Predictors of Hospitalization Two Years After Treatment for First-Episode Psychosis
Abstract
Objective
This study examined the predictors of inpatient hospital admission after early intervention for psychosis services (EIPS) treatment. Specifically, the study examined the sociodemographic and clinical factors that were associated with hospital admissions in the two years after discharge from EIPS.
Methods
The characteristics of 231 patients enrolled in an EIPS program in Christchurch, New Zealand, between 2000 and 2005 were examined. Information about post-EIPS hospital admissions was obtained from the New Zealand Health Information Service. Poisson regression models were used to estimate bivariate and multivariate associations between number of post-EIPS hospital admissions and a range of predictors, including hospitalization history.
Results
A majority of patients (78%) were hospitalized before or during EIPS treatment, and 29% were hospitalized after the program. Older age, Māori ethnicity, a domestic partnership at referral, lower Global Assessment of Functioning scores, and hospitalization before and during treatment were significant predictors of the number of post-EIPS hospitalizations.
Conclusions
The results of the study suggest that the number of hospital admissions after participation in the EIPS program may be reduced by addressing issues related to the cultural appropriateness of treatment, engagement with partners and families, and overall adjustment and functioning and by efforts to limit hospitalization before and during EIPS treatment.
One of the key goals in the development of early intervention for psychosis services (EIPS) was to improve care during the early stages of psychosis and to shift the burden of treatment from inpatient hospitalization to outpatient programs. Individuals diagnosed as having psychotic disorders are more likely to be hospitalized than individuals with other psychiatric disorders (1,2), and these hospitalizations are more likely to occur earlier in the course of the illness (3). Furthermore, hospitalization has been shown to be associated with poorer recovery rates (4) and is a considerably more expensive treatment alternative, accounting for up to 80% of mental health service costs (5,6).
Evidence suggests that EIPS programs reduce rates of hospital admission while the patient is receiving the services (7,8), but some individuals who receive treatment with EIPS eventually require inpatient treatment. Less clear are the factors or processes that may be related to increased risks of hospitalization for individuals attending EIPS (9). In order to refine and improve EIPS, further information is needed concerning program and individual factors that increase the risk that an individual will require inpatient hospitalization after treatment.
Several studies have examined the clinical and sociodemographic factors associated with risk of hospitalization for first-episode psychosis. These factors include presence and persistence of positive symptoms, lack of clinical improvement, poor medication adherence, substance abuse, diagnosis of schizophrenia, earlier hospitalization, younger age at first admission, male gender, racial or ethnic minority status, unemployment, low social support, low social status, and homelessness (9–18). These characteristics are similar to those generally associated with hospitalization for psychosis (19,20).
An additional method for determining risk of hospitalization among EIPS patients may be to examine their clinical characteristics before and during EIPS treatment. These measures may include symptom scores or global measures of adaptation and functioning.
This study examined the predictors of inpatient hospital admission after treatment with EIPS for first-episode psychosis. Specifically, the study examined the sociodemographic and clinical factors that were associated with an increased number of hospital admissions among patients of an EIPS program in the two years after discharge. Data were collected as part of a prospective cohort study of hospital admissions after EIPS treatment.
Methods
The EIPS program that is the focus of this study is located in Christchurch, New Zealand. It aims to provide intensive, phase-specific, multimodal treatment to the patient and family members as close as possible to the time of onset of first-episode psychosis. The EIPS program is a multidisciplinary service that uses an assertive outreach approach for the first two years after a first episode of any form of psychosis (21).
Participants
The following inclusion and exclusion criteria were used for the study. Patients were between the ages of 16 and 30, had confirmed psychotic features, were presenting with psychosis for the first time, and had previously received treatment with antipsychotics for fewer than 12 weeks. There were no exclusion criteria, although patients whose IQ was below 70 or who were currently within the criminal justice system would not have been referred to the program.
Between February 2000 and February 2005, 282 patients were admitted to the EIPS program. A total of 38 patients who were not experiencing a first episode of psychosis and eight patients who were still in treatment in February 2007 were excluded from further analysis. The remaining 236 patients had the opportunity to receive treatment for first-episode psychosis for at least two years and were included in the study.
Assessment
Psychiatrist interview.
