2013
DOI: 10.1177/0163278713492982
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Racial, Income, and Marital Status Disparities in Hospital Readmissions Within a Veterans-Integrated Health Care Network

Abstract: Hospital readmission is an important indicator of health care quality and currently used in determining hospital reimbursement rates by Centers for Medicare & Medicaid Services. Given the important policy implications, a better understanding of factors that influence readmission rates is needed. Racial disparities in readmission have been extensively studied, but income and marital status (a postdischarge care support indicator) disparities have received limited attention. By employing three Poisson regression… Show more

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Cited by 8 publications
(7 citation statements)
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“…In the optimized model, based on a priori knowledge and clinical plausibility [33][34][35][36][37][38], we included social factor variables that are available in the MCBS, in addition to the variables used in the reference model above (i.e., the optimized model included patient's age, comorbidities, mechanical ventilation use, and all of the following variables). As the use of arbitrary statistical cutoffs for variable selection (e.g., univariate screening) has been criticized [39,40], we have selected the predictors in the final model based on a priori knowledge.…”
Section: Candidate Predictor Variables and Model Derivationmentioning
confidence: 99%
“…In the optimized model, based on a priori knowledge and clinical plausibility [33][34][35][36][37][38], we included social factor variables that are available in the MCBS, in addition to the variables used in the reference model above (i.e., the optimized model included patient's age, comorbidities, mechanical ventilation use, and all of the following variables). As the use of arbitrary statistical cutoffs for variable selection (e.g., univariate screening) has been criticized [39,40], we have selected the predictors in the final model based on a priori knowledge.…”
Section: Candidate Predictor Variables and Model Derivationmentioning
confidence: 99%
“…This lack of statistical control in the predictive model is problematic for hospitals that primarily serve diverse patients [12,13]. At the hospital level, previous research has consistently indicated hospitals serving minority populations experience higher readmissions for HF, AMI, PN, and diabetes [14,15]. County characteristics that correlate highly with readmission rates include minority and vulnerable populations, socioeconomic status (negative correlation), educational level (negative correlation), high unemployment rate, a large number of individuals who never married, and a high percentage of Medicare recipients per 100,000 residents [15][16][17].…”
Section: Introductionmentioning
confidence: 99%
“…At the hospital level, previous research has consistently indicated hospitals serving minority populations experience higher readmissions for HF, AMI, PN, and diabetes [14,15]. County characteristics that correlate highly with readmission rates include minority and vulnerable populations, socioeconomic status (negative correlation), educational level (negative correlation), high unemployment rate, a large number of individuals who never married, and a high percentage of Medicare recipients per 100,000 residents [15][16][17]. At the individual level, Martsolf et al (2016) reported race, ethnicity, and low socioeconomic status affect hospitals' excess readmission ratios and significantly impact hospitals' risk for penalties.…”
Section: Introductionmentioning
confidence: 99%
“…Among the socio-demographic characteristics shown to impact readmission, patient age appears to have the most consistent relationship with risk of readmission [20242526]. Other demographic variables—such as gender, residential address, and marital status—are also potential predictors of readmission, however these are not avoidable or modifiable [817272829303132], as does the patient’s health insurance status [172930]. Coinsurance is a predetermined specific percentage of money that patient pays for healthcare services, typically is due at the time of service, and then the insurance agency pays the rest [33].…”
Section: Introductionmentioning
confidence: 99%