Impact of socioeconomic status measures on hospital profiling in New York City

Socioeconomic and Demographic Determinants of Readmission Rates in Congestive Heart Failure Patients: Insights From the Nationwide Readmissions Database

Background Congestive heart failure (CHF) is a leading cause of hospitalizations and readmissions, placing a significant burden on the healthcare system. Identifying factors associated with readmission risk is crucial for developing targeted interventions and improving patient outcomes. This study aimed to investigate the impact of socioeconomic and demographic factors on 30-day and 90-day readmission rates in patients primarily admitted for CHF. Methods The study was carried out using a cross-sectional study design, and the data were obtained from the Nationwide Readmissions Database (NRD) from 2016 to 2020. Adult patients with a primary diagnosis of CHF were included. The primary outcomes were 30-day and 90-day all-cause readmission rates. Multivariable logistic regression was used to identify factors independently associated with readmissions, including race, ethnicity, insurance status, income level, and living arrangements. Results A total of 219,904 patients with a primary diagnosis of CHF were used in the study. The overall 30-day and 90-day readmission rates were 17.3% and 23.1%, respectively. In multivariable analysis, factors independently associated with higher 30-day readmission risk included Hispanic ethnicity (OR 1.18, 95% CI 1.03-1.35), African American race (OR 1.15, 95% CI 1.04-1.28), Medicare insurance (OR 1.24, 95% CI 1.12-1.38), and urban residence (OR 1.11, 95% CI 1.02-1.21). Higher income was associated with lower readmission risk (OR 0.87, 95% CI 0.79-0.96 for highest vs. lowest quartile). Similar patterns were observed for 90-day readmissions. Conclusion Socioeconomic and demographic factors, including race, ethnicity, insurance status, income level, and living arrangements, significantly impact 30-day and 90-day readmission rates in patients with CHF. These findings highlight the need for targeted interventions and policies that address social determinants of health and promote health equity in the management of CHF. Future research should focus on developing and evaluating culturally sensitive, community-based strategies to reduce readmissions and improve outcomes for high-risk CHF patients.

Population Characteristics and Organ Procurement Organization Performance Metrics

Importance

In 2020, the Centers for Medicare & Medicaid Services (CMS) issued a final rule updating the Organ Procurement Organization (OPO) Conditions for Coverage. This rule evaluates OPO performance based on an unadjusted donation rate and an age-adjusted transplant rate; however, neither considers other underlying population differences.

Objective

To evaluate whether adjusting for age and/or area deprivation index yields the same tier assignments as the cause, age, and location consistent (CALC) tier used by CMS.

Design, Setting, and Participants

This retrospective cross-sectional study examined the performance of 58 OPOs from 2018 to 2020 across the entire US. A total of 12 041 778 death records were examined from the 2017 to 2020 National Center for Health Statistics' Restricted Vital Statistics Detailed Multiple Cause of Death files; 399 530 of these met the definition of potential deceased donor. Information about 42 572 solid organ donors from the Scientific Registry of Transplant Recipients was also used. Statistical analysis was performed from January 2017 to December 2020.

Exposure

Area deprivation of donation service areas and age of potential donors.

Main Outcome and Measures

OPO performance as measured by donation and transplant rates.

Results

A total of 399 530 potential deceased donors and 42 572 actual solid donor organs were assigned to 1 of 58 OPOs. Age and ADI adjustment resulted in 19.0% (11 of 58) to 31.0% (18 of 58) reclassification of tier ratings for the OPOs, with 46.6% of OPOs (27 of 58) changing tier ranking at least once during the 3-year period. Between 6.9% (4 of 58) and 12.1% (7 of 58) moved into tier 1 and up to 8.6% (5 of 58) moved into tier 3.

Conclusions and Relevance

This cross-sectional study of population characteristics and OPO performance metrics found that adjusting for area deprivation and age significantly changed OPO measured performance and tier classifications. These findings suggest that underlying population characteristics may alter processes of care and characterize donation and transplant rates independent of OPO performance. Risk adjustment accounting for population characteristics warrants consideration in prospective policy and further evaluation of quality metrics.

Impact of Socioeconomic Status on Mortality and Readmission in Patients With Heart Failure With Reduced Ejection Fraction: The ARIC Study

