Copyright 7992 by The Cerontological Society of America The Cerontologist Vol.32, No. 5,634-640
A statewide probability sample of 1,625 Massachusetts elderly was studied prospectively over a decade to ideniify risk profiles for long-term care (LTC) institutionalization. Previous admission to a LTC institution, age, basic ADL disability, and restricted outside mobility were the strongest individual predictors of institutionalization. Examining profiles of risk factors dramatically increased the ability to predict 10-year risk of admission. Key words: Long-term care, Smoking, Community-dwelling elderly, Informal support
High-risk Profiles for Nursing Home Admission1
The risk of entering a nursing home is high: almost a third of men and just over half of women who turned 65 in 1990 are expected to enter a nursing home at least once before they die (Kemper & Murtaugh, 1991; Murtaugh, Kemper, & Spillane, 1990). The increasing growth of the elderly population coupled with a longer life expectancy will certainly increase the demand for nursing home care in the coming decades (Kane & Kane, 1987). Previous research on personal risk factors for nursing home entry has revealed consistent individual predictors of institutionalization (Branch & jette, 1982; Chiswick, 1976; Dunlop, 1976; Greenberg & Cinn, 1979; Hing, 1987; Liu & Manton, 1983; McCoy & Edwards, 1981; Morris, Sherwood, & Cutkin, 1988; Murtaugh, Kemper, & Spillane, 1990; Palmore, 1976; Shapiro & Tate, 1988; Vincente, Wiley, & Carrington, 1979; Weissert & Scanlon, 1983; Wingard et al., 1990). The most commonly identified personal risk factors include advanced age, Caucasian race, physical disability, mental impairment, living without a spouse, and the presence of specific medical conditions. Although the impact of individual characteristics on the risk of entering a long-term care (LTC) institution is certainly useful to know, a person with one risk factor is not necessarily an appropriate target for preventive intervention (Branch, 1988; Shapiro & Tate, 1988). Many elderly persons presumed to be at high risk of institutionalization have remained in the
community for extended periods, whereas those presumably at low risk sometimes enter a nursing home (General Accounting Office, 1982; Hicks et al., 1981; Manheim & Hughes, 1986; Shapiro & Webster, 1984; Skellie, Mobley, & Coan, 1982; Weissert et al., 1980). Risk profiles for nursing home admission are very useful for projecting further demand for nursing home care, developing interventions to prevent the need for institutionalization, and designing noninstitutional alternatives to costly long-term institutional care. Shapiro and Tate (1988) illustrated the advantage of examining a combination of personal traits to predict the probability of short-term (21/2 years) and long-term risk (7 years) of institutionalization in a cohort of elderly persons living in Manitoba, Canada. Developing a risk profile for institutionalization reduced the number of false positives and provided a more accurate picture of those at high risk. Using a similar approach with a regional sample from Massachusetts, Morris and his colleagues developed a four-category risk classification system based on combinations of measures of functional status, age, health status, demographics, and social supports (Morris, Sherwood, & Gutkin, 1988). Their high-risk profile for institutionalization consisted of individuals with functional impairment, specific medical conditions, and advanced age. In their study, 36% of those classified as high risk were admitted to a nursing home within 48 months, compared with 2.6% of those classified as low risk (Morris, Sherwood, & Gutkin, 1988). This article tests a model of potential risk factors for nursing home use and estimates the predictive power of risk factor profiles for 10-year risk of nursing home admission by an elderly cohort from Massachusetts. By replicating the approach used by Shapiro and Tate (1988), this analysis helps clarify the profile of the older person at highest risk of entering the nursing home over an entire decade.
1 This analysis was supported in part by a grant from the Carter Center of Emory University, Atlanta, CA. Data collection has been supported by grants from the Massachusetts Department of Public Health, the U.S. Administration on Aging, the National Center for Health Services Research, and the Health Care Financing Administration. The authors thank Dr. Stuart Lipsitz for his helpful suggestions regarding modeling and computing. Address correspondence to Dr. Jette and reprint requests to Dr. Branch. ^New England Research Institute, 9 Galen St., Watertown, MA 02172. JAbt Associates, Inc., 55 Wheeler St., Cambridge, MA 02138. "Boston University School of Medicine.
