Examining Rural Hospital 

Bypass for Inpatient Services

DECEMBER 2020 PRESENTED TO:

Darci Graves

CMS Office of Minority Health

Centers for Medicare & 

Medicaid Services

7500 Security Boulevard

Baltimore, MD 21244

PRESENTED BY: 

Alana Knudson

Shena Popat

Wen Hu

Curt Mueller

Victoria Hallman

CMS Alliance to Modernize 

Healthcare (Health FFRDC)

7515 Colshire Drive

McLean, VA 22102

Cristina Miller

CMS Office of Minority Health

Suggested citation: CMS Office of Minority Health. Examining Rural Hospital Bypass for 

Inpatient Services. Baltimore, MD: Centers for Medicare & Medicaid Services; December 2020. 

Paid for by the U.S. Department of Health and Human Services.
i 

Table of Contents 

Introduction................................................................................................................................... 1

Purpose of the study .................................................................................................................... 2

Data Sources and Methods........................................................................................................... 2

Quantitative Data and Analysis .................................................................................................. 2

Defining Rural Hospitals............................................................................................................ 3

Defining Rural Hospital Markets (Hospital Service Areas)....................................................... 3

Defining Rural Hospital Bypass................................................................................................. 3

Variables and Outcomes............................................................................................................. 4

Statistical Analysis...................................................................................................................... 5

Qualitative Data Collection and Analysis................................................................................... 5

Quantitative Findings................................................................................................................... 6

Beneficiary Demographic Characteristics.................................................................................. 6

Market and Hospital Factors....................................................................................................... 7

Hierarchical Analysis for Beneficiaries: Demographic Characteristics Associated with 

Avoidable Bypass..................................................................................................................... 11

Listening Session Observations ................................................................................................ 12

Access to Care....................................................................................................................... 13

Perceptions of Quality and Trust in Providers ...................................................................... 14

Out-of-Pocket Costs and Insurance Coverage ...................................................................... 15

Personal Preferences............................................................................................................. 15

Limitations................................................................................................................................... 16

Conclusion and Next Steps......................................................................................................... 16

References.................................................................................................................................... 19

Appendix A: Supporting Tables and Figures........................................................................... 21

Appendix B: Detailed Methodology .......................................................................................... 32
ii

List of Tables and Figures

Figure 1. Avoidable Rural Hospital Bypass by Medicare FFS Beneficiary Race and 

Ethnicity.......................................................................................................................................... 7

Table 1. Home Hospital Rating (Rural PPS Hospitals) versus Distant Hospital HCAHPS 

Overall Quality Star Rating ............................................................................................................ 8

Figure 2. Home Hospital Bypass Factors for Rural PPS Hospitals ................................................ 9

Figure 3. Home Hospital Bypass Factors for CAHs ..................................................................... 10

Figure 4. Adjusted Hospital Bypass Rate by Age Category......................................................... 11

Table 2. Adjusted Hospital Bypass Rate by Dual Eligible Status ................................................ 12

Figure A.1. Study Design and Patient Selection........................................................................... 21

Figure A.2. Geographic Distribution of Rural home Hospitals by Hospital Type ....................... 22

Table A.1. Top 50 DRGs Included in the Analysis ...................................................................... 22

Table A.2. Home Hospital Characteristics, 2018 ......................................................................... 24

Table A.3. Home Hospital Market Characteristics, 2018 ............................................................. 24

Table A.4. Inpatient Stays by Home Hospital Type, 2018 ........................................................... 25

Table A.5. Rural Hospital Bypass by Medicare Beneficiary Characteristics, 2018..................... 25

Table A.6a. Linear Regression Model for Rural PPS Hospital Avoidable Bypass ...................... 26

Table A.6b. Variable Profiling for Rural PPS Hospitals .............................................................. 27

Table A.7a. Linear Regression Model for CAH Avoidable Bypass ............................................. 28

Table A.7b. Variable Profiling for CAHs ..................................................................................... 29

Table A.8a. Hierarchical Logistic Regression Model (HLRM) for Patient and Inpatient 

Stays for Surgical DRGs in Rural Hospitals (PPS and CAH) ...................................................... 30

Table A.8b. Hierarchical Logistic Regression Model (HLRM) for Patient and Inpatient 

Stays for Medical DRGs in Rural Hospitals (PPS and CAH) ...................................................... 31

Table B.1. Interview Participants by Type ................................................................................... 37
1 

INTRODUCTION

Rural hospitals are the anchor for health care delivery in most rural communities and provide 

access to essential health care services for the approximately 4 million Medicare beneficiaries 

who live in rural America. Rural hospitals also contribute to their local economies, often serving 

as significant economic engines and major employers [1]. The presence of a hospital in a rural 

community has direct and indirect effects on the local economy—on average, critical access 

hospitals (CAHs) employ 127 employees and pay $6 million in wages, salaries, and benefits, and 

also generate an additional 43 jobs and $1.1 million in secondary wages, salaries, and benefits. 

Health care often accounts for 10 to 15 percent of jobs in a rural community [2]. 

Rural hospitals also face challenges. Many rural hospitals inherently experience lower patient 

volumes than urban hospitals due to population density and geography. Low patient volumes 

contribute to the financial vulnerability of rural hospitals [3]. While rural residents may receive 

inpatient care in their local community, some rural residents receive inpatient services at 

hospitals that are not their nearest rural hospital (rural hospital bypass). When patients bypass 

their local rural hospital for the same services that are available locally, it can further threaten the 

sustainability of their local rural hospital [4]. 

A limited number of rural hospital bypass studies have been published in the last 25 years. Most 

rural hospital bypass studies using quantitative data (i.e., claims data) were limited to a few 

select states and found bypass rates that ranged from 25 percent to 50 percent [5] [6] [7]. 

Qualitative studies of rural patients showed an even greater bypass rate that ranged from 16 

percent to 70 percent [8]. 

Previous studies have explored patient and hospital market characteristics associated with rural 

hospital bypass. For complex procedures, such as thoracic surgery, rural hospital bypass is to be 

expected because those services are not likely to be offered at a patient’s local rural hospital. 

Rural patients often cite limited services and lack of specialty care as reasons for bypassing their 

local rural hospital [9]. Recognizing the services offered in rural hospitals, some studies 

controlled for the types of services for which patients are accessing care locally and outside of 

their home community. Across inpatient and outpatient admissions, patients are more likely to 

access care nearby for emergency and urgent care than for elective or scheduled care [5] [8]. One 

recent study found that smaller hospitals (i.e., hospitals with fewer beds), less profitable 

hospitals, and those closer to larger hospitals were associated with higher bypass [9].

Several studies identified patient characteristics associated with higher rates of rural hospital 

bypass. For example, a study conducted in a few selected states found that younger patients, 

males, and racial/ethnic minorities were more likely to bypass their local rural hospital [7]. Other 

studies found that privately insured patients were more likely to bypass their local rural hospital, 

compared to Medicare and uninsured patients. Medically complex and higher-risk patients are 

also more likely to bypass their local hospital [5] [6]. 

Rural patients may choose not to seek care at their nearest rural hospital for many reasons. 

Patient perception of the local hospital, including the perception of quality and reputation of 

local services and providers, may affect a patient’s choice to seek care locally [9]. Satisfaction 

with the local hospital is also associated with rural hospital bypass [9]. While patients may not 

favor a local rural hospital due to limited services or specialty care, increasing the number of 
2 

services and technologies offered by rural hospitals has only a small effect on patient decisionmaking [10].

Purpose of the study

The purpose of the study is to explore the drivers of rural hospital bypass for inpatient services.

The study was guided by the following research questions: 

1. What demographic characteristics are associated with rural hospital bypass? 

2. Why did patients receive inpatient care at a hospital that is not their nearest rural 

hospital? 

3. How does the local supply of primary care and specialty care services affect rural hospital 

bypass? 

4. How do insurance networks or cost affect decisions to use rural hospitals? 

DATA SOURCES AND METHODS

We conducted a national mixed methods study using quantitative methods and Medicare fee-forservice (FFS) claims combined with a qualitative case study approach. Quantitative analyses 

were completed prior to qualitative data collection to inform case study selection. A detailed 

description of the study methodology is included in Appendix B. 

Quantitative Data and Analysis 

The quantitative study was designed as a retrospective cohort study. The Medicare acute care 

hospital inpatient claims and enrollment files for calendar year (CY) 2018 were the primary data 

used for the analysis. Only rural fee-for-service (FFS) Medicare beneficiaries who had coverage 

for all 12 months during CY 2018 and had at least one inpatient stay were included in the 50-

state analysis. Medicare Advantage and beneficiaries with end-stage renal disease (ESRD) were 

excluded from the analysis.

The 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient claims were merged 

with the Medicare Master Beneficiary Summary File to provide demographic characteristics and 

other information such as patients’ hierarchical condition category (HCC) scores and dual 

eligible status (i.e., individuals who receive both Medicare and Medicaid benefits). Other 

hospital and market characteristics data were merged with the inpatient claims, including RuralUrban Commuting Area (RUCA) codes from the United States Department of Agriculture’s 

Economic Research Service, the Rural Hospital List from the University of North Carolina at 

Chapel Hill Cecil G. Sheps Center for Health Services Research (Sheps Center), a list of Tribal 

hospitals generated by the Indian Health Service (2019), the CMS Provider of Services file, the 

Area Health Resource File (HRSA), American Hospital Association (AHA) Annual Survey, 

County Health Rankings, Hospital Consumer Assessment of Healthcare Providers and Systems 

(HCAHPS) data from Hospital Compare, and the CMS geographic variation public use file. 
3 

Defining Rural Hospitals 

All general acute care hospitals located in rural ZIP codes, including critical access hospitals 

(CAHs) and rural Prospective Payment System (PPS) hospitals,i were included in the analysis. 

The hospitals were categorized as rural or urban facilities based on the RUCA code for the 

hospital’s ZIP code (rural includes RUCA codes ≥ 4 and urban includes RUCA codes < 4). 

RUCA codes, which are based off of the Office of Management and Budget metropolitan 

statistical area and micropolitan statistical area definitions, were used to define rural and urban. 

Hospitals that did not provide general medical services (i.e., psychiatric hospitals, long-term care 

hospitals, and specialty hospitals) were not included in the analysis. 

Defining Rural Hospital Markets (Hospital Service Areas)

For this analysis, hospital service areas (HSAs) are defined by the ZIP codes that a rural hospital 

serves. The goals of this analysis required construction of an HSA for every hospital located in a 

rural ZIP code treating Medicare FFS inpatients and assignment of beneficiaries to one HSA. 

Each HSA was defined by ranking the ZIP codes for Medicare beneficiaries by Medicare 

inpatient volume of the hospital and identifying those ZIP codes that accounted for 80 percent of 

Medicare inpatient volume [11] [12]. Patients who received services at the rural hospital and 

reside in one of the ZIP codes that did not comprise the top 80 percent of inpatient stays were not 

considered to be part of an HSA. If the 80 percent threshold only captures one beneficiary ZIP 

code, the HSA for the hospital contains only one residential ZIP code. 

There were instances in which ZIP codes, and the rural Medicare beneficiaries who live in those 

ZIP codes, were assigned to multiple HSAs. For beneficiaries with multiple HSA assignments, 

we identified the type of markets in which they reside by calculating the distance between each 

beneficiary’s ZIP code centroid (the point marking the center of the ZIP code) and the ZIP code 

centroid for each hospital used by the patient. ii We then used this calculation to determine the 

HSA that is the closest to beneficiary’s residence and estimate whether any other HSAs

including that ZIP code were less than or greater than 30 miles away [13] [14]. This distinction 

between HSA markets was used (as described below) to avoid assigning a hospital stay as bypass 

if the hospital stay was actually at a reasonably close hospital other than at the nearest hospital. 

