Predicting Rehospitalization of Persons with Severe Mental Illness.
Despite the early optimism that accompanied attempts at deinstitutionalization, a large number of these individuals did not improve in functioning, nor did they adapt as well as originally hoped to the community after being discharged (Lamb, 1982; Stein & Test, 1982;). The high rate of SPMI readmissions to psychiatric facilities characterizes their inability to remain in the community for extended periods of time (Bassuk & Gerson, 1978). However, some individuals with SPMI are not repeatedly hospitalized and others break out of this "revolving-door cycle" (Caton, 1984; Stein & Test, 1982; Talbott, 1979; Krebaum, 1998; Turner, Korman, Lumpkin, & Hughes, 1998). Increased emphasis on environment as a critical factor in the recovery of persons with SPMI has led to greater success in their adjustment to living in the community. This is particularly evident among those advocating a rehabilitation model which stresses that the environment must be altered in order to accommodate a person's disability and support optimal functioning (Anthony & Blanch, 1989; Anthony, Cohen, & Farkas, 1990).
Persons with SPMI are always at risk for rehospitalization. It has been estimated that approximately 30-40% will be rehospitalized six months post discharge, rising to 35-50% after one year, and 65-75% after five years (Anthony et al., 1990). The questions of interest are when is rehospitalization likely to occur, and what factors predict it? Such findings would be important to those who design and implement community-based treatment programs, as well as to mental health researchers and clinicians interested in improving the quality of life for persons with SPMI and addressing issues of relapse.
Research findings have established a number of relevant variables associated with repeated hospitalizations and the difficulty persons with SPMI have in remaining in the community (Gooch & Leff, 1996; Hoffmann, 1994). For example, one study suggested that the best explanatory factors for readmission were male gender, being younger, a high number of previous admissions, diagnosis of manic-depressive psychosis, and living in a non staffed group home (Dayson, Gooch, & Thornicroft, 1992; Thornicroft, Gooch, & Dayson, 1992). Another study indicated that the best predictors of rehospitalization were medication noncompliance, comorbid alcohol abuse, types and extent of outpatient service use, access to care, quality of life, ethnicity, and gender (Sullivan, Wells, Morgenstern, & Leake, 1995).
The role that gender plays in recidivism is unresolved as one study found higher rates for females (Setze & Bond, 1985), while five studies found males more likely to be rehospitalized (Bene-Kociemba, Cotton, & Frank, 1979; McCrainie & Mizell, 1978; Miller & Willer, 1976; Solomon, Davis, & Gordon, 1984; Sylvester & Bean, 1989). On the other hand, 14 studies found no relationship between gender and recividism (Appleby & Desai, 1987; Axelrod & Wetzler, 1989; Byers & Cohen, 1979; Franklin, Kitteredge, & Thrasher, 1975; Geller, Fischer, Simon, Wirth-Cauchon, 1990; Green, 1988, Hughes, Joyce, & Stanley, 1987; Junginger, 1990; Miller, Beck, & Fraps, 1984; Rosenfield, 1991; Sands, 1984; Solomon, et al., 1984; Solomon, Gordon, & Davis, 1984; Snowden & Holschuch, 1992).
However, few studies have examined variables that predict the timing of rehospitalization, as well as factors that may predict longer versus shorter time in the community for persons with SPMI. Additionally, no follow-up data or tests of the predictive accuracy of relevant variables are currently available.
This study seeks to fill in some gaps in the knowledge and understanding concerning persons with SPMI by employing multivariate Kaplan-Meier (Kaplan & Meier, 1958) and Cox (1972) regression statistical procedures. The analytical task of this study was to develop risk-assessment models from which a subject's rehospitalization risk can be credibly assessed. Little is known about using risk-assessment and classification systems in decision making with persons with SPMI, especially for those with multiple hospitalizations. Moreover, those systems that are in use are flawed because of their dominant concern with service needs and inattention to risk of rehospitalization. Determining these models may provide a pragmatic approach for establishing treatment protocols and rehabilitation and supervision requirements for persons with SPMI that may help them to have greater success in remaining in the community. Using a four-year follow-up period, this study examines the length of stay in the community for a sample of SPMI individuals and identifies specific predictor variables related to diagnosis, severity of illness, number of prior hospitalizations, community placement, family support, race, and gender, etc. Two models were constructed which best fit the data. Each model provided a measure of the risk of rehospitalization based on the predictor variables in the model.
