Number 31 n November 18, 2010

Factors Influencing Rural Residents’ Utilization of

Urban Hospitals

by Margaret Jean Hall, Ph.D.; Jill Marsteller, M.S., Ph.D.; and Maria Owings, Ph.D.

Division of Health Care Statistics

Abstract

Objective—To examine, using nationally representative data, which patient,

hospital, and county characteristics influence rural residents’ urban

hospitalization.

Methods—Rural residents hospitalized in urban hospitals (crossovers) are

compared with those hospitalized in rural hospitals (noncrossovers). National

Hospital Discharge Survey data were merged with Area Resource File and

Centers for Medicare & Medicaid Services data to study rural inpatients’

characteristics; hospital descriptors; and county or state socioeconomic and health

service variables. Multivariate logistic regression analysis identified covariates of

the likelihood of being a crossover.

Findings—About one-third of the rural resident hospitalizations in 2003

were in urban hospitals. Other factors constant, those requiring greater resources

had higher odds of crossing over, as did younger inpatients, those transferred

from other hospitals, receiving surgery, and with mental diagnoses or congenital

anomalies. Males, emergency admissions, and intervertebral disk disorder

inpatients had lower odds of crossing over compared with those who were not in

these categories. Crossover patients’ hospitals had higher Medicare case mix

indices than hospitals used by noncrossovers. Rural inpatients in government

hospitals, rather than proprietary or non-profit hospitals, had greater odds of

crossing over, as did rural patients from counties with lower population density,

fewer hospital beds, more hospitals, more commuters, and lower per capita

income compared with those in other categories.

Conclusions—Rural hospitals continue to be an important source of inpatient

care, but rural residents travel to urban hospitals in some specific instances.

Keywords: inpatients • National Hospital Discharge Survey • hospital choice •

hospital bypassing

Introduction

In the 1980s, rural hospitals were

closing at an alarming rate, and this

trend continued, though at a slower

pace, during the 1990s (1). Loss of a

rural community’s hospital, particularly

if it is the sole hospital, not only affects

access to inpatient care, but also to

outpatient, 24-hour emergency room,

and long-term care, as well as other

services the hospitals provide to

community residents. The supply of

physicians and other health care

professionals may also dwindle in areas

without hospitals.

To help preserve access to hospital

care for rural residents, the Medicare

program established a number of special

inpatient reimbursement categories for

rural hospitals (1,2), which were

designed to help rural hospitals

overcome the financial hurdles they face

primarily due to their low volume. But,

even with these programs, rural

hospitals can maintain financial viability

only if a sufficient number of residents

in their communities receive their

inpatient care locally rather than

elsewhere (3). Information on the

number and characteristics of rural

residents who remain in, and those who

leave, rural areas for hospitalization is

important for rural and urban hospitals,

for policymakers, and for payers.

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Center for Health Statistics 
Page 2 National Health Statistics Reports n Number 31 n November 18, 2010

The phenomenon of leaving rural

areas to be hospitalized has been the

subject of prior research spanning a

number of years and has been referred

to as out-of-area hospitalization,

out-migration, and bypassing local

hospitals. In this study, we refer to rural

patients whose hospitalization takes

place in urban hospitals as ‘‘crossovers’’

and others as ‘‘noncrossovers.’’

During the 1980s, nearly 30 percent

of rural Medicare beneficiaries were

treated in urban hospitals (4), and during

the 1990s the average was 32 percent

(5). The stability of these numbers was

surprising given changes in hospital care

during that time period, including

increases in managed care enrollment

and multihospital chains (5).

Two multivariate analyses of

national data on rural Medicare

beneficiaries were conducted—one using

fiscal year 1987 data (4) and the other

1994–1995 data (3). Both found that

Medicare beneficiaries who were

younger, male, had cardiovascular or

psychiatric conditions, needed surgery,

or had greater or specialized resource

needs were more likely to be

hospitalized in an urban hospital over a

rural hospital. The 1987 study (4) found

that Medicare disabled patients, and

those who had more diagnoses, and the

1994–1995 study (3) found that those

with longer patient or physician ties,

were more likely to obtain hospital care

locally. The later study found that,

although rural Medicare beneficiaries in

general were more likely to be

hospitalized in hospitals closer, rather

than farther, from their residences, those

who had higher income, more education,

and who had a previous admission

within a year to a nonlocal hospital

were more likely to bypass their closest

rural hospitals.

Other smaller studies (6–11) in

specific geographical areas found that

many of the same variables were

important in what they refer to as

hospital choice. Included among these

was distance from an urban area. In

addition, some studies found county

hospital resources and types of hospitals

chosen were important variables to

consider (8,12–14). Most notable about

these smaller studies was the great

variation in the percentages of rural

patients who crossed over—from 18%

to 64% for various locations (6,7,12).

Importantly, some of these studies

assessed the effects of different types of

health insurance on being a crossover.

By including national data for

patients covered by all payers, this study

is the first to attempt to determine the

proportions of rural hospitalized patients

who go to rural and urban hospitals, and

the factors that may influence the

decision to crossover for both Medicare

and non-Medicare patients. It also

utilizes more recent data, and includes

data on characteristics of patients, the

hospitals in which they are hospitalized,

and attributes of the counties and states

where they live.

