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Health Serv Res. 2010 December; 45(6 Pt 1): 1796–1814.
PMCID: PMC2976829
NIHMSID: NIHMS224717

State Regulatory Enforcement and Nursing Home Termination from the Medicare and Medicaid Programs

Abstract

Objectives

Nursing homes certified by the Medicare and/or Medicaid program are subject to federally mandated and state-enforced quality and safety standards. We examined the relationship between state quality enforcement and nursing home terminations from the two programs.

Study Design

Using data from a survey of state licensure and certification agencies and other secondary databases, we performed bivariate and multivariate analyses on the strength of state quality regulation in 2005, and nursing home voluntary terminations (decisions made by the facility) or involuntary terminations (imposed by the state) in 2006–2007.

Principal Findings

Involuntary terminations were rarely imposed by state regulators, while voluntary terminations were relatively more common (2.16 percent in 2006–2007) and varied considerably across states. After controlling for facility, market, and state covariates, nursing homes in states implementing stronger quality enforcement were more likely to voluntarily terminate from the Medicare and Medicaid programs (odds ratio=1.53, p=.018).

Conclusions

Although involuntary nursing home terminations occurred rarely in most states, nursing homes in states with stronger quality regulations tend to voluntarily exit the publicly financed market. Because of the consequences of voluntary terminations on patient care and access, state regulators need to consider the effects of increased enforcement on both enhanced quality and the costs of termination.

Keywords: Nursing home regulation, Medicare and Medicaid, termination, state variation

Nursing home services are largely paid for by public insurances and are regulated by the federal and state governments (Walshe and Regulating 2001; Harrington, Mullan, and Carrillo 2004;). In 2007, the annual cost of care provided to the nation's 1.6 million elderly and disabled nursing home patients was U.S.$131 billion, of which 60 percent was paid for by the Medicare and Medicaid programs (Hartman et al. 2009). In order to be eligible for public payment under the two programs, nursing homes must comply with the minimum quality and safety standards set forth by federal legislations. It is estimated that over 95 percent of all homes are Medicare/Medicaid certified. A recent study reported that between 1996 and 2005, approximately 11 percent of a longitudinally tracked group of facilities terminated from Medicare and Medicaid (Zinn et al. 2009).

Involuntary termination from federal certification is an important tool used by state regulators to enforce federal quality standards. For nursing homes, termination from certification means that they cannot receive Medicare or Medicaid reimbursement, which likely leads to financial strains. After termination from certification, a facility may be sold to a new owner or closed entirely, and its Medicare and Medicaid residents would need to be moved to another nursing home unless the facility is sold. If the facility ultimately closes, all residents, irrespective of payer source, will be displaced. Transfer of residents to another nursing home entails a burden on the family, which needs to identify an alternative arrangement, and may place residents at risk for adverse health consequences due to changes in environment, caregivers, and service routines (Friedman et al. 1995; Capezuti et al. 2006;). On a macrolevel, the closure of a facility shrinks the supply of nursing home beds in the community, and it may affect access to care in areas of limited supply. In contrast, evidence suggests that nursing homes with poor quality of care or lower occupancy rate are more likely to terminate (either voluntarily or involuntarily) (Angelelli et al. 2003; Zinn et al. 2009;) and ultimately close (Castle 2005; Castle et al. 2009;). Therefore, termination from federal certification may help remove poorly performing and less efficient facilities from the market and increase the overall performance of the industry.

Because of concerns about possible service disruptions and the potential political advocacy power of the nursing home industry in shaping state regulations and politics (Kronebusch 1997), state regulators use enforced terminations as a “policy of last resort”—many states do not impose involuntary terminations at all and others impose them sparingly. Table 1 shows the number and percent of involuntary terminations by state in 2006–2007. Thirty-two states had no involuntary terminations. The other 18 had between one and eight, with the highest number of states, seven, having only one enforced termination in this period.

Table 1
Nursing Home Terminations in 2006–2007 and State Regulatory Stringencies in 2005

However, nursing homes may choose to voluntarily terminate from the Medicare and Medicaid programs (Angelelli et al. 2003). Indeed, voluntary terminations are more common than involuntary terminations. During the same period of 2006–2007, there were 353 voluntary terminations nationwide compared with only 47 involuntary terminations. And unlike involuntary terminations, voluntary terminations are widespread, with only five states having had none in this period (see Table 1). Yet the consequences of voluntary terminations (e.g., resident relocation and service disruptions) tend to be the same as in the case of state-imposed terminations.

