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Predicting the length of hospital stay of psychiatry patients using signal detection analysis
Psychiatry Research 210 (2013) 1211–1218
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Predicting the length of hospital stay of psychiatry patients using signal detection analysis Fase Badriah a, Takeru Abe a,b, Yoshihiro Nabeshima a, Kouji Ikeda c, Kenji Kuroda c, Akihito Hagihara a,n a
Kyushu University, Graduate School of Medicine, Department of Health Services Management and Policy, Higashi-ku, Fukuoka 812-8582, Japan Waseda University, Faculty of Human Sciences, Department of Health Sciences and Social Welfare, 2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan c Hannan Hospital, 277 Handa-minamino-cho, Naka-ku, Sakai City, Osaka 599-8263, Japan b
art ic l e i nf o
a b s t r a c t
Article history: Received 11 July 2012 Received in revised form 9 September 2013 Accepted 15 September 2013
In Japan, the length of hospital stay (LOS) at psychiatric institutions often exceeds a year, and factors related to such stays have been identified. However, we do not know how multiple patient, hospital, and physician factors interact to determine LOS. Patient data were collected from a psychiatric hospital in Osaka, Japan. We developed subgroups, which were determined by interactions related to LOS using signal detection theory. In acute or emergency wards, five factors related to LOS were identified, and subjects were categorized into six subgroups. The indices obtained by the five factors ranged 2.49–3.47 for odds ratio, 0.47–0.84 for sensitivity, 0.40–0.76 for specificity, and 0.52–0.71 for positive predictive value. In general wards, five factors related to LOS were identified, and subjects were categorized into six subgroups. The indices obtained by the five factors ranged 3.02–5.36 for odds ratio, 0.58–0.86 for sensitivity, 0.37–0.68 for specificity, and 0.85–0.92 for positive predictive value. Psychiatrists who have been practicing longer in acute or emergency wards appear to have significantly longer stay of patients, and older or more severe patients tend to be in need of longer inpatient care. Our results provide findings that may be helpful in decreasing LOS at psychiatric hospitals. & 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Outcome Prediction Signal detection analysis
1. Introduction In recent years, medical costs have been increasing because of the aging society and the progress of medical technologies in Japan. Revenue has been decreasing due to the economic recession. Thus, it is crucial to decrease medical costs. As the first step toward this end, it is important to understand the characteristics of patient care. Evaluated in terms of widely accepted measures such as the length of stay (LOS) and medical costs (Söderberg et al., 2005), psychiatric care in Japan has the following characteristics. First, the number of patients diagnosed with “mental and behavioral disorders” was 301,400 in 2007, and this number was the largest among all medical diagnosis groups (Ministry of Health, Labour and Welfare, 2008a, 2008b). Second, the mean length of stay (LOS) of patients in psychiatric institutions was 290.6 days, and this was the longest among all medical diagnosis groups in Japan (Ministry of Health, Labour and Welfare, 2008a, 2008b).
n
Corresponding author. Tel.: þ 81 92 642 6950. E-mail addresses: [email protected] (F. Badriah), [email protected] (T. Abe), [email protected] (Y. Nabeshima), [email protected] (K. Ikeda), [email protected] (K. Kuroda), [email protected] (A. Hagihara). 0165-1781/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.psychres.2013.09.019
Additionally, the mean LOS of patients in psychiatric institutions in Japan is longer than that of psychiatric patients in other countries (Oshima et al., 2003), such as 3–4 months as the mean LOS in Israel (Levine and Rbinowitz, 2009; Levine et al., 2011) and Denmark based on national registries in both countries. Thus far, patient, psychiatrist, and institutional characteristics have been reported as factors related to LOS in psychiatric care. Patients who had higher Global Assessment of Functioning (GAF) scores or who did not require restraint had shorter lengths of institutional stay than those who had lower GAF scores or required restraint (Abe et al., 2011). Patients who used community care, such as services offered by daycare centers, were found to have a shorter LOS than patients who did not use community care (Imai et al., 2005). The type of psychiatric condition also played a role in LOS; vascular dementia, mental retardation, and schizophrenia were associated with longer LOS in Japan (Imai et al., 2005). Moreover, male and older patients (Fujita and Sato, 2004; Chung et al., 2009), or male and forensic patients (Levine, 2008), were at increased risk of longer stay at a psychiatric institution. Patients admitted involuntarily tended to stay longer than patients who were voluntarily admitted (Imai et al., 2005; Abe et al., 2011). As for psychiatrist characteristics, it has been reported that lower quality medical care was associated with older physicians
