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Chain ReactionMartin Kitchener PhD a , Ciaran O'Neill PhD b &Charlene Harrington PhD ca Department of Social and Behavioral Sciences ,University of California , San Francisco, USAb University of Ulster , U.K.c Department of Social and Behavioral Sciences ,University of California , San Francisco, USAPublished online: 11 Oct 2008.

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GENERAL ARTICLE

Chain Reaction:An Exploratory Study

of Nursing Home Bankruptcy in California

Martin Kitchener, PhD

University of California, San Francisco

Ciaran O’Neill, PhD

University of Ulster, U.K.

Charlene Harrington, PhD

University of California, San Francisco

Martin Kitchener is Associate Adjunct Professor, Department of Social and BehavioralSciences, University of California, San Francisco. His research interests include health careorganization, policy, and long-term care.

Ciaran O’Neill is Professor, University of Ulster, U.K. His research concentrates onhealth economics and policy.

Charlene Harrington is Professor, Department of Social and Behavioral Sciences,University of California, San Francisco. Her research interests include health policyand long-term care for the elderly and disabled.

Address correspondance to: Dr. Martin Kitchener, Department of Social and Be-havioral Sciences, University of California, San Francisco, 3333 California Street,Suite 455, San Francisco, CA 94118 (E-mail: martink@itsa.ucsf.edu).

This research was funded by the California Research Bureau’s Contract ResearchFund, Grant #L-1821, at the request of Senate Rules Committee. The views expressedin the paper are those of the authors and do not necessarily reflect those of the Califor-nia Research Bureau or the California State Senate.

Journal of Aging & Social Policy, Vol. 17(4) 2005Available online at http://www.haworthpress.com/web/JASP

2005 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J031v17n04_02 19

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ABSTRACT. This paper reports on an exploratory study of nursinghome bankruptcy. From state and industry data regarding nearly 1,000California facilities, it was possible to identify 155 homes in five chains(multi-facility organizations) that were operating in bankruptcy in 2000.When compared with facilities in non-bankrupt chains, while the bank-rupt chain facilities had significantly worse financial liquidity, higheradministrative costs, and higher payables to related parties, they also hadmore Medicare residents, fewer Medicaid residents, better solvency, andwere located in less competitive county markets and in areas with higherMedicaid reimbursement rates. These findings indicate that, rather thanfacility characteristics and local market factors, strategic decisions takenat the corporate (chain) level are the major determinants of nursing facil-ity bankruptcy status. [Article copies available for a fee from The HaworthDocument Delivery Service: 1-800-HAWORTH. E-mail address: <docdelivery@haworthpress.com> Website: <http://www.HaworthPress.com> © 2005 by TheHaworth Press, Inc. All rights reserved.]

KEYWORDS. Nursing homes, chains, bankruptcy, policy

INTRODUCTION

In 2001, there was mounting concern over reports that 1,800 of thenation’s 16,500 nursing homes were operating in bankruptcy while re-structuring under protection of Chapter 11 of the U.S. Bankruptcy Code(American Health Care Association [AHCA], 2001). In contrast to theknowledge of hospital failures derived from national datasets and so-phisticated models (Lee & Alexander, 1999), fewer data and limited re-search presented policymakers with an inadequate basis from which toassess the nature or scope of nursing home bankruptcy (Centers forMedicare and Medicaid Services [CMS], 2002a).

At the national level, the nursing home industry depicted a “rising tide”of bankruptcy, attributed it to the 1998 introduction of the MedicareNursing Facility Prospective Payment System (PPS) and demandedhigher reimbursement rates to ensure facility survival and quality (Apple-by, 1999; AHCA, 2001). By contrast, the U.S. General Accounting Of-fice (GAO, 2000) ascribed the bankruptcy of five of the largest chains(multi-facility organizations) to factors including poor business deci-sions. In California, as in other states, a lack of research evidence left per-ceptions to be shaped by providers’ framing of the issue and mediacoverage of a small number of sudden facility closures involving: the

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traumatic transfer of frail residents, the state’s incurring temporary ad-ministration costs of $2m (California Department of Health Services[CDHS], 2002a), and a review of state oversight procedures (Bonnet,2001).

