National Invitational Conference on Long-Term Care Data Bases: Conference Package. V. EXPLOITATION OF THE LONGITUDINAL NATURE OF THE SURVEYS AND LINKED MEDICARE SERVICE DATA

05/01/1987

In this section we discuss how the cross-temporal nature of the file can be exploited in several types of analyses of transition. The longitudinal nature of the file can be exploited in several ways. The first, and most basic, is simply to analyze changes in characteristics between 1982 and 1984. This is illustrated in Table 7 for disability and institutional status.

TABLE 7. Percentage Distribution of Case Status in 1984 by Case Status in 1982
1982 Status 1984 Status
Nondisabled IADL Only 1-2 ADL 3-4 ADL 5-6 ADL Institutional Deceased -
Nondisabled 79.66 4.54 3.17 1.12 1.02 1.76 8.73 100.00
IADL Only 12.18 39.39 19.13 4.73 4.20 5.66 14.72 100.00
1 or 2 ADLs 7.10 14.10 32.87 12.36 6.35 7.49 19.73 100.00
3 or 4 ADLs 4.74 4.13 17.22 22.05 18.62 9.98 23.26 100.00
5 or 6 ADLs 4.13 4.49 7.19 8.84 30.00 9.60 35.75 100.00
Institutional 1.48 1.06 0.95 1.07 1.05 53.71 40.67 100.00
Aged-in 89.85 3.08 2.23 1.45 1.32 0.94 1.13 100.00
-
All Cases in 1984 60.32 7.06 6.80 3.28 3.39 6.81 12.35 100.00
All Cases in 1982 63.28 8.52 9.70 4.21 4.74 9.56 0.00 100.00
Totals may not add to 100.00 due to rounding.

Down the left hand side of the table we present the percentage distribution of persons (weighted counts) by their status in 1982. Across the table we provide the status (including death) of persons in 1984. We see that the largest proportion of persons remain in the same state that they were in 1982, or, for the most disabled, they experienced high death rates. Interestingly, there are also sizeable numbers of persons who had long term (2+ years) functional improvements. For persons with 5 to 6 ADL's in 1982 nearly a quarter improved status by 1984. Given the high mortality rate for persons with this level of impairment (~36%) and only a moderate level of institutionalization, this suggests that the functional impairment is driven by an acute morbid condition that often produce death, but also often result in improved LTC functional status.

This table can also be stratified by other variables. For example, in Table 8, we have decomposed changes in disability by age.

TABLE 8. Percentage Distribution of Case Status in 1984 by Case Status in 1982, By Age
1982 Status 1984 Status
Nondisabled IADL Only 1-2 ADLs 3-4 ADLs 5-6 ADLs Institutional Deceased
Nondisabled
65 to 74 years 86.38 3.56 1.91 0.88 0.63 0.58 6.06
75 to 84 years 71.30 6.21 4.75 1.34 1.47 2.93 12.01
85+ years 45.39 7.30 9.51 2.71 3.14 9.33 22.61
IADL Only
65 to 74 years 17.00 45.09 16.47 3.67 3.49 3.15 11.13
75 to 84 years 10.32 35.93 20.85 5.53 4.40 7.14 15.84
85+ years 1.58 30.55 22.90 5.93 5.97 9.66 23.41
1 or 2 ADLs
65 to 74 years 8.91 17.84 35.60 12.66 5.73 4.35 14.90
75 to 84 years 6.95 13.92 33.22 11.05 4.97 7.72 22.18
85+ years 4.19 7.81 27.37 14.32 10.06 12.61 23.64
3 or 4 ADLs
65 to 74 years 7.56 5.68 24.07 24.56 15.20 4.62 18.33
75 to 84 years 3.85 3.60 16.37 21.47 19.35 11.93 23.43
85+ years 1.44 2.40 6.96 18.78 23.22 15.77 31.43
5 or 6 ADLs
65 to 74 years 5.24 7.05 8.90 10.07 31.70 6.10 30.95
75 to 84 years 4.87 4.39 6.54 8.80 30.24 10.96 34.20
85+ years 1.22 0.70 5.60 6.99 26.98 12.81 45.71
Institutional
65 to 74 years 4.05 1.41 1.99 2.30 1.42 60.26 28.57
75 to 84 years 1.53 1.67 1.37 0.89 1.25 55.25 38.03
85+ years 0.35 0.35 0.13 0.71 0.70 49.50 48.26

In Table 8 we see that there are decided shifts with age to higher levels of disability and that, at advanced ages (85+) there is a lower proportion of persons who improve and a higher proportion who become more disabled.

The above discrete state, discrete time description of transitions can become unreliable as one attempts to stratify those transitions by more than one additional variable (e.g., stratify them by age and marital status and the cell sizes become small). To deal with this problem it is necessary to use some type of regression model where the transitions are made functions of a set of covariates. Usually, in order to conduct such modeling, some assumptions have to be made about the form of the dependency of the transitions on the covariates (e.g., in the Cox regression model it is assumed that each covariate has a proportional impact on the transitions).

Such models simply describe the probability of a change in state, as the states are described in the survey, over the two-year period. Obviously, this does not tell us how to exploit the rich data on the use of different types of Medicare Part A services over the two-year period. These data can be exploited to different degrees by models with different degrees of sophistication.

One approach is to simply aggregate different types of service use over specified periods of time. The problem with this approach is that it can produce severely misleading results due to the fact that events like death--or even other types of service use--represent constraints on the amounts of specific types of services that an individual consumes in a given period of time. Thus, a person who was in a nursing home for a year and used 20 days of home health care in a two-year period, used home health care twice as fast when eligible as a person who, in a two-year period used no other type of service.

To account for these differences in exposures, one must calculate life table type measures of ex- posure for each of the types of services. In these life tables one can deal with the constraints that death, or consumption of other types of services, represents. An illustration of these types of life tables is presented in Figure 4.

FIGURE 4. HOSPITAL EPISODES: unavailable at the time of HTML conversion--will be added at a later date.

What we have done is to plot the proportion of persons who stay a given number of days in hospitals in 1982 and in 1984 for (a) community disabled persons, (b) persons in institutions at the time of the survey, (c) non-disabled, non-institutionalized persons, and (d) a residual group of non-respondents. To read the table note that the vertical axis describes the proportion of all hospital admissions in the specified year who were still in the hospital after X days. The horizontal axis represents the hospital LOS in days. We see that, in both 1982 and 1984, all groups experienced a decline in LOS and that non-disabled persons had the shortest stays, community disabled the next shortest stays and institutionalized persons the third longest stays, i.e., the hospital LOS was correlated with severity of the chronic conditions the person had. In calculating such tables one has to be careful to post-weight observations, and to deal with "exposure" weights based upon when the survey was conducted in the year, or misleading results may be produced.

These life tables can be used to describe how different types of services are used in a specified period of time after adjusting for exposure differences. To combine that longitudinal information with data from the surveys on chronic functional and health problems can be done by certain multivariate procedures.