Background Low socioeconomic status (SES) is associated with a higher risk of heart failure (HF). The contribution of individual and neighborhood SES to the prognosis and quality of care for HF with reduced ejection fraction is not clear yet has important implications. Methods and Results We examined 728 participants of the ARIC (Atherosclerosis Risk in Communities) study (mean age, 78.2 years; 34% Black participants; 46% women) hospitalized with HF with reduced ejection fraction (ejection fraction <50%) between 2005 and 2018. We assessed associations between education, income, and area deprivation index with mortality and HF readmission using multivariable Cox models. We also evaluated the use of guideline-directed medical therapy (optimal: ≥3 of ß-blockers, mineralocorticoid receptor antagonist, angiotensin-converting enzyme inhibitors, or angiotensin receptor blockers; acceptable: at least 2) at discharge. During a median follow-up of 3.2 years, 58.7% were readmitted with HF, and 74.0% died. Low income was associated with higher mortality (hazard ratio [HR], 1.52 [95% CI, 1.14-2.04]) and readmission (HR, 1.45 [95% CI, 1.04-2.03]). Similarly, low education was associated with mortality (HR, 1.27 [95% CI, 1.01-1.59]) and readmission (HR, 1.62 [95% CI, 1.24-2.12]). The highest versus lowest area deprivation index quartile was associated with readmission (HR, 1.69 [95% CI, 1.11-2.58]) but not necessarily with mortality. The prevalence of optimal guideline-directed medical therapy and acceptable guideline-directed medical therapy was 5.5% and 54.4%, respectively, but did not significantly differ by SES. Conclusions Among patients hospitalized with HF with reduced ejection fraction, low SES was independently associated with mortality and HF readmission. A targeted secondary prevention approach that focuses intensive efforts on patients with low SES will be necessary to improve outcomes of those with HF with reduced ejection fraction.

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.

Demographic and social determinants of cognitive dysfunction following hospitalization for COVID-19

BACKGROUND

Persistent cognitive symptoms have been reported following COVID-19 hospitalization. We investigated the relationship between demographics, social determinants of health (SDOH) and cognitive outcomes 6-months after hospitalization for COVID-19.

METHODS

We analyzed 6-month follow-up data collected from a multi-center, prospective study of hospitalized COVID-19 patients. Demographic and SDOH variables (age, race/ethnicity, education, employment, health insurance status, median income, primary language, living arrangements, and pre-COVID disability) were compared between patients with normal versus abnormal telephone Montreal Cognitive Assessments (t-MOCA; scores<18/22). Multivariable logistic regression models were constructed to evaluate predictors of t-MoCA.

RESULTS

Of 382 patients available for 6-month follow-up, 215 (56%) completed the t-MoCA (n = 109/215 [51%] had normal and n = 106/215 [49%] abnormal results). 14/215 (7%) patients had a prior history of dementia/cognitive impairment. Significant univariate predictors of abnormal t-MoCA included older age, ≤12 years of education, unemployment pre-COVID, Black race, and a pre-COVID history of cognitive impairment (all p < 0.05). In multivariable analyses, education ≤12 years (adjusted OR 5.21, 95%CI 2.25-12.09), Black race (aOR 5.54, 95%CI 2.25-13.66), and the interaction of baseline functional status and unemployment prior to hospitalization (aOR 3.98, 95%CI 1.23-12.92) were significantly associated with abnormal t-MoCA scores after adjusting for age, history of dementia, language, neurological complications, income and discharge disposition.

CONCLUSIONS

Fewer years of education, Black race and unemployment with baseline disability were associated with abnormal t-MoCA scores 6-months post-hospitalization for COVID-19. These associations may be due to undiagnosed baseline cognitive dysfunction, implicit biases of the t-MoCA, other unmeasured SDOH or biological effects of SARS-CoV-2.

The Impact of a Standardized Discharge Process on 30-Day Readmissions for Patients on Outpatient Parenteral Antibiotic Treatment

Introduction: Outpatient parenteral antibiotic treatment (OPAT) is associated with shorter length of hospital stay and reduced cost. Yet, patients discharged home on OPAT are at risk of hospital readmissions due to adverse events and complications. Although the impact of a multidisciplinary approach to readmission has been assessed by previous studies, addition of an innovative technology has not been evaluated for OPAT. This study examines the impact of a multidisciplinary approach including automated voice calls on 30-day readmissions of OPAT patients. Methods: A post-discharge transitional care process (PDTCP) targeting OPAT patients was implemented in fall 2016. This process included an automated telephone patient engagement service and coordination among pharmacy, nursing, medicine, and social work personnel. The patients on OPAT received automated telephone calls at 2, 9, 16, 28, and 40 days post-discharge to ensure medication availability and adherence and to circumvent issues that would otherwise result in an emergency room visit or readmission to the hospital. Results: A total 429 voice calls were made to 148 patients from November 8, 2016 to February 28, 2019. Overall, 61% (n = 90/148) of the patients were successfully reached by the automated voice system. The patients who were reached by the automated voice system were less likely to be readmitted than those not reached (18.9% vs 41.4%; relative risk (RR) 0.46, 95% CI 0.27-0.77, P = .003). Conclusion: Our study demonstrated that a multidisciplinary approach involving the use of automated telephone calls was associated with decreased hospital readmissions.

Effects of race and socioeconomic status on treatment for localized renal masses in New York City

INTRODUCTION

Partial nephrectomy (PN) is the preferred treatment for localized renal masses (LRM), however its use is not uniform across patient socioeconomic (SES) factors. Our hypothesis is that the effect of increased SES on surgical management of LRMs in New York City (NYC) will not be the same for Black and White patients.

