Copyright 7992 by The Cerontological Society of America The Cerontologist Vol. 32, No. 6, 805-«12
This study used data from the Channeling Demonstration to investigate the relationship between program participation, utilization of formal in-home services, and client satisfaction in an elderly population. Age, being male, severe ADL dependency, living alone with no informal support, provision of basic case management services, and utilization of formal in-home services were significant predictors of satisfaction. Key Words: Long-term care, Case management
The Relationship Between Program Participation, Use of Formal In-home Care, and Satisfaction with Care in an Elderly Population1
This investigation explored the relationship between participation in a comprehensive home and community-based long-term care (known as "Channeling") intervention, use of formal in-home services, and client satisfaction with care. Satisfaction with care enhances the quality of life of elderly individuals, and therefore is an important outcome to be considered in any program evaluation. By learning how comprehensive home and community-based long-term care interventions affect client satisfaction with care, policymakers and planners may be better able to tailor programs and target increasingly limited resources. Those aspects of an intervention that directly affect client satisfaction with care should be among the factors given special consideration. Although other researchers have assessed the total effects of the Channeling intervention on a variety of patient outcomes (Applebaum et al., 1988; Kemper et al., 1986), the effects have not been separated into their direct and indirect component parts. As a result, previous investigators have been able to ascertain that the Channeling intervention did make a difference; however, prior research has not provided a quantitative analysis of how the intervention made a difference. This study focuses on the identification of program factors that determine client satisfaction, especially those that have a direct effect on client satisfaction with care. Two research questions are of primary interest:
2) Is the total effect of participation in a program intervention on client satisfaction indirect, mediated solely by the utilization of formal in-home services? In other words, is it only utilization of formal in-home services, and not other aspects of a long-term care intervention (such as case management, additional funds for non-Medicarecovered services, and utilization of communitybased care), that directly affects client satisfaction with care? The Intervention, Research Design, and Data Sources The data for this study come from the National Long Term Care Demonstration (better known as the "Channeling Demonstration"). The Channeling Demonstration was a home and community-based intervention, funded by the federal government between 1982 and 1985 and conducted in 10 sites throughout the United States. Two models of the intervention were evaluated: the basic model, which included outreach, screening, care planning, service utilization, and program monitoring; and the financial control model, which covered the same elements as the basic model plus additional features, including a funds pool to finance non-Medicarecovered home and community-based care. Eligibility criteria for participation included: age (65 or older); functional disability; unmet needs or a fragile informal support system; community residence; and finally, for those living in sites where the financial control model was to be implemented, eligibility for Part A of Medicare. During the demonstration, individuals who were initially screened and determined eligible were randomly assigned to one of the two treatment groups or the control group in each location. (Control group members continued to receive whatever services
1) What are the determinants of client satisfaction? and 1 An earlier version of this paper was presented at the annual meeting of the American Public Health Association in Atlanta, November 1991. The author thanks Sally Stearns, PhD, and Elizabeth Mutran, PhD for comments on an earlier version of this paper. ^University of NC at Chapel Hill, School of Public Health, Dept. HPAA, CB # 7400,1101 McCavran-Creenberg Hall, Chapel Hill, NC 27599-7400.
Vol. 32, No. 6,1992
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Donna J. Rabiner, MHA2
Conceptual Model
Two endogenous variables were of particular interest — utilization of formal in-home services and client satisfaction with service arrangements (see Figure 1). Andersen and Newman's model of individual utilization (1973) was adopted as the conceptual model for the utilization equation. This model, which divides the determinants of utilization into three domains — predisposing, enabling, and need factors — has been widely endorsed by health services researchers in the assessment of utilization for elderly (Wolinsky et al., 1983; Wolinsky & Johnson, 1991; Cafferata, 1987; Evaschwick et al., 1984; Weissert et al., 1980a, 1980b; Stoller, 1982) and nonelderly populations (Morgan, 1980; Andersen et al., 1983). The selection of a conceptual model for the client satisfaction equation was less straightforward. A sim-
e
e2
t
UHllutlon
SatJii action
Figure 1. Conceptual model of relationship of variables with service utilization and client satisfaction.
806
plified version of the model proposed by Shortell and colleagues (1977) was chosen due to its uniqueness in conceptualizing the relationships among these variables from a multiequation perspective. (A simultaneous equation model was needed in order to separate the total effects of the intervention into their respective direct and indirect components). In this model, Andersen and Newman's individual predisposing, enabling, and baseline need factors are defined as "structural" variables since they are considered to be unalterable characteristics. Program service utilization, access, continuity of care, and physician performance are conceptualized as "process" variables since they represent behavior involved in the seeking and delivery of care (Shortell et al., 1977). Finally, "program outcomes" are defined as the final results or endproducts of the health service activity and include measures such as diastolic blood pressure and patient satisfaction. In this study, Andersen and Newman (1973) and Shortell and colleagues' (1977) models have been modified and combined into one overall theoretical framework. Specifically, predisposing, enabling, and need factors are hypothesized to directly affect utilization of formal in-home services, which in turn is anticipated to directly determine client satisfaction. Second, some predisposing, enabling, and need factors are directly linked to client satisfaction. Finally, since it is anticipated that some unmeasured factors in both the utilization and client satisfaction equations will be correlated across equations (e.g., patient diagnosis, severity of illness, etc.), a correlation between the error terms of the two simultaneous equations has been included in the conceptual model developed here.