The primary aim of this study is to provide a foundation of evidenceabout nursing home bankruptcy for policymakers, the public, and futureresearchers. In the absence of comparable national data on facility bank-ruptcy, this exploratory study had two main goals: (1) to identify thescale and nature of nursing home bankruptcy in California, and (2) touse the state’s rich facility-level dataset to identify characteristics ofbankrupt homes that might provide early warning signals to regulators,and which could be further examined (e.g., within predictive models) ifnational data become available. The study is reported in the four mainsections of this paper: (1) conceptual framework, (2) research designand methods, (3) findings, and (4) discussion of findings with referenceto national developments, areas for future research, and policy implica-tions.

CONCEPTUAL FRAMEWORK

Because of the dearth of research into nursing home bankruptcy, theconceptual model for this study was derived from nursing home coststudies (Aaronson et al., 1995) and analyses of hospital bankruptciesand closures (Wertheim & Lynn, 1993). While some variables em-ployed in studies of hospital failure (e.g., leadership tenure) are notavailable for nursing homes, two key findings from these literatureswere incorporated into our framework. First, facility characteristics(e.g., size) and market factors were incorporated (Lee & Alexander,1999). Second, because much of the variation contained within facilityfinancial data is explained by single ratios of leverage, profitability, andliquidity (Zeller et al., 1997), a single ratio of each characteristic was in-cluded in the framework.

Facility Characteristics

Following nursing home cost studies and hospital failure research,facility characteristics were predicted to be associated with facilitycosts and hence, with the likelihood of bankruptcy (Wertheim & Lynn,1993).

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Number of Beds (Size). Hospital studies suggest that as the relativenumber of resources increases, facilities are better placed to avoid bank-ruptcy (Cleverly, 1985). Similarly, nursing home analyses report asso-ciations between size and outcomes, including: higher net patientrevenues (HCIA & Arthur Andersen, 1999), better facility financial sta-tus (Cohen & Spector, l996), and fewer closures (Angelelli et al., 2003).California nursing home cost reports for 1999 show that facilities with1-59 beds had an average loss of income per patient day of $4.86, com-pared with earnings of $1.82 per patient day for facilities with 60-99beds, and earnings of $2.55 per patient day for facilities with 100 ormore beds (California Office of Statewide Health Planning and Devel-opment [COSHPD], 2002).

Occupancy Rates. Nursing home studies report that facilities withlower occupancy rates have higher average patient costs (Ullmann, 1984)and are more likely to close because of poor quality of care (Angelelli etal., 2003). Hospital studies also suggest that low occupancy affects fa-cility financial position and hence, the likelihood of bankruptcy (Wert-heim & Lynn, 1993).

Chain Membership. Cost studies of hospitals and nursing homes re-port that chain facilities have generally lower operating costs (Arling etal., 1991; McKay, 1991) that could lead to better financial status, andhence reduce the likelihood of bankruptcy.

Ownership Type. While nursing home ownership type (e.g., for-profit, not-for-profit) is associated with facility outcomes including li-censing termination (Angelelli et al., 2003) and closure (Kitchener etal., 2004), non-profit and government facilities are omitted from thisstudy because they are not eligible to declare Chapter 11 bankruptcy.

Revenue and Cost Factors

Medicare and Private Pay Residents. Because Medicare skilled nurs-ing facility (SNF) reimbursement rates are set at the federal level andare higher than most state Medicaid rates (Swan et al., 2001), facilitieswith higher percentages of Medicare residents may have higher reve-nues and net incomes. While higher percentages of Medicare residentsmight once have reduced the likelihood of bankruptcy, followingMedicare PPS in 1998, the average Medicare reimbursement rate forfreestanding facilities declined from $305 in 1997 to $240 in 1999. Thismay have increased the risk of bankruptcy among facilities with higherpercentages of Medicare residents (AHCA, 2001).