PATIENTS AND METHODS

Patients were identified from the New York State Cancer Registry (NYSPACED) treated for LRMs with PN or radical nephrectomy from 2004 to 2016. We identified patients' home neighborhoods through Public Use Microdata Areas (PUMA) in NYSCAPED and used a US Census SES index. Logistic regression was used to determine the association of race and SES on receipt of PN, controlling for age, ethnicity, gender, and diagnosis year.

RESULTS

On unadjusted analyses, patients from higher PUMA SES quartiles were more likely to receive PN (OR = 1.07, P < 0.05), while Black patients were less likely to receive PN as compared to White patients (OR = 0.66, P < 0.001). Multivariable analysis showed a significant interaction between race and SES quartile (interaction P = 0.005) such that the effect of PUMA SES on receipt of PN was modified by race. PN receipt for Black vs. White patients was significantly different within the highest SES quartile (OR = 0.44, P < 0.001), but not within the lowest.

CONCLUSION

In NYC, patients from higher SES quartile neighborhoods had significantly increased odds for receipt of PN for LRMs. As neighborhood SES quartile increased, White patients were significantly more likely to receive PN, while Black patients were not.

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.

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.

Assessment of Racial/Ethnic Disparities in Hospitalization and Mortality in Patients With COVID-19 in New York City

IMPORTANCE

Black and Hispanic populations have higher rates of coronavirus disease 2019 (COVID-19) hospitalization and mortality than White populations but lower in-hospital case-fatality rates. The extent to which neighborhood characteristics and comorbidity explain these disparities is unclear. Outcomes in Asian American populations have not been explored.

OBJECTIVE

To compare COVID-19 outcomes based on race and ethnicity and assess the association of any disparities with comorbidity and neighborhood characteristics.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study was conducted within the New York University Langone Health system, which includes over 260 outpatient practices and 4 acute care hospitals. All patients within the system's integrated health record who were tested for severe acute respiratory syndrome coronavirus 2 between March 1, 2020, and April 8, 2020, were identified and followed up through May 13, 2020. Data were analyzed in June 2020. Among 11 547 patients tested, outcomes were compared by race and ethnicity and examined against differences by age, sex, body mass index, comorbidity, insurance type, and neighborhood socioeconomic status.

EXPOSURES

Race and ethnicity categorized using self-reported electronic health record data (ie, non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and multiracial/other patients).

MAIN OUTCOMES AND MEASURES

The likelihood of receiving a positive test, hospitalization, and critical illness (defined as a composite of care in the intensive care unit, use of mechanical ventilation, discharge to hospice, or death).

RESULTS

Among 9722 patients (mean [SD] age, 50.7 [17.5] years; 58.8% women), 4843 (49.8%) were positive for COVID-19; 2623 (54.2%) of those were admitted for hospitalization (1047 [39.9%] White, 375 [14.3%] Black, 715 [27.3%] Hispanic, 180 [6.9%] Asian, 207 [7.9%] multiracial/other). In fully adjusted models, Black patients (odds ratio [OR], 1.3; 95% CI, 1.2-1.6) and Hispanic patients (OR, 1.5; 95% CI, 1.3-1.7) were more likely than White patients to test positive. Among those who tested positive, odds of hospitalization were similar among White, Hispanic, and Black patients, but higher among Asian (OR, 1.6, 95% CI, 1.1-2.3) and multiracial patients (OR, 1.4; 95% CI, 1.0-1.9) compared with White patients. Among those hospitalized, Black patients were less likely than White patients to have severe illness (OR, 0.6; 95% CI, 0.4-0.8) and to die or be discharged to hospice (hazard ratio, 0.7; 95% CI, 0.6-0.9).

CONCLUSIONS AND RELEVANCE

In this cohort study of patients in a large health system in New York City, Black and Hispanic patients were more likely, and Asian patients less likely, than White patients to test positive; once hospitalized, Black patients were less likely than White patients to have critical illness or die after adjustment for comorbidity and neighborhood characteristics. This supports the assertion that existing structural determinants pervasive in Black and Hispanic communities may explain the disproportionately higher out-of-hospital deaths due to COVID-19 infections in these populations.

Predictors of Heart Failure Readmission in a High-Risk Primarily Hispanic Population in a Rural Setting

BACKGROUND

High risk for readmission in patients with heart failure (HF) is associated with Hispanic ethnicity, multimorbidity, smaller hospitals, and hospitals serving low-socioeconomic or heavily Hispanic regions and those with limited cardiac services. Information for hospitals caring primarily for such high-risk patients is lacking.

OBJECTIVE

The aim of this study was to identify factors associated with 30-day HF readmission after HF hospitalization in a rural, primarily Hispanic, low-socioeconomic, and underserved region.

METHODS

Electronic medical records for all HF admissions within a 2-year period to a 107-bed hospital near the California-Mexico border were reviewed. Logistic regression was used to identify independent predictors of readmission.

