Nursing Homes: Private Investment Homes Sometimes Differed from Others in Deficiencies, Staffing, and Financial Performance
Published: 2011-07-15T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: Private investment (PI) firms' acquisition of several large nursing home chains led to concerns that the quality of care may have been adversely affected. These concerns may have been in part due to PI firms' business strategies and their lack of financial transparency compared to publicly traded companies. In September 2010, GAO reported on the extent of PI ownership of nursing homes and firms' involvement in the operations of homes they acquired. In this report, GAO examined how nursing homes that were acquired by PI firms changed from before acquisition or differed from other homes in: (1) deficiencies cited on state surveys, (2) nurse staffing levels, and (3) financial performance. GAO identified nursing homes that had been acquired by PI firms from 2004 through 2007 and then used data from CMS's Online Survey, Certification, and Reporting system and Medicare Skilled Nursing Facility Cost Reports to compare these PI homes to other forprofit and nonprofit homes. For PIacquired homes, GAO also compared homes for which the operations and real estate were owned by the same firm to those that were not. Because research has shown that other variables influence deficiencies, staffing, and financial performance, GAO statistically controlled--that is adjusted--for several factors, including the percent of residents for whom the payer is Medicare, facility size, occupancy rate, market competition, and state. Any differences GAO found cannot necessarily be attributed to PI ownership or acquisition.
section_title: What GAO Found
paragraphs: On average, PI and other for-profit homes had more total deficiencies than nonprofit homes both before (2003) and after (2009) acquisition. PI-acquired homes were also more likely to have been cited for a serious deficiency than nonprofit homes before, but not after, acquisition. Serious deficiencies involve actual harm or immediate jeopardy to residents. From 2003 to 2009, total deficiencies increased and the likelihood of a serious deficiency decreased in PI homes; these changes did not differ significantly from those in other homes. Reported average total nurse staffing ratios (hours per resident per day) were lower in PI homes than in other homes in both 2003 and 2009, but the staffing mix changed differently in PI homes. Staffing mix is the relative proportion of registered nurses (RN), licensed practical nurses (LPN), and certified nurse aides (CNA). RN ratios increased more from 2003 to 2009 in PI homes than in other homes, while CNA ratios increased more in other homes than in PI homes. The increase in RN ratios in PI homes from 2003 to 2009 was greater if the same PI firm acquired both operations and real estate than if not. The financial performance of PI homes showed both cost increases from 2003 to 2008 and higher margins in those years when compared to other for-profit or nonprofit homes. Facility costs as well as capital-related costs for PI homes increased more, on average, from 2003 to 2008 than for other ownership types. The increase was less if the same PI firm acquired both the operations and real estate than if it did not. In 2008, PI homes reported higher facility costs than other for-profit homes (but lower costs than nonprofit homes) and higher capital-related costs than other ownership types. Despite increased costs, PI homes also showed increased facility margins and the increase was not significantly different from that of other for-profit homes. In contrast, the margins of nonprofit homes decreased. Although the acquisition of nursing homes by PI firms raised questions about the potential effects on quality of care, GAO's analysis of data from before and after acquisition did not indicate an increase in the likelihood of serious deficiencies or a decrease in average reported total nurse staffing. The performance of these PI homes was mixed, however, with respect to the other quality variables GAO examined. We found differences among PI-acquired homes that reflected management decisions made by the firms and, to varying degrees, some of the changes in the PI firms we studied were consistent with attempts to increase their homes' attractiveness to higher paying residents. HHS provided CMS's observations on our methodology. CMS suggested an alternative to our "before and after" acquisition methodology to take into account the fact that PI firms acquired nursing homes at different points in time during 2004 through 2007. One of the studies we cited used such a methodology and we believe that the use of different methodologies enhances the understanding of an issue. CMS also identified a number of additional approaches for exploring the relationship between PI ownership and quality. We agree that such approaches merit future attention. CMS also acknowledged that the report is an important step toward better understanding the effect of nursing home ownership on the quality of care provided to residents.

section_title: Letter
paragraphs: The acquisition by private investment (PI) firms of several large nursing  home chains led to congressional and media attention in 2007 stemming  from concerns that the quality of resident care may have been adversely  affected. For example, a 2007 New York Times article reported that PI  firms had reduced nursing home costs and increased profitability by  cutting registered nurse (RN) staffing. These concerns may have been  due in part to PI firms’ business strategies and their lack of financial  transparency compared to publicly traded companies. PI firms may hold  their investments for relatively short time frames while they attempt to  improve financial and operating performance. In addition, they may place  large levels of debt on the acquired company. Since the ownership  interests of PI firms generally are not publicly traded on a stock  exchange, the nursing home companies acquired by such firms are not  subject to the same federal financial disclosure requirements, making  their finances and management less transparent than publicly traded  companies.
Together, the Medicare and Medicaid programs funded about $89 billion  for nursing home care for elderly and disabled individuals in 2009.  Medicaid, which funds about two-thirds of all nursing home resident days,  pays for individuals who typically require long-term custodial care, such  as help with bathing and toileting. Medicare, which funds about   12 percent of nursing home resident days, pays for individuals who  require more intensive skilled care for a relatively short period of time  following a hospital stay. The Centers for Medicare & Medicaid Services  (CMS) oversees both programs and contracts with state survey agencies  to conduct inspections, known as standard surveys, and complaint  investigations to determine whether nursing homes that participate in the  Medicare and Medicaid programs are complying with federal quality  standards. State surveyors cite deficiencies when a nursing home is  found to be out of compliance with these standards, which include a  requirement that homes have sufficient nursing staff. Research has  shown both deficiencies and nurse staffing levels to be indicators of the  quality of care in nursing homes.
You asked us to examine the impact of PI ownership on the quality of  care provided and on nursing homes’ financial performance. This report  builds on our September 2010 report, which addressed the extent of PI  ownership of nursing homes and the involvement of PI firms in the  operations of homes they acquired. We reported that PI firms acquired  about 1,900 unique nursing homes from 1998 through 2008. In this  report, we examine how nursing homes that were acquired by PI firms  changed from before acquisition or differed from other homes with regard  to (1) health deficiencies cited on state surveys, (2) nurse staffing levels,  and (3) financial performance.
To determine whether PI-owned nursing homes changed from before  acquisition or differed from other nursing homes in deficiencies, nurse  staffing levels, or financial performance, we (1) identified nursing homes  that had been acquired by PI firms from 2004 through 2007 and   (2) compared data from before and after PI acquisition of these homes to  data from other for-profit and nonprofit homes. The PI homes we studied  were acquired by the top 10 PI acquirers of nursing homes we identified  in our September 2010 report and were still owned by the same PI firm in  2009. We included homes for which a PI firm acquired the operations,  the real estate, or both. We obtained data for our outcome variables from  CMS: deficiency and nurse staffing data came from CMS’s Online  Survey, Certification, and Reporting system (OSCAR) and data regarding  financial performance came from Medicare Skilled Nursing Facility (SNF)
Cost Reports. OSCAR is the only national, uniform data source that  contains data on nursing home deficiencies and nurse staffing. Medicare  SNF Cost Reports are the only publicly available source of financial data  on most Medicare providers.    Deficiencies. We examined total deficiencies and whether there were  any “serious” deficiencies using data from both standard surveys and  complaint investigations. Deficiencies are categorized into levels  according to the number of residents potentially or actually affected  and the degree of relative harm involved. Serious deficiencies are  those at the levels indicating actual harm or immediate jeopardy  (actual or potential death or serious injury). As we have noted in prior  reports, state surveys may underestimate deficiencies.    Nurse staffing. We examined the total number of nursing hours per  resident per day (nurse staffing ratios), as well as ratios for each of  three types of nursing staff separately—RNs, licensed practical  nurses (LPN), and certified nurse aides (CNA). Nurse staffing data are  self-reported by nursing homes.    Financial performance. We examined (1) facility costs per resident  day, defined as the total facility costs—including both operating and  capital costs—divided by total resident days; (2) capital-related costs  per resident day, defined as capital-related costs allocated to nursing  home resident care divided by nursing home resident days; and   (3) facility margins, defined as the amount of total facility revenues  exceeding total facility costs, divided by total facility revenues.  Financial data are self-reported by nursing homes.
Data analyses. To determine whether the PI, other for-profit, and  nonprofit homes we studied differed from one another, we analyzed data  from two points in time, one before and one after our target acquisition  period of 2004 to 2007. In general, we analyzed data from 2003 and 2009  (for deficiencies and staffing) or 2003 and 2008 (for financial  performance). The 2008 and 2009 data were the latest available, which  allowed as much time as possible for any changes associated with PI  acquisition to take effect. We included data from before PI acquisition so  we could determine whether the post acquisition data reflected  preexisting differences. Throughout this report, we refer to the homes that  were acquired by PI firms as “PI homes,” even when referring to 2003,  which preceded our target acquisition period. We included data from  other types of nursing homes so we could determine whether any  changes from before to after acquisition reflected changes that occurred  regardless of type of ownership. For PI-acquired homes, we also  compared homes for which the operations and real estate were owned by  the same firm to those that were not. Because research has shown that  other variables can influence deficiencies, staffing, and financial  performance, we statistically controlled—that is adjusted—for these  variables when analyzing our data. This adjustment allowed us to  examine data from homes with different types of ownership after  neutralizing the effect of these variables. Our control variables included  membership in a chain, payer mix (i.e., the percent of residents for whom  the payer is Medicare, Medicaid, or another source), facility size (number  of beds), occupancy rate, market competition (based on the number of  beds in each county), and geographic location (state). Payers other than  Medicare and Medicaid include private insurance, religious organizations,  the Department of Veterans Affairs, residents who pay for their own care,  and others. Unless otherwise specified, all results that we present are  based on our adjusted analyses and are statistically significant at the   0.05 level. To provide context, we show the unadjusted values in our  figures and also describe the key differences that were significant in our  analyses of adjusted data.