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Medicaid Residents. Studies have identified a positive relationshipbetween percentage of Medicaid residents and factors such as costs perday (Harrington et al., 1998) and the likelihood of closure (Angelelli etal., 2003). California facilities with higher levels of Medicaid residentsmay be at greater risk of bankruptcy because, as noted earlier, Medicaidreimbursement rates are lower than Medicare and private pay rates(COSHPD, 2002).

Reimbursement Rates and Geographic Regions. While urban nursinghomes have been found to have higher costs (Ullmann, 1984) and bemore likely to close (Angelelli et al., 2003), rural facilities are reportedto have more financial problems (Smith et al., 1992). To take geograph-ical differences in costs of living into account, California sets its Medi-caid reimbursement rate to vary by size and geographic region. Patientday rates for homes with 59 beds or less are: $87.26 in the Los Angeles(L.A.) Region, $100.28 in the (San Francisco) Bay Area counties, and$93.31 in all other counties (COSHPD, 2002). While facilities in regionswith higher Medicaid reimbursement rates may be less likely to experi-ence bankruptcy, this would depend on the extent to which Medicaidrates cover the regional costs.

Expenditure Factors

Administrative Costs. Administrative costs may influence the finan-cial viability of nursing facilities in two ways (Wertheim & Lynn,1993). On one hand, paying sufficient wages and benefits to adminis-trators may help attract and retain individuals who may have the ca-pacity to identify and deal with financial problems early. Against this,administration represents an overhead, which if allowed to becomeunnecessarily high, could endanger the financial position of a facility.

Maintenance Costs. High maintenance costs could place a facilityin financial jeopardy as well as indicating that the building is in needof remodeling or rebuilding. This would drive up maintenance costsand probably other costs as well, for example, administration dealingwith contractors, etc. A case study of nursing home closures in Michi-gan identified high maintenance costs among older homes as an im-portant factor (Hirschel, 2002).

Funds Paid to Related Parties. As more nursing facilities join chains,an increasing number have financial relationships with parent organi-zations (related parties). From parents, facilities may receive fundsthat can help with operating costs, but many also pay administrative

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fees to parents. It could be expected that “better” facility net flows(funds received minus funds paid) could improve the stability of a fa-cility and hence be negatively associated with bankruptcy.

Nurse Staffing Levels. Nurse staffing levels vary widely; they are ahighly significant positive factor in average operating costs, and theymay have negative effects on facility financial outcomes (Bliesmer etal., 1998). On the other hand, where facilities compete on staffing andquality, higher staffing could lead to higher revenues and improve thefinancial status of the facility.

Financial Status Indicators

While financial status (as indicated by measures of liquidity, leverage,and solvency) is central within bankruptcy studies, Zeller et al. (1997)demonstrated that most variance within measures can be explained withthe use of a single ratio for each. In general, higher liquidity (acid test ra-tio) indicates a stronger financial position, higher leverage (liabilities toasset ratio) indicates financial problems, and higher profitability (net in-come ratio) indicates better financial health. Thus, facility bankruptcyshould be negatively associated with profitability and liquidity, and bepositively associated with leverage.

Resident Characteristics

Socio-Demographic Factors. Higher facility percentages of the aged85 and over population should increase the casemix of residents (andthus the per-patient cost of care) and could weaken the financial posi-tion of the facility (Ullmann, 1990). Higher disability rates among Afri-can Americans may have a negative effect on the net income of facilitieswith higher proportions of such residents (Headen, 1992). While the ev-idence is mixed on this issue (Ullmann, 1990), homes with higher pro-portions of minorities may also provide a proxy for low-income areas,which in turn could negatively impact on facility viability.