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in all specializations, including psychiatry (Choudhry et al., 2005). Older psychiatrists were less uniform than younger psychiatrists in prescribing medications for mood disorders (West et al., 2000). Male psychiatrists were more likely to prescribe antidepressants for depressive episodes than were female psychiatrists (Epstein et al., 2001). A higher risk of suicide has been reported among the patients of older male psychiatrists (Lee and Lin, 2009). However, the gender of psychiatric care workers, such as psychiatrists or social workers, was not found to be related to patient LOS (Bowman et al., 2001). Institutional characteristics also influence LOS (Westert et al., 1993). LOS tended to be longer in institutions with higher numbers of outpatients or greater inpatient capacity (Imai et al., 2005), whereas it tended to be shorter in institutions with a large number of psychiatrists and psychiatric nurses (Imai et al., 2005; Chung et al., 2009). Patients staying in private institutions were likely to have longer stays than patients admitted to public institutions, and LOS was longer at institutions with a shortage of personnel compared with well-staffed institutions (Fujita and Sato, 2004). The type of hospital to which a patient was admitted was related to LOS (Chung et al., 2009). When a patient is admitted to a psychiatric hospital, multiple factors related to characteristics of the patient, the hospital, and the psychiatrist providing care are likely to interact to determine LOS of the patient. However, we do not know how the interactions among three, four, or more variables relate to LOS because there are typically problems with multi-collinearity, and the interpretation of higher-order interactions is very complex within the framework of a linear model. To avoid these problems, we used signal detection analysis (SDA) and analyzed higher-order interactions among variables that were potentially related to LOS among psychiatric patients. Because SDA can analyze the joint and interactive effects of factors and identify subgroups with common characteristics, it is easy to interpret the findings, and intervention approaches can be tailored to each subgroup's characteristics (Abe et al., 2012). Furthermore, if we can categorize groups by expected length of stay at the time of hospital admission, it might be possible to take steps to decrease LOS in psychiatric care. The purpose of the study was twofold. The first was to use SDA to reveal how higher-order interactions among patient and physician factors at the time of hospital admission relate to LOS. The second purpose was to determine whether SDA can provide information that can be effective in decreasing LOS at a psychiatric hospital.
2. Methods 2.1. Participants This was an epidemiological study, obtaining patient data from one psychiatric hospital in Osaka, Japan. This privately owned institute has 690 beds: 50 for emergency care, 60 for acute care, 460 for general psychiatric care, and 120 for long-term care. The hospital provides healthcare in neurology, psychosomatic internal medicine, internal medicine, dentistry, and psychiatry and serves a population of about 840,000 people within a distinct area having little or no overlap with the catchment areas of neighboring hospitals. An increasing number of medical institutions have participated in the Diagnostic Procedure Combination (DPC) program to standardize healthcare in Japan. As of 2008, 1728 hospitals were participating or were ready to participate in this program. Together, these hospitals contained 460,000 beds, equivalent to 50.2% of all the hospital beds in Japan (i.e., 911,000 beds). We collected data from the Form 1 file of the DPC program database at the psychiatric hospital for two periods: between 1.09.2007, and 31.08.2008, and between 1.04.2009, and 31.03.2010. The data for the first period (“Sample 1”) included inpatients in acute or emergency wards (n¼ 843) and inpatients in general wards (n ¼840). The data for the second period (“Sample 2”) included inpatients in acute or emergency wards (n¼ 769) and inpatients in general wards (n¼ 879). The Form 1 file contains patient background and healthcare information per one hospitalization (Ministry of Health, Labour and Welfare, 2008a, 2008b). In addition to the DPC data, the present study also
