In addition to determining whether channeling impacts differed by model, we also tested whether impacts on key channeling outcomes differed across sites and for various subsets of the sample defined by characteristics of the sample members. The baseline and screen characteristics used to form these subsets, described more fully n Grannemann et al. (1986), include:
- Impairment on activities of daily living (extremely severe, severe, moderate, mild/none)
- Continence (incontinent, need help with device to be continent, continent)
- Unmet needs (high, medium, low)
- Living arrangement (alone without current informal support, alone with current support, with own child(ren), with someone but not with child)
- Health system contact (in nursing home at randomization, on nursing home wait list, in a hospital or referred to channeling by a hospital or nursing home, referred by a home-health agency, referred by family or other source or self)
- Medicaid eligibility (eligible at baseline, not eligible but would be within 3 months after entering nursing home, would not be eligible)
- Cognitive impairment (severe, moderate, mild/none)
All of these characteristics were also explanatory variables in the standard regression model given in equation 1. (See Table III.1 for sample means of these variables.)
To obtain estimated impacts for the 3 to 5 subgroups formed by each of the classifying variables, the standard regression was modified as follows:
|(2)||Y = a0 + aTT + a1X1 + a2X2 + aT1T*X1 + aSS + aTST*S + e,|
where X1 is a vector that contains the binary variables representing the characteristics defining the subgroups and X2 contains the other explanatory variables used in the standard regression model.24 This equation was estimated separately for the basic and financial control models, to reduce the number of parameters and simplify the calculation of impacts and standard errors.
The estimate of channeling's impact obtained from this model is
Impact = aT + aT1X1 + aTSS,
which depends on the set of 8 characteristics defining the subgroups. Estimated impacts for a particular subgroup were calculated by setting the variables in X1 representing the classifying characteristic of interest at 1 for the category for which impact estimates were desired and 0 for the other categories of this characteristic, and setting all of the other characteristics in X1 at the sample mean. Impacts were estimated in this way for each subgroup defined by each of the classifying variables. Standard errors of these estimated impacts were computed and used to form t-statistics to test whether impacts were significantly different from zero.
The primary tests conducted, however, were of whether the estimated impacts differed from each other across the subgroups defined by each of the classifying variables.25 The hypothesis that no such difference occurred was tested by performing for each classifying characteristic an F-test of whether the coefficients in aT1 (or aTS for tests of equivalence across sites) on the binary variables representing that characteristic were equal to zero. Given the large number of such tests, however, we first jointly tested all of the coefficients in aT1 to determine whether they were equal to zero. Rejection of this hypothesis indicated that channeling impacts on a given outcome did vary with at least one of the classifying characteristics. In such cases, the F-tests for each characteristic were then examined to determine with which of the characteristics channeling impacts varied. More details on the computation and interpretation of these test statistics is given in Grannemann et al. (1986).