Resident Acuity (Casemix). Nursing facility studies have shown astrong positive relationship between acuity (casemix) and nurse staffingtime and hence, cost (Fries et al., 1994). As staff hours increase, thecosts for a facility should increase, and, if not adequately reimbursed,higher facility casemix could increase the likelihood of bankruptcy.

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Market Competition

Nursing home and hospital studies suggest that in more competitivecounty markets, private pay rates and facility income may be lower(Zinn, 1994; Banaszak-Holl et al., 1996) and that facilities are morelikely to fail (Angelelli et al., 2003). Thus, more competitive countymarkets were expected to be associated with facility bankruptcy.

RESEARCH DESIGN AND METHODS

Sample

All free-standing, licensed, and/or certified nursing facilities operat-ing in California in 2000 were considered for inclusion in the study (n =1,156). Hospital-based nursing homes (211) were excluded becausenone was reported to be in bankruptcy, and cost report data for these fa-cilities are not comparable with those of freestanding facilities. All gov-ernment and other non-profit facilities (160) were omitted because theyare not eligible to file for Chapter 11 bankruptcy status. Forty-one free-standing homes were omitted because they either did not file cost re-ports for the study period and/or had extensive missing data. Thus, thefinal study sample comprised the 955 freestanding California facilitiesthat were comparable in terms of data availability and eligibility to op-erate under Chapter 11 bankruptcy status.

Variables and Data Sources

The primary sources of data for this study were the cost and utiliza-tion reports compiled from the uniform reports that all California fa-cilities submit for all payers (COSHPD, 2002). Table 1 displays thevariables selected to operate our conceptual framework, their defini-tions, data sources, and the hypotheses for facility bankruptcy.

All facility and market characteristics (Herfindahl Index) were taken,or calculated, from COSHPD (2001) cost reports. Consistent with stan-dard practice in bankruptcy studies, revenue and cost variables werestandardized by resident day, and all financial data were examined forthe year prior to bankruptcy (1998-99). Resident characteristics weretaken from facility utilization reports (COSHPD, 2001). Because otheracuity measures were not available for this study, facility average needfor assistance with activities of daily living (ADL) data were taken from

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26 JOURNAL OF AGING & SOCIAL POLICY

TABLE 1. Variables, Measures, Data Sources, and Hypotheses

Variables Measures Data Sources BankruptcyHypotheses

Facility Characteristics

Number of Beds Total number of licensed beds. COSHPDCost Report

-

Occupancy Rate Resident days divided by bed days. COSHPDCost Report

-

Revenue Factors

Percent MedicareResident Days

Days paid by Medicare as percent of total dayscare per facility.

COSHPDCost Report

+

Percent MedicaidResident Days

Days paid by Medicaid as percent of total dayscare per facility.

COSHPDCost Report

-

Los Angeles Region Facilities receiving lower Medicaidreimbursement rate for L.A. counties.

MedicaidRate Areas

+

Bay Area Region Facilities receiving higher Medicaidreimbursement rate for Bay Area Counties.

MedicaidRate Areas

_

Expenditure Factors

Administrative Costsper Resident Day

Total administrative costs standardized byresident days.

COSHPDCost Report

+

Maintenance Costsper Resident Day

Total maintenance costs standardized byresident days.

COSHPDCost Report

+

Net Related PartyPayables PerResident Day

Total Receivables minus Payables fromrelated party companies standardized perresident day.

COSHPDCost Report

_

Nurse Staffing Hoursper Resident Day

Total productive hours (excluding vacations,sick days, mealtimes) for: full-time, part-time,and contract staff; directors of nursing;supervisory and registered nurses (RN),licensed practical/vocational (LVN/LPN) forthe year, standardized by resident days.

COSHPDCost Report

+

Financial Indicators

Profitability Net Income Margin: ratio of net income tototal healthcare revenue. Higher ratio showsa stronger position.