investigated the medical records of 3331 patients who were discharged between the two periods. Patient outcomes that were either “died during stay” or “unknown” were excluded from further analysis. Additionally, data from patients whose LOS exceeded 1 year were excluded. This was a small, heterogeneous group (F2 [schizophrenia, schizotypal, and delusional disorders]: 8%, F3 [mood (affective) disorders]: 3%), and the length of stay was influenced by social factors (i.e., no family or personal support) rather than by clinical or institutional factors. The Institutional Review Board of the hospital approved the present study. 2.2. Variables The outcome variable was LOS. As factors potentially related to the outcome, we used the following patient characteristics: age; gender; arrival by ambulance (yes, no); admission type according to the Mental Health Act (voluntary and involuntary, which included medical protection, compulsory, and emergency); referral from other institution (yes, no); seclusion (number of days); restraint (number of days); comorbidity at admission (yes, no); comorbidity during hospital stay (yes, no); primary ICD-10 diagnosis; and GAF score at hospital admission. The GAF scale measures psychological, social, and occupational functioning on a hypothetical continuum of mental health-illness (American Psychiatric Association, 1994). GAF scores range from 1 to 100, and higher scores denote better functioning. The primary ICD-10 diagnosis was categorized according to the International Classification of Diseases 10th revision (ICD-10) (World Health Organization, 2010) as follows: organic, including symptomatic, mental disorders (F0); Alzheimer's disease (G3); mental and behavioral disorders due to psychoactive substance use (F1); schizophrenia, schizotypal, and delusional disorders (F2); mood (affective) disorders (F3); neurotic, stress-related, and somatoform disorders (F4); and epilepsy and recurrent seizures (G40). “Others” include behavioral syndromes associated with physiological disturbances and physical factors (F5); disorders of adult personality and behavior (F6); mental retardation (F7); disorders of psychological development (F8); and other diseases. Voluntary admission involved situations in which the patient was admitted on a voluntary basis. A medical protection admission occurred, as legally mandated, when the patient refused hospital admission, but a family member consented to the admission or when a designated psychiatrist determined that the admission was necessary for medical and safety reasons. A compulsory admission occurred when two designated psychiatrists decided, and the governor of a prefecture (or the mayor of a large city) concurred, that admission was necessary to prevent a patient from harming him/herself or others despite the patient's refusal to be admitted voluntarily. An emergency admission involved admission to a designated emergency psychiatric hospital for less than 72 h when a designated psychiatrist judged that the mental health and safety of a patient was at risk but consent could not be obtained from the patient or a family member, and the possibility of involuntary admission was absent. The physician factors we investigated were physician's gender and number of years of psychiatric practice. These data were retrieved from the medical records of the 3331 patients who were discharged during the two periods. 2.3. Analysis The analyses were performed by ward type (“acute or emergency” and “general”). The data of patients discharged between 1.09.2007, and 31.08.2008, (sample 1) were used for a prediction analysis, and the data of patients discharged between 1.04.2009, and 31.03.2010, (sample 2) were used for a validation analysis. According to a tariff in Japan, medical cost for inpatient care changes before or after 1 or 3 months from hospital admission in an “acute or emergency” or “general” ward, respectively, and hospital stay longer than 1 month in an “acute or emergency” ward or 3 months in a “general” ward are not recommended since longer hospital stay could lead a patient to lower quality of life and less social involvement (Ministry of Health, Labour and Welfare, 2009). Thus, LOS≦30 days in acute or emergency wards and LOS≦90 days in general wards were studied. Signal detection analysis (SDA) (ROC 5.0 software [The Aging Clinical Research Center, 2008]) was performed on sample 1 to identify factors related to hospital discharge within 30 days from admission in acute or emergency wards or to hospital discharge within 90 days from admission in general wards. SDA is a recursive partitioning and nonparametric process that assesses combinations of independent variables that are divided into two subgroups according to a selected criterion, such as a dichotomous variable or a specified cutoff point for a continuous variable. Recursive partitioning has been used in