RESULTS

A total of 189 unique patients had 30-day follow-up data. Patients were primarily Hispanic (71%), male (58%), and overweight or obese (82.5%) with 4 or more chronic conditions (83%) and a mean age of 68 years. The 30-day HF readmission rate was 5.3%. Early readmission was associated with history of HF, more previous emergency department (ED) and hospital visits, higher diastolic blood pressure and hypokalemia at presentation, shorter length of stay, and higher heart rate, diastolic blood pressure, and atrial fibrillation (AF) at discharge. Using logistic regression, previous 6-month ED visits (odds ratio, 1.5; P = .009) and AF at discharge (odds ratio, 5.7; P = .039) were identified as independent predictors of 30-day HF readmission.

CONCLUSIONS

Previous ED use and AF at discharge predicted early HF readmission in a high-risk, primarily Hispanic, rural population in a low-socioeconomic region.

Development and Validation of a County-Level Social Determinants of Health Risk Assessment Tool for Cardiovascular Disease

PURPOSE

Social determinants of health (SDoH) have been linked to a variety of health conditions, but there are no multivariate measures of these determinants to estimate the risk of morbidity or mortality in a community. We developed a score derived from multivariate measures of SDoH that predicts county-level cardiovascular disease (CVD) mortality.

METHODS

Using county-level data from 3,026 US counties, we developed a score considering variables of neighborhood socioeconomic status, food/lifestyle environment, and health care resource availability and accessibility to predict the 3-year average (2015-2017) age-adjusted county-level mortality rate for all CVD. We used one 50% random sample to develop the score and the other to validate the score. A Poisson regression model was developed to estimate parameters of variables while accounting for intrastate correlation.

RESULTS

The index score was based on 7 SDoH factors: percentage of the population of minority (nonwhite) race, poverty rate, percentage of the population without a high school diploma, grocery store ratio, fast-food restaurant ratio, after-tax soda price, and primary care physician supply. The area under the curve for the development and validation groups was similar, 0.851 (95% CI, 0.829-0.872) and 0.840 (95% CI, 0.817-0.863), respectively, indicating excellent discriminative ability. The index had better predictive performance for CVD burden than other area-level indexes: poverty only (area under the curve= 0.808, P <.001); the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI) (area under the curve =0.786, P <.001); and the Agency for Healthcare Research and Quality's Socioeconomic Status (AHRQ-SES) index (area under the curve =0.835, P = .03).

CONCLUSIONS

Our validated multivariate SDoH index score accurately classifies counties with high CVD burden and therefore has the potential to improve CVD risk prediction for vulnerable populations and interventions for CVD at the county level.

Association of Medicaid Eligibility With Surgical Readmission Among Medicare Beneficiaries

IMPORTANCE

The Centers for Medicare & Medicaid Services is beginning to consider adjusting for social risk factors, such as dual eligibility for Medicare and Medicaid, when evaluating hospital performance under value-based purchasing programs. It is unknown whether dual eligibility represents a unique domain of social risk or instead represents clinical risk unmeasured by variables available in traditional Medicare claims.

OBJECTIVE

To assess how dual eligibility for Medicare and Medicaid is associated with risk-adjusted readmission rates after surgery.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study was conducted of 55 651 Medicare beneficiaries undergoing general, vascular, and gynecologic surgery at 62 hospitals in Michigan between January 1, 2014, and December 1, 2016. Representative cohorts were derived from traditional Medicare claims (n = 29 710) and the Michigan Surgical Quality Collaborative (MSQC) clinical registry (n = 25 941), which includes additional measures of clinical risk. Statistical analysis was conducted between April 10 and July 15, 2019. The association between dual eligibility and risk-adjusted 30-day readmission rates after surgery was compared between models inclusive and exclusive of additional measurements of clinical risk. The study also examined how dual eligibility is associated with hospital profiling using risk-adjusted readmission rates.

EXPOSURES

Dual eligibility for Medicare and Medicaid.

MAIN OUTCOMES AND MEASURES

Risk-adjusted all-cause 30-day readmission after surgery.

RESULTS

There were a total of 3986 dual-eligible beneficiaries in the Medicare claims cohort (2554 women; mean [SD] age, 72.9 [6.9] years) and 1608 dual-eligible beneficiaries in the MSQC cohort (990 women; mean [SD] age, 72.9 [6.8] years). In both data sets, higher proportions of dual-eligible beneficiaries were younger, female, and nonwhite than Medicare-only beneficiaries (Medicare claims cohort: female, 2554 of 3986 [64.1%] vs 12 879 of 25 724 [50.1%]; nonwhite, 1225 of 3986 [30.7%] vs 2783 of 25 724 [10.8%]; MSQC cohort: female, 990 of 1608 [61.6%] vs 12 578 of 24 333 [51.7%]; nonwhite, 416 of 1608 [25.9%] vs 2176 of 24 333 [8.9%]). In the Medicare claims cohort, dual-eligible beneficiaries were more likely to be readmitted (15.5% [95% CI, 13.7%-17.3%]) than Medicare-only beneficiaries (13.3% [95% CI, 12.7%-13.9%]; difference, 2.2 percentage points [95% CI, 0.4-3.9 percentage points]). In the MSQC cohort, after adjustment for more granular measures of clinical risk, dual eligibility was not significantly associated with readmission (difference, 0.6 percentage points [95% CI, -1.0 to 2.2 percentage points]). In both the Medicare claims and MSQC cohorts, adding dual eligibility to risk-adjustment models had little association with hospital ranking using risk-adjusted readmission rates.

