Assessing cost effects of nursing-home-based geriatric nurse practitioners

Health Care Financing Review, Spring, 1990 by Joan L. Buchanan, Robert M. Bell, Sharon B. Arnold, Christina Witsberger, Robert L. Kane, Judith Garrard

Case study interviews conducted early in the evaluation revealed that, in 4 of the 30 nursing homes in the treatment group, the GNP role was not implemented (Kane et al., 1988). In these homes, the newly trained GNP was actually employed full time as either the nursing home administrator or the director of nursing. As a result, observations from these homes were treated separately in the analysis.

Using ordinary least-squares regression, we developed analysis of covariance (ANOCOVA) models to study the effects of GNPs on both per diem operating costs and the imputed cost of medical services used per patient day at risk. Our inferences about GNP effects follow from comparing results across the six cells of the design: period (pre, post) crossed with treatment status (control, implemented GNP, nonimplemented GNP). ANOCOVA models allowed us to adjust for differences among cells that were explained by nursing home and patient characteristics.

Independent covariates

In both the home- and patient-level analyses, we controlled for nursing home characteristics and patient characteristics. These are summarized briefly in the following sections.

Nursing-home-level analyses

Our work draws from earlier studies on nursing home cost functions in which the researchers controlled for structural, service intensity, and patient case-mix characteristics (Walsh, 1979; Meiners, 1982; Bishop, 1979; Lee and Birnbaum, 1979; Bishop, 1980; Jensen and Birnbaum, 1979; Holahan, Cohen, and Scanlon, 1983; Mennemeyer, 1979; Schlenker and Shaughnessy, 1984). Structural variables are used to describe the type and location of the nursing home. They include facility size, occupancy late, facility ownership, membership in a chain, certified level of care, whether hospital based or freestanding, State, and urban or rural setting. Service intensity variables relate to the types of services and frequency with which they are provided to nursing home residents. Patient case-mix variables capture patient characteristics and the need for nursing services.

Variables used in the cost function estimation are defined in Table 1. Means and standard deviations for GNP and control homes for the variables in our sample are given in Table 2. Approximately 58 percent of the facilities in the sample were for-profit nursing homes; 69 percent were licensed for Medicare skilled nursing care, and another 9 percent for Medicaid skilled care. One-quarter of the homes were small (less than 75 beds), and another one-quarter had 150 beds or more. Forty-two percent were part of a chain: 23 percent were in small chains of less than five homes and the remaining 19 percent were part of larger chains. One-third of the facilities in the sample had some kind of residential apartments or day care programs run in conjunction with the nursing home, and 15 percent had a formal hospital affiliation. One-third of the sample was located in a rural area (that is, outside metropolitan statistical areas). The only significant difference in nursing home characteristics between the GNP and control nursing homes was that GNP homes were more likely to be in rural areas.