Defining Rural Hospital Bypass

We identified the inpatient stays for rural Medicare FFS beneficiaries and determined the 

facilities where the beneficiaries received inpatient hospital services. The closest hospital that 

rural Medicare beneficiaries are attributed to is considered the “home” hospital. Each inpatient 

stay was assigned to one of three HSA markets: 

• In-Market Inpatient Stays: Inpatient stays at rural hospitals that are either the home 

hospital, or are ≤ 30 miles away. In-Market Inpatient Stays were not considered to be 

rural hospital bypasses.

• Rural to Other Rural Bypass: Inpatient stays at distant rural hospitals >30 miles away

 

i

 Rural Prospective Payment System (PPS) hospitals include all general, acute care hospitals located in rural ZIP codes (RUCA 

≥4) that receive reimbursement for inpatient services under the inpatient prospective payment system (IPPS).

ii

 A centroid is a point (usually on a map) that defines the center (in this case, of the zip code). The centroid (point) is represented 

by longitude and latitude coordinates. In this study, the ZIP code centroid is used to approximate the Euclidean (or straight

line) distance between two ZIP codes.
4 

• Rural to Urban Bypass: Inpatient stays at distant urban hospitals

Recognizing that some rural hospital bypass was due to the availability of services, we 

characterized bypasses as “unavoidable” – an inpatient stay at a distant hospital for a service not 

available at the home hospital - or “avoidable” – an inpatient stay at a distant hospital for a 

service that is also available at the home hospital. We limited the inpatient stays to the most 

common 50 diagnostic related groups (DRGs), out of the 765 DRGs provided by rural hospitals 

in CY 2018 (Appendix A, Table A.1) and determined the availability of services in each HSA. In 

Appendix A, Figure A.1 provides a diagrammatic presentation of the study design and selection 

process.

Variables and Outcomes

Variables in the study included those at the inpatient hospital stay level, beneficiary level, 

hospital level, and community level. In addition to DRG, we included elective inpatient stays as 

a binary indicator. For beneficiary level factors, Medicare beneficiaries were categorized by sex 

and age categories. The RTI race code was used to identify race/ethnicity (non-Hispanic White, 

Black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska Native). We also included 

dual eligible status and the beneficiary’s 2018 health risk score based on the HCC riskadjustment models as a proxy estimate of patient’s severity of illness. 

We accounted for the following hospital level variables for home hospitals: number of beds; 

hospital ownership type (i.e., private vs. public); HCAHPS measures of overall hospital rating 

and hospital recommendation; and presence of a cancer program approved by American College 

of Surgeons. Three additional variables were derived using Medicare claim and enrollment data: 

hospital telehealth service flag, Medicare Advantage penetration, and percentage of dual eligible 

Medicare beneficiaries by HSA. The telehealth services flag was defined for those hospitals that 

delivered at least one telehealth office visit or consultation service to a Medicare patient during 

an inpatient stay. 

The following HSA and community level indicators were included: 

• Health insurance coverage: percentage of households comprised of individuals who are 

18-64 years of age without health insurance by county, 2017 

• Medicare market: Medicare Advantage penetration and percentage of dual eligible 

Medicare beneficiaries by HSA, 2018 

• Physicians per capita: primary care physicians (i.e., MDs and DOs) and specialists per 

1,000 population by county, 2017 

• Education: percentage of residents with high school and higher education diploma(s) by 

county, 2017 

• Population wealth: median household income by county, 2017 

• Social cohesion: Number of membership associations per 10,000 population by county, 

2017iii

 

iii The number of membership associations was used as a proxy for social connectedness. Additional information is available 

through County Health Rankings at https://www.countyhealthrankings.org/explore-health-rankings/measures-datasources/county-health-rankings-model/health-factors/social-and-economic-factors/family-social-support/social-associations. 
5 

• Average HCC score for Medicare population who reside in the county, 2017 

• Geography: rural-urban commuting area codes (RUCA); Census region: Northeast, 

South, West, Midwest 

• State policy: Medicaid expansion state [15]

The primary outcome in the analysis was whether the inpatient stay was an avoidable bypass. At 

the inpatient stay level, the avoidable rural hospital bypass rate was the percentage calculated 

from the total number of avoidable bypasses divided by total number of hospital inpatient stays 

for the Medicare beneficiaries who reside in the HSA, excluding unavoidable bypasses. At the 

beneficiary level, the beneficiary was flagged as having an avoidable bypass if at least one of 

his/her inpatient stays during CY 2018 was an avoidable bypass. 

Statistical Analysis

We developed statistical models to assess the impact of community, hospital, patient, and stay 

level characteristics on avoidable hospital bypass. The exploratory data analysis before model 

testing was conducted to: 

1. Describe rural hospital bypass among Medicare FFS beneficiaries for inpatient care; 

2. Identify factors influencing avoidable rural hospital bypass for inpatient services; and 

3. Quantify the relationship between avoidable bypass and patient demographics. 

The results of the exploratory data analysis are expressed as frequencies and percentages for 

patient and hospital characteristics by home hospital type—rural PPS hospitals and CAHs. This 

study compared quality measures of the home and distant hospitals for beneficiaries who 

bypassed the home facility. Multiple linear regression models were used to identify factors 

associated with avoidable bypass; separate estimates were obtained for home PPS and CAHs. 

Linear regression is based on some important assumptions, which we addressed during the 

analysis process. [16] Statistical significance was set at the p< 0.05. Regression coefficients and 

standard errors are included in Appendix A. 

Qualitative Data Collection and Analysis 

Results of the quantitative analysis were used to inform the collection of qualitative data from 

key stakeholders. Due to the COVID-19 public health emergency (PHE), we conducted virtual 

listening sessions with state-level representatives in four states. The four states chosen for case 

studies—Georgia, Kansas, Michigan, and Montana—were selected to represent geographic and 

market diversity, including Census region, rural/frontier population and areas, Medicaid 

expansion/non-expansion, and market factors. 

We invited stakeholders from each state, including representatives from: 

• CMS Regional Office Rural Health Coordinators; 

• State Health Insurance Assistance Programs (SHIPs);

• State Offices of Rural Health and State Flex Directors; 

• State Hospital Associations; and

• State Rural Health Associations. 
6 

As these were listening sessions and open to those who wanted to participate, we welcomed 

participants to invite colleagues and other stakeholders within their state who might have input 

on the topic of rural hospital bypass. As a result, representatives from rural hospitals, community 

health centers, and rural health clinics also participated in some state listening sessions. 

In advance of the listening sessions, participants were sent a spreadsheet that included their 

state’s median rural hospital avoidable bypass rate and a list of all the state’s rural hospitals that 

were classified as having a lower or higher bypass rate based on the state median. Five listening 

sessions were completed in July and August 2020, with one listening session per selected state 

and one with representatives from SHIPs. Each listening session was 60 to 90 minutes in length. 

The listening session discussion guide included questions about drivers of home hospital use and 

drivers of home hospital bypass. 

QUANTITATIVE FINDINGS

Overall, about one in five Medicare beneficiaries (20 percent) resided in a rural area. Of the 12.2 

million rural Medicare beneficiaries, the majority were White (89 percent) and enrolled in FFS 

Medicare (75 percent in CAH HSAs; 70 percent in rural PPS HSAs). By comparison, about 59 

percent of urban Medicare beneficiaries were enrolled in FFS Medicare. 

This study included 1,002,373 inpatient stays for 749,053 rural Medicare FFS beneficiaries

assigned to 2,051 HSAs. Slightly over half of the rural hospitals (57 percent) were CAHs, and 

the balance were rural PPS hospitals (43 percent). The majority of rural PPS hospitals (70 

percent) have 26-100 beds, and two-thirds are located in micropolitan areas, the most densely 

populated rural areas (RUCA 4-6) with populations between 10,000 and 49,999. In contrast, half 

of the CAHs (50 percent) are located in rural noncore areas, the most sparsely populated rural 

areas (RUCA 10) with populations less than 10,000. Generally, rural PPS hospitals are 

concentrated in the eastern region of the U.S., whereas CAHs are concentrated in the central and 

western regions (Appendix A, Figure A.2). 

Of rural Medicare FFS beneficiaries who had at least one inpatient stay in CY 2018 the average 

inpatient stays per patient was 1.5. Almost one-fourth of rural Medicare beneficiaries (23

percent) had two or more inpatient stays in CY 2018. Although one-fifth of the rural Medicare 

FFS population are dual eligible Medicare beneficiaries (20 percent), they accounted for almost 

one-third of all rural Medicare beneficiary inpatient stays (31 percent).

The following sections describe beneficiary demographic characteristics, and market and 

hospital factors related to avoidable rural hospital bypass.

Beneficiary Demographic Characteristics 

Two-thirds of Medicare FFS beneficiaries (68 percent) had one in-market inpatient stay or all inmarket stays (i.e., inpatient stays at home hospital), while one-third of beneficiaries (32 percent) 

had at least one avoidable bypass in CY 2018 (i.e., inpatient stay at distant hospital). 

Among all age groups, rural Medicare beneficiaries who were 85 years and over had the lowest 

avoidable bypass (23 percent for rural PPS hospitals; 24 percent for CAHs), while rural 

Medicare beneficiaries in the age 65 to 74 years group had the highest avoidable bypass (37 

percent for rural PPS hospitals; 36 percent for CAHs). 
7 

Hispanic, Black, and American Indian/Alaska Native rural Medicare beneficiaries have slightly 

higher avoidable bypass rates compared with the white population (Figure 1). Asian/Pacific 

Islander and American Indian/Alaska Native beneficiaries are grouped into a single category in 

the beneficiary-level regression analysis due to small numbers. Beneficiary demographic 

characteristics are included in Appendix A, Table A.5. 

Figure 1. Avoidable Rural Hospital Bypass by Medicare FFS Beneficiary Race and Ethnicity

NOTE: Avoidable bypass rate is the percentage of rural Medicare beneficiaries with at least one inpatient stay at a distant 

hospital even though those services were available at their home hospital. Percentage of beneficiaries is the percentage of 

rural Medicare beneficiaries included in the analysis by RTI race code. 

Source: Estimates were produced using 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient claims

and Medicare enrollment data.

Market and Hospital Factors 

The average distance from a rural Medicare beneficiary’s residence to a distant hospital was 

about 55 miles to urban hospitals and 57 miles to other rural hospitals. About 85 percent of 

avoidable bypasses were to distant hospitals in metropolitan areas (RUCA 1), while almost 10

percent of avoidable bypasses were to distant hospitals in micropolitan areas (RUCA 4-6).

HCAHPS Overall Quality Star Ratingiv of Hospital scores were examined to determine if there 

were differences between the quality of the home rural PPS hospitalsv and the distant hospitals 

where rural Medicare beneficiaries received care. Regardless of the home hospital HCAHPS 

overall star rating, rural Medicare beneficiaries who bypassed rural PPS hospitals most often 

chose hospitals with an HCAHPS overall rating of three or greater (Table 1). Eighty-eight 

percent of rural Medicare beneficiaries who resided in lower quality star rating (1, 2) home 

HSAs (≥3) and bypassed their home hospital chose hospitals with higher quality star ratings 

 

iv For additional information, see Technical Notes for HCAHPS Star Ratings at https://hcahpsonline.org/globalassets/hcahps/starratings/tech-notes/2017-10_star-ratings_tech-notes.pdf. 

v CAHs were excluded because almost half of the CAHs (47 percent) are missing HCAHPS overall rating.