In addition, to determine whether there were systematic differences  among nursing homes acquired by PI firms from 2004 to 2007 in  outcomes we studied, we conducted a series of analyses in which we  separately compared each of five PI firms’ homes to all other PI-acquired  nursing homes in our study. We restricted our analyses to those PI firms  and homes for which we could identify both the PI owner of operations  and real estate and those PI firms for which we determined we had data  from a sufficient number of homes.    For three PI firms’ homes, the same PI firm acquired both operations  and real estate.    For two PI firms that acquired nursing home operations, a different PI  firm acquired the real estate.
In each of five separate analyses, we compared the homes owned by a  PI firm to all other PI homes in our larger aggregate analysis, including  homes owned by the other firms we studied and any other homes owned  by that PI firm (e.g., those for which we could not identify the real estate  owner). Again, we adjusted for other variables that can influence  deficiencies, staffing, and financial performance. Unless otherwise  specified, all results that we present were statistically significant at the  0.05 level in analyses of adjusted data. We also interviewed  representatives of PI firms that acquired nursing home operations, real  estate, or both, and representatives of companies that operate PI-owned  homes and, if their homes were part of our firm level analyses, we  discussed the results for their homes.
For all analyses, we excluded nursing homes when extreme values  suggested data entry or other reporting errors. We performed data  reliability checks on the list of PI homes we compiled and on data we  used from OSCAR, Medicare’s Provider of Services, and Medicare SNF  Cost Reports. We also reviewed relevant documentation and discussed  these data sources with knowledgeable officials and industry experts. In  addition to our statistical analyses, we reviewed published research on  the quality and costs of nursing home care, our prior work on nursing  homes, and other relevant documentation. We interviewed officials from  CMS and experts on nursing home quality and costs. We reviewed all  data for soundness and consistency and determined that they were  sufficiently reliable for our purposes.
Limitations. Our analyses have several important limitations. Our  findings cannot be generalized beyond the PI-acquired nursing homes we  studied, which were limited to only those homes acquired from 2004 to  2007 by the 10 largest PI acquirers of nursing homes. Because they may  have been caused by other uncontrolled and unquantified variables, the  differences between PI-acquired and other nursing homes that we  observed cannot necessarily be attributed to PI ownership and the  differences we observed from before to after acquisition cannot  necessarily be attributed to PI acquisition. Despite these limitations, our  analyses do provide a reasonable basis for comparing deficiencies, nurse  staffing, and financial performance of the PI-owned homes we studied to  each other and to other types of nursing homes at two points in time.
We conducted this performance audit from January 2010 to July 2011 in  accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives. A more detailed description of  our scope and methodology can be found in appendix I.
subsections: section_title: Background
paragraphs: Over the last decade, nursing home ownership and operating structures  have continued to evolve, including an increase in private investment  ownership of nursing homes and the development of more complex  structures.
subsections: section_title: Nursing Home Ownership and Operations
paragraphs: Nursing home ownership varies in terms of profit status, level of  management involvement, number of homes owned, and whether the real  estate of homes is owned or leased.    Profit status. Owners may be for-profit, nonprofit, or government  entities; about two-thirds of nursing homes are for-profit businesses.  In general, for-profit businesses, which may be publicly traded or  privately owned, have a goal of making profits that are distributed  among the owners and stockholders. In contrast, a nonprofit entity  receives favorable tax status because it may not operate for the  benefit of nor distribute revenues to private interests.    Management involvement. Nursing home owners vary in terms of their  involvement in management of the business: they may be the  operators, and hold the state license, or they may contract with  separate licensed entities to manage the day-to-day operations.    Number of homes owned. Owners or operators may have only one  facility or they may have multiple facilities across one or more states  that are part of a chain. Owners or operators may also have multiple  chains. According to a study conducted for the Department of Health  and Human Services, about half of nursing homes are part of a  chain.    Real estate. Owners or operators do not necessarily own the real  estate where care is delivered, but instead may lease it. The  separation of real estate assets from the operations may be done to  obtain financing or in an attempt to protect real estate assets from  malpractice claims. Furthermore, the owners, leaseholders, and  operators may or may not be owned by the same or related entities.
PI firm nursing home ownership. In general, PI firms use a combination  of investment capital and debt financing to acquire companies, including  nursing home companies, with a goal of making a profit and eventually  returning that profit to investors and the firm. As we noted in our prior  report, some of the 10 PI firms we studied acquired both the operations  and the real estate of nursing home chains while others only acquired the  real estate. The former firms sit on the chains’ boards of directors and  told us that their role is to provide strategic direction rather than directing  day-to-day operations. In contrast, PI firms we studied that only  purchased real estate do not sit on the nursing home chains’ boards of  directors. Among the PI firms that shared their reasons for investing in  the nursing home industry, most cited the increased demand for long- term care due to an aging population. We also reported that the  investment time horizons and objectives of PI firms vary. Some PI firms  purchased the homes with a planned short-term “exit strategy” and others  intended to hold the investment over the long term. PI firm managers  said they are able to make business improvements that their publicly  traded competitors may be less willing to make because they generally  are not subject to periodic disclosure requirements about their financial  performance and therefore are not tied to producing profits on a quarterly  basis. In addition, PI firms have said that they increase the operator’s  access to funding that can be used to increase staff wages, enhance  operations, or modernize facilities and which ultimately may result in  improved quality of care.
PI firm business strategies. PI firms may pursue different business  strategies with respect to the types of residents they want to attract and  the efficiency of their operations. Researchers have found that some  nursing homes may specialize in caring for residents with certain care  needs or Medicare residents. Care for such residents may result in higher  levels of reimbursement. Indeed, prior to and after acquisition, PI homes  we studied had a higher average percentage of residents whose care was  reimbursed by Medicare compared to other for-profit and nonprofit  homes. After acquisition, the percentage of residents in PI homes whose  care was paid for by a source other than Medicare or Medicaid was  higher on average than in other for-profit homes, but lower than in  nonprofit nursing homes.
Prior to acquisition, the average occupancy rates in PI homes were not  significantly different from other homes. However, after acquisition in  2009, the average occupancy rates in PI homes were higher than other  for-profit homes, although they did not differ significantly from nonprofit  homes’ occupancy rates.
section_title: Federal Oversight of Nursing Home Quality
paragraphs: The Social Security Act requires all nursing homes that participate in  Medicare and Medicaid to undergo periodic assessments of compliance  with federal quality standards. It also includes certain ownership  reporting requirements. Under contract with CMS, state survey agencies  conduct standard surveys, which occur once a year, on average, and  complaint investigations as needed. A standard survey involves a  comprehensive assessment of about 200 federal quality standards. In  contrast, complaint investigations generally focus on a specific allegation  regarding resident care or safety made by a resident, family member, or  nursing home staff member. Deficiencies identified during either  standard surveys or complaint investigations are classified in 1 of 12  categories according to their scope (i.e., the number of residents  potentially or actually affected) and severity (i.e., the potential for or  occurrence of harm to residents). Serious deficiencies indicate care  problems that have resulted in actual harm or immediate jeopardy (actual  or potential for death or serious injury) for one or more residents.
We, CMS, and other researchers have examined the rates of deficiency  citations, by state and among groups of nursing homes, to track trends in  the proportion of homes with serious deficiencies and better understand  recurring care problems. Our prior reports identified considerable  interstate variation in citations for serious deficiencies on standard  surveys and the understatement of serious deficiencies on those  surveys. Although several studies have shown that for-profit nursing  homes generally have a greater number of total deficiency citations than  nonprofit homes, others have found no statistical difference in total  deficiency citations between for-profit and nonprofit homes. Similarly,  research that examined differences in the citations for serious  deficiencies has not consistently found a difference between for-profit and  nonprofit homes. One study examined the effect of PI acquisition on  total and serious deficiencies; it did not find a significant difference from  before to after PI acquisition. A different study that examined the impact  of ownership of nursing home operations and real estate found that  deficiency rates were similar across homes regardless of whether or not  ownership was split between different entities.
section_title: Nursing Home Staffing
paragraphs: Nursing homes employ three types of nursing staff—RNs, LPNs, and  CNAs. The responsibilities and salaries of these three types of staff are  related to their level of education. The staffing mix—that is, the balance a  nursing home maintains among RNs, LPNs, and CNAs—is generally  related to the needs of the residents served. For example, a higher  proportion of RNs may be employed to meet residents’ needs in homes  that serve greater numbers of residents with acute care needs or those  with specialty care units (such as units for residents who require  ventilators). However, homes may not be able to pursue their ideal  staffing mix because of RN shortages in certain geographic areas. High  turnover among licensed nurses and CNAs may also affect staffing mix.
Licensed Nurses and Nurse Aides    RNs have at least a 2-year degree and are licensed in a state. Due to  their advanced training and ability to provide skilled nursing care, RNs  are paid more than other nursing staff. Generally, RNs are  responsible for managing residents’ nursing care and performing  complex procedures, such as starting intravenous feeding or fluids.    LPNs have a 1-year degree, are also licensed by the state, and  typically provide routine bedside care, such as taking vital signs.    CNAs are nurse aides or orderlies who work under the direction of  licensed nurses, have at least 75 hours of training, and have passed a  competency exam. CNAs’ responsibilities usually include assisting  residents with eating, dressing, bathing, and toileting. In a typical  nursing home, CNAs have more contact with residents than other  nursing staff and provide the greatest number of hours of care per  resident per day. CNAs generally are paid less than RNs and LPNs.