The implications for resident care, quality, and local access warrant an investigation of the causes for termination, and in particular the role that state policy and regulations play in encouraging voluntary terminations. In this paper, we present a heuristic model explaining the way state policy affects nursing home decisions to leave the Medicare and Medicaid programs, and we examine empirically the evidence for these decisions. It is important to note that while the majority of terminated facilities eventually close (as the data presented in “Discussion” suggest), termination is not synonymous with closure. Rather, as has been shown for acute care hospitals (Zajac and Shortell 1989; Meyer, Brooks, and Goes 1990; Alexander, D'Aunno, and Succi 1996;), termination or substantial reduction of public funding creates a strong environmental jolt that has profound impacts on facilities' operations and financing and is, therefore, likely to increase the likelihood of closure. When undertaken voluntarily, termination from public certification is a crucial decision made by the current management of the nursing home. Whether undertaken voluntarily or not, it is likely a precursor to further major business decisions which would change the nature of the organization and which may include either closure, selling the facility to another owner, or changing a market niche to serving exclusively private-pay patients.

NURSING HOME DECISIONS TO VOLUNTARILY TERMINATE FROM THE MEDICARE AND MEDICAID PROGRAMS

We consider nursing home termination decisions in a neoclassical economic framework. Approximately two-thirds of nursing homes are for-profit and thus can be assumed to make decisions designed to maximize profits given the market and regulatory constraints that they face. The objectives driving the operation of nonprofit private facilities are less clear and are likely to be heterogeneous. Some may be “for-profits in disguise,” that is, actually striving to maximize profit despite their nonprofit status (Weisbrod 1988). Others may maximize quality or services to a population with a particular religious affiliation. All the private nonprofits are, however, bound by a break-even constraint as well as the same market and regulatory constraints that for-profit facilities face.1 Therefore, the responses of facilities of different ownerships to market and regulatory constraints, while different in magnitude, are in the same direction. Thus, in the theoretical discussion below we do not distinguish between nursing homes of different ownership types.2

We expect that the decision to voluntarily terminate from the Medicare and Medicaid programs would be reached by a nursing home because it would conclude that it is financially more advantageous than to continue to participate in these programs. The benefits to participation are access to Medicare postacute care patients, to Medicaid long-term care patients, and to the attendant revenues that they generate.

Medicare patients are those who are admitted following a hospital stay and require rehabilitation and skilled nursing care. They are generally considered attractive because the Medicare payment rate is relatively high (Troyer 2002; Grabowski 2007;). They are typically more expensive than long-term care patients, and the Medicare reimbursement rate recognizes that. Many nursing homes offer specialized nursing and medical services to attract these patients.

Medicaid patients, who are long-term, custodial care patients, are much less desirable from a financial perspective. Because Medicaid payment rates are typically much lower than the corresponding private-pay rates, facilities that can attract private-pay patients tend to prefer them. However, most facilities cannot fill their long-term care beds with private-pay patients only, and the question that they face typically is not whether to evade Medicaid patients altogether, but rather at what level of payer mix to operate, recognizing that many of those admitted as private pay will “spend-down” their assets during their stay and will become Medicaid patients. Thus, the benefit of participation in the Medicaid program is not only the revenues generated by Medicaid patients but also access to private-pay patients who anticipate “spending-down” to Medicaid sometime during their stay, and who may have never entered the facility if it were not Medicaid certified.

Participation in the Medicare and Medicaid programs also entails costs, such as administrative costs due to various reporting requirements. In particular, participation requires meeting the quality and safety standards mandated by the federal government and enforced by each state. Nursing homes are subject to an annual inspection (referred to as “survey”) in which a multidisciplinary team of surveyors inspects the facility's compliance with a large (several hundred) number of standards and issues deficiency citations if the standards are not met. Cited deficiencies can be followed by more severe sanctions, including monetary penalties, repeated surveys, forced change of management, and involuntary termination from the Medicare and Medicaid program. In addition, citations are available to the public through federal and state quality report cards (Angelelli et al. 2003); thus, they can influence the demand for the facility (i.e., as a result of better-informed consumer choices) and increase market competition based on quality.3

Statistics show that the vast majority of nursing homes (92 percent in 2006) have at least one deficiency citation in any given year (Centers for Medicare and Medicaid 2007) and on average have 7.1 deficiencies, indicating that most nursing homes find it economically more attractive to maintain a level of quality that is consistent with noncompliance with at least some standards. This suggests that compliance with the quality regulation component of the Medicare and Medicaid programs is costly.