32% 34%

38%

30%

34%

38%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Non-Hispanic White Black (AfricanAmerican)Hispanic Asian/Pacific

Islander

American Indian /

Alaska Native

Unknown or Other

Avoidable Bypass Rate Percentage of Beneficiaries
8 

while only 6 percent of rural Medicare beneficiaries who resided in higher quality star rating 

home HSAs (≥3) and bypassed their home hospital chose hospitals with lower quality star ratings 

(1, 2) (p<0.001).

Table 1. Home Hospital Rating (Rural PPS Hospitals) versus 

Distant Hospital HCAHPS Overall Quality Star Rating

Home Hospital Star Rating

(Rural PPS Only)

Distant 

Hospital 

Star 

Rating: 1 

Distant 

Hospital 

Star 

Rating: 2 

Distant 

Hospital 

Star 

Rating: 3 

Distant 

Hospital 

Star 

Rating: 4 

Distant 

Hospital 

Star 

Rating: 5 

1 0% 24% 46% 26% 3%

2 0% 10% 44% 39% 5%

3 0% 6% 50% 36% 6%

4 0% 5% 48% 39% 7%

5 0% 11% 47% 31% 9%

N/A 0% 9% 42% 44% 3%

NOTE: Percentages of all inpatient claims for avoidable bypass of a rural PPS hospital by home HSA rating. About 1

percent of inpatient stays were excluded due to missing HCAHPS overall rating. CAHs were excluded because almost half 

of the CAHs (47 percent) have missing HCAHPS overall rating scores. 

Source: Estimates were produced using 2017 HCAHPS Overall Quality Star Rating from Hospital Compare. 

https://data.medicare.gov/Hospital-Compare/Patient-survey-HCAHPS-Hospital/dgck-syfz

Multiple linear regression results are included in Appendix A, Table A.6a for home PPS 

hospitals and Table A.7a for home CAHs. The regression coefficients represent the marginal 

effect of changing one variable while holding all the other variables constant. The most 

important variables associated with avoidable rural hospital bypasses are included in Figure 2 for 

rural PPS hospitals and Figure 3 for CAHs. 

Rural hospitals where inpatient telehealth consultation services were available had significantly

lower avoidable bypass. Rural PPS hospitals that used telehealth services had an average bypass 

rate of 26 percent compared to 33 percent for facilities that did not use telehealth services 

(p<0.005). The avoidable bypass rate is negatively associated with the supply of primary care 

physicians (p<0.005); hospital markets with a greater supply of primary care physicians had 

lower avoidable bypass. Bypasses are directly associated with the MA penetration rate 

(p<0.005); hospital markets with a greater percentage of Medicare beneficiaries with MA had 

higher avoidable bypass. 

The regression analysis also suggests that PPS hospitals in counties with more hospital beds per 

capita have lower bypass rates (P<0.0001). PPS hospitals in Medicaid expansion states, those 

hospitals with an approved cancer program, and hospitals located in counties with higher median 

incomes had lower avoidable bypass (p=0.003, <0.001, <0.001 respectively) (See Appendix X 

Table X). In contrast, those PPS hospitals with lower patient recommendation scores (HCAHPS 

measure of willingness to recommend hospital) tended to have higher bypass rates (P=0.0095). 
9 

Figure 2. Home Hospital Bypass Factors for Rural PPS Hospitals

NOTE: This chart is a graphical representation of results included in Appendix A, Table A.6a. The points are the 

coefficient (marginal effect) estimates for the factors on the y-axis; a factor greater than zero implies that higher hospital 

bypass rate is positively related to higher factor values; a factor less than zero implies the negative relationship between 

hospital bypass and factors. With all other independent variables held constant, a one unit increase in primary care 

physicians per 1,000 county population is associated with an 8 percent decrease in the avoidable hospital bypass rate for 

rural PPS hospitals. Compared to hospitals that did not use telehealth services, bypass rates of hospitals that used 

telehealth services are 3 percent lower for rural PPS hospitals. When the MA penetration rate increases by 1 percent, 

bypass rates for rural PPS hospitals increase by 0.2 percent. There is a 0.1 percent decrease in the avoidable bypass rate for 

rural PPS hospitals for every one unit increase in the number of beds. 

Source: Estimates were produced using 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient claims, 

Area Health Resource File (HRSA), Provider of Services file, and County Health Rankings. 

Similar to rural PPS hospitals, avoidable bypass for CAHs that provided inpatient telehealth 

services was 28 percent compared to 33 percent for those that did not provide telehealth services 

(p=0.03). The avoidable bypass rate is negatively associated with the supply of primary care 

physicians (p<0.005), i.e, hospital markets with a greater supply of primary care physicians had 

lower avoidable bypass. Bypasses are directly associated with the MA penetration rate 

(p<0.005); hospital markets with a greater percentage of Medicare beneficiaries with MA had 

higher avoidable bypass. CAHs that were located in the Midwest and northeast regions, and 

CAHs in counties with stronger social cohesion had lower avoidable bypass rates (P=0.003, 

=0.001, <0.0001, respectively). A hospital’s avoidable bypass rate does not appear to be 

associated with hospital ownership (private vs. non-private), education level in the county atlarge, the share of dual eligible beneficiaries among Medicare enrollees, and the 18-64 age 

population without health insurance. 
10

Figure 3. Home Hospital Bypass Factors for CAHs

NOTE: This chart is a graphical representation of results included in Appendix A, Table A.7a. The points are the 

coefficient (marginal effects) estimate for the factors on the y-axis; a factor greater than zero implies that higher hospital 

bypass rate is positively related to higher factor values; a factor less than zero implies the negative relationship between 

hospital bypass and factors. With all other independent variables held constant, a one unit increase in primary care 

physicians per 1,000 county population is associated with a 6 percent decrease in the avoidable hospital bypass rate for 

CAHs. Compared to hospitals that did not use telehealth services, bypass rates of hospitals that used telehealth services are 

2 percent lower for CAHs. When the MA penetration rate increases by 1 percent, bypass rates for CAHs increase by 0.2 

percent. With every one unit increase in social cohesion per 10,000 county population, there is a 0.3 percent decrease in 

the avoidable bypass rate for CAHs.

Source: Estimates were produced using Medicare Provider Analysis and Review files (MedPAR) inpatient claims, Area 

Health Resource File (HRSA), Provider of Services file, and County Health Rankings. 
11

Hierarchical Analysis for Beneficiaries: Demographic Characteristics Associated with 

Avoidable Bypass 

Four models were used to examine patients’ inpatient stays based on the following categories: 

PPS surgical DRGs; CAH surgical DRGs; PPS medical DRGs; and CAH medical DRGs.vi (A 

full table of results are shown in Appendix A, Tables A.8a and A.8b.) The odds of bypassing a 

patient’s home hospital was significantly lower among rural Medicare beneficiaries who were 

older or dual eligible (p<0.0001) across all four models; however, the difference between 

beneficiaries age <65 years and 65-74 years is not statistically significant (p>0.05) in surgical 

DRG models (Figure 3 and Table 2). 

Figure 4. Adjusted Hospital Bypass Rate by Age Category 

NOTE: The points are the estimated hospital bypass rates for age groups that are adjusted of other factors in the four DRG 

models. DRG is the Diagnostic Related Group that is used to classify the type of hospitalization. There are two clinical 

types of DRG. A medical DRG is one where no operating room (OR) procedure is performed. When an OR procedure is 

performed, a surgical DRG is assigned.

Source: Estimates were produced using 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient claims 

and Medicare enrollment data. 

 

vi Among the top 50 DRGs, there are six surgical DRGs and 44 medical DRGs. The observed the bypass for surgical DRGs is 

higher than for medical DRGs (45 percent versus 27 percent). Therefore, four separate models were developed for 

beneficiaries with surgical DRGs in PPS and CAH home hospitals and beneficiaries with medical DRGs in PPS and CAH 

home hospitals. The four patient-level null models suggested that there was significant variation in the odds of bypassing 

across the HSAs (p<0.001), which indicates the hierarchical logistic regression model is an appropriate model for this analysis. 

51% 48%

39%

24%

42%

39%

32%

23%

33%

29%

25% 20%

37%

33%

28%

20%

0%

10%

20%

30%

40%

50%

60%

<65 65-74 75-84 85+

PPS Surgical CAH Surgical PPS Medical CAH Medical
12

Table 2. Adjusted Hospital Bypass Rate by Dual Eligible Status

Dual 

Eligible

Surgical 

DRGs: 

Bypass 

Rate

Surgical 

DRGs:

Lower 

95% CI

Surgical 

DRGs:

Upper 

95% CI

Medical 

DRGs:

Bypass 

rate

Medical 

DRGs:

Lower 

95% CI

Medical 

DRGs:

Upper 

95% CI

PPS

No 48% 46% 50% 30% 29% 32%

Yes 32% 30% 35% 23% 22% 24%

CAH

No 40% 37% 44% 33% 32% 34%

Yes 27% 23% 31% 25% 23% 26%

NOTE: The estimated hospital bypass rates for Medicare and Medicaid dual eligible status are adjusted of other factors in 

the four DRG models. DRG is the Diagnostic Related Group that is used to classify the type of hospitalization. There are 

two clinical types of DRG. A medical DRG is one where no operating room (OR) procedure is performed. When an OR 

procedure is performed, a surgical DRG is assigned

Source: Estimates were produced using 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient claims 

and Medicare enrollment data. 

Sex and race/ethnicity were not found significantly associated with bypass rate (P>0.05) in both 

surgical DRG models. 

The majority of the 138,721 surgical inpatient admissions (70 percent) were classified as elective

surgical stays; slightly over half of these stays (53 percent) were avoidable bypass. Less than 

one-third of non-elective surgical stays (29 percent) were avoidable bypass (p<0.0001); no 

differences were found between PPS hospitals and CAHs. 

For medical DRG models, males were 16 percent more likely to bypass their home hospitals than 

females (PPS model: OR 1.16, 95% CI 1.12-1.20, p<.0001; CAH model: OR 1.17, 95% CI 1.10-

1.23, p<.0001). In assessing patient health risks, with a one-unit increase in the HCC score, we 

expect to see an increase in the odds of bypassing by about 9 percent for CAHs and 8 percent for 

rural PPS hospitals (p<0.001). Black and Hispanic beneficiaries in CAH HSAs were more likely 

than other rural beneficiaries to bypass their home hospitals to seek care for medical DRG 

inpatient stays (p<0.0001). 

Overall, there was no difference in avoidable bypass based on whether the admission source was 

elective or emergent/urgent in CAH HSAs; however, patients were 80 percent more likely to 

bypass their home PPS hospitals when the admission was emergent or urgent. (OR: 0.19, CI: 

0.15-0.24, p<0.0001).vii 

Listening Session Observationsviii

Listening session participants discussed their perceptions of the drivers influencing patient 

decisions to receive care in their home community or seek care outside of their home 

community. The most frequently cited reasons for using local services were convenience or 

 

vii Admission types are nested within patients that are located within the home hospital. The covariance structure test indicates the 

patient effect was not significant for both rural PPS hospitals and CAHs (p>0.05). Therefore, we developed a two level logistic 

regression model with inpatient stays clustered within HSA.

viii The observations summarized below reflect only the perceptions of listening session participants and what was discussed 

during these sessions. 
13

limited transportation options, perceptions of quality of care, and provider relationships. Across 

listening sessions, participants noted that it was their perception that when patients receive care 

in a larger community, it is unlikely that they will come back to receive care locally in the future.

