Rural Hospitals: Factors That Affect Risk of Closure

Published by the Government Accountability Office on 1990-06-19.

Below is a raw (and likely hideous) rendition of the original report. (PDF)

                                             RURAL HOSPITALS
                                             Factors That Affect
                                             Risk of Closure

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             United States
GAO          General Accounting  Office
             Washington, D.C. 20548

             Human Resources           Division


             June 19,199O

             Dear Congressional Requesters:’

             Because of your concern about increasing numbers of rural hospital
             closures and their impact on elderly and poor rural residents, you asked
             us to look at a series of related issues. This report addresses one of the
             issues-the factors making rural hospitals vulnerable to closure.”
             Appendices II and III contain our methodology and supporting tables.
             Another report, to be issued later this year, will address your remaining
             questions and provide a broader discussion of factors influencing

             Rural hospitals have faced an enormous challenge in the last decade,
Background   responding to the changing role of hospitals in the current medical envi-
             ronment. Many medical procedures and illnesses that once required hos-
             pitalization now are treated in outpatient settings, thus reducing the
             demand for inpatient services among both rural and urban populations.
             Moreover, new technologies that greatly enhance the diagnostic and
             treatment capability of the health care system are costly and require
             high volume use to justify their purchase. Since urban hospitals are on
             average larger and have a higher volume of patients, they have an
             advantage in obtaining this new technology.

             Studies show that rural hospitals provide a core of basic services to
             local rural communities. Also, they are more likely to provide long-term
             care nursing services than urban hospitals of similar size,:’ Although
             most rural hospitals provide care for less complex medical conditions,
             they still must compete for patients with the more technologically
             sophisticated urban hospitals. Changes in the delivery of health services
             and in the structure of hospitals’ insurance reimbursement appear to
             complicate rural hospitals’ ability to attract patients and generate

             The most important change in the reimbursement of hospitals occurred
             in 1983 when the Congress established a prospective payment system
             (PPS) for inpatient services provided to Medicare beneficiaries, PPSsets

             ’ Congressional requesters are listed in app. I.

             %ther issues included the impact of closures on (I) rural residents’ accessto hospital care and (2)
             Medicare program costs.

             “See L. G. Hart, R. A. Rosenblatt, and B. A. Amundson, “Is There a Role for the Rural Hospital‘?”
             Working Paper, Vol. 1, No. 1, WAMI Rural Health Research Center, IJniversity of Washington, 1989.

             Page 1                                             GAO/HRD-90-134   Factors   in Rural Hospital   Closures
                        E239993                                       i

                        payment at a predetermined amount, based on the 1981 average cost of
                        treatment for each patient with a similar diagnosis or requiring a similar
                        treatment procedure. These payments are adjusted for certain hospital
                        characteristics and updated annually. Hospitals with costs below this
                        amount make a profit from the system; those with costs above the pre-
                        determined payment lose. The intent of PPS was to control costs by
                        giving hospitals financial incentives to deliver services more efficiently.

                        During the first 3 years of PPS (roughly fiscal years 1984-86), the
                        majority of urban and rural hospitals profited from treating Medicare
                        patients, but profits declined each year. Urban hospitals, however, aver-
                        aged higher PPS profits than rural hospitals. By the 4th year of PPS, most
                        rural hospitals lost money on their Medicare patients, while most urban
                        hospitals still profited, although substantially less than in previous
                        years. However, the difference between urban and rural PPS profits may
                        be narrowing since the fourth year of PPS, as the Congress has increased
                        the standardized amount (the amount on which PPS payments are based)
                        at a higher rate for rural than for urban hospitals.

                        Declining utilization and changes in hospital payment systems have
                        placed considerable financial pressures on both urban and rural hospi-
                        tals. From 1986 to 1988,260 U.S. hospitals closed-about half in rural
                        areas. Although many studies have described the characteristics of
                        closed hospitals, there have been few efforts to identify the combination
                        of factors that most increase a hospital’s risk of closure in the era of
                        Medicare’s PPS.

                        Our objectives in this study were to (1) determine the financial charac-
Objectives, Scope,and   teristics associated with rural hospital closures,4 (2) determine the role
Methodology             of Medicare payment in rural closures, and (3) identify the operating
                        and environmental characteristics associated with financial distress and
                        a high risk of closure. In our analysis, we compared the operating, envi-
                        ronmental, and financial characteristics of hospitals that closed with
                        those that remained open. We used a statistical technique, logistic
                        regression, to identify factors that may contribute to rural hospitals’
                        risk of closure. Our analysis included all rural and urban nonfederal,
                        short-stay general hospitals in operation in 1985.” Factors assessed for

                        4A closure was defined as the discontinuance of the provision of inpatient acute care medical services
                        for any time period during 1980-88.
                        “To identify these hospitals, we used the American Hospital Association (AHA) Annual Survey data
                        files and the Medicare Hospital Cost Report Information System (HCRIS) Minimum Data Set.

                        Page 2                                       GAO/HRD90-134       Factors   In Rural Hospital   Closures

                     their effect on hospitals’ risk of closure are identified in table III. 1. We
                     assessed the likelihood of closure during the 4-year time period 1985-88
                     (see app. II for further details on our methodology).

                     Closed rural hospitals suffered substantial and increasing financial
Results in Brief     losses during the 3 years before closure. Their losses were due primarily
                     to their high cost per case relative to other, similar hospitals. Losses on
                     Medicare patients were not a major factor causing most hospital clo-
                     sures. Indeed, in the 3 years before closure, most hospitals made more or
                     lost less money from treating Medicare patients than from treating other
                     patients. However, for about a third of rural closures with fewer than
                     60 beds, the converse was true; they lost more from treating Medicare
                     patients than from treating other patients. Consequently, Medicare may
                     have contributed disproportionately to the losses and thus the financial
                     distress of this group.

                     Contrary to the perception of many, a hospital’s location in a rural area
                     did not raise the risk of closure over and above that of a comparable
                     urban hospital. Rather, rural hospitals were more vulnerable to closure
                     because several factors associated with a high risk of closure were more
                     prevalent among these hospitals. One factor-low      occupancy-
                     increased this vulnerability substantially and tends to be associated
                     with hospitals having high costs relative to other hospitals. Other major
                     factors associated with a higher risk of closure were small size and own-
                     ership by a for-profit entity. These findings suggest that strategies to
                     prevent rural closures should target hospitals with high risk factors
                     rather than all rural hospitals.

                     Between 1986 and 1988, the rate of hospital closures was 29 percent
Characteristics of   higher in rural than in urban areas (5.3 vs. 4.1 per 100).(i As shown in
Urban and Rural      table 1, closed rural hospitals were predominantly small hospitals
Closures Differ      (fewer than 60 beds) and were about equally distributed among the
                     ownership types.7 Also, over three-quarters of rural hospitals had occu-
                     pancy rates of less than 40 percent. In contrast, urban closures were
                     more evenly distributed across the hospital bed size groups and were
                     largely nonprofit and for-profit hospitals. Fewer (60 percent) of the

                     “(No. of community hospital closures in 1986-88/k&J no. of hospitals of this type in 1985) X 100 = 4-
                     year rate of hospital closure. See table III.2 for closure rates by bedsize, types of ownership, occu-
                     pancy, and census regions.

