J. Mick Tilford, PhD

We appreciate the opportunity to address the RTI team’s main concerns of our study. Our paper demonstrated that CMS Hierarchical Condition Categories (HCC) risk scores are related to patient experience in home health.

 These findings suggest that HCC risk scores should be considered when evaluating the performance of home health agencies. However, the RTI team raised 2 concerns regarding the validity of agency-level HCC risk scores. First, HCC risk scores are prone to upcoding by providers across the spectrum, resulting in an upward bias. Statistically, upcoding will change the HCC risk score but not the SD. Our findings are based on the SD of HCC risk scores and therefore unlikely to be affected by upcoding. Secondly, the RTI team indicated that agency-level HCC risk scores likely include outliers, whereas the random sampling used in the Home Health Care Consumer Assessment of Healthcare Providers and Services (HHCAHPS) is unlikely to be affected by outliers, due to the exclusion of patients with certain conditions.

 The exclusion criteria are not based on the HCC risk scores. Additionally, most patients treated by agencies still remain in the sampling pool, regardless of their outlier scores. Theoretically, a randomizing process that selects patients from each agency will yield the characteristics of the sample that are similar to those of the patients cared by the agency. Thus, the outliers at the agency level of HCC risk scores and the outliers of HCC risk scores from the randomized HHCAHPS sampling should not differ significantly.

The RTI team also identified 2 methodological issues: (1) the reporting of excessive correlation as it relates to the authors’ combined use of state fixed effects and race variables and (2) our use of controls for agency profit/nonprofit status and term of certification by Medicare and how these factors are reflected in the agency’s public reporting. The correlation of most of our independent variables was less than 0.20, with the exception of ownership and tenure years with CMS. Therefore, the correlation in our study is not a concern (

). States vary in racial composition, but the correlation between racial/ethnic composition and state fixed effects is not our primary focus. States certify and regulate home health agencies and through these policies affect the practice of home health agencies.

 Additionally, agencies with different ownership and CMS tenure years respond to public reporting differently.

 Thus, controlling for those confounders (ie, state fixed effects, ownership of agency, and number of tenure years) is necessary. We reanalyzed the data by excluding ownership, number of years certified by CMS, and state fixed effects and then estimated the association between HCC risk scores and patient experience (

). The coefficients in the model without those variables remain significant and are larger than those in our original paper.

For the reporting issue about the effect size, the RTI team recommended 3 levels using a small size (difference of 1 point), a medium size (3 points), and a large size (≥5 points). Our presentation is based on the association between the change of 1 SD in HCC scores and the change in the percent patient experience. We believe that we should provide the association instead of arbitrarily deciding a small, medium, or large effect size for the readers.

Finally, although the RTI team is unable to adjust HCC scores for patient experience due to limitations of the data and time constraints, our findings, at least, provide evidence for stakeholders that HCC risk scores can influence patient experience at the agency level.

 Department of Health Policy and Management, College of Public Health, University of Arkansas for Medical Sciences (HFC, JMT, RFS), Little Rock, AR; Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine (FW), St. Louis, MO.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (HFC, FW); acquisition of data (HFC, RFS); analysis and interpretation of data (HFC, JMT, FW); drafting of the manuscript (HFC, JMT); critical revision of the manuscript for important intellectual content (HFC, JMT, RFS, FW); statistical analysis (HFC, RFS); and provision of patients or study materials (HFC, RFS).

 Hsueh-Fen Chen, PhD, University of Arkansas for Medical Sciences, 4301 W Markham St, Mail Slot 820-12, Little Rock, AR 72205. Email: hchen@uams.edu.

1. Chen HF, Tilford JM, Wan F, Schuldt R. CMS HCC risk scores and home health patient experience measures. 

2. Home Health Care CAHPS survey: protocols and guidelines manual. Home Health CAHPS website. homehealthcahps.org/Portals/0/PandGManual.pdf. Published January 2019. Accessed December 16, 2019.

3. Kim H, Norton EC. Practice patterns among entrants and incumbents in the home health market after the prospective payment system was implemented. 

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4. Polsky D, David G, Yang J, Kinosian B, Werner R. The effect of entry regulation in the health care sector: the case of home health. 

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5. Jung K, Shea D, Warner C. Agency characteristics and changes in home health quality after Home Health Compare. 

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Articles

The authors of “CMS HCC Risk Scores and Home Health Patient Experience Measures” respond to a letter to the editor.

Reply to “Risk Adjustment in Home Health Care CAHPS”

 To understand the association between agency-level CMS Hierarchical Condition Categories (HCC) risk scores and patient experience measures for home health.