The most frequently cited drivers of hospital bypass were access to care, perceptions of quality 

and trust, cost/insurance, and personal preferences. 

Access to Care

Listening session participants noted that patients may choose to seek care locally for more 

common illnesses or urgent care needs, but may be more willing to travel to a distant hospital for 

services they consider to be complex. A few participants noted that physician turnover or gaps in 

local health care services have led patients to seek care outside of their community. Even with 

visiting specialists and telehealth, some participants noted that rural hospitals may not be able to 

provide a health care service at the time it is needed. Once those patients are established with 

another physician at a distant hospital, the likelihood increases that they will not seek care in

their rural community for that service line in the future. A few participants noted that rural 

hospitals serve small populations and may only treat five to 10 instances of a condition in a year; 

suggesting that many rural hospitals have stopped delivering babies due to difficulty in 

maintaining staff skills with low volumes. One participant commented that overall inpatient days 

are declining nationally, creating greater challenges to maintain access to hospital services in 

rural communities.

The role of telehealth in access to care was also a big point of discussion. A few participants 

noted that telehealth used in emergency rooms has increased the community’s faith in the skills 

and abilities of the local rural hospital, particularly in those facilities staffed by advanced 

practice clinicians. One participant commented on the benefit of telehealth for specialty care, 

including care that may not be available in the entire state. The use of telehealth may be 

beneficial to those who lack reliable transportation and reduces travel time for both the patient 

and the provider. While COVID-19 has expanded the use of telehealth, a few participants noted 

that some rural communities still lack broadband or other wireless telecommunication causing 

difficulties for either the provider or patient to access telehealth services. CMS telehealth 

flexibilities allowed during the COVID-19 PHE has expanded the use of telehealth, and a few 

participants noted the recent change, allowing Federally Qualified Health Centers and Rural 

Health Clinics to serve as originating sites, has increased the provision of telehealth. 

Participants from all states noted that limited transportation options, particularly for elderly 

residents and residents with low incomes (e.g., Medicare and Medicaid patients), contribute to 

the convenience of receiving care in a patient’s local area. Transportation challenges are 

exacerbated when distances to other facilities are great, such as in frontier areas. For both the 

patient and family members or friends, transportation is a concern for supporting the patient 

during an inpatient stay. 

A few participants commented on the role of emergency medical services (EMS) in exacerbating 

rural hospital bypass. When a patient is located between multiple hospitals, EMS drivers may 

choose where they want to take a patient. One participant commented that EMS might choose to 

bypass the local hospital, without input from the patient or their family member. In response, 

another participant correctly noted that emergency transportation coverage by Medicare requires 

that EMS take a patient to the nearest hospital. Finally, another participant relayed stories of 

patients or their family members instructing EMS to avoid a particular hospital. In two states, 
14

participants noted limited EMS capacity in rural areas, resulting in longer waits for transfers to 

higher levels of care, such as a tertiary hospital. 

Perceptions of Quality and Trust in Providers

Patient perceptions of quality and trust in local services based on previous experiences, word-ofmouth endorsements, or reputation were noted by listening session participants as a common

driver in the health care decision-making process. Listening session participants noted that rural 

communities tend to be socially connected and that patient experiences—both good and bad—

spread quickly throughout the community and can have lasting influence on where patients 

choose to seek care. A few participants noted that hospitals with known financial challenges 

appear to be less stable and, as a result, appear to have lower quality. One participant noted that 

aging facilities affected perceptions of the quality of care offered. Often, patients want to receive 

care in a larger, newer facility located in an urban area. One participant noted that retirement 

destinations located in rural areas within their state attract reputable physicians who move to 

those rural communities to retire. A few participants noted that patients have privacy concerns 

with receiving treatment from neighbors who are also their health care providers. One participant 

noted that if patients were able to experience the level of care provided in rural settings, 

including faster and friendlier care, they would choose to seek care at their local rural hospital. 

When asked about HCAHPS scores, all participants indicated that patients are likely not aware 

of Hospital Compare and do not use HCAHPS to determine where to seek care. One participant 

commented that family members might be more likely to assess hospital quality when choosing a 

hospital for an elderly patient. 

Listening session participants commented that existing provider relationships and provider 

referrals affect where patients receive care. A few participants noted that patients might have 

long-standing relationships with providers who have seen multiple generations of families. The 

concept of “loyalty” was noted by a few participants, including when a physician is recruited to a 

neighboring community. One participant commented that if a patient seeks local primary care 

services, they are more likely to be admitted to their rural hospital. Another participant 

commented that a rural hospital relying on travelling providers results in a perceived lack of 

continuity of care. Many participants noted that patients would not push back if their primary 

care provider referred them to a hospital in another community. Some participants commented 

that patients, who have established relationships with specialty providers in another community, 

would want to receive their inpatient care where those providers are located. One participant 

commented that providers located in larger communities may not have accurate information 

about the services available in the rural community and do not send their patients to rural 

facilities for local care. 

A few participants commented on the role of health system affiliations. For example, specialty 

providers may be affiliated with larger health systems, and those providers will admit patients to 

the affiliated hospital. The purchase of a small rural hospital by a larger system drove patients to 

receive inpatient care at the larger hospital. One participant also noted the influence of 

accountable care organizations (ACOs) and bundled payments, commenting that they have seen 

those relationships affect referral patterns. Bundled payments tend to align incentives for 

providers, allowing them to work closely together and were particularly influential for deciding 

where to refer patients for care in certain post-acute care settings (e.g., CAH swing bed versus 

other facility). 
15

Most participants spoke about the limited role of health care marketing in rural communities. A 

few participants did not believe marketing heavily influenced patients; one participant noted that 

most Medicare patients have already formed opinions on local facilities. While one participant 

mentioned the capacity of larger, urban facilities to produce sleek marketing, other participants 

noted that marketing is often a “real challenge” for rural facilities due to budgetary constraints 

and a lack of local media outlets, such as TV and radio stations. All participants concurred that 

the primary method for sharing information about health care facilities in rural communities was 

word-of-mouth from community members. However, one participant acknowledged that relying 

on word-of-mouth experiences may result in the spread of inaccurate information, especially 

when spread through social media platforms. One participant noted that the SORH, state hospital 

association, and Flex Program provide rural hospitals with information describing the value of 

the hospital to the community and why the hospital is an essential business, particularly in 

counties where the hospital is the largest employer. The information is intended for use by 

hospitals to demonstrate to the community that they need to support the hospital like other local 

businesses to maintain those services. 

Out-of-Pocket Costs and Insurance Coverage 

Some participants mentioned that out-of-pocket costs and insurance coverage affect where 

patients seek care. A few participants noted that they believe rural hospitals serve a large 

proportion of Medicare and Medicaid patients, who may have fewer options and limited 

transportation. One participant commented that some rural providers offer concierge servicesix

and do not accept Medicare or Medicaid. Some participants commented that insurance networks 

affect where patients receive care and noted that Medicare Advantage plans may not cover 

services provided at rural hospitals. One participant noted that Tribal members might choose an 

Indian Health Service (IHS) hospital because of costs. A few participants commented that 

patients shop for certain types of services, such as elective procedures and MRIs. Others stated

that sometimes care is more expensive in the rural hospital, particularly CAHs, and may 

influence bypass behavior. A few participants mentioned that some patients make decisions 

based on transportation costs, including emergency transportation costs. 

Personal Preferences

Personal preferences also play a role in rural hospital bypass behavior. These preferences include 

proximity to family members, commuting behaviors, and other personal circumstances. Family 

support is a key factor in patient decision-making. A few participants commented that patients 

might seek care locally to stay closer to family members, while other participants commented 

that patients might seek care in distant communities where children or other family members 

live. One participant commented that rural patients might leave their home communities to seek 

care at hospitals where a family member works. Another participant noted that Tribal members 

might prefer to receive culturally competent care at IHS facilities. One participant commented 

that family members might also influence where EMS takes a patient. Participants from one state 

commented that there is a large snowbird population, who have second homes in other states 

(e.g., Arizona). Those patients may choose to receive care while living in another state. One 

participant also noted that patients with higher incomes might choose to seek care at top-rated 

 

ix Concierge care is a primary care model for which patients pay a membership fee for a set of health care services, including 

Medicare-covered services, and other amenities, such as same-day appointments. More information is available at 

https://www.medicare.gov/coverage/concierge-care. 
16

tertiary care centers. A few participants commented that where patients seek care might be 

aligned with where they travel for shopping or work. An additional comment was that COVID19 may have led to fewer bypasses as patients view the risk of going to a small hospital to be less 

than that at a larger hospital. 

Participants also commented on a number of general concerns related to providing care in a rural 

community. Many participants noted workforce recruitment and retention challenges, 

particularly as many rural providers retire. Some stated that workforce pipeline programs and 

loan forgiveness might help attract new providers to rural communities. In some rural 

communities, COVID-19 has led to greater recruitment activity as providers are looking for 

opportunities to move away from urban centers. One participant commented on alternative 

models for rural hospitals, including an option without inpatient beds that maintains access to 

24/7 emergency care. 

LIMITATIONS

This study has a few limitations to consider. First, the quantitative analysis is limited to inpatient 

acute hospital care for rural FFS Medicare beneficiaries in CY 2018 and represents only a 

portion of the patients served and services provided by rural hospitals in their communities. 

Additionally, Medicare FFS claims data has limitations in terms of understanding clinical 

severity and patient conditions, despite patients having similar diagnoses or DRGs. While 

patients with higher degrees of clinical complexity or severity may be appropriately referred to a 

higher-level facility outside of their home HSA, these referrals are classified as a bypass in this 

analysis, potentially overestimating the avoidable rural hospital bypass rate. It was also not 

possible to capture in the models the influence of patients’ and families’ perceptions of rural 

hospitals and providers, community good will, and the interplay of patient/family and provider 

decision-making. 

The qualitative data collection plan, to capture rural community members’ perspectives on 

hospital utilization, shifted due to the evolving COVID-19 PHE. The in-person listening sessions 

with rural Medicare beneficiaries, rural community leaders, and rural hospital administrators and 

clinicians were cancelled when COVID-19 cases increased in rural communities. Instead, virtual 

listening sessions were held with state and regional stakeholders who provided insights regarding 

their observations and/or experiences with rural hospital utilization. Although these stakeholders 

were very knowledgeable about the rural hospitals and communities in their state or region, some 

participants had not resided in rural communities nor had direct experiences accessing rural 

health care. It should also be noted that listening session observations represent the perspectives 

of 24 participants who bring perspectives that are specific to their roles, locations, and previous 

experiences.

CONCLUSION AND NEXT STEPS

This study creates the first national analysis of avoidable rural hospital bypass for Medicare 

beneficiaries, expanding upon on previous rural hospital bypass studies based on a limited 

number of states. We developed a methodology to define hospital service areas (HSAs) that are 

targeted and suitable for rural Medicare populations; explored drivers of rural hospital bypass 

based on hospital and market characteristics; and identified factors associated with likelihood of 

patients’ bypassing their home hospitals. 
17

As expected, rural hospital characteristics associated with a higher likelihood of retaining 

Medicare FFS beneficiaries include high quality ratings, high number of primary care physicians 

in the HSA, large number of beds, and offering telehealth and advanced care. Patient 

characteristics associated with a lower likelihood of bypassing their local rural hospital include 

older Medicare FFS beneficiaries, females, and dually eligible beneficiaries. Travel to a distant 

hospital may be more difficult for these rural Medicare beneficiaries given limited transportation 

options in many rural communities. Qualitative findings confirmed the challenges that many 

older rural Medicare beneficiaries have in either accessing care outside of their home community

due to limited transportation options, or the concern of placing a burden on their family or 

caregiver to provide transportation. 

