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Exploring Inequities in Care Transitions

Sharon Post and Bonnie Ewald
December 19, 2014
In 2010, 29.9% of African American Medicare beneficiaries in Chicago who were hospitalized were readmitted within 30 days of discharge, more than 50% higher than the national average readmission rate of 19.2%. The stark disparity in this statistic led our Health & Medicine Center for Long-Term Care Reform team to dig deeper on readmissions-related statistics and learn about any best practices to address the gaps that could lead to such a disparity. We are pleased to share our findings in a new report, “Addressing Inequities in Care Transitions.” What follows here is a brief explanation of our approach to this investigation, and a report of our findings.

As ACA  implementation proceeds, 30-day readmission rates have been getting a lot of press due to the Medicare reimbursement penalties levied on hospitals  that have high readmission rates. We have also seen more attention to critiques of the use of the readmission rates as a quality metric. The Center for Long-Term Care Reform has been watching the debate over readmission rates with interest because, since 2008, Health & Medicine has partnered closely with some Illinois hospitals and community agencies on the Bridge Model of transitional care. The Bridge Model aims to improve the experience for older adults and their caregivers when leaving the hospital and going home – which includes trying to prevent readmissions. Transitions from one care setting to another present a lot of challenges for the patient, their caregiver(s), their family and friends, and the system. People can fall through the cracks at any number of junctures. For that reason, it is very important to be aware of trends that suggest who may be more likely to experience a difficult transition, and to develop ways to improve the system so they do not experience unnecessary, disruptive hospital readmissions.

We have learned from direct experience with Bridge that many things impact whether an older adult is readmitted to the hospital; some reasons are medical in nature, while others are related to psychosocial, community, or environmental issues – and in any given readmission case, it is likely that more than one of these factors was at play. When conducting our research for this report, we found that the literature confirms this list of influences.

Our report highlights many of the socioeconomic correlations in readmission rates. Patient characteristics such as race and income have been shown in study after study to be correlated with differential readmission rates. (Spoiler alert: being African American or low-income means you have a higher risk of readmission.)

On top of patient-level characteristics, the setting in which patients receive care has also been shown to impact readmission rates. We looked at readmission penalties of Chicago-area hospitals, and found that hospitals serving a majority of African Americans as their patient population were more likely to have a higher readmission rate. Other hospital characteristics, such as being public or having low nurse staffing levels, were also correlated with higher readmission rates.

Finally, in recognition that many things impact one’s health beyond one’s own characteristics or those of the place where one receives care, we looked at readmission rates in connection with community statistics. Not surprisingly, living in an area with low median income increases readmission risk. One study stated that the readmission risk associated with living within the most disadvantaged neighborhoods was similar to that of having chronic pulmonary disease.

These sobering trends deserve to be highlighted and addressed. Because the disparities in readmission rates are inextricably linked to socioeconomic status, we opt to use language of inequities. (The CDC says that the difference, or “disparity,” in rates becomes an “inequity” if it can be tied to “differences in social, economic, environmental or healthcare resources.”)

While the literature clearly shows inequities in readmission rates, it is severely lacking in evidence showing ways to mitigate these very inequities. Because of this, we decided to look more broadly at types of interventions that have been shown to reduce disparities in a variety of health-related metrics. We summarize these approaches in the report, and analyze these lessons for how they might translate to transitional care and the Bridge Model specifically.

This lack of evidence regarding mitigating strategies mirrors challenges that we have had in our work with the Bridge Model: while Bridge has shown success in reducing readmissions among those the model works directly with, we have had challenges in teasing apart Bridge impact data in regard to race or class. Moreover, the setting of care and the community one lives in are critical in supporting individuals and preventing readmissions, so a singular intervention like Bridge may not be adequate to reduce the inequities we see in the numbers. Therefore, we highlight certain policy approaches that hold promise but need testing.

To highlight some data found in this report, and more broadly on trends in hospitalization rates in general, Bonnie Ewald of Health & Medicine will be speaking at the American Society of Aging’s 2015 Aging in America conference on trends and inequities in hospitalization patterns. Health & Medicine looks forward to continuing to investigate the factors that impact one’s ability to transition home safely from the hospital, and we hope that you will reach out to us with any questions, comments, or ideas for future work.

Contact us:
Sharon Post: Director, Center for Long-term Care Reform, spost@hmprg.org
Bonnie Ewald: Program Coordinator, the Center for Long-term Care Reform and Bridge Model National Office, bewald@hmprg.org

Read the report below, or click here to download. You may also down-load a one page summary here.