                     7The three ownership types were public, private nonprofit, and for-profit

                     Page 3                                        GAO/HRD-90-134     Factors    in Rural Hospital   Closures

                      closed urban hospitals had occupancy rates of less than 40 percent. Dif-
                      ferences among closed hospitals reflect, in part, differences that exist
                      among open rural and urban hospitals. For example, more rural than
                      urban public hospitals closed, but there were many more rural than
                      urban public hospitals in operation during this period.

                      Both rural and urban hospitals that closed had substantial and
Financial Symptoms    increasing financial losses on patient care during the 3 years prior to
of Distress Precede   closure (see table 111.3).Hospitals that remained open also generally
Closure               declined in profitability during fiscal years 1984-87.N As would be
                      expected, the closed hospitals’ decline was much steeper, and they were
                      less profitable throughout the period. Losses among closed hospitals
                      were generally a result of their higher median cost per discharge com-
                      pared with open hospitals. Median cost per case!’in the year prior to
                      closure was 24-29 percent higher in rural hospitals that closed than in
                      rural hospitals that remained open. Yet the hospitals that later closed
                      generally were not treating more complex medical cases.1”

                      One cause of high costs per case prior to closure was low occupancy.
                      Closed hospitals had occupancy rates that were 35 to 52 percent lower
                      in the year prior to closure than those that remained open. At lower
                      rates of occupancy, a hospital’s fixed costs represent a greater propor-
                      tion of its operating costs, raising the cost per discharge. Low occupancy
                      also may partly explain the higher staffing ratios” found in the closed

                      Medicare was not a major factor contributing to the financial decline
Medicare LossesNot    and closure of most rural or urban hospitals that closed between 1985
a Major Factor in     and 1988. Before closure, hospitals generally fared better from treating
Closures              Medicare patients than from treating other patients. For example,
                      during the 2nd year before closure, most hospitals lost from treating
                      Medicare patients, but their losses generally were less on Medicare

                      ‘Data are for hospital cost reporting periods for fiscal years 1984-87.

                      “Total expenses per discharge, adjusted to exclude outpatient and other expenses not related to pro-
                      vision of acute care hospital services.

                      ‘“We used the Medicare case mix index as an indicator of the complexity of a hospital’s patients (see
                      app. II).

                      ’ ‘Full-time equivalent personnel per average daily patient census, adjusted to exclude outpatient and
                      other nonacute care hospital services.

                      Page 4                                        GAO/HRD-90-134      Factors   in Rural Hospital   Closures

Table 1: Comparison of Charactirtics   of
Open and Closed Hospltalr

                                            Number of hostAtalr
                                                                                            Closed hospitals,
                                                                                            1985-88 (N ~280)

                                                                                                                                      h5”2” PIa

                                            Size (percent)
                                            6-49                                                 77.1      38.3                39.5                   5.3
                                            50-99                                                19.3      27.5                33.2                  14.9
                                            100-199                                               3.6      23.3                20.9                  257
                                            200+                                                  0.0      10.8                 6.4                  54.2

                                            OwnershID (Percent)
                                            Public, nonfederal                                   30.7       8.3                43.8                  14.7
                                            Private, nonprofit                                   39.3      51.7                47.6                  67.4
                                            For-orofit                                           30.0      40.0                 8.6                  17.9

                                            Occupancy (percent)
                                            Less than 20                                         33.6      19.2                 9.6                   1.9
                                            20-39                                                45.0      40.8                42.7                  14.2
                                            40-60                                                14.3      27.5                34.3                  33.9
                                            61or more                                             7.1      12.5                13.4                  50.0

                                            Census region (percent)
                                            North Central:
                                              E.N. Central                                       10.7      18.3                14.2                  17.3
                                              W.N. Central                                       11.4       9.2                23.1                   6.3
                                              New England                                           1.4     1.7                     2.4               6.1
                                              Mid-Atlantic                                          4.3    14.2                     4.0              16.0
                                            South:                                                                                                  --
                                              South Atlantic                                      7.9       6.7                12.5                  15.4
                                              ES. Central                                        11.4       5.8                12.1                    5.5
                                            -KS.   Central                                       37.1      25.8                15.9                  12.3
                                              Mountain                                           10.0       2.5                  9.6                  3.8
                                              Pacific                                             5.7      15.8                     6.4              17.3
                                            Note: Data are for 1985.
                                            %ural4-year rate of hospital closure was 5.3 percent.
                                            bUrban 4-year rate of hospital closure was 4.1 percent.

                                            Page 5                                         GAO/HRD-90434   Factors   in Rural Hospital          Cloeures



                      patients than on their business as a whole. About three-quarters of
                      the closed hospitals either profited from Medicare patients or fared
                      better from treating Medicare patients than other patients in the 2nd
                      year before closing.

                      The smallest closed rural hospitals, however, lost significantly more on
                      Medicare than other open or closed hospitals. Specifically, 35 percent of
                      the rural closures with fewer than 50 beds had PPS operating margins (a
                      measure of profitability on Medicare patients) that were lower than
                      their total operating margins (a measure of overall profitability on
                      patient care). This compares with about 19 percent of larger rural and
                      urban hospitals that closed.iz

                      For most small rural closures, we concluded that large Medicare losses
                      were primarily due to their relatively high cost per discharge. Although
                      the revenue of closed and open small rural hospitals was not very dif-
                      ferent,‘:’ the median cost per discharge was dramatically higher (27 per-
                      cent) for the closed hospitals in the year prior to closure. Despite this
                      finding, we cannot rule out that Medicare may have contributed dispro-
                      portionately to the financial distress and closure of 35 percent of these
                      hospitals and 19 percent of other closures.

                      We found that a number of underlying factors made hospitals more vul-
Multiple Factors      nerable to closure. Four factors had a large effect and were considered
Contribute to Rural   of particular importance. I4Hospitals that had fewer than 100 beds, had
Hospitals’ Closure    occupancy rates of 40 percent or less, were owned by a for-profit entity,
                      or were located in either the Northeastern or Southern regions of the
                      United StatesI” were at least 3 times more likely to close than the com-
                      parison groups we analyzed.l”

                      “Analysis based on 2nd year before closing.

                      “‘The median revenue per discharge was 6 percent lower in the closed than open hospitals.

                       ‘^‘A large effect was measured by the odds ratios presented in table 111.4.See app II, section on “Sta-
                      tistical Techniques” for further discussion of odds ratios.