psychiatry (Andreescu et al., 2008). The subgroups identified are mutually exclusive, allowing for maximal discrimination among subgroups in a dichotomous outcome. SDA is appropriately and ideally applied for an exploratory analysis, particularly when higher-order interactions among independent variables are anticipated. Thus, linearity and normality in residuals from a model's equation or multicollinearity among independent variables, as in multivariate analysis, need not be considered (Kraemer, 1992, 2004; Kiernan et al., 2001; Rodriguez et al., 2002; James et al., 2005). The SDA partitioning process identifies unknown combinations of certain independent variables to maximize both sensitivity and specificity in predicting LOS. This optimally efficient
F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218 variable or cutoff point would be determined by the maximum chi-square statistic (Kraemer, 1992, 2004; Kiernan et al., 2001; Rodriguez et al., 2002; James et al., 2005). After selecting the first variable, the program repeats partitioning for each subgroup using independent variables until the stopping rules are applied. The stopping rules for the partitioning processes are triggered when (1) no further predictors occur in a newly formed subgroup, (2) no additional significant variables are detected at p o0.05, or (3) the number of subjects in the newly divided group becomes too small (n≦10) (Kraemer, 1992, 2004; Kiernan et al., 2001; Rodriguez et al., 2002; James et al., 2005). The Cochran–Mantel–Haenszel test was conducted to compare proportions of patients with LOS≦30 days in newly divided subgroups in acute or emergency wards or of patients with LOS≦90 days in newly divided subgroups in general wards between the prediction and validation samples (i.e., samples 1 and 2) (Atienza et al., 2006; Hair et al., 2010). Specifically, the validation sample (sample 2) was categorized into mutually exclusive subgroups based on the information provided by SDA using the prediction sample (sample 1), and the proportion of patients with LOS≦30 days or with LOS≦90 days was calculated. The split-sample is validated if no significant difference in proportions of patients with LOS≦30 days in acute or emergency wards or of patients with LOS≦90 days in general wards is found between samples 1 and 2 (Atienza et al., 2006; Hair et al., 2010). Using sample 1, the subgroups were compared according to the study variables listed in Table 1. Analysis of variance (ANOVA) for continuous variables and tests of independence for dichotomous or discrete variables were used. Two-tailed p-values o0.05 were deemed to indicate statistical significance. Analyses were conducted using SPSS software (version 19; SPSS, Chicago, IL, USA).
3. Results Descriptive characteristics of the study population in acute or emergency wards are shown in Table 1, and characteristics of the study population in general wards are shown in Table 2. In acute or emergency wards, the numbers of patients in samples 1 and
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2 were 843 and 769, respectively. The mean LOS was 42.82 7 42.78 (days) in Sample 1 and 41.54 7 30.65 (days) in Sample 2, and there was no significant difference between Samples 1 and 2 (t¼ 0.69, d.f. ¼1526, P ¼0.75). There was a significant difference between Samples 1 and 2 with respect to prevalence of admission type (χ 2 ¼16.69, d.f. ¼1, P o0.001), prevalence of diagnosis (t ¼12.88, d. f.¼ 6, P ¼0.05), and mean length of psychiatric practice (t¼ 2.30, d.f. ¼45, P o0.05) (Table 1). In general wards, the numbers of patients in samples 1 and 2 were 840 and 879, respectively. The mean LOS was 52.83 767.61 (days) in Sample 1 and 48.53 760.33 (days) in Sample 2; the difference was not statistically significant (t¼ 1.39, d.f. ¼1717, P¼ 0.16). There was a significant difference between Samples 1 and 2 with respect to mean age (t¼ 2.03, d.f. ¼1717, P ¼0.04), prevalence of diagnosis (χ 2 ¼ 25.93, d.f. ¼6, Po 0.001), and prevalence of comorbidity during hospital stay (χ 2 ¼4.66, d.f. ¼1, P¼ 0.03) (Table 2). As shown in Fig. 1, in acute or emergency wards in the prediction sample (Sample 1), the proportion of LOS≦30 days was 50.2%. The SDA identified five factors and six distinct subgroups. Factors related to LOS≦30 days were (1) patients with a GAF score of more than 50 at admission; (2) not diagnosed with F2; (3) patients age younger than 50; (4) arrival by ambulance; and (5) psychiatrists with less than 12 years of practice. The proportions of patients with LOS≦30 days ranged between 30.1% and 71.0% among the subgroups. The subgroups were numbered in descending order of the proportion with LOS≦30 days (Table 3 and Fig. 1). As shown in Fig. 2, in general wards in the prediction sample (Sample 1), the proportion of LOS≦90 days was 81.1%.