During interviews at baseline and discharge, psychiatrists used DSM-IV criteria to assess clinical diagnosis and substance abuse in the previous month (22). They also used the Positive and Negative Syndrome Scale (PANSS) to assess symptoms of psychosis (23) and the Global Assessment of Functioning (GAF) (24). At baseline only, psychiatrists also determined the duration of untreated illness—the time between onset of prodromal symptoms, defined as unusual behavioral symptoms (25), and onset of adequate treatment, defined as the start of antipsychotic medication. Insight at baseline and discharge was assessed by using the Schedule for the Assessment of Insight (26).
Case manager interview.
During interviews at baseline and discharge, a case manager collected information about patients’ demographic characteristics, including ethnicity (Māori versus non-Māori), age at referral, living arrangement at referral; receipt of government unemployment benefits (except student loans), and education. A case manager also administered the Quality of Life Scale (QLS) (27) and the Health of the Nation Outcome Scales (HoNOS). In addition, patients were asked to complete a custom-written “engagement with program” measure defined as “willingness to participate in program as appropriate.”
Measures of hospitalization.
Hospitalization history before and during treatment in the EIPS program and the total number of admissions in the two years after discharge from the EIPS program were obtained from clinical information in the New Zealand Health Information Service Mental Health Information National Collection (MHINC). Given that all psychiatric inpatient facilities in New Zealand are publicly funded, full information was available for all 24 district health boards, except the Capital and Coast District Health Board, which did not supply MHINC data between 2006 and 2009.
Sample retention
A total of 231 patients completed two years of EIPS treatment. Data were available for 204 (88%) to 219 (95%) baseline assessments and 196 (84%) to 202 (89%) discharge assessments. [The number of respondents for each factor is available online as a data supplement to this article.] Staff and administrative issues were responsible for the missing assessments. The sociodemographic characteristics of this cohort have previously been reported (21). Patients were young (mean±SD age=22.4±3.4), male (N=169, 73%), and single (N=213, 92%); were of European ancestry (N=159, 69%); lived with their parents (N=134, 58%); and were unemployed (N=139, 60%). Half (N=113, 49%) were diagnosed as having a history of substance abuse or dependence, predominantly involving alcohol and cannabis. The median duration of untreated illness was 180 days, and the mean length of treatment in the EIPS program was 82.7±40.4 weeks, with a range of seven to 175 weeks.
Over half the patients were admitted to the hospital before (N=134, 58%) and during (N=120, 52%) EIPS treatment, with 78% (N=180) hospitalized either before or during EIPS treatment. Finally, 67 (29%) patients were hospitalized in the two years after discharge from the EIPS program.
All patients accepted into the program are part of a longitudinal incidence cohort. Ethical approval was obtained from the New Zealand Health and Disability Ethics Committee.
Statistical analyses
To examine the extent to which demographic and other factors were predictive of post-EIPS hospitalization, a multivariate Poisson regression model was fitted to the data by using the total number of hospital admissions after discharge from the EIPS program as the outcome measure. The full set of demographic and other factors were entered into the model as predictors, with the exception of the measure of the total number of admissions during the EIPS program, which was redundant with the measure of the number of days admitted to hospital during the program. The model used methods of forward and backward variable elimination to identify a stable and parsimonious set of predictors. The final model included the set of statistically significant (p<.05) predictors, as well as terms controlling for gender, schizophrenia spectrum disorder diagnosis, duration of untreated psychosis, and positive PANSS symptoms at discharge. Although statistically nonsignificant, these terms were included to control for the possible influence of demographic and clinical factors that were not accounted for by the remaining predictors. With all covariates assigned their mean values, the final model was used to calculate the predicted number of admissions post-EIPS. We also report estimates of marginal effects derived from the fitted model, which represent the expected change in the dependent variable resulting from a one-unit change in the independent variable. All models were fitted by using SAS, version 9.2 (28).
In addition to the analyses described above, a further multivariate Poisson regression analysis was conducted in which the variables representing hospitalization before and during EIPS were replaced by a pair of dichotomous dummy variables representing a three-level classification of no hospitalization before or during EIPS, hospitalization before or during the EIPS program, or hospitalization both before and during the EIPS program.
Results
Hospitalization after EIPS
Seventy-one percent (N=163 of 231) of patients were not hospitalized in the two years after EIPS treatment. Among those who were hospitalized, the number of hospitalizations ranged from one (18%; N=41) to five (1%; N=3), with a mean of .51±.99. [A figure showing the observed frequency distribution of hospitalization after the EIPS program is available online as a data supplement to this article.]