COSHPDCost Report

-

Liquidity Acid Test Ratio: cash plus marketablesecurities divided by total current liabilities.Higher ratios indicate a stronger financialposition.

COSHPDCost Report

-

Solvency Liability to Assets Ratio: total liabilities to totalassets. Higher ratios show a weaker position.

COSHPDCost Report

+

Resident Characteristics

Percent Aged Over 85 Annual percent of facility residents aged over85 years.

COSHPDUtilization Rpt

+

Percent Black Annual percent of African American facilityresidents.

COSHPDUtilization Rpt

+

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the federal On-Line Survey Certification and Reporting (OSCAR) sys-tem for 1999 (CMS, 2002b). The region variable was derived from thethree California Medicaid reimbursement rate areas (COSHPD, 2002).

Identification of Bankrupt Facilities

Two stages of fieldwork were required to identify facilities operatingin bankruptcy in California. First, using multiple federal and industryinformation sources and a press search conducted by the California Re-search Bureau (CRB), a list was compiled of eight bankrupt chains op-erating facilities in California in 2000. No bankrupt independent (non-chain) nursing homes were identified. Second, the California nursinghome licensing information system (CDHS, 2002b) was used to iden-tify 155 Californian facilities in the eight bankrupt chains.

Analysis

Given the exploratory nature of the project, the available data, andthe sample characteristics (all identified bankrupt facilities being chainmembers), the dataset was analyzed descriptively in three main steps.First, for each nursing home in the study, a record file was created tocomprise bankruptcy status in 2000 (yes/no) plus facility variablesfor the previous year. Second, descriptive statistics were computedon all variables for four categories of facility: (a) all freestanding(955); (b) non-bankrupt independent (188); (c) bankrupt chain mem-

Kitchener, O’Neill, and Harrington 27

Variables Measures DataSources

BankruptcyHypotheses

Percent Hispanic Annual percent of Latino facility residents. COSHPDUtilization

Rpt

+

Acuity The average percentage of residents that aretotally dependent in three activities of daily living(ADLs) eating, toileting, transfer from bed/chair.

OSCARData

+

MarketConcentration

Herfindahl for Daysof Care

The Herfindahl Index (HI) for each county was calcu-lated using total days of care for each facility incounty in 1999. Days of care per facility were dividedby days of care in its county. For each county, facilityproportions were squared and summed to create HI.Index range 0-1, with higher values representing lesscompetition/more concentration.

COSHPDCost

Report

-

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bers (155); and (d) non-bankrupt chain members (612). Because someof the variable distributions were slightly skewed, the data were nor-malized using a series of Box Cox transformations (Greene, 1997). Forvariables whose range covered non-positive values (e.g., net income),the amount necessary to make the minimum value positive was added tothe distribution. Third, differences in means (Z) tests were conducted toidentify significant differences between facilities in bankrupt chainsand facilities in non-bankrupt chains. To account for the multiple com-parisons made, results are reported with Bonferroni corrections (Bland &Altman, 1995).

FINDINGS

This study of nursing home bankruptcy in California produced threesets of unexpected findings. First, the California state agency responsi-ble for nursing home industry oversight did not monitor bankruptcy in2002 and could not identify bankrupt facilities. Second, all cases ofnursing home bankruptcy identified in this study were members of eightchains operating in the state. Unlike hospital failure studies (Wertheim &Lynn, 1993), no independent facilities were identified as operating inbankruptcy. Four of the identified bankrupt chains (113 Californian fa-cilities) filed in 1999 and remained in bankruptcy through 2000 (Lenox,Sun, Aspen & Vencor). The other four chains (42 Californian facilities)entered bankruptcy in 2000 (IHS, Mariner, Hermitage, and TLC). Fiveof the bankrupt chains were national, and three (Lenox, Aspen, andTLC) were smaller, regional chains. With 155 bankrupt homes in 2000(just over 16,000 beds), California’s 16% rate of bankrupt facilities washigher than the 11% national average (AHCA, 2001).

