The Effect of Rural Hospital Closures
on Community Economic Health
George M. Holmes, Rebecca T. Slifkin, Randy K. Randolph,
and Stephanie Poley
Objective. To examine the effect of rural hospital closures on the local economy.
Data Sources. U.S. Census Bureau, OSCAR, Medicare Cost Reports, and surveys of
individuals knowledgeable about local hospital closures.
Study Design. Economic data at the county level for 1990–2000 were combined with
information on hospital closures. The study sample was restricted to rural counties
and other community economic measures. Models included both leading and lagged
be fully realized by the community.
Data Collection. Information on closures was collected by contacting every state
hospital association, reconciling information gathered with that contained in the Amer-
ican Hospital Association file and OIG reports.
Principal Findings. Results indicate that the closure of the sole hospital in the com-
munity reduces per-capita income by $703 (po0.05) or 4 percent (po0.05) and in-
creases the unemployment rate by 1.6 percentage points (po0.01). Closures in
communities with alternative sources of hospital care had no long-term economic im-
pact, although income decreased for 2 years following the closure.
Conclusions. The local economic effects of a hospital closure should be considered
when regulations that affect hospitals’ financial well-being are designed or changed.
Key Words. Hospital closure, community economy, economic development
Hospitalsaregenerally considered to bethelocusof rural healthcaresystems.
Not only are important health services based at hospitals, but many of a
community’s health care personnel are either directly employed by or sup-
ported by the local hospital. Further, hospitals are often considered vital to
local economies as they bring outside dollars into the communities via third-
party payors, provide jobs, stimulate local purchasing, and help attract in-
dustry and retirees (Doeksen et al. 1997). As such, the closureof a hospital can
r Health Research and Educational Trust
have detrimental effects on a rural community. The rapid succession of hos-
pital closures throughout the 1980s and 1990s helped stimulate legislation,
such as creation of Critical Access Hospitals (hospitals that accept certain
restrictions and are reimbursed 101 percent of cost from Medicare), designed
to ensure the financial viability of small rural hospitals.
status has risen beyond expectations; as on August 2004, 959 small rural
hospitals (over 40 percent of all rural hospitals) have opted out of Prospective
Payment System by converting (Flex Monitoring Team 2005). In some policy
circles, concern has been expressed about the effect on the Prospective Pay-
ment System of so many hospitals taking advantage of the protection of cost-
based reimbursement (MEDPAC 2003). In light of these concerns, this is an
opportune time to more accurately assess the economic importance of small
rural hospitals to their communities, and to estimate the potential impact of
their closure, should favorable reimbursement policies be changed.
The effect of hospital closures on the health of community members has
been relatively well documented and is not the focus of this study. For ex-
ample, Reif, Des Harnais, and Bernard (1999) study six communities expe-
riencing a hospital closure and conclude that hospital closures decrease access
to health care, whereas Rosenbach and Dayhoff (1995) find that per-capita
Medicare expenditures grew at a slower rate in communities experiencing a
closure is perceived to have negative economic effects on a rural community
(Hart, Pirani, and Rosenblatt 1991a), although few studies have directly
measured the effect. A number of studies have attempted to estimate the role
of hospitals in their local economies as evidence of the direct and indirect
impact a closure would have, by either comparing the closure communities’
economies to those of control groups, or through input/output (I/O) analysis.
In one of the earliest studies, Christianson and Faulkner modeled the
contribution of rural hospitals to local economies and found an estimated
Address correspondence to George M. Holmes, Ph.D., Cecil G. ShepsCenter for Health Services
Chapel Hill, NC. Rebecca T. Slifkin, Ph.D., Randy K. Randolph, M.R.P, and Stephanie Poley,
B.A., are with the Cecil G. Sheps Center for Health Services Research, University of North
Carolina at Chapel Hill, Chapel Hill, NC.