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The Gerontologist
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Alan M. Jette, PhD,2 Laurence G. Branch, PhD,3 Lynn A. Sleeper, ScD,2 Henry Feldman, PhD,2 and Lisa M. Sullivan, PhD4
Data
Table 1. Demographic Characteristics of the MHCPS Sample at Wave 1 (Baseline) Characteristic
%
Age
65-69 70-74 75-79 80-84 85 + (n) Race White Nonwhite (n) Gender Women Men (n) Living situation Alone With others (n) Marital status Married Widowed/separated/divorced/never married (n) Annual income (1974) < $5,000 $5,000-$9,999 $10,000 + (n) Usual occupation White collar Blue collar (n) Education Crade school or less Some high school College or beyond (n)
35.3 27.1 19.2 11.9 6.5 (1,617)
Study Variables
The conceptual framework used to guide variable selection for this analysis was Andersen's behavioral model, which presumes that differential use of a health service such as a nursing home is a function of three classes of variables: (1) personal attributes that predispose individuals to seek care; (2) enabling factors such as income and social support that influence access to care; and (3) need factors as reflected by health status, disease, and functional disability (Andersen & Aday, 1978; Andersen & Newman, 1973). We examined the predictive power of 11 predisposing, 7 enabling, and 18 need factors previously found to influence risk of institutionalization that were available from questionnaire items included in the first three waves of the MHCPS. Table 3 lists all potential predictors considered in the model specification. Impaired mental status was excluded from the model because it was not measured at Wave 3 of the study. It was, however, highly collinear with selfreported neurologic condition, which was included in the analysis. Almost all independent variables were dichotomized prior to statistical modeling to more closely replicate the analyses of Shapiro and Tate(1988). For this analysis, nursing homes and chronic disease hospitals were considered LTC institutions. In-
99.0 1.0 (1,625) 60.0 40.0 (1,625) 31.4 68.6 (1,625) 51.1 48.9 (1,615) 58.1 28.2 13.0 (1,442) 41.9 58.1 (1,573) 38.1 44.4 17.4 (1,566)
Note: N's differ due to missing data at baseline.
Table 2. Overview of Sample Transitions Across the Four Waves of the MHCPS (N = 1,625)
VVavel Wave 2 (1.25 years) Wave 3 (6 years) Wave 4 (10 years)
Vol. 32, No. 5,1992
Community
Nursing home
Between-wave nursing home admissions
1,625(100%) — 1,317(81%) — 824(51%) — 540 (33%)
0 (0%) — 26 (2%) — 60 (4%) — 62 (4%)
48 (3%) — 148(11%) — 165 (20%) —
635
Lost to Deceased
follow-up
0 (0%) — 103 (6%) — 422 (26%) — 690 (42%)
0 (0%) — 179(11%) — 319 (20%) — 333 (20%)
Downloaded from http://gerontologist.oxfordjournals.org/ at New York University on September 2, 2015
under age 70, 27% were 70-74 years old, and 38% were 75 years and older. At baseline, a majority rated their health as good or excellent. Less than 10% of the sample reported one or more ADL dependencies. Over three-quarters were categorized as having one or more IADL limitations. Half of the sample noted one or more chronic conditions. The MHCPS cohort was reinterviewed at intervals of 1.25 (Wave 2), 6 (Wave 3) and 10 years (Wave 4) after baseline, or, as appropriate, tracked to a nursing home or through death records. At each wave, data were obtained from cohort members by a structured interview at each respondent's place of residence. A summary of respondent transitions across the four waves of the MHCPS can be found in Table 2. A comparison of characteristics of persons who were alive in the community and participated in all four waves of the MHCPS with those who were lost to follow-up by Wave 4 reveals that the continuing participant sample underrepresented the old old, the unmarried, and those with lower pretax household income (Branch & Stuart, 1985).