Operationalization of Model
In-home Utilization Equation In keeping with the conceptual framework outlined above, the following predisposing variables were selected to operationalize the utilization equation at 6 and 12 months (see Table 1 for variable definitions): age, sex (male), two variables for race (black and Hispanic), and two variables for extent of social support (no support and hours of informal home care). Three enabling variables were included, namely, the assignment to the two treatment groups (basic treatment and financial control treatment) and the indicator variable for residence in sites where the financial control model was in effect (model type). The need dimension was operationalized by several variables: prior use of formal home care, number of hours of formal in-home care received per week, and three variables for dependency with activities of daily living (extremely severe, highly severe, and moderately severe ADL). Finally, the logged numbers of in-home visits by formal caregivers during a representative target week at 6- and 12-month follow-up were selected as the dependent variables for utilization. The Gerontologist
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were available to them in their communities.) All participants were interviewed at baseline and then followed at 6-month intervals for 12 or 18 months. Detailed information was collected on: demographics, service utilization, program expenditures, health outcomes, client satisfaction, and caregiver wellbeing. The primary sources of this information were client questionnaires with trained interviewers, Medicare/Medicaid claims data, program data (for the financial control model), provider records, and official mortality reports (Kemper et al., 1986). For this analysis, data from three public use files were used — namely, the Baseline File, the Formal Community Service Analysis File, and the Quality of Life Analysis File. Over the life of the demonstration, 11,769 applicants were screened, 9,890 of whom were determined to be eligible. In all, 6,341 were randomly assigned. Given the substantial death rate among this population and interview noncompletion, Channeling data set research samples ranged from 3,372 to 6,326, depending on the analysis (Kemper et al., 1986). All individuals for whom complete data were available at 6- or 12-month follow-up were included in the analyses (N = 3,920 at 6 months and N = 3,387 at 12 months).
Table 1. Descriptive Statistics and Variable Definitions Baseline descriptive statistics Variable
Mean
SD
Minimum Maximum 1 0 0 0 0 0 0 0
6 1 1 1 1 1 1 1
0 0
168 1
0 0 0 0
168 1 1 1
-1.370
1.330
-1.290
1.480
0
2
0
2
'Age was categorized as follows: 1 = 64-69 years of age, 2 = 7074 years of age, 3 = 75-79 years of age, 4 = 80-84 years of age, 5 = 85-89 years of age, and 6 = 90+ years of age. b Male = 1 if the individual was male and 0 otherwise. 'Black = 1 if the individual was black and 0 otherwise, and Hispanic = 1 if the individual was hispanic and 0 otherwise. White was the omitted racial category in the models estimated in this analysis. "Impairment levels were defined hierarchically. Extremely severe ADL = 1 if the individual was impaired on all five areas (eating, transfer, toileting, dressing, and bathing), and 0 otherwise; Highly severe ADL = 1 if the individual was impaired in all areas but eating, and 0 otherwise; Moderately severe ADL = 1 if the individual was impaired on toileting and/or dressing and bathing, and 0 otherwise. The omitted ADL category was Mild/no ADL dependency, indicating that the individual needed assistance at most with bathing. r No support = 1 if the individual lives alone and has no informal support and 0 if he/she either lives with others or lives alone but has informal support. •Number of hours/week of informal in-home care at baseline. »Prior visits = 1 if the individual received formal care within the last 2 months (prior to baseline) and 0 otherwise. h Number of hours/week of formal in-home care at baseline. 'Financial control treatment = 1 if the individual was in the financial control model treatment group and 0 otherwise. 'Basic treatment = 1 if the individual was in the basic model treatment group and 0 otherwise. k Model type = 1 if the individual resided in sites where the financial control model was to be implemented, and 0 otherwise. 'Log (predicted visits) at 6 and 12 months were continuous dependent variables measuring the log of the number of visits by formal caregivers during a representative target week. This measure was taken at 6- and 12-month follow-up. In order to calculate the log of this continuous dependent variable, it was necessary to transform values that were equal to 0, since the log (0) is undefined. In this analysis, values of 0 were recoded to .1 in order that the log transformation would be possible. •"Satisfaction with service arrangements at 6 and 12 months were the two ordinal dependent variables in this analysis. The variables were coded such that 0 = dissatisfied with service arrangements, 1 = partly satisfied with service arrangements, and 2 = satisfied with service arrangements.
Vol.32, No. 6,1992
The following predisposing and need factors were hypothesized to have a direct effect on satisfaction with care at 6 and 12 months: age, gender, race (both black and Hispanic), extent of social support (both no support and hours of informal care), all three levels of ADL dependency status, and utilization of formal in-home services at 6 or 12 months. In addition, since it was important to test whether there was a direct link between the intervention and patient satisfaction, the intervention variables, basic and financial control treatments, were directly linked to client satisfaction. (It was anticipated that these two linkages would not be direct, but rather would be mediated through utilization. Therefore, the expectation was that these two paths would not be statistically significant, indicating that there was no direct effect of being enrolled in the Channeling intervention, in and of itself, on client satisfaction.) Finally, satisfaction with care at 6 and 12 months were ordinal dependent variables with range 0-2 (0 = not satisfied, 1 = partly satisfied, and 2 = satisfied). The following baseline variables were not hypothesized to directly affect satisfaction with service arrangements at 6 and 12 months: residence in sites where the financial control (versus the basic) model was in effect (model type), and the two variables for receipt of baseline formal in-home care (prior visits and hours of formal care). These variables were expected to directly affect utilization of formal in-home services, which, in turn, would directly affect satisfaction with care at follow-up; therefore, no direct paths to satisfaction were estimated for these three background variables. Hypothesized Direct Effects