This also suggests that for the majority of nursing homes the optimal operating point, that is, the point of balance between the costs of compliance with quality standards and the benefits of participation in Medicare and Medicaid, was achieved at a level of less than full compliance. For some nursing homes (about 1 percent annually, see Table 1), the costs may exceed the benefits, leading them to the decision of voluntary termination.

The balance point, which determines voluntary termination may depend on facility-specific factors related to organizational objectives, efficiency of operation, and ability to compete in the marketplace. It may also depend on characteristics of the market where the facility operates. For example, nursing homes in more competitive markets may face more price stress and may be more likely to have voluntary terminations than other homes.

In particular, our focus in this paper is the hypothesis that differences in the stringency of state quality regulations and enforcement have an impact on the voluntary termination decisions of nursing homes. Prior studies showed that states varied substantially in their oversight process and sanction approaches, both in terms of regulatory inputs such as state budgets and surveyor training and staffing, and in terms of outputs such as the threshold for issuing citations and determining the severity and scope of violations, and the stringency of imposed sanctions on noncompliant facilities (Harrington and Carrillo 1999; Walshe and Harrington 2002; Harrington, Mullan, and Carrillo 2004;). Walshe and Harrington (2002) reported that in 2000, state budgets funding the survey process ranged from U.S.$94 to U.S.$770 per nursing home bed, and surveyor staff ranged from 1 full-time equivalent (FTE) per 102 beds to 1 FTE per 2,790 beds. In addition, Harrington, Mullan, and Carrillo (2004) found that the average number of deficiencies cited per facility ranged from 2.0 to 11.4 across states, and the total number of civil monetary penalties (CMPs) issued to noncompliant facilities varied similarly.

This interstate variation in quality enforcement may reflect states' idiosyncrasy and propensity to regulate, and their varied abilities to identify serious care problems and enforce sanctions during annual inspections (U.S. Government Accountability Office 2008). In addition, states have discretion to set their own standards of care that are higher than those coded by federal legislations. Harrington, Mullan, and Carrillo (2004), for example, have shown that the application of state regulations tends to be determined by state political environment, state-level supply and competition of nursing home services, and related state policies such as Medicaid reimbursement rate. As a result, the costs of participating in the Medicare and Medicaid programs that arise due to meeting these standards, or, alternatively, due to choosing not to meet the standards and hence face deficiency citations and their sequela, vary across states. We hypothesized that nursing homes are more likely to voluntarily terminate from the Medicare and Medicaid programs when they operate in states with more stringent regulatory programs.

METHODS

Data

We used the National Online Survey, Certification, and Reporting (OSCAR) data to obtain information on facility termination status (whether terminated and type of termination) in 2006 and 2007. Other facility characteristics were obtained from the OSCAR in 2005. Although with limitations, the OSCAR data are generally considered reliable and accurate, and widely used in nursing home policy analyses (Grabowski 2007).

We linked the nursing home database with (1) data on state quality regulations obtained from a survey of state agencies (described below); (2) linked Medicare enrollment file and nursing home Minimum Data Set that defined the market boundary for each facility; (3) U.S. census data; (4) the CMS area wage index; and (5) state Medicaid reimbursement rates in 2004 as recently reported (Grabowski, Zhanlian, and Mor 2008).

State Enforcement Stringency as a Key Explanatory Variable

Following Harrington, Mullan, and Carrillo (2004), we conducted a structured survey of all states' licensing and certification agency directors to collect data on the number, type, and distribution of federal and state deficiencies, and federal and state CMPs issued in 2005. Although the OSCAR contains deficiencies according to federal standards, it generally does not contain information on CMPs according to state standards of care. Previous studies have shown that a number of the most enforcement-oriented states issue their own state deficiencies and CMPs in addition to federal penalties. Therefore, without a survey to collect both federal and state data, stringency of state regulations would be underestimated in the most enforcement-oriented states.

The survey data were used to develop a state regulatory stringency index described in Harrington and colleagues, as follows: The composite “stringency” index was scored and standardized according to five measures of state quality enforcement and sanctions: (1) average number of deficiencies issued per facility, (2) percent of facilities with no deficiencies, (3) percent of facilities with a deficiency at G level or higher (actual harm or serious jeopardy to residents), (4) percent of facilities with substandard care, and (5) average number of CMPs issued per facility. Harrington and colleagues showed that the derived index largely captured states' propensity to quality enforcement that tended to be determined by the unique political, economic, and market conditions in each state.