468 HSR: Health Services Research 41:2 (April 2006)
$686,405 to $1,083,282 (US$ in 1978) in community income was generated
directly and indirectly by hospital expenditures; income multiplier estimates
were lessthan 2 for90 percentofthecommunities(ChristiansonandFaulkner
1981). McDermott et al. (1991) used hospital survey data to estimate the eco-
nomic impact of a hospital on its host community and found that the com-
bined induced and direct effects, on average, were $54,739 per hospital bed
(1991). Studies using I/O analysis, which uses observed data on business and
ofa changeinone sectorofthe economyonothers,havefound similarresults.
For example, Doeksen, Gerald, and Altobelli (1990) simulated the effect of a
hospital closure in rural Oklahoma and estimated that over a 5-year period
approximately 78 jobs, $1.7 million in income, $452,100 in retail sales, and
$9,100 in sales tax revenue would be lost because of the closure. Similar
conclusions were reached using data from three Texas communities (Doek-
by bed size. They found that the estimated economic multipliers increased in
magnitude with hospital bed size, but did not specifically estimate the effect of
closure using I/O analysis.
While each of these studies suggests that a hospital closure would have
negative economic consequences for rural communities, other research has
indicated little to no effect on the rural community because of hospital closure.
Pearson and Tajalli (2003)found thathospital closure doesnot appeartocause
short- or long-term harm to the economies of their rural host communities.
economic indicators were examined for trends and none were found to have
Probst et al.(1999) compared economic indicators in closure communities to a
control group of nonclosure communities and failed to find a statistically sig-
nificant difference in income trends in the closure counties relative to the
comparison counties. Stensland et al.(2002) examined the effect of 42 hospital
no effect on short-term or long-term economic growth of those areas.
Predominantly, the literature on the economic effects of hospital clo-
sures has relied on I/O analysis. Whereas I/O analysis has been useful in
furthering the methodology of measuring hospital closure effects to include
spending induced by the hospital business, the technique is limited in many
ways. First, it is not designed to calculate ‘‘amenity’’ effects of a hospital
The Effect of Rural Hospital Closures 469
closure——the absence of a local hospital may discourage retirees and busi-
nesses from moving into the community. Secondly, because of a lack of data
on these small rural markets, I/O analysis for rural areas often relies on na-
tional purchasing trends, rather than local purchasing patterns, to calculate
economic multipliers. Third, I/O treats the study region (often a county) as an
isolated economy and tends to ignore market area considerations, which may
lead to over- or under-estimation of the effects. Finally, I/O analysis does not
offer measures of precision in the estimates. The concept of standard errors
(SEs) is critical in ascertaining the degree of confidence one has in the results,
and I/O has no such ability.
In this paper, we estimate the effect of hospital closure on the local
economy using multivariate regression methods that do not require the use of
acontrolgroup consistingofcommunitiesnotexperiencingahospital closure.
a community, and we extend the hospital closure literature in two new di-
mensions. First, we differentiate between the impact of a hospital closure in a
communitywhereanother hospital remains open andclosurein a community
with no other proximal access to hospital services. This distinction is impor-
tant because many of the ways that a closure can affect local economies, such
astheamenityeffect,canbemitigatedbythe presence ofa near-byalternative
hospital. Second, our analysis considers whether the economic conditions in
communities where a hospital has closed can be attributed to the closure, or
whether poor economic conditions preceded (and perhaps contributedto) the
closure. Our methodology allows this assessment without the necessity of
identifying appropriate controls, a difficult task as there may be intrinsic dif-
ferences between financially struggling communities where hospitals ulti-
mately close and those where they remain open.
A variety of data sources were used for the study. Information on hospital
closures was obtained from a database we have constructed and maintained
that includes hospital closures from 1990 to the present. The database iden-
tifiedclosures byreconcilinginformation froma number ofsources, including
of Service File, the HHS Office of the Inspector General (OIG), and the
American Hospital Association (AHA). Closures reported in any of these
470 HSR: Health Services Research 41:2 (April 2006)
the purpose of this study, a hospital is considered closed if short-term acute
care services provided by that entity cease in a community. For example, if a
hospital builds a new plant within the same zipcodeand relocation to the new
asa closure.Othersources, suchOIG,concentratemore onbricksandmortar
rather than a community’s access to a hospital, and would consider that event
to include one closure and one new opening; thus the number of hospital
closures may vary dependent on the source of information.