CONCLUSIONS AND RELEVANCE

This study suggests that dual eligibility for Medicare and Medicaid may reflect unmeasured clinical risk of readmission in claims data. Policy makers should consider incorporating more robust measures of social risk into risk-adjustment models used by value-based purchasing programs.

Financial toxicity is associated with worse physical and emotional long-term outcomes after traumatic injury

BACKGROUND

Increasing health care costs and high deductible insurance plans have shifted more responsibility for medical costs to patients. After serious illnesses, financial responsibilities may result in lost wages, forced unemployment, and other financial burdens, collectively described as financial toxicity. Following cancer treatments, financial toxicity is associated with worse long-term health-related quality of life (HRQoL) outcomes. The purpose of this study was to determine the incidence of financial toxicity following injury, factors associated with financial toxicity, and the impact of financial toxicity on long-term HRQoL.

METHODS

Adult patients with an Injury Severity Score of 10 or greater and without head or spinal cord injury were prospectively followed for 1 year. The Short-Form-36 was used to determine overall quality of life at 1 month, 2 months, 4 months, and 12 months. Screens for depression and posttraumatic stress syndrome were administered. The primary outcome was any financial toxicity. A multivariable generalized estimating equation was used to account for variability over time.

RESULTS

Five hundred patients were enrolled, and 88% suffered financial toxicity during the year following injury (64% reduced income, 58% unemployment, 85% experienced stress due to financial burden). Financial toxicity remained stable over follow-up (80-85%). Factors independently associated with financial toxicity were lower age (odds ratio [OR], 0.96 [0.94-0.98]), lack of health insurance (OR, 0.28 [0.14-0.56]), and larger household size (OR, 1.37 [1.06-1.77]). After risk adjustment, patients with financial toxicity had worse HRQoL, and more depression and posttraumatic stress syndrome in a stepwise fashion based on severity of financial toxicity.

CONCLUSION

Financial toxicity following injury is extremely common and is associated with worse psychological and physical outcomes. Age, lack of insurance, and large household size are associated with financial toxicity. Patients at risk for financial toxicity can be identified, and interventions to counteract the negative effects should be developed to improve long-term outcomes.

LEVEL OF EVIDENCE

Prognostic/epidemiologic study, level III.

Racial and Ethnic Differences in 30-Day Hospital Readmissions Among US Adults With Diabetes

IMPORTANCE

Differences in readmission rates among racial and ethnic minorities have been reported, but data among people with diabetes are lacking despite the high burden of diabetes and its complications in these populations.

OBJECTIVES

To examine racial/ethnic differences in all-cause readmission among US adults with diabetes and categorize patient- and system-level factors associated with these differences.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study includes 272 758 adult patients with diabetes, discharged alive from the hospital between January 1, 2009, and December 31, 2014, and stratified by race/ethnicity. An administrative claims data set of commercially insured and Medicare Advantage beneficiaries across the United States was used. Data analysis took place between October 2016 and February 2019.

MAIN OUTCOMES AND MEASURES

Unplanned all-cause readmission within 30 days of discharge and individual-, clinical-, economic-, index hospitalization-, and hospital-level risk factors for readmission.

RESULTS

A total of 467 324 index hospitalizations among 272 758 adults with diabetes (mean [SD] age, 67.7 [12.7]; 143 498 [52.6%] women) were examined. The rates of 30-day all-cause readmission were 10.2% (33 683 of 329 264) among white individuals, 12.2% (11 014 of 89 989) among black individuals, 10.9% (4151 of 38 137) among Hispanic individuals, and 9.9% (980 of 9934) among Asian individuals (P < .001). After adjustment for all factors, only black patients had a higher risk of readmission compared with white patients (odds ratio, 1.05; 95% CI, 1.02-1.08). This increased readmission risk among black patients was sequentially attenuated, but not entirely explained, by other demographic factors, comorbidities, income, reason for index hospitalization, or place of hospitalization. Compared with white patients, both black and Hispanic patients had the highest observed-to-expected (OE) readmission rate ratio when their income was low (annual household income <$40 000 among black patients: OE ratio, 1.11; 95% CI, 1.09-1.14; among Hispanic patients: OE ratio, 1.11; 95% CI, 1.07-1.16) and when they were hospitalized in nonprofit hospitals (black patients: OE ratio, 1.10; 95% CI, 1.08-1.12; among Hispanic patients: OE ratio, 1.08; 95% CI, 1.05-1.12), academic hospitals (black patients: OE ratio, 1.16; 95% CI, 1.13-1.20; Hispanic patients: OE ratio, 1.12; 95% CI, 1.06-1.19), or large hospitals (ie, with ≥400 beds; black patients: OE ratio, 1.11; 95% CI, 1.09-1.14; Hispanic patients: OE ratio, 1.09; 95% CI, 1.04-1.14).