Facility, Market, and State Covariates

Based on prior literature, we included in multivariate analyses a set of facility-, market-, and state-specific covariates hypothesized to affect facility's termination decisions (Angelelli et al. 2003; Castle 2005; Castle et al. 2009; Zinn et al. 2009;). Facility-level covariates included the excess number of health-related deficiency citations a nursing home received in 2005, occupancy rate, total number of residents, proportion of Medicare residents, proportion of Medicaid residents, and whether the facility is chain affiliated, for-profit, or hospital-based (Angelelli et al. 2003; Castle 2005; Castle et al. 2009; Zinn et al. 2009;). Because of state variations in issuing citations, we defined the excess number of health-related deficiency citations as the difference between the number of a facility's health-related deficiency citations and the state average number of health-related citations issued to all nursing homes in the state. In this way we controlled for the within-state variation of facility quality when estimating the independent impact of state regulatory stringency on termination.

The market for each nursing home was defined based on patterns of new admissions during 2005. For methodological details, see Zwanziger, Mukamel, and Indridason (2002) and Mukamel, Spector, and Bajorska (2005). Briefly, we used the zip code of patient residence in the year before nursing home admission (obtained from the Medicare enrollment file) to define market boundaries, where the core market of a facility included the zip code areas that together made up 70 percent or more total admissions. The remaining 30 percent of admissions were not used to define the market because based on our empirical analyses, they tended to come from a large number of zip codes, with each contributing only one or a few admissions.

We then defined market-level covariates that first included a measure of market competition based on the Herfindahl–Hirschmann Index (HHI). The HHI was first calculated as the sum of squared shares of new admissions to all nursing homes in a zip code and then aggregated to the market level. Market competition was defined as 1-HHI, ranging from 0 (no competition or monopoly) to 1 (perfect competition). Two exogenous demand variables included in the analyses were percent of older population (≥75 years) in the market and percent of women in labor force in the market (Nyman 1985); and an exogenous supply variable included in the model was the CMS hospital wage index aggregated at the market level (a measure of local labor price). These demand and supply measures have been shown to affect nursing home operational characteristics such as cost of care (Mukamel, Spector, and Bajorska 2005), quality (Cohen and Spector 1996; Grabowski and Hirth 2003;), resource utilization (Cohen and Spector 1996), and staff turnover (Mukamel et al. 2009) above and beyond market competition, and thus may shift facility decisions of market exit.

We identified two state-level covariates as state policy controls. The state Medicaid reimbursement rate was obtained from a previous report by Grabowski, Zhanlian, and Mor (2008), who showed that the payment rate varied considerably across states. State Medicaid payment rate was expected to determine the financial status—and thus market entry/exit behaviors—of nursing homes, given the fact that Medicaid is the dominant payer of institutional long-term care (Hartman et al. 2009). State generosity of Medicaid payment may also influence state design and applications of quality regulations (Harrington, Mullan, and Carrillo 2004). For example, where Medicaid services are reimbursed at a lower rate, state quality inspectors may be more lenient to issue deficiency citations and impose sanctions, recognizing the tough financial situation faced by facilities in the state.

Another state-level policy control was the minimum level of licensed nurse hours per resident day mandated by each state (Harrington, Swan, and Carrillo 2007). The state nurse-staffing standard was obtained from the survey of each state. It was expected that state minimum staffing standard would be correlated with nursing home profitability and thus the likelihood of market exit, because the mandated staffing level tended to increase input cost of patient care (Harrington, Swan, and Carrillo 2007).

STATISTICAL ANALYSIS

We performed multivariate analysis of voluntary terminations focusing on for-profit and nonprofit nursing homes. We excluded the approximately 900 government-owned facilities from the analysis because their market incentives tend to differ substantially from those of nursing homes of private owners. Logistic regression models were estimated where the dependent variable equaled 1 if the facility voluntarily terminated in 2006–2007 and 0 if it did not. The key independent variable was the state stringency index in 2005, which for ease of interpretation, was retransformed to 1 if the original index >0 (higher than the national average stringency level) and 0 otherwise. Regression models controlled for the facility, market, and state covariates in 2005 described above, and they accounted for the clustering of nursing homes within states using White (1980) robust standard errors for covariance estimates.

RESULTS

State Variation in Regulatory Stringency

The indexed regulatory stringency was standardized to a mean of 0 and standard deviation of 1. It varied greatly across states (Table 1), ranging from 4 or higher in states such as Connecticut, Washington, DC, Kansas, and Oregon, to below −2.5 in Hawaii, Kentucky, Pennsylvania, Rhode Island, South Dakota, and Wyoming.