 We extracted variables from the 2014 Medicare Provider Utilization and Payment Data for Home Health Agencies and Home Health Compare file. We applied fixed-effects models for the analyses. Our dependent variables included both global and composite patient experience measures. The 2 global patient experience measures were the patient’s overall rating of care provided by the agency (

) and the patient’s willingness to recommend the home health agency to others (

). The 3 composite patient experience measures were how often the patient felt the provider gave care in a professional way (

), how well the home health team communicated with the patient (

), and whether the home health team discussed medicines, pain, and home safety with the patient (

 Increased agency-level CMS HCC risk scores were negatively associated with all patient experience measures: 

 ≤.001). Several covariates, including the percentage of racial/ethnic minority beneficiaries, ownership of the agency, and number of tenured years with the Medicare program, were significantly associated with patient experience measures.

 A negative association exists between CMS HCC risk scores and patient experience measures. To avoid unintended consequences, patient experience measures need further risk adjustment under the CMS 5-star patient survey rating system and the Home Health Value-Based Purchasing pilot program.

The Home Health Compare website uses patient experience measures to construct a 5-star patient survey rating system for the public. These measures are also quality performance metrics used in the Home Health Value-Based Purchasing (HHVBP) pilot program.

Increases in CMS Hierarchical Condition Categories risk scores were negatively associated with patient experience measures for home health at the agency level.

Evaluating the practice patterns of home health agencies and monitoring access to care for racial/ethnic minorities and beneficiaries with complicated clinical conditions under the 5-star patient survey rating system and HHVBP pilot program is recommended.

Patient experience is a critical quality dimension of healthcare and is also associated with patient outcomes, such as patient safety and readmissions.

 In 2010, CMS used the Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) survey to assess patient experience for home health care.

 In 2016, CMS used the results from the HHCAHPS survey to create a 5-star patient survey rating system and made it publicly available.

 Patient experience is also part of quality metrics in the Home Health Value-Based Purchasing (HHVBP) pilot program that financially rewards or penalizes home health agencies in 9 states.

In the United States, about 12,000 home health agencies deliver care to approximately 3.5 million Medicare beneficiaries at their residences.

 Medicare home health beneficiaries are homebound and often experience polypharmacy.

 They have, on average, 4.2 diagnostic conditions and have a higher prevalence of limitations in activities of daily living and cognitive function than general Medicare beneficiaries.

 Most of them receive assistance from informal caregivers to meet their medical and daily needs.

 Home health providers not only conduct professional care (eg, physical therapy and wound care) but also educate beneficiaries and their informal caregivers about how to conduct safe care (eg, proper use of medications and oxygen inhalation) and coordinate necessary resources to meet patients’ needs and expectations at home.

Evidence from surveys for health plans, hospitals, community health centers, and primary care physicians shows that several factors affecting patient experience are beyond health providers’ control. The most

common factors are patient sociodemographics, self-reported health status, depression, survey language, mode of survey (eg, mail or telephone interview), and whether the survey was completely by a proxy.

 Other studies focusing on hospital care found that patients with high severity of illness and complicated conditions were likely to have lower ratings of patient experience than their counterparts. They concluded that risk factors used in patient experience for hospital care insufficiently adjusted for clinical conditions that affected patient experience ratings.

In home health, CMS and the HHCAHPS coordination team reported that the factors associated with patient experience are (1) patient sociodemographics, including age and education; (2) self-reported health status; (3) whether patients live alone; (4) self-reported mental/emotional status; (5) non-English survey response; (6) a diagnosis of schizophrenia or dementia/cerebral degeneration; and (7) whether the survey was completely by a proxy.

 These 7 factors are currently used to adjust patient experience for the 5-star patient survey rating system and the HHVBP pilot program. A study also found that minorities had lower ratings of patient experience for their home health care.

Medicare home health beneficiaries are among the most vulnerable Medicare populations. Whether the risk factors currently used by CMS adequately adjust patient experience for home health care is largely unknown. Our study aimed to fill this critical knowledge gap by examining the interplay between agency-level CMS Hierarchical Condition Categories (HCC) risk scores (details in Methods section) and the patient experience metrics. We used the agency-level CMS HCC risk score as a proxy variable for the clinical and functional conditions of home health beneficiaries within home health agencies. Associations between patient experience measures and agency-level CMS HCC risk scores from our study provide critical information about whether the current risk-adjusted model for patient experience measures needs modification.