Methods

Conceptual framework—

Hospital choice literature

The factors found to influence

hospital choice can be grouped into

patient characteristics, physician’s

referral patterns, hospital characteristics,

and county characteristics. National data

on most of these major categories were

included in this analysis with the

exception of physician factors. The only

variable included for physicians was a

measure of physician supply in the

patient’s county of residence.

Patient characteristics considered

important included demographic

characteristics, clinical factors, length of

hospital stay, whether an admission was

on an emergency basis or was a transfer

from another hospital, and source of

payment. Payment source could directly

affect hospital choice, as in the case of

health maintenance organizations

(HMOs) or managed care, by limiting

coverage to certain hospitals that are

owned by, or have contractual

arrangements with, the managed care

plan. But fee-for-service (FFS) coverage

could also affect choice of hospital if

coverage was more generous for

hospitals listed on a preferred provider

list. The terms under which hospital care

is covered could effectively encourage

or discourage crossing over to urban

areas for inpatient care by requiring

patients using certain hospitals to pay

more out of pocket, or incur more debt,

than those who used others.

Hospital characteristics of

importance included the scope and level

of services offered by hospitals (9,15),

actual or perceived hospital quality

(8,12,13,16) and, not previously

included in other known national

models, hospital ownership. Hospital

policies relating to care not covered by

insurance could vary by ownership, so

this variable could provide important

information about barriers affecting

hospital choice.

A county characteristic deemed

important in prior studies was proximity,

or ease of access to urban areas (7,10).

Other county-level demographic

characteristics, like population density

and socioeconomic status, play an

important role because of their potential

influence on the ability of the local area

to offer and support more specialized

services. County health service variables

provide valuable contextual information

about how resource rich or poor the

rural resident’s county is.

Lastly, state certificate of need

(CON) programs could be an important

influence on the extent to which

regionalization of services is

encouraged, and hence on the number of

crossover hospitalizations.

Data

This research used the 2003

National Hospital Discharge Survey

(NHDS), consisting of 319,530

randomly sampled discharges from a

national probability sample of 426

nonfederal, short-stay or general

hospitals conducted by the Centers for

Disease Control and Prevention’s

National Center for Health Statistics.

Data on infants born during the sampled

hospitalizations were excluded from the

analysis. Because persons with multiple

hospital discharges in a year can be

sampled more than once, NHDS

produces estimates for discharges, not

persons. Throughout this report the

terms discharges, patients, and inpatients

are used interchangeably. A full

description of the survey has been
National Health Statistics Reports n Number 31 n November 18, 2010 Page 3

published elsewhere (17). Of the overall

number of NHDS discharges, 33,434

hospital discharges of rural county

residents were analyzed for this report.

Using ZIP Codes from NHDS

internal files, we identified hospital and

patient Federal Information Processing

Standards (FIPS) county codes. All of

the hospitals had a ZIP Code, but these

codes were missing for 2% of the

patients. These patients were dropped

from the analysis because urban or rural

designations were needed for both the

patient’s county of residence and the

county of the hospital in order to

determine whether or not a patient was

a crossover patient. After having

identified counties for NHDS patients

and hospitals included in this study, the

June 2003 Office of Management and

Budget (OMB) classification of counties

(18) was used to assign an urban or

rural status. Counties identified by the

OMB classification to be metropolitan

were considered to be urban, and all

others (micropolitan and noncore

counties) were considered to be rural.

We also merged NHDS data with

contextual demographic and health

services data on the patient’s county of

residence from the 2005 Area Resource

File (ARF) (19). The 2005 ARF

generally contains data from 2003. Data

on commuting patterns to urban areas

from the University of Washington (20)

were also merged with the NHDS data.

We also augmented the NHDS file

with two files from the Centers for

Medicare & Medicaid Services (CMS).

The first one contained data on payment

weights for each Diagnosis Related

Group (DRG). The DRG for each

patient stay had already been added to

each NHDS discharge but, using this

first CMS file, we were able to add the

DRG payment weight to each record

indicating the level of resources used

during the hospital stay.

To the NHDS file we also added

data from a second CMS file containing

case mix index (CMI) scores for each

hospital. These scores were calculated

by CMS for each hospital by averaging

the DRG weights for Medicare patients

served in a year. This measure gives a

relative indicator of the level of

specialized care offered by a hospital.

Out of the 426 NHDS hospitals, 56 did

not have a CMI index on the CMS file.

For 47 of these hospitals, 2005 CMI

scores were obtained from the American

Hospital Directory (http://

www.ahd.com). For the remaining nine

hospitals, CMI scores were calculated

by averaging the DRG weights of

discharges included in the NHDS with

Medicare payment or, if there were no

Medicare patients, those with all

payment sources. We tested this method

by comparing the CMIs of hospitals

obtained from the CMS file to the CMIs

calculated using the method just

described. Results were highly

correlated (Pearson’s r=0.79).

Finally, a measure of state-level

Certificate of Need (CON) stringency

was obtained from Popescu et al., 2006

(21), who based it on the 2000–2003

National Directories of Health Planning

Policy and Regulatory Agencies and the

work of Conover and Sloan (22).

Independent variables

Patient characteristics

Demographic characteristics of

hospitalized rural residents analyzed in

this study included sex, race, and age.

Patients’ principal expected payment

source was categorized as Medicare,

Medicaid, private insurance, health

maintenance organization or preferred

provider organization (HMO or PPO),

self-pay, or other. Private insurance was

used as the reference category in the

logistic model.