Associations between hospitalization and predictors
A Poisson regression model was used to estimate the bivariate associations between number of post-EIPS hospitalizations and a range of predictors (Table 1). Several demographic, diagnostic, and other patient factors were significantly (p<.05) associated with an increased number of post-EIPS hospital admissions. They included ethnicity, older age at referral, a domestic partnership at referral, lack of educational qualifications, a diagnosis of schizophrenia spectrum disorder, increased number of days admitted before EIPS treatment, increased number of days admitted to hospital during EIPS treatment, and increased number of hospitalizations during EIPS treatment. In addition, males were marginally (p<.10) more likely to have had more post-EIPS hospitalizations.
| Admissions | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total(N=231) | 0(N=163) | 1(N=41) | 2(N=15) | 3(N=5) | >4(N=7) | Pa | |||||||
| Factor | N | % | N | % | N | % | N | % | N | % | N | % | |
| Demographic | |||||||||||||
| Male | 166 | 72 | 122 | 75 | 26 | 63 | 10 | 67 | 4 | 80 | 4 | 57 | .098 |
| Māori ethnicity | 40 | 17 | 23 | 14 | 7 | 17 | 5 | 33 | 2 | 40 | 3 | 43 | <.001 |
| Age at referral (M±SD) | 22.5±3.38 | 22.2±3.4 | 22.9±3.5 | .523.0±3.7 | 22.7±3.0 | 24.6±2.7 | .004 | ||||||
| Domestic partnership at referral | 18 | 8 | 11 | 7 | 2 | 5 | 4 | 31 | 1 | 20 | 0 | 0 | .029 |
| Without educational qualifications | 68 | 33 | 45 | 31 | 11 | 31 | 6 | 55 | 4 | 80 | 2 | 29 | .036 |
| Diagnostic | |||||||||||||
| Schizophrenia spectrum disorder | 131 | 57 | 88 | 54 | 23 | 56 | 12 | 80 | 2 | 40 | 6 | 86 | .019 |
| Duration of untreated illness (M±SD days) | 198.9±465.5 | 472.7±908.4 | 465.4±639.2 | 420.6±504.8 | 1112.5±1047.7 | 742.0±555.1 | .175 | ||||||
| Treatment | |||||||||||||
| Days admitted to hospital before EIPS treatment (M±SD) | 28.3±69.1 | 22.1±69.8 | 35.0±51.1 | 39.7±53.8 | 53.6±37.5 | 96.7±125.1 | <.001 | ||||||
| Days admitted to hospital during EIPS treatment (M±SD) | 39.1±78.9 | 30.4±68.5 | 61.8±110.0 | 59.7±75. 1 | 42.8±58.0 | 67.7±72.2 | <.001 | ||||||
| Hospitalizations during EIPS treatment (M±SD) | .9±1.3 | .7±1.0 | 1.2±1.5 | 1.9±1.5 | 2.2±1.8 | 1.9±1.8 | <.001 | ||||||
| Length of EIPS treatment (M±SD weeks) | 86.3±39.3 | 87.3±40.8 | 82.0±36.4 | 95.9±33.7 | 75.4±34.8 | 74.4±36.0 | .300 | ||||||
| Symptoms and functioning | |||||||||||||
| Unemployed at discharge | 79 | 39 | 47 | 33 | 17 | 52 | 9 | 60 | 4 | 80 | 2 | 33 | .002 |
| Substance abuse at discharge | 53 | 27 | 32 | 23 | 12 | 35 | 4 | 31 | 2 | 50 | 3 | 43 | .006 |
| Engagement with program (M±SD score)b | 2.9±1.3 | 3.0±1.3 | 2.9±1.1 | 2.5 ±.9 | 2.0±1.6 | 1.5±1.2 | <.001 | ||||||
| GAF at discharge (M±SD score)c | 68.8±14.5 | 70.9±13.9 | 67.2±14.8 | 63.9±11.4 | 53.8±25.0 | 53.0±7.7 | <.001 | ||||||
| HoNOS at discharge (M±SD score)d | 5.8±6.3 | 4.8±6.0 | 7.0±5.9 | 8.7±6.5 | 15.2±10.2 | 8.0±4.4 | <.001 | ||||||
| Insight at baseline (M±SD score)e | 8.8±4.3 | 9.2±4.4 | 8.8±3.9 | 7.0±4.4 | 7.8±1.0 | 4.8±1.5 | .001 | ||||||
| Insight at discharge (M±SD score)e | 9.5±4.2 | 10.0±4.1 | 9.1±4.1 | 7.1±5.1 | 9.8±2.2 | 6.4±3.8 | <.001 | ||||||
| PANSS positive symptoms at discharge (M±SD score)f | 10.1±4.4 | 9.5±3.8 | 10.9±5.3 | 11.8±4.4 | 11.0±8.0 | 12.7±6.3 | <.001 | ||||||
| QLS at discharge (M±SD score)g | 75.7±28.9 | 78.8±28.1 | 71.2±30.8 | 71.5±28.3 | 52.5±30.0 | 54.0±26.2 | <.001 | ||||||
a
Wald chi square test of linear association from Poisson regression
b
Possible scores range from 0 to 4, with higher scores indicating greater engagement with program.