We merged 2 national Medicare databases at the home health agency level, the 2014 Medicare Provider Utilization and Payment Data for Home Health Agencies (PUPDHHA) and the 2014 Home Health Compare file.

 The PUPDHHA provides several agency-level variables based on 100% of Medicare enrollment and fee-for-service claims data during the 2014 calendar year.

 These variables include an agency-level HCC risk score based on the HCC risk scores from all of the beneficiaries cared for by a particular home health agency, provider identification number, number of distinct beneficiaries without a low-utilization payment episode (with ≤4 home health visits during an episode of home health care, namely, up to 60 days), and number of beneficiaries from different racial/ethnic groups.

The Home Health Compare file is updated quarterly by CMS.

 It consists of provider identification numbers and patient experience measures extracted from the HHCAHPS survey. The Home Health Compare file also provides characteristics of home health agencies, including ownership and CMS certification year. We extracted agency-level patient experience measures based on data collected from January 1, 2014, through December 31, 2014.

We used a cross-sectional study design in which the home health agency was the unit of analysis. The study sample consisted of Medicare-certified home health agencies in 50 states and the District of Columbia. Home health agencies with fewer than 60 patients were excluded because they were exempted from the HHCAHPS survey.

The dependent variables are the 5 risk-adjusted agency-level patient experience measures from the 2 global questions and 3 composite measures extracted from CMS Home Health Compare. The HHCAHPS survey has 2 global questions; they include the “overall rating of care” provided by the home health agency (hereafter termed 

) and “patient willingness to recommend the home health agency to family or friends” (hereafter termed 

 in the survey ranges from 0 to 10. CMS reports the percentage of 

for an agency based on the percentage of patients who gave their home health care a rating of 9 or 10. The choices in the survey for 

 include (1) definitely no, (2) probably no, (3) probably yes, and (4) definitely yes. The percentage of 

 at the agency level is the number of patients who answered “definitely yes” divided by the total number of patients.

For the composite measures, CMS uses the data from the other 17 patient experience questions from the HHCAHPS survey. Each of these 3 measures is calculated from 4 or more topically related survey questions.

 The resulting composite measures include the following: “how often the home health team gives care in a professional way” (hereafter termed 

), “how well the home health team communicates with patients” (hereafter termed 

), and whether or not the “home health team discuss[ed] medicines, pain, and home safety with patients” (hereafter termed 

). The HHCAHPS survey website provides detailed information for the composite measures.

The risk-adjusted global and composite measures for patient experience at the agency level are measured as a percentage. A higher percentage of patient experience measures indicates that the patients perceive higher-quality care from home health agencies.

The key independent variable is the agency-level HCC risk score, extracted from PUPDHHA.

 The agency-level HCC risk score is the sum of CMS HCC risk scores from individual Medicare home health beneficiaries divided by the total number of Medicare home health beneficiaries for an individual home health agency.

 CMS constructs a CMS HCC risk score for an individual Medicare home health beneficiary as a risk factor to calculate a beneficiary’s likelihood of unplanned hospitalization and emergency department visits within 60 days of home health care for public reporting.

 The CMS HCC risk score for an individual Medicare home health beneficiary is a function of the beneficiary’s age, gender, original reason for Medicare entitlement, prior care setting, clinical conditions from CMS HCC, and their interaction terms. The CMS website provides detailed information about the CMS HCC risk score for home health beneficiaries.

In addition to the average CMS HCC risk score at the agency level, previous studies show that beneficiaries of different races/ethnicities rate their respective patient experience surveys differently.

 Therefore, we included variables describing the percentage of beneficiaries who were African American, Hispanic, and of other racial group, which includes beneficiaries who were not white, African American, or Hispanic. Finally, because the characteristics of home health agencies are associated with the quality of care provided,

 we included 2 dummy variables for ownership of a home health agency (ie, not-for-profit and public home health agencies, with for-profit home health agencies as the reference group) and the number of years that home health agencies had been certified by the Medicare program in the analytical models.

Home health agencies practice within a given state, facing that state’s regulations, which may differ from those of other states. Additionally, there is high variation in home health utilization across states, with $176 per beneficiary per year for the states at the 10th percentile and $866 per beneficiary per year for those at the 90th percentile.

 We applied fixed-effects models at the state level, with robust standard errors for each patient experience measure to account for differences in regulations and other state characteristics that affect the practice of home health agencies. Stata 14.2 (StataCorp; College Station, Texas) was used for data management and analyses. We used xtreg for the fixed-effects model.