Table 1 Demographic and medical characteristics of patients discharged from acute or emergency wards. Sample 1 (n¼ 843)
Age (years) Gender Male Female Arrival by ambulance No Yes Admission type Voluntary Involuntary Referral from other institutions No Yes Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD-10 F0þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission No Yes Comorbidity during hospital stay No Yes GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender Male Female Length of psychiatric practice (years)
Sample 2 (n¼ 769)
Mean/frequency
S.D. (%)
Range
Mean/frequency
S.D. (%)
Range
43.06
16.41
(13–90)
43.79
15.75
(14–90)
371 472
44.01 55.99
369 400
47.98 52.02
χ 2 ¼2.56, d.f. ¼ 1, P ¼0.11
585 258
69.40 30.60
563 206
73.21 26.79
χ 2 ¼2.86, d.f. ¼ 1, P ¼0.10
327 516
38.79 61.21
224 545
29.13 70.87
χ 2 ¼16.69, d.f. ¼ 1, Po 0.001
445 398 2.77 0.74
52.79 47.21 7.75 3.34
390 379 5.41 1.18
50.72 49.28 11.59 4.21
χ 2 ¼0.69, d.f. ¼ 1, P ¼0.41
60 78 410 169 53 10 63
7.12 9.25 48.64 20.05 6.29 1.19 7.47
35 92 361 152 53 3 73
4.56 11.96 46.94 19.77 6.89 0.39 9.49
χ 2 ¼12.88, d.f. ¼ 6, P ¼0.05
714 129
84.70 15.30
643 126
83.62 16.38
χ 2 ¼0.35, d.f. ¼ 1, P¼ 0.55
823 20 43.11 42.82 (n¼ 25)
97.74 2.26 15.51 42.78
755 14 40.45 41.54 (n¼ 22)
98.18 1.82 13.78 30.65
χ 2 ¼0.59, d.f. ¼ 1, P ¼0.44
20 5 11.56
80.00 20.00 9.49
16 6 14.05
72.73 27.27 10.07
(0–78) (0–44)
(10–100) (1–352)
(1–38)
(0–117) (0–74)
(0–80) (1–231)
t¼ 0.91, d.f. ¼1610, P¼ 0.37
t¼ 5.32, d.f. ¼ 1322, P o 0.001 t¼ 2.35, d.f. ¼1462, P ¼0.02
t¼ 3.65, d.f. ¼ 1609, Po 0.001 t¼ 0.69, d.f. ¼ 1526, P ¼0.49
χ 2 ¼3.18, d.f. ¼ 1, P ¼0.75 (3–40)
t¼ 2.30, d.f. ¼ 45, P¼ 0.02
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Table 2 Demographic and medical characteristics of patients discharged from general care wards. Sample 1 (n¼840)
Age (years) Gender Male Female Arrival by ambulance No Yes Admission type Voluntary Involuntary Referral from other institutions No Yes Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD-10 F0 þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission No Yes Comorbidity during hospital stay No Yes GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender Male Female Length of psychiatric practice (years)
Sample 2 (n¼ 879)
Mean/frequency
S.D. (%)
Mean/frequency
S.D. (%)
Range
47.43
18.57
49.27
19.16
(8–92)
340 500
40.55 59.45
375 504
42.66 57.34
χ 2 ¼ 0.85, d.f. ¼ 1, P¼ 0.36
750 90
89.30 10.70
791 88
89.99 10.01
χ 2 ¼ 0.23, d.f. ¼ 1, P¼ 0.36
593 247
70.51 29.49
612 267
69.62 31.38
χ 2 ¼ 0.19, d.f. ¼ 1, P ¼0.66
425 415 1.58 0.87
50.54 49.46 9.76 5.51
496 383 2.07 1.46
56.43 43.57 10.49 8.28
χ 2 ¼ 5.88, d.f. ¼ 1, P¼ 0.02
70 36 244 214 61 126 89
8.32 4.28 29.01 25.45 7.25 14.98 10.70
93 37 197 178 75 172 127
10.58 4.21 22.41 20.25 8.53 19.57 14.45
χ 2 ¼ 25.93, d.f. ¼ 6, Po 0.001
699 141
83.23 16.77
755 124
85.89 14.11
χ 2 ¼ 2.36, d.f. ¼ 1, P¼ 0.12
821 19 51.85 52.83 (n ¼25)
97.63 2.37 16.31 67.61
843 36 52.19 48.53 (n¼ 22)
95.50 4.50 15.17 60.33
χ 2 ¼ 4.66, d.f. ¼ 1, P ¼0.03
19 6 11.92
76.00 24.00 9.32
16 6 14.05
72.73 27.27 10.07
(15–96)
(0–215) (0–107)
(0–80) (1–364)
(1–38)