Clinical characteristics were

measured using a number of different

data items. One was whether or not the

admission to the hospital was an

emergency. These data were obtained

from the admission type variable in the

NHDS, which included five choices:

emergency, urgent, elective, newborn,

and not available or unknown.

Newborns were excluded from this

study. For this analysis, patients in the

emergency category were compared

with all others (including the 9% with

no stated admission type). Other data

were obtained from the admission

source item in NHDS. The admission

source item has response categories of

physician referral, clinical referral,

HMO referral, transfer from a hospital,

transfer from a skilled nursing facility,

transfer from other health facility,

emergency room, court or law

enforcement, other, and not available.

For this study, those patients transferred

from a hospital were compared with all

others (including the 6% with the

‘‘other’’ or ‘‘not available’’ admission

sources).

Also included in the model were

dummy variables indicating whether or

not patients had a first-listed diagnosis

of ischemic heart disease, mental

disorders, childbirth, intervertebral disc

disorders, conditions originating in the

perinatal period, or congenital

anomalies. These diagnoses were

identified from relevant research

literature (4–6), or from initial bivariate

analysis of the NHDS data (23,24),

which indicated that they were

correlated with crossover status.

We used a summary indicator of the

number of comorbidities, such as

diabetes or heart failure, which was

computed using the Charlson Index as

adapted by Deyo et al (25). Scores

range from 0 to 16, with higher values

indicating greater patient comorbidity

burden and treatment complexity. As

described earlier, the DRG weight

obtained from CMS files was linked

with patients’ DRGs already included on

the NHDS file. Higher DRG weights

would indicate greater relative resource

use for that particular hospitalization.

The DRG also included information

indicating whether or not a patient

received surgery.

Hospital characteristics

In addition to classification as urban

or rural, hospital characteristics included

teaching status as an indirect measure of

quality (16) and ownership (for-profit or

proprietary, government, or not for

profit). Federal hospitals are not

included in the NHDS so the

government category included state,

county, and city or town ownership. Not

for profit was used as the reference

category in the logistic regression.

The Medicare 2003 CMI for

hospitals is based on the average DRG

weight of the hospitals’ Medicare

patients. Hospitals with higher CMI

indices have Medicare patients with
Page 4 National Health Statistics Reports n Number 31 n November 18, 2010

higher resource requirements with the

implication that they provide more

sophisticated equipment and specialized

care. We assume that a hospital’s

Medicare CMI index would be

representative of all its patients. As

described earlier, CMI index values

were imputed for nine of the 426

hospitals. In the logistic model, scores

on the CMI were divided into five

groups based on quintiles using the

weighted distribution of the values: very

high, high, middle, low, or very low.

Quintiles were used because of the

skewed distribution of the values for

this variable for the hospitals in this

study. The ‘‘high’’ group was used as

the reference category.

County and state characteristics

The share of the rural resident’s

county population that commuted daily

to an urban county for work was

obtained from the University of

Washington (20) and was used as a

proxy for accessibility of the urban area

(i.e., closer in distance or time or both).

A higher proportion of daily commuters

from a county to an urban area is

considered to indicate greater

accessibility.

The ARF provided socioeconomic

data for the counties where the rural

inpatients lived, including county

population density, percentage of

residents with a college education, and

per capita income. ARF measures of

health service resource availability

included rate of active nonfederal

physicians (excluding doctors of

osteopathy) per 1,000 population;

number of short-term general (STG)

hospitals in the county; rate of STG

hospital beds (set up and staffed) per

1,000 population; whether or not the

county had a rural referral center (which

more closely resembles an urban

hospital in patients, staff, and service

types), and the rate of HMO penetration

in 2003.

Because hospital choice could be

influenced by state restrictions on

provider provision of services (e.g.,

through CON programs) we included a

previously developed measure (21) of

no, low, or high CON program

stringency. The reference group for the

logistic regression was no CON

program.

Statistical analysis

Sampled data were weighted to

produce national statistics. Differences

between crossovers and noncrossovers

on the variables, or subcategories of

variables, contained in Table 1 were

tested using the two-sided t-test. The

significance level indicated in Table 1

was the 0.05 level. All error estimates

were derived from SUDAAN, which

accounts for the complex sampling

design of the NHDS (26).

In addition to descriptive analyses,

multivariate logistic regression tested the

likelihood of a hospitalized rural

resident being a crossover (1) compared

with a noncrossover (0). We analyzed

groups of variables in steps, adding each

group sequentially in four separate

regressions, in order to estimate the

portion of the variance that was

explained by patient, hospital, county,

and state variables.

Collinearity among the variables

included in the model was checked

using Pearson’s r and, in general, the

level of correlation was low. The

bedsize variable was highly correlated

with teaching hospital (r=.62) and CMI

index (r=.76) and so bedsize was

dropped from subsequent models. Some

other variables included in descriptive

analyses were dropped from the final

multivariate model because they were

not statistically significant and their

exclusion did not alter other effects. One

measure, length of stay, was excluded

because it was found to be endogenous

to the dependent variable based on a

Durbin-Wu-Hausman test (27). A few

variables without independent effects

were retained because of their

conceptual importance or to allow

comparability with other research

results.

