Article

Racial/Ethnic Disparities in Potentially Preventable Readmissions: The Case of Diabetes

Center for Delivery, Organization and Markets, Agency for Healthcare Research and Quality, 540 Gaither Rd, Rockville, MD 20850, USA.
American Journal of Public Health (Impact Factor: 4.55). 10/2005; 95(9):1561-7. DOI: 10.2105/AJPH.2004.044222
Source: PubMed

ABSTRACT

Considerable differences in prevalence of diabetes and management of the disease exist among racial/ethnic groups. We examined the relationship between race/ethnicity and hospital readmissions for diabetes-related conditions.
Nonmaternal adult patients with Medicare, Medicaid, or private insurance coverage hospitalized for diabetes-related conditions in 5 states were identified from the 1999 State Inpatient Databases of the Healthcare Cost and Utilization Project. Racial/ethnic differences in the likelihood of readmission were estimated by logistic regression with adjustment for patient demographic, clinical, and socioeconomic characteristics and hospital attributes.
The risk-adjusted likelihood of 180-day readmission was significantly lower for non-Hispanic Whites than for Hispanics across all 3 payers or for non-Hispanic Blacks among Medicare enrollees. Within each payer, Hispanics from low-income communities had the highest risk of readmission. Among Medicare beneficiaries, Blacks and Hispanics had higher percentages of readmission for acute complications and microvascular disease, while Whites had higher percentages of readmission for macrovascular conditions.
Racial/ethnic disparities are more evident in 180-day than in 30-day readmission rates, and greatest among the Medicare population. Readmission diagnoses vary by race/ethnicity, with Blacks and Hispanics at higher risk for those complications more likely preventable with effective postdischarge care.

Download full-text

Full-text

Available from: H. Joanna Jiang
  • Source
    • "Furthermore, we defined multiple-hospitalization patients as those who had two or more hospital admissions during a year's time. Whereas previous literature has looked specifically at time between discharge and readmission[5,6], the MEPS data were not complete enough in many cases to measure time between hospitalizations that precisely. "

    Preview · Article · Dec 2016 · BMC Endocrine Disorders
  • Source
    • "However, this study does have limitations which should be considered in the interpretation of results. First, we did not account for racial and ethnic disparities consistently documented to be related to readmission[29,31,39,40]. However, the prevalence of ethnic minorities is so low in the population covered by the Quebec trauma system that these factors were unlikely to have influenced our results[11]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Unplanned readmissions cost the US economy approximately $17 billion in 2009 with a 30-day incidence of 19.6%. Despite the recognised impact of socio–economic status (SES) on readmission in diagnostic populations such as cardiovascular patients, its impact in trauma patients is unclear. We examined the effect of SES on unplanned readmission following injury in a setting with universal health insurance. We also evaluated whether additional adjustment for SES influenced risk-adjusted readmission rates, used as a quality indicator (QI).
    Full-text · Article · Jan 2016 · Injury
  • Source
    • "This trend has been seen in many other studies [7,32], likely reflecting the fact that a large percentage of the hospital resources in our country are utilized by a small percentage of patients [10]. In previous studies, demographic factors such as marital status, age and gender have been shown to be predictive of 30-day readmission [21,33,34]. Single marital status was a predictor in the combined model, which may suggest a lack of power to detect a significant finding in the smaller cohorts. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis. The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.
    Full-text · Article · Aug 2014 · BMC Medical Informatics and Decision Making
Show more