Thedatabasecontains,amongotherinformation,the nameand location
of the hospital, the year it closed, and information detailing the closure, when
available(e.g.,whetherit reopenedasanothertypeofhealthcare facility,such
as a long term care hospital, or whether the building was left unoccupied by
the closure). The year of closure was verified and occasionally modified to
reflect the date of the hospital’s final Medicare cost report. For this study, we
define a hospital as rural according to the 1993 MSA status as defined by the
Office of Management and Budget and we only include hospital closures that
occurred in nonmetropolitan counties.
Hospital data were obtained from theAHA’s annual survey of hospitals,
OSCAR and Medicare Cost Reports. These files provide information on the
hospital utilization, costs and revenues, staffing, total wages and salaries, and
the geographic location of the hospital. We also obtain information on com-
Bureau. Because of data limitations, we define communities as counties. Al-
though finer geographic measures would be preferred, economic outcome
data are difficult to obtain longitudinally at the sub-county level.
The study data are longitudinal and consist of all counties for the years
1990–2000. Although some county characteristics are not available in every
year, the most critical elements for our analysis are. Various measures of
economichealth of thecounty areused as dependent variables,including per-
capita income (PCI), unemployment rates, the size of the labor force, and the
population in the county. Each of these measures is expected to reflect the
overall economic health of a community.
There are at least three ways that a hospital closure can adversely affect the
the purpose of this study, we define these as follows:
The Effect of Rural Hospital Closures471
Direct. The closure of a hospital generates job loss. While some employees
may find alternative employment within the community, other workers
(especially health professionals) must depart the community to find
employment. The exodus of these workers decreases the total value of
goods and services produced in the community. To the extent that health
professionals have incomes above the average income in the community, the
average income will fall if health professionals leave. It is also possible that
many of the former employees of the hospital would find employment at
wages lower than those previously earned at the hospital.
Indirect and Induced. Hospitals can be major purchasers of goods and services
within the community such as laundry services or construction. Thus,
employment, which further reverberates as those businesses and their
employees reduce consumption of other goods and services and other firms
throughout the local economy are affected. Second, the hospital provides an
incentive for nonresidents to visit the community, whether to receive
treatment or to visit a patient. These individuals will likely purchase some
products or services in the community during their visit (for example, a hotel
room, meals, flowers).
Amenity. For some businesses and retirees, proximity to a hospital may be an
important consideration in deciding where to locate, and the absence of a
hospital may discourage businesses or retirees from locating in a community,
retarding future economic development.
We posit that the economic health of the community can be specified as a
linearfunction ofcommunity characteristics.Inthediscussionthatfollows,we
for the other outcomes considered.
INCOMEct¼ aHctþ Xctb þ ttþ mcþ ect
where subscripts c and t denote county and time, respectively. Indicator Hct
equals one if and only if county c has a hospital in time t. Xctis a vector of
county characteristics. Unobservable (to the analyst) terms ttand mcallow
time-specific and county-specific shocks, respectively.
472 HSR: Health Services Research 41:2 (April 2006)
Estimation of the effect of a hospital closure (i.e., when DHct5
Hc,t?Hc,t?15 ?1) is achieved by exploiting the longitudinal data struc-
ture to control for unobserved time-invariant factors affecting the economic
health of the community. Operationally, we specify a fixed effects model.
The parameter a is identified by variation in Hctover time within a county.
That is, the model estimates the effect of hospital closure by comparing the
economic health of the community before the closure with the economic
that compares communities with hospital closures to communities without
hospital closures if counties experiencing a closure are unobservedly different
from those counties that did not experience a closure. One advantage of a
fixed effect model is that the hospital closure indicator is allowed to be
correlated with county-specific time-invariant factors (mc) unobserved by the
We allow the effect of the closure to vary based on the number of years
since closure. In the index year (the year in which the hospital closes), the
effect may be different from subsequent years for two reasons. First, the clo-
sure likely does not occur on January 1, so the measured economic effect will
effect may not fully manifest in the first year. Operationally, we account for
this by including lagged terms of the closure effect.