                      ‘“The South was defined as the West South Central, South Atlantic, and East South Central regions of
                      the United States. The Northeast was defined as the New England and Middle Atlantic census

                      “‘We defined our threshold of high-risk as 3 times the risk of closure of the comparison group
                      included in the analysis. See table III.4 to identify the comparison “reference group” for each

                      Page 6                                         GAO/HRD-90-134      Factors   in Rural Hospital   Closures

                           Rural hospitals are more vulnerable as a group because several of the
                           characteristics associated with hospitals’ higher risk of closure (e.g.,
                           smaller size, low patient volume) are more prevalent among rural than
                           urban hospitals. However, their vulnerability appears to be a result of
                           these characteristics rather than their location in a rural area. Control-
                           ling for differences in operating characteristics, rural hospitals did not
                           face a greater risk of closure than urban hospitals during the 1985-88

Characteristics of         We found that a number of hospital operating and environmental char-
Hospitals at Risk of       acteristics (bed size, occupancy, percent Medicaid days, case mix, area
                           wages, ownership, and geographic region) had a modest to large effect
Closure                    on the risk of closure (see table 111.4).With the exception of ownership
                           status, the effect of these characteristics on the risk of closure did not
                           vary significantly between rural and urban hospitals.17

                           When we examined the effect of each characteristic while holding the
                           other characteristics constant, we found that:

                       . Hospitals with fewer than 50 beds and those with 50-99 beds were 12
                         and 4 times more likely to close, respectively, than hospitals with 200 or
                         more beds.
                       l Hospitals with very low volume (occupancy rates of less than 20 per-
                         cent) were 9 times more likely to close than hospitals with occupancy
                         rates of 61 percent or more. Hospitals with low to modest occupancy
                         rates (20-39 percent) were 4 times more likely to close than hospitals
                         with occupancy rates of 61 percent or more.
                       l Hospitals owned by a for-profit entity were more likely to close than
                         publicly owned hospitals. This effect was larger for rural than urban
                         hospitals. While rural for-profit hospitals were 8 times as likely to close
                         as publicly owned rural hospitals, urban for-profit hospitals were 5
                         times as likely to close as publicly owned urban hospitals (see
                         table 111.5).
                       . Hospitals with a relatively large percentage of Medicaid inpatient days
                         (11 percent or more) had a 1.5 times higher risk of closure than hospi-
                         tals with fewer Medicaid days.

                           17This statement is based on the statistical insignificance of most of the “interaction terms” in our
                           model. See app. II for information on interaction terms.

                           Page 7                                         GAO/HRD90-134       Factors   in Rural Hospital   Closures


. Hospitals with higher case mix indexes had a lower risk of closure. A
  lo-percent higher case mix for the average hospital reduced the risk of
  closure by 33 percent.lH
l Hospitals facing higher labor costs were more likely to close. For a
  lo-percent increase in the area wage index, the probability of closure
  increased by about 23 percent.‘”
l Hospitals in the North Central regions had twice the risk of closure of
  those in the West; hospitals in the Northeast and South had about 4
  times the risk of closure of hospitals in the West.

    Our analysis did not provide evidence that hospitals with a large share
    of Medicare patients were at greater risk of closure. When controlling
    for differences in hospitals’ operating characteristics, the odds of clo-
    sure were not greater for hospitals with Medicare inpatient days of
    60 percent or more when compared to hospitals with an average percent
    of Medicare days. acB
                        This finding did not differ for urban and rural

    Our data do not permit distinguishing between the effects of each indi-
    vidual factor used to indicate a declining or depressed economy. How-
    ever, we found that, as a group, factors such as the unemployment rate
    and low per capita income were important determinants of risk.?’ Evi-
    dence from a descriptive analysis we presented in another reporP also
    showed that hospitals in areas with low per capita income and increased
    unemployment were more likely to be financially distressed. We will fur-
    ther explore the role of economic factors in closure in the report we plan
    to issue later this year.

    IsThis response is measured at the mean of the case mix index (1.13). At other levels of the case mix
    index, the estimated effect will differ since the logit function is not a linear relationship.

    “‘This response is measured at the mean of the wage index (0.98). At other levels of the wage index,
    the estimated effect will differ since the logit function is not a linear relationship.

    ““We found some evidence that having relatively few Medicare days increased a hospital’s risk of
    closure. This finding, however, appears to be sensitive to the data source and the number of observa-
    tions in the analysis and therefore, should be interpreted cautiously. See app. II for a discussion of the
    limitations of the data source.

    “‘All the market area demand variables were tested as a group: median income, median education,
    unemployment, population density, population age 66 and over, change in population, population,
    and Herfindahl index.

    22Rural Hospitals: Federal Leadership and Targeted Programs Needed (GAOIHRD-90-67) June
    1990, p, 19.

    Page 8                                         GAO/HRD-90-134      Factors   in Rural Hospital   Closures

Rural Location Did Not     We found that closure rates, not adjusted for differences in hospital
Increase Risk of Closure   operating characteristics, were higher for rural than urban hospitals
                           during the 1985-88 time period. However, when holding operating and
                           environmental characteristics constant, only for-profit rural hospitals
                           had a slightly higher risk of closure than urban hospitals. Since for-
                           profit hospitals represent a small share of all rural hospitals (fewer than
                           10 percent), the effect of this finding on the number of closures was
                           negligible. Our analysis suggests that rural hospitals’ higher closure rate
                           was due to a greater prevalence of high-risk characteristics (e.g., small
                           size, low occupancy) among them. Thus, any strategies to prevent rural
                           closures should target rural hospitals with high-risk characteristics,
                           rather than all hospitals located in a rural area.

                           We are sending copies of this report to the Secretary of Health and
                           Human Services, the Director of the Office of Management and Budget,
                           and other interested parties. We also will make copies available to
                           others on request. Please call me at (202) 275-5451 if you or your staff
                           have any questions concerning the report. The major contributors to this
                           report are listed in appendix IV.

                           Janet L. Shikles
                           Director of Health Financing
                              and Policy Issues

                           Page 9                           GAO/HRD-90-134   Factors   in Rural Hospital   Closures

Appendix I
List of Congressional
Appendix II                                                                                                   13
Objectives, Scope,and   Financial Characteristics Associated With Closure
                        Factors Associated With Risk of Closure
Appendix III                                                                                                  21
Supporting Tables
Appendix IV                                                                                                   27
Major Contributors to
This Report
Tables                  Table 1: Comparison of Charactistics of Open and Closed                                    5
                        Table III. 1: Factors Suspected to Influence Risk of                                  21
                            Hospital Closure
                        Table 111.2:Rural and Urban Community Hospital Closure                                22
                            Rates (1985-88)
                        Table 111.3:Hospitals’ Median PPS Margins, Operating                                  23
                            Margins, and Total Margins (1984-87)
                        Table 111.4:Likelihood of a Community Hospital Closure                                24
                            by Selected Hospital Characteristics (1985-88):
                            Logistic Regression Results
                        Table 111.5:Likelihood of Closure by Bed Size and                                     25
                            Ownership: Adjusted Rates
                        Table 111.6:Logit Estimates of Hospital Closure                                       26

                        Page 10                         GAO/IiRD-90-134   Factors   in Rural Hospital   Closures



        AHA        American Hospital Association
        ARF        Area Resource File
        HCFA       Health Care Financing Administration
        HCIA       Health Care Investment Analysts
        HCRIS      Medicare Hospital Cost Report Information System
        HHS        Department of Health and Human Services
        MSA        metropolitan statistical area
        PPS        prospective payment system

        Page 11                         GAO/IUD-W-134   Factors   in Rural Hospital   Closures
Appendix I