The Massachusetts Health Care Panel Study (MHCPS), begun in 1974-1975, was a prospective cohort study of 1,625 people age 65 and older living in Massachusetts (Branch, 1988). The original MHCPS sample was a statewide probability sample of 403 area segments, stratified by eight planning regions and within regions by central city, other urban, and other place. This was a strict probability sample with no substitution of people or housing units once a specific unit was selected. An initial 79% response rate was achieved. As Table 1 illustrates, the sample was predominantly female (60%), and 99% white. At Wave 1 of the MHCPS, 35% of respondents were
Table 3. Independent Variables and Coding Scheme Variable coding
Variable Predisposing characteristics Gender Age Education level
1 2 0 1 0
= = = = =
Variable Need factors Perceived visual acuity (with correction)
male female < 85 years 85 + years grade school
Perceived hearing acuity (with correction) Current physical health problem Perceived health
1 = high school + Marital status Usual occupation Fear of one's neighborhood Consider rest home
Consider elder housing Recent weight change
Smoking status Enabling factors Living situation Income (1974) Relatives nearby Hospitalization in previous year Home care in previous year Physician visits in previous year Rehabilitation services in previous year
= married = not married = white collar = blue collar = no fear = fear = yes = no = yes = no = no = yes = often = sometimes/rarely = ever or current = never
0 1 0 1 0 1 0 1 0 1 0 1 0 1
= alone = with others = < $5,000 = $5,000 + = yes = no = no = yes = no = yes = 0-2 = >2 = no = yes
Frequency of getting out Basic ADL disability
Mobility disability
1 = yes (W1), excellent, good (W2, W3) 2 = no (W1), fair, poor (W2, W3) 0 = excellent/good 1 = fair/poor 0 = yes 1 = no 0 = excellent/good 1 = fair/poor 0 = daily 1 = less than daily 0 = no 1 = 1 + disability
0 = no 1 = 1 + disability
Instrumental disability
0 = no 1 = 1 + disability
Cardiovascular condition
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
Diabetes Musculoskeletal condition Digestive condition Neurologic condition Respiratory condition Emotional condition Cancer Total number of chronic conditions
= no = yes = no = yes = no = yes = no = yes = no = yes = no = yes = no = yes = no = yes = none = 1 + condition
Note: W1 = Wave 1; W2 = Wave 2; W3 = Wave 3.
We modeled the time effects (three waves) using two dummy variables. This method provided flexibility by not constraining the form of the regression parameter estimates; such flexibility was desirable in light of the different wave lengths. Exploratory modeling revealed that including time as continuous variables (time and time squared) resulted in risk factor parameter estimates similar to those in which time was included as a dummy variable. The model differed from that used in a simple logistic regression analysis because the data contain multiple responses from the same person over time. The repeated measures approach allowed for the inclusion of time-varying covariates in the model. For example, a change in marital status or living situation was incorporated into the analysis. However, baseline age of the respondent as measured at Wave 1 was used at all waves so that changes in age would not be confounded with time effects. The repeated measures analysis also allowed us to use all available information on a respondent, thereby minimizing case exclusion due to incomplete follow-up information at a subsequent wave. An adjustment to the covariance matrix of the parameter estimates was employed (Liang & Zeger, 1986) to account for the correlation among re-
formation on a LTC admission was collected at Waves 2, 3, and 4 from the personal interview, from a proxy respondent, from a death certificate, and from a tracking procedure with significant others for all decedents. A respondent was considered institutionalized regardless of the length of stay in the institution. Missing from this categorization were those lost to follow-up. Analysis
Model Specification In the first stage of the analysis we identified individual risk factors for LTC institutionalization within three subgroupings of variables: predisposing, enabling, and need. Each model was conditional on time effects and covariate information from the previous wave. Individual risk factors and selected interactions among them were identified using a repeated measures logistic regression model. In this model, the dependent variable is binary (1 = LTC institutionalization, 0 = remained in the community). The three submodels, containing variables significant at the .15 level or less, were combined to arrive at a final model that retained terms significant at the .05 level.
636
The Gerontologist
Downloaded from http://gerontologist.oxfordjournals.org/ at New York University on September 2, 2015
Eat right
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
Variable coding
sponses; therefore, our modeling procedure did not violate the independence assumption of regression analysis. The data were analyzed with a SAS macro by Lipsitz and Harrington (1990), which implemented the methods of Liang and Zeger. The model was of the form: logit (p) = po
p2x2
(p4x4
if a simplifying (Markovian) assumption is made regarding the independence of admission at a given wave and admission at two waves prior. The validity of this assumption was examined and confirmed. Each of the terms in the above expression is estimable due to the presence of a prior wave status covariate in our model. The 10-year probabilities of LTC institutionalization were estimated using the final model shown in Table 4, with age dichotomized for ease of interpretation. Standard errors of the estimated 10-year probabilities were obtained by jackknifing.
p,xr)
where po, . . . , pr were model parameters, x, and x2 were indicators for Waves 3 and 4, respectively, x3 was an indicator for nursing home (NH) status at the previous wave, x 4 , . . . , xr were predictors of interest, and p = Prob (nursing home admission | xu...,x) = P r o b ( V = 7 | xu . . . , x r ) . The model was used to estimate odds ratios for potential risk factors and interactions between predictors. Interactions for each potential predictor by gender and age were examined. In addition, we examined interactions of selected need factors with marital status, living situation, and proximity of relatives. These particular interactions were selected based on previous research or their policy relevance. Odds ratios for particular factors along with their 95% confidence intervals were obtained by exponentiating linear combinations of the parameter estimates. There were 2,448 observations with all information available to estimate the final regression model: 1,149 at Wave 1, 756 at Wave 2, and 543 at Wave 3.