Researchers have found that higher total and RN staffing levels are  typically associated with higher quality of care as shown by a wide range  of indicators, including deficiencies and health outcomes. Lower total  nurse staffing levels and lower levels of RN staffing have been linked to  higher rates of deficiency citations. In addition, higher total nurse staffing  ratios (hours per resident per day), and higher levels of RN staffing in  particular, have been associated with better health outcomes (such as  fewer cases of pressure ulcers, urinary tract infections, malnutrition, and  dehydration) as well as improved residents’ functional status. A home’s  management of its nurse staffing has the potential to affect the quality of  resident care, as well. For example, nursing staff turnover complicates  nursing homes’ efforts to train their staff and can contribute to quality  problems.
There are no federal minimum standards linking nurse staffing to the  number of residents but a number of states have such standards. By  statute, nursing homes that participate in Medicare and Medicaid are  required to have sufficient nursing staff to provide nursing and related  services to allow each resident to attain or maintain the highest  practicable physical, mental, and psychosocial well-being. In addition to  this general requirement, every nursing home must have 24 hours of  licensed nurse (RN or LPN) coverage per day, including one RN on duty  for at least 8 consecutive hours per day, 7 days per week. In contrast,  one researcher reported that, as of 2010, 34 states had established  minimum requirements for the number of nurse aide or direct care hours,  which ranged from about 0.4 to 3.5 hours per resident per day.
In 2000, CMS examined the impact of nurse staffing on quality of care in  nursing homes. CMS concluded that a minimum nurse staffing ratio of  2.75 hours per resident day was needed to maintain quality of care, while  also noting a preferred ratio of 3 hours and an optimal ratio of 3.9 hours.  For RNs, CMS concluded that the minimum ratio should be 0.2 hours,  with a preferred ratio of 0.45 hours. The average acuity of nursing home  residents has increased since that report was issued. CMS did not  recommend establishing minimum federal nurse-staffing standards, in  part because staffing needs vary with residents’ care needs and  management or nursing practices (such as training or policies affecting  the retention of nursing staff) can influence the quality of care.
Studies of trends in nurse staffing in the last few years have noted an  increase in total nurse staffing and in licensed nurse staffing. In addition,  several studies have shown that for-profit nursing homes generally have  lower nurse staffing ratios, and lower RN ratios, than nonprofit homes.  One study examined the effect of PI ownership on nurse staffing; it found  that RN staffing declined after PI acquisition, but this decline had begun  prior to acquisition. This study also found an increase in CNA staffing  after PI acquisition. A different study that examined the impact of  ownership of nursing home operations and real estate on nurse staffing  found that RN staffing was higher when real estate was owned than when  it was leased or when ownership arrangements were mixed.
section_title: Costs of Care and Profitability
paragraphs: Nursing home costs are determined by the mix of residents and the  management of a home’s resources to meet its residents’ needs. The  costs of caring for any particular nursing home resident vary with the type  of services and amount of care needed. Residents who require low- intensity nursing and therapy or custodial care, like the typical Medicaid  resident, are less costly, in part because their care needs are not as  heavily dependent on the services of licensed nurses. Medicare  beneficiaries are typically more costly than Medicaid residents, have  shorter stays, and are admitted with the expectation that they will  rehabilitate, recover, and return to their residences. A growing share of  nursing home residents requires rehabilitation therapies and intensive  skilled nursing care, such as parenteral feeding and ventilator care that  previously were provided primarily in hospital settings; these residents are  more costly because they require more skilled nursing and therapy staff  and specialized equipment.
Salaries and labor-related costs for nursing and other staff account for  more than half of a nursing home’s operating costs. Therefore a home’s  decisions about its staffing mix are a key determinant of the home’s costs.  To a lesser extent, the nursing home’s management of its capital  assets—buildings, land, and equipment—also influences the home’s  costs. New nursing homes and those that have been recently renovated  may have additional expenses associated with facility construction and  renovation that older buildings do not.
In addition to a home’s occupancy rate, profitability is influenced by  several other factors, including payment rates, the mix of residents, and  the nursing homes’ management of resources. Medicare’s and 21 states’  Medicaid payment rates are prospectively set per diem amounts that take  into account the relative care needs of the resident. Under such  payment systems, nursing homes have an incentive to provide care at a  cost below the payment amount because they can retain any excess  revenue not spent providing care. Although Medicare generally pays for  the care of the nursing home residents with the most complex care  needs, Medicare and private insurance have the highest payment rates  for nursing home care and, on average, reimburse homes more than the  costs of care. On the other hand, industry representatives perennially  express concerns that Medicaid payment rates in many states are so low  that they do not cover the costs of providing care. Some nursing homes  trying to increase their profitability may focus on reducing their costs, by  providing fewer or less expensive services. Other homes trying to  increase their profitability may staff their homes and renovate their  buildings to attract the better-paying Medicare and private insurance  residents that will enhance their revenues or profits. We and the Medicare  Payment Advisory Commission have reported that for-profit nursing  homes have a greater profit on their Medicare line of business than  nonprofit homes, on average.
The relationship between costs, profitability, and quality of care in nursing  homes differs depending on how the home’s resources are deployed. A  home that increases its nurse staffing or adopts a new technology to  improve the quality of care may also reduce its profitability because it  increased costs without increasing revenues. However, some  expenditures may prevent additional costs or increase revenues and  therefore lead to improved profitability. For example, an expense can  prevent subsequent, costly care needs, such as when higher levels of RN  staffing result in reduced levels of infections. As another example,  expenses that boost the attractiveness of the home to better paying  residents may also improve the home’s profitability, whether or not such  expenses improve the quality of care.
section_title: PI Homes Had More Total Deficiencies than Nonprofit Homes and Were More Likely to Have Had a Serious Deficiency Before but Not After Acquisition
paragraphs: PI homes, like other for-profit homes, had more total deficiencies than  nonprofit homes in both 2003 and 2009. In 2009, PI homes did not differ  significantly from nonprofit homes in the likelihood of a serious deficiency,  but in 2003 the likelihood was higher in homes that were subsequently  acquired by PI than in nonprofit homes. From 2003 to 2009, total  deficiencies increased and the likelihood of a serious deficiency  decreased in PI homes; the changes in these deficiency measures from  2003 to 2009 in other for-profit and nonprofit homes did not differ  significantly from the changes in PI homes.
subsections: section_title: PI Homes Had More Total Deficiencies than Nonprofit Homes
paragraphs: On average, PI homes had more total deficiencies than nonprofit homes  in both 2003 and 2009. (See fig. 1.) PI homes did not differ significantly  from other for-profit homes in total deficiencies in either year. Total  deficiencies in PI homes increased from 2003 to 2009; this change was  not significantly different from the change in other homes. Among PI  homes, total deficiencies did not differ significantly as a function of  whether the same firm acquired the operations and real estate or not.
Our examination of total deficiencies in each of five PI firms’ homes  indicated some differences between PI firms, but the differences we  observed generally existed prior to acquisition and persisted after  acquisition. For example, in comparison to other homes acquired by PI  firms, total deficiencies were lower in both 2003 and 2009 in homes of  one firm and were greater in both years in homes of a second firm.
section_title: Compared to Nonprofit Homes, PI Homes Were More Likely to Have Had a Serious Deficiency Before but Not After Acquisition
paragraphs: In 2009, PI homes did not differ significantly from nonprofit homes in the  likelihood of a serious deficiency when we controlled for other explanatory  factors, even though PI homes were more likely than nonprofit homes to  have had a serious deficiency in 2003. (See fig. 2.) The likelihood of a  serious deficiency in other for-profit homes was not significantly different  from PI homes in either year. The likelihood of a serious deficiency  decreased from 2003 to 2009 in PI homes, and this change was not  significantly different from the change in other for-profit and nonprofit  homes. In addition, the likelihood that a PI home would have had a  serious deficiency in 2009 did not differ significantly as a function of  whether the same firm owned both the operations and real estate or not,  although in 2003, the likelihood was significantly lower in homes for which  the same PI firm acquired both operations and real estate.
Our examination of serious deficiencies in each of five PI firms’ homes  indicated some differences between PI firms, but these differences  existed prior to acquisition and persisted after acquisition. In comparison  to other homes acquired by PI firms, the likelihood was lower in both 2003  and 2009 in homes of one firm and was greater in both years in homes of  a second firm.
section_title: Reported Total Nurse Staffing Ratios Were Lower in PI Homes, but Reported RN Ratios Increased More in PI Homes than Other Homes
paragraphs: On average, total reported nurse staffing ratios (hours per resident per  day) were lower for PI homes than for other types of homes in both 2003  and 2009, but PI homes’ reported RN ratios—the most skilled component  of total nurse staffing—increased more from 2003 to 2009. On average,  reported ratios for LPNs—the other type of licensed nurse—also  increased from 2003 to 2009 in PI homes; this change was not  significantly different from the change from 2003 to 2009 in other for-profit  and nonprofit homes. In contrast, reported CNA ratios for PI homes did  not change significantly from 2003 to 2009, but increased for other types  of homes.
subsections: section_title: Average Reported Total Nurse Staffing Ratios Were Lower for PI Homes than Other Homes in Both 2003 and 2009
paragraphs: In both 2003 and 2009, PI homes reported lower average total nurse  staffing ratios than other types of homes. (See fig. 3.) Average reported  total nurse staffing ratios for PI homes increased from 2003 to 2009; this  change was not significantly different from either other for-profit or  nonprofit homes. The unadjusted average total nurse staffing ratios  reported in 2009 for each ownership type exceeded the ratio identified as  “preferred” by CMS in its 2000 report, but fell short of the level CMS  identified as “optimal.”