List of CongressionalRequesters

U.S. Senators   James R. Sasser
                Kent Conrad
                J. James Exon
                John D, Rockefeller IV
                Carl M. Levin
                Richard Shelby
                Tom Daschle
                Lloyd Bentsen
                Strom Thurmond
                Bob Graham
                Harry Reid
                Larry Pressler
                John C. Danforth
                Steve Symms
                Ernest F. Hollings
                Thad Cochran
                Terry Sanford
                Albert Gore, Jr.
                Quentin N. Burdick
                Donald W. Riegle, Jr.
                Christopher S. Bond
                Mitch McConnell
                Barbara Mikulski
                Howard M. Metzenbaum
                Robert W. Kasten, Jr.
                John Breaux ’
                Charles E. Grassley
                John Heinz
                Sam Nunn
                J. Bennett Johnston
                Tom Harkin
                William L. Armstrong

                Page 12                  GAO/IiRD-99-134   Factors   in Rural Hospital   Closures
Appenldix II

Objectives, Scope,and Methodology

               This study focused on community hospitals that closed during 1985-88,
               the period after implementation of Medicare’s prospective payment
               system. The specific objectives were to (1) determine the financial char-
               acteristics associated with rural closures, (2) determine the role of Medi-
               care payment in rural closures, and (3) identify hospital operating and
               environmental characteristics associated with financial distress and the
               risk of closure

               Through a review of the literature and discussions with experts in the
               field, we identified hospital and environmental characteristics that were
               suspected or documented as related to closure. We then constructed
               indicators of these measures from several data sources. Information on
               the organizational characteristics, utilization history, and financial per-
               formance of rural and urban community hospitals was obtained from
               the Medicare Hospital Cost Report Information System 1’1+31-I’PS4’   Min-
               imum Data Set and the American Hospital Association’s Annual Survey
               and closure files for 1980-88. Information on the external operating envi-
               ronment of hospitals was obtained from the Department of Health and
               Human Services’s 1988 Area Resource File (ARF).

               During the initial phase of this work, we validated a sample of urban
               and rural community hospital closures listed in the AIIA closure files
               (1980-87). One state was randomly selected from each of the nine census
               regions, and all reported closures in that state were validated. Hospitals
               listed as questionable 1988 closures in a 1989 publication were also vali-
               dated.:! We used the AIIA definition of a community hospital as a
               nonfederal, short-term, general and other specialty hospital, whose
               facilities are available to the public. A closure was defined as the discon-
               tinuance of the provision of inpatient acute care medical services for
               any time period during 1980-88. Any hospital that closed and reopened
               during the study period was classified as a temporary closure but not
               excluded. Hospitals that did not meet our criteria for closure (for
               example, consolidations, changes of ownership) were excluded from the
               list of closures.

               ’ l&porting periods for fiscal years 1984-87.
               “‘AHA Closure List Questioned,” Modern Healthcare, Mar. 3, 1989, p. 6., and “AHA’s ‘86,‘87 Closure
               Data Questioned,” Modern Ilealthcare, Mar. 17,1989, p. 6,

               Pa@? 13                                         GAO/HRD-90-134   Factors   in Rural Hospital   Closures
                       Appendix II
                       Objectives, Scope, and Methodology

                       To identify the financial characteristics associated with closure, we ana-
Financial              lyzed data on two commonly used measures of hospitals’ profitability
Characteristics        (total operating margin:! and total margin ) in the 4 years before closure.
Associated With        In this analysis, we compared the financial characteristics of open and
                       closed hospitals, stratified by size and urban/rural location. Using bivar-
Closure                iate techniques, we compared open and closed hospitals’ median profit
                       margins, costs, and revenues5 and calculated rates of closure for hospi-
                       tals, given certain levels of profitability.

                       Additional analyses were undertaken to assess Medicare’s contribution
                       to the overall profits and losses of rural and urban hospitals. We com-
                       pared PPS costs and revenues of closed and open hospitals stratified by
                       size and urban/rural location. Also, we compared hospitals’ PPSmargins”
                       with their total operating margins. For hospitals that experienced PPS
                       losses, we compared their PPSand operating margins to determine
                       whether their losses on Medicare patients were more severe than on
                       other patients.

                       PPSpayment rules changed during our study period (for example, pay-
                       ment rates increasingly were based on national average costs, rather
                       than hospitals’ own costs). Therefore, while our methodology allows us
                       to assess whether PPSpayment was a major factor influencing the clo-
                       sure of hospitals between 1985 and 1988, these results must not be
                       assumed to reflect the pattern for more recent or future closures.

                       To identify factors that might contribute to a hospital’s financial dis-
Factors Associated     tress and ultimate closure, we used several approaches. First, we com-
With Risk of Closure   pared the characteristics of closed and open hospitals. Second, we

                       “The operating margin is used to measure profitability on all patient care operations and is: (net
                       patient revenue - operating expenses)/net patient revenue. Because for many hospitals, net patient
                       revenue does not include all operating revenue, this measure understates operating profitability by an
                       estimated l-l/‘2 to 2 percent (HCIAestimate; see footnote 6).
                       4The total margin measures overall profitability and is: (total revenue - expenses)/total revenue

                       “Much of this work was undertaken through a GAO contract with a private firm, Health Care Invest-
                       ment Analysts (IICIA), Inc. HCIA also obtains its data from the Medicare cost reports. However, it
                       obtains the original cost report source documents and has accessto financial information that is not
                       included in the Medicare HCRIS data set. HCIA extracted more detailed information on the closed
                       hospitals than were available through HCRIS. For measures that could be constructed using both the
                       GAO and HCIA data sets, we found that the estimates resulted in similar patterns and trends.
                       “The PI’S margin is used to measure profitability on Medicare patients and is calculated: (PI’S oper.
                       ating revenue - PI’S operating costs)/PPS operating revenue. Our PPS margin does not include a
                       hospital’s capital costs or capital cost reimbursement.

                       Page 14                                       GAO/HRD-90-134      Factors   in Rural Hospital   Closures

                          Appendix II
                          Objectives, Scope, and Methodology

                          computed closure rates for urban and rural hospitals by factors we sus-
                          pected influenced the risk of closure. Using a statistical technique,
                          logistic regression, we assessed the individual and combined influence of
                          the multiple factors associated with closure. This technique also per-
                          mitted us to assess the effect of a hospital’s location in an urban or rural
                          area, while holding constant other factors that could influence closure.

A Description of the      We used a multivariate logit model to quantify the impact of hospital
Closure Model             operating and environmental characteristics on the probability of clo-
                          sure. We observed the status of 5,524 community hospitals (nonfederal,
                          short-stay general hospitals) between 1985 and 1988. During this
                          period, 260 hospitals closed. To estimate the statistical relationship
                          between the likelihood of closure and our selected characteristics, we
                          obtained maximum likelihood estimates from a logistic function.7 The
                          dependent variable in this model is the closure status of the hospital
                          during 198588. The variable equals 1 if the hospital closed between
                          1985 and 1988; otherwise the value of the variable is 0.

