Reducing Nursing Home Use Through Community Long-Term Care: An Optimization Analysis Using Data from the National Channeling Demonstration. Variables in the Model


The dependent variables in the analysis (transition indicators for movement into and out of nursing home care) have been described above. Independent variables in the model consist of community services which are central to the analysis here, and a variety of factors which previous research has indicated are significant predictors of nursing home use (Greene, Lovely and Ondrich, 1992).

The service variables consist of measures, in hours per month, of formal community services received by each individual in the sample. We measure four categories of community service: home nursing, home health aide, personal care aide, and housekeeper. These represent the vast majority (over 90 percent) of total in-home services consumed by the sample. Other categories (e.g. physical therapy) were not used with sufficient frequency to permit reliable statistical estimation of their effects.

In the regressions, services are entered interacted with client impairment indicators found to be substantial predictors of differential impact for that service on nursing home risk. Interactions are paired symmetrically with both the presence and absence of the indicated functional impairment. In two cases (the interaction of home health aide services with the absence of severe cognitive impairment and the interaction of personal care aide services with the absence of severe ADL impairment) the interaction terms were small and statistically insignificant, but had the "wrong" sign in at least one of the logits. Because we believe the anomalous signs are due to sampling error, and because such sign inversions would severely hinder convergence in the optimization algorithm, these two coefficients were set to zero in the analysis. Obviously, with multiple services which may themselves be interactive, both among themselves and with other client characteristics, more elaborate specifications are possible and reasonable. But our experience with these was that collinearity problems become so prevalent in the estimations that it seems likely that experimental rather than observational studies will be necessary to pursue these more subtle and complex specifications.

Information on formally supplied services in the NLTCCD dataset is drawn from surveys administered at the sixth month and again at the twelfth month of the demonstration. The survey instrument uses retrospective questioning that required participants to recall the total hours of services, by type, received in the previous week of community residence from all sources. Because of the retrospective nature of the questioning, we assume that the “snapshot” of service hours reported at the time of the six-month survey (rescaled from weeks to months) is representative of the actual hours received in months 1 through 6. Similarly, information reported at the twelve-month survey is imputed to months 7-12. The longitudinal measurement of community service use in the NLTCCD involved a number of complications, our treatment of which is detailed in Appendix A, which also explains the price data used.

The other regressors fall roughly into three categories: (1) personal and demographic characteristics, (2) indicators of health and impairment status, and (3) location and demonstration-specific factors. In our models, these variables are set to their baseline values: only service levels are permitted to vary over time. Hence the perspective taken is one of a prospective or predictive planning model with services presumed to be potentially subject to manipulation during the planning period. While a time-varying regressor approach would be of greater theoretical interest, such a specification would introduce endogeneity and identification problems that could not be resolved with available data.

Personal and demographic characteristics as measured included dummy variables indicating whether the individual was African-American, Hispanic-American, female, a homeowner, or lived alone. Included also are quantitative variables for monthly household income, age (years), and number of surviving children (as a proxy for availability of family support).

Impairment and health-related variables include indicators of whether the individual was severely impaired in ADL, IADL or cognition, or used a wheelchair. Measured as a continuous variable is self-rated health.

Site and demonstration-related variables included the nursing home bed supply in the client site (beds per thousand over age 65), whether the individual was in the demonstration treatment or control group in a "basic" or "financial control" demonstration site [these were the two different intervention modes for the NLTCCD--see Carcagno and Kemper (1988) for details]. In general, "financial control" sites were established in areas with more extensive service systems than "basic" sites, and permitted case managers to authorize purchase of additional services--in contrast to "basic" sites where case managers worked with existing services. The nursing home bed rate, as a proxy for availability, may be expected to influence nursing home use.

Because the non-service regressors serve only as control variables in this study, because many have been considered in other studies using similar methodology (e.g. Branch and Jette, 1982; Weissert and Cready, 1989), and because an extensive treatment of their measurement and the rationale for their inclusion in models predicting nursing home use using the NLTCCD data have been provided elsewhere (Greene and Ondrich, 1990; Garber and MaCurdy, 1989), we will for sake of brevity not repeat these discussions here. Before presenting results of the logit analysis, we outline the optimization problem they will be used to solve.

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