The third surprising finding of this study was that, as shown in Table 2,few facility or market factors were associated with the bankruptcy sta-tus of individual facilities. Against expectations from hospital researchand nursing home cost studies, bankrupt chain facilities were no differ-ent in terms of occupancy rates and size when compared with homes innon-bankrupt chains. In terms of revenues, facilities in bankrupt chainshad higher percentages of Medicare days of care. Contrary to expecta-tions, facilities in bankrupt chains had lower percentages of Medicaiddays of care than facilities in non-bankrupt chains.

In terms of expenditure factors, as expected, when compared withhomes in non-bankrupt chains, facilities in bankrupt chains had higheradministration costs and had less advantageous net flows of funds to re-

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Kitchener, O’Neill, and Harrington 29

TABLE 2. Comparison of Free-Standing For-Profit Bankrupt and Non-Bank-rupt Chain Facilities in California, 1999-2000

All FacilitiesN = 955

Non-Bankrupt,Independent

FacilitiesN = 188

BankruptChain

Facilities1

N = 155

Non-BankruptChain Facilities

N = 612

Mean (Median) Mean (Median) Mean (Median) Mean (Median)

FacilityCharacteristics

Occupancy Rate 87.29%(89.92)

86.91%(89.61)

88.56%(91.14)

87.08%(89.72)

Mean(Median)

SD Mean(Median)

SD Mean(Median)

SD Mean(Median)

SD

Number of Beds 103.92(99.00)

49.59 94.10(92.50)

51.64 105.76(99.00)

41.38 106.46(99.00)

50.55

Revenue Factors

Percent MedicareResident Days

6.47(5.90)

5.20 5.11(3.80)

6.40 8.89**(8.10)

5.07 6.27(5.70)

4.59

Percent MedicaidResident Days

66.37(74.00)

25.30 63.97(75.25)

30.25 61.43*(67.80)

21.45 68.35(75.90)

24.33

Los Angeles region(lower Medicaidreimbursement)

32.25% 48.02% 20.00%** 34.31%

Bay Area region(higher Medicaidreimbursement)

17.49% 42.11% 25.16%** 13.89%

Expenditure Factors

Administrative Costsper Resident day

18.66(17.52)

7.92 18.12(16.13)

8.92 20.84**(20.19)

8.73 18.27(17.38)

7.28

Maintenance Costsper Resident Day

11.96(11.42)

3.05 12.68(11.86)

3.64 11.82(11.45)

2.32 11.78(11.29)

2.99

Net Related PartyPayables perResident Day

�0.09(0.00)

1.43 0.05(0.00)

0.19 �0.53*(�0.04)

3.18 �0.02(0.00)

0.75

Nurse Staffing HoursPer Resident Day

3.03(2.90)

0.71 3.14(3.00)

0.91 3.05(3.00)

0.49 2.99(2.90)

0.68

Financial Indicators

Net Income MarginProfitability

1.47(2.26)

10.87 1.57(2.13)

10.04 2.96(3.71)

7.42 1.06(1.79)

11.80

Acid Test Ratio Liquidity 0.30(0.02)

0.87 0.71(0.22)

1.42 0.15**(0.01)

0.70 0.22(0.01)

0.61

Liability to AssetsRatio Solvency

0.87(0.67)

1.15 0.85(0.68)

0.94 0.62**(0.43)

0.57 0.94(0.72)

1.30

ResidentCharacteristics

Percent Aged Over 85 38.97(40.83)

17.33 41.48(45.22)

19.32 40.25(41.05)

15.07 37.87(38.92)

17.4

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lated parties (i.e., they paid more than they received). There were no dif-ferences in nursing hours per resident day or in maintenance costs perday. When compared with homes in non-bankrupt chains, facilities inbankrupt chains displayed significantly weaker financial liquidity buthad superior solvency and no difference in profitability. Thus, weakerliquidity was the only indicator of financial jeopardy of bankrupt chainfacilities.