Our examination of reported average total nurse staffing ratios for each of  five PI firms indicated some differences between firms. We found that the  change in these ratios from 2003 to 2009 in one PI firm’s homes was not  as great as the increase for other PI-acquired homes; in 2009, total nurse  staffing ratios for that firm’s homes were lower than for other PI-acquired  homes. Representatives of the nursing home operator for homes of this  PI firm told us that they had focused on and reduced staff turnover since  2003.
section_title: Staffing Mix Changed, with Average Reported RN Ratios Increasing More for PI Homes than Other Homes but CNA Ratios Increasing More for Other Homes than PI Homes
paragraphs: The staffing mix in PI homes—the balance of RNs, LPNs, and CNAs— changed from 2003 to 2009, and the changes in staffing were different in  PI homes than in other types of homes. Average reported ratios for RNs  (one type of licensed nursing staff) increased more from 2003 to 2009 in  PI homes than other types of homes. Average ratios for LPNs (the other  type of licensed nursing staff) also increased in PI homes from 2003 to  2009, but the change in PI homes did not differ significantly from the  change in other for-profit and nonprofit homes. In contrast, average  reported ratios for CNAs (who are not licensed) did not change  significantly from 2003 to 2009 for PI homes, but increased for both other  types of homes.
RN ratios. In 2009, average reported RN ratios for PI homes were  greater than other for-profit homes and were also greater than nonprofit  homes, when we controlled for other explanatory factors. (See fig. 4.)  Average reported RN ratios for PI homes increased from 2003 to 2009,  and this increase was greater than the change for both other types of  homes. In 2003, average reported RN ratios for PI homes did not differ  significantly from other for-profit homes when we controlled for other  explanatory factors and were lower than for nonprofit homes. These ratios  were greater for nonprofit homes than for other for-profit homes in both  2003 and 2009. The unadjusted average RN ratios reported in 2009 for  each ownership type—PI, other for-profit, and nonprofit homes—fell short  of the ratios identified as “preferred” by CMS in its 2000 report.
In 2009, average reported RN ratios were higher if the same PI firm  acquired both operations and real estate than if not. The increase in these  ratios from 2003 to 2009 for PI homes was greater if the same PI firm  acquired both operations and real estate than if not. (See fig. 5.) In 2003,  average reported RN ratios did not differ significantly as a function of  whether the same PI firm acquired both operations and real estate or not  when we controlled for other explanatory factors.
Our examination of RN ratios for five PI firms’ homes indicated some  differences between firms. We found that the increase from 2003 to 2009  was greater for homes of two firms than for other homes acquired by PI.  Representatives of the owners and operators of these homes told us that  these homes generally had high levels of RN staff before acquisition  either because they served a large proportion of short-term residents with  high acuity or rehabilitation needs in one case, or because they treated  residents in specialized care units (such as ventilator units).
Representatives of each firm also said that increasing RN staff was part  of an ongoing strategy to expand their capacity to care for such residents.  For homes of the third PI firm, the change from 2003 to 2009 in RN ratios  was not as great as the increase for other PI homes. This firm’s  representatives told us that training can be more important than the  number of staff and so they have focused their efforts on training and  reducing staff turnover. The change in average reported RN ratios from  2003 to 2009 for two sets of homes for which different PI firms acquired  the operations and real estate was less than the increase for other PI  homes. The operator of one of these sets of homes told us that they had  focused on promoting stable nursing leadership.
LPN ratios. Average reported LPN ratios were lower for PI homes than  other homes in both 2003 and 2009 when we controlled for other  explanatory factors. For PI homes, these ratios increased from 2003 to  2009; this increase was not significantly different than the change for  either other type of homes. Among PI homes, LPN ratios did not differ  significantly as a function of whether the same firm acquired the  operations and real estate or not.
CNA ratios. Average reported CNA ratios were lower for PI homes than  other homes in both 2003 and 2009. (See fig. 6.) Average reported CNA  ratios for PI homes did not change significantly from 2003 to 2009, but  increased for both other types of homes. Among PI homes, CNA ratios  did not differ significantly as a function of whether the same firm acquired  the operations and real estate or not when we controlled for other  explanatory factors.
Our examination of the CNA ratios for five PI firms’ homes indicated some  differences between firms. In comparison to other homes acquired by PI  firms, we found that for one set of homes where different PI firms  acquired the operations and real estate these ratios were lower in 2009,  but did not differ significantly in 2003. For another set of homes where  different PI firms acquired the operations and real estate, these ratios  were higher in 2009, but did not differ significantly in 2003.  Representatives of the operator for the nursing homes with lower CNA  ratios in 2009 told us that they had acquired labor-saving technology and  focused on reducing turnover. They reported that turnover of nursing staff  that provide direct care to residents in their homes had been 90 percent in  2003, but was 59 percent in 2009.
section_title: PI Homes’ Financial Performance Showed Cost Increases and Higher Facility Margins Compared to Other Homes
paragraphs: The financial performance of PI homes showed both cost increases and  higher margins when compared to other for-profit or nonprofit homes.  Specifically, facility costs per resident day for PI homes increased more,  on average, from before acquisition (2003) to after acquisition (2008) than  other for-profit and nonprofit homes. Among PI-acquired homes, we  observed less of an increase if the same PI firm owned the operations  and real estate than if not. The results were similar when we examined  capital-related costs, a component of facility costs. Despite increased  costs, PI homes also showed increased facility margins but the increase  was not significantly different from the change in other for-profit homes. In  contrast to PI and other for-profit homes, the margins of nonprofit homes  decreased.
subsections: section_title: Facility Costs, Including Capital-Related Costs, Increased for PI Homes and This Increase Was Greater than for Other Homes
paragraphs: Both facility costs per resident day and a component of those costs— capital related costs per resident day—increased in PI homes from 2003  to 2008 and this increase was greater than for other for-profit and  nonprofit homes.
Facility costs. In both 2003 and 2008, PI homes reported lower facility  costs per resident day, on average, than nonprofit homes even though  these costs increased more in PI homes from 2003 to 2008 than in both  nonprofit homes and other for-profit homes. (See fig. 7.) Facility costs  include all costs associated with maintaining and operating a nursing  home, such as staff salaries, administrative costs, and capital-related  costs. While PI homes did not differ significantly from other for-profit  homes in 2003 when we controlled for other explanatory factors, they  reported higher costs in 2008.
The increase in facility costs per resident day from 2003 to 2008 was  less, on average, if the same PI firm acquired both the operations and  real estate than if it did not. (See fig. 8.) While the latter group of homes  reported lower costs in 2003, these two groups reported costs in 2008  that did not differ significantly after we controlled for other explanatory  factors.
Our examination of facility costs for each of five PI firms indicated some  differences among firms. In comparison to other homes acquired by PI,  the increase in facility costs from 2003 to 2008 was greater in one set of  homes where different PI firms owned the operations and real estate but  the change was not as great in another PI firm’s homes.
Capital-related costs. Average capital-related costs per resident day in  PI homes increased from 2003 to 2008 and this change was greater for  PI homes than for other types of homes. (See fig. 9.) Capital-related costs  are a component of total facility costs that capture mortgage payments,  rents, depreciation, taxes and insurance, as well as land and building  improvements, including upgrades to equipment. Although capital- related costs were lower in PI homes than in other for-profit and nonprofit  homes in 2003 when we controlled for other explanatory factors, they  were higher than both other types of homes in 2008.
The average increase in capital-related costs from 2003 to 2008 was less  if the same PI firm acquired both operations and real estate than if not.  (See fig. 10.) Additionally, capital-related costs were lower in both years if  the same PI firm acquired both the operations and real estate than if not,  when we controlled for other explanatory factors.
Our examination of capital-related costs for each of five PI firms’ homes  indicated some differences between firms. Two PI firms’ homes showed  increases that were greater than other homes acquired by PI firms:   (1) one of these sets of homes, for which different PI firms acquired the  operations and real estate, reported lower capital-related costs in 2003  than other PI homes, but higher costs in 2008 and (2) the other firm’s  homes reported higher capital-related costs than other PI homes in both  2003 and 2008. A representative of the latter PI firm told us that they had  secured a $100 million line of credit for the modernization of the firm’s  nursing homes. Investment in the homes had been ongoing prior to  acquisition, this representative said, but the homes’ access to capital had  increased after acquisition. In contrast, the change in capital-related costs  for the remaining three firms’ homes was not as great as the increase in  other PI homes. Two of these three firms’ homes reported lower capital- related costs in both 2003 and 2008. Representatives from a nursing  home chain owned by one of these firms commented that the majority of  investments were in staffing. They noted that, in contrast, their peers had  invested in their own facilities to attract the highest paying residents.  Representatives from another firm that owned nursing home real estate,  but not operations commented that, depending on the resident population  served and the location of the home, renovations aimed at attracting more  acute (and higher paying) residents may not pay off. For example, homes  in a rural area might not be able to attract the appropriate staff and mix of  residents to make renovations aimed at treating more acute-care  residents worth the costs. However, they told us that these older, rural  homes still effectively serve a segment of the market despite the lower  level of capital investment.
section_title: Facility Margins for PI Homes Increased and Were Higher on Average than for Other Homes
paragraphs: Facility margins for PI homes were, on average, higher in 2003 and 2008  than for other for-profit and nonprofit homes. (See fig. 11.) Facility  margins in PI homes increased from 2003 to 2008; this increase was not  significantly different from the average change for other for-profit homes,  but was greater than the change in margins for nonprofit homes. In fact,  facility margins for nonprofit homes decreased from 2003 to 2008. The  increase in facility margins among PI homes from 2003 to 2008 was not  significantly different, on average, if the same PI firm acquired both the  homes’ operations and the real estate than if it did not. However, facility  margins for the former were, on average, higher both in 2003 and 2008.
