The key lynchpin in researching and enforcing policy directives around nursing home ownership is having timely, detailed data about ownership structures and management arrangements. Importantly, federal datasets are not yet able to facilitate these tasks adequately. Online Survey and Certification Automated Record (OSCAR) data offer only cursory information about ownership, including for-profit and chain status and, where relevant, the name and organizational type of the parent company. Even the very straightforward task of identifying facilities with the same chain owner can be difficult with OSCAR data, as this field in the database is an open-ended text-field subject to slight variations and errors in data entry. In addition, the Provider Enrollment, Chain, and Ownership System (PECOS) data have faced multiple implementation challenges to date and have not yet fulfilled their purpose to provide detailed information on ownership structures and changes over time. PECOS data may ultimately fulfill their potential and prove to be a reliable national tool for ownership-related inquiry. At this time, however, the most viable option to pursue these types of questions is to seek ownership data from state licensure agencies, which play an important regulatory role in nursing home oversight.
To this end, we obtained detailed nursing home ownership data from the State of Texas. Based on our assessment, the State of Texas and a few other states (e.g., New Jersey, Illinois, and California) seemed to be ahead of the curve in its nursing home tracking systems, potentially offering a useful example for federal policymakers and other states to consider. In particular, the Ownership Management and Tracking System (OMT), maintained by the Texas Department of Health, includes information on ownership of nursing homes multiple layers deep to the level of the individual person. These data, available back in time, are collected when nursing home entities apply for licensure (at inception and every two years subsequently) and when ownership structures change. The data also include information about management companies used by operators (e.g., for staffing or payroll) as well as limited, cross-sectional information about property ownership, an emerging area of interest for policymakers.
Using these data, we sought first to understand the evolution of Texas nursing home ownership structures over time, including the use of limited liability structures (e.g., limited liability companies or LLCs); the role of management companies; and the overall complexity of corporate structures. Second, we sought to understand the relationship between corporate structure and a range of facility characteristics, including quality of care and staffing.
The core analyses in this project were conducted based on a comprehensive dataset compiled from the Texas OMT dataset and merged with data from the OSCAR system. The former is managed by the Texas Department of Health, while the latter falls under federal jurisdiction in the Centers for Medicare and Medicaid Services (CMS). The design and roles of these data sources are discussed below.
OMT Data. The Texas OMT system is a large database that summarizes the ownership and management details of health facilities in Texas over time, roughly from 2000 through 2007. Analyses of this dataset focus on two types of entries: facilities, which refer to the brick-and-mortar buildings in which nursing home services are provided, and entities, which are the businesses and people with controlling stakes in either the ownership of the facility’s license or in the management of these facilities. The roles of these entities are complex and are explained in the table descriptions below, as well as in the section on the compilation of the master dataset.
The OMT data were obtained through a Data Use Agreement with the State of Texas and were accessible in Microsoft Access as a series of tables that are linked together through various facility and entity identifiers. Our analyses used seven of the available tables:
Facility Demographics -- Shows the address, contact and licensure information for all Texas facilities. This table also categorizes the facilities based on type (Nursing, Assisted Living, Intermediate Care Facility for the Mentally Retarded, or Unlicensed), though our analyses were limited to nursing homes.
Facility Ownership -- Details the various controlling entities for each facility in the OMT database. Facility ownership refers specifically to ownership of the facility’s license to operate (e.g., as a provider in the Medicare and Medicaid programs). Each entry is specific to the controlling entity and the ownership stake. For example, a facility with four separate controlling entities--such as, for instance, four companies that each owns a 25 percent stake--would have four entries only if the stake amounts were constant over the whole time range. If, however, the ownership stake of any of the controlling entities changed during the time range, each distinct stake amount for that entity would have its own entry. To facilitate the analysis, these ownership stakes (and the start and end dates of their incidence) are included in the table. These numbers generally sum to 100 percent at any given time, but due to errors in data accumulation and entry this is not always the case.
Facility Management -- Details the various managing entities for each facility in the OMT database. This table is analogous to that for facility ownership.
Hierarchy -- Explores the complex hierarchical arrangements of the entities listed in Table 2 and Table 3. In the previous two tables, only top-level owners and managers are listed, whereas this table shows the deeper levels of ownership. For example, if the owner of a nursing home was a limited partnership, only the name of that company would be shown in Table 2; each limited partner would be listed as a controlling party of that partnership in Table 4, however. Similarly, if a corporate structure had five branching levels of ownership, each of these entities would have its own entry in the hierarchy table. As with the other tables, each entry also has both the stake amount and the start and end dates. This allows identity down to the individual level. Importantly, the entire structure cannot be seen from any individual entry. Thus, to explore the complete structure of any given entity, the entire file would have to be searched iteratively for every incidence of the various players in either the entity or controlling party columns. This process is explored in the Methods section.