On average, rural residents travel at least an hour (approximately 60 miles) when seeking care at 

distant hospitals. A number of factors contribute to the hospital bypass decision-making process, 

including, patient and family perceptions and provider trust, provider referrals, and EMS dropoff decisions. Patient choice plays an important role in bypassing home hospitals for surgical 

admissions, particularly because these admissions are not urgent or emergent. When stakeholders 

were asked if publicly available hospital quality data were used to inform patients’ hospital 

bypass decisions, participants indicated that hospital quality information is most often based on 

word-of mouth, not existing hospital quality data sources like HCAHPS. Stakeholders may 

consider how to construct more rural-relevant quality measures that provide useful information 

for rural providers to improve their quality and community perception. In addition, stakeholders 

observed that learning and action networks (LANs) may support rural providers in better 

understanding what important health care services, such as telehealth services, are valued by the 

community that will lead to an increase in utilization of local health care services. 

Access to local primary care providers also plays an important role in rural Medicare 

beneficiaries choosing to use their home hospital. Rural hospitals with the lowest bypass rates 

had the greatest number of primary care providers per population. Listening session participants 

noted the importance of the relationship between primary care providers and rural Medicare 

beneficiaries, particularly the loyalty that patients feel for their providers. These relationships 

often extend beyond the rural Medicare beneficiary to other members of the family, which 

contributes to rural Medicare beneficiaries choosing their home hospitals – there is a strong, 

long-lasting trust in the local health care system. In addition, rural communities with a large 

number of primary care providers often have greater access to specialty care providers, which 

increases the likelihood that rural Medicare beneficiaries seek inpatient hospital care locally. 

Even though Medicare Advantage (MA) has rapidly expanded in urban areas during the last 

decade, MA penetration is still low in rural areas. In rural areas where MA penetration is highest, 

there is also a higher bypass rate among Medicare FFS beneficiaries. Based on stakeholders’ 

observations, some MA plans do not contract with rural hospitals,x which affect overall revenue 

and may reduce the financial viability of providing some services locally. Likewise, some 

stakeholders observed that out-of-pocket costs may be higher at rural hospitals, particularly for 

diagnostic and laboratory services at CAHs, resulting in rural Medicare beneficiaries shifting 

their inpatient hospital care to a distant location where they receive other health care services for 

less cost. 

 

x

 Under §422.116, an MA plan must demonstrate it has an adequate contracted provider network sufficient to provide access to 

covered services.
18

As health care systems continue to evolve, information on access to health care services, 

especially inpatient hospital care, for rural Medicare beneficiaries is essential. Medicare plays a 

vital role in sustaining access to essential health care services in many rural communities. Rural 

hospitals play a pivotal role in supporting the health and economic stability of the rural 

communities they serve.
19

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21

APPENDIX A: SUPPORTING TABLES AND FIGURES

Figure A.1. Study Design and Patient Selection

NOTE: A total of 1,002,373 acute hospital inpatient stays were identified and included; a total 15,781 out of 37,856 

residential ZIP codes were assigned to rural HSAs; a total 2,051 out of 4,282 home HSAs classified as rural. 

Source: Estimates were produced using 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient 

claims. 

MEDPAR FFS nonESRD rural inpatient 

claims (N=2,071,459)

Top 50 DRG 

inpatient claims

(N=1,199,752)

Total 1,168,099 

inpatient claims 

HSA:

Total 2,051 home 

hospitals 

Exclude beneficiaries with missing 

or invalid zip code 

(N=31,653)

Unique Medicare 

beneficiary count

N=749,053

Analysis claim data by 

excluding unavoidable 

bypasses and transfers

N=1,002,373
22

Figure A.2. Geographic Distribution of Rural home Hospitals by Hospital Type

NOTE: Total of 1,168 CAHs (orange) and 883 rural PPS hospitals (blue) are included. 

Source: Hospitals were identified using the U.S. Hospital List from the NC Rural Health Research Program, 

available at https://www.shepscenter.unc.edu/programs-projects/rural-health/data/.

Table A.1. Top 50 DRGs Included in the Analysis

DRG 

Number DRG Description

Number 

of HSAs

Total 

Number of 

Inpatient 

Claims

064 Intracranial Hemorrhage or Cerebral Infarction with MCC 1,872 16,656

065 Intracranial Hemorrhage or Cerebral Infarction with CC or Tissue 

Plasminogen Activator (TPA) in 24 Hours 1,945 24,601

281 Acute Myocardial Infarction Discharged Alive with CC 1,796 12,791

280 Acute Myocardial Infarction Discharged Alive with MCC 1,827 19,178

377 Gastrointestinal (GI) Hemorrhage with MCC 1,768 10,611

853* Infectious and Parasitic Diseases with Operating Room (OR) Procedure 

with MCC 1,892 16,055

552 Back Problems without MCC 1,852 10,455

330* Major Small and Large Bowel Procedures with CC 1,847 12,750

378 Gastrointestinal (GI) Hemorrhage with CC 1,951 28,647

208 Respiratory System Diagnosis with Ventilator Support ≤ 96 Hours 1,752 12,330

066 Intracranial Hemorrhage or Cerebral Infarction without CC/MCC 1,738 8,750

470* Major Hip and Knee Joint Replacement or Reattachment of Lower 

Extremity without MCC 2,040 104,847

682 Renal Failure with MCC 1,851 19,609

885 Psychoses 1,518 12,329

309 Cardiac Arrhythmia and Conduction Disorders with CC 1,913 21,064

069 Transient Ischemia without Thrombolytic 1,670 8,767

483* Major Joint/Limb Reattachment Procedure of Upper Extremities 1,901 16,379

310 Cardiac Arrhythmia and Conduction Disorders without CC/MCC 1,874 15,866
23

DRG 

Number DRG Description

Number 

of HSAs

Total 

Number of 

Inpatient 

Claims

313 Chest Pain 1,641 8,733

308 Cardiac Arrhythmia and Conduction Disorders with MCC 1,836 15,009

871 Septicemia or Severe Sepsis without Mechanical Ventilation (MV) >96 

Hours with MCC 2,034 123,042

683 Renal Failure with CC 1,926 28,212

291 Heart Failure and Shock with MCC or Peripheral Extracorporeal Membrane 

Oxygenation (ECMO) 2,015 71,026

698 Other Kidney and Urinary Tract Diagnoses with MCC 1,609 10,175

312 Syncope and Collapse 1,765 11,758

177 Respiratory Infections and Inflammations with MCC 1,777 15,433

389 Gastrointestinal (GI) Obstruction With CC 1,829 11,929

640 Miscellaneous Disorders of Nutrition Metabolism Fluids and Electrolytes 

with MCC 1,712 9,165

392 Esophagitis Gastroenteritis and Miscellaneous Digestive Disorders without 

MCC 1,992 33,025

481* Hip and Femur Procedures except Major Joint with CC 1,935 18,049

812 Red Blood Cell Disorders without MCC 1,775 11,821

390 Gastrointestinal (GI) Obstruction without CC/MCC 1,744 8,247

872 Septicemia or Severe Sepsis without Mechanical Ventilation (M) >96 

Hours without MCC 1,976 36,569

292 Heart Failure and Shock With CC 1,974 24,106

189 Pulmonary Edema and Respiratory Failure 1,936 35,614

638 Diabetes with CC 1,742 11,434

178 Respiratory Infections and Inflammations with CC 1,653 8,031

193 Simple Pneumonia and Pleurisy with MCC 1,991 37,542

689 Kidney and Urinary Tract Infections with MCC 1,801 16,296

948 Signs and Symptoms without MCC 1,829 9,794

603 Cellulitis without MCC 1,980 24,572

641 Miscellaneous Disorders of Nutrition Metabolism Fluids and Electrolytes 

without MCC 1,984 24,078

293 Heart Failure and Shock without CC/MCC 1,836 11,010

690 Kidney and Urinary Tract Infections without MCC 2,006 35,279

190 Chronic Obstructive Pulmonary Disease with MCC 1,954 37,743

191 Chronic Obstructive Pulmonary Disease with CC 1,879 19,750

194 Simple Pneumonia and Pleurisy with CC 2,015 40,078

192 Chronic Obstructive Pulmonary Disease without CC/MCC 1,857 12,249

195 Simple Pneumonia and Pleurisy without CC/MCC 1,951 14,796

247* Percutaneous Cardiovascular Procedures with Drug-Eluting Stent without 

MCC 1,946 21,849

NOTE: DRG is the Diagnostic Related Group that is used to classify the type of hospitalization. DRG is the Diagnostic 

Related Group that is used to classify the type of hospitalization. There are two clinical types of DRG. A medical DRG is 

one where no operating room (OR) procedure is performed. When an OR procedure is performed, a surgical DRG is 

assigned. CC is complication or comorbidity. MCC is major complication or comorbidity.

* =Surgical DRGs

Source: Estimates were produced using 2018 MedPAR inpatient claims.
24

Table A.2. Home Hospital Characteristics, 2018 

Rural PPS 

Hospital:

N 

Rural PPS 

Hospital:

% 

CAH:

N 

CAH:

% 

Number of Beds

≤ 25 46 5% 1,168 100%

26 to 50 306 35%

51 to 100 308 35%

101 to 200 179 20%

201+ 44 5%

RUCA Code

Micropolitan (4,5,6) 571 65% 166 14%

Small town (7,8,9) 254 7% 581 36%

Noncore (10) 58 29% 421 50%

Other Characteristics

IHS hospitals 19 7 

Closed hospitals as of January 2020 14 7 

NOTE: Total 2,051 home hospitals are included. Rural-Urban Commuting Areas (RUCA) Code is defined by the U.S. 

Department of Agriculture Economic Research Service. 

Source: Estimates were produced using 2018 MedPAR inpatient claims, 2018hospital list from the Sheps Center, and 2018 

Provider of Services file.

Table A.3. Home Hospital Market Characteristics, 2018

Rural PPS 

Hospital:

N 

Rural PPS 

Hospital:

% 

CAH:

N 

CAH:

% 

Urban 

Hospital:

N 

Urban 

Hospital:

% 

Total Medicare Population 8,596,908 3,646,533 50,158,191 

FFS 5,999,264 70% 2,722,519 75% 29,613,965 59%

Dual Eligible 1,238,290 21% 515,849 19% 5,006,231 17%

NOTE: Medicare population were classified into Rural (PPS and CAH) and Urban markets based on Medicare 

beneficiary’s residential zip codes that crosswalked with RUCA codes.