CONCLUSIONS AND RELEVANCE

In this study, black patients with diabetes had a significantly higher risk of readmission than members of other racial/ethnic groups. This increased risk was most pronounced among lower-income patients hospitalized in nonprofit, academic, or large hospitals. These findings reinforce the importance of identifying and addressing the many reasons for persistent racial/ethnic differences in health care quality and outcomes.

Hospital Variation in 30-Day Mortality for Patients With Stroke; The Impact of Individual and Municipal Socio-Demographic Status

Background

Thirty‐day mortality after hospitalization for stroke is commonly reported as a quality indicator. However, the impact of adjustment for individual and/or neighborhood sociodemographic status (SDS) has not been well documented. This study aims to evaluate the role of individual and contextual sociodemographic determinants in explaining the variation across hospitals in Norway and determine the impact when testing for hospitals with low or high mortality.

Methods and Results

Patient Administrative System data on all 45 448 patients admitted to hospitals in Norway with an incident stroke diagnosis from 2005 to 2009 were included. The data were merged with data from several databases to obtain information on vital status (dead/alive) and individual SDS variables. Logistic regression models were compared to estimate the predictive effect of individual and neighborhood SDS on 30‐day mortality and to determine outlier hospitals. All individual SDS factors, except travel time, were statistically significant predictors of 30‐day mortality. Of the municipal variables, only the municipal variable proportion of low income was statistically significant as a predictor of 30‐day mortality. Including sociodemographic characteristics of the individual and other characteristics of the municipality improved the model fit. However, performance classification was only changed for 1 (out of 56) hospital, from “significantly high mortality” to “nonoutlier.”

Conclusions

Our study showed that those stroke patients with a lower SDS have higher odds of dying after 30 days compared with those with a higher SDS, although this did not have a substantial impact when classifying providers as performing as expected, better than expected, or worse than expected.

Augmenting community-level social determinants of health data with individual-level survey data

Social determinants of health (SDH) such as education and socioeconomic status are strongly associated with health and health outcomes. Incorporating SDH variables into clinical data sets could therefore improve the accuracy of predictive analytics, but individual-level SDH are rarely available and must be inferred from community-level data. We propose a method for doing so leveraging the joint probability distribution of the basic demographics available from the patient's clinical record and known community-level SDH. We demonstrate the method using two data sets, the New York City (NYC) subset of the US census data and the NYC Health and Nutrition Estimation Survey (NYCHANES) and provide sample results for 2 census tracts in NYC. The advantage of this approach is that it does not simplistically assume that all residents within a census tract share the same average/median socioeconomic status, but instead recognizes and leverages the strong known associations between demographics and SDH within localities. Results could explain some of the discrepancies appearing in the SDH-big data literature. Future studies are needed for using the augmented SHD to improve clinically relevant use cases, such as predictive analytics.

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

Background:

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

Methods and Results:

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

Conclusions:

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

Racial and Socioeconomic Differences Manifest in Process Measure Adherence for Enhanced Recovery After Surgery Pathway

BACKGROUND

Adherence to care processes and surgical outcomes varies by population subgroups for the same procedure. Enhanced recovery after surgery pathways are intended to standardize care, but their effect on process adherence and outcomes for population subgroups is unknown.

OBJECTIVE

This study aims to demonstrate the association between recovery pathway implementation, process measures, and short-term surgical outcomes by population subgroup.

DESIGN

This study is a pre- and post-quality improvement implementation cohort study.

SETTING

This study was conducted at a tertiary academic medical center.

INTERVENTION

A modified colorectal enhanced recovery after surgery pathway was implemented.

PATIENTS

Patients were included who had elective colon and rectal resections before (2013) and following (2014-2016) recovery pathway implementation.

MAIN OUTCOME MEASURE

Thirty-day outcomes by race and socioeconomic status were analyzed using a difference-in-difference approach with correlation to process adherence.

RESULTS

We identified 639 cases (199 preimplementation, 440 postimplementation). In these cases, 75.2% of the patients were white, and 91.7% had a high socioeconomic status. Groups were similar in terms of other preoperative characteristics. Following pathway implementation, median lengths of stay improved in all subgroups (-1.0 days overall, p ≤ 0.001), but with no statistical difference by race or socioeconomic status (p = 0.89 and p = 0.29). Complication rates in both racial and socioeconomic groups were no different (26.4% vs 28.8%, p = 0.73; 27.3% vs 25.0%, p = 0.86) and remained unchanged with implementation (p = 0.93, p = 0.84). By race, overall adherence was 31.7% in white patients and 26.5% in nonwhite patients (p = 0.32). Although stratification by socioeconomic status demonstrated decreased overall adherence in the low-status group (31.8% vs 17.1%, p = 0.05), white patients were more likely to have regional pain therapy (57.1% vs 44.1%, p = 0.02) with a similar trend seen with socioeconomic status.