Table 2 shows the hypothesized effects between the background and dependent variables in this analysis. The proposed relationships were based on findings reported both in the utilization literature (cited above) and the patient/client satisfaction literature (see Hall & Dornan, 1988; Hulka et al., 1970, 1975; Lebow, 1974; Linn & Greenfield, 1982; Mutran, 1990; Pascoe, 1983; Tessler & Mechanic, 1975; Ware et al., 1975; Ware, Snyder, & Wright, 1976; Ware, DaviesAvery, & Stewart, 1978; Zastowny, Roghmann, & Hengst, 1983; Zastowny, Roghmann, & Cafferata, 1989). Question marks indicate where no consensus exists in the literature regarding the direction of the effects. Descriptive Statistics
The typical Channeling participant (see Table 1) was approximately 77.5 years of age, female (74%), of white ethnic origin (74%), and dependent in activities of daily living (19.4% were extremely severely dependent in eating, transfer, toileting, dressing, and bathing, 35.4% were highly severely dependent in all areas except eating, 23.5% were moderately severely dependent, meaning that they could eat and transfer and either toilet or dress, but could not bathe themselves, and only 21.7% were only mildly 807
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3.462 1.496 Age category" .263 .440 Male" .414 Black1 .219 .041 .198 Hispanic0 .194 .395 Extremely severe ADLd .354 .478 Highly severe ADLd .424 .236 Moderately severe ADLd .074 .261 No support Hours of informal 11.69 25.95 in-home care' .492 Prior visits" .585 Hours of formal 7.22 19.14 in-home careh .472 Financial control treatment1 .335 .450 Basic treatment1 .283 .515 .500 Model type" Log (predicted visits) -.082 .678 at 6 months1 Log (predicted visits) -.210 .596 at 12 months1 Satisfaction at .656 6 months'" 1.599 Satisfaction at 1.579 .66 at 12 months"1
Client Satisfaction Equation
Table 2. Hypothesized Direct Effects for Utilization and Satisfaction Equations at 6 and 12 months
Variable
+ ? ? + + + +
+ ? ? ? + + +
+
+ NAa
+ + + + NA
NA NSb NS NA +
Note: Question marks have been placed in the table where no consensus currently exists in the literature regarding the direction of these specific effects. a NA = not applicable, meaning that no direct effect was hypothesized between this variable and satisfaction with care. b NS = not significant. It was hypothesized that once utilization of formal in-home care was included in the model, neither treatment would directly affect client satisfaction with care.
Findings
or not at all dependent, meaning that they were fully independent, with the possible exception of needing assistance in bathing). In addition, the average Channeling participant either lived with others or lived alone but had informal support (only 7.4% both lived alone and had no informal support), receiving 11.7 hours/week of informal in-home care at baseline. Approximately 59% of all Channeling participants utilized formal in-home care prior to the Channeling intervention, typically receiving 7.2 hours/week of formal in-home care at baseline. Approximately 34% of all study participants were randomly assigned to the financial control model treatment group, 28% were assigned to the basic model treatment group, and the remaining 38% were randomly assigned to a control group. Of all the participants in the study, 51% lived in locations where the financial control model was in effect. Methods
Ordinary least squares (OLS) was used to analyze the utilization equations at 6 and 12 months. Estimation of client satisfaction was somewhat more complicated, however, because: 1) client satisfaction was operationalized as an ordinal dependent variable in the Channeling data set and 2) errors in the utilization and client satisfaction equations were expected to be correlated due to unmeasured common factors (e.g., patient diagnosis, severity of illness, etc.) that were not available. Given the endogeneity of the utilization variables, and the fact that the error terms in the utilization and satisfaction equations were hypothesized to be correlated, it was necessary to use a two-stage least squares (2SLS) rather than an 808
For the Utilization Equation Ordinary least squares estimation of utilization at 6 months revealed that being black, extremely or highly severe ADL dependency status, prior use of formal in-home care, being in either treatment group, and residence in a site with the financial control model in effect directly increased utilization of formal in-home services at 6 months (see Table 3). On average, and when holding all else in the model constant, being black increased the number of formal in-home visits at 6 months by .156 (see Table 4). Similarly, extremely severe ADL dependency increased the number of formal in-home visits by .21, highly severe ADL dependency increased the number of formal in-home visits by .17, prior utilization of formal care increased them by 1.22, being in the financial control model treatment group increased them by 1.87, being in the basic model treatment group increased them by .34, and residency in sites with the financial control model in effect increased utilization by .19. It was appropriate to reject the null hypothesis that the coefficient estimates for all variables in the utilization equation at 6 months were equal to 0 (F[14,3,905] = 45.044, p < .01). The adjusted R2 statistic for this equation was .1359, indicating that 13.59% of the total variation in the logged utilization of formal in-home care at 6 months was explained by this model. Results from the OLS analysis of utilization of formal in-home care at 12 months indicated that being black, extremely severe ADL dependency status, prior use of formal in-home care at baseline, number of hours of formal in-home care, being assigned to The Gerontologist
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Age category Male Black Hispanic Extremely severe ADL Highly severe ADL Moderately severe ADL No support Hours of informal in-home care Prior visits Hours of formal in-home care Financial control treatment Basic treatment Model type Predicted visits
Utilization Satisfaction 6 and 12 months 6 and 12 months
OLS procedure for this estimation (Kennedy, 1985; Gujarati, 1988). 2SLS was used to "purify" the utilization variables (Y1) at 6 and 12 months of the influence of the disturbance associated with the satisfaction variables (e2) at 6 and 12 months (Gujarati, 1988). This was accomplished by regressing Y1 on all of the predetermined variables in the system (step 1), obtaining predicted values of Y1 (V1A), replacing the original values of V1 with V1 * in the satisfaction equations (Y2), and then applying ordered probit techniques on the satisfaction equations so transformed (Gujarati, 1988; Kennedy, 1985). Among the consequences of using 2SLS are that the correlation between predicted utilization (V1A) and the error associated with each satisfaction equation (e2) is removed (asymptotically), and the estimators thus obtained are consistent (Gujarati, 1988; Kennedy, 1985; Kmenta, 1986). Ordered probit techniques were used in this study to predict client satisfaction with care at 6 and 12 months; attrition-corrected ordered probit analyses were also performed to assess whether differential attrition from the sample significantly altered the study findings.