There were 7756 home health agencies with data for patient experience measures available in the Home Health Compare file. Due to missing data, such as the agency-level CMS HCC risk score or race/ethnicity, the total number of observations included in our analytical models was 7637.

 presents descriptive statistics. For the overall patient experience measures, the average of 

with score 9 or 10 was about 84%, and the average of 

 with “definitely yes” was about 79%. For the composite measures, the averages of 

were 88%, 85%, and 84%, respectively. The average agency-level HCC risk score was 2.14. Home health agencies, on average, had 16% African American and 10% Hispanic beneficiaries, and about 2% of beneficiaries identified their race/ethnicity as other or unknown. Among our study sample, 21% of home health agencies were not-for-profit, and 6% of home health agencies were public. Home health agencies had an average of about 18 tenured years with the Medicare program.

The results from the fixed-effects models, in 

, showed that increases of 1 SD in agency-level CMS HCC risk scores (0.37 in Table 1) significantly lowered 

 by about 0.75% (coefficient, —2.04, multiplied by 0.37) (

For the control variables related to race/ethnicity, increases in the percentage of African American beneficiaries were negatively associated with patient experience measures in 

 <.001). Similarly, an increase in the percentage of beneficiaries of other racial/ethnic group was negatively associated with patient experience measures in 

 <.001). We did not observe a significant association between the percentage of Hispanic beneficiaries and patient experience measures.

Characteristics of home health agencies were also associated with patient experience measures. Not-for-profit home health agencies had patient experience measures that were about 1 to 3 percentage points higher than for-profit home health agencies in all aspects: 

 <.001). Public home health agencies had patient experience measures that were about 2 to 4 percentage points higher than for-profit home health agencies in all aspects: 

 <.001). Increases in years tenured with the Medicare program were significantly associated with more positive patient experience measures of 

Our findings showed that increases in agency-level CMS HCC risk score were significantly associated with lower scores for all 5 patient experience measures. The findings indicated that current risk factors insufficiently adjust for the variation in beneficiaries’ clinical and functional conditions that affects patient experience. The differences in patient experience based on current risk factors partially reflect the differences in case mix among home health agencies.

CMS uses risk-adjusted patient experience to construct the 5-star patient survey rating system posted on the Home Health Compare website. The system was developed in an effort to improve the quality of home health care by allowing consumers the opportunity to find information on their community’s providers and choose the best providers. However, the CMS 5-star patient survey rating system of home health based on current risk factors could be misleading. Evidence shows that CMS public reporting affects market shares for health plans, hospitals, and nursing homes.

 Future studies examining how the CMS 5-star patient survey system affects market shares for home health agencies with high and low HCC risk scores are recommended.

Risk-adjusted patient experience is part of quality metrics in the HHVBP pilot program that have a financial impact on home health agencies. Considering the negative association between agency-level CMS HCC risk score and patient experience measures, home health agencies with a high proportion of clinically complicated beneficiaries are likely to be financially penalized under the HHVBP pilot program. Home health agencies can also simply dump or avoid beneficiaries with complicated conditions to improve their performance in patient experience measures, rather than investing resources in truly improving patient experience. Access to care for beneficiaries with complicated clinical and functional conditions can become problematic, given the fact that more than 80% of home health agencies are for-profit entities that pursue profit maximization.

 We also observed that increases in the percentage of African Americans and other racial/ethnic minority populations were negatively associated with patient experience measures, with 1 exception (

). Our findings are consistent with evidence from a recent patient-level study, which indicated that minority home health beneficiaries had lower patient experience rates in 

 than home health beneficiaries who were non-Hispanic whites.

 Evidence from the same study also showed that Hispanic, African American, Asian, and Native Hawaiian or Pacific Islander home health beneficiaries had better experience measures in 

 in our study are positive but not statistically significant. It is unclear why minorities gave a high rating for their home health providers discussing medicines, pain, and home safety but gave a low rating for the other measures. This may be explained by cultural differences among racial/ethnic groups regarding pain tolerance and differing attitudes among patients or informal caregivers about medication or patient safety. However, future studies that focus on the cultural differences affecting the rating of patient experience are needed.

Evidence from other studies showed that compared with not-for-profit agencies, for-profit agencies provided higher-cost and lower-quality home health care, including worsened process of care, poor functional improvement, and high rates of avoidable hospitalizations and bedsores.

 Our study found that compared with public and not-for-profit agencies, for-profit agencies had poorer patient experience performance in all 5 measures. We also found that increases in the number of tenured years with the Medicare program increased patient experience measures by about 0.01% to 0.04%. Over the past decade, the number of home health agencies increased dramatically, from about 7500 in 2000 to about 12,400 in 2014. This increase was partly due to the high profit margin from therapy visits in the Medicare home health payment system.