One concern may be that random shocks to the economic health of the
community (e) may adversely affect the financial viability of the hospital and
lead to a hospital closure. Formally, we might expect E(H0ctec,t?1) 6¼ 0. For
example, a bad economic year in the community may induce the hospital to
close in the next year. Failure to control for this may lead to spurious findings
of an effect when none exists. We examine this potential bias by including an
indicator for whether the hospital closes in the subsequent year. If this con-
dition, commonly known in labor economics as an Ashenfelter Dip from
We include a series of indicator variables to allow the effect of a hospital
closure to vary depending on the number of years since the hospital closed.
There are two types of variables. The first is a state variable that captures the
overall effect of closure. This variable is positive if and only if the hospital has
1994 and 1 from 1995 until the end of the sample period. We also include a
series of dummy variables for each year pre- and postclosure. The ‘‘short run’’
effect——the effect in the first couple years——is the sum of the overall closure
The Effect of Rural Hospital Closures473
variable with the current year. Thus, in 1996, the effect of a hospital closure is
the sum of the ‘‘overall’’ effect and the ‘‘hospital closed last year’’ coefficients.
We expect a negative coefficient on the ‘‘has no hospital’’ or ‘‘a hospital
closed’’ variables, as the closure should negatively affect the economy. We
expect a negative coefficient on the leading year indicator to capture the
preclosure dip in the economy. We expect the current and lagged terms to be
positive and of shrinking value as the effect of the closure is not fully realized
for a couple years.
the closure of any hospital (‘‘Number of Hospital Closures’’), which captures
the direct and indirect effects of the closure. Thesecond measure indicates the
closure of the only hospital (‘‘County Has No Hospital’’) in the community.
This variable captures the amenity effect that the ‘‘Closure of Any Hospital’’
measure does not include. We specify two years of lagged effects. The model
not presented. SEs are corrected for heteroskedasticity using the White (1980)
To measure further the amenity effect of a hospital closure, we calculate
the proportion of the county population that is located within 15 miles of a
hospital, before and after closure, using census tract estimates of population.
This approach allows for cross-county effects in that the 15 mile radius is
drawn irrespective of county borders. If a hospital closure reduces the pro-
point, the amenity effect should be small. Conversely, a hospital closure that
decreases the proportion within 15 miles by 80 percentage points would be
expected to have a large amenity effect.
Ultimately, the goal of the paper is to estimate the difference between the
the hospital closure. The difficulty, of course, is that the counterfactual is not
observed forcountiesthatdo notexperiencea closure;this must beestimated.
If hospital closures are not correlated with unobserved factors, then one could
estimate models using, for example, a propensity-score approach as used by
Probst et al. (1999). An alternative method (Stensland et al. 2002), is to use a
longitudinal approach and compare the outcomes for closure counties with
the outcomes of nonclosure counties (used as the estimate of the counterfac-
tual). Although this approach is appealing in its simplicity, it could produce
474 HSR: Health Services Research 41:2 (April 2006)
biased estimates if nonclosure counties are systematically different than clo-
sure counties. In preliminary analyses, we found that closure counties have a
and postclosure) compared with nonclosure counties. These results suggest
that in our data the nonclosure counties may not be a meaningful comparison
group. Therefore, we restrict our sample to those counties experiencing a
closure at least once in the sample time period (1992–1998 because of the
requirements of leading and lagged closure terms). In our approach, in any
given year counties that eventually will experience a closure but have not yet
experienced one serve as the estimate of what economy of the closure county
would have been in the absence of a closure.
The final sample includes 134 counties and the economy of each county
is measured at seven points in time (once per year 1992–1998); thus the total
is presented in Table 1. The shaded rows represent the years included in the
sample. The nonshaded rows are not included in the study sample per se but
contribute to the estimation because closures in these years contribute to the
identification of the model. That is, although 1992 is the first year included in
the sample, closures in 1991 affect the estimates because the once-lagged
closure term is positive for that county in 1992. Note that the total number of
closures in the sample is 140 as some counties experienced multiple closures.
Table1: Number of Closures
YearNumber ANY ClosuresNumber ONLY Closures
Only years 1992–1998 (shaded) are included in the model; closures in the other years contribute
due to lagged and leading terms. That is, a 1990 closure contributes to the model because in 1992
the twice-lagged term is nonzero.