Facility Provider Numbers -- Connects the OMT facility identifiers, which are specific to this database, to the Medicare provider IDs which are used in systems like the OSCAR. This table was used extensively for merging files.
Owner Table -- This table gives identifying and contact information for all of the entities of Table 2, Table 3 and Table 4. The table also categorizes each entity based on whether it is a person or business, as well as by its function, as in Table 7.
Type Codes -- Translates the controlling entity and entity type codes given in Table 4 and Table 6. For businesses, these codes generally referred to Limited Partnerships (LPs), Limited Liability Partnerships (LLPs) and Corporations (LLCs), General Partnerships (GPs), sole proprietorships, for-profit corporations, not-for-profit corporations, etc. For individuals, these tended to be general partners, limited partners, presidents and chairmen of the board, major stockholders, directors and secretaries, etc. Detailing the features of these different ownership types is beyond the scope of this report; however, it is important to note that some of these structuring options have varying degrees of liability for controlling entities. Sole proprietorship has the greatest liability for the owner, as this business arrangement is characterized by the owner and business being recognized as the same entity (i.e., profits and losses are classified as personal taxes, not corporate taxes, and the normal rules about corporate liability not extending to individual owners do not apply). In contrast, for-profit and not-for-profit corporations are distinct legal entities from their shareholders. For-profit and not-for-profit corporations refer to the corporate structures and not solely to the proprietary status. For instance, a for-profit nursing home may use a for-profit corporate structure, an LLC structure, an LP structure, etc. At the same time, however, under a for-profit corporate structure, liabilities from part of the corporation (e.g., a nursing home) extend to the corporation as a whole. GPs--a group of 2+ general partners who all share the risks, liabilities, debts and profits of a company--share some of the same features of sole proprietorship. However, in the Texas data, this corporate structure is used almost exclusively in combination with the limited partnership model. The LP model limits the liability of investors up to their level of investment, and they receive a dividend-like payment instead of a percentage of the profits. The LLP model is used rarely in the Texas data. Unlike a limited partnership where there are some GPs and some LPs, the LLP structure limits the liabilities of all partners. Each investor takes an active role in management, but they are each insulated from any liability due to misconduct by another member. Finally, the LLC is an entity with features of both a corporation and a partnership (especially a LLP). Like a corporation, LLC owners have limited personal liability for the debts and actions of the LLC. Like a partnership, the LLC provides management flexibility and the benefits of pass-through taxation.
Real Estate Data. A separate Texas dataset shows, for each facility, the owner of the brick-and-mortar facility and the land on which the facility is located, which often differed from the owner of the facility’s license itself (i.e., the licensee and the real estate owner were not always the same). Unlike the tables in the main OMT database, this table provided information only on the most recent owner (i.e., it is cross-sectional), so these data were not useful for our longitudinal analyses. This table also shows the type of real estate ownership as a series of dummy variables: lease, sublease, mortgage, lien, note, deed of trust, warranty or other. Some of these correspond to ownership, while others refer to renting.
The real estate file is imperfect in two main respects. First, several facilities have more than one owner listed even though the table is supposedly designed to show a single top-level owner. Second, the categories listed above are not mutually exclusive, thus making it very complicated to designate an entity, as we intended, as either an owner or a renter.
OSCAR Data. For several of our analyses, we merged OMT data for facilities with widely-used data from the OSCAR system. OSCAR contains survey and certification data for all Medicaid and Medicare-certified facilities in the United States.7 Collected and maintained by CMS, the OSCAR data include information about whether homes are in compliance with federal regulatory requirements. Nursing homes submit facility, resident, and staffing information. Deficiencies are entered into OSCAR by survey agencies when facilities are found to be out of compliance with federal regulatory standards (regular inspections occur every 9-15 months; complaint investigations can occur at any time). Each deficiency is categorized into one of 17 areas and rated by scope and severity (on an ascending scale ranging from “A” to “L” in order of increasing severity). OSCAR data have important limitations that should be noted, including a lack explicit auditing procedures of facility-reported information, potential variation in the survey process across states, and possible under-reporting of serious quality problems.8,9