The SDA identified five factors and six distinct subgroups. Factors related to LOS≦90 days were (1) patients with a GAF score of more than 40 at admission; (2) patients age younger than 52; (3) patients with a GAF score of more than 60 at admission; (4) not diagnosed as F3; and (5) not diagnosed as F2. The proportions of patients whose LOS was≦90 days ranged between 61.3% and 95.6% among the subgroups. The subgroups were numbered in descending order of the proportion with LOS≦90 days (Table 3 and Fig. 2). To verify the results of SDA, we used Sample 2 data and performed prediction analyses. As Table 4 shows, there were no significant differences in proportions of patients with LOS≦30 days in acute or emergency wards or in proportions of patients with LOS≦90 days in general wards between samples 1 and 2 (Table 4). The subgroups created by SDA using sample 1 were validated. Tables 5 and 6 summarize the six subgroups in acute or emergency and general wards in terms of the initial explanatory variables. In acute or emergency wards, there was no significant difference among the six subgroups with respect to explanatory variables except for patient's gender, diagnosis of G40, and physician's gender (Table 5). In general wards, there was no significant difference among the six subgroups with respect to explanatory variables except for patient's gender (Table 6).
4. Discussion In the present study, we applied SDA to DPC data and demonstrated how factors related to patients and physicians mutually
(0–111) (0–139)
(10–80) (1–343)
t¼ 2.03, d.f. ¼1717, P ¼0.04
t¼ 1.00, d.f. ¼1716, P¼ 0.32 t¼ 1.75, d.f. ¼ 1536, P¼ 0.08
t¼ 0.45, d.f. ¼1717, P ¼0.65 t¼ 1.39, d.f. ¼ 1717, P¼ 0.16
χ 2 ¼ 1.15, d.f. ¼ 1, P¼ 0.28 (3–40)
t¼ 2.43, d.f. ¼45, P¼ 0.02
interacted to determine LOS at a psychiatric hospital; these effects varied with the type of ward. First, the time that the psychiatrist had been practicing was a significant predictor of LOS among psychiatric patients in acute or emergency wards but not among psychiatric patients in general wards (Figs. 1 and 2). A possible explanation for the association between longer durations of practice by psychiatrists and shorter LOS of patients is that experienced psychiatrists may rely on their experience and become less familiar with newer treatment advances, such as emergency psychiatry (which was only introduced in Japan in 1995), compared to younger psychiatrists. Second, SDA identifies subgroups via combinations of the different independent variables while predicting LOS between acute or emergency wards and general wards (Figs. 1 and 2). In acute or emergency wards, LOS was influenced by two different interactions: (1) the patient's GAF score at admission, the patient's age and the number of years the psychiatrist has been practicing and (2) the patient's GAF score at admission, their F2 diagnosis and whether they arrived by ambulance. This finding is a totally new one. Among the factors that were related to LOS, all have been previously reported (West et al., 2000; Epstein et al., 2001; Fujita and Sato, 2004; Choudhry et al., 2005; Chung et al., 2009; Abe et al., 2011). In general wards, conversely, LOS was influenced by two different interactions: (1) the patient's GAF score at admission, the patient's age and the patient's F2 diagnosis and (2) the patient's GAF score at admission, the patient's age and the patient's F3 diagnosis. This finding is also a new one. Among the factors that were related to LOS, all have been previously reported (West et al., 2000; Epstein
F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218
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Fig. 1. The results of signal detection analysis of factors related to LOS≦30 days in acute or emergency wards.