Results
Profile Analysis Table 4. Final Prediction Model of the 10-year Risk of LTC Institutionalization Among Massachusetts Elderly Persons
In the second phase of the analysis, the rate of institutionalization was estimated for clusters of risk factors included in the final prediction model. The conditional probability of nursing home admission at a particular wave given fixed values x1; . . . , x, was: Prob(V =
Risk factor Nursing home admission in previous wave vs. no admission Predisposing characteristics Smoker (ever vs. never)3 Men Women Age (X+ 5 years vs. X years)3 Income < $5,000 Income > $5,000 Enabling factors Income (< $5,000 vs. > $5,000)a Age 65-69 Age 70-74 Age 75-79 Age 80-84 Age 85-89 Age 90 + Fearful in own neighborhood (yes vs. no) Need factors Neurological condition 8 (present vs. absent) Distant relatives Nearby relatives Basic ADL disabilities (3= 1 vs. none) Getting out (< daily vs. at least daily)
.exp exp (p 0
prxr)
This probability was estimated by substituting the parameter estimates of po, . . . , fi r from the final prediction model into the equation. Wave-specific probability estimates were then used to calculate the overall probability of institutionalization within 10 years for profiles of risk factors. This overall probability was based on but has a more complicated form than that for a probability at any given wave. Suppose Vj = 1 if institutionalization occurs during Wave j and V, = 0 otherwise. Then the probability of enteringa nursing home within 10years (i.e., the end of the study), given a particular risk profile X = x (that is, [X, = xu X2 = x2, . . . , X, = xr]) can be expressed as: Prob(V2 = 1 or V3 = 1 or V4 = 1 | X = x) = P(Y2 = 1 | X = x) + P(Y, = 1 | X = x) + P(YA = 1 | X = x) - P(Y3 = 1 | Y2 = 1, X = x) P(Y2 = 1 | X = x) - P(YA = 1 | X = x) P(Y2 = 1 | X = x) W = 1 | y3 = 1 ; X = x) P(Y3 = 1 | X = x) + P(YA = 1 | v, = 1, x = x) P(v3 = 1 I y2 = 1, x = x) P(y2 = 1
95% Cl
5.17*
(1.35,19.85)
2.61* 1.02
(1.09,6.26) (0.63,1.65)
1.60*** 2.35***
(1.36,1.90) (1.76,3.13)
3.82** 2.61** 1.78 1.22 1.08 0.74
(1.64,8.91) (1.43,4.79) (0.64, 4.95) (0.83,1.80) (0.63,1.85) (0.34,1.58)
1.97*
(1.11,3.50)
15.70** 2.73
(5.15,47.88) (0.81,9.18)
2.84***
(1.74,4.64)
1.78**
(1.23,2.58)
a The effects of these variables are conditional on a second factor due to significant interaction terms. *p*s.O5; * * p « . 0 1 ; ***p*£.001.