Results

Descriptive analysis

In 2003, 7.3 million rural residents

in the U.S. were hospitalized in

short-stay hospitals. Most of these,

68 percent or 4.9 million inpatients,

used rural hospitals. The other 2.4

million (32%) were crossovers to urban

hospitals. Table 1 presents a description

of these rural inpatients by their

crossover status, including demographic

and medical characteristics, as well as

hospital, county, and state

characteristics. Because of low

reliability, estimates with relative

standard errors of more than 30 percent,

or that are based on fewer than 30

records, are not presented in this table.

This analysis shows that patients

with certain characteristics were

significantly more likely to be

crossovers, that is, males, younger

patients, those with higher DRG

weights, with a surgical DRG,

hospitalized for ischemic heart disease,

and those transferred from another

hospital. Patients with an expected

payment source of Medicare were

significantly less likely to be crossovers.

One hospital characteristic was also

found to be statistically significant.

Patients in hospitals with higher

Medicare CMIs were more likely to be

crossovers. Lastly, some characteristics

of the counties where inpatients lived

were statistically significant. Crossovers

were more likely to live in counties with

lower per capita income and with a

lower rate per 1,000 population of

short-term general hospital beds.

Multivariate analysis

Table 2 provides adjusted odds

ratios for variables in four logistic

regression models. Model 1 shows

patient characteristics only, Model 2

adds hospital characteristics, Model 3

adds characteristics of patients’ county

of residence, and the final model, Model

4, adds state-level CON regulation. The

results of the final, most comprehensive,

model (Model 4) will be discussed in

this section. Changes in effects that

occurred across the bivariate and the

four multivariate models will be

included in the discussion.

Looking at the final model (Model

4), several factors distinguished between

crossovers and noncrossovers after

controlling for other variables. With

regard to patient attributes, female
National Health Statistics Reports n Number 31 n November 18, 2010 Page 5

patients and those who were younger

had higher odds of being crossovers

than male and older patients. Patients

with a surgical compared with a

nonsurgical DRG, a higher rather than a

lower DRG weight, those hospitalized

for mental disorders or congenital

anomalies (compared with those with

other diagnoses), and those who had

been transferred from another hospital

rather than coming from other or

unknown admission sources also had

higher odds of being crossovers.

Emergent patients, compared with those

with other and unknown admission

types, and those hospitalized for

intervertebral disk disorders, had lower

odds of being crossovers.

In terms of hospital characteristics,

other things held constant, crossovers

had higher odds of being treated in

hospitals with higher rather than lower

CMI scores, and in government

hospitals rather than not-for-profit

hospitals.

A number of county characteristics

resulted in higher odds of being a

crossover. Patients living in counties

with a larger number of short-term

general hospitals, and those with a

larger percentage of the population

commuting to work in urban areas, had

higher odds of being crossovers

controlling for other factors. Those who

lived in counties with a greater

population density, higher per capita

income, and a higher rate of short-term

general hospital beds per population,

had lower odds of being a crossover

compared with those in other counties.

Taken alone, patient characteristics

explained 17 percent of the variability in

the dependent variable according to the

pseudo R-squared value. Adding the

hospital-level variables accounted for an

additional 43 percent of the variance for

a total of 60 percent. After adding in the

patient’s county socioeconomic and

health services characteristics the

percentage of the variation explained

was 71 percent—an increase of

11 percent. The inclusion of the single

state-level variable, CON stringency, did

not add much to the multiple R-squared

for this model. Though conceivably the

contributions of the different sets of

variables would have changed if entered

into the model in a different order, we

chose the above order because it

matched our conceptual framework (in

which the patient’s needs are the origin

of the hospitalization decision) and other

studies that added variables in a similar

order, and because using a different

order did not appreciably alter the

results.

Discussion

In this section, we primarily discuss

significant findings from our final model

(Model 4), which included patient,

hospital, county, and state variables. We

also discuss changes in the pattern of

results as we moved from bivariate to

multivariate analyses.

Patient characteristics

About two-thirds of rural patients

were hospitalized in rural hospitals. This

percentage is similar to that found in

earlier national studies (4,5) of rural

Medicare patients (Table 1).

Younger patients had greater odds

of crossing over to urban hospitals than

older patients, consistent with prior

national and regional studies

(3,4,7,10,14,28). Older patients have

been found to have more impediments

to travel, including unwillingness to

travel to urban areas for hospitalization

(7,8,11,28).

Our bivariate findings and prior

research (4,14) indicated that males

were significantly more likely to be

crossovers than females. But in our

multivariate analyses this held only in

Model 1, which included only patient

characteristics. Controlling for county

characteristics (Model 3), we found the

reverse, that males had lower odds of

crossing over than females. Thus some

of the higher prevalence of crossing

over among males found in other studies

appears to have been due to

uncontrolled variation at the county

level. Those hospitalized for

intervertebral disc disorders (IDD) had

lower odds of crossing over in Models

1, 3, and 4. This suggests that local

access to IDD treatment varies by

county and that these patients stayed

local when possible.

Unlike previous research (9,11,28)

we did not find that being a black rural

resident was associated with using a

rural rather than urban hospital. Other

variables in our model may account for

effects formerly thought to be due to

race. It is also possible that missing race

data for 20% of the discharges, or the

relatively small numbers of rural black

patients included in the NHDS, could

have influenced these findings.