Our examination of facility margins for each of five PI firms’ homes  indicated some differences between firms. We found that two firms’  homes showed an increase in facility margins that was greater than other  homes acquired by PI we studied. Representatives of one of these firms  told us that increased margins were the result of increased spending in  the homes with a focus on investments in technology, staffing, and  treating higher acuity residents. They told us that the strategy of the  nursing home chain they acquired had not changed and that both  increased spending and margins were present before the acquisition.  Two firm’s homes showed a change in facility margins that was less than  other PI homes. Representatives for the nursing home chain operating  one of these two sets of homes commented that they had not been  focused on the margins; the chain’s chief executive officer noted that he  was evaluated by its PI owner based on the quality of care provided, not  margins.
section_title: Concluding Observations
paragraphs: The acquisition of nursing homes by private investment firms has raised  questions about the potential effects on the quality of care. Our analyses  did not find an increase in the likelihood of serious deficiencies or a  decrease in average reported total nurse staffing for the PI-acquired  homes we studied. In fact, reported RN staffing increased more in PI- acquired homes than other homes. However, the performance of these PI  homes was mixed with respect to the other quality variables we  examined. For example, PI-acquired homes had more total deficiencies  and lower total nurse staffing ratios than nonprofit homes, both before  and after acquisition. Also, despite concerns that PI firms might cut costs  to improve profitability, we found that reported facility costs increased in  the PI-acquired homes we studied. Margins also increased in the PI- acquired homes we studied from before to after acquisition, while they  decreased in nonprofit homes. It is possible to increase both costs and  margins because certain expenditures may prevent subsequent, costly  care, or increase a home’s attractiveness to better paying residents. PI- acquired homes were more similar to for-profit than to nonprofit homes  with respect to the change in margins and total deficiencies, but were like  neither for-profit nor nonprofit homes with respect to the change in  staffing mix and capital-related costs. In addition, compared to homes for  which the same PI firm acquired both operations and real estate, PI- acquired homes for which ownership was split had lower reported RN  ratios, higher reported capital-related costs, and lower reported facility  margins in the period after acquisition.
Our findings were consistent with the fact that PI firms we studied are to  varying degrees attempting to increase the attractiveness of their homes  to higher paying residents, including those whose care is reimbursed by  Medicare. The homes acquired by the PI firms we studied had a higher  average proportion of Medicare residents both before and after  acquisition. Our analyses and interviews with PI firm officials revealed  differences in their management approaches. For example:    Officials at two PI firms noted that they were continuing the existing  strategy of the homes they acquired by expanding the capacity to care  for residents with high acuity or specialized needs. Consistent with  their strategies, both firms’ homes reported a greater increase in RN  staffing from 2003 to 2009 than other PI-acquired homes. One of  these firms indicated that facility modernization, which was associated  with its strategy, had continued since acquisition and in fact access to  capital for such improvements had increased after acquisition. Both  firms’ homes showed an increase in facility margins that was greater  than the other PI homes we studied.    Officials at a third PI firm stated that training can be more important  than the number of staff and so focused on training and reducing staff  turnover. They also stated that they did not focus on facility  improvements to the same degree as other PI firms. The increase in  facility margins for this firm’s homes was less than for other PI firms.  We also found that the likelihood of a serious deficiency for this firm’s  homes was lower than for other PI firms’ homes in both 2003 and  2009.
section_title: Agency Comments and Our Evaluation
paragraphs: We provided a draft of this report to the Department of Health and Human  Services (HHS) for comment and also invited the PI firms from which we  obtained information for this report to review the draft. In its written  comments, HHS provided CMS’s observations on our methodology.  HHS’s comments are reproduced in appendix II. CMS suggested an  alternative to our “before and after” acquisition methodology to take into  account the fact that PI firms acquired nursing homes at different points in  time during 2004 through 2007. In addition, CMS identified a number of  alternative analyses that it believed could help to explore the relationship  between PI ownership and quality. CMS also acknowledged that the  report is an important step toward better understanding the effect of  nursing home ownership on the quality of care provided to residents. In  general, representatives of the PI firms commented that the report  handled a complex topic well and that its conclusions were fair and  balanced. Several also commented that our acknowledgement of  limitations to our analyses was important.
The alternative methodology presented in CMS’s comments would tailor a  pre and post analysis to the year prior to each PI firm’s acquisition of a  nursing home chain and to a time point after the acquisition. One of the  studies we cited used such a methodology. We chose to use a different  methodology and believe that the use of different methodologies  enhances the understanding of an issue. Our methodology used 2003  (pre) and 2008/2009 (post) for nursing homes acquired by PI firms from  2004 to 2007, irrespective of the specific year in which the acquisition  occurred. We selected the 2004 through 2007 timeframe because it was  the period of heaviest PI acquisition of nursing home chains. Finally, CMS  said that the exclusion of homes acquired from 2004 through 2007 but  sold by PI firms by 2009 could have biased our results. However, only 6  homes were excluded because they were sold and another 55 were  excluded because we could not verify they were still owned by the  acquiring PI firm in 2009. These exclusions represented less than   5 percent of the PI homes we studied. We believe these exclusions were  appropriate and that it is unlikely that such a small share of homes would  have notably affected our findings.
CMS also suggested a number of alternative approaches for exploring the  relationship between private investment and quality of care, such as   (1) using measures derived from its Five-Star Quality Rating System,   (2) examining the citation of serious deficiencies on successive surveys,  and (3) studying the association between aggregate staffing payroll and  quality of care. We agree that there are other approaches that can be  used to study the relationship between ownership and nursing home  quality of care. We chose well-defined measures of deficiencies and  nurse staffing that we and others have used to study nursing home  quality.
In a few instances, CMS’s comments did not accurately describe our  findings. For example, CMS stated that the increase in capital-related  costs at PI-acquired homes from 2003 to 2008 was related largely to  improving the attractiveness of facilities—facility modernization—to higher  paying residents. However, we concluded that the increase in RN staffing  from 2003 to 2009 was a key aspect of PI firms’ strategies to attract  higher acuity, higher paying residents. In addition, CMS states that our  study shows that CNA and total nurse staffing ratios decreased in PI  homes. Rather, we report that average reported CNA ratios for PI homes  did not change significantly from 2003 to 2009 and that average reported  total nurse staffing ratios for PI homes increased from 2003 to 2009.  Finally, we did not find that average total staffing ratios for any PI firms’  homes decreased or were unchanged from 2003 to 2009. Instead, we  reported that average total staffing increased in PI homes, although the  increase in one firm’s homes was not as great as in other PI homes.
subsections: section_title: PI Firms
paragraphs: Representatives of most of the PI firms who provided oral comments  generally told us that the report handled a complex topic well and they  appreciated our statement of limitations of our methodology. However,  several were concerned that the presentation of the report over- emphasized results that reflected poorly on PI firms. Representatives of  two firms specifically mentioned that the report presented negative  findings first, saving the more positive results for later and suggested that  not everyone would read far enough to learn about the positive findings  relative to the PI firms we studied or to read GAO’s conclusions. For  example, we discuss total deficiencies and staffing before turning our  attention to subsets of these measures—serious deficiencies and RN  staffing. In serious deficiencies, PI firms’ homes were comparable to  nonprofit homes and in RN staffing they compared favorably to nonprofit  homes. However, we believe we present the findings fairly and in a logical  order.
In addition, representatives of several PI firms provided specific  comments on our findings about deficiencies and staffing. Regarding  deficiencies cited on standard surveys and complaint investigations, one  PI firm representative stated that the survey process resulted in more  scrutiny of for-profit homes than nonprofit nursing homes. We consider  cited deficiencies, particularly serious deficiencies, important measures of  quality of nursing home care and our research has found that they  represent real lapses in the care provided. Regarding our analysis of  staffing ratios, the representatives of one firm stated that our analysis did  not take into account staff efficiency. These representatives said that they  had invested in labor saving technology. While staff efficiency may offset  the need for more staff, in our analyses we could not measure or control  for differences in staff efficiency using our datasets. The representatives  of a different firm commented that we did not address changes in therapy  staffing, noting that therapy staff had increased in its homes and that this  increase offset some of the need for CNA staff. In our analysis of staffing,  we chose to focus on nurse staffing because other research has  associated it with quality of care.
In general, representatives of the PI firms said that our findings on facility  costs and margins were consistent with their own analyses. However,  representatives of one firm explained that what we called “costs” they  considered “investments.” They said that money spent to train staff,  modernize facilities, and adopt electronic medical records reduced errors,  prevented subsequent costs, and also improved care. On Medicare cost  reports, such expenditures are generally known as costs. A different PI  firm commented that our finding that capital-related costs were higher  when ownership was split was logical because rents for an operator are  generally higher than mortgage payments and may result in lower  margins and discourage investments in RN staffing. A few PI firms also  stated that the Medicare cost reports were not necessarily accurate with  respect to capital-related costs. We acknowledged that the data in the  Medicare cost reports are self-reported and have limitations, but all  nursing homes are subject to the same reporting requirements and  limitations and thus these data are comparable across the groups we  analyzed.
We incorporated technical comments provided by CMS and the  representatives of PI firms as appropriate.
