Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Readmission Rates

Abstract

Under the Hospital Readmissions Reduction Program (HRRP) of the Centers for Medicare & Medicaid Services (CMS), hospitals with excess readmissions for select conditions and procedures are penalized. However, readmission rates are not risk adjusted for socioeconomic status (SES) or race/ethnicity. We examined how adding SES and race/ethnicity to the CMS risk-adjustment algorithm would affect hospitals’ excess readmission ratios and potential penalties under the HRRP. For each HRRP measure, we compared excess readmission ratios with and without SES and race/ethnicity included in the CMS standard risk-adjustment algorithm and estimated the resulting effects on overall penalties across a number of hospital characteristics. For the 5 HRRP measures (heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and total hip or knee arthroplasty), we used data from the Healthcare Cost and Utilization Project’s State Inpatient Databases for 2011-2012 to calculate the excess readmission ratio with and without SES and race/ethnicity included in the model. With these ratios, we estimated the impact on HRRP penalties and found that risk adjusting for SES and race/ethnicity would affect Medicare payments for 83.8% of hospitals. The effect on the size of HRRP penalties ranged from −14.4% to 25.6%, but the impact on overall Medicare base payments was small—ranging from −0.09% to 0.06%. Including SES and race/ethnicity in the calculation had a disproportionately favorable effect on safety-net and rural hospitals. Any financial effects on hospitals and on the Medicare program of adding SES and race/ethnicity to the HRRP risk-adjustment calculation likely would be small.

Reliability of the American Community Survey Estimates of Risk-Adjusted Readmission Rankings for Hospitals Before and After Peer Group Stratification

“…However, equivalent discourse over the quality of data available for SES adjustment has been lacking. To date, there have been no fewer than 8 studies 28,29,30,31,32,33,34,35 that have attempted to contrast hospital readmission rates against socioeconomic data derived from the ACS. However, to our knowledge, no study has attempted to disentangle its findings from the reliability of the estimates.…”

Impact of hospitals’ Referral Region racial and ethnic diversity on 30-day readmission rates of older adults

“…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.…”

“…Many studies have explored the impact of county [12,17], hospital [13,21], and patient characteristics [2,8]individually on hospitals' performance on 30-day readmission for congestive HF, AMI, and PN. However, to our knowledge, no study has explored the impact of community diversity in a hospital referral region (HRR) as it relates to its demographics and racial/ethnic makeup, on a hospital's 30-day readmission rate.…”

Data concordance between ESRD Medical Evidence Report and Medicare claims: is there any improvement?

“…Differences in health care delivery based on local and regional factors, items beyond patient or provider control, are also topics that have been highlighted over the last several years ( Bernheim et al, 2016 ; Kshirsagar et al, 2017 ; Manickam et al, 2017 ; Martsolf et al, 2016a ; Martsolf et al, 2016b ). For example, regional differences have been associated with outcomes in the ESRD setting, particularly when the West coast is compared against other regions of the US ( Almachraki et al, 2016 ; Kshirsagar et al, 2017 ; Mu et al, 2018 ), despite near universal care and coverage offered in the US for ESRD.…”