Having greater resource needs

(average DRG weight) and receiving

surgery increased the odds of crossing

over. Clinical variables, particularly

those designed to measure the need for

specialized care, have been established

as important predictors in numerous

other studies (4–8,28). We found

significant effects on being crossovers

of patients with congenital anomalies

after county characteristics were

included in the multivariate analyses

(Model 3). There was no prior literature

studying the effects of this diagnosis on

crossing over.

Our bivariate analyses found that

those hospitalized for ischemic heart

disease (IHD) were more likely to be

crossovers. The literature also found

having heart disease or cardiovascular

surgery was positively related to

crossing over (4–6). In our multivariate

analyses, however, after hospital

characteristics were added (Models 2–4),

IHD was no longer statistically

significantly related to being a

crossover. It may be that the receipt of

care in hospitals with high case mix

indices, which some IHD and other

patients require, was actually what

appeared to raise the odds of being a

crossover in the absence of hospital

covariates.

On the other hand, being

hospitalized for mental disorders and

being an emergent patient were each not

statistically significant in bivariate

analysis, or in Models 1 or 2, but were

included in further models because of

literature supporting their importance.

When county characteristics were added

(Models 3 and 4), both having a mental

disorder and being an emergent patient

were associated with being a crossover

though in different directions. Patients

with mental disorders were more likely
Page 6 National Health Statistics Reports n Number 31 n November 18, 2010

to be crossovers, and emergent patients

were less likely to be crossovers. For

mental disorders, researchers have

speculated these effects were due to

psychiatric care being less available in

rural areas (29), and to the desire for

anonymity one may be more likely to

get in an urban hospital (7,30). Other

research on emergent patients noted that

the immediacy of these patients’ need

for hospitalization removes much, if not

all, of the choice of hospital (6).

Transferred patients were found to

have greater odds of crossing over

(Model 4). This finding suggests that the

first hospital (which may or may not

have been rural) could not provide all of

the care the patient needed (29).

In our bivariate findings, Medicare

patients were significantly less likely to

be crossovers. Prior research from

selected states and local areas found that

those with private insurance were more

likely to cross over (6), and that those

who were uninsured (self-pay) (6,9), or

on Medicaid (9), were less likely to

crossover. However, none of the

payment sources exerted a statistically

significant independent effect in our

multivariate analyses. It is

understandable that the observed

bivariate effect of Medicare would not

hold up in the multivariate context,

where we controlled for age (Medicare

patients are predominantly aged 65

years and over). This study not only

included data on insurance coverage, but

also controlled for characteristics of the

hospitals where crossovers received care

(including hospital ownership that may

be related to offering uncompensated

care), and for county and state variables.

The effects of less comprehensive

private health insurance offered in rural

areas (31,32) could also have affected

our findings. This issue is addressed

further below along with managed care

findings.

Hospital characteristics

Unlike in prior research (10), we

did not find that patients in teaching

hospitals were significantly more likely

to be crossovers, either in our bivariate

or multivariate analyses. Patients in

hospitals with higher hospital CMIs,

however, had consistently greater odds

of being crossovers in bivariate and

multivariate analyses (Models 2–4).

Other similar measures of the level of

specialized care offered were positively

related to crossover status in prior

research (1,8), suggesting that the level

of specialized care offered is responsible

for crossing over to urban areas rather

than teaching status per se.

Hospitalization in a government,

compared with a nonprofit, hospital was

associated with significantly greater

odds of being a crossover in Models

2–4. There are no known prior national

studies assessing the effects of hospital

ownership on being a crossover, but one

explanation could be that government

hospitals are more likely to offer

relatively unprofitable services that are

disproportionately needed by poor and

underserved patients, and are thus

considered the caregivers of last resort

(33). A 2005 U.S. Government

Accountability Office study (34) found

that government hospitals often devoted

substantially larger shares of their

patient operating expenses to

uncompensated care than other types of

hospitals. Rural residents are more likely

to be self-employed, work for small

employers, and have lower wages and

more limited benefits, including less

comprehensive health insurance (31,32).

This underinsurance and government

hospitals’ commitment to offering

uncompensated care could explain why

crossovers had greater odds of being

hospitalized in government facilities.

County characteristics

Living in a county with a larger

proportion of commuters to urban areas

was significantly associated in

multivariate Models 3 and 4 with

increased odds that patients would

crossover for hospitalization. Other

studies have found that various

measures of distance and accessibility to

hospitals were important influences

(7,10). The commuter result may also

demonstrate rural residents’ comfort

with traveling to the city.

Rural patients living in counties

with higher per capita income had lower

odds of being crossovers in prior

research (28), in our bivariate analysis,

and in our multivariate models. The

result for higher per capita income could

indicate that the county would have the

ability to raise the initial funds needed

to bring in physicians and other

specialized resources, and enough

people with the comprehensive

insurance to cover this care afterwards.

When individual income, rather than

county per capita income, has been

studied, however, those with higher

income have been found to be more

likely to crossover (3).

A higher county population density

was also associated with decreased odds

of crossing over for hospital care. A

certain population density would be

required to create sufficient demand to

support the provision of specialized

services locally. Furthermore, rural

hospitalized residents with more STG

hospital beds per capita in the counties

where they lived were significantly less

likely to be crossovers in our bivariate

analysis, and had lower odds of crossing

over to urban areas than those in

counties with fewer of these beds in

Models 3 and 4. This finding, supported

by other research (28), may be because

more beds per population increased the

likelihood that hospitals would

specialize and thus offer more

sophisticated care locally. On the other

hand, patients in counties with a higher

number of STG hospitals (at a constant

level of beds per capita) had greater

odds of crossing over than those in

counties with fewer hospitals. This may

suggest that instead of offering more

specialized services, a greater number of

STG hospitals may indicate many

smaller hospitals offering basic services

in a larger number of local communities.