As agreed with your offices, unless you publicly announce the contents of  this report earlier, we plan no further distribution until 30 days from the  report date. At that time, we will send copies to the Secretary of Health  and Human Services, the Administrator of the Centers for Medicare &  Medicaid Services, and other interested parties. In addition, the report will  be available at no charge on the GAO Web site at http://www.gao.gov.
If you or your staff have any questions about this report, please contact  me at (202) 512-7114 or at dickenj@gao.gov. Contact points for our  Offices of Congressional Relations and Public Affairs may be found on  the last page of this report. GAO staff who made key contributions to this  report are listed in appendix III.
section_title: Appendix I: Scope and Methodology
paragraphs: To determine whether nursing homes that are owned by private  investment (PI) firms differ from other nursing homes in deficiencies cited  on state surveys, nurse staffing levels, or financial performance, we   (1) identified nursing homes for which PI firms had acquired the  operations or the real estate or both from 2004 through 2007 and   (2) compared data from before and after acquisition of these homes to  data from other nursing homes, including other for-profit homes and  nonprofit homes. In addition, we reviewed published research on the  quality and costs of nursing home care, our prior work on nursing homes,  and other relevant documentation. We interviewed officials from the  Centers for Medicare & Medicaid Services (CMS); representatives of PI  firms that acquired nursing home operations, real estate, or both;  representatives of companies that operate PI-owned nursing homes; and  experts on nursing home quality and costs. This appendix provides  information about (1) our data sources and the development of our  analytic datasets, (2) our analytic approach, and (3) data reliability and  limitations.
subsections: section_title: Data Sources and Development of Analytic Datasets
paragraphs: Based on our earlier work identifying the top 10 PI acquirers of nursing  homes, we developed a list of homes acquired by PI firms from 2004  through 2007. We chose 2004 through 2007 as our target acquisition  interval because these were the years during which PI firms acquired the  greatest number of nursing homes. We obtained data for our outcome  variables from CMS. We used CMS’s Online Survey, Certification, and  Reporting system (OSCAR) as our source of data regarding deficiencies,  nurse staffing, and characteristics of all the nursing homes we analyzed,  including PI, other for-profit, and nonprofit homes. OSCAR is the only  national, uniform data source that contains this information. We used  Medicare Skilled Nursing Facility (SNF) Cost Reports as our source of  data regarding the financial performance of nursing homes. These reports  are the only publicly available source of financial data on most Medicare  providers and are a primary source of data used by CMS and others to  examine nursing homes’ financial performance.
subsections: section_title: Identification of Homes with Different Types of Ownership
paragraphs: We identified nursing homes with three types of ownership: PI-owned,  other for-profit, and nonprofit.
PI-owned nursing homes. We developed a list of nursing homes owned  by the top 10 PI acquirers of nursing homes identified in our September  2010 report using information that these firms provided and other  sources, such as nursing home chain Web sites. These 10 PI firms  accounted for almost 90 percent of the nursing homes that were acquired  by PI firms from 1998 through 2008. We included homes for which a PI  firm acquired operations, real estate, or both, and were still owned by the  acquiring PI firm in 2009. To compare data from before and after  acquisition, we excluded homes acquired before 2004 or after 2007. We  also reviewed information from the PI firms and other sources to  determine whether the same PI firm acquired both the operations and real  estate of these homes. When we could not determine whether the same  PI firm owned both the operations and the real estate for a particular  home—for example, when we knew that a PI firm owned the real estate  for most, but not all, of the homes for which it owned operations, but we  did not know which specific homes those were—we assigned it to the  group with that firm’s usual ownership pattern.
Other for-profit and nonprofit homes. We used OSCAR to identify the  for-profit and nonprofit nursing homes that we compared to PI homes. To  ensure that our comparison groups were appropriate, we excluded homes  that were hospital-based or government-owned in 2009 (because they  differ from other nursing homes in important ways, including resident  needs and financial performance) and homes that were not certified by  Medicare in 2009 (because almost all homes owned by the PI firms in our  review were Medicare-certified). We also excluded homes for which we  could not identify data from both before and after our target acquisition  interval.
section_title: Identification of Nursing Home Characteristics
paragraphs: OSCAR also includes data on nursing home characteristics, including  profit status; chain affiliation; facility size as indicated by the number of  beds certified by Medicare, Medicaid, or both; and state. OSCAR also  includes information about the number of residents and their payers,  which we used to calculate the percentage of residents whose care was  paid by Medicare, Medicaid, or a source other than Medicare or Medicaid,  and occupancy rate.
section_title: Identification of Datasets for Our Outcome Measures
paragraphs: We identified separate datasets for our analyses of deficiencies, nurse  staffing, and financial performance.
Deficiencies. To examine deficiencies, we used OSCAR data. OSCAR  includes data about deficiencies that were cited during standard surveys  of nursing homes (which are to be conducted, on average, every   12 months) and during complaint investigations, along with the dates of  those surveys and investigations, allowing comparison of data from  different points in time. Deficiencies identified during either type of survey  are placed into 1 of 12 categories, identified by letter, according to the  number of residents potentially or actually affected and the degree of  relative harm involved. (See table 1.) Throughout this report, we refer to  deficiencies at the actual harm and immediate jeopardy levels as serious  deficiencies.
To examine deficiencies, we sought OSCAR data from a single standard  survey of each home from both 2003 and 2009, but used data from  alternate years in a small proportion of the nursing homes in our  analyses. Specifically, if no state standard survey was available from  2003 or 2009, we substituted data from 1 year later, if available;  otherwise, we used data from 1 year before—with the constraint that the  data for PI-acquired homes had to be from before the acquisition and at  least 1 year after acquisition. For example, if 2009 data were not available  for a particular home, we sought 2010 data, if available; otherwise, we  used 2008 data with the constraint that the data must be from 1 year after  acquisition for PI-acquired homes. We also collected OSCAR data on  deficiencies cited during complaint investigations in calendar years 2003  and 2009. To avoid double counting, we excluded any complaint  deficiencies that matched a deficiency cited in a standard survey that was  conducted within 15 days of the complaint investigation. We refer to all  data used in our analyses of deficiencies as having been from 2003 or  2009.
We included data from 12,956 nursing homes in our analyses of  deficiencies, of which 1,270 were PI-owned in 2009 and had been  acquired from 2004 through 2007. Because we used data from 2003 and  2009 for homes acquired anytime from 2004 through 2007, the amount of  time between the surveys that identified any deficiencies and PI  acquisition varied. In most cases, the surveys were within 3 years of  acquisition.
Nurse staffing. We calculated four different staffing ratios, that is,  nursing hours per resident per day: registered nurse (RN) ratios, licensed  practical nurse (LPN) ratios, certified nurse aid (CNA) ratios, and total  nurse staffing ratios (i.e., the total number of nursing hours, whether by  RNs, LPNs, or CNAs, per resident per day). In each case, we included  full-time, part-time, and contract hours, but we excluded hours reported  for performing administrative duties or as Directors of Nursing. When  calculating CNA staffing, we also included two other types of nursing  staff—nurse aides in training and medication aides.
We used the same set of nursing homes included in our analyses of  deficiencies to analyze nurse staffing, but excluded homes from the  staffing analyses if the data related to staffing appeared to represent data  entry or other reporting errors. Specifically, we excluded facilities that, in  either 2003 or 2009, reported    more residents than beds,   more than 10 percent of the home’s beds as not certified for Medicare    0 total nursing hours per resident per day,   24 or more total nursing hours per resident per day, or staffing and census data that resulted in nurse staffing ratios that were  three or more standard deviations above the mean, indicating that  they were statistical outliers.
We included data from 11,522 nursing homes in our analyses of staffing  ratios, of which 1,176 were PI-owned in 2009 and acquired from 2004  through 2007.
Financial performance. To examine nursing homes’ financial  performance, we used Medicare SNF cost reports to compute three  measures:    Facility costs per resident day, defined as the total facility costs— including both operating and capital costs—divided by total resident  days.    Capital-related costs per resident day, defined as capital-related costs  allocated to nursing home resident care divided by total resident  days.    Facility margins, defined as the amount of total facility revenues  exceeding total facility costs, divided by total facility revenues.
All Medicare-certified nursing homes—or SNFs—must submit cost  reports on an annual basis to CMS. The cost report contains provider  information—such as facility characteristics, utilization data, costs, and  financial data—generally covering a 12-month period of operations based  on the provider’s fiscal year. The cost report contains utilization and cost  information on Medicare-covered services, and also contains information  for services provided to all residents, regardless of payer.
We used cost report data for the provider’s fiscal years 2003 and 2008  because fiscal year 2009 Medicare SNF cost reports were not available at  the time we collected our data. For PI-acquired homes, we ensured that  these data were from before and after acquisition. Our analyses of  financial data also required information from OSCAR about facility  characteristics such as the percentage of residents whose care was paid  by Medicare or Medicaid and occupancy rate. We sought OSCAR data  from calendar years 2003 and 2008, and if these data were not available,  we substituted data from 1 year after, if available, otherwise 1 year  before. We refer to all data used in our analyses of financial  performance as having been from 2003 or 2008.