Table 3. Results from the OLS Estimation of Formal In-Home Service Utilization (Log) at 6 and 12 months
Variable Intercept Age category Male Black Hispanic Extremely severe ADL Highly severe ADL
No support Hours of informal in-home care
Coefficient at 12 months
(SE)
(SE)
-1.238** (.109) .013 (.018) -.087 (.062) .145* (.066) -.068 (.138) .192* (.086) .160* (.074) .077 (.080) .054 (.105) -.001 (.001)
Prior visits
.796** (.058)
Hours of formal in-home care
.002 (.001) 1.057** (.079)
Financial control treatment Basic treatment Model type
.293** (.079) .174* (.078)
Variable Black Extremely severe ADL Highly severe ADL Prior visits Financial control treatment Basic treatment Model type
-1.273** (.121)
.0056 (.021) .026 (.071)
.186* (.075) .008 (.159) .194* (.098)
Transformed estimate at 12 months
.156 .211 .173 1.22 1.878 .340 .190
.204 .215 NSb .779 1.53 .430 .303
"The calculation of the effects of dummy variables in semilogarithmic equations was based on Halvorsen and Palmquist (1980). When interpreting dummy variables in semilogarithmic equations (such as the two utilization equations analyzed above), it is necessary to correct for the fact that the derivative of the two dependent variables with respect to each dummy variable does not exist. Rather, it measures the discontinuous effect on each dependent variable when the dummy variable equals 1 versus when it equals 0. Therefore, it is necessary to transform the significant dummy variable coefficients in each of the two utilization equations. b NS = not significant at 12-month follow-up.
.124 (.082) .003
(.089) -.026 (.110)
.001 (.001) .576** (.065) .0045*" (.002) .929** (.089) .357** (.088) .265** (.099)
For the Client Satisfaction Equation
Note: F statistic at 6 months = 45.044 with 14, 3,905 df, p < .01; at 12 months = 27.809 with 14, 3,372 df, p < .01. Adjusted R2 at 6 months = .1359; at 12 months = .0998.
*p< .05; **p< .01.
either treatment group, and residence in sites with the financial control model directly increased utilization of formal in-home care at 12 months (see Table 3). Interpretation of the statistically significant coefficient estimates is similar to that presented for the 6month utilization equation. (For example, on average and when holding all else in the model constant, being in the financial control treatment group increased the number of formal in-home visits at 12 months by 1.53). (See Table 4 for all transformed significant coefficients at 12-month follow-up.) It was also appropriate to reject the null hypothesis that all coefficient estimates for all variables in the utilization equation at 12 months were equal to 0 (/T14,3,372] = 27.809, with p < .01). The adjusted R2 value for this model was .0998, indicating that 9.98% of the total variation in the logged utilization of formal in-home care at 12 months was explained by the model. Finally, when the predictors of utilization of formal in-home care at 6 and 12 months were compared, results were generally found to be consistent in terms of the magnitude, direction, and significance of the effects (see Tables 3 and 4). Vol.32, No. 6,1992
Transformed estimate at 6 months
Results from the ordered probit analysis at 6 months revealed that age, being male, both extremely severe and highly severe ADL dependency status, and hours of informal care/week directly increased client satisfaction at 6 months, whereas being Hispanic and having no informal support system directly decreased client satisfaction at 6 months (see Table 5). As expected, being in either treatment group did not directly affect client satisfaction when all else in the model was held constant. Finally, being black and having moderately severe ADL dependency status were not directly related to satisfaction with care at 6 months. The chi-square statistic for this equation was 99.183 with 12 df (p < .01), indicating that the null hypothesis that all coefficient estimates of the variables were equal to 0 could be rejected. The ordered probit estimation of satisfaction with care at 12 months revealed that age, being male, and each level of ADL dependency directly increased satisfaction with service arrangements at 12 months, whereas having no support system directly decreased satisfaction with care at 12 months (see Table 5). Predicted utilization of formal in-home visits at 12 months positively affected satisfaction with care at p < .094. Although being in the financial control model treatment group did not directly affect satisfaction with care at 12 months, being in the basic model treatment group did have a direct positive effect on satisfaction with care when all else in the model was held constant. The chi-square statistic for this model was 90.814 with 12 df, again indicating that the null hypothesis could be rejected. The predictors of client satisfaction with care were compared at 6 and 12 months. Although the numerical value of the parameters varied to some degree over the 6- to 12-month period, as would be expected, the magnitude and direction of the effects remained the same. When comparing the statistical
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Moderately severe ADL
Coefficient at 6 months
Table 4. Transformation of Significant OLS Dummy Variable Coefficients in Utilization Equations at 6 and 12 months'
attrition did not appear to play a significant role in the analysis, the results reported here remain in their uncorrected form.