The Effect of Rural Hospital Closures475
Forty-two hospitals that were the only hospital in the county closed in the
1990–1999 period. The remaining 98 hospitals that closed were located in
counties with at least one other hospital when the hospital closed. Table 2
presents the mean, minimum, and maximum values of population, per capita
income, and the unemployment rate for the study sample counties in 1992.
The average county had a population of 26,766, a per capita income (US$
1990) of 14,119 and an unemployment rate of 8.12 percent. Compared with
counties without a closure, at the beginning of the study period (1992) the
closure counties had a larger population (not shown, p50.02) but were sta-
tistically identical in income and unemployment rate. The geographical dis-
tribution is also presented. Most of the sample is located in the Midwest and
South census regions, although the distribution of sample counties is statis-
tically equal to the distribution of nonmetropolitan counties (not shown,
Figure 1 presents the average per capita income (adjusted to US$1990)
forthree groups of counties. Thefirstgroup consists of counties that neverhad
a hospital during the time period under observation. The second group is the
set of counties that have hospitals but did not lose any. The third group is the
set of hospitals that lose a hospital over the time frame. Counties in this group
are omitted from the calculations once they lose a hospital. This implies that
the any change in the averages are not caused by hospital closures. Any
difference in time trend, therefore, captures underlying differences in the
counties not directly caused by a hospital closure because we omit counties that
experienced a closure. Note that although the average incomes are similar in
1990, counties losing a hospital have a slower rate of income growth than
counties not losing a hospital. This suggests that including ‘‘nonclosure’’
Table2: Summary Statistics
Variable (1992 Value)MeanRange
Per capita income ($1990)
Number in sample
476 HSR: Health Services Research 41:2 (April 2006)
counties as a control group may bias the estimate of the estimated effect of the
Fixed effect regression models were estimated using various economic indi-
cators as dependent variables (Table 3). The estimates suggest that hospital
if the hospital is the only hospital in the community. Model 1 shows that
in real PCI of roughly $703 (in 1990 currency). The lagged term is insignif-
year of the closure. Although this is inconsistent with the amenity hypothesis
which postulates the loss of the amenity (hospital) acts as an impediment to
economicgrowth, thepattern of the magnitudes and signs of the coefficients is
data might lead to statistically significant estimates. Although not marked in
the table, by dividing the coefficient by the SE and using t-statistic tables, one
can see the once-lagged estimate here is significant at the 10 percent level
Per Capita Income (1990 Dollars)
Counties are omitted from calculations after they lose a hospital.
Average per Capita Income, by County Type
Never Has Hospital
Never Loses a Hospital
Subsequently Loses a Hospital
Figure1: Mean per Capita Income and Time Trend, by County Type
The Effect of Rural Hospital Closures 477
(p50.06). This pattern tends to hold in the models with other outcomes as
well. Model 2 indicates that closure of the only hospital leads to a 4 percent
decrease in PCI, again fully realized in the first year of closure. Unemploy-
ment increases by 1.6 percentage points as a result of the closure, with only
about half of that effect in the year of the closure. Neither the population nor
the labor force appears to be affected by the hospital closure.
Overall, while the loss of the sole hospital imparts a significant negative
effect on the county economy, there is little evidence that a hospital closure
Table3: Regression Results
Closure of ANY hospital
Had ANY hospital closure323.167
Leading and lagged terms
Any closure in t11
Any closure in t
Any closure in t?1
Any closure in t?2
Closure of ONLY hospital
County has no hospital
Leading and lagged terms
Sole closure in t11 323.167
Sole closure in t
Sole closure in t?1
Sole closure in t?2
nSignificant at 5%.
nnSignificant at 1%.
Estimated White (1980) standard errors in parentheses.
Year and county fixed effects also included but not listed here.
478 HSR: Health Services Research 41:2 (April 2006)
when another institution remains open affects the county’s economy in the
long run. Income (whether measured in levels or logs) does diminish with the
closure of any hospital, but again the pattern (as well as the insignificance on
the ‘‘Had ANY Hospital Closure’’ variable) suggests that the effect is short-
lived. Indeed, in models in which we use three lagged terms, the third lag was
It is instructive to compare our results to estimates from I/O models.