This study was designed to focus on the length of time following discharge until a patient was re-admitted to the hospital. The survival analysis statistical methods are well suited to the study of rehospitalization (Maltz, 1984; Schmidt & Witte, 1988). These methods allow for the tracking of time as well provide more information than traditional "static" models of rehospitalization which merely measure the proportion of subjects who are rehospitalized within some fixed period of time - typically a Chi-square analysis of frequency.
"Survival analysis" was designed for longitudinal data based on the occurrence of events where an event is defined as a qualitative change that can be situated in time. It only requires that there be a transition from one discrete state to another where one just needs to know when the change occurs. Although historically used by insurance companies as actuarial tables, it has also been a tool for predicting terminal illness and treatment intervention (Berkson & Gage, 1950; Cutler & Ederer, 1958). The name is somewhat unfortunate in that it encourages a highly restricted view of the potential applications of these methods.
For example, it is very useful to the social and natural sciences to study the onset of disease, course of treatment outcome (Emslie, Rush, Weinberg, Gullion, Rintelmann, Hughes, 1997), earthquakes, births, marriages, retirements, arrests, and particularly the outcome of various rehabilitative treatment interventions, etc. Different fields tend to call these analyses by different names: reliability analysis (engineering); failure time analysis (engineering); event history analysis (sociology); duration analysis (economics); and transition analysis (economics). The recent statistical texts for the social sciences still refer to it as survival analysis, however (Allison, 1995), as do the current versions of commercially available computer statistical packages (Green, Salkind, & Akey, 1997). There are many variants of survival analysis dependent on the nature of the data, potential covariates, as well as the number of groups being compared (e.g., life tables, Kaplan-Meier estimators, proportional hazards regression, competing risk models, exponential regression, log-normal regression, and discrete-time methods). Excellent in-depth reviews of survival analysis are available (Allison, 1995; Altman, 1991; Collett, 1994; Peto, et al., 1976, 1977).
The study sample consisted of 93 male and 70 female subjects ranging in age from 18 to 64 years (M = 36, SD = 10.3). Seventy-seven subjects were African American, 75 were Caucasian Americans, seven subjects were Hispanic Americans and four were Asian Americans. Inclusion criteria for each participant in this study required that each subject was at least 18 years of age, discharged to a residence within Dallas County, and met criteria for the mental health "priority population," as defined by the Texas Department of Mental Health and Mental Retardation (TDMHMR, 1994). The priority population for mental health services consisted of SPMI adults with schizophrenia, major depression, or manic depressive disorder, or other severely disabling mental disorders which require crisis resolution or ongoing and long-term support and treatment.
Subjects were recruited as they became eligible for discharge from the state hospital. Subjects were interviewed and hospital medical records were reviewed to obtain information relevant to independent variables. Variables included demographic data, duration and severity of psychopathology, types of support, as well as data reflecting the conditions under which a subject was admitted to the hospital and the type of discharge residence. Each subject was followed for a period of approximately four years from the date of discharge. Subjects were tracked through the Client Assignment and Registration (CARE) system, a statewide data communications network that links state schools, state hospitals, state centers, and community mental health and mental retardation (MHMR) centers to a central database that is the depository for statewide client information. It provides a cumulative account of each client's use of mental health and/or mental retardation services. Additionally, the CARE system provides information regarding location and dates of hospitalizations. At the conclusion of the follow-up period, the CARE System database was reviewed for each subject's activity during that period to determine if a subject was rehospitalized. If a subject was rehospitalized during the follow-up period, the date of readmission was indicated in the CARE system and an exact length of time spent in the community prior to rehospitalization was calculated.