Information on the size, dispersion, and

level of sophistication of hospitals

should be taken into account in future

analyses to see if this, indeed, is the

case.

Just as individually being enrolled

in an HMO or PPO was not statistically

significant in our analyses, neither was

living in a county with a higher

penetration of managed care. Prior

studies on the effects of HMO and other

managed care coverage on crossing over

had conflicting results (6,9,28).
National Health Statistics Reports n Number 31 n November 18, 2010 Page 7

Managed care has been slow to spread

to rural areas (35) and, where it does

exist, may have fewer restrictions to

encourage provider participation, which

may make it more similar to unmanaged

care.

We did not attempt to determine

which patients ‘‘should’’ have crossed

over to receive the most appropriate

care. In our data, we only know which

patients did cross over. It is possible that

one or more of the barriers to access

that were discussed previously in this

paper resulted in some patients not

being able to get more specialized care

in urban areas. Knowing the extent to

which this occurs would be useful in

assessing rural access to care and

represents a fertile area for future

inquiry.

By examining in turn patient,

hospital, county, and state variables, this

research not only considered a larger

number of variables than most studies of

hospital choice, but it also provided

some indication of the relative

importance of each of the categories.

The majority of the variance was

explained by characteristics of the

hospitals where care was given. Hence,

prior studies that did not include

hospital characteristics may have missed

a vital component of the process and

could have yielded incomplete, and

possibly misleading, results.

Limitations of this study include the

fact that patients who were hospitalized

more than once during a year could

have been sampled for the NHDS more

than once. Such a person could have

been a crossover for one hospitalization

and a noncrossover for another. These

discharges could be within the same

episode of illness (e.g., when a rural

patient started in a rural hospital and

transferred to an urban hospital), or

when more time separated the

hospitalizations. However, data on all

sampled hospitalizations are included

here and provide valuable information

about each distinct stay.

Some of the NHDS variables used

in this analysis had missing values. A

specific admission type was missing for

9% of the discharges and a specific

admission source for 6%. Twenty

percent of NHDS discharges had race

missing. Prior research on race

underreporting in the NHDS (36) found

that hospitals that did not report race

were likely to have a higher proportion

of white discharges than hospitals that

reported race. Nonetheless, some of the

records with race not stated would have

been for black patients and this,

combined with the relatively small

numbers of rural black patients included

in the NHDS, could contribute to our

failure to detect an independent effect of

race that some prior studies had found.

We felt that failure to include data on

race in the model would have been a far

more serious shortcoming.

Hospital choice is in actuality a

more complex and dynamic process than

our, or other models in the literature,

suggest. A patient’s condition is what

leads to a referral for inpatient care in

the first place, and so patient

characteristics are included in analyses

of this issue. It is probable that what the

patient needs, and the hospitals they or

their physicians feel are able to meet

those needs, are the most important

influences in selecting where to go for

inpatient care. A rural patient with

complex needs may only be able to

receive specialized care by crossing over

to an urban hospital. In cases where the

patient’s needs can be met in either a

rural or urban hospital, other factors

(such as patient preferences to stay near

home) may play a more prominent role.

In addition, the extent to which an

individual patient really has a choice

varies. The set of facilities from which a

patient could ‘choose’ might be limited

to only one, or only a few, in terms of

the link between services needed and

services provided, advice of providers,

insurance coverage, and availability of

beds. In emergencies, the need for

immediate care could effectively limit

the choice to the closest facility.

Because this is a study of hospital

discharges, we do know that some

choice was made, and the patient,

physician(s), emergency care providers,

or family (or likely several of these)

took part in the decision-making

process.

In this and most other studies on

this topic, no data were included on

availability of public and other

transportation to urban areas, travel

costs, out-of-pocket costs, and additional

time off from work (often unpaid) for

patients or their family members if

hospitalization took place in an urban

area. Further, little data exist on

physicians’ and families’ influence on

the choices made, patients’ assessments

of the quality of care of various

hospitals, indicators of cultural and

ethnic viewpoints, or other personal

views likely to influence hospital choice,

all of which should be explored in

future research.

Conclusions

Early studies identified the elderly,

the needy, and the disabled as the

groups likely to be the most adversely

affected by rural hospital closures,

because these patients were less likely

to travel to urban hospitals for inpatient

care. In an attempt to maintain local

access to hospital care for the most

vulnerable, as well as other rural

residents, multiple federal programs

were enacted to ease the financial

burden on rural hospitals. These

programs were largely targeted to areas

where hospital closure would present a

hardship for the residents (e.g., in the

case where there is only one hospital in

the community).

Even with this financial assistance,

rural hospitals could not have continued

to operate if a sufficient number of rural

patients did not use them for care. Most

hospitalizations of rural residents are

indeed in rural hospitals. The third of

the rural resident hospitalizations that

occur in urban hospitals are more likely

to involve specialized levels of care that

are not offered in most rural hospitals.