Factors Included in the   The independent variables included in our model are characteristics of
Closure Model             the hospital and its market environment. For each hospital, expected
                          financial performance depends on projected revenues and costs over the
                          4-year period, 1985-88. The operating characteristics included in the
                          regression model are considered direct or indirect determinants of hospi-
                          tals’ revenues and costs. We recognize that in some cases, the variables
                          are indicators of more than one operating characteristic of a hospital
                          affecting costs and revenues. For example, a hospital’s bed size is an
                          indicator of its capacity, capital costs, and mix of services.

                          The regression model was used to assess the effects of the independent
                          variables on the likelihood of closure, while controlling for the effects of
                          the other hospital and market characteristics. For variables obtained
                          from the AHA Annual Survey, we used 1985 values of the variables to
                          estimate the relationship between closure and the observed hospital or

                          7The logistic function is a nonlinear estimation technique that is appropriate when the dependent
                          variable is dichotomous. Here the technique is necessary because only two conditions are considered
                          for each institution-either   it remained open during the entire period or it closed. The estimates were
                          Jxrformed with the author-supported SAS logistic procedure. For a detailed description of the logit
                          model, see #JanKmenta’s Elements of Econometrics, 2nd ed. (New York: MacMillan Publishing Co.,
                          1986), or Robert S. Pindyck and Daniel L. Rubinfeld, Econometric Models and Economic Forecasts,
                          2nd ed. (New York: McGraw-Hill Hook Co.), 1981.

                          Page 16                                        GAO/HRD-90-134     Factors   in Rural Hospital   Closures
                                   Appendix II
                                   Objectives, Scope, and Methodology

                                   market characteristics.” For those obtained from the Medicare cost
                                   reports, we used data from hospital cost reporting periods beginning
                                   during fiscal year 1985. The variables included in the final regression
                                   model are described below.

Hospital Operating and Financial   Location. This variable classified a hospital as urban or rural. An urban
Characteristics                    hospital was one located within a metropolitan statistical area (MSA). A
                                   rural hospital was one outside an MSA.!'

                                   Bed size. Hospitals were grouped in one of four categories: fewer than 50
                                   beds; 50-99 beds; loo-199 beds; and 200 beds or more. This factor mea-
                                   sures hospital size and is an indicator of a hospital’s capacity, capital
                                   costs, and mix of services.

                                   Ownership. Hospitals were classified as either for-profit, private non-
                                   profit, or public nonfederal. This variable measures differences in risk
                                   due to the incentives and constraints facing these institutions. Also, it is
                                   an indicator of the potential availability of nonpatient sources of rev-
                                   enue from either community fundraising efforts or government

                                   Occupancy rate. A hospital’s occupancy rate was defined as the ratio of
                                   a hospital’s average daily census”’ to the average number of staffed beds
                                   maintained during the reporting period. Hospitals were categorized into
                                   one of four occupancy groups: less than 20 percent, 20-39 percent, 40-60
                                   percent, and greater than 60 percent. Occupancy rate is an indicator of a
                                   hospital’s patient volume, which is a determinant of revenues and per
                                   patient costs. II

                                   Percent Medicare inpatient days. Hospitals were classified into three
                                   groups: low Medicare population (less than or equal to 35 percent Medi-
                                   care inpatient days); average-size Medicare population (36-59 percent

                                   ‘Vah*     *-r 1985 wftrq’ not a’     able for all the variables used in estimating the model. When 1986
                                   valul         missi         +I-    dividual characteristics of hospitals, we used the closest reported value
                                   fro;           1’                   .bthe number of usable observations. If no reported value was available
                                   in il                              1 data were reported, we used that information.
                                   !q.,                          ural generally used by Medicare’s PJ’S
                                   “‘A\                    ,’ inpatients, excluding newborns, receiving care each day during the reporting
                                   ’ ’ For ou    ,~~.,L.ical
                                                          approach to yield meaningful results, a hospital’s occupancy rate should be pre-
                                   determi:      i.e., observed at least 1 year prior to closure. Occupancy data for all closures were for
                                   prior ye      Data for the 1985 closures were from the 1983 and 1984 AIIA annual surveys.

                                   Page 16                                            GAO/HRD-90-134      Factors   in Rural Hospital   Closures


                                 Appendix II
                                 Objectives, Scope, and Methodology

                                 Medicare inpatient days); and Medicare-dependent (60 percent Medicare
                                 inpatient days or more). This factor is an indicator of hospital’s patient
                                 and payer mix and, more specifically, its reliance on a federal govern-
                                 ment payer source.

                                 Percent Medicaid days. Hospitals were classified into two groups: low/
                                 modest Medicaid population (less than 11 percent Medicaid inpatient
                                 days) and high Medicaid population (greater than or equal to 11 percent
                                 Medicaid inpatient days). This factor is also an indicator of a hospital’s
                                 patient and payer mix and, more specifically, its reliance on a state gov-
                                 ernment payer source.

                                 Medicare wage index. The wage index was entered into the model as a
                                 continuous variable. It is a relative measure of labor costs for each MSA
                                 and for rural areas of each state. The index has unique values for each
                                 MSA in the United States. This number is assigned to each urban hospital
                                 located in that MSA. For rural hospitals, however, the measure is consid-
                                 erably less precise. The wage index contains one value for non-MsA areas
                                 in each state. Consequently, rural hospitals within each state are
                                 assigned the same index value.

                                 Medicare case mix index. The case mix index was entered into the model
                                 as a continuous variable. It is a measure of the costliness of Medicare
                                 inpatients at a hospital relative to the national average cost of treating
                                 all Medicare patients. The case mix index is also considered a measure of
                                 the complexity of the medical cases treated at a hospital. It therefore
                                 affects hospital revenues as well as costs.

Environmental Characteristics:   Population density. This factor measures the population density of the
Market Area Demand’”             county in which the hospital is located and is an indicator of the poten-
                                 tial demand for services. The data are for the 1980 population per
                                 square mile.

                                 Per capita income. Data were on the 1986 median per capita income of
                                 county residents, This factor is an indicator of consumer purchasing
                                 power in the area, the extent of health insurance coverage, and the eco-
                                 nomic health of the area.

                                 “All the hospital market area demand characteristics were entered into the regression model as
                                 continuous variables.

                                 Page 17                                     GAO/HRD-90-134 Factors in Rural Hospital      Closures
                                   Appendix II
                                   Objectives, !3cope, and Methodology

                                   Median education. Data were for the 1980 median level of education of
                                   county residents. This factor is an indicator of counties’ relative levels
                                   of need for and use of services,

                                   Change in population. This factor measured the percentage change in
                                   the hospital county’s population from 1980 to 1985. It is a measure of the
                                   area’s growth, which affects the demand for health services.

                                   Population. This factor measures the 1985 population of the county in
                                   which the hospital is located and thus indicates the potential demand
                                   for hospital services.

                                   Population over 65 years old. Data were for the number of county
                                   residents over 65 years of age in 1980. The measure is included to cap-
                                   ture the effects of the population’s age composition on the demand for
                                   hospital services.

                                   County’s unemployment rate. Data were for the percentage of the
                                   county’s civilian labor force unemployed in 1985. This factor is an indi-
                                   cator of the economic health of the county.