 Among all new home health agencies, 95% were for-profit entities, which are more likely to target therapy visits with high profit margins.

 The Medicare program is the primary payer for home health care and paid about $18 billion in 2015.

 The Medicare program strives to improve the quality and efficiency of care for Medicare home health beneficiaries. However, with the high cost and lower quality of care associated with for-profit agencies and with the rapidly growing number of for-profit agencies, CMS’ efforts to improve quality while controlling rising home health costs are limited because the license approval system and regulations for home health agencies occur at the state level. To overcome this problem, both payment reforms at the federal level and modifications at the state level for licensure and regulation are necessary to improve the quality and control the cost of home health care.

Our study has limitations. First, we used data at the agency level. The patient experience measures are based on the HHCAHPS survey from nonhospice, nonmaternity home health patients who are 18 years or older.

 However, CMS HCC risk scores are based on Medicare beneficiaries who are 65 years or older.

 Although the sources of patient data provided by the HHCAHPS survey and the CMS HCC risk score were not completely the same, the majority of home health beneficiaries in our sample were Medicare beneficiaries who were 65 years or older

 and were more likely to be in the random sampling of the HHCAHPS survey. Therefore, the inconsistent patient sources among the HHCAHPS survey and CMS HCC risk scores are less likely to affect the estimates in our study. However, we recommend that future studies use patient-level data to examine CMS HCC risk scores and patient experience measures.

Secondly, due to missing data, we excluded 119 home health agencies that qualified for the HHCAHPS survey. These agencies were more likely to be for-profit home health agencies with lower patient volume than those included in our study. Additionally, the HHCAHPS survey exempted home health agencies that had fewer than 60 patients per year, and thus these agencies were excluded from our study.

Our findings have research and policy implications that apply to the 5-star patient survey rating system and the HHVBP pilot program. In terms of research, access to care for minorities and beneficiaries with complicated clinical conditions needs to be carefully monitored under the 5-star patient survey rating system and the HHVBP pilot program. Additionally, it is necessary to investigate the practice patterns of home health agencies and the home health market and to determine how home health agencies with a high proportion of beneficiaries who have advanced clinical and functional conditions fare. In terms of policy implications, the current risk factors used to adjust patient experience measures need to be modified. The CMS HCC risk score is based on Medicare home health beneficiaries’ enrollment data and inpatient and outpatient claims data.

 The enrollment data are collected by CMS, and inpatient and outpatient claims data are provided by hospitals and physicians, so there is little chance for home health agencies to practice upcoding that may affect the CMS HCC risk score. Thus, it is feasible to include the CMS HCC risk score of a home health beneficiary as a risk factor in the current risk adjustment for patient experience measures in an effort to avoid potential unintended consequences.

The authors thank Madison Hedrick, MA, of the Science Communication Group at the University of Arkansas for Medical Sciences, who provided editing for the original version of the manuscript.

Department of Health Policy and Management (H-FC, JMT, RS), and Department of Biostatistics (FW), College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR.

 Concept and design (H-FC, FW); acquisition of data (H-FC, RS); analysis and interpretation of data (JMT, FW); drafting of the manuscript (H-FC, JMT); critical revision of the manuscript for important intellectual content (H-FC, JMT, FW, RS); statistical analysis (H-FC); and provision of patients or study materials (RS).

 Hsueh-Fen Chen, PhD, Department of Health Policy and Management, College of Public Health, University of Arkansas for Medical Sciences, 4301 W Markham St, Mail Slot 820-12, Little Rock, AR 72205. Email: hchen@uams.edu.

1. Isaac T, Zaslavsky AM, Cleary PD, Landon BE. The relationship between patients’ perception of care and measures of hospital quality and safety. 

. 2010;45(4):1024-1040. doi: 10.1111/j.1475-6773.2010.01122.x.

2. Carter J, Ward C, Wexler D, Donelan K. The association between patient experience factors and likelihood of 30-day readmission: a prospective cohort study. 

. 2018;27(9):683-690. doi: 10.1136/bmjqs-2017-007184.

Risk adjustment for patient experience measures needs to be modified by including the CMS Hierarchical Condition Categories (HCC) risk scores of home health beneficiaries.

CMS HCC Risk Scores and Home Health Patient Experience Measures

 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).

 A longitudinal study design from 2013 to 2016.

 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. 

 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 (

 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.

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,

 the number of selected conditions increased from 3 in 2013 to 6 in 2015. CMS plans to add more conditions going forward.