Source: Estimates were produced using 2018 MedPAR inpatient claims and Medicare enrollment data.
25

Table A.4. Inpatient Stays by Home Hospital Type, 2018

Rural Home 

Hospital 

Characteristics

All 765 

DRG 

Inpatient 

FFS 

Claims: 

Number 

of Stays

All 765 

DRG 

Inpatient 

FFS 

Claims: 

Number 

of Benes

Top 50 

DRGs:

Number 

of Stays

Top 50 

DRGs:

Number of 

Beneficiaries 

Top 50 

DRGs:

Inmarket

Stays

Top 50 

DRGs:

Avoidable 

Stays

Top 50 

DRGs:

Percent Inmarket

Stays

All Rural Hospitals 2,071,459 1,336,799 1,199,752 867,228 711,782 290,591 59%

Rural PPS

Hospitals 1,460,043 936,011 849,163 609,546 531,526 207,465 63%

CAH 606,616 398,991 350,589 258,506 180,256 83,126 51%

NOTE: Rural home hospital markets were classified into Rural (PPS and CAH) markets based on Medicare beneficiary’s 

residential zip codes that crosswalked with RUCA codes. Total 2,051 home hospital HSAs were identified. Avoidable 

stays are the inpatients hospital admissions for which rural Medicare beneficiaries received acute care at a distant hospital 

even those those services were avaiable in home hospital. Percent In-market stays is the percentage of inpatient stays for 

which rural Medicare beneficiaries received acute care at home hospital HSA.

Source: Estimates were produced using 2018 MedPAR inpatient claims. 

Table A.5. Rural Hospital Bypass by Medicare Beneficiary Characteristics, 2018

Rural PPS 

Hospital:

In-market 

Stays

Rural PPS 

Hospital:

Avoidable 

Bypass

Rural PPS 

Hospital:

% of 

Bypass

CAH:

In-market 

Stays

CAH:

Avoidable 

Bypass

CAH:

% of 

Bypass

Sex

Male 160,935 78,794 33% 56,923 33,405 37%

Female 214,278 91,363 30% 76,797 36,558 32%

Age Group

<65 52,796 26,559 33% 14,355 9,298 39%

65-74 112,250 63,213 36% 37,330 25,369 40%

75-84 117,740 52,450 31% 43,180 22,857 35%

85+ 92,427 27,935 23% 38,855 12,439 24%

RTI Race Code

Non-Hispanic White 330,635 147,796 31% 124,018 63,137 34%

Black (AfricanAmerican) 26,091 12,548 32% 3,253 2,417 43%

Hispanic 7,977 4,581 36% 2,830 2,121 43%

Asian/Pacific Islander 1,497 572 28% 316 193 38%

American Indian / 

Alaska Native 5,646 2,703 32% 2,083 1,202 37%

Unknown or Other 3,367 1,957 37% 1,220 893 42%

Dual Eligible: No 251,108 125,658 33% 92,326 52,118 36%

Dual Eligible: Yes 124,105 44,499 26% 41,394 17,845 30%

Beneficiaries with at least 

3 chronic conditions 343,047 151,480 31% 122,174 61,903 34%

Average HCC score 1.83 (1.45 ) 1.81(1.57) 1.72 (1.33) 1.80 (1.55)

NOTE: The total rural Medicare beneficiaries with non-missing inpatient stays data for the 50 most common DRGs is 

749,053. In-market stays is the inpatient stays for which rural Medicare beneficiaries received acute care at home hospital 

HSA. Avoidable bypass is the number of inpatient stays for which rural Medicare beneficiaries received acute care at a 

distant hospital even though those services were available at home hospital. 

Source: Estimates were produced using 2018 MedPAR inpatient claims and Medicare enrollment data.
26

Table A.6a. Linear Regression Model for Rural PPS Hospital Avoidable Bypass

Coefficient

Standard 

Error t Value Pr > |t|

Statistical

Significance

Intercept 0.514 0.130 3.96 <.0001

Hospital Level

Percentage of MA beneficiaries, HSA 0.204 0.037 5.46 <.0001 ***

Bed count -0.001 0.000 -4.26 <.0001 ***

Bed count squared <0.001 <0.001 2 0.05

Cancer program approved by American 

College of Surgeons (1=yes, 0=no )

-0.045 0.013 -3.59 <.001 ***

Telehealth office visits or consultation 

(1=yes, 0=no)

-0.032 0.009 -3.46 <.001 ***

HCAHPS: % definitely recommend the 

hospital

-0.001 0.001 -2.6 <.01 **

Percentage of dual eligible beneficiaries, 

HSA

-0.167 0.088 -1.9 0.06

Hospital Ownership: Private -0.003 0.009 -0.3 0.76

State/Region Indicator

State Medicaid Expansion (1= not 

adopted, 0=yes)

0.039 0.013 2.99 0.003 ***

Census Region: Mid-West (1=yes, 0=no ) -0.040 0.019 -2.09 0.04 * 

Census Region: South (1=yes, 0=no ) 0.033 0.019 1.75 0.08

Census Region: Northeast (1=yes, 0=no ) -0.016 0.022 -0.76 0.45

RUCA code 10 -0.024 0.019 -1.27 0.21

RUCA code 4 0.009 0.011 0.8 0.42

County Market Characteristics

County Primary Care Physicians, MD+DO 

per 1,000 population, 2017 -0.089 0.027 -3.26 0.001 ***

County median household income per 

1,000 population, 2017 0.002 0.001 3.73 <.001 ***

Percentage of residents with high school 

and higher education diploma, ZIP code -0.002 0.001 -1.72 0.09

Percentage age 18-64 years without health 

insurance, county, 2017 -0.002 0.001 -1.72 0.09

County social cohesion per 10,000 

population -0.002 0.001 -1.61 0.11

County average HCC score 0.102 0.073 1.4 0.16

County specialists per 1,000 population, 

2017 -0.011 0.013 -0.85 0.40

NOTE: The model dependent variable is hospital-level avoidable bypass percentage. Rural PPS hospital model included 

852 hospitals, 29 Rural hospitals that have no HCHAPS and 2 hospitals with less than 20 inpatient stays are excluded

from modeling. The model R2 is 0.33. States that had not adopted Medicaid expansion as of January 1, 2018 include AL, 

FL, GA, KS, MS, MO, NC, OK, SC, SD, TN, TX, WI, WY, VA, NE, UT, ID (https://www.kff.org/medicaid/issuebrief/status-of-state-medicaid-expansion-decisions-interactive-map/).

* = statistical significance at p < 0.005, p < 0.01, and p < 0.05, respectively.

Source: Estimates were produced using 2018 MedPAR inpatient claims, 2018 Area Health Resource File (HRSA), 2018 

Provider of Services file, and 2018 County Health Rankings. 
27

Table A.6b. Variable Profiling for Rural PPS Hospitals 

Variable Mean

Std. 

error Min

1st

Percentile

25th

Percentile Median

75th

Percentile

99th

Percentile Max

Avoidable bypass 0.30 0.15 0 0.05 0.19 0.29 0.40 0.72 0.78

Census Region: 

Northeast 0.10 0.30 0 0 0 0 0 1 1 

Census Region: 

South 0.51 0.50 0 0 0 1 1 1 1 

Census Region: MidWest 0.27 0.44 0 0 0 0 1 1 1 

State Medicaid 

Expansion not 

adopted

0.51 0.50 0 0 0 1 1 1 1 

RUCA code 4 0.64 0.48 0 0 0 1 1 1 1 

RUCA code 10 0.06 0.24 0 0 0 0 0 1 1 

County average HCC 

score

0.98 0.09 0.61 0.76 0.93 0.98 1.03 1.2 1.42

Percentage of 

resident with high 

school and higher 

education diploma, 

ZIP code

85.20 6.72 46.4 67.1 81.25 86 90.1 96.7 100

County Primary Care 

Physicians, MD+DO 

per 1,000 population, 

2017

0.58 0.26 0 0.18 0.40 0.52 0.70 1.45 2.06

County specialists

per 1,000 population, 

2017

0.66 0.57 0 0 0.33 0.53 0.84 2.62 8.04

County median 

household income, 

2017

44,653 10,985 14,914 25,122 37,373 43,257 49,721 80,110 110,190

Percentage ages 18-

64 years without 

health insurance, 

county, 2017

13.72 6.24 3.4 5.2 8.3 12.85 18.05 30.2 39.6

County social 

cohesion 12.00 3.94 0 2.93 9.46 11.77 14.54 21.84 29.20

Percentage of MA 

beneficiaries, HSA 0.25 0.13 0.001 0.02 0.16 0.25 0.33 0.58 0.65

Percentage of dual 

eligible beneficiaries, 

HSA

0.22 0.08 0.03 0.07 0.16 0.20 0.26 0.50 0.63

Hospital Ownership: 

Private 0.57 0.50 0 0 0 1 1 1 1 

Telehealth services 0.39 0.49 0 0 0 0 1 1 1 

Bed count 85.05 60.34 4 19 47 67 102 286 566

HCAHPS: % 

definitely 

recommend the 

hospital

67.74 8.60 31 45 62 68 74 87 93

NOTE: Table shows the mean, standard errors and percentiles of the variables in linear regression model for Rural PPS 

hospital.

Source: Estimates were produced using 2018 MedPAR inpatient claims, 2018 Area Health Resource File (HRSA), 2018 

Provider of Services file, and 2018 County Health Rankings. 
28

Table A.7a. Linear Regression Model for CAH Avoidable Bypass

Coefficient

Standard 

Error t Value Pr > |t|

Statistical

Significance

Intercept 0.261 0.114 2.28 0.02

Hospital Level

Percentage of MA beneficiaries, HSA 0.227 0.033 6.84 <.0001 ***

Telehealth office visits or consultation 

(1=yes, 0=no)

-0.024 0.011 -2.24 0.03 * 

Percentage of dual eligible 

beneficiaries, HSA

-0.122 0.089 -1.37 0.17

State/Region Indicator

State Medicaid Expansion (1=no, 

0=yes)

0.005 0.012 0.37 0.71

Census Region: Mid-West (1=yes, 

0=no)

-0.049 0.016 -3.04 0.003 **

Census Region: South (1=yes, 0=no ) 0.000 0.018 0 0.996

Census Region: Northeast (1=yes, 

0=no)

-0.076 0.023 -3.27 0.001 **

RUCA code 10 -0.021 0.010 -2.13 0.03 * 

RUCA code 4 0.004 0.015 0.28 0.78

County Market Characteristics

County Primary Care Physicians, 

MD+DO per 1,000 population, 2017 -0.060 0.016 -3.87 <.0001 ***

County social cohesion per 10,000 

population -0.003 0.001 -4.47 <.0001 ***

County average HCC score 0.178 0.069 2.58 0.01 * 

County median household income per 

1,000 population, 2017 0.001 0.001 1.1 0.27

NOTE: The model dependent variable is hospital-level avoidable bypass percentage. CAH model included 1,157 hospitals, 

11 CAHs with less than 20 inpatient stays are excluded from modeling. The model R2 is 0.18. States that had not adopted 

Medicaid expansion as of January 1, 2018 include AL, FL, GA, KS, MS, MO, NC, OK, SC, SD, TN, TX, WI, WY, VA, 

NE, UT, ID (https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map/).

* = statistical significance at p < 0.005, p < 0.01, and p < 0.05, respectively.