Environmental Characteristics:     Herfindahl index. This index is a measure of the concentration of bed
Market Structure                   capacity in a county. It is computed by adding together the square of the
                                   percentage share of total county acute care beds controlled by each hos-
                                   pital.‘” The index is used as an indicator of the competitiveness of the
                                   market environment.

Environmental   Characteristics:   Region. The nine U.S. Census regions were collapsed into four summary
Other                              categories: (1) North Central (East North Central and West North Cen-
                                   tral regions); (2) Northeast (New England and Middle Atlantic regions);
                                   (3) South (South Atlantic, East South Central, and West South Central
                                   regions); and (4) West (Mountain and Pacific) regions. This variable is
                                   an indicator of differences in costs and revenues not accounted for by
                                   other variables in the model. For example, it is intended to capture the
                                   effect of regional differences in practice patterns and resource costs.

                                   “‘That   is, Herfindahl index = Z i s12,where s, = (hospital i’s bedsize/total county beds) X 100.

                                   Page 18                                         GAO/HRD-90-134      Factors   in Rural Hospital   Closures
                         Appendix II
                         Objectives, Scope, and Methodology

Statistical Techniques   When two variables have a joint effect over and above the effects of
Used in the Analysis     each factor separately it is considered “interaction.“l.’ To statistically
                         test whether the effect of a hospital’s location in a rural or urban area
                         was consistent across the levels of the other variables in the model, an
                         interaction term for urban/rural location and each variable identified in
                         table III.4 was tested in the regression.

                         The logistic regression results are presented in table III.4 as adjusted
                         odds ratios. The odds ratio is a measure of association that approxi-
                         mates the relative risk of occurrence of an event (for example, closure).
                         The reported odds ratio indicates the effect of a particular factor (e.g.,
                         having fewer than 50 beds), controlling for the effects of the other vari-
                         ables in the model. The estimate of the effect, reflected in the odds ratio,
                         is a net effect for a particular variable. If there were no significant dif-
                         ferences between two groups, their odds would be equal, and the ratio of
                         their odds would be one. The greater the odds ratio differs from one, in
                         either direction, the larger the effect it represents. The odds ratios were
                         computed in relation to a defined reference group.

                         We used the odds ratio to assess whether a factor had a large or small
                         effect on the risk of closure. Determining what qualifies as a large effect
                         was not simple, however, for two reasons. First, the independent vari-
                         ables in our regression model include both categorical variables (e.g.,
                         small hospital versus large hospital) and continuous variables (e.g.,
                         wage index). Comparing the size of the effect of, say, a change in the
                         wage index to that of a change in hospital size is not straightforward,
                         because the change from “small hospital” to “large hospital” is not
                         equivalent to a one unit change in the wage index (e.g., from 0.5 to 1.5).
                         Second, the size of the effects of the categorical variables depends on
                         our choice of categories that define the variables. For example, the esti-
                         mated effect of hospital size will likely differ if “small hospital” is
                         defined as “fewer than 50 beds” versus “fewer than 150 beds.”

                         We used the logistic function to compute adjusted closure rates for sub-
                         groups of hospitals (table 111.5).Adjusted rates were calculated by mul-
                         tiplying the coefficients (see table 111.6)of the logistic regression
                         equation by either the characteristic mean or proportion, and then per-
                         forming the logistic transformation. The coefficients provide an adjust-
                         ment factor for differences in the risk of closure resulting from the

                         ’ ‘For further detail see David G. Kleinbaum and Lawrence L. Kupper, Applied Regression Analysis
                         and Other Multivariable Methods (Boston: Duxbury Press, 1978),pp. 333, 176, and 180.

                         Page 19                                     GAO/HRD-99-134    Factors   in Rural Hospital   Closures
                             Appendix II
                             Objectives, Scope, and Methodology

                             varying characteristics of rural and urban hospitals.lE The adjusted
                             rates give an estimate of the probability of closure when the hospital
                             characteristics are comparable.11’

Quality and Limitations of   The data used in this analysis were the best available sources of infor-
the Data and Measures        mation. Of the 260 closures, only 8 (3.1 percent) were deleted from the
                             regression model because data were not available on some of the vari-
                             ables included in the model. Of the 8 hospitals, 3 were rural and 5 were

                             We were, however, concerned about missing data on an important inde-
                             pendent variable, percent Medicare days. For this variable, we were
                             missing HCRIS data on 39 closed hospitals. To maximize the number of
                             observations, we used AHA annual survey data for the hospitals for
                             which we were missing data. We found that the risk of closure for hospi-
                             tals with a relatively small percentage of Medicare days (fewer than 36
                             percent) was sensitive to the data source or the number of observations
                             in the model. As such, this finding should be interpreted cautiously.

                             Two limitations of the measures used in this analysis also deserve men-
                             tion. Since we have not studied possible variations in hospital
                             accounting practices, the operating and total margin data should be
                             interpreted as general indicators of the profitability of the hospital
                             groups presented, rather than as precise measurements. Further, county
                             level data are imperfect measures of a hospital’s market as they are
                             derived for a county, a geographic area defined for political purposes. In
                             some cases, a county may represent a reasonable approximation of a
                             hospital’s market area; however in other cases a hospital’s market area
                             may be larger or smaller than the county boundaries. Neither of these
                             limitations were considered to have jeopardized the study’s potential to
                             identify hospitals’ major risk factors for closure.

                             ‘“For this study, adjustment variables were ownership, size, occupancy, percent Medicare days, per-
                             cent Medicaid days, area wage index, case mix index, Herfindahl index, median income, median edu-
                             cation, unemployment rate, population density, change in population, population, percent population
                             white, and percent population over age 65.

                             “‘For further detail, see Kleinbaum and Kupper, pp. 218-220

                             Page 20                                      GAO/HRD-90-134     Factors   in Rural Hospital   Closures
Appendix III

Supporting Tables

Table 111.1:Factor8 Surpected to
Influence Risk of Hospital Closure   Characterirtic                                Meanurea
                                     Hospital operating and financial characteristic
                                     Hospital type and location                           Rural/urban status
                                                                                          Multihospital svstem member
                                     Capacity and utilization                             Bedsize
                                                                                          Occupancy rate
                                     Patient and payer mix                                Percent Medicare days
                                                                                          Percent Medicaid davs
                                     Long-term care services                              Swing bed program
                                                                                          Long-term care unit
                                     Revenues, expenses, and profitability                Area wage index
                                                                                          Teachina status
                                                                                          Medicare case mix index
                                     Environmental characteristic
                                     Market area supply and competitionb                  No. hospitals in county
                                                                                          Beds per 1,000
                                                                                          Adjacent to a MSA
                                                                                          Phvsicians per 1,000
                                                                                          Herfindahl index
                                                                                          Skilled nursing beds
                                     Market area demandb                                  Median income
                                                                                          Median education
                                                                                          Unemployment rate
                                                                                          Population density
                                                                                          Change in population
                                     Other                                                Census region
                                     aMeasures included in the final regression model are defined in app. II
                                     %ounty measures.