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.

 The Mississippi Delta region is known for poor population health, inadequate healthcare infrastructure, and being among the most impoverished areas in the United States.

 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.

 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,

 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. 

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.

 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.

 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.

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.

 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.

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.

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.

Delta Region and Delta State Hospitals’ Performance Relative to Other Hospitals on Readmissions

presents the means and SDs of study variables from 2013 to 2016 for each hospital group and the statistical differences among the 3 hospital groups (

 values ranged from <.01 to <.001). Given the definition of 30-day readmission ratios in HRRP, a ratio greater than 1 indicates that the hospital’s predicted 30-day readmission is higher than the national expected 30-day readmission, and vice versa. During the study years for all study conditions, Delta region and Delta state hospitals had higher readmission than the national average, whereas other hospitals had lower readmission than the national average. For example, the average 30-day readmission ratio for heart failure from 2013 to 2016 was 1.036 and 1.018 for Delta region hospitals and Delta state hospitals, respectively, whereas it was 0.996 for other hospitals.

consists of 3 subfigures that show the number of qualified hospitals and present the trend in risk-adjusted 30-day readmission ratios for each study condition for each hospital category in each year from 2013 to 2016. The 30-day readmission ratios in the Figure were extracted from CMS without adjusting for hospital and community characteristics. For Delta region hospitals, there were about 170 hospitals per year for pneumonia and heart failure and about 80 hospitals per year for AMI. For Delta state hospitals, there were about 445 hospitals per year for pneumonia and heart failure and about 300 per year for AMI. The total numbers of other hospitals per year for pneumonia, heart failure, and AMI were about 2410, 2380, and 1800, respectively. 

Part A of the Figure shows that, for pneumonia, Delta state hospitals had an average readmission ratio of 1.028 in 2013 but gradually reduced it to 1.016 in 2016, whereas Delta region hospitals maintained a consistent average readmission ratio of 1.018 during the study period. As seen in part B of the Figure, Delta region hospitals reduced readmission ratios for heart failure from 1.041 in 2014 to 1.027 in 2016. However, their average readmission ratios were still higher than those of Delta state hospitals, which remained stable at approximately 1.018 from 2013 to 2016. For AMI in part C of the Figure, average readmission ratios for Delta state hospitals were reduced from 1.023 in 2013 to 1.014 in 2016, but they remained high for Delta region hospitals, ranging from 1.017 to 1.023 across the study years. 

Potential Factors Associated With Variation in Readmission Across the Hospital Groups

Hospital characteristics among Delta region, Delta state, and other hospitals were different. Table 1 shows that Delta region hospitals had a higher percentage of public hospitals (36.06%), had a lower percentage of major teaching hospitals (5.65%), and were relatively smaller (168 beds) than Delta state (20.72%, 10.77%, and 237 beds, respectively) and other hospitals (18.49%, 13.23%, and 230 beds, respectively). Delta region and Delta state hospitals had a higher percentage of Medicare patients (42.42% and 40.74%, respectively) than other hospitals (35.73%). Delta region and Delta state hospitals also had a higher percentage of Medicaid patients than other hospitals (20.50%, 17.63%, and 12.47%, respectively).

Community characteristics where hospitals operated were quite different across the 3 hospital categories. Delta region hospitals operated in communities with fewer PCPs per 1000 population (0.61), a higher percentage of persons in poverty (23.07%), and fewer home health agencies (4.58), but a higher number of SNF beds per 1000 population (8.78), than communities where Delta state and other hospitals operated (0.70 PCPs per 1000 population, 17.81% persons in poverty, 55.30 home health agencies, and 6.89 SNF beds per 1000 population for Delta state hospitals; and 0.76, 16.19%, 50.67, and 5.70, respectively, for other hospitals). Delta region hospital communities had a significantly higher black population (33.57%) and a lower Hispanic population (3.01%) than the other 2 hospital categories (14.49% and 11.04% black population and 7.03% and 16.44% Hispanic population for Delta state and other hospital communities, respectively).

, the coefficients for Delta region and Delta state hospitals represent the differences in 2013 readmission ratios between Delta region and other hospitals and between Delta state hospitals and other hospitals, respectively (coefficients and standard errors in Table 2 were multiplied by 10). The coefficient for the interaction between Delta region hospitals and year dummy variable for 2014 indicates the difference in mean readmission ratios from 2013 to 2014 between Delta region and other hospitals. The same interpretation applies to the interaction among Delta region hospitals and year dummy variables for 2015 and 2016 and interactions among Delta state hospitals and year dummy variables for 2014, 2015, and 2016. 