Source: Estimates were produced using 2018 MedPAR inpatient claims, 2018 Area Health Resource File (HRSA), 2018 

Provider of Services file, and 2018 County Health Rankings. 
29

Table A.7b. Variable Profiling for CAHs

Variable Mean

Std. 

error Min

1st

Percentile

25th

Percentile Median

75th

Percentile

99th

Percentile Max

Avoidable bypass 0.31 0.16 0 0.03 0.19 0.29 0.41 0.75 0.98

Census Region: 

Northeast 0.06 0.23 0 0 0 0 0 1 1 

Census Region: 

South 0.23 0.42 0 0 0 0 0 1 1 

Census Region: 

Mid-West 0.49 0.50 0 0 0 0 1 1 1 

State Medicaid 

Expansion not 

adopted

0.43 0.50 0 0 0 0 1 1 1 

County average 

HCC score

0.91 0.09 0.64 0.70 0.85 0.91 0.96 1.14 1.28

Percentage of 

residents with 

high school and 

higher education 

diploma, ZIP code

87.07 6.73 53.60 65.00 84.10 88.70 91.90 96.70 99.00

County Primary 

Care Physicians, 

MD+DO per 

1,000 population, 

2017

0.56 0.35 0.00 0.00 0.33 0.50 0.71 1.64 4.31

County specialists 

per 1,000 

population, 2017

0.32 0.60 0.00 0.00 0.05 0.18 0.40 2.12 8.04

County median 

household 

income, 2017

47,628 10,189 18,846 25,453 40,995 47,340 53,098 79,611 93,940

Percentage of MA 

beneficiaries, 

2017 

0.19 0.15 0.00 0.00 0.06 0.16 0.28 0.61 0.67

Percentage of dual 

eligible 

beneficiaries, 

HSA

0.18 0.08 0.03 0.06 0.13 0.16 0.21 0.42 0.60

Percentage ages

18-64 years 

without health 

insurance, 2017

13.11 6.10 3.80 5.00 8.40 11.50 16.30 30.00 38.40

County social 

cohesion 14.03 6.85 0.00 0.00 9.59 13.20 17.42 34.93 52.31

RUCA code 4 0.11 0.31 0 0 0 0 0 1 1 

RUCA code 10 0.36 0.48 0 0 0 0 1 1 1 

Telehealth 

services 0.21 0.41 0 0 0 0 0 1 1 

NOTE: Table shows the mean, standard errors and percentiles of the variables in linear regression model for CAH. 

Source: Estimates were produced using 2018 MedPAR inpatient claims, 2018 Area Health Resource File (HRSA), 2018 

Provider of Services file, and 2018 County Health Rankings. 
30

Table A.8a. Hierarchical Logistic Regression Model (HLRM) for Patient and Inpatient Stays for 

Surgical DRGs in Rural Hospitals (PPS and CAH) 

Characteristics

Rural 

PPS 

Hospital:

OR

Rural PPS 

Hospital:

95% CI

Rural PPS 

Hospital:

Statistical

Significance*

CAH:

OR

CAH:

95% CI

CAH:

Statistical

Significance

Sex: ref - Female

Male 1.037 0.979-1.098 1.073 0.961-1.198

Age Category: ref - 65-74

<65 1.102 0.996-1.219 1.138 0.909-1.425

75-84 0.682 0.64-0.728 *** 0.753 0.666-0.851 ***

85+ 0.339 0.307-0.373 *** 0.462 0.384-0.557 ***

Race: Black ref - No

Yes 0.943 0.812-1.094 0.686 0.378-1.246

Race: Hispanic ref - No

Yes 1.028 0.833-1.269 0.843 0.549-1.295

Race: Asian/Pacific 

Islander, and American 

Indian/Alaska Native ref - 

No

Yes 1.073 0.836-1.376 1.533 0.925-2.542

Dual Eligible status: ref - 

No

Yes 0.528 0.485-0.574 *** 0.551 0.462-0.658 ***

HCC score 1 0.974-1.027 1.124 1.061-1.191 ***

Stay level PPS and CAH 

combined

Elective =1 (ref=0) 3.519 3.264-3.794 ***

Measure of variation Intercept 

Variance 

Estimates 

(SE)

ICC Intercept 

Variance 

Estimates 

(SE)

ICC

Hospital level 0.98 

(0.08)

22.9% *** 2.61 

(0.24)

44.3% ***

Deviance from Null Model 903.66 129.39

NOTE: The surgical HLRM Models were developed by ~30% of randomly selected sample and validated by crossvalidation. The model sample size for rural PPS is 25,540 and for CAH is 8,091 respectively. Odds Ratio (OR) represents 

the odds that a bypass occurred at the characteristics category, compared to the odds of bypass occuring in the reference 

category. The intraclass correlation coeeficient (ICC) indicates the proportion of the total variance of bypass that is due to 

hospital is 23% in PPS model and 44% in CAH model. Deviance from null model is calculated as -2 Log Likelihood(LL) 

of Null model - -2LL of full model. Positive deviance implies better fit.

* = statistical significance at p < 0.005. 

Source: Estimates were produced using 2018 MedPAR inpatient claims and Medicare enrollment data. 
31

Table A.8b. Hierarchical Logistic Regression Model (HLRM) for Patient and Inpatient Stays for 

Medical DRGs in Rural Hospitals (PPS and CAH) 

Characteristics

Rural PPS 

Hospital:

OR

Rural PPS 

Hospital:

95% CI

Rural PPS 

Hospital:

Statistical 

Significance*

CAH:

OR

CAH:

95% CI

CAH:

Statistical

Significance*

Sex: ref - Female

Male 1.159 1.117-1.202 *** 1.165 1.102-1.23 ***

Age Category: ref - 

65-74

<65 1.229 1.161-1.301 *** 1.317 1.199-1.446 ***

75-84 0.855 0.817-0.895 *** 0.817 0.763-0.875 ***

85+ 0.626 0.595-0.659 *** 0.497 0.461-0.536 ***

Race: Black ref - No

Yes 0.954 0.887-1.027 1.465 1.236-1.736 ***

Race: Hispanic ref - 

No

Yes 1.068 0.969-1.261 1.434 1.202-1.712 ***

Race: Asian/Pacific 

Islander, and 

American 

Indian/Alaska Native 

ref - No

Yes 1.068 0.922-1.237 1.209 0.995-1.469

Dual Eligible status: 

ref - No

Yes 0.683 0.654-0.713 *** 0.671 0.628-0.717 ***

HCC score 1.077 1.064-1.089 *** 1.087 1.067-1.108 ***

Stay level 

Elective =1 (ref=0) 0.972 0.83-1.138 0.193 0.152-0.244 ***

Measure of variation Estimates

(SE)

ICC Estimates

(SE)

ICC

Hospital level 0.69 (0.04) 17.3% *** 0.68

(0.04)

17.1% ***

Deviance from Null 

Model

977.92 697.74

NOTE: 

The Medical HLRM Models were developed by ~30% of randomly selected sample and validated by cross-validation. The 

model sample size for rural PPS is 69,106 and for CAH is 29,148 respectively. Odds Ratio (OR) represents the odds that a 

bypass occurred at the characteristics category, compared to the odds of bypass occurring in the reference category. The 

intraclass correlation coeeficient (ICC) indicates the proportion of the total variance of bypass that is due to hospital is 

17% in both PPS model and CAH model. Deviance from null model is calculated as -2 Log Likelihood(LL) of Null model 

- -2LL of full model. Positive deviance implies better fit.

* = statistical significance at p < 0.005.

Source: Estimates were produced using 2018 MedPAR inpatient claims and Medicare enrollment data. 
32

APPENDIX B: DETAILED METHODOLOGY

We conducted a national mixed methods study using quantitative methods and Medicare fee-forservice (FFS) claims combined with a qualitative case study approach. Quantitative analyses 

were completed prior to qualitative data collection to inform case study selection. 

Quantitative Data and Analysis 

The quantitative study was designed as a retrospective cohort study. The Medicare acute care 

hospital inpatient claims and enrollment files for calendar year (CY) 2018 were the primary data 

used for the analysis. Only rural fee-for-service (FFS) Medicare beneficiaries who had coverage 

for all 12 months during CY 2018 and had at least one inpatient stay were included in the 50-

state analysis. Medicare Advantage and beneficiaries with end-stage renal disease (ESRD) were 

excluded from the analysis.

The 2018 Medicare Provider Analysis and Review files (MedPAR) inpatient claims were merged 

with the Medicare Master Beneficiary Summary File to provide demographic characteristics and 

other information such as patients’ hierarchical condition category (HCC) scores and dual 

eligible status (i.e., individuals who receive both Medicare and Medicaid benefits). Other 

hospital and market characteristics data were merged with the inpatient claims, including RuralUrban Commuting Area (RUCA) codes from the United States Department of Agriculture’s 

Economic Research Service, the Rural Hospital List from the University of North Carolina at 

Chapel Hill Cecil G. Sheps Center for Health Services Research (Sheps Center), a list of Tribal 

hospitals generated by the Indian Health Service (2019), the CMS Provider of Services file, the 

Area Health Resource File (HRSA), American Hospital Association (AHA) Annual Survey, 

County Health Ranking, Hospital Consumer Assessment of Healthcare Providers and Systems 

(HCAHPS) data from Hospital Compare, and the CMS geographic variation public use file. 

Variables of interest from these datasets provided information on hospital and neighborhood 

characteristics, such as hospital type (e.g., CAH or prospective payment system [PPS] status), 

number of beds, hospital ownership, income per capita, Medicaid concentration, Medicaid 

program indicators, physicians per capita, and “ruralness” or degree of rurality. 

Defining Rural Hospitals 

All acute care hospitals that located in rural ZIP codes, including critical access hospitals 

(CAHs) and hospitals paid under Medicare’s prospective payment systems (PPS), were included 

in the analysis. RUCA codes, which are based on the Office of Management and Budget 

metropolitan statistical area and micropolitan statistical area definitions, were used to define 

rural and urban. The hospitals were categorized as rural or urban facilities based on the RUCA 

code for the hospital’s ZIP code (rural includes RUCA codes ≥ 4 and urban includes RUCA 

codes < 4). Hospitals that did not provide general medical services (i.e., psychiatric hospitals, 

long-term care hospitals, and specialty hospitals) were not included in the analysis. 

Defining Rural Hospital Markets (Hospital Service Areas)

For this analysis, hospital service areas (HSAs) are defined by the ZIP codes that a rural hospital 

serves. The goals of this analysis required construction of an HSA for every hospital located in a 
33

rural ZIP code treating Medicare FFS inpatients and assignment of beneficiaries to one HSA.

xi

Each HSA was defined by ranking the ZIP codes for Medicare beneficiaries by Medicare 

inpatient volume of the hospital and identifying those ZIP codes that accounted for 80 percent of 

Medicare inpatient volume [11] [12]. Patients who received services at the rural hospital and 

reside in one of the ZIP codes that did not comprise the top 80 percent of inpatient stays were not 

considered to be part of an HSA. If the 80 percent threshold only captures one beneficiary ZIP 

code, the HSA for the hospital contains only one residential ZIP code. 

This approach to constructing HSAs allowed for some overlap in ZIP codes. There were 

instances in which ZIP codes, and the rural Medicare beneficiaries who live in those ZIP codes,

were assigned to multiple HSAs. A maximum of seven rural HSAs for certain beneficiaries was 

identified using this assignment method. For beneficiaries with multiple HSA assignments, we 

identified the type of markets in which they reside by calculating the distance between each 

beneficiary’s ZIP code centroid (the point marking the center of the ZIP code) and the ZIP code 

centroid for each hospital used by the patient. xii We then used this calculation to determine the 

HSA that is the closest to beneficiary’s residence and estimate whether any other HSAs

including that ZIP code were less than or greater than 30 miles away [13] [14]. This distinction 

between HSA markets was used (as described below) to avoid assigning a hospital stay as bypass 

if the hospital stay was actually at a reasonably close hospital other than at the nearest hospital. 