I x = x),
Vol. 32, No. 5,1992
Odds ratio for institutionalization in 10 years
637
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The data in Table 2 illustrate the risk of entering a LTC institution between each wave of the study. The crude incidence of LTC admission was 2.9% between Waves 1 and 2, 11.2% between Waves 2 and 3, and 20.0% between Waves 3 and 4. The proportion living in a LTC institution at each wave was considerably smaller than the proportion entering between waves because some died after entering a LTC institution or were discharged back to the community. Table 4 presents the final regression model of individual risk factors for admission to a LTC facility during the decade of follow-up. Prior institutionalization, age and income interactions, and indicators of need were the strongest predictors of institutionalization. Elders who reported a LTC admission at a
Table 5. Probability of Nursing Home Entry within 10 Years by Age Probability of institutionalization at 10 years Probability estimate (standard error) Gender
Male
Female
Male
Female
Age
A statewide probability sample of 1,625 Massachusetts elderly was studied prospectively over a decade to ideniify risk profiles for long-term care (LTC) institutionalization. Previous admission to a LTC institution, age, basic ADL disability, and restricted outside mobility were the strongest individual predictors of institutionalization. Examining profiles of risk factors dramatically increased the ability to predict 10-year risk of admission. Key words: Long-term care, Smoking, Community-dwelling elderly, Informal support
High-risk Profiles for Nursing Home Admission1
The risk of entering a nursing home is high: almost a third of men and just over half of women who turned 65 in 1990 are expected to enter a nursing home at least once before they die (Kemper & Murtaugh, 1991; Murtaugh, Kemper, & Spillane, 1990). The increasing growth of the elderly population coupled with a longer life expectancy will certainly increase the demand for nursing home care in the coming decades (Kane & Kane, 1987). Previous research on personal risk factors for nursing home entry has revealed consistent individual predictors of institutionalization (Branch & jette, 1982; Chiswick, 1976; Dunlop, 1976; Greenberg & Cinn, 1979; Hing, 1987; Liu & Manton, 1983; McCoy & Edwards, 1981; Morris, Sherwood, & Cutkin, 1988; Murtaugh, Kemper, & Spillane, 1990; Palmore, 1976; Shapiro & Tate, 1988; Vincente, Wiley, & Carrington, 1979; Weissert & Scanlon, 1983; Wingard et al., 1990). The most commonly identified personal risk factors include advanced age, Caucasian race, physical disability, mental impairment, living without a spouse, and the presence of specific medical conditions. Although the impact of individual characteristics on the risk of entering a long-term care (LTC) institution is certainly useful to know, a person with one risk factor is not necessarily an appropriate target for preventive intervention (Branch, 1988; Shapiro & Tate, 1988). Many elderly persons presumed to be at high risk of institutionalization have remained in the
community for extended periods, whereas those presumably at low risk sometimes enter a nursing home (General Accounting Office, 1982; Hicks et al., 1981; Manheim & Hughes, 1986; Shapiro & Webster, 1984; Skellie, Mobley, & Coan, 1982; Weissert et al., 1980). Risk profiles for nursing home admission are very useful for projecting further demand for nursing home care, developing interventions to prevent the need for institutionalization, and designing noninstitutional alternatives to costly long-term institutional care. Shapiro and Tate (1988) illustrated the advantage of examining a combination of personal traits to predict the probability of short-term (21/2 years) and long-term risk (7 years) of institutionalization in a cohort of elderly persons living in Manitoba, Canada. Developing a risk profile for institutionalization reduced the number of false positives and provided a more accurate picture of those at high risk. Using a similar approach with a regional sample from Massachusetts, Morris and his colleagues developed a four-category risk classification system based on combinations of measures of functional status, age, health status, demographics, and social supports (Morris, Sherwood, & Gutkin, 1988). Their high-risk profile for institutionalization consisted of individuals with functional impairment, specific medical conditions, and advanced age. In their study, 36% of those classified as high risk were admitted to a nursing home within 48 months, compared with 2.6% of those classified as low risk (Morris, Sherwood, & Gutkin, 1988). This article tests a model of potential risk factors for nursing home use and estimates the predictive power of risk factor profiles for 10-year risk of nursing home admission by an elderly cohort from Massachusetts. By replicating the approach used by Shapiro and Tate (1988), this analysis helps clarify the profile of the older person at highest risk of entering the nursing home over an entire decade.
1 This analysis was supported in part by a grant from the Carter Center of Emory University, Atlanta, CA. Data collection has been supported by grants from the Massachusetts Department of Public Health, the U.S. Administration on Aging, the National Center for Health Services Research, and the Health Care Financing Administration. The authors thank Dr. Stuart Lipsitz for his helpful suggestions regarding modeling and computing. Address correspondence to Dr. Jette and reprint requests to Dr. Branch. ^New England Research Institute, 9 Galen St., Watertown, MA 02172. JAbt Associates, Inc., 55 Wheeler St., Cambridge, MA 02138. "Boston University School of Medicine.