Without controlling for hospital and community factors, the results from the models showed that, compared with other hospitals, Delta region and Delta state hospitals had 2.1% (

<.001) higher readmission ratios, respectively, for pneumonia; 4.2% (

<.05) higher readmission ratios for heart failure; and 2.1% (

<.01) higher readmission ratios for AMI. The differences in mean pneumonia readmission ratios between 2015 and 2013 and between 2016 and 2013 for Delta state hospitals were 1.0% (

<.001), respectively, lower than those for other hospitals. The difference in mean heart failure readmission ratios between 2016 and 2013 for Delta region hospitals was 1.5% (

<.05) lower than that for other hospitals. 

After controls for hospital and community factors, the significant difference in readmission ratios for pneumonia and AMI between Delta region and other hospitals in 2013 became insignificant (

>.05). The significance level between Delta and other hospitals for heart failure in 2013 weakened from 

<.05. The differences in mean readmission ratios for heart failure between 2016 and 2013 for Delta region hospitals were 1.6% (

<.01) lower than those for other hospitals. For pneumonia and AMI, the results for differences in mean readmission ratios from 2014 to 2013 and from 2015 to 2013 between Delta region and other hospitals remained unchanged after controlling for hospital and community characteristics. For Delta state and other hospitals, the difference in readmission ratios for the study conditions between these 2 hospital groups in 2013 remained significant (

 <.001 for pneumonia and AMI). Additionally, the difference in mean readmission ratios between Delta state and other hospitals during the study period remained similar from the models with and without controls for hospital and community characteristics. 

Some hospital and community characteristics were significantly associated with 30-day readmission ratios. The factors that were positively associated with readmission ratios were major teaching hospitals (

<.05 for both heart failure and AMI) and the percentage of black population (

values ranged from <.01 to <.001) for all 3 study conditions. The percentage of Medicare patients, number of acute care hospital beds, number of SNF beds, and unemployment rate were each positively associated with 30-day readmission ratios for pneumonia (

 <.05 for all). However, an increase in the number of PCPs per 1000 population was associated with lower readmission ratios for heart failure (

<.05). Unexpectedly, increases in the percentage of persons in poverty were associated with lower readmission ratios for AMI (

Our findings showed that disparities in readmissions among the 3 hospital groups exist and that hospital and community factors contribute to those disparities. In the models without controls for hospital and community characteristics, the findings showed that Delta region and Delta state hospitals had poorer performance on 30-day readmissions for all study conditions than did other hospitals. After controlling for those characteristics, the differences in 30-day readmission ratios between Delta region and other hospitals were weakened for all study conditions and became insignificant for pneumonia and AMI. However, the differences in 30-day readmission ratios between Delta state and other hospitals remained unchanged after controlling for hospital and community characteristics. A possible explanation is that community factors in our study were based on hospitals’ locations rather than on patients’ residential areas. Some patients in the Delta region might bypass regional hospitals and travel to Delta state hospitals for treatment, then return after hospitalization to the Delta, where there is a lack of postacute care resources. Under HRRP, hospitals are accountable for their patients’ readmissions after the initial hospitalization, regardless of where patients reside and where they receive postacute care. Further studies based on patient-level data to examine this potential explanation are needed. 

Our findings, consistent with those of previous studies,

 showed that major teaching hospitals and a higher percentage of black population were associated with higher 30-day readmission ratios for all study conditions. Major teaching hospitals are part of the safety net that serves indigent populations. The variables of major teaching hospitals and percentage of black population may reflect community contextual factors that affect health outcomes but are not currently well measured. Although 30-day readmissions may be the result of poor transitions from the hospital to the community, our findings indicate that 30-day readmissions were also affected by community resources beyond the hospitals’ control. CMS has implemented HRRP and continues payment reform toward value-based purchasing. To avoid unduly penalizing hospitals serving the poor, adjusting outcome measures to include measures of community contextual factors needs to be considered.

Unexpectedly, we found that an increase in the percentage of persons in poverty was associated with lower readmission ratios for AMI. Mortality rates are high for patients who have had an AMI if they cannot access treatment in a timely manner. Although evidence indicates that a hospital’s mortality rate is not associated with 30-day readmissions for the general Medicare population with AMI,

 future studies focusing on the relationship between mortality and readmissions for low-income individuals with AMI are recommended. 