Other than having significantly fewer Hispanic residents, nursinghomes in bankrupt chains had no significant difference in resident char-acteristics (percentages of residents over age 85, Black residents, andresidents with higher acuity levels). Finally, and perhaps most surpris-ingly, higher percentages of facilities in bankrupt chains were located inless competitive county markets and in regions where the Medicaid re-imbursement rates were higher (e.g., the Bay Area).

DISCUSSION AND CONCLUSION

Studies of nursing home costs and hospital failures suggested that fa-cility and market characteristics would be predictors of nursing homebankruptcy. This exploratory study of nursing home bankruptcy in Cal-ifornia reports, however, that all bankrupt facilities were members of

30 JOURNAL OF AGING & SOCIAL POLICY

TABLE 2 (continued)

All FacilitiesN = 955

Non-Bankrupt,Independent

FacilitiesN = 188

BankruptChain

Facilities1

N = 155

Non-BankruptChain Facilities

N = 612

Mean (Median) Mean (Median) Mean (Median) Mean (Median)

Percent Black 10.21(4.30)

15.49 10.26(3.89)

16.57 8.64(3.51)

13.52 10.61(4.76)

15.62

Percent Hispanic 10.98(7.69)

11.67 10.13(6.81)

10.35 8.19*(4.82)

10.35 11.95(8.82)

12.24

Percent Dependent inADLS

32.24(32.33)

14.22 34.54(34.67)

15.92 29.65(29.67)

13.39 32.20(32.33)

13.77

Market Concentration

Herfindahl for Daysof Care

0.05(0.02)

0.09 0.05(0.02)

0.09 0.07**(0.03)

0.12 0.05(0.02)

0.09

1Z test of the difference in transformed means was conducted with Bonferroni corrections for all comparisonsbetween bankrupt chain facilities and non-bankrupt chain facilities.*p < 0.05, **p < 0.01

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chains and while they had significantly worse financial liquidity, higheradministrative costs, and higher payables to related parties, they alsohad more Medicare residents, fewer Medicaid residents, better sol-vency, and were located in less competitive county markets and in areaswith higher Medicaid reimbursement rates.

The finding that bankrupt chain facilities had higher rates of Medi-care patients does offer some support to providers’ claims regarding thenegative impact of Medicare PPS (AHCA, 2001). However, a recentGAO (2002) study found that the 10 largest chains had a median Medi-care margin of 18.2% in 1999 and 25.2% in 2000. Moreover, whileprofit margins (for all payers) for all facilities were about 1.8% in 2000,the largest chains had margins of double this amount (3.8%). This sug-gests that although chains’ bankruptcy decisions may have been im-pacted by the change in the Medicare payment system, their Medicareoperations continued to be profitable in 1999 and 2000.

Overall, these study findings indicate that strategic decisions taken atthe corporate (chain) level are the major determinant of nursing facilitybankruptcy. Noting that some nursing home chain bankruptcy filingsmay have involved separate petitions for operating divisions (geo-graphic or functional), it seems that corporations could have enteredonly some of their facilities into bankruptcy. While this point and ourfindings underscore the general contention of the GAO (2000) thatsome chain bankruptcies may have had little to do with facility viability,they raise two important questions about the nature of nursing homebankruptcy. First, because this study was unable to identify any bank-rupt-independent facilities in California, national research is required toestablish whether this is a state-level phenomenon or whether, unlikeindependent hospitals, independent nursing homes do not enter bank-ruptcy. Any revealed difference could be explained by the possibilitythat, when compared with nursing homes, independent hospitals enterbankruptcy because they are harder to sell or close. However, at thisstage, it is not known whether any independent nursing homes have en-tered bankruptcy in the nation.