Table 5. Results from Ordered Probit Estimation of Satisfaction with Care at 6 and 12 Months
Coefficient at 6 months
Coefficient at 12 months
(SE)
(SE)
Intercept
1.10*** (.081) .038*** (.014)
.913*** (.095) .045*** (.015)
.261*** (.048) -.020 (.051) -.266*** (.106) .200*** (.066)
.199*** (.052) -.030 (.055) -.185 (.115) .230*** (.072) .211*** (.059) .129*** (.062) -.246*** (.074) .0014 (.0009) .033 (.087)
Age category Male Black Hispanic Extremely severe ADL Highly severe ADL Moderately severe ADL No support Hours of informal in-home care Financial control treatment Basic treatment Predicted visits
.157*** (.056) .051 (.059) -.220*** (.076) .0021*** (.0007) .038 (.077) .0686 (.051) .099*** (.049)
.211*** (.056) .108* (.064)
Nofe: The chi-square statistic for satisfaction with care at 6 months = 99.183 with 12 df, p < .01; at 12 months = 90.814 with 12 df,p
This study used data from the Channeling Demonstration to investigate the relationship between program participation, utilization of formal in-home services, and client satisfaction in an elderly population. Age, being male, severe ADL dependency, living alone with no informal support, provision of basic case management services, and utilization of formal in-home services were significant predictors of satisfaction. Key Words: Long-term care, Case management
The Relationship Between Program Participation, Use of Formal In-home Care, and Satisfaction with Care in an Elderly Population1
This investigation explored the relationship between participation in a comprehensive home and community-based long-term care (known as "Channeling") intervention, use of formal in-home services, and client satisfaction with care. Satisfaction with care enhances the quality of life of elderly individuals, and therefore is an important outcome to be considered in any program evaluation. By learning how comprehensive home and community-based long-term care interventions affect client satisfaction with care, policymakers and planners may be better able to tailor programs and target increasingly limited resources. Those aspects of an intervention that directly affect client satisfaction with care should be among the factors given special consideration. Although other researchers have assessed the total effects of the Channeling intervention on a variety of patient outcomes (Applebaum et al., 1988; Kemper et al., 1986), the effects have not been separated into their direct and indirect component parts. As a result, previous investigators have been able to ascertain that the Channeling intervention did make a difference; however, prior research has not provided a quantitative analysis of how the intervention made a difference. This study focuses on the identification of program factors that determine client satisfaction, especially those that have a direct effect on client satisfaction with care. Two research questions are of primary interest:
2) Is the total effect of participation in a program intervention on client satisfaction indirect, mediated solely by the utilization of formal in-home services? In other words, is it only utilization of formal in-home services, and not other aspects of a long-term care intervention (such as case management, additional funds for non-Medicarecovered services, and utilization of communitybased care), that directly affects client satisfaction with care? The Intervention, Research Design, and Data Sources The data for this study come from the National Long Term Care Demonstration (better known as the "Channeling Demonstration"). The Channeling Demonstration was a home and community-based intervention, funded by the federal government between 1982 and 1985 and conducted in 10 sites throughout the United States. Two models of the intervention were evaluated: the basic model, which included outreach, screening, care planning, service utilization, and program monitoring; and the financial control model, which covered the same elements as the basic model plus additional features, including a funds pool to finance non-Medicarecovered home and community-based care. Eligibility criteria for participation included: age (65 or older); functional disability; unmet needs or a fragile informal support system; community residence; and finally, for those living in sites where the financial control model was to be implemented, eligibility for Part A of Medicare. During the demonstration, individuals who were initially screened and determined eligible were randomly assigned to one of the two treatment groups or the control group in each location. (Control group members continued to receive whatever services
1) What are the determinants of client satisfaction? and 1 An earlier version of this paper was presented at the annual meeting of the American Public Health Association in Atlanta, November 1991. The author thanks Sally Stearns, PhD, and Elizabeth Mutran, PhD for comments on an earlier version of this paper. ^University of NC at Chapel Hill, School of Public Health, Dept. HPAA, CB # 7400,1101 McCavran-Creenberg Hall, Chapel Hill, NC 27599-7400.
Vol. 32, No. 6,1992
805
Downloaded from http://gerontologist.oxfordjournals.org/ at University of Sussex on September 2, 2015
Donna J. Rabiner, MHA2
Conceptual Model
Two endogenous variables were of particular interest — utilization of formal in-home services and client satisfaction with service arrangements (see Figure 1). Andersen and Newman's model of individual utilization (1973) was adopted as the conceptual model for the utilization equation. This model, which divides the determinants of utilization into three domains — predisposing, enabling, and need factors — has been widely endorsed by health services researchers in the assessment of utilization for elderly (Wolinsky et al., 1983; Wolinsky & Johnson, 1991; Cafferata, 1987; Evaschwick et al., 1984; Weissert et al., 1980a, 1980b; Stoller, 1982) and nonelderly populations (Morgan, 1980; Andersen et al., 1983). The selection of a conceptual model for the client satisfaction equation was less straightforward. A sim-
e
e2
t
UHllutlon
SatJii action
Figure 1. Conceptual model of relationship of variables with service utilization and client satisfaction.
806
plified version of the model proposed by Shortell and colleagues (1977) was chosen due to its uniqueness in conceptualizing the relationships among these variables from a multiequation perspective. (A simultaneous equation model was needed in order to separate the total effects of the intervention into their respective direct and indirect components). In this model, Andersen and Newman's individual predisposing, enabling, and baseline need factors are defined as "structural" variables since they are considered to be unalterable characteristics. Program service utilization, access, continuity of care, and physician performance are conceptualized as "process" variables since they represent behavior involved in the seeking and delivery of care (Shortell et al., 1977). Finally, "program outcomes" are defined as the final results or endproducts of the health service activity and include measures such as diastolic blood pressure and patient satisfaction. In this study, Andersen and Newman (1973) and Shortell and colleagues' (1977) models have been modified and combined into one overall theoretical framework. Specifically, predisposing, enabling, and need factors are hypothesized to directly affect utilization of formal in-home services, which in turn is anticipated to directly determine client satisfaction. Second, some predisposing, enabling, and need factors are directly linked to client satisfaction. Finally, since it is anticipated that some unmeasured factors in both the utilization and client satisfaction equations will be correlated across equations (e.g., patient diagnosis, severity of illness, etc.), a correlation between the error terms of the two simultaneous equations has been included in the conceptual model developed here.