c
GAF, Global Assessment of Functioning. Possible scores range from 0 to 100, with higher scores indicating a greater level of functioning.
d
HoNOS, Health of the Nation Outcome Scales. Possible scores range from 0 to 60, with higher scores indicating a lower level of functioning.
e
Measured by the Schedule for the Assessment of Insight. Possible scores range from 0 to 14, with higher scores indicating greater insight.
f
PANSS, Positive and Negative Syndrome Scale. Possible scores range from 7 to 49, with higher scores indicating greater levels of positive symptoms.
g
QLS, Quality of Life Scale. Possible scores range from 0 to 126, with higher scores indicating greater quality of life.
Several measures of symptoms and functioning were also significantly (p<.05) associated with an increased number of post-EIPS hospital admissions. These measures included unemployment at discharge, substance abuse at discharge, lower program engagement score, lower GAF score at discharge, higher HoNOS score at discharge, lower baseline and discharge insight score, lower QLS score at discharge, and higher PANSS positive-symptom score at discharge.
Risk factors for post-EIPS hospitalization
To examine the extent to which the factors listed in Table 1 were predictive of post-EIPS hospitalization outcomes, a multivariate Poisson regression model using the total number of post-EIPS admissions as the outcome measure was fitted to the data. Using the final fitted model to predict the number of post-EIPS admissions, with all covariates assigned mean values, resulted in an estimate of 1.80 admissions.
Table 2 shows the parameter estimates, standard errors, and tests of significance for each predictor in the final version of the multivariate model, as well as estimates of marginal effects and 95% confidence intervals. The Poisson regression analyses revealed that after control of all other factors, several measures were statistically significant predictors of the total number of post-EIPS hospital admissions, including the total number of days admitted pre-EIPS, the total number of days admitted during EIPS treatment, a domestic partnership at referral, Māori ethnicity, older age at referral, and lower GAF score at discharge. In general, these results suggest that demographic factors (age, ethnicity, and domestic partnership), hospitalization history, and a general measure of functioning were the primary predictors of the total number of post-EIPS hospitalizations.
| Multivariate model | Marginal effects | ||||
|---|---|---|---|---|---|
| Predictor | Unstandardizedcoefficient | SE | pa | Estimate | 95% CI |
| Male gender | –.165 | .283 | .559 | –.080 | –.267 to .167 |
| Māori ethnicity | .761 | .277 | .006 | .442 | .110 to .775 |
| Age at referral to EIPS | .103 | .034 | .002 | .048 | .023 to .072 |
| Domestic partnership | .687 | .320 | .032 | .455 | .011 to .899 |
| Schizophrenia spectrum disorder | –.356 | .282 | .206 | –.175 | –.346 to –.004 |
| Duration of untreated illness | –.000 | .000 | .685 | –.000 | –.000 to .000 |
| Days admitted to hospital before EIPS treatmentb | .005 | .002 | .002 | .002 | .001 to .003 |
| Days admitted to hospital during EIPS treatment | .003 | .001 | .005 | .001 | .001 to .002 |
| GAF score at dischargec | –.030 | .011 | .009 | –.014 | –.022 to –.006 |
| PANSS positive symptom score at discharged | –.007 | .031 | .822 | –.003 | –.025 to .018 |
a
Wald chi square
b
EIPS, early intervention for psychosis services
c
GAF, Global Assessment of Functioning
d
PANSS, Positive and Negative Syndrome Scale
Sensitivity analysis
As noted in Methods, an additional analysis was conducted in which a pair of dichotomous dummy variables representing a three-level classification of hospitalization was substituted for the variables representing the number of days of hospitalization before and during EIPS treatment. The results of this analysis were congruent with those reported in Table 2, suggesting that the study results were robust after substitution of alternative specifications of the measures of prior hospitalization.