Some rural counties, those with

more people and greater financial

means, have specialized care in their

own local hospitals. But less densely

populated rural counties, and those with

lower average incomes, are not likely to

have the resources to support specialized

hospital care locally (8). Without an

increase in the rate of hospital beds per

population, having more hospitals may

improve access to basic services closer

to home, but more hospitals does not

necessarily mean greater access to
Page 8	 National Health Statistics Reports n Number 31 n November 18, 2010

hospitals with specialized care in rural

areas. Residents of these areas have, and

probably will continue to have, higher

odds of crossing over to urban hospitals.

Greater coordination between rural

and urban hospitals is important so

referrals are easily arranged.

Improvements in the provision of

transportation, and the means to pay for

it, would eliminate another barrier to

ensuring equal access to urban hospital

care (37). At a broader level, federal or

other programs to expand coverage for

the uninsured and the underinsured

could help ensure access to profit,

not-for-profit, and government urban

inpatient hospital care. Some states have

already begun to offer programs to

expand coverage by providing financial

incentives for insurers to offer it, and

residents to buy it (38).

It is possible that an important

factor responsible for the extensive use

of rural hospitals is that patients prefer

to receive care closer to home. But these

hospitals’ ability to attract a substantial

share of inpatients is contingent upon

rural residents and their doctors

continuing to believe that these rural

hospitals offer the care they need, and

that this care is of comparable quality to

what they would get in urban hospitals.

Due to pressure from federal agencies,

private employers, consumers, and

health care professionals, rural hospitals,

often as part of networks, have followed

the lead of larger hospitals in gathering

and analyzing data on the quality of

care, and in using these data to

continuously monitor and improve their

performance (39). The legislation that

established the critical access hospital

program requires these small rural

hospitals to meet certain quality

standards and provides funds for

quality-related activities (40,41).

Without these quality assessment and

improvement efforts, rural hospitals

could run the risk of losing patients who

decide that the inconvenience of

traveling to urban areas for hospital care

is outweighed by the better care and

outcomes they anticipate receiving if

they were hospitalized in larger, more

sophisticated, high-tech hospitals.

The Patient Protection and

Affordable Care Act (P.L.111–148), and

the Health Care and Education

Reconciliation Act (P.L.111–152), both

passed in 2010, are expected to have

far-reaching effects on the health care

system and on access to, and quality of,

care in both rural and urban areas. It

will be important to track the effects of

these laws on rural residents’

hospitalization in rural and urban areas.

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Page 10 National Health Statistics Reports n Number 31 n November 18, 2010

Table 1. Descriptive data on rural residents who were hospitalized in urban hospitals (crossovers) and those who were hospitalized in

rural hospitals (noncrossovers), United States, 2003

All rural residents’

hospitalizations Crossovers Noncrossovers

N = 7,292,000 N=2,357,000 N=4,935,000

Variables Estimate SE Estimate SE Estimate SE

Patient characteristics

Demographic characteristics

Male (%)** .................................... 42 0.6 47 0.7 40 0.7

Average age (in years)**............................ 57 0.8 52 1.3 59 1.0

Black (%) ..................................... 6 1.8 8 2.0 * *

Diagnostic and other clinical characteristics

Hospitalized for (first-listed diagnosis) (%)

Ischemic heart disease**............................ 8 0.6 11 1.0 6 0.7

Mental disorder ................................. 6 1.1 6 1.4 6 1.5

Childbirth ..................................... 8 0.7 8 1.1 7 0.8

Intervertebral disc disorder .......................... 1 0.2 2 0.3 * *

Condition originating in the perinatal period ................ 0.4 0.1 1 0.2 * *

Congenital anomaly............................... 0.4 0.1 1 0.2 * *

Surgical DRG (%)** ................................ 26 1.7 43 1.8 18 1.5

Average DRG weight** .............................. 1.16 0.03 1.51 0.04 1.00 0.02

Average Charlson/Deyo comorbidity index .................. 1.06 0.04 1.05 0.05 1.07 0.06

Administrative characteristics

Expected source of payment (%)

Medicare** .................................... 51 1.3 42 1.6 56 1.7

Medicaid ..................................... 14 1.0 16 1.3 13 1.2

Private insurance ................................ 19 1.1 22 1.5 18 1.4

HMO or PPO .................................. 8 1.3 10 1.3 6 1.7

Self pay ...................................... 4 0.3 4 0.5 4 0.4

Other or not stated ............................... 4 0.5 6 1.0 3 0.7

Emergent (percent) ................................ 29 2.6 23 1.8 31 3.6

Transferred from another hospital (%)** .................... 4.4 0.9 11 1.8 1 0.4

Hospital characteristics

Teaching hospital (%) ............................... 8 1.9 21 3.4 * *

Ownership (%)

Not-for-profit ................................... 73 5.7 67 5.7 75 8.0

Government ................................... 14 4.2 21 5.0 * *

For-profit ..................................... * * 13 2.8 * *

Medicare case mix index (average)** ..................... 1.26 0.03 1.35 0.02 1.22 0.05

County characteristics

County demographic and socioeconomic characteristics (average)

Population density per square mile ..................... 60 7.4 56 5.6 62 10.4

Percent in county commuting to urban area for employment ...... 12 1.3 13 1.3 11 1.7

Percent with a college degree ........................ 15 0.6 14 0.7 15 0.8

Per capita income**............................... 22,400 351 21,400 333 23,000 466