LIMITATIONS

Data were collected primarily for quality improvement purposes.

CONCLUSIONS

Differences in outcomes by race and socioeconomic status did not arise following implementation of an enhanced recovery pathway. Differences in process measures by population subgroups highlight differences in care that require further investigation. See Video Abstract at http://links.lww.com/DCR/A386.

Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVES

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

DESIGN

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

SETTING

Acute care, Medicare participating hospitals from 2008 to 2011.

PATIENTS

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

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Impact of Socioeconomic Status on Patients Supported With a Left Ventricular Assist Device: An Analysis of the UNOS Database (United Network for Organ Sharing)

BACKGROUND

Low socioeconomic status (SES) is a known risk factor for heart failure, mortality among those with heart failure, and poor post heart transplant (HT) outcomes. This study sought to determine whether SES is associated with decreased waitlist survival while on left ventricular assist device (LVADs) support and after HT.

METHODS AND RESULTS

A total of 3361 adult patients bridged to primary HT with an LVAD between May 2004 and April 2014 were identified in the UNOS database (United Network for Organ Sharing). SES was measured using the Agency for Healthcare Research and Quality SES index using data from the 2014 American Community Survey. In the study cohort, SES did not have an association with the combined end point of death or delisting on LVAD support (P=0.30). In a cause-specific unadjusted model, those in the top (hazard ratio, 1.55; 95% confidence interval, 1.14-2.11; P=0.005) and second greatest SES quartile (hazard ratio 1.50; 95% confidence interval, 1.10-2.04; P=0.01) had an increased risk of death on device support compared with the lowest SES quartile. Adjusting for clinical risk factors mitigated the increased risk. There was no association between SES and complications. Post-HT survival, both crude and adjusted, was decreased for patients in the lowest quartile of SES index compared with all other SES quartiles.

CONCLUSIONS

Freedom from waitlist death or delisting was not affected by SES. Patients with a higher SES had an increased unadjusted risk of waitlist mortality during LVAD support, which was mitigated by adjusting for increased comorbid conditions. Low SES was associated with worse post-HT outcomes. Further study is needed to confirm and understand a differential effect of SES on post-transplant outcomes that was not seen during LVAD support before HT.

Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score

BACKGROUND

Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission.

METHODS

We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model.

RESULTS

There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722.

CONCLUSIONS

Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.

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

OBJECTIVE

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

DATA SOURCES

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

STUDY DESIGN

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

PRINCIPAL FINDINGS

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

CONCLUSIONS

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

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

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

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

OBJECTIVE

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Geographic Variation in Trends and Disparities in Acute Myocardial Infarction Hospitalization and Mortality by Income Levels, 1999-2013

IMPORTANCE

During the past decade, the incidence and mortality associated with acute myocardial infarction (AMI) in the United States have decreased substantially. However, it is unknown whether these improvements were consistent across communities of different economic status and geographic regions since efforts to improve cardiovascular disease prevention and management may have had variable impact.

OBJECTIVE

To determine whether trends in US county-level, risk-standardized AMI hospitalization and mortality rates varied by county-based median income level.

DESIGN, SETTING, AND PARTICIPANTS

In this observational study, county-level risk-standardized (age, sex, and race) hospitalization and 1-year mortality rates for AMI from January 1, 1999, to December 31, 2013, were measured for Medicare beneficiaries 65 years or older. Data analysis was performed from June 2 through December 1, 2015. Counties were stratified by median income percentile using 1999 US Census Bureau data adjusted for inflation: low- (<25th), average- (25th-75th), or high- (>75th) income groups.

MAIN OUTCOMES AND MEASURES

The effect of income on the slope of AMI hospitalizations and mortality, measured as differences in the rate of change in AMI hospitalizations and mortality by county income and by the 4 US geographic regions, and a possible lag effect among low-income counties.

RESULTS

In the 15-year study period, AMI risk-standardized hospitalization and mortality rates decreased significantly for all 3 county income groups. Mean hospitalization rates were significantly higher among low-income counties compared with high-income counties in 1999 (1353 vs 1123 per 100 000 person-years, respectively) and in 2013 (853 vs 648 per 100 000 person-years, respectively). One-year mortality rates after hospitalization for AMI were similar across county income groups, decreasing from 1999 (31.5%, 31.4%, and 31.1%, for high-, average-, and low-income counties, respectively) to 2013 (26.2%, 26.1%, and 25.4%, respectively). Income was associated with county-level, risk-standardized AMI hospitalization rates but not mortality rates. Increasing 1 interquartile range of median county consumer price index-adjusted income ($12 000) was associated with a decline in 46 and 37 hospitalizations per 100 000 person-years for 1999 and 2013, respectively; interaction between income and time was 0.56. The rate of decline in AMI hospitalizations was similar for all county income groups; however, low-income counties lagged behind high-income counties by 4.3 (95% CI, 3.1-5.9) years. There were no significant differences in trends across geographic regions.