Variation in Nursing Home Termination

Nursing home termination also varied substantially across states (Table 1). The national 2-year voluntary termination rate in 2006–2007 was 2.16 percent, with Washington, DC, reporting the highest rate (10 percent), and Alaska, Maine, North Dakota, Nevada, and Wyoming reporting no voluntary termination. State voluntary termination rate was correlated with the state regulatory stringency index (Pearson correlation=0.27, p<.05). Table 2 shows that compared with nursing homes not terminated in 2006–2007, voluntarily terminated nursing homes tended to be smaller, have lower occupancy rate, and be nonchain affiliated, for profit, or hospital based (and thus with higher percentage of Medicare residents). These voluntarily terminating facilities also tended to be located in markets with higher competition and states with lower Medicaid reimbursement rate.

Table 2
Characteristics of Nursing Homes, 2005

There were only 47 involuntary terminations in 2006–2007, with over 30 states having no facilities involuntarily terminated. State involuntary termination rate did not correlate with state stringency of quality enforcement (Pearson correlation=−0.04, p=.76). Compared with nonterminated nursing homes, involuntarily terminated nursing homes tended to have more deficiency citations and to be larger, for profit, or hospital based (Table 2).

Determinants of Voluntary Termination

Table 3 shows that in multivariate analyses, variables expected to be associated with increased ability to generate revenues and profits—such as increased occupancies, higher patient census, and higher Medicare coverage—lowered the odds of termination; variables that are likely associated with lower revenues and profits, including poor quality measured by excess deficiency citations (which lowers the residual demand the nursing home faces) and increased competition, increased the odds of termination. Hospital-based facilities were also much more likely to terminate, possibly because their per diem cost of care was higher than that in freestanding facilities while the current Medicare prospective payment for skilled nursing care tends not to distinguish between the two types of facilities (Liu and Black 2003; Medicare Payment Advisory Commission 2007;). Therefore, under the “uniform” Medicare payment, hospital-based skilled nursing facilities are more likely to lose money. In addition, their existence may depend on how much they help improve the margin of their affiliated hospital (Medicare Payment Advisory Commission 2007), and this precarious financial situation may make them more vulnerable to terminations. The estimated effects of these factors are consistent with findings of recent studies on nursing home terminations and closures (Angelelli et al. 2003; Castle 2005; Castle et al. 2009; Zinn et al. 2009;).

Table 3
Relationship of State Regulatory Stringency in 2005 and Nursing Home Voluntary Termination in 2006–2007

After controlling for these covariates, nursing homes in states with stronger regulatory enforcement (stringency indices >0) were 50 percent more likely to voluntarily terminate than nursing homes in other states (odds ratio [OR]=1.53, p=.018). This indicates that state regulatory programs have an impact on the termination decisions made by facilities above and beyond other factors that may affect facility operations.

Sensitivity analyses confirmed that when the original stringency index was included in the model as a continuous variable, the estimated OR=1.08 (p=.022). We also recategorized states into tertile groups where we found that compared with nursing homes in states with low regulatory stringency (index <−1.62), nursing homes had an OR=1.17 (p=.472) for voluntary termination if they were in states with medium regulatory stringency (−1.62<index<1.36), and had an OR=1.68 (p=.014) if they were in states with high regulatory stringency (index >1.36). To test whether nursing home behavior differed by ownership, we repeated the regression analyses on samples stratified by ownership type as follows: for profit, nonprofit, hospital based, freestanding, chain affiliation, and nonchain affiliation. The results were similar to the main results and are available from the author upon request.

DISCUSSION

We found considerable state variations in the stringency with which states apply quality regulations and sanctions and the rate of nursing home voluntary terminations from Medicare/Medicaid programs. Involuntary terminations, those imposed by the state as the ultimate sanction for inadequate care, were rare and occurred in only about one-third of the states in 2006–2007. This study also found an independent effect of state quality enforcement efforts on nursing home voluntary termination decisions, where facilities were more likely to voluntarily terminate from the Medicare/Medicaid program in states with more stringent regulatory enforcement policies, everything else being equal.

While prior studies found that nursing home and market characteristics predicted facility terminations and closures (Angelelli et al. 2003; Castle 2005; Castle et al. 2009; Zinn et al. 2009;), this is the first study to show that a facility's voluntary termination decisions are also highly dependent on state overall approaches to regulating quality. As discussed earlier, terminations from the Medicare and Medicaid programs—both voluntary and involuntary—tend to disrupt services for nursing home residents and their families. Clearly, these consequences to resident care are recognized by state regulators who impose involuntary terminations sparingly. Findings in this study, however, suggest that state quality regulations may in another way affect the local supply of nursing home beds by driving facilities' voluntary market exit.