Recall that I/O models contain no uncertainty measures, so it is difficult to
compare the models statistically. Furthermore, I/O models use aggregate
measures (total income of the community) while we are using average meas-
ures (PCI). A quick back-of-the-envelope comparison is instructive, however.
Doeksen, Loewen, and Strawn (1990) estimate a decrease of $901,400 in
during that year, this decrease translates (using their county) to a 0.8 percent
change in income compared with our estimate of 0.5 percent (computed as
0.025 ? 0.017 ? 0.040 1 0.027 5 0.5 for the Log(PCI) model in Table 3).
Given the different approaches, the similarity in our findings is striking.
As mentioned previously, we explore an alternative measure of
the economic impact of hospital closures by calculating the proportion of a
county’s population that resides within 15 miles of an operating short-term
general hospital. This method helps compensate for some of the specificity
lost by our inability to construct models at the community-, rather than
GIS software allows the drawing of 15-mile-radius circles around a hos-
pital; these circles are then mapped onto census tracts to estimate the pro-
portion of county population located within 15 miles of a hospital. We
estimate population-weighted measures at the census tract level, using the
2000 census, to provide a more appropriate estimate of ‘‘hospital coverage.’’
The closure of a hospital serving much of a county’s land area but a small
serving a small land area but a large population. We use the population
measure as the treatment variable in the regressions in this subsection, al-
though the area-weighted measure yielded similar but slightly less significant
results. This class of models is comparable with those of the previous sub-
section in simplicity, but uses a continuous measure of hospital closure rather
than a simple dichotomous measure. Table 4 presents the results of these
models. The anticipated sign is opposite that of the previous models; an in-
crease in the proportion of a county served by a hospital should increase the
economic health of the community.
The Effect of Rural Hospital Closures 479
In general, results match our expectations. A 10 percent decrease in the
coverageofa county(abouthalftheaveragechangefora countylosingitssole
hospital) leads to a $130 (or about 0.9 percent as estimated by the model with
log PCI) decrease in per capita income,and a 0.3 percent point increase in the
unemployment rate. The leading terms here are statistically significant, sug-
gesting that a negative shock to the unemployment rate of a county may be
when using a completely different metric of hospital closure we find statistically
significant effects of the anticipated sign. Although the magnitudes are slightly
the true coverage of the hospital. Thus, these models provide additional
Table4: Radii Measures
Pct of population
within 15 miles of hospital
Leading and lagged terms
Change in population
within 15 miles of
hospital in t11
Change in population
within 15 miles of
hospital in t
Change in population
within 15 miles of
hospital in t?1
Change in population
within 15 miles of
hospital in t?2
nSignificant at 5%.
nnSignificant at 1%.
Estimated White (1980) standard errors in parentheses.
Year and county fixed effects also included but not listed here.
480 HSR: Health Services Research 41:2 (April 2006)
support for our main finding that a closure of the sole hospital affects the
economy of the county.
CONCLUSION AND POLICY IMPLICATIONS
Although we find that a hospital closure per se does not negatively affect the
long-run economic health of a community, losing the sole hospital in the
county results in a considerable negative effect on the economy. We account
for endogenous closure by including leading terms of closures and find little
evidence of an Ashenfelter Dip; this may be because of our careful construc-
tion of a control group. Although there are no guarantees that it is the hospital
closure per se that led to the economic decline of the county, our estimates
reconcile with previous work and seem reasonable.
The results presented here suggest that the closure of a rural county’s
sole hospital decreases the economic well-being of the community and likely
places the local economy in a downward cycle that may be very difficult to
recover from. This effect was not only statistically significant but policy sig-
income of 1.5 percent. The finding that the economic impact is an issue in
communities where the sole hospital closed, an event that would almost al-
ways occur in rural areas, suggests important considerations for policy makers
involved with hospital regulation. The traditional charge of health care reg-
ulators has been to increase economic efficiency, which places a particularly
acute financial pressure on small rural hospitals. Because of low volumes it is
difficult for these facilities to manage profitability under fixed reimbursement
systems such as Medicare’s PPS, as they experience significantly greater var-
iability ininpatientdemandacrossyears, with aresultant instability inaverage
costs per discharge (Dalton, Holmes, and Slifkin 2003a,b).