CONCLUSIONS AND RELEVANCE

Hospitalization and mortality rates of AMI declined among counties of all income levels, although hospitalization rates among low-income counties lag behind those of the higher income groups. These findings lend support for a more targeted, community-based approach to AMI prevention.

The 30-day metric in acute heart failure revisited: data from IN-HF Outcome, an Italian nationwide cardiology registry

AIMS

Unplanned readmissions early after a discharge from acute heart failure hospitalization are common and have become a reimbursement benchmark and marker of hospital quality. However, the competing risk of short-term post-discharge mortality is substantial.

METHODS AND RESULTS

Using data from the prospective, nationwide Registry IN-HF Outcome, we analysed the incidence and predictors of 30-day mortality or readmissions and associated days-alive-out-of-hospital (DAOH) in 1520 patients discharged alive after admission for acute heart failure. Within 30 days after discharge, 94 patients (6.2%) were readmitted (91% for cardiovascular causes; 60% recurrent heart failure) and 42 (2.8%) died, 10 of which occurred during readmission. Overall, 126 patients (8.3%) met the combined endpoint. By multivariable logistic regression, worsening chronic heart failure as clinical presentation [odds ratio (OR) 1.83, 95% confidence interval (CI) 1.21-2.77, P = 0.005), inotropes during admission (OR 2.19, 95% CI 1.40-3.43, P = 0.0006), length of stay (OR 1.02, 95% CI 1.01-1.04, P = 0.002) and renin-angiotensin system inhibitors at discharge (OR 0.52, 95%CI 0.35-0.77, P = 0.001) independently predicted 30-day all-cause mortality and/or readmission (c-statistic = 0.695). Per cent 30-day DAOH was lower in patients with in-hospital inotrope use, no renin-angiotensin system inhibitors prescription at discharge, New York Heart Association III-IV class at discharge, and correlated inversely with length of stay and age.

CONCLUSION

A clinical and biohumoral profile consistent with chronic advanced heart failure and end-organ damage identifies acute heart failure patients discharged home from cardiology units, who are at highest risk of early death and/or readmission. These findings have practical implications for tailoring specific follow-up.

Socioeconomic status and ethnicity of deceased donor kidney recipients compared to their donors

Public perception and misperceptions of socioeconomic disparities affect the willingness to donate organs. To improve our understanding of the flow of deceased donor kidneys, we analyzed socioeconomic status (SES) and racial/ethnic gradients between donors and recipients. In a retrospective cohort study, traditional demographic and socioeconomic factors, as well as an SES index, were compared in 56,697 deceased kidney donor and recipient pairs transplanted between 2007 and 2012. Kidneys were more likely to be transplanted in recipients of the same racial/ethnic group as the donor (p < 0.001). Kidneys tended to go to recipients of lower SES index (50.5% of the time, p < 0.001), a relationship that remained after adjusting for other available markers of donor organ quality and SES (p < 0.001). Deceased donor kidneys do not appear to be transplanted from donors of lower SES to recipients of higher SES; this information may be useful in counseling potential donors and their families regarding the distribution of their organ gifts.

Utility of socioeconomic status in predicting 30-day outcomes after heart failure hospitalization

BACKGROUND

An individual's socioeconomic status (SES) is associated with health outcomes and mortality, yet it is unknown whether accounting for SES can improve risk-adjustment models for 30-day outcomes among Centers for Medicare & Medicaid Services beneficiaries hospitalized with heart failure.

METHODS AND RESULTS

We linked clinical data on hospitalized patients with heart failure in the Get With The Guidelines-Heart Failure database (January 2005 to December 2011) with Centers for Medicare & Medicaid Services claims and county-level SES data from the 2012 Area Health Resources Files. We compared the discriminatory capabilities of multivariable models that adjusted for SES, patient, and hospital characteristics to determine whether county-level SES data improved prediction or changed hospital rankings for 30-day all-cause mortality and rehospitalization. After adjusting for patient and hospital characteristics, median household income (per $5000 increase) was inversely associated with odds of 30-day mortality (odds ratio, 0.97; 95% confidence interval, 0.95-1.00; P=0.032) and the percentage of people with at least a high school diploma (per 5 U increase) was associated with lower odds of 30-day rehospitalization (odds ratio, 0.95; 95% confidence interval, 0.91-0.99). After adjustment for county-level SES data, relative to whites, Hispanic ethnicity (odds ratio, 0.70; 95% confidence interval, 0.58-0.83) and black race (odds ratio, 0.57; 95% confidence interval, 0.50-0.65) remained significantly associated with lower 30-day mortality, but had similar 30-day rehospitalization. County-level SES did not improve risk adjustment or change hospital rankings for 30-day mortality or rehospitalization.

CONCLUSIONS

County-level SES data are modestly associated with 30-day outcomes for Centers for Medicare & Medicaid Services beneficiaries hospitalized with heart failure, but do not improve risk adjustment models based on patient characteristics alone.

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

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