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Understanding Factors Associated With Readmission Disparities Among Delta Region, Delta State, and Other Hospitals
Hsueh-Fen Chen, PhD; Adrienne Nevola, MPH; Tommy M. Bird, PhD; Saleema A. Karim, PhD; Michael E. Morris, PhD; Fei Wan, PhD; and J. Mick Tilford, PhD
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Understanding Factors Associated With Readmission Disparities Among Delta Region, Delta State, and Other Hospitals

Hsueh-Fen Chen, PhD; Adrienne Nevola, MPH; Tommy M. Bird, PhD; Saleema A. Karim, PhD; Michael E. Morris, PhD; Fei Wan, PhD; and J. Mick Tilford, PhD
Revisions of the Hospital Readmissions Reduction Program should consider including community characteristics in risk adjustment models and adding mechanisms that recognize improvement given the uniqueness of the Mississippi Delta region.
ABSTRACT

Objectives: To understand the factors that potentially account for differences in 30-day readmission ratios for pneumonia, heart failure, and acute myocardial infarction (AMI) among hospitals in the Mississippi Delta region (Delta region), in Delta states excluding the hospitals in the Delta region (Delta state), and in the rest of the nation (other).

Study Design: A longitudinal study design from 2013 to 2016.

Methods: The dependent variables were 30-day readmission ratios for AMI, heart failure, and pneumonia. The key independent variables were 2 hospital categories (Delta region and Delta state), year dummies for 2014-2016, and the interactions among hospital categories and year dummies. We conducted 2 analyses for each study condition by estimating models with and without controls for hospital and community characteristics. 

Results: The coefficients for the interactions among year dummies and Delta region and Delta state hospitals were negative, indicating that Delta region and Delta state hospitals had higher reductions in readmissions than did other hospitals. After controlling for hospital and community characteristics, the disparities in readmissions for pneumonia and AMI in 2013 between Delta region and other hospitals were weakened (>.05). Major teaching hospitals and percentage of black population were positively associated with readmissions for all study conditions (P values ranged from <.05 to <.001).

Conclusions: Disparities in 30-day readmissions for the study conditions among Delta region, Delta state, and other hospitals were reduced under the Hospital Readmissions Reduction Program (HRRP). However, community factors that are not currently used for adjustment in HRRP were associated with readmission ratios. Revisions of HRRP should consider including community characteristics in risk adjustment models.

Am J Manag Care. 2018;24(5):e150-e156
Takeaway Points
  • Disparities in 30-day readmissions for pneumonia, heart failure, and acute myocardial infarction among Delta region hospitals and other hospitals in the nation, and Delta state and other hospitals, were reduced from 2013 to 2016. 
  • Adding community characteristics in the analytical models weakened the significant difference in 30-day readmission ratios for all 3 study conditions among Delta region and other hospitals. 
  • Consideration of sociodemographic status and addition of a mechanism that recognizes hospitals’ improvement in the Hospital Readmissions Reduction Program would lessen the program’s unintended consequences that may threaten the healthcare delivery system in the Mississippi Delta region and Delta states.
The Hospital Readmissions Reduction Program (HRRP) seeks to financially incentivize hospitals to reduce 30-day readmissions. Since 2013, hospitals have received Medicare payment reductions if their 30-day readmissions of selected conditions were higher than the national average. Although the payment reduction was capped at 3% beginning in 2015,1 the number of selected conditions increased from 3 in 2013 to 6 in 2015. CMS plans to add more conditions going forward.1

Since its implementation, HRRP has spurred controversy and has been the focus of many critiques. One of the most common criticisms is that there is inadequate adjustment for factors beyond hospitals’ control, as evidence has indicated that patients’ sociodemographic status and hospital and community characteristics were associated with 30-day readmissions.2-13 The Mississippi Delta region is known for poor population health, inadequate healthcare infrastructure, and being among the most impoverished areas in the United States.14,15 Hospitals in this region likely are located in rural areas and serve as the sole hospital for the surrounding community. How 30-day readmission ratios, defined by CMS in HRRP, may differ between hospitals in this unique region and those in the rest of the nation, and what underlying factors may account for these differences, are largely unknown. 

