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

Nursing-home-level variables included geography, ownership and institutional status of the home, and number and type of patients accepted. Patient-level variables included information about demographic characteristics; admission status (when admitted, place from which admitted, etc.); and function. Consequently, differences arising from nursing home and patient characteristics were not attributed to the GNPs. Variance components models were used to estimate the magnitude of nursing home effects that remained unexplained by the ANOCOVA models. These models provided correct standard errors for GNP effects and other nursing-home-level coefficients.

In a variance components model, the analytic approach that we have chosen, the unexplained variance term is decomposed into the variation attributable to various sources. In our models, we introduced one variance component for nursing home effects and another for an interaction between post-period and home effects. The latter variance component allows for the possibility that nursing home effects differ between periods. Estimates of these random-effects models confirmed that the two hypothesized variance components were significant. The variance components model provides formulas that have been used to adjust the t-statistics for the coefficients of all home-level variables in the tables that follow. Because nursing home effects were fairly stable over time, the adjustment to the coefficients for post-period, post-period with GNP role implemented, and post-period with GNP role not implemented are slight compared with those for other home-level variables.

New admissions

The signs on most of the home-level variables were consistent with our expectations, although it is clear from the adjusted t-statistics that most were not significant at the 5-percent level (Table 5). Patients in for-profit homes had lower per diem expenditures, and those in homes licensed for skilled care had higher average expenditures. Patients from very large homes and those from small homes used fewer medical services than those from average-sized homes; patients from small homes were consistently the lowest users. Patients from homes with a formal hospital affiliation used more services, and patients in rural areas used less.

Patient age is a strong predictor of per diem expenditures. Relative to the young elderly (those 65-74 years of age), both the young (under 65 years of age) and old (75 years of age or over) had lower use patterns. Expenditures fell monotonically with age among the elderly, and the lowest use is observed among patients 95 years of age or over. Patients admitted directly from the hospital and those admitted to skilled care used more services; those admitted from the hospital used more than patients who were admitted to skilled care. Patients who were covered by Medicaid at admission had lower service use than non-Medicaid patients. A greater number of nursing therapies used during the first 2 weeks after admission also significantly predicts greater use of other medical services. Expenditures dropped continuously with length of stay, as observed earlier.