Discussion
This study examined the sociodemographic and clinical predictors of posttreatment hospitalization in a sample of EIPS patients. Approximately 29% of the sample was hospitalized in the two years after discharge from the program. This rate was lower than the rate reported for similar samples of individuals who had received EIPS treatment (1,29,30).
Bivariate associations between the sociodemographic and clinical predictors and the number of post-EIPS hospital admissions showed that several demographic and personal factors were significantly (p<.05) related to a greater number of hospital admissions, including Māori ethnicity, older age at first referral, diagnosis of schizophrenia spectrum disorder, unemployment, lower levels of education, domestic partnership at referral, lower scores on clinical measures of functioning, substance abuse, poorer quality of life, a higher level of positive symptoms, and lower levels of program engagement. Admission to the hospital both before and during EIPS treatment was significantly (p<.001) related to a greater number of post-EIPS hospital admissions. These findings are congruent with previous research examining the risk factors for hospitalization among first-episode psychosis patients more generally (9–18). In addition, the finding that Māori ethnicity was related to an increased number of post-EIPS hospital admissions was in agreement with previous findings suggesting that rates of hospitalization are higher for this population (31,32).
Multivariate modeling showed that only a subset of sociodemographic and clinical predictors were statistically significantly (p<.05) related to the number of post-EIPS hospital admissions. These factors included a domestic partnership at referral, Māori ethnicity, older age at referral, lower GAF scores, and hospitalization both before and during attendance at the EIPS program. Of particular note, the analyses showed that most of these predictors were related to either demographic characteristics or hospitalization measures, with only a single clinical indicator (GAF score) being associated with increased number of post-EIPS hospital admissions. The results of a sensitivity analysis suggest that these findings were robust after substitution of alternative specifications of the measures of prior hospitalization. The findings of a significant association between hospitalization both before and during the EIPS program and the number of post-EIPS admissions to hospital are also in agreement with a wide range of research that has shown linkages between earlier hospitalization and increased risks of later hospitalization (15,33–35).
The overlap between the factors that predict hospitalization among patients experiencing psychosis and patients having their first episode suggests that these characteristics and measures may help to identify patients who are likely to have a greater number of hospital admissions after EIPS treatment. On the other hand, the findings of the multivariate analysis suggest that although clinical indicators of symptoms and functioning may help to identify individuals who are likely to have a greater number of hospital admissions after the program, the factors that most strongly influenced the number of post-EIPS hospital admissions were related to demographic characteristics and prior experience of hospitalization. One implication of this finding is that EIPS services may be enhanced by finding ways to improve outcomes for individuals whose demographic characteristics are associated with greater vulnerability to hospitalization. For example, program improvements may be put in place to assist members of ethnic minority groups to have better outcomes, perhaps through the development of services that are more culturally appropriate (32,36). In addition, the finding that individuals in a domestic partnership were more likely to have a greater number of post-EIPS hospital admissions may indicate the need to adopt approaches to treatment that involve a wider family unit (37,38).
Although a direct causal link was not established, it may be speculated that the observed linkages between hospitalization both before and during the EIPS program and post-EIPS hospitalization suggest that reducing exposure to hospitalization early in first-episode psychosis treatment leads to a smaller number of hospitalizations after the conclusion of treatment. However, it is unclear whether the observed linkages indicate that individuals who were hospitalized before or during the program were less well than those who were not hospitalized, suggesting that hospitalization may be a proxy measure for an unmeasured variable pertaining to “wellness” or that earlier hospitalization had some iatrogenic effect that increased the number of hospitalizations after treatment. Indeed, it may be the case that both of these explanations are true to some degree. Further research is required to disentangle the extent to which relative wellness or iatrogenic effects contribute to increasing the number of hospitalizations after treatment of first-episode psychosis.
Conclusions
This study had a number of limitations, including lack of interrater reliability information and the use of consensus measures for the clinical measures and missing data for post-EIPS hospitalizations for ten to 12 patients from the original cohort, which may have reduced the precision of the analyses. Despite these limitations, however, the results of this study suggest that several factors are related to an increased number of hospital admissions after EIPS treatment, including ethnic minority status, older age at referral, relationship status, general functioning, and hospitalization both before and during EIPS treatment.
Acknowledgments and disclosures
This article was made possible through continued financial support for the research position at Totara House from the Mental Health Division of the Canterbury District Health Board. MHINC data were provided by Jane Perrott from New Zealand Health Information Service and John Beveridge from the Canterbury District Health Board. The authors also thank the Totara House team for their commitment to completing the measures.
The authors report no competing interests.
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