County health service characteristics (average)

Rate of active MDs per 1,000 population ................. 1.2 0.09 1.0 0.08 1.2 0.13

Number of short-term general hospitals................... 1.7 0.17 1.6 0.19 1.7 0.21

Rate of short-term general hospital beds per 1,000 population** . . . 3.6 0.29 2.9 0.15 3.9 0.41

Percent HMO penetration ........................... 9 1.2 8 0.7 9 1.8

Percent had rural referral center ....................... 17 4.2 14 1.9 * *

State characteristics

Certificate of need program (%)

None ........................................ 51 7.8 52 9.7 50 10.3

Low stringency .................................. 34 8.5 30 8.1 36 11.8

Medium or high stringency .......................... * * * * * *

** Difference significant at 0.05 level using a t-test.

* Figure does not meet standards of reliability or precision. Estimates with a relative standard error of more than 30 percent, or that are based on a sample size of fewer than 30 records, are

considered to be too unreliable to report.

NOTE: N is the weighted estimate and SE is standard error.
 

SOURCE: 2003 National Hospital Discharge Survey, National Center for Health Statistics, Centers for Disease Control and Prevention.
 
National Health Statistics Reports n Number 31 n November 18, 2010 Page 11

Table 2. Multiple logistic regression results on factors associated with rural patient crossover to urban hospitals: United States, 2003

Selected characteristics Model 1 Model 2 Model 3 Model 4

Patient characteristics Odds ratio

Sex: male ....................................... **1.16 0.93 **0.86 **0.86

Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . **0.98 **0.98 **0.97 **0.97

Black .......................................... 1.45 2.12 1.40 1.40

Average DRG weight ................................ **1.53 **1.20 **1.26 **1.26

Average Charlson/Deyo comorbidity index ................... **1.06 0.98 0.96 0.96

Surgical DRG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . **2.37 **1.56 **1.62 **1.60

Emergent admission ................................. 0.84 0.69 **0.61 **0.60

Transferred from another hospital ......................... **9.13 **6.72 **6.41 **6.35

Hospitalized for:

Ischemic heart disease .............................. **1.82 1.27 1.37 1.35

Mental disorder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.61 2.90 **4.33 **3.98

Intervertebral disc disorder. . . . . . . . . . . . . . . . . . . . . . . . . . . . **2.26 0.68 **0.62 **0.64

Childbirth ...................................... 1.11 1.21 1.11 1.13

Congenital anomaly ................................ 1.68 2.62 **5.37 **5.98

Condition originating in perinatal period .................... 1.19 3.29 4.03 4.48

Expected source of payment:

Medicare vs. private insurance ......................... 1.00 1.18 1.28 1.31

Medicaid vs. private insurance ......................... 0.93 0.97 0.94 0.94

HMO or PPO vs. private insurance ...................... 1.20 0.79 0.90 0.96

Self pay vs. private insurance .......................... 1.04 0.90 0.92 0.97

Other or not stated vs. private insurance ................... 1.57 1.61 1.23 1.24

pseudo R2 = 0.17

Hospital characteristics

Teaching hospital ................................... ... 0.18 0.38 0.40

Ownership

Profit vs. nonprofit ................................. ... 1.33 1.41 1.63

Government vs. nonprofit ............................ ... **5.62 **6.16 **6.34

Medicare Case Mix Index (CMI)..........................

Very low vs. high. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... **0.07 **0.03 **0.03

Low vs. high .................................... ... **0.02 **0.01 **0.01

Middle vs. high . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... **0.14 **0.05 **0.05

Very high vs. high . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... **32.59 **35.55 **35.12

pseudo R2 = 0.60

County-level characteristics

Demographic and socioeconomic characteristics ...............

Population density per square mile....................... ... ... **0.99 **0.99

Percent commuting to urban area for employment ............. ... ... **1.08 **1.08

Percent with a college degree.......................... ... ... 1.06 1.07

Per capita income ................................. ... ... **0.82 **0.81

Health services characteristics

Rate of active MDs per 1,000 population ................... ... ... **1.33 1.29

Number of short-term general hospitals .................... ... ... **1.63 **1.54

Rate of short-term general hospital beds per 1,000 population...... ... ... **0.72 **0.74

Had rural referral center ............................. ... ... 0.90 0.93

HMO penetration.................................. ... ... 0.96 0.96

pseudo R2 = 0.71

State-level characteristics

Certificate of Need Program . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Low stringency program vs. no program ................... ... ... ... 0.60

Medium or high stringency program vs. no program ............ ... ... ... 0.84

pseudo R2 = 0.71

** Significant at the 0.05 level.

. . . Category not applicable.

SOURCE: CDC/NCHS, 2003 National Hospital Discharge Survey.
Page 12 National Health Statistics Reports n Number 31 n November 18, 2010

Acknowledgments

This report was prepared in the Division of Health Care Statistics. This report was edited by Demarius V. Miller and Laura Drescher, CDC/NCHM/

Division of Creative Services, Writer-Editor Services Branch; and typeset by Annette F. Holman, CDC/OSELS/NCHS/OD/Office of Information Services,

Information Design and Publishing Staff.

Suggested citation

Hall MJ, Marsteller J, Owings MF. Factors

influencing rural residents’ utilization of urban

hospitals. National health statistics reports; no

31. Hyattsville, MD: National Center for Health

Statistics. 2010.

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