634
The Gerontologist
Downloaded from http://gerontologist.oxfordjournals.org/ at New York University on September 2, 2015
Alan M. Jette, PhD,2 Laurence G. Branch, PhD,3 Lynn A. Sleeper, ScD,2 Henry Feldman, PhD,2 and Lisa M. Sullivan, PhD4
Data
Table 1. Demographic Characteristics of the MHCPS Sample at Wave 1 (Baseline) Characteristic
%
Age
65-69 70-74 75-79 80-84 85 + (n) Race White Nonwhite (n) Gender Women Men (n) Living situation Alone With others (n) Marital status Married Widowed/separated/divorced/never married (n) Annual income (1974) < $5,000 $5,000-$9,999 $10,000 + (n) Usual occupation White collar Blue collar (n) Education Crade school or less Some high school College or beyond (n)
35.3 27.1 19.2 11.9 6.5 (1,617)
Study Variables
The conceptual framework used to guide variable selection for this analysis was Andersen's behavioral model, which presumes that differential use of a health service such as a nursing home is a function of three classes of variables: (1) personal attributes that predispose individuals to seek care; (2) enabling factors such as income and social support that influence access to care; and (3) need factors as reflected by health status, disease, and functional disability (Andersen & Aday, 1978; Andersen & Newman, 1973). We examined the predictive power of 11 predisposing, 7 enabling, and 18 need factors previously found to influence risk of institutionalization that were available from questionnaire items included in the first three waves of the MHCPS. Table 3 lists all potential predictors considered in the model specification. Impaired mental status was excluded from the model because it was not measured at Wave 3 of the study. It was, however, highly collinear with selfreported neurologic condition, which was included in the analysis. Almost all independent variables were dichotomized prior to statistical modeling to more closely replicate the analyses of Shapiro and Tate(1988). For this analysis, nursing homes and chronic disease hospitals were considered LTC institutions. In-
99.0 1.0 (1,625) 60.0 40.0 (1,625) 31.4 68.6 (1,625) 51.1 48.9 (1,615) 58.1 28.2 13.0 (1,442) 41.9 58.1 (1,573) 38.1 44.4 17.4 (1,566)
Note: N's differ due to missing data at baseline.
Table 2. Overview of Sample Transitions Across the Four Waves of the MHCPS (N = 1,625)
VVavel Wave 2 (1.25 years) Wave 3 (6 years) Wave 4 (10 years)
Vol. 32, No. 5,1992
Community
Nursing home
Between-wave nursing home admissions
1,625(100%) — 1,317(81%) — 824(51%) — 540 (33%)
0 (0%) — 26 (2%) — 60 (4%) — 62 (4%)
48 (3%) — 148(11%) — 165 (20%) —
635
Lost to Deceased
follow-up
0 (0%) — 103 (6%) — 422 (26%) — 690 (42%)
0 (0%) — 179(11%) — 319 (20%) — 333 (20%)
Downloaded from http://gerontologist.oxfordjournals.org/ at New York University on September 2, 2015
under age 70, 27% were 70-74 years old, and 38% were 75 years and older. At baseline, a majority rated their health as good or excellent. Less than 10% of the sample reported one or more ADL dependencies. Over three-quarters were categorized as having one or more IADL limitations. Half of the sample noted one or more chronic conditions. The MHCPS cohort was reinterviewed at intervals of 1.25 (Wave 2), 6 (Wave 3) and 10 years (Wave 4) after baseline, or, as appropriate, tracked to a nursing home or through death records. At each wave, data were obtained from cohort members by a structured interview at each respondent's place of residence. A summary of respondent transitions across the four waves of the MHCPS can be found in Table 2. A comparison of characteristics of persons who were alive in the community and participated in all four waves of the MHCPS with those who were lost to follow-up by Wave 4 reveals that the continuing participant sample underrepresented the old old, the unmarried, and those with lower pretax household income (Branch & Stuart, 1985).