Thus, regulations imposed to increase hospital efficiency may have
spillover effects; the economy is affected if the regulations induce the
hospital to close. These economic effects, of course, compound any negative
effects on health and health care access in rural communities because of the
have created barriers to receipt of crucial emergency services (Reif, Des
Harnais, and Bernard 1999), increased travel time to inpatient care with sub-
stantial effecton outcomesinthecaseofcertainclinicalconditions(Fleminget
al. 1995; Reif, Des Harnais, and Bernard 1999), and resulted in decreased
utilization (Rosenbach and Dayhoff 1995) and a loss of access to a proximate
The Effect of Rural Hospital Closures481
source of primary health care (Bindman et al. 1990). Combined with the
decrease in physician supply because of hospital closure (Hart, Pirani, and
Rosenblatt 1991b) and the economic downturn demonstrated in this paper,
access to primary health care would likely continue to decrease.
It should be noted, however, that the closure of a hospital is not a ran-
volumes, were in poor financial condition, and had for-profit ownership
(McKay and Coventry 1995; Rosenbach and Dayhoff 1995). It is reasonable
to ask whether hospitals that close because of low volumes are necessary
small population base, or are providers that are not utilized by their commu-
nities, and so are appropriately closed. To the extent that the latter is true, one
perspective is that a hospital closure, while painful for a community, is the
market’s mechanism for enforcing minimum quality standards. There is,
however, almost no evidence regarding the quality of care in hospitals that
have closed, just as there is almost no research on quality of care in small rural
hospitals generally (IOM 2005). One study that interviewed physicians in
closure communities found that over three-quarters of those interviewed felt
that thequalityofcare inthe closedhospital wasaverage orbetter(Piranietal.
Although it was beyond the scope of our analysis to examine the
in these hospital closures allows for discussion of appropriate policies to
protect health care access in rural America. Policy makers have shown that
they are willing to accept a certain degree of inefficiency and/or economic
risk, such as that associated with the Critical Access Hospital designation, in
order to avoid the negative spiral associated with a hospital closure.
In the absence of empirical work, one can make the assumption that the
universe of closed hospitals includes both high-quality institutions with an
insufficient population base in their community to be able to financially sur-
vive, as well as institutions that were underutilized by their community
create reimbursement policy that helped sustain all necessary providers
with adequate quality of care, while allowing those of low quality to close.
An implicit choice when designing reimbursment policy is whether it is pref-
erable to preserve access for as many rural communities as possible, recog-
nizing that some institutions will be kept open that possibly should have
closed, or whether it is preferable to allow the market to dictate closure of low
482 HSR: Health Services Research 41:2 (April 2006)
will also close because of the financial realities of operating in a sparsely
Factors that are associated with closures, such as quality concerns, can
also be addressed by policy makers. Consistent with national quality im-
provement efforts, the CAH enabling legislation also created the medicare
rural hospital flexibility program, which supports quality initiatives in rural
hospitals by requiring CAHs to have a credential and quality assurance
mechanism and state certification beforeconversion,aswellasprovidesfunds
for quality-related activities (Casey and Moscovice 2004). The linking of the
financial protection of cost-based reimbursement with a program to improve
quality of care shows recognition of both the importance of small hospital
survival to rural communities as well as the need to provide support for im-
provement in services. The findings from this analysis support the continu-
ation of such initiatives.
This research was funded by the Federal Office of Rural Health Policy, co-
operative agreement #6U1CRH00027-03. All opinions expressed here are
those of the authors and are not necessarily those of ORHP. Helpful com-
mentsprovided by participantsatthe2003 NationalRuralHealthAssociation
and Southern Economic Association annual meetings and the Sheps Center
Work in Progress Lunch, two anonymous referees, and Catherine McLaugh-
lin. All remaining errors are, of course, our own.
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