Operationalization of Model
In-home Utilization Equation In keeping with the conceptual framework outlined above, the following predisposing variables were selected to operationalize the utilization equation at 6 and 12 months (see Table 1 for variable definitions): age, sex (male), two variables for race (black and Hispanic), and two variables for extent of social support (no support and hours of informal home care). Three enabling variables were included, namely, the assignment to the two treatment groups (basic treatment and financial control treatment) and the indicator variable for residence in sites where the financial control model was in effect (model type). The need dimension was operationalized by several variables: prior use of formal home care, number of hours of formal in-home care received per week, and three variables for dependency with activities of daily living (extremely severe, highly severe, and moderately severe ADL). Finally, the logged numbers of in-home visits by formal caregivers during a representative target week at 6- and 12-month follow-up were selected as the dependent variables for utilization. The Gerontologist
Downloaded from http://gerontologist.oxfordjournals.org/ at University of Sussex on September 2, 2015
were available to them in their communities.) All participants were interviewed at baseline and then followed at 6-month intervals for 12 or 18 months. Detailed information was collected on: demographics, service utilization, program expenditures, health outcomes, client satisfaction, and caregiver wellbeing. The primary sources of this information were client questionnaires with trained interviewers, Medicare/Medicaid claims data, program data (for the financial control model), provider records, and official mortality reports (Kemper et al., 1986). For this analysis, data from three public use files were used — namely, the Baseline File, the Formal Community Service Analysis File, and the Quality of Life Analysis File. Over the life of the demonstration, 11,769 applicants were screened, 9,890 of whom were determined to be eligible. In all, 6,341 were randomly assigned. Given the substantial death rate among this population and interview noncompletion, Channeling data set research samples ranged from 3,372 to 6,326, depending on the analysis (Kemper et al., 1986). All individuals for whom complete data were available at 6- or 12-month follow-up were included in the analyses (N = 3,920 at 6 months and N = 3,387 at 12 months).
Table 1. Descriptive Statistics and Variable Definitions Baseline descriptive statistics Variable
Mean
SD
Minimum Maximum 1 0 0 0 0 0 0 0
6 1 1 1 1 1 1 1
0 0
168 1
0 0 0 0
168 1 1 1
-1.370
1.330
-1.290
1.480
0
2
0
2
'Age was categorized as follows: 1 = 64-69 years of age, 2 = 7074 years of age, 3 = 75-79 years of age, 4 = 80-84 years of age, 5 = 85-89 years of age, and 6 = 90+ years of age. b Male = 1 if the individual was male and 0 otherwise. 'Black = 1 if the individual was black and 0 otherwise, and Hispanic = 1 if the individual was hispanic and 0 otherwise. White was the omitted racial category in the models estimated in this analysis. "Impairment levels were defined hierarchically. Extremely severe ADL = 1 if the individual was impaired on all five areas (eating, transfer, toileting, dressing, and bathing), and 0 otherwise; Highly severe ADL = 1 if the individual was impaired in all areas but eating, and 0 otherwise; Moderately severe ADL = 1 if the individual was impaired on toileting and/or dressing and bathing, and 0 otherwise. The omitted ADL category was Mild/no ADL dependency, indicating that the individual needed assistance at most with bathing. r No support = 1 if the individual lives alone and has no informal support and 0 if he/she either lives with others or lives alone but has informal support. •Number of hours/week of informal in-home care at baseline. »Prior visits = 1 if the individual received formal care within the last 2 months (prior to baseline) and 0 otherwise. h Number of hours/week of formal in-home care at baseline. 'Financial control treatment = 1 if the individual was in the financial control model treatment group and 0 otherwise. 'Basic treatment = 1 if the individual was in the basic model treatment group and 0 otherwise. k Model type = 1 if the individual resided in sites where the financial control model was to be implemented, and 0 otherwise. 'Log (predicted visits) at 6 and 12 months were continuous dependent variables measuring the log of the number of visits by formal caregivers during a representative target week. This measure was taken at 6- and 12-month follow-up. In order to calculate the log of this continuous dependent variable, it was necessary to transform values that were equal to 0, since the log (0) is undefined. In this analysis, values of 0 were recoded to .1 in order that the log transformation would be possible. •"Satisfaction with service arrangements at 6 and 12 months were the two ordinal dependent variables in this analysis. The variables were coded such that 0 = dissatisfied with service arrangements, 1 = partly satisfied with service arrangements, and 2 = satisfied with service arrangements.
Vol.32, No. 6,1992
The following predisposing and need factors were hypothesized to have a direct effect on satisfaction with care at 6 and 12 months: age, gender, race (both black and Hispanic), extent of social support (both no support and hours of informal care), all three levels of ADL dependency status, and utilization of formal in-home services at 6 or 12 months. In addition, since it was important to test whether there was a direct link between the intervention and patient satisfaction, the intervention variables, basic and financial control treatments, were directly linked to client satisfaction. (It was anticipated that these two linkages would not be direct, but rather would be mediated through utilization. Therefore, the expectation was that these two paths would not be statistically significant, indicating that there was no direct effect of being enrolled in the Channeling intervention, in and of itself, on client satisfaction.) Finally, satisfaction with care at 6 and 12 months were ordinal dependent variables with range 0-2 (0 = not satisfied, 1 = partly satisfied, and 2 = satisfied). The following baseline variables were not hypothesized to directly affect satisfaction with service arrangements at 6 and 12 months: residence in sites where the financial control (versus the basic) model was in effect (model type), and the two variables for receipt of baseline formal in-home care (prior visits and hours of formal care). These variables were expected to directly affect utilization of formal in-home services, which, in turn, would directly affect satisfaction with care at follow-up; therefore, no direct paths to satisfaction were estimated for these three background variables. Hypothesized Direct Effects