The Massachusetts Health Care Panel Study (MHCPS), begun in 1974-1975, was a prospective cohort study of 1,625 people age 65 and older living in Massachusetts (Branch, 1988). The original MHCPS sample was a statewide probability sample of 403 area segments, stratified by eight planning regions and within regions by central city, other urban, and other place. This was a strict probability sample with no substitution of people or housing units once a specific unit was selected. An initial 79% response rate was achieved. As Table 1 illustrates, the sample was predominantly female (60%), and 99% white. At Wave 1 of the MHCPS, 35% of respondents were
Table 3. Independent Variables and Coding Scheme Variable coding
Variable Predisposing characteristics Gender Age Education level
1 2 0 1 0
= = = = =
Variable Need factors Perceived visual acuity (with correction)
male female < 85 years 85 + years grade school
Perceived hearing acuity (with correction) Current physical health problem Perceived health
1 = high school + Marital status Usual occupation Fear of one's neighborhood Consider rest home
Consider elder housing Recent weight change
Smoking status Enabling factors Living situation Income (1974) Relatives nearby Hospitalization in previous year Home care in previous year Physician visits in previous year Rehabilitation services in previous year
= married = not married = white collar = blue collar = no fear = fear = yes = no = yes = no = no = yes = often = sometimes/rarely = ever or current = never
0 1 0 1 0 1 0 1 0 1 0 1 0 1
= alone = with others = < $5,000 = $5,000 + = yes = no = no = yes = no = yes = 0-2 = >2 = no = yes
Frequency of getting out Basic ADL disability
Mobility disability
1 = yes (W1), excellent, good (W2, W3) 2 = no (W1), fair, poor (W2, W3) 0 = excellent/good 1 = fair/poor 0 = yes 1 = no 0 = excellent/good 1 = fair/poor 0 = daily 1 = less than daily 0 = no 1 = 1 + disability
0 = no 1 = 1 + disability
Instrumental disability
0 = no 1 = 1 + disability
Cardiovascular condition
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
Diabetes Musculoskeletal condition Digestive condition Neurologic condition Respiratory condition Emotional condition Cancer Total number of chronic conditions
= no = yes = no = yes = no = yes = no = yes = no = yes = no = yes = no = yes = no = yes = none = 1 + condition
Note: W1 = Wave 1; W2 = Wave 2; W3 = Wave 3.
We modeled the time effects (three waves) using two dummy variables. This method provided flexibility by not constraining the form of the regression parameter estimates; such flexibility was desirable in light of the different wave lengths. Exploratory modeling revealed that including time as continuous variables (time and time squared) resulted in risk factor parameter estimates similar to those in which time was included as a dummy variable. The model differed from that used in a simple logistic regression analysis because the data contain multiple responses from the same person over time. The repeated measures approach allowed for the inclusion of time-varying covariates in the model. For example, a change in marital status or living situation was incorporated into the analysis. However, baseline age of the respondent as measured at Wave 1 was used at all waves so that changes in age would not be confounded with time effects. The repeated measures analysis also allowed us to use all available information on a respondent, thereby minimizing case exclusion due to incomplete follow-up information at a subsequent wave. An adjustment to the covariance matrix of the parameter estimates was employed (Liang & Zeger, 1986) to account for the correlation among re-
formation on a LTC admission was collected at Waves 2, 3, and 4 from the personal interview, from a proxy respondent, from a death certificate, and from a tracking procedure with significant others for all decedents. A respondent was considered institutionalized regardless of the length of stay in the institution. Missing from this categorization were those lost to follow-up. Analysis
Model Specification In the first stage of the analysis we identified individual risk factors for LTC institutionalization within three subgroupings of variables: predisposing, enabling, and need. Each model was conditional on time effects and covariate information from the previous wave. Individual risk factors and selected interactions among them were identified using a repeated measures logistic regression model. In this model, the dependent variable is binary (1 = LTC institutionalization, 0 = remained in the community). The three submodels, containing variables significant at the .15 level or less, were combined to arrive at a final model that retained terms significant at the .05 level.
636
The Gerontologist
Downloaded from http://gerontologist.oxfordjournals.org/ at New York University on September 2, 2015
Eat right
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
Variable coding
sponses; therefore, our modeling procedure did not violate the independence assumption of regression analysis. The data were analyzed with a SAS macro by Lipsitz and Harrington (1990), which implemented the methods of Liang and Zeger. The model was of the form: logit (p) = po
p2x2
(p4x4
if a simplifying (Markovian) assumption is made regarding the independence of admission at a given wave and admission at two waves prior. The validity of this assumption was examined and confirmed. Each of the terms in the above expression is estimable due to the presence of a prior wave status covariate in our model. The 10-year probabilities of LTC institutionalization were estimated using the final model shown in Table 4, with age dichotomized for ease of interpretation. Standard errors of the estimated 10-year probabilities were obtained by jackknifing.
p,xr)
where po, . . . , pr were model parameters, x, and x2 were indicators for Waves 3 and 4, respectively, x3 was an indicator for nursing home (NH) status at the previous wave, x 4 , . . . , xr were predictors of interest, and p = Prob (nursing home admission | xu...,x) = P r o b ( V = 7 | xu . . . , x r ) . The model was used to estimate odds ratios for potential risk factors and interactions between predictors. Interactions for each potential predictor by gender and age were examined. In addition, we examined interactions of selected need factors with marital status, living situation, and proximity of relatives. These particular interactions were selected based on previous research or their policy relevance. Odds ratios for particular factors along with their 95% confidence intervals were obtained by exponentiating linear combinations of the parameter estimates. There were 2,448 observations with all information available to estimate the final regression model: 1,149 at Wave 1, 756 at Wave 2, and 543 at Wave 3.