Second, if national research established that independent nursinghomes do not enter bankruptcy, the question arises as to what does hap-pen to failing nursing homes? While the present study was not designedto assess the outcomes of nursing home bankruptcy, among our sample,bankruptcy was followed by facility closure in only two chains (eighthomes) in California. Moreover, only 32 freestanding California nurs-ing facilities closed during the five-year period 1997-2001 (Kitchener etal., 2004). It is also worth noting that one of the small bankrupt chains

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that closed facilities in California had serious quality-of-care problemsthat may have been more important factors for closure than the facili-ties’ financial status.

In light of this and other evidence that very few independent nursinghomes close (Angelelli et al., 2004), it seems that failing independentnursing homes either continue to operate (possibly in a precarious state)or are sold. It has been reported that following bankruptcy restructuring,some large chains such as Sun have sold, or plan to sell, some of theirCalifornian facilities. Further evidence on the empirical nature of nurs-ing home failure will require national analyses of the competing risks(closure, bankruptcy, and sale) facing different types of nursing homes(e.g., large and small chain-members vs. independent homes) to matchwork conducted in fields of hospitals (Lee & Alexander, 1999).

Further research is also needed to examine the implications of chainbankruptcy for quality of care, and for issues of health policy and gover-nance. At the national level, since five of the largest nursing homechains declared bankruptcy in 1999-2000, one has changed its name,none has proceeded to corporate dissolution, and no evidence hasemerged of widespread facility closures. Rather, most chains have re-duced their number of beds and reported improving financial positionsto Wall Street investors. By 2002, revenues among the top chainsranged from $1.3 billion to $2.5 billion, and earnings before interest,taxes, depreciation, amortization, and rent (EBITDAR) for the largestseven chains averaged 11% (CMS, 2003).

The policy significance of nursing home chain bankruptcy came intofocus when, in response to mounting concern rather than evidence,Congress used the Balanced Budget Refinement Act ([BBRA], 1999)and the Benefits Improvement and Patient Protection Act ([BIPA],2000) to reinstate temporarily some of the per diem reimbursement thatnursing homes lost under Medicare PPS. These two provisions wereworth an estimated $1.7 billion to the nursing home industry in 2003(CMS, 2003). The development of such policy in the absence of evi-dence underscores recommendations made in a recent Institute of Medi-cine (2001) report regarding the need to better monitor the financial andoperational arrangements of the nursing home industry, particularly na-tional chains.

The information deficit concerning nursing facility bankruptcy is in-creasingly problematic in the light of growing concerns regarding cor-porate governance. As some of the larger chains emerge from bank-ruptcy with new leaders, there has been acknowledgement of (prior)managerial problems. For example, the new Chair and CEO of Sun ad-

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mitted, “If you look at the companies in our business that didn’t go intoChapter 11, they didn’t grow in the same way, they didn’t accumulatedebt in the same way. They took the same hit on reimbursement thatothers did, but it didn’t force them into Chapter 11” (Piotrowski, 2002).In light of this admission, the findings presented here, and because gov-ernment provides 71% of revenues for the large corporate nursing homechains (CMS, 2002a), they could be required to provide federal andstate governments with more and better information to demonstrate thatvulnerable residents are being cared for in stable facilities.

In the absence of a national effort such as the one recommended bythe Institute of Medicine (2001), networks of information exchangesshould be established between states. Data from these efforts could thenbe made available to consumers, policymakers, and regulators by post-ing them on nursing home websites maintained by CMS and roughlyhalf of all states (Harrington et al., 2003). A model for developmentmay emerge from California where the facility-level financial data andthe bankruptcy data collected for this study are reported on a publicwebsite. This study underscores the need to collect and analyze such fi-nancial and bankruptcy data on a national basis to better inform the pub-lic and reduce the potential for nursing home policy to be driven bypartial understandings of phenomena such as bankruptcy.

RECEIVED: 09/03REVISED: 06/04

ACCEPTED: 09/04

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