustment may preserve incentives to care for patients with complex conditions under Medicare bundled payment programs.