Results
Profile Analysis Table 4. Final Prediction Model of the 10-year Risk of LTC Institutionalization Among Massachusetts Elderly Persons
In the second phase of the analysis, the rate of institutionalization was estimated for clusters of risk factors included in the final prediction model. The conditional probability of nursing home admission at a particular wave given fixed values x1; . . . , x, was: Prob(V =
Risk factor Nursing home admission in previous wave vs. no admission Predisposing characteristics Smoker (ever vs. never)3 Men Women Age (X+ 5 years vs. X years)3 Income < $5,000 Income > $5,000 Enabling factors Income (< $5,000 vs. > $5,000)a Age 65-69 Age 70-74 Age 75-79 Age 80-84 Age 85-89 Age 90 + Fearful in own neighborhood (yes vs. no) Need factors Neurological condition 8 (present vs. absent) Distant relatives Nearby relatives Basic ADL disabilities (3= 1 vs. none) Getting out (< daily vs. at least daily)
.exp exp (p 0
prxr)
This probability was estimated by substituting the parameter estimates of po, . . . , fi r from the final prediction model into the equation. Wave-specific probability estimates were then used to calculate the overall probability of institutionalization within 10 years for profiles of risk factors. This overall probability was based on but has a more complicated form than that for a probability at any given wave. Suppose Vj = 1 if institutionalization occurs during Wave j and V, = 0 otherwise. Then the probability of enteringa nursing home within 10years (i.e., the end of the study), given a particular risk profile X = x (that is, [X, = xu X2 = x2, . . . , X, = xr]) can be expressed as: Prob(V2 = 1 or V3 = 1 or V4 = 1 | X = x) = P(Y2 = 1 | X = x) + P(Y, = 1 | X = x) + P(YA = 1 | X = x) - P(Y3 = 1 | Y2 = 1, X = x) P(Y2 = 1 | X = x) - P(YA = 1 | X = x) P(Y2 = 1 | X = x) W = 1 | y3 = 1 ; X = x) P(Y3 = 1 | X = x) + P(YA = 1 | v, = 1, x = x) P(v3 = 1 I y2 = 1, x = x) P(y2 = 1
95% Cl
5.17*
(1.35,19.85)
2.61* 1.02
(1.09,6.26) (0.63,1.65)
1.60*** 2.35***
(1.36,1.90) (1.76,3.13)
3.82** 2.61** 1.78 1.22 1.08 0.74
(1.64,8.91) (1.43,4.79) (0.64, 4.95) (0.83,1.80) (0.63,1.85) (0.34,1.58)
1.97*
(1.11,3.50)
15.70** 2.73
(5.15,47.88) (0.81,9.18)
2.84***
(1.74,4.64)
1.78**
(1.23,2.58)
a The effects of these variables are conditional on a second factor due to significant interaction terms. *p*s.O5; * * p « . 0 1 ; ***p*£.001.
I x = x),
Vol. 32, No. 5,1992
Odds ratio for institutionalization in 10 years
637
Downloaded from http://gerontologist.oxfordjournals.org/ at New York University on September 2, 2015
The data in Table 2 illustrate the risk of entering a LTC institution between each wave of the study. The crude incidence of LTC admission was 2.9% between Waves 1 and 2, 11.2% between Waves 2 and 3, and 20.0% between Waves 3 and 4. The proportion living in a LTC institution at each wave was considerably smaller than the proportion entering between waves because some died after entering a LTC institution or were discharged back to the community. Table 4 presents the final regression model of individual risk factors for admission to a LTC facility during the decade of follow-up. Prior institutionalization, age and income interactions, and indicators of need were the strongest predictors of institutionalization. Elders who reported a LTC admission at a
Table 5. Probability of Nursing Home Entry within 10 Years by Age Probability of institutionalization at 10 years Probability estimate (standard error) Gender
Male
Female
Male
Female
Age