The Kaplan-Meier [K-MI (Kaplan & Meier, 1958) method was initially used to determine the survival function for the entire sample and preliminary univariate survival analyses were conducted. A multivariate K-M using Cox proportional hazards regression (Cox, 1972) was then performed using a stepwise selection procedure that included all of the variables in a single analysis. The modeling strategy added or deleted predictor variables based on a change in the fit of the model that was significant at p [is less than] .10 as reflected by change in the log likelihood (Collett, 1994). The use of p [is less than] 0.10 helps to minimize making a Type II error of not including a variable that may actually contribute to the overall predictor model. Variables were checked for the validity of the proportional hazard assumption. The final models were checked for interactions and the presence of outliers. This study was limited to a four-year follow-up. Sixty-two observations were censored (censored refers to incomplete survival time data in that the exact survival time is not known due to a subject being lost to follow-up or a subject still being followed at the end of the study period and in the tracking system).
The regression method proposed by Cox (1972) allows for multiple predictor variables to be evaluated simultaneously. Cox regression was done two ways; with and without the residential program and case management variables. This was done for two reasons. First, residential program and case management differed from other independent variables in that they were not necessarily measured at baseline but at some point during the follow-up period. The decision to assign a consumer to a residential program and/or case management services is made based on an assessment of intensity for need of special assistance (TDMHMR, 1996). Therefore, assignment to case management and/or a residential program may be related to some other factor (i.e., a greater degree of disability) that was not manifest at baseline and, subsequently, assignment to case management and/or to a residential program may be indicative of patients that have significant differences. The second reason was since residential program and case management were very strong univariate predictors, it was possible that their presence might "wash out" the effects of the other predictors if they were highly correlated with each other.
The follow-up time ranged from 1,104 days (37 months) to 1,632 days (54 months), with a median follow-up time of 1,362 days (45 months). During the follow-up period, 101 subjects (62%) were rehospitalized. The survival time data for 62 subjects were censored. Twenty-nine of the 62 censored observations consisted of those subjects who remained in the community (47%) at the end of the study. The survival times for these subjects were censored at the last day of the study. Thirty-three of the 62 censored observations consisted of subjects that were indicated as inactive status in the CARE system which signifies that such individuals are not currently being served by the MHMR system (approximately 20% of the total study sample). The typical reasons for status as inactive are that individuals do not require continuing services, or they may have moved out of state or dropped out of treatment. In any case, subjects on inactive status did not have any contact with the state mental health system since their last assignment was closed and were considered lost to follow-up.
The KM survival estimate was calculated for the entire sample. The survival function for the entire sample is presented in Figure 1. The median survival time was 365 days. Following (KM) analyses of the independent variables, the simultaneous effect of multiple variables on survival patterns was examined (Peto et al., 1977).
When residential program (restricted to community based services operated by local or state MHMR) and case management were excluded, the final model contained three variables: race, number of prior hospitalizations, and type of discharge residence. This model [[chi square] (6, N = 163) = 18.49, p [is less than] .01] is presented in Table 1. The risk of return to the hospital was 1.46 for African Americans compared to Caucasians. For each increase of five prior hospitalizations, the risk of return to the hospital was 0.45. Subjects discharged to live with a parent or relative, in a group home, or in a supported housing environment, had 0.45 the likelihood of returning to the hospital in comparison to subjects discharged to live in their own house or apartment. Subjects discharged to live in a nursing home or personal care home had 0.63 of the likelihood of returning to the hospital as those subjects discharged to live in their own house or apartment. Individuals who were discharged to jail had 0.55 the risk of return as subjects discharged to live in their own house or apartment. Being discharged to a boarding home, hotel, or shelter did not differ from those discharged to live in their own house or apartment in terms of the likelihood of returning to the hospital. Severity of psychopathology, diagnosis, and degree of family support did not predict who would remain the longest in the community without rehospitalization.