Defining Rural Hospital Bypass

We identified the inpatient stays for rural Medicare FFS beneficiaries and determined the 

facilities where the beneficiaries received inpatient hospital services. The closest hospital that 

rural Medicare beneficiaries are attributed to is considered the “home” hospital. Each inpatient 

stay was assigned to one of three HSA markets: 

• In-Market Inpatient Stays: Inpatient stays at rural hospitals that are either the home 

hospital, or are ≤ 30 miles away. In-Market Inpatient Stays were not considered to be 

rural hospital bypasses.

• Rural to Other Rural Bypass: Inpatient stays at distant rural hospitals >30 miles away

• Rural to Urban Bypass: Inpatient stays at distant urban hospitals 

Recognizing that some rural hospital bypass was due to the availability of services, we 

characterized bypasses as “unavoidable” – an inpatient stay at a distant hospital for a service not 

available at the home hospital - or “avoidable” – an inpatient stay at a distant hospital for a 

service that is also available at the home hospital. To determine the availability of services in 

each HSA, we compiled a list of diagnostic related groups (DRGs) from the most common 50 

DRGs (Appendix A, Table A.1) for each hospital. If the beneficiary’s DRG code on the claim 

appeared in the list of DRGs for that beneficiary’s home hospital, we classified this particular 

discharge as an “avoidable” bypass, as the DRG could be provided at the beneficiary’s home 

 

xi We determined that the HSAs developed by the Dartmouth Atlas were not appropriate for this analysis, because they were 

generally constructed for urban areas. The Dartmouth Atlas creates HSAs by assigning ZIP codes to the hospital area where 

the greatest proportion of their Medicare residents were hospitalized, allowing for only one hospital service area per ZIP code 

and for more than one hospital to comprise a single HSA.

xii A centroid is a point (usually on a map) that defines the center (in this case, of the zip code). The centroid (point) is represented 

by longitude and latitude coordinates. In this study, the ZIP code centroid is used to approximate the Euclidean (or straight 

line) distance between two ZIP codes.
34

hospital. If the beneficiary’s DRG code did not appear in the list of DRGs for that beneficiary’s 

home hospital, we classified this discharge as an “unavoidable” bypass, as the DRG could not be 

provided at the beneficiary’s home hospital. We limited the inpatient stays to the most common 

50 diagnostic related groups (DRGs), out of the 765 DRGs provided by rural hospitals in CY 

2018 (Appendix A, Table A.1) and determined the availability of services in each HSA. Both 

unavoidable bypass and all inpatient stays that were admitted as a transfer from other hospitals, 

skilled nursing facilities (SNFs), hospices and Ambulatory Surgical Centers (ASCs) were 

excluded. Appendix A, Figure A.1 provides a diagrammatic presentation of the study design and 

selection process.

Variables and Outcomes 

Variables included those at the stay level, beneficiary level, hospital level, and community level. 

In addition to DRG, we included elective inpatient staysxiii as a binary indicator. For patient level 

factors, Medicare beneficiaries were categorized by sex and age categories (i.e., <65 years, 65–

74 years, 75–84 years, and ≥ 85 years). The RTI race codexiv was used to identify race/ethnicity 

(non-Hispanic white, black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska 

Native). We also included dual eligible status and the beneficiary’s 2018 health risk score based 

on the HCC risk-adjustment models as a proxy estimate of patient’s severity of illness. 

We accounted for the following hospital level variables for home hospitals: number of beds; 

hospital ownership types (private vs. public); HCAHPS measures of overall hospital rating and 

hospital recommendation; and presence of a cancer program approved by American College of 

Surgeons. Three additional variables were derived using Medicare claim and enrollment data: 

hospital telehealth service flag, Medicare Advantage penetration, and percentage of dual eligible 

Medicare beneficiaries by HSA. A telehealth services flag was defined for each hospital based 

on whether at least one telehealth office visit or consultation service was billed during the 

patient’s inpatient stay.xv 

Random forest classification regression and clustering analysis were applied to reduce 

community level variables [17]. The following HSA and community level indicators were

included: 

• Health insurance coverage: percentage of households comprised of individuals who are 

18-64 years of age without health insurance by county, 2017 

• Medicare market: Medicare Advantage penetration and percentage of dual eligible 

Medicare beneficiaries by HSA, 2018 

 

xiii Admission source as defined in the Chronic Conditions Data Warehouse available at 

https://www2.ccwdata.org/web/guest/data-dictionaries. 

xiv RTI race code as defined in the Chronic Conditions Data Warehouse available at https://www2.ccwdata.org/web/guest/datadictionaries. 

xv Telehealth professional claims were identified as physician or practitioner services furnished to beneficiaries in hospitals 

through a telecommunications system.Telehealth services were identified using place of service code=02 and HCPCS code 

G0425–G0427, G0406–G0408, 99231–99233, G0508, G0509 with identifier 95 or GT, as described in the Telehealth Services 

MLN Booklet available at.https://www.cms.gov/Outreach-and-Education/Medicare-Learning-NetworkMLN/MLNProducts/Downloads/TelehealthSrvcsfctsht.pdf. 
35

• Physicians per capita: primary care physicians (i.e., MDs and DOs) and specialists per 

1,000 population by county, 2017 

• Education: percentage of residents with high school and higher education diploma(s) by 

county, 2017 

• Population wealth: median household income by county, 2017 

• Social cohesion: Number of membership associations per 10,000 population by county, 

2017xvi

• Average HCC score for Medicare population who reside in the county, 2017 

• Geography: rural-urban commuting area codes (RUCA); Census region: North-east, 

South, West, Midwest 

• State policy: Medicaid expansion state [15]

The primary outcome in the analysis was whether the inpatient stay was an avoidable bypass 

(coded as “1”). At the beneficiary level, the beneficiary was flagged as having an avoidable 

bypass if at least one of his/her inpatient stays during CY 2018 was an avoidable bypass. At the 

inpatient stay level, when aggregating the inpatient stays within a home hospital, the home 

hospital avoidable bypass rate was calculated as the percentage of the total number of avoidable 

bypasses divided by total number of hospital inpatient stays from the Medicare beneficiaries who 

reside in the HSA excluding unavoidable bypasses. 

Statistical Analysis

We developed statistical models to assess the impact of community, hospital, patient, and stay 

level characteristics on avoidable hospital bypass. The exploratory data analysis before model 

testing was conducted to: 

1. Describe rural hospital bypass among Medicare FFS beneficiaries for inpatient care; 

2. Identify factors influencing avoidable rural hospital bypass for inpatient services; and 

3. Quantify the relationship between avoidable bypass and patient demographics. 

The results of the exploratory data analysis are expressed as frequencies and percentages for 

patient and hospital characteristics by home hospital type—rural PPS hospitals and CAHs. This 

study compared quality measures of the home and distant hospitals for beneficiaries who 

bypassed the home facility. Multiple linear regression models were used to identify factors 

associated with avoidable bypass; separate estimates were obtained for home PPS and CAHs. 

Linear regression is based on some important assumptions, which we addressed during the 

analysis process [16]. The residual errors were examined to ensure it was normally distributed, 

and collinearity among independent variables were diagnosed to assess the stability and variance 

of the regression estimates. Significance tests were two-tailed and statistical significance was set 

at the p< 0.05. Regression coefficients and standard errors are included in Appendix Tables. 

Beneficiaries who live within an HSA are likely to be more similar than randomly selected 

beneficiaries (within HSA beneficiaries are “naturally clustering”). Hence, a multilevel design 

 

xvi The number of membership associations was used as a proxy for social connectedness. Additional information is available 

through County Health Rankings at https://www.countyhealthrankings.org/explore-health-rankings/measures-datasources/county-health-rankings-model/health-factors/social-and-economic-factors/family-social-support/social-associations. 
36

was considered suitable for the analysis of data with hierarchical structures to account for 

variability within and between HSAs and beneficiaries. Hierarchical generalized logistic 

regression models (HGLMs) [18] with random intercepts were estimated to assess how patient 

demographic, socio-economic status (dual eligible), and admission elective choices affected 

home hospital bypass. In hierarchical models, levels are counted from the bottom up. The highest 

level in our data is the HSA. Patients are nested within HSA. The outcome variable, avoidable 

bypass, is measured for patients (at level 1) residing in an HSA (at level 2). The probability of 

bypassing home hospitals was related to a set of patient characteristics and a random HSA effect. 

Two models were constructed to judge the goodness-of-fit of the model. The first model (null 

model) did not contain any exposure variables. This model was fitted to decompose the total 

variance that existed between HSAs. The second full model expanded the null model to include 

all fixed effects (patient characteristics). We conducted deviance test to assess model fit by 

examining differences in the -2 log likelihood (-2LL) between null and full model [19]. The 

Inter-class Correlation coefficient (ICC) [20] was computed to measure the proportion of 

variability in the chance of bypassing that lies between HSAs, which provides information 

regarding to the importance and effect of the cluster (HSA).

To account for the possibility of type I error (false positives) as the modeling samples are large, 

we adopted a stricter threshold for statistical significance of p<0.005 [21] [22]. The results of 

fixed effects are shown as odds ratios with their 95% confidence intervals (CIs) in Appendix A, 

Tables A.8a and A.8b. 

Qualitative Data Collection and Analysis 

Results of the quantitative analysis were used to inform the collection of qualitative data from 

key stakeholders. The initial study design included in-depth qualitative data collection through 

site visits to four rural HSAs located in two states in April and May 2020. Given that health 

systems were responding to the COVID-19 public health emergency (PHE) beginning in March 

2020, qualitative data collection was delayed until June 2020. Beginning in June 2020, we 

contacted State Offices of Rural Health (SORH) and rural hospital leaders in Georgia and 

Michigan to assess feasibility of conducting virtual listening sessions with community 

stakeholders. As COVID-19 cases in those states continued to grow, virtual listening sessions did 

not appear to be a viable option. As a result, we changed the study design to focus on state-level 

representatives to share their experiences and perspectives from their stakeholders.

The alternative study design involved virtual listening sessions with state-level representatives in 

four states. The four states chosen for case studies—Georgia, Kansas, Michigan, and Montana—

were selected to represent geographic and market diversity, including Census region, 

rural/frontier population and areas, Medicaid expansion/non-expansion, and market factors. 

We invited stakeholders from each state, including representatives from: 

• CMS Regional Office Rural Health Coordinators; 

• State Health Insurance Assistance Programs (SHIPs);

• State Offices of Rural Health and State Flex Directors; 

• State Hospital Associations; and

• State Rural Health Associations.
37

As these were listening sessions and open to those who wanted to participate, we welcomed 

participants to invite colleagues and other stakeholders within their state who might have input 

on the topic of rural hospital bypass. As a result, representatives from rural hospitals, community 

health centers, and rural health clinics also participated in some state listening sessions (Table 

B.1). 

Table B.1. Interview Participants by Type 

Organization Number of Participants

Health Care Facility 8 

State Health Insurance Assistance Program (SHIP) 6 

State Office of Rural Health 6 

Hospital Association 2 

Federal Government 1 

University 1 

Total 24

In advance of the listening sessions, participants were sent a spreadsheet that included their 

state’s median rural hospital avoidable bypass rate and a list of all the state’s rural hospitals that 

were classified as having a lower or higher bypass rate based on the state median. Five listening 

sessions were completed in July and August 2020, with one listening session per selected state 

and one with representatives from SHIPs. Each listening session was 60 to 90 minutes in length. 

The listening session discussion guide included questions about drivers of home hospital use and 

drivers of home hospital bypass. Listening session transcripts were coded in NVivo 12 (QSR 

International, 2018).