As such, state implementations of quality regulation should be contemplated in terms of both their costs and their benefits. Policy makers have long believed that monitoring and regulating nursing home services is critical to ensuring quality; broad consensus exists that government oversight of the industry is necessary because nursing home residents are often too frail to act as assertive consumers, and because of the major role that public financing plays in the delivery of nursing home care. As codified in the OBRA 1987, government regulations in the nursing home marketplace are grounded in the belief that effective regulatory enforcement—together with other quality approaches such as market competition—can ensure that nursing homes “attain or maintain the highest practicable physical, mental, and psychosocial well-being of each resident” (Institute of Medicine 1986). Indeed, there is some evidence for regulatory success. Since the OBRA 1987 was adopted, nursing homes have shown decreased uses of physical restraints and antipsychotic medications, decreased rates of pressure ulcers and hospital admissions among long-term residents, and increased staffing levels (Shorr, Fought, and Ray 1994; Garrard, Chen, and Dowd 1995; Mor et al. 1997; Zhang and Grabowski 2004;).

However, these quality effects should be weighed against the costs imposed by a more stringent regulatory system (Walshe and Regulating 2001; Walshe and Harrington 2002;), which include not only the incremental costs of enforced compliance with service standards but also the costs to local communities and patients who may suffer from disrupted nursing home services. The question we raise is whether the benefits from the increased strength of regulations justify the costs of potential loss of publicly financed beds due to terminations, and where the right balance is.

We caution that we can only raise this issue. We can neither quantify the benefits of stronger quality enforcements, nor the costs they impose. We do note, however, that extensive analyses continue to document ongoing quality and safety shortcomings in nursing facilities, including inappropriate medication use, untreated pains, persistent use of physical restraints (Castle 2002; Jenq et al. 2004; Briesacher et al. 2005;), and a recent finding that 20 percent of U.S. nursing homes still operate with severe and dangerous care deficiencies (General Accounting Office 2003). Furthermore, while there certainly are more voluntary than involuntary terminations, the number of voluntary terminations is small, averaging about 1 percent of all nursing homes per year, and there are factors other than regulatory stringency that drive terminations. For example, the recent burgeoning of community long-term care alternatives such as assisted living has decreased the use of institutional services (Gruneir et al. 2007), which may contribute to the overall increased market exits of nursing facilities over time (Castle et al. 2009). Therefore, even if states were to relax their regulatory programs, they would not be able to avert all the terminations that are currently occurring.

One limitation of this study is that we have not addressed entry decisions into nursing home markets. It is likely that the regulatory environment influences not only market exit but new entries or expansions of existing service lines in a facility. In addition, for voluntarily or involuntarily terminated facilities, we could not determine whether they were ultimately closed, sold to another owner, or switched to serving exclusively private patients. However, although these different events may have potentially different implications for service access and quality, termination from public certifications and regulations itself indicates performance failure of the current management (Zinn et al. 2009) and represents a necessary condition and significant threat for financial viability that foreshadows any of these business events. Using 2008 and 2009 OSCAR files, we also confirmed that over 90 percent of terminated facilities were not operated under a different ownership in 2008–2009, which suggests the likelihood that most terminated facilities were eventually closed (although a small number of them could serve private patients only). Finally, our analyses were cross-sectional and could not track longitudinal trend of nursing home terminations, that is, caused by, for example, recent increase of noninstitutional long-term care services (Gruneir et al. 2007) and that may vary across states.

In summary, we found that although involuntary nursing home terminations were rarely imposed in most states, nursing homes in states with stronger quality regulations tend to voluntarily terminate from the Medicare and Medicaid programs. Further research is required to better understand the impact of state regulatory policies on facilities' market entry and exit decisions. Such research would guide policy makers and state regulators as they make choices that determine the nature of the state survey and certification program.

Acknowledgments

Joint Acknowledgment/Disclosure Statement: The authors greatly acknowledge the funding by the National Institute on Aging under grant AG027420.

Disclosures: None.

Disclaimers: None.

NOTES

1Nonprofit facilities receive tax exemptions and may have access to other funds such as donations and grants. But in general, their market and regulatory constraints do not differ from those of for-profit facilities. In addition, another category of nursing homes is government owned (approximately 5 percent of all facilities). They were not considered in this study because their organizational objectives are dictated by the local or state governments that own them and their operations are subsidized by public revenues; as a result, the constraints that they face are quite different from those of private facilities.