When residential program and case management were included in the analysis, the only significant predictors were race and residential program. The second model [[chi square] (3, N = 163) = 89.1, p [is less than] .0001) is presented in Table 2. The model indicates that compared to Caucasians who were not assigned to a residential program (reference category), African American subjects who were not assigned to a residential program had 4.3 times greater risk of return to the hospital. However, the difference between racial groups lessens when assignment to a residential program has occurred. Caucasians who were assigned to a residential program were 17 times more likely to return to the hospital than Caucasians who were not assigned to a residential program. Similarly, African Americans assigned to a residential program were 16.8 times more likely to return to the hospital than Caucasians who were not assigned to a residential program.
During the four year follow-up period, 101 subjects were rehospitalized. When the rate of rehospitalization was examined at 6 months (31%), 12 months (46%) and at approximately four years (62%) of follow-up, the recidivism rates demonstrated remarkable consistency with the base rates for rehospitalization among persons with severe and persistent mental illness (SPMI) reported by others (e.g., Anthony, et al., 1990; Goering, Wasylenki, Lancee, & Freeman, 1984). The sample used in this study appears comparable to the samples used in other studies. Demographic characteristics of the study sample such as age, gender, and ethnicity as well as frequency and length of time before rehospitalization were also consistent with persons with SPMI described in other studies (e.g., Bachrach, 1988; Caton, Koh, Fleiss, Barrow, & Goldstein, 1985). With the exception of race, demographic factors had little impact on significant differences in survival expectancy. With the data well balanced with respect to African Americans (47%) and Caucasians (53%), the finding that Caucasians had a median survival time of more than twice that of African Americans (617 days versus 307 days) is noteworthy. Although demographic variables have generally not been associated with rehospitalization rates (Avison & Speechley, 1987), it has been demonstrated that African Americans are at greater risk of becoming a non-attender to community mental health center aftercare services (Moseley, 1994). Other differences between African Americans and their Caucasian counterparts have been observed in prevalence rates of mental disorders, likelihood of involuntary commitment, representation in research samples, presentation of psychiatric symptoms and resulting diagnoses, and differing attitudes toward seeking treatment and care (Adebimpe, 1994; Adebimpe, Chu, Klein, & Lange, 1982).
Shorter survival time among African Americans might also be viewed as an indication that urban African Americans may be quicker to access psychiatric services as a cultural phenomenon (Neighbors & Taylor, 1985) and have higher utilization rates of psychiatric emergency room services (Claassen, et al., 2000a; Claassen, Hughes, Gilfillan, Roose, & Basco, 2000b). Although higher utilization rates of services can carry a negative connotation for a group that "taxes" or "burdens" the system, higher utilization rates by African Americans may be an indication of cultural differences in help-seeking behavior or availability of resources. Exit interviews in an extensive study of psychiatric emergency room use indicated that such services were frequently viewed as the first choice of service utilization because of 24-hour, seven-day-a-week convenience, location, and no need for appointments (Claasen, 1995; Claassen, et al., 2000a, 2000b). The finding that African Americans have less than half the median survival expectancy and a greater risk of rehospitalization, indicates the need for further research to understand the reasons for less frequent hospitalizations between African Americans and their Caucasian counterparts.
The variable that had the most powerful effect on remaining in the community was assignment to a residential program. The strength of this variable was such that when it was included in the multivariate analysis, the resulting model consisted of only race and residential program as predictors. All other variables dropped out. In order to address the question of why assignment to a residential program is such a powerful factor, further explanation is necessary.
A residential program as defined in this study was a community-based service operated by the local or state MHMR system or a third party under contract to the local or state MHMR. A decision to assign a person to a residential program was a clinical decision made by local mental health professionals. Additionally, assignment to a residential program differed from other variables in that it was not measured at baseline, but at some time during the follow-up period. This poses some caveats to interpretation and prediction because predictions of time in the community cannot be made from baseline measurements. Rather, the process is a retrospective stratification (Peto et al., 1977) where it is determined that once a person is assigned to a residential program, his/her likelihood of return to the hospital increases substantially. System administrators need to be made aware of these more high risk facilities and consider additional resource and program evaluation to attempt to minimize the risk for these individuals. The risk ratio of 17.0 for Caucasians and the almost equivalent risk ratio of 16.8 for African Americans are evidence of this outcome. However, little in the way of predicting time to rehospitalization can be made until a subject is assigned to a residential program.
