RESUMEN
Importance: Equity is an essential domain of health care quality. The Centers for Medicare & Medicaid Services (CMS) developed 2 Disparity Methods that together assess equity in clinical outcomes. Objectives: To define a measure of equitable readmissions; identify hospitals with equitable readmissions by insurance (dual eligible vs non-dual eligible) or patient race (Black vs White); and compare hospitals with and without equitable readmissions by hospital characteristics and performance on accountability measures (quality, cost, and value). Design, Setting, and Participants: Cross-sectional study of US hospitals eligible for the CMS Hospital-Wide Readmission measure using Medicare data from July 2018 through June 2019. Main Outcomes and Measures: We created a definition of equitable readmissions using CMS Disparity Methods, which evaluate hospitals on 2 methods: outcomes for populations at risk for disparities (across-hospital method); and disparities in care within hospitals' patient populations (within-a-single-hospital method). Exposures: Hospital patient demographics; hospital characteristics; and 3 measures of hospital performance-quality, cost, and value (quality relative to cost). Results: Of 4638 hospitals, 74% served a sufficient number of dual-eligible patients, and 42% served a sufficient number of Black patients to apply CMS Disparity Methods by insurance and race. Of eligible hospitals, 17% had equitable readmission rates by insurance and 30% by race. Hospitals with equitable readmissions by insurance or race cared for a lower percentage of Black patients (insurance, 1.9% [IQR, 0.2%-8.8%] vs 3.3% [IQR, 0.7%-10.8%], P < .01; race, 7.6% [IQR, 3.2%-16.6%] vs 9.3% [IQR, 4.0%-19.0%], P = .01), and differed from nonequitable hospitals in multiple domains (teaching status, geography, size; P < .01). In examining equity by insurance, hospitals with low costs were more likely to have equitable readmissions (odds ratio, 1.57 [95% CI, 1.38-1.77), and there was no relationship between quality and value, and equity. In examining equity by race, hospitals with high overall quality were more likely to have equitable readmissions (odds ratio, 1.14 [95% CI, 1.03-1.26]), and there was no relationship between cost and value, and equity. Conclusion and Relevance: A minority of hospitals achieved equitable readmissions. Notably, hospitals with equitable readmissions were characteristically different from those without. For example, hospitals with equitable readmissions served fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Implementation of an equitable readmission measure must consider unequal distribution of at-risk patients among hospitals.
Asunto(s)
Equidad en Salud , Disparidades en Atención de Salud , Hospitales , Medicare , Readmisión del Paciente , Calidad de la Atención de Salud , Anciano , Humanos , Población Negra , Estudios Transversales , Hospitales/normas , Hospitales/estadística & datos numéricos , Medicare/normas , Medicare/estadística & datos numéricos , Readmisión del Paciente/estadística & datos numéricos , Estados Unidos , Negro o Afroamericano/estadística & datos numéricos , Blanco/estadística & datos numéricos , Equidad en Salud/economía , Equidad en Salud/estadística & datos numéricos , Disparidades en Atención de Salud/economía , Disparidades en Atención de Salud/etnología , Disparidades en Atención de Salud/estadística & datos numéricos , Evaluación del Resultado de la Atención al Paciente , Calidad de la Atención de Salud/economía , Calidad de la Atención de Salud/normas , Calidad de la Atención de Salud/estadística & datos numéricosAsunto(s)
Equidad en Salud , Disparidades en Atención de Salud , Hospitales , Readmisión del Paciente , Indicadores de Calidad de la Atención de Salud , Determinantes Sociales de la Salud , Humanos , Equidad en Salud/normas , Equidad en Salud/estadística & datos numéricos , Disparidades en Atención de Salud/normas , Disparidades en Atención de Salud/estadística & datos numéricos , Hospitales/normas , Hospitales/estadística & datos numéricos , Readmisión del Paciente/normas , Readmisión del Paciente/estadística & datos numéricos , Indicadores de Calidad de la Atención de Salud/normas , Determinantes Sociales de la Salud/estadística & datos numéricos , Estados Unidos/epidemiologíaRESUMEN
BACKGROUND: Despite documented associations between social determinants of health and outcomes post-congenital heart surgery, clinical risk models typically exclude these factors. OBJECTIVES: The study sought to characterize associations between social determinants and operative and longitudinal mortality as well as assess impacts on risk model performance. METHODS: Demographic and clinical data were obtained for all congenital heart surgeries (2006-2021) from locally held Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources Society of Thoracic Surgeons Congenital Heart Surgery Database data. Neighborhood-level American Community Survey and composite sociodemographic measures were linked by zip code. Model prediction, discrimination, and impact on quality assessment were assessed before and after inclusion of social determinants in models based on the 2020 Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model. RESULTS: Of 14,173 total index operations across New York State, 12,321 cases, representing 10,271 patients at 8 centers, had zip codes for linkage. A total of 327 (2.7%) patients died in the hospital or before 30 days, and 314 children died by December 31, 2021 (total n = 641; 6.2%). Multiple measures of social determinants of health explained as much or more variability in operative and longitudinal mortality than clinical comorbidities or prior cardiac surgery. Inclusion of social determinants minimally improved models' predictive performance (operative: 0.834-0.844; longitudinal 0.808-0.811), but significantly improved model discrimination; 10.0% more survivors and 4.8% more mortalities were appropriately risk classified with inclusion. Wide variation in reclassification was observed by site, resulting in changes in the center performance classification category for 2 of 8 centers. CONCLUSIONS: Although indiscriminate inclusion of social determinants in clinical risk modeling can conceal inequities, thoughtful consideration can help centers understand their performance across populations and guide efforts to improve health equity.
Asunto(s)
Procedimientos Quirúrgicos Cardíacos , Cardiopatías Congénitas , Determinantes Sociales de la Salud , Humanos , Cardiopatías Congénitas/cirugía , Cardiopatías Congénitas/mortalidad , Masculino , Femenino , Procedimientos Quirúrgicos Cardíacos/mortalidad , Lactante , Preescolar , Medición de Riesgo/métodos , Niño , Recién Nacido , New York/epidemiologíaRESUMEN
This cross-sectional study assesses US pediatric inpatient psychiatric capacity and identifies state-level variation in access to pediatric inpatient psychiatric beds.