2However, in empirical analyses of nursing home terminations, ownership type was included as a covariate given our assumption here that the responses to market and regulatory constraints of for-profit versus nonprofit nursing homes were different in magnitude (but not in direction). We also performed stratified analyses to verify the assumption that the direction of responses is the same.

3In addition to the federally mandated quality standards and annual inspections, states have licensure requirements for all nursing homes irrespective of their federal certification status. Because it would require a major effort to identify state regulations, we have focused on the federally mandated (and state-enforced) requirements, which may affect prospective market behaviors.

SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article:

Appendix SA1: Author Matrix.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

REFERENCES

  • Alexander JA, D'Aunno TA, Succi MJ. Determinants of Profound Organizational Change: Choice of Conversion or Closure among Rural Hospitals. Journal of Health and Social Behavior. 1996;37(3):238–51. [PubMed]
  • Angelelli J, Mor V, Intrator O, Feng Z, Zinn J. Oversight of Nursing Homes: Pruning the Tree or Just Spotting Bad Apples? Gerontologist. 2003;43(Spec. No. 2):67–75. [PubMed]
  • Briesacher BA, Limcangco MR, Simoni-Wastila L, Doshi JA, Levens SR, Shea DG, Stuart B. The Quality of Antipsychotic Drug Prescribing in Nursing Homes. Archives of Internal Medicine. 2005;165(11):1280–5. [PubMed]
  • Capezuti E, Boltz M, Renz S, Hoffman D, Norman RG. Nursing Home Involuntary Relocation: Clinical Outcomes and Perceptions of Residents and Families. Journal of American Medical Directors Association. 2006;7(8):486–92. [PubMed]
  • Castle NG. Nursing Homes with Persistent Deficiency Citations for Physical Restraint Use. Medical Care. 2002;40(10):868–78. [PubMed]
  • Castle NG. Nursing Home Closures and Quality of Care. Medical Care Research and Review. 2005;62(1):111–32. [PubMed]
  • Castle NG, Engberg J, Lave J, Fisher A. Factors Associated with Increasing Nursing Home Closures. Health Services Research. 2009;44(3):1088–109. [PMC free article] [PubMed]
  • Centers for Medicare and Medicaid. Nursing Home Data Compendium, 2007 Edition. Baltimore, MD: Centers for Medicare and Medicaid; 2007.
  • Cohen JW, Spector WD. The Effect of Medicaid Reimbursement on Quality of Care in Nursing Homes. Journal of Health Economics. 1996;15(1):23–48. [PubMed]
  • Friedman SM, Williamson JD, Lee BH, Ankrom MA, Ryan SD, Denman SJ. Increased Fall Rates in Nursing Home Residents after Relocation to a New Facility. Journal of the American Geriatrics Society. 1995;43(11):1237–42. [PubMed]
  • Garrard J, Chen V, Dowd B. The Impact of the 1987 Federal Regulations on the Use of Psychotropic Drugs in Minnesota Nursing Homes. American Journal of Public Health. 1995;85(6):771–6. [PubMed]
  • General Accounting Office. Nursing Home Quality: Prevalence of Serious Problems, while Declining, Reinforces Importance of Enhanced Oversight (Publication No. GAO-03-561) Washington, DC: General Accounting Office; 2003.
  • Grabowski DC. Medicare and Medicaid: Conflicting Incentives for Long-Term Care. Milbank Quarterly. 2007;85(4):579–610. [PubMed]
  • Grabowski DC, Hirth RA. Competitive Spillovers across Non-Profit and For-Profit Nursing Homes. Journal of Health Economics. 2003;22(1):1–22. [PubMed]
  • Grabowski DC, Zhanlian F, Mor V. Medicaid Nursing Home Payment and the Role of Provider Taxes. Medical Care Research and Review. 2008;65(4):514–27. [PMC free article] [PubMed]
  • Gruneir A, Lapane KL, Miller SC, Mor V. Long-Term Care Market Competition and Nursing Home Dementia Special Care Units. Medical Care. 2007;45(8):739–45. [PubMed]
  • Harrington C, Carrillo H. The Regulation and Enforcement of Federal Nursing Home Standards, 1991–1997. Medical Care Research and Review. 1999;56(4):471–94. [PubMed]
  • Harrington C, Mullan JT, Carrillo H. State Nursing Home Enforcement Systems. Journal of Health Politics, Policy and Law. 2004;29(1):43–73. [PubMed]