Additionally, there is little information about what happens to individuals to get to a point where a decision is made to assign them these services. The decision to assign someone to a residential program is a clinical decision made by local mental health professionals (TDMHMR, 1996). Implied in this determination is that the level of functioning and disability and the level of intensity for need of special assistance has been identified by a mental health professional. In this light, assignment to a residential program seems to be related to some other factor that is being registered but not directly evaluated (i.e., a greater degree of disability) and, subsequently, assignment to a residential program identifies patients who may be fundamentally different from those persons who are not assigned to these programs.
A fundamental requirement in the management and rehabilitation of persons with severe and persistent mental illness is an appropriately supportive and structured living arrangement (Baxter & Hopper, 1982; Lamb, 1981). Other interventions are of little benefit until people feel secure and are stabilized in their living situations. As such, an individual's residence with appropriate structure and support may be the greatest factor in successful adaptation to prolonged survival in the community. When individuals fail to remain in the community, it seems that maintaining a living arrangement is one of the first things to have deteriorated. Assignment to a residential program tends to reflect a lower level of functioning and a greater intensity of needs. Consequently, these services may reflect the need for help in securing and maintaining a place to live along with the supportive services that typically accompany them. Case management and assignment to a residential program were strong predictors of being readmitted to the hospital. However, this may not be due to actual prediction but, rather, these variables may represent a first step toward rehospitalization. The current study does not have sufficient information to distinguish who is likely to receive either of these services after discharge.
The significant finding that the type of residence to which a subject was discharged with regards to the time an individual remained in the community is consistent with Segal & Avirams' (1978) notion that environmental characteristics are more predictive of outcome than personal characteristics. Outcome studies of persons with psychiatric disabilities conclude that persons with severe mental illness value independence and productivity more than any other treatment outcome (Anthony & Blanch, 1989; Wilson, 1992). This has led to placing greater emphasis on housing and support needs, making consumers' preferences a priority in developing housing and community supports for people with SPMI. Consumers of mental health services value freedom and autonomy, stability, security, and privacy, and consistently report a preference for independent living in their own apartment or house and not in residential mental health programs or facilities (Tanzman, 1993). Consumers who experience these conditions also report higher degrees of satisfaction (Anthony & Blanch, 1989; Tanzman, 1993; Wilson, 1992). The largest proportions of subjects (33%) in this study were discharged to live in their own homes. Although individuals discharged to their own home or residence represented the largest proportion of subjects across the five types of discharge residences, their median survival time (434 days) was exceeded by more than two times by subjects discharged to live with a parent or relative, in a group home, or in supported housing (1,155 days), and those discharged to jail. Discharge to jail or prison does not provide clear comparisons, as legal entanglements are a confounding factor for survival time, especially if a subject was incarcerated for an extended period of time. In addition, if a subject received psychiatric services while incarcerated, this can be viewed as a type of "hospitalization" which may have artificially increased the median survival time of persons discharged from the hospital to jail.
The longest median survival time (1,155 days) was demonstrated by subjects discharged to live with a parent or relative, in a group home, or in supported housing and twice that of those discharged to their own home or apartment. Individuals who live in a parent's house or relative's home, group home, or supported housing type of residence may have greater levels of support, as well as support in closer proximity, than individuals who live independently. This may be a reason for the longer survival time of these types of residence over discharge to one's own home or apartment. Alternatively, living independently may be an indirect reflection of lack of family support due to a variety of reasons. It is not uncommon to find that SPMI individuals often "burn the bridges" to ongoing family relationships (Doll, 1976; Hatfield, 1986).