We created different datasets to examine our three calculated measures  of financial performance. For each measure, we excluded nursing homes  if the cost report covered less than 10 or more than 14 months and those  that did not have Medicare SNF cost reports or OSCAR data from both  time periods. We also excluded nursing homes for which the data  appeared to represent data entry or other reporting anomalies or were  statistical outliers.    Facility costs. Data for our analyses of facility costs were from 9,616  nursing homes, of which 1,089 were PI-owned in 2009 and acquired  from 2004 through 2007. We excluded homes that, in either 2003 or  2008, reported    no facility costs or  facility costs per resident day that were more than two times the  interquartile range below the 25th or above the 75th percentile.    Capital costs. Data for our analyses of capital-related costs were from  9,707 nursing homes, of which 1,088 were PI-owned in 2009 and  acquired from 2004 through 2007. We excluded facilities that, in either  2003 or 2008, reported    no capital-related costs or  capital costs per resident day that were more than two times the  interquartile range below the 25th or above the 75th percentile.    Facility margins. Data for our analyses of facility margins were from  8,630 nursing homes, of which 955 were PI-owned in 2009 and  acquired from 2004 through 2007. We excluded facilities that, in either  2003 or 2008, reported    no facility revenues or missing margins or  facility margins that were in the top or bottom 1 percent of all  homes we studied, regardless of type of ownership.
Table 2 lists the variables we included in our datasets, describes our  operational measures of these variables, and identifies the sources of the  data we used to calculate these measures.
section_title: Analytic Approach
paragraphs: We conducted both aggregated data analyses and analyses of data from  specific PI firms’ homes. Unless otherwise specified, all results that we  present were statistically significant at the 0.05 level in analyses of  adjusted data.
subsections: section_title: Aggregate Data Analyses
paragraphs: We used panel regression models to determine, at the aggregate level,  whether nursing homes that were acquired by PI firms from 2004 through  2007 differed significantly, before and/or after the acquisition, from other  nursing homes in our outcome variables—deficiencies, nurse staffing  levels, or financial performance. Using these models, we compared  outcome data from homes with different types of ownership (PI, other for- profit, and nonprofit) at each of two points in time (2003 and 2009 for  deficiencies and staffing, and 2003 and 2008 for financial performance)  and we examined whether there were differences between years for PI  homes and whether any such differences were similar to any differences  between years in the other for-profit and nonprofit homes. We included  data from before PI acquisition so we could determine whether the  postacquisition data reflected preexisting differences. We included data  from other types of nursing homes so we could determine whether any  changes from before to after acquisition reflected changes that occurred  regardless of type of ownership. We also compared data from PI homes  for which the same firm acquired both operations and real estate to data  from PI homes for which the same firm did not acquire both operations  and real estate.
Our panel regression models statistically controlled for variables that  research has shown can influence nursing home deficiencies, staffing,  and financial performance. These variables were (1) the percentage of  residents for whom the payer was Medicare in 2003 and 2009; (2) the  percentage of residents for whom the payer was neither Medicare nor  Medicaid in 2003 and 2009; (3) chain affiliation in 2009; (4) facility size as  indicated by the number of beds certified by Medicare, Medicaid, or both  in 2009; (5) occupancy rate in 2003 and 2009; (6) market competition in  2003 and 2008; and (7) geographic location (state).
We used random effects models rather than fixed effects models to  measure not only the change in outcomes for the same nursing home  groups over time, but also the difference between groups at each point in  time. Moreover, we wanted to accurately reflect the change over time in  our control variables and their effects on our outcome variables— something that can be accomplished using a random effects model, but  not a fixed effects model.
Illustration. To illustrate our analytic strategy, consider the example of  reported RN ratios. Unadjusted average (or mean) reported RN ratios are  presented in table 3, along with the number of homes in our analyses.
Our panel models analyze the data to identify the size and statistical  significance of differences between means. Statistical significance is  indicated by the probability (P-value) of coefficients calculated by the  panel regression for the comparisons it tests. The specific comparisons  tested by our panel regressions are based on independent variables and  their interactions. Our panel regression models included a main effect for  year and a main effect for ownership type (PI, other for-profit, and  nonprofit). The models also included an interaction between year and  ownership type, which allowed for the comparison of data between  different types of ownership at each point in time as well as the difference  between years. Therefore, the five terms in the model are year, other  for-profit homes, nonprofit homes, year by other for-profit homes, and  year by nonprofit homes. The interpretation of the model terms are as  follows: (1) the main effect year measures the difference between 2003  and 2009 for PI homes, (2) the main effect for other for-profit measures  the difference between PI and other for-profits in 2003, (3) the main effect  for nonprofit measures the difference between PI and nonprofits in 2003,  (4) the interaction effect of year by other for-profit measures the  difference between PI and other for-profits in the change from 2003 to  2009, and (5) the interaction effect year by nonprofit measures the  difference between PI and nonprofits in the change from 2003 to 2009.
Table 4 shows the results of our panel regression analysis of reported RN  ratios without including control variables—that is, the coefficients and  associated P-values for tested comparisons. With unadjusted data, the  coefficients calculated by the panel regression can be calculated directly  from the means in table 3. For example, the coefficient shown in table 4  for the difference between other for-profit homes and PI homes in 2003 is  -0.023, which is the difference between the relevant means shown in  table 3: 0.275 minus 0.298. As another example, the coefficient shown in  table 4 for the change from 2003 to 2009 for PI homes is 0.100, which is  the change from 2003 to 2009 for PI homes shown in table 3. Similarly,  the coefficient of -0.068 in table 4 indicates the difference in the change in  RN ratio from 2003 to 2009 between other for-profit and PI homes and is  equal to the difference between the change for other for-profit homes and  the change for PI homes shown in table 3: (0.032 minus 0.100).
In contrast, table 5 shows the results of a parallel panel analysis of the  reported RN ratios using the same independent variables described  above, but in this second analysis, we included our control variables.  When the regression model includes control variables, coefficients can  not be calculated directly from means. The change in key results between  table 4 and table 5 reflects the impact of control variables on RN ratios.  For example, when we controlled for these variables, we found that the  average reported RN ratios for PI homes did not differ significantly from  those of other for-profit homes in 2003.
To examine differences between means that were not directly addressed  in our panel regressions, we conducted chi-square tests. For example,  after applying our panel regressions, we used chi-square tests to  determine whether there were significant differences between other for- profit and nonprofit homes.
Deficiencies. To apply a panel model regression to deficiencies, we first  examined the data to select an appropriate statistical model and ensure  that the data were consistent with relevant statistical assumptions. Our  measure of total deficiencies was a count of how many deficiencies were  cited in the nursing home. Count variables can be modeled by a negative  binomial regression. Coefficients from a negative binomial model  represent the expected log-count of an event and can be transformed into  incidence-rate ratios, which represent how much more or less the  expected incidence rate is for one group in comparison to another. In this  report, we refer to these ratios as total deficiencies.
When we examined the data regarding whether a home was cited for a  serious deficiency or not, we determined that a different panel regression  model was most appropriate. Because a relatively small proportion of  nursing homes were cited for serious deficiencies, and most homes with  any serious deficiency had no more than two, our measure was whether  or not a home had been cited for any serious deficiencies. For such  binary outcomes, a logistic regression model is appropriate. Logistic  regression model coefficients represent log-odds ratios and can be  transformed to odds ratios, which indicate how much more or less likely  the odds are for a binary (yes/no) event to occur for one group in  comparison to another. In this report, we refer to these ratios as the  likelihood of a serious deficiency.
Nurse staffing. After excluding nursing homes with staffing ratios that  appeared to represent data entry or other reporting errors, the distribution  of each staffing ratio approximated a normal distribution, so we used an  Ordinary Least Squares panel regression model to analyze these data.
Financial performance. After excluding nursing homes with extreme  values, the distributions of facility costs per resident day and capital- related costs per resident day were highly positively skewed, that is, they  were not distributed normally or symmetrically around the average. We  transformed these variables by taking their natural logarithms; the  resultant distributions were consistent with the relevant statistical  assumptions. We used Ordinary Least Squares panel regression models  to analyze the log-transformed values.
After excluding nursing homes with extreme values, facility margins  approximated a normal distribution, so we used an Ordinary Least  Squares panel regression model to analyze the data. We conducted two  additional regression analyses of facility margins in which we controlled  for case mix (the average acuity of the residents in a nursing home) and  other sources of revenue (such as home health or hospice care). We do  not report these analyses because each variable was correlated with  payer mix and controlling for them did not increase the amount of  variability that was accounted for by our models.
section_title: Results of Aggregate Analyses Adjusting for Control Variables
paragraphs: (a – b) (a – b) (a – b)
RN ratio (a – b) (a – b) (a – b) (a – b) (a – b)
Facility  margins (a – b) -  (a – b) (a – b) (a – b)
RN ratio (a – b) (a – b) (a – b) (a – b) (a – b)
Facility  margins (a – b)
Notes. Data were adjusted to control for the influence of chain affiliation, payer mix, facility size,  occupancy rate, market competition, and state so that one can make comparisons holding these  other variables constant.
Cell entries indicate the relationship between two values, labeled (a) and (b) in first column. There  were three possible relationships between the two values: If (a) was significantly higher than (b), the  cell contains a +; if (a) did not differ significantly from (b), the cell is blank; and if (a) was significantly  lower than (b), the cell contains a -. Our standard for statistical significance was p < .05.  Data regarding deficiencies and nurse staffing were from 2009; data regarding financial performance  were from 2008.
section_title: Firm-Level Data Analyses
paragraphs: In addition, to determine whether there were systematic differences  among nursing homes owned by PI firms in outcomes we studied, we  conducted a series of analyses in which we separately compared each of  five PI firms’ homes to all other PI-acquired nursing homes in our study.  We restricted our analyses to those homes for which we could identify  both the PI owner of operations and real estate and those PI firms for  which we determined we had data from a sufficient number of homes.    For three PI firms’ homes, the same PI firm acquired both operations  and real estate.    For two PI firms that acquired the nursing home operations, a different  PI firm acquired the real estate.