                                     Page 21                                        GAO/HRD-90-134      Factors   in Rural Hospital   Closures
                                        Appendix III
                                        Supportlng Tables

Table 111.2:Rural and Urban Community
Horpltal Closure Rate3 (198588)                                                Rural hospitals                          Urban hospitals
                                                                                     4-year closure                           4-year closure
                                                                                        rate per 100                             rate per 100
                                        All hospitals                         No.          hospitals                   No.          hospitals
                                                                               140                   5.3               120                     4.1
                                        Bed size
                                        6-49                                   108                   9.9                46                    23.7
                                        --                                      27                   3.2                33                     7.4
                                        -_                                       5                   1.0                28                     3.8
                                        200 or more                              0                   0.0                13                     0.9
                                        Public, nonfederal                      43                   3.8                10                     2.4
                                        Private, nonprofit
                                        ____I___-                               55                   4.5                62                     3.2
                                        For-orofit                              42                  16.4                48                     8.8
                                        Occupancy (percent)
                                        Less than 20                            47                  16.4                23                    30.3
                                        20-39                                   63       --          5.6                49                    11 .o
                                        40-60                                   20                   2.3                33                     3.4
                                        61 or more                              10                   2.9                15                     1.1
                                        Percent Medicare days
                                        Less than 36                            30                   5.4                20                     3.5
                                        36-59                                   68                   5.2                92                     4.3
                                        600r more                               22                   6.2                 8                     4.0
                                        Census region
                                        North Central-
                                           E.N. Central                         15                   4.1                22                     4.4
                                           W.N. Central                         16                   2.7                11                     6.0
                                            New Enaland                          2                   3.3                  2                    1.2
                                            Middle Atlantic                      6                   5.8                 17                    3.7
                                        l_-.South Atlantic                      11                   3.4 -               8                     1.8
                                            ES Central                          16                   5.1                 7                     4.4
                                            W.S. Central                        52                  11.6                31                     8.3
                                        -Mountain                               14                   5.6                  3                    2.8
                                            Pacific                              8                   4.8                 19                    3.8
                                        a(Number of community hospital closures in 198588/total number of hospitals of this type in 1985) x 100
                                        = 4-year rate of hospital closure.

                                        Page 22                                       GAO/HRD-90-124       Factors   in Rural Hospital   Closures
                                                                Appendix III
                                                                Supporting Tables

Table 111.3:Hospitals’ Median PPS Margins, Operating Margins, and Total Margins (1984-87)
Figures are profit.._.margins
^__I__-__^--_-..                as a percent of hospital revenues.a
                      ._-..--_--_~.--                                                                                                                                        -
                                                                  Closed hospitals (years prior to
                                                                             closure)                                            Open hospital (PPS year)
-...-.-- of.._...
                 .         profit
                       ..-..-.       margin
                               -.---~_--..~..--...---.--   ~.          4        3       2         1                             1         2           3                          4
All hospitals
       .._...                    ~-.             ------            N~62~ N=l18
                                                                            ---.      N=l66           N=166            N=4,584"       N=4,954         N=4,948         N-4,913
PPS     margin . ~. -~. ~~~----~._-.---.-.-----
     ..-....                                                      __._ 9            3     -5            -12                  12             11               6               2
..._.___       margin
        - - ..__...~  .~~. _~~.-_~ .~ .._.~~-.__~~                    -4         -8‘    -15             -22                   2                 0            -1            -2
Total margin                                                             1       -3
                                                                               ..___-___  -8            -1.5                  6              4               3               3
Rural hospitals          -. ..~      .~    ~~_~..
                                               ~~~.                         N=70
                                                                   N=36___-____.-.     N=97            N=90            N=2,338        N=2,362         N=2,304         N=2,305
PPS margin                                                              0        -1     -11             -21                   8              7                    1        -2
Operating margin                                                      -8      -11       -16             -25                     0          -2              -3              -3
Total margin                                                          -2         -5       -7            -15                   4              3               2               2
._--      hospitals
               --..-_ .._- . ~~_-~~. -...                          N=26     N=48       N=69            N=76            N=2,246        N=2,592         N=2,644         N=2,608
PPS margin                                                             13          7        3             -4                 15                14                 9          5
Operating margin                                                      -1         -6     -13             -20                   4                  1                0         -1

_-._-. margin
Total   --_-^ . ..^._.._.. _..._._....-. --.-~                            2        -2            -8     -15                     7               5                 4              3
Rural hospitals with 6-49 beds
PPS margin                                                             -1           -2       -13        -23                     7                6                0         -2
Operating margln                                                       -8         -13        -18
                                                                                          ..___         -24                   -3               -5            -7             -7
-._--  margln
Total .__.
                   -.-__        .~                                     -2          -5            -9     -14                     3                2                0              0
Rural  hospital8
l___l..__._._        with 50-99 beds
          __...... _---_.-.---~--.--.--..-~.
PPS margin                                                                0          3           -5     -10                     9                8             2            -1
Operating    margin
                .. . ..-...~.-~...                                   ___- d        -5       -11         -25                     0              -2            -3             -3
Total marain                                                              d          0           -2     -18                     4                3             2              2
Urban hospitals with 6-49 beds                              _____.----.            -.
.I.I   margin
             ._.-. I_..I._.__._~
                                 -- _--_____--                          d         8         7            -9                    10              14              6              5
Operating margin                     _---...~ .--_~-____ ---.-___._     d       -3      -10             -22                     0              -2            -6             -6
Total   margln
                      ..- .--... -~.      .-                            d
                                                                  -- ~~--.___-  -3 ___-. -8             -15                     4                3                0              0
Urban hospitals with 50-99 beds                          ..___--~-        ___..
--..   .--...-- _~...--.~-. -__-----                                    d
                                                   .--- .___ --_____-----___.    10         2            -1                    13              13                 8           4
Operating      margin
“. . .-._- ._.. -.- ..----.- _-...- ___-.--                  ____       d       -8      -12             -21                     1               0            -1             -2
Total marain                                                                  d    -2            -8     -19                     4               3                 2           2
                                                                %ince there were some mrssing data for each median reported, the number of observations differs
                                                                slightly for each computation.

                                                                ‘All usable data for open and closed hospitals were included. Because we combine data for hospitals
                                                                that closed in different years, the number of observations varies due to data availability in the years prior
                                                                to closure. For example, no data were available 2-4 years prior to closure for hospitals that closed in

                                                                CFewer open hospitals were analyzed in PPS year 1 because many hospitals appeared to have incorrect
                                                                data. Data were edited based on screens used by HHS, HCIA, and the Prospective Payment Assess-
                                                                ment Commrssion.

                                                                dNot calculated due to small number of observations.