Table 2 shows the hypothesized effects between the background and dependent variables in this analysis. The proposed relationships were based on findings reported both in the utilization literature (cited above) and the patient/client satisfaction literature (see Hall & Dornan, 1988; Hulka et al., 1970, 1975; Lebow, 1974; Linn & Greenfield, 1982; Mutran, 1990; Pascoe, 1983; Tessler & Mechanic, 1975; Ware et al., 1975; Ware, Snyder, & Wright, 1976; Ware, DaviesAvery, & Stewart, 1978; Zastowny, Roghmann, & Hengst, 1983; Zastowny, Roghmann, & Cafferata, 1989). Question marks indicate where no consensus exists in the literature regarding the direction of the effects. Descriptive Statistics
The typical Channeling participant (see Table 1) was approximately 77.5 years of age, female (74%), of white ethnic origin (74%), and dependent in activities of daily living (19.4% were extremely severely dependent in eating, transfer, toileting, dressing, and bathing, 35.4% were highly severely dependent in all areas except eating, 23.5% were moderately severely dependent, meaning that they could eat and transfer and either toilet or dress, but could not bathe themselves, and only 21.7% were only mildly 807
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3.462 1.496 Age category" .263 .440 Male" .414 Black1 .219 .041 .198 Hispanic0 .194 .395 Extremely severe ADLd .354 .478 Highly severe ADLd .424 .236 Moderately severe ADLd .074 .261 No support Hours of informal 11.69 25.95 in-home care' .492 Prior visits" .585 Hours of formal 7.22 19.14 in-home careh .472 Financial control treatment1 .335 .450 Basic treatment1 .283 .515 .500 Model type" Log (predicted visits) -.082 .678 at 6 months1 Log (predicted visits) -.210 .596 at 12 months1 Satisfaction at .656 6 months'" 1.599 Satisfaction at 1.579 .66 at 12 months"1
Client Satisfaction Equation
Table 2. Hypothesized Direct Effects for Utilization and Satisfaction Equations at 6 and 12 months
Variable
+ ? ? + + + +
+ ? ? ? + + +
+
+ NAa
+ + + + NA
NA NSb NS NA +
Note: Question marks have been placed in the table where no consensus currently exists in the literature regarding the direction of these specific effects. a NA = not applicable, meaning that no direct effect was hypothesized between this variable and satisfaction with care. b NS = not significant. It was hypothesized that once utilization of formal in-home care was included in the model, neither treatment would directly affect client satisfaction with care.
Findings
or not at all dependent, meaning that they were fully independent, with the possible exception of needing assistance in bathing). In addition, the average Channeling participant either lived with others or lived alone but had informal support (only 7.4% both lived alone and had no informal support), receiving 11.7 hours/week of informal in-home care at baseline. Approximately 59% of all Channeling participants utilized formal in-home care prior to the Channeling intervention, typically receiving 7.2 hours/week of formal in-home care at baseline. Approximately 34% of all study participants were randomly assigned to the financial control model treatment group, 28% were assigned to the basic model treatment group, and the remaining 38% were randomly assigned to a control group. Of all the participants in the study, 51% lived in locations where the financial control model was in effect. Methods
Ordinary least squares (OLS) was used to analyze the utilization equations at 6 and 12 months. Estimation of client satisfaction was somewhat more complicated, however, because: 1) client satisfaction was operationalized as an ordinal dependent variable in the Channeling data set and 2) errors in the utilization and client satisfaction equations were expected to be correlated due to unmeasured common factors (e.g., patient diagnosis, severity of illness, etc.) that were not available. Given the endogeneity of the utilization variables, and the fact that the error terms in the utilization and satisfaction equations were hypothesized to be correlated, it was necessary to use a two-stage least squares (2SLS) rather than an 808
For the Utilization Equation Ordinary least squares estimation of utilization at 6 months revealed that being black, extremely or highly severe ADL dependency status, prior use of formal in-home care, being in either treatment group, and residence in a site with the financial control model in effect directly increased utilization of formal in-home services at 6 months (see Table 3). On average, and when holding all else in the model constant, being black increased the number of formal in-home visits at 6 months by .156 (see Table 4). Similarly, extremely severe ADL dependency increased the number of formal in-home visits by .21, highly severe ADL dependency increased the number of formal in-home visits by .17, prior utilization of formal care increased them by 1.22, being in the financial control model treatment group increased them by 1.87, being in the basic model treatment group increased them by .34, and residency in sites with the financial control model in effect increased utilization by .19. It was appropriate to reject the null hypothesis that the coefficient estimates for all variables in the utilization equation at 6 months were equal to 0 (F[14,3,905] = 45.044, p < .01). The adjusted R2 statistic for this equation was .1359, indicating that 13.59% of the total variation in the logged utilization of formal in-home care at 6 months was explained by this model. Results from the OLS analysis of utilization of formal in-home care at 12 months indicated that being black, extremely severe ADL dependency status, prior use of formal in-home care at baseline, number of hours of formal in-home care, being assigned to The Gerontologist
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Age category Male Black Hispanic Extremely severe ADL Highly severe ADL Moderately severe ADL No support Hours of informal in-home care Prior visits Hours of formal in-home care Financial control treatment Basic treatment Model type Predicted visits
Utilization Satisfaction 6 and 12 months 6 and 12 months
OLS procedure for this estimation (Kennedy, 1985; Gujarati, 1988). 2SLS was used to "purify" the utilization variables (Y1) at 6 and 12 months of the influence of the disturbance associated with the satisfaction variables (e2) at 6 and 12 months (Gujarati, 1988). This was accomplished by regressing Y1 on all of the predetermined variables in the system (step 1), obtaining predicted values of Y1 (V1A), replacing the original values of V1 with V1 * in the satisfaction equations (Y2), and then applying ordered probit techniques on the satisfaction equations so transformed (Gujarati, 1988; Kennedy, 1985). Among the consequences of using 2SLS are that the correlation between predicted utilization (V1A) and the error associated with each satisfaction equation (e2) is removed (asymptotically), and the estimators thus obtained are consistent (Gujarati, 1988; Kennedy, 1985; Kmenta, 1986). Ordered probit techniques were used in this study to predict client satisfaction with care at 6 and 12 months; attrition-corrected ordered probit analyses were also performed to assess whether differential attrition from the sample significantly altered the study findings.