We focused on hospitals in the Delta region (hereafter called Delta region hospitals) and in Delta states. The Delta region, as defined by the Mississippi Delta Regional Authority (MDRA), includes 252 counties in 8 states: Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee.16 Most of these counties are in close physical proximity to the Mississippi River, hence their distinction as Delta region counties. Hospitals located outside of the Delta region but still in those 8 Delta states (hereafter called Delta state hospitals) are likely to be affected by the Delta region’s characteristics, as patients may travel within their home state to receive care. For instance, although the eastern counties of Arkansas are part of the Delta region as defined by the MDRA, the western counties of Arkansas are not; thus, the entire state of Arkansas is considered a Delta state. Given evidence from 30-day readmission studies and the unique characteristics in the Delta region discussed above,2-15 we hypothesized that disparities in 30-day readmissions among Delta region, Delta state, and other hospitals exist and that hospital and community factors accounted for those disparities. 

DATA AND METHODS

Data

We used several publicly available data sources. The HRRP Supplemental Data Files provided readmission ratios for conditions included in HRRP and hospital identification numbers for hospitals qualified to participate in HRRP from 2013 to 2016. The MDRA website provided a list of Mississippi Delta states and the counties making up the Mississippi Delta region.16 CMS Provider of Services (POS) Files (2010-2013) provided hospital characteristics and county and state codes where hospitals were located. The Area Health Resources Files (AHRF) and the Social Characteristics Table from the American Community Survey (ACS) provided county characteristics. 

Risk-adjusted 30-day readmission ratios for qualified hospitals are published annually and are based on 3 years of claims data prior to the year published.1 To account for this lag from measurement to publication, data on hospital and community characteristics were taken from the measurement years for the corresponding publication year. Specifically, we used a midpoint year to represent the value of a given covariate over the 3-year measurement period. For example, the 2013 HRRP Supplemental Data File provides readmission ratios based on data between July 2008 and June 2011; to represent this measurement period, we used 2010 hospital and community characteristics from POS, AHRF, and ACS files. In the present study, we reported the years of readmission ratios that were published rather than the years of claims data used for calculating readmission ratios.

Measures

The dependent variables were 30-day readmission ratios for acute myocardial infarction (AMI), heart failure, and pneumonia, extracted from the CMS HRRP Supplemental Data Files. The readmission ratios in HRRP are based on the predicted readmission from the model with a hospital-specific random effect for a given hospital divided by the expected readmission from the model with an overall average hospital effect from the nation.1,17 In our study, we chose AMI, heart failure, and pneumonia because HRRP has included these conditions since its inception in 2013. It is expected that hospitals have gained substantial experience with preventing 30-day readmissions for patients with these conditions. 

There were 3 categories of independent variables of interest. The first consisted of 2 indicator variables for Delta region hospitals and Delta state hospitals, with other hospitals in the nation (hereafter referred to as other hospitals) as the reference group. The second category contained 3 dummy variables for study years of 2014, 2015, and 2016, with 2013 as the reference group. The final category included the interactions among Delta region hospitals and study years and among Delta state hospitals and study years. 

For hospital characteristics, we included ownership (for-profit and public hospitals, with not-for-profit as the reference group), teaching status (major teaching and minor teaching hospitals, with nonteaching hospitals as the reference group), hospital size (defined as the certified bed count), and the percentage of Medicare and Medicaid patients. For county-level community characteristics, we included: (1) number of primary care physicians (PCPs) per 1000 population, (2) number of hospital beds per 1000 population, (3) number of skilled nursing facility (SNF) beds per 1000 population, (4) number of home health agencies, (5) percentage of persons in poverty, (6) percentage of black population, (7) percentage of Hispanic population, and (8) unemployment rate.

Study Sample

We defined the study sample as all hospitals qualified for HRRP. Maryland hospitals are exempted from HRRP as the state has its own hospital readmission reduction incentive program.17

Study Design and Analysis

We applied a retrospective longitudinal study design, with the hospital as the unit of analysis. We conducted a trend analysis for risk-adjusted 30-day readmission ratios for AMI, heart failure, and pneumonia separately for each of the 3 hospital groups. For multivariate analyses, we treated hospitals as a random effect factor to control for the correlation of the readmission ratio measures across study periods within each hospital. To identify the underlying factors that may account for the differences in 30-day readmissions among the 3 hospital groups, we conducted 2 analyses for each study condition by estimating models with and without controls for hospital and community characteristics, with clustering of hospitals within the state to adjust standard errors.


 
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