In each of five separate analyses, we compared the homes owned by a  PI firm to all other PI homes in our larger aggregate analysis, including  homes owned by the other firms we studied and any other homes owned  by that PI firm (e.g., those for which we could not identify the real estate  owner). Again, we statistically controlled for other variables that may  influence deficiencies, staffing, and financial performance. Unless  otherwise specified, all results that we present were statistically significant  at the 0.05 level in analyses of adjusted data. To better understand  differences among the nursing homes owned by these PI firms, we also  interviewed representatives of PI firms that acquired nursing home  operations, real estate, or both, and representatives of companies that  operate PI-owned homes and, if their homes were part of our firm-level  analyses, we discussed the results for their homes.
section_title: Data Reliability and Limitations
paragraphs: There are several important limitations to our findings: The results of our  analyses can not be generalized beyond the PI-acquired nursing homes  in our review. In addition, the differences between PI-acquired and other  nursing homes that we observed cannot necessarily be attributed to PI  ownership because they may have been caused by other uncontrolled  and unquantified variables, such as specific characteristics of the  particular sets of homes or particular PI firms in our review or the fact that  these homes changed ownership, rather than the effect of PI ownership  per se. Moreover, although our data for homes that were acquired by PI  firms came from before and after the PI firm acquired them, we cannot  assume that any difference we observed between the data from 2003 and  the data from 2008 or 2009 were due to acquisition by the PI firm  because other things could have occurred between those years. For  example, changes we observed could have occurred after 2003, but  before acquisition by the PI firm.
In addition, each of our measures has limitations:  PI ownership. Our sample of PI-acquired homes did not include all PI- owned homes. Specifically, to compare data from before and after  acquisition by a PI firm, we excluded PI-owned homes that were acquired  before or after our target acquisition interval. Moreover, the 10 PI firms in  our sample acquired about 94 percent of the nursing homes that were  acquired by PI firms from 2004 through 2007; we could not identify the  other approximately 6 percent of PI-acquired nursing homes, and as a  result, some homes that we classified as other for-profit or nonprofit  homes may have been PI-owned.
Deficiency data. We have previously documented inconsistencies in  states’ citation of deficiencies. Our analyses controlled for variation  across states, but may not have captured all variation associated with  state surveys. In addition, deficiency data provide incomplete  information about quality of care. Although cited deficiencies indicate  problems with the quality of care that were identified during a survey, the  absence of cited deficiencies does not necessarily indicate that the quality  of care was good because surveyors may have failed to identify and cite  actual quality problems.
Staffing data. Although OSCAR was the most suitable data source  available for our analyses, OSCAR staffing data have several limitations.  First, OSCAR provides a 2-week snapshot of staffing and a 1-day  snapshot of residents at the time of the survey, so it may not have  accurately depicted a facility’s staffing or number of residents over a  longer period. Second, staffing is reported across the entire facility, while  the number of residents is reported only for Medicare- and Medicaid- certified beds; as a result, our calculations may have overstated staffing  ratios for homes with noncertified beds. Third, neither CMS nor the  states regularly attempt to verify the accuracy of the OSCAR staffing  data, and at least some studies question these data. For example,  research in one state suggested systematic inaccuracies, with larger and  for-profit homes being more likely to report higher levels of RN staffing in  OSCAR than in their audited state Medicaid cost reports.
Financial data. Although Medicare cost reports provided the most  suitable data for our analyses, they are not routinely audited and are  subject to minimal verification, so they may contain inaccuracies. Since  the implementation of the Medicare prospective payment system (in 1998  for SNFs), providers are no longer reimbursed directly on the basis of  costs, and some have raised concerns that the quality and level of effort  providers put into accurately completing Medicare cost reports may have  eroded. In addition, the Medicare program limits the amount of capital- related costs that may be reported—for example, by limiting the reporting  of certain financing costs associated with acquisition of a facility. If a  provider’s financing costs exceed these limits, the provider’s full financing  costs cannot be reported. As a result, a portion of the providers’ reported  margins may be needed to offset these unreported financing costs. Also,  for about one-third of PI homes, our 2008 financial performance data are  from less than 1 year after acquisition. Thus, our postacquisition time  period may not fully capture any impact of PI ownership on the home’s  financial performance.
Despite these limitations, our analyses do provide a reasonable basis for  comparing deficiencies, nurse staffing, and financial performance of the  PI-owned homes we studied to each other and to other types of nursing  homes at two points in time. We reviewed all data for soundness and  consistency and determined that they were sufficiently reliable for our  purposes. We performed data reliability checks on the list of PI homes we  compiled, OSCAR, Medicare’s Provider of Services, and Medicare SNF  cost report data we used, reviewed relevant documentation, and  discussed these data sources with knowledgeable officials and industry  experts. We also reviewed published research on the quality and costs of  nursing home care, our prior work on nursing homes, and other relevant  documentation. We interviewed officials from CMS; representatives of PI  firms that acquired nursing home operations, real estate, or both;  representatives of companies that operate PI-owned nursing homes; and  experts on nursing home quality and costs.
section_title: Appendix II: Comments from the Department of Health and Human Services
section_title: Appendix III: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Acknowledgments
paragraphs: In addition to the contact name above, Walter Ochinko, Assistant  Director; Dae Park, Assistant Director; Kristen Joan Anderson; Jennie  Apter; Ramsey Asaly; Leslie V. Gordon; Dan Lee; Jessica Smith; and  Sonya L. Vartivarian made key contributions to this report.
section_title: Related GAO Products
paragraphs: Nursing Homes: More Reliable Data and Consistent Guidance Would  Improve CMS Oversight of State Complaint Investigations. GAO-11-280.  Washington, D.C.: April 7, 2011.
Nursing Homes: Complexity of Private Investment Purchases  Demonstrates Need for CMS to Improve the Usability and Completeness  of Ownership Data. GAO-10-710. Washington, D.C.: September 30,  2010.
Poorly Performing Nursing Homes: Special Focus Facilities Are Often  Improving, but CMS’s Program Could Be Strengthened. GAO-10-197.  Washington, D.C.: March 19, 2010.
Nursing Homes: Addressing the Factors Underlying Understatement of  Serious Care Problems Requires Sustained CMS and State Commitment.  GAO-10-70. Washington, D.C.: November 24, 2009.
Nursing Homes: Opportunities Exist to Facilitate the Use of the  Temporary Management Sanction. GAO-10-37R. Washington, D.C.:  November 20, 2009.
Nursing Homes: CMS’s Special Focus Facility Methodology Should Better  Target the Most Poorly Performing Homes, Which Tended to Be Chain  Affiliated and For-Profit. GAO-09-689. Washington, D.C.: August 28,  2009.
Nursing Homes: Federal Monitoring Surveys Demonstrate Continued  Understatement of Serious Care Problems and CMS Oversight  Weaknesses. GAO-08-517. Washington, D.C.: May 9, 2008.
Nursing Home Reform: Continued Attention Is Needed to Improve Quality  of Care in Small but Significant Share of Homes. GAO-07-794T.  Washington, D.C.: May 2, 2007.
Nursing Homes: Efforts to Strengthen Federal Enforcement Have Not  Deterred Some Homes from Repeatedly Harming Residents.   GAO-07-241. Washington, D.C.: March 26, 2007.
Nursing Homes: Despite Increased Oversight, Challenges Remain in  Ensuring High-Quality Care and Resident Safety. GAO-06-117.  Washington, D.C.: December 28, 2005.
Nursing Home Quality: Prevalence of Serious Problems, While Declining,  Reinforces Importance of Enhanced Oversight. GAO-03-561.  Washington, D.C.: July 15, 2003.
Skilled Nursing Facilities: Medicare Payments Exceed Costs for Most but  Not All Facilities. GAO-03-183. Washington, D.C.: December 31, 2002.
Skilled Nursing Facilities: Available Data Show Average Nursing Staff  Time Changed Little after Medicare Payment Increase. GAO-03-176.  Washington, D.C.: November 13, 2002.
Skilled Nursing Facilities: Providers Have Responded to Medicare  Payment System by Changing Practices. GAO-02-841. Washington,  D.C.: August 23, 2002.
Nursing Homes: Quality of Care More Related to Staffing than Spending.  GAO-02-431R. Washington, D.C.: June 13, 2002.
Nursing Homes: Sustained Efforts Are Essential to Realize Potential of  the Quality Initiatives. GAO/HEHS-00-197. Washington, D.C.:   September 28, 2000.
Nursing Homes: Aggregate Medicare Payments Are Adequate Despite  Bankruptcies. GAO/T-HEHS-00-192. Washington, D.C.: September 5,  2000.
Skilled Nursing Facilities: Medicare Payment Changes Require Provider  Adjustments but Maintain Access. GAO/HEHS-00-23. Washington, D.C.:  December 14, 1999.
Nursing Home Care: Enhanced HCFA Oversight of State Programs  Would Better Ensure Quality. GAO/HEHS-00-6. Washington, D.C.:  November 4, 1999.
Nursing Home Oversight: Industry Examples Do Not Demonstrate That  Regulatory Actions Were Unreasonable. GAO/HEHS-99-154R.  Washington, D.C.: August 13, 1999.
Nursing Homes: Proposal to Enhance Oversight of Poorly Performing  Homes Has Merit. GAO/HEHS-99-157. Washington, D.C.: June 30, 1999.
Nursing Homes: Complaint Investigation Processes Often Inadequate to  Protect Residents. GAO/HEHS-99-80. Washington, D.C.: March 22,  1999.
Nursing Homes: Additional Steps Needed to Strengthen Enforcement of  Federal Quality Standards. GAO/HEHS-99-46. Washington, D.C.:   March 18, 1999.
California Nursing Homes: Care Problems Persist Despite Federal and  State Oversight. GAO/HEHS-98-202. Washington, D.C.: July 27, 1998.