Table 3. Results from the OLS Estimation of Formal In-Home Service Utilization (Log) at 6 and 12 months
Variable Intercept Age category Male Black Hispanic Extremely severe ADL Highly severe ADL
No support Hours of informal in-home care
Coefficient at 12 months
(SE)
(SE)
-1.238** (.109) .013 (.018) -.087 (.062) .145* (.066) -.068 (.138) .192* (.086) .160* (.074) .077 (.080) .054 (.105) -.001 (.001)
Prior visits
.796** (.058)
Hours of formal in-home care
.002 (.001) 1.057** (.079)
Financial control treatment Basic treatment Model type
.293** (.079) .174* (.078)
Variable Black Extremely severe ADL Highly severe ADL Prior visits Financial control treatment Basic treatment Model type
-1.273** (.121)
.0056 (.021) .026 (.071)
.186* (.075) .008 (.159) .194* (.098)
Transformed estimate at 12 months
.156 .211 .173 1.22 1.878 .340 .190
.204 .215 NSb .779 1.53 .430 .303
"The calculation of the effects of dummy variables in semilogarithmic equations was based on Halvorsen and Palmquist (1980). When interpreting dummy variables in semilogarithmic equations (such as the two utilization equations analyzed above), it is necessary to correct for the fact that the derivative of the two dependent variables with respect to each dummy variable does not exist. Rather, it measures the discontinuous effect on each dependent variable when the dummy variable equals 1 versus when it equals 0. Therefore, it is necessary to transform the significant dummy variable coefficients in each of the two utilization equations. b NS = not significant at 12-month follow-up.
.124 (.082) .003
(.089) -.026 (.110)
.001 (.001) .576** (.065) .0045*" (.002) .929** (.089) .357** (.088) .265** (.099)
For the Client Satisfaction Equation
Note: F statistic at 6 months = 45.044 with 14, 3,905 df, p < .01; at 12 months = 27.809 with 14, 3,372 df, p < .01. Adjusted R2 at 6 months = .1359; at 12 months = .0998.
*p< .05; **p< .01.
either treatment group, and residence in sites with the financial control model directly increased utilization of formal in-home care at 12 months (see Table 3). Interpretation of the statistically significant coefficient estimates is similar to that presented for the 6month utilization equation. (For example, on average and when holding all else in the model constant, being in the financial control treatment group increased the number of formal in-home visits at 12 months by 1.53). (See Table 4 for all transformed significant coefficients at 12-month follow-up.) It was also appropriate to reject the null hypothesis that all coefficient estimates for all variables in the utilization equation at 12 months were equal to 0 (/T14,3,372] = 27.809, with p < .01). The adjusted R2 value for this model was .0998, indicating that 9.98% of the total variation in the logged utilization of formal in-home care at 12 months was explained by the model. Finally, when the predictors of utilization of formal in-home care at 6 and 12 months were compared, results were generally found to be consistent in terms of the magnitude, direction, and significance of the effects (see Tables 3 and 4). Vol.32, No. 6,1992
Transformed estimate at 6 months
Results from the ordered probit analysis at 6 months revealed that age, being male, both extremely severe and highly severe ADL dependency status, and hours of informal care/week directly increased client satisfaction at 6 months, whereas being Hispanic and having no informal support system directly decreased client satisfaction at 6 months (see Table 5). As expected, being in either treatment group did not directly affect client satisfaction when all else in the model was held constant. Finally, being black and having moderately severe ADL dependency status were not directly related to satisfaction with care at 6 months. The chi-square statistic for this equation was 99.183 with 12 df (p < .01), indicating that the null hypothesis that all coefficient estimates of the variables were equal to 0 could be rejected. The ordered probit estimation of satisfaction with care at 12 months revealed that age, being male, and each level of ADL dependency directly increased satisfaction with service arrangements at 12 months, whereas having no support system directly decreased satisfaction with care at 12 months (see Table 5). Predicted utilization of formal in-home visits at 12 months positively affected satisfaction with care at p < .094. Although being in the financial control model treatment group did not directly affect satisfaction with care at 12 months, being in the basic model treatment group did have a direct positive effect on satisfaction with care when all else in the model was held constant. The chi-square statistic for this model was 90.814 with 12 df, again indicating that the null hypothesis could be rejected. The predictors of client satisfaction with care were compared at 6 and 12 months. Although the numerical value of the parameters varied to some degree over the 6- to 12-month period, as would be expected, the magnitude and direction of the effects remained the same. When comparing the statistical
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Moderately severe ADL
Coefficient at 6 months
Table 4. Transformation of Significant OLS Dummy Variable Coefficients in Utilization Equations at 6 and 12 months'
attrition did not appear to play a significant role in the analysis, the results reported here remain in their uncorrected form.
Table 5. Results from Ordered Probit Estimation of Satisfaction with Care at 6 and 12 Months
Coefficient at 6 months
Coefficient at 12 months
(SE)
(SE)
Intercept
1.10*** (.081) .038*** (.014)
.913*** (.095) .045*** (.015)
.261*** (.048) -.020 (.051) -.266*** (.106) .200*** (.066)
.199*** (.052) -.030 (.055) -.185 (.115) .230*** (.072) .211*** (.059) .129*** (.062) -.246*** (.074) .0014 (.0009) .033 (.087)
Age category Male Black Hispanic Extremely severe ADL Highly severe ADL Moderately severe ADL No support Hours of informal in-home care Financial control treatment Basic treatment Predicted visits
.157*** (.056) .051 (.059) -.220*** (.076) .0021*** (.0007) .038 (.077) .0686 (.051) .099*** (.049)
.211*** (.056) .108* (.064)
Nofe: The chi-square statistic for satisfaction with care at 6 months = 99.183 with 12 df, p < .01; at 12 months = 90.814 with 12 df,p