                                                                Page 23                                          GAO/HRD-90-134      Factors     iu Rural Hospital    Closures

                                        Appendix III
                                        Supporting    Tables

Table llL4: Llkellhood of a Communlty
HO#pltOl Closure by Selected Horpltal                                                                  All commun/y ;o$ltals In 1985
Characterlstlce (1985-88): Logistic                                                                                 =      1
Regrerrion Result3                                                                                                          (95% confidence
                                        Characterlstlc                                           Adjusted odds ratiob               interval)
                                        Rural                                                                      1.25                           (.63 . 2.47’
                                        Urban                                                          Reference group
                                        Bed size
                                        Fewer than 50                                                                 11.72
                                                                                                                                 ______- (5.56 - 24.65:
                                        50-99                                                                     4.23                    (2.08. 8.55:
                                        100-199                                                                   2.13                           (1.05 _ 4.29‘
                                        200 or more                                                    Reference QrouD
                                        Ownership                                                                                                        -
                                        Public                                                                     0.22                             c.09 - 50’
                                        Private nonprofit                                                          0,71                           (.42 - 1.21’
                                        Private for-profit                                             Reference group
                                        Occupancy (percent)
                                        Less than 20                                                                   8.97                  (4.87 16.41
                                        20-39                                                                          4.06                   (2.34 - 7.02
                                        40-60                                                                                                 (1.11 -3.45
                                        61 or more                                                     Reference arouD
                                        Percent Medicare days
                                        Fewer than 36                                                              2.82                          (2.03 3.95:
                                        60 or more                                                        ~-
                                                                                                                                                  (.72 - -1.57
                                        36-59                                                          Reference group
                                        Percent Medicaid days
                                        11 or more                                                                 1.48 _____~~~~_. (1.08 - 2.02
                                        Fewer than 11                                                  Reference group
                                        Region                                                                          -_____             ...._~ --....--
                                        North Central                                                                  2.04                      (1.17 - 3.52
                                        Northeast                                                                      3.71           .~         (1.76 7.81
                                        South                                                                          4.24                      (2.28 - 7.98
                                        West                                                           Reference group
                                        Location and ownerrhlp
                                        Rural & public                                                             0.59                           (.23 - 1.54
                                        Rural & private nonprofit                                                  0.35                             (.18 - .70
                                        Rural & for-profit                                             Reference group
                                        Case mix Index
                                        (Meanx1.13      f .15)                                                         0.67d
                                        Wage index
                                        (Mean=0.98      + .17)                                                         1 .24c
                                        aThis table reports selected variables, including all the statisttcally   significant vartables In the model

                                        Page 24                                            GAO/HRD-SO-134         Factors   in Rural Hospital      Closures
                                           Appendix lII
                                           Supporting Tables

                                           bThe odds ratio approximates the relative risk of occurrence of an event such as closure. If there were
                                           no significant differences between two groups, their odds would be equal and the ratio of their odds
                                           would be one. The odds ratios in this table are computed in relation to a defined reference group. Thus,
                                           for example, hospitals with 50-99 beds were 4 times as likely to close as the reference group of hospi-
                                           tals with 200 or more beds.
                                           ‘Odds are significant at the 95 percent confidence level
                                           dOdds are significant at the 99 percent confidence level.

Table 111.5:Likelihood of Closure by Bed
Size and Ownership: Adjusted Rates                                                                 Four-year closure rate per 100 hospitals
                                                                                                       Rural hospitals        Urban hospitals
                                           Bed size
                                           Fewer than 50                                                           4.32                          7.09
                                           50-99                                                                   1.60                          2.68
                                           100-199                                                                 0.81                          1.37
                                           200 or more                                                             0.38                          0.65
                                           Public, nonfederal                                                      0.57                          0.77
                                           Private, nonDrofit                                                      1.11                          2.49
                                           Private for-profit                                                      4.28                          3.46
                                           Note: See app. II for a discussion of adjusted rates.

                                           Page 25                                         GAO/HRD-90434      Factors   in Rural Hospital   Closures
                                          Appendix III
                                          Supporting Tables

Table 111.6:Logit Estimates of Hospital
Closure                                                                                                                                  Standard
                                          Variable                                                                 Coefficient               error
                                          Rural                                                                            ,221                ,347

                                          Occupancy (less than 20%                                                        2.194                ,312
                                          Occupancy (20-39%)                                                              1.401                ,281
                                          Occuoancv (40-60%)                                                               ,674                ,287

                                          Bed size (6-49)                                                                 2.461                ,383
                                          Bed size (50-99)                                                                1.442                ,364
                                          Bed size (100-199)                                                               ,755                ,363

                                          Public                                                                        -1.529                 ,428
                                          Nonorofit                                                                       -.337                ,265
                                          Rural & public                                                                  -.525                ,489
                                          Rural & nonprofit                                                             -1.044                 ,352

                                          Medicare case mix index                                                       -3.589                 ,792

                                          Area wage index                                                                 2.176              1.056

                                          Hi h Medicare inpatient days
                                          (68% or more)                                                                     ,064               ,205
                                          Few Medicare inpatient days
                                          (35% or fewer)                                                                  1.035                ,168
                                          High Medicaid inpatient days
                                          (11% or more)                                                                     ,393               ,161

                                          --                                                                              1.446                ,326
                                          North Central                                                                    ,713                ,285
                                          Northeast                                                                       1.310                .38C

                                          Herfindahl indexa                                                              -.243                 ,255

                                          Population over aae 65”                                                           ,042               ,032
                                          Population densitya                                                               ,155               ,198
                                          Percent change in population                                                   -.016                 ,011
                                          Unemployment rate (1985)                                                       -.OOl                 ,025
                                          Per capita income (1986)a                                                      -.784                 ,412
                                          Median education level (1986)                                                     ,083               .13E
                                          Populationa                                                                    -.002                 ,002

                                          Constant                                                                     -4.4932                 .24E
                                          aWemultiplied these coefficients by 10,000.

                                          Page 26                                       GAO/HRD-SO-134   Factors     ln Rural Hospital    Closurer
Appendix IV

Major Contributors to This Report

                  Mark V. Nadel, Associate Director for National and Public Health Issues,
Human Resources      (202) 276-6195
Division,         Edwin P.Stropko, Assistant Director
                  Marsha LillieIBlanton, Evaluator-in-Charge
Washin&ion, DC.   Suzanne M. Felt, Evaluator
                  Elizabeth A. Wennar, Evaluator
                  C. Robert DeRoy, Evaluator (Computer Science)
                  Patrick Redmon, Economist
                  Steve Machlin, Statistician
                  Lester Baskin, Intern

(10SBOR)          Page 27                         GAO/HRD90-134   Factors   ln Rural Hospital   Closures
Keqwsts          for copies of (iA0 report,s should be sent, to:

I1.S. Wwral    Accout~tit~g Office
Post. Office 130x 60 15
(;aitJwrslmrg,  Maryland 20877

‘I’t~lq,hoIlt”    20%27!%;241

‘I’ht~ first five copies of each report      are free. Additional   copies are
82.00 tVlc11.

There is il 25% discount        on orders for 100 or more copies mailed to 8
sitlglth ;itItlrtw.

Ordt~rs must be pwpaitl by cash or by check or money order made
out t,o t.ht* SuI)t-‘rint,t~nti~lIt of D0cumt~nt.s.
                                             First-Class Mail     ’
                                           Post,age & Fees Paid   i
                                             Permit No. GlOO      :
Of’fic~ial Ihsitwss
l’t~tl;~lt y f’or I’rivwt t’ lJsc* !kH~O