These findings suggest that although independent living arrangements are important to the consumer and may appear to be more cost effective, there is a trade-off in the tendency to also have shorter duration of time in the community before being rehospitalized. The calculated risk ratios support this conclusion, that compared to one's own home or apartment, the risk of return to the hospital is less for all other types of residences, with the exception of subjects discharged to a boarding home, hotel, or shelter.
Board-and-care homes, single-room-occupancy hotels, and shelters in this study were not affiliated with a mental health facility and were not therapeutically oriented. Staffs were usually nonprofessional, and care was custodial rather than therapeutic. As such, the level of support was very low, if any existed at all. This study further emphasizes the importance that living arrangements play in the survival outcome of persons with SPMI and reiterates the importance of residential planning as a cornerstone of comprehensive care for persons with SPMI (Bachrach, 1994).
This study demonstrated the application of K-M and Cox regression statistics to predict rehospitalization of the SPMI where exact length of times are taken into account. Findings suggest that specific factors and variables can be identified that predict how long an individual remains in the community and subsequent rehospitalization. The knowledge gained in this study can be generalized and used to serve persons with SPMI with greater precision and effectiveness by providing mental health professionals with information that can be used to increase the time between admissions. Another area that requires further investigation is examining the factors that lead to a person being assigned to case management and to a residential program. Identification of these factors would allow prediction from baseline. Nonetheless, the findings from this study provide a foundation from which to further understand the needs of persons with SPMI and how best to help them.
This research was supported in part by the Mental Health Connections, a partnership between Dallas County Mental Health Mental Retardation and the Department of Psychiatry of the University of Texas Southwestern Medical Center - Dallas. Funding is from the Texas State Legislature and the Dallas County Hospital District.
The project could not have been completed without the cooperation and assistance of the Dallas County MHMR administration and staff, the Superintendent's Office and staff of Terrell State Hospital, and the members of the Research Core of Mental Health Connections. We would like to express our gratitude to these organizations for their support in helping make this project a success. A portion of this work served to meet the requirements of Doctor of Philosophy for the first author supervised by the second author.
Table 1: Cox Regression Model Fitted to Significant Variables from All Subsets (Excluding Residential Program and Case Management)
95% Risk Confidence Variable(1) B SE B Ratio Interval Discharge residence: Parents' house/apt, -0.805 0.291 0.45 0.25-0.79 relative's home/apt; group home; supported housing. Boarding home/ 0.018 0.277 1.01 0.59-1.75 hotel/shelter Nursing home; personal -0.462 0.399 0.61 0.29-1.38 care home Jail or prison -0.590 0.359 0.55 0.28-1.12 Number of previous 0.144 0.088 1.16 0.97-1.37 Hospitalizations(2) Race 0.38 0.21 1.46 0.96-2.21
(1) Discharge Residence of own home or apartment was used as the reference category.
(2) Number of previous hospitalizations is in increments of five.
[chi square] (6, N = 163) = 18.5, p<.01
Table 2: Cox Regression Model Fitted to Significant Variables from All Subsets (Including Residential Program and Case Management)
95% Risk Confidence Variable(1) B SE B Ratio Interval Caucasian Residential program 2.834 0.445 17.01 7.10-40.73 African American No residential program 1.458 0.459 4.30 1.75-10.56 Residential program 2.819 0.450 16.76 6.93-40.51
(1) Caucasian and not assigned to a residential program was used as the reference category.
[chi square](3, N = 163) = 89.1, p<.0001
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Masaki M. Yamada Maurice Korman
University of Texas Southwestern Medical Center at Dallas
Carroll W. Hughes University of Texas Southwestern Medical Center at Dallas and Terrell State Hospital
Carroll W. Hughes, Ph.D., Director Psychology, Terrell State Hospital, P.O. Box 70, Terrell, Texas 75160 Email: firstname.lastname@example.org
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|Author:||Hughes, Carroll W.|
|Publication:||The Journal of Rehabilitation|
|Date:||Apr 1, 2000|
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