Although Delta region and Delta state hospitals have higher readmission ratios than other hospitals, the trends in the Figure, along with the negative coefficients for the interaction terms between study years and Delta region and Delta states in Table 2, indicate that Delta region and Delta state hospitals reduced 30-day readmissions more than other hospitals did over the study period. That is, overall disparities in 30-day readmissions for the study conditions among Delta region, Delta state, and other hospitals were reduced from 2013 to 2016 under HRRP. 

Our study has limitations. First, the 30-day readmission ratios in our study were extracted from CMS, which weights the ratios by a Bayesian hierarchical shrinkage approach that aligns small hospitals closer to the national average.

 Delta region hospitals might receive higher weights than Delta state and other hospitals given the smaller average size of Delta region hospitals (Table 1). Second, we used county-level community characteristics. It is recommended that future studies measure community characteristics at a smaller geographic level, such as zip code or census tract, and sociodemographic status at the patient level to more precisely quantify the impact of these factors on readmissions among regions. Finally, there were variations in 30-day readmission ratios within each hospital group. Investigating the underlying factors associated with such variations within hospital groups is strongly recommended. 

Despite these limitations, the findings from our study have implications for future studies and the revision of HRRP. Although our study and previous studies found that community factors matter, how these factors affect readmissions for each study condition is not fully understood. Future studies using multilevel approaches to investigate the pathways from disadvantaged communities to 30-day readmissions for CMS selected conditions are recommended.

Regarding policy implications, the goal of HRRP is to motivate hospitals to reduce 30-day readmissions. Evidence shows that readmissions for the study conditions have been reduced nationwide each year

 and, thus, reach a new lower benchmark annually. To continue to motivate Delta region and Delta state hospitals and help them avoid Medicare patient revenue loss from HRRP penalties, stakeholders and policy makers should consider crediting hospitals for continuously reducing 30-day readmissions, even if their readmission rates are above the national average. Such an approach is consistent with CMS’ adoption of an improvement score in the Hospital Value-Based Purchasing Program.

 The inclusion of an improvement mechanism that can be used to compare hospitals’ current readmission rates with their past readmission rates could mitigate limitations from unobserved factors associated with 30-day readmissions and would recognize the health needs of unique geographical areas. This would likely reduce the unintended consequences of HRRP that may, with reductions in Medicare reimbursement, threaten the healthcare delivery system in the Mississippi Delta region and other similarly underserved areas.

This study was presented at the AcademyHealth Annual Research Meeting in June 2017.

Department of Health Policy and Management (HFC, AN, TMB, SAK, MEM, JMT), and Department of Biostatistics (FW), College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR.

The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Concept and design (HFC, TMB, SAK, MEM, FW, JMT); acquisition of data (HFC, AN); analysis and interpretation of data (HFC, TMB, SAK, MEM, FW, JMT); drafting of the manuscript (HFC, AN); critical revision of the manuscript for important intellectual content (AN, TMB, SAK, MEM, FW, JMT); and statistical analysis (HFC, AN). 

Hsueh-Fen Chen, PhD, Department of Health Policy and Management, College of Public Health, University of Arkansas for Medical Sciences, 4301 W Markham St, Mail Slot 820-12, Little Rock, AR 72205. Email: hchen@uams.edu.

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.

Understanding Factors Associated With Readmission Disparities Among Delta Region, Delta State, and Other Hospitals

To explore the feasibility of applying value-based insurance designs (V-BIDs) to the treatment of mental health disorders and address any additional challenges posed.

This study consisted of 3 steps. First, we reviewed the historical literature on V-BIDs and challenges revealed by various programs. Second, we reviewed the literature on the cost, scope, and various treatment options for mental health disorders. Third, we analyzed potential challenges in applying V-BIDs to mental health disorders.

Many challenges exist in applying V-BID to the management and treatment of physical and mental health disorders, such as getting buy-in from insurance companies and from large employers, and adherence issues for those with diminished capabilities to comprehend program benefits and those lacking family support. Additional challenges specific to mental health disorders include: a) privacy (ie, sensitivity issues) in implementing the program in certain settings; and b) sociodemographic variables, along with perceptions of mental disorder severity and need that currently affect the take-up of mental health services.

 Research projects focused on applying V-BID to mental health disorders that address these challenges and demonstrate cost savings will be needed (ie, comparative effectiveness research studies), along with additional information on changes in disability-adjusted life-years, and on-demand  responses across different mental health services, populations, and care settings.

	Prior studies examining value-based insurance design (V-BID) have focused on their application to the treatment of chronic physical disease. Here, application of V-BID to mental health disorders is examined.

This study examined the application of value-based insurance design to the treatment of mental health disorders and addresses any additional challenges. 

Author | J. Mick Tilford, PhD

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