  • Harrington C, Swan JH, Carrillo H. Nurse Staffing Levels and Medicaid Reimbursement Rates in Nursing Facilities. Health Services Research. 2007;42(3, part 1):1105–29. [PMC free article] [PubMed]
  • Hartman M, Martin A, McDonnell P, Catlin A. National Health Spending in 2007: Slower Drug Spending Contributes to Lowest Rate of Overall Growth Since 1998. Health Affairs (Millwood) 2009;28(1):246–61. [PubMed]
  • Institute of Medicine. Improving the Quality of Care in Nursing Homes. Washington, DC: Institute of Medicine; 1986.
  • Jenq GY, Guo Z, Drickamer M, Marottoli RA, Reid MC. Timing in the Communication of Pain among Nursing Home Residents, Nursing Staff, and Clinicians. Archives Internal Medicine. 2004;164(14):1508–12. [PubMed]
  • Kronebusch K. Medicaid and the Politics of Groups: Recipients, Providers, and Policy Making. Journal of Health Politics, Policy and Law. 1997;22(3):839–78. [PubMed]
  • Liu K, Black KJ. Hospital-Based and Freestanding Skilled Nursing Facilities: Any Cause for Differential Medicare Payments? Inquiry. 2003;40(1):94–104. [PubMed]
  • Medicare Payment Advisory Commission. Report to Congress: Promoting Greater Efficiency in Medicare. Washington, DC: Medicare Payment Advisory Commission; 2007.
  • Meyer AD, Brooks GR, Goes JB. Environmental Jolts and Industry Revolutions: Organizational Responses to Discontinuous Change. Strategic Management Journal. 1990;11:93–110.
  • Mor V, Intrator O, Fries BE, Phillips C, Teno J, Hiris J, Hawes C, Morris J. Changes in Hospitalization Associated with Introducing the Resident Assessment Instrument. Journal of American Geriatrics Society. 1997;45(8):1002–10. [PubMed]
  • Mukamel DB, Spector WD, Bajorska A. Nursing Home Spending Patterns in the 1990s: The Role of Nursing Home Competition and Excess Demand. Health Services Research. 2005;40(4):1040–55. [PMC free article] [PubMed]
  • Mukamel DB, Spector WD, Limcangco R, Wang Y, Feng Z, Mor V. The Costs of Turnover in Nursing Homes. Medical Care. 2009;47(10):1039–45. [PMC free article] [PubMed]
  • Nyman JA. Prospective and “Cost-Plus” Medicaid Reimbursement, Excess Medicaid Demand, and the Quality of Nursing Home Care. Journal of Health Economics. 1985;4(3):237–59. [PubMed]
  • Shorr RI, Fought RL, Ray WA. Changes in Antipsychotic Drug Use in Nursing Homes during Implementation of the OBRA-87 Regulations. Journal of American Medical Association. 1994;271(5):358–62. [PubMed]
  • Troyer JL. Cross-Subsidization in Nursing Homes: Explaining Rate Differentials among Payer Types. Southern Economic Journal. 2002;68:750–73.
  • U.S. Government Accountability Office. Federal Monitoring Surveys Demonstrate Continued Understatement of Serious Care Problems and CMS Oversight Weakness. Washington, DC: U.S. Government Accountability Office; 2008.
  • Walshe K, Harrington C. Regulation of Nursing Facilities in the United States: An Analysis of Resources and Performance of State Survey Agencies. Gerontologist. 2002;42(4):475–87. [PubMed]
  • Walshe K, Regulating US. Nursing Homes: Are We Learning from Experience? Health Affairs (Millwood) 2001;20(6):128–44. [PubMed]
  • Weisbrod BA. The Nonprofit Economy. 1st Edition. Cambridge, MA: Harvard University Press; 1988.
  • White H. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica. 1980;48:817–30.
  • Zajac EJ, Shortell SM. Changing Generic Strategies: Likelihood, Direction, and Performance Implications. Strategic Management Journal. 1989;10:413–30.
  • Zhang X, Grabowski DC. Nursing Home Staffing and Quality under the Nursing Home Reform Act. Gerontologist. 2004;44(1):13–23. [PubMed]
  • Zinn J, Mor V, Feng Z, Intrator O. Determinants of Performance Failure in the Nursing Home Industry. Social Science and Medicine. 2009;68(5):933–40. [PubMed]
  • Zwanziger J, Mukamel DB, Indridason I. Use of Resident-Origin Data to Define Nursing Home Market Boundaries. Inquiry. 2002;39(1):56–66. [PubMed]

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