Table 3 Factors obtained from signal detection analysis and test-performance. In acute or emergency ward (n¼ 843) Outcome: LOS≦30days
GAF score at admission 50
Age 50 years old
F2 diagnosis
Years in practice 12 years
Arrival by ambulance
Odds ratio Sensitivity Specificity Positive predictive value
2.61 0.54 0.69 0.64
3.47 0.84 0.40 0.71
2.49 0.60 0.62 0.52
2.91 0.47 0.76 0.59
2.79 0.53 0.71 0.66
In general ward (n¼ 840) Outcome: LOS≦90days
GAF score at admission 40
Age 52 years old
F2 diagnosis
GAF score at admission 60
F3 diagnosis
Odds ratio Sensitivity Specificity Positive predictive value
3.68 0.86 0.37 0.86
3.66 0.68 0.64 0.92
5.36 0.77 0.61 0.96
3.02 0.58 0.68 0.85
4.05 0.80 0.50 0.90
et al., 2001; Fujita and Sato, 2004; Choudhry et al., 2005; Chung et al., 2009; Abe et al., 2011) with the exception of the diagnosis of mood disorders (F3), which may be explained by differences in financial incentives in Japan. Third, older patients who were involuntarily admitted with F2 or F3 diagnoses received inpatient care for longer durations (Tables 5 and 6). These patients may have required more resources for inpatient care, which consequently
led to longer LOS. In summary, our results based on SDA appear to indicate an effective means for decreasing LOS in psychiatric hospitals. These findings have important practical implications. First, in terms of hospital administration, we suggest that clinicians greater numbers of years of practice receive more training on discharge planning and facilitation of community based care to comply with
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Fig. 2. The results of signal detection analysis of factors related to LOS≦90 days in general wards.
Table 4 Subgroups and hospital discharge in prediction and validation samples. Group 1 (%)
Group 2 (%)
Group 3 (%)
Group 4 (%)
Group 5 (%)
Group 6 (%)
Cochran Mantel–Haenszel
d.f.
P
Proportions of patients discharged within 30 days from acute or emergency wards Sample 1 (n¼ 843) 71.00 66.32 58.62 41.35 Sample 2 (n¼ 769) 63.50 62.65 39.53 39.47
32.76 51.22
30.12 28.80
1.65
1
0.20
Proportions of patients discharged within 90 days from general care wards Sample 1 (n¼ 840) 95.64 89.90 80.36 Sample 2 (n¼ 879) 93.75 85.40 76.74
64.75 70.49
61.04 64.02
0.03
1
0.87
68.75 72.22
Note: Sample 1 was used for prediction, and Sample 2 was used for validation.
the shift from inpatient care to outpatient care, reduce medical costs and challenge the social stigmas associated with mental illness. Second, for patients in those groups with short estimated LOS, if they choose to use community care after discharge from the hospital, LOS can be shortened by applying for community care use at the time of hospital admission, which would avoid extending the hospital stay while the patients wait for available community care. Community care, such as services offered by daycare
centers, is frequently suggested for patients upon discharge from psychiatric hospitals (Imai et al., 2005). Third, patients with F2 or F3 diagnoses and severe conditions require more inpatient care. Community psychiatric care should be promoted in Japan. However, the promotion of this care requires careful consideration of the patients' clinical conditions. Further development of effective community care, especially care designed for those with severe conditions, is necessary.
F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218
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Table 5 Characteristics of identified subgroups of patients in acute or emergency wards (Sample 1, n ¼ 843). Subgroups
1 (n¼ 269)
Age (years) Gender (Female) Arrival by ambulance (Yes) Admission type (Involuntary) Referral from other institutions (Yes) Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD-10 F0þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission (Yes) Comorbidity during hospital stay (Yes) GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender (Female) Length of psychiatric practice (years)
33.75
2 (n¼ 95)
3 (n¼ 29)
4 (n ¼133)
5 (n¼ 58)
6 (n ¼259)
42.77
65.17
49.76
64.05
42.23
F(5,837) ¼73.53, Po 0.001
56.13%
47.37%
48.28%
57.14%
53.45%
59.85%
χ 2 ¼5.36, d.f. ¼ 5, P¼ 0.37
22.30%
100.00%
6.90%
0.00%
31.03%
32.05%
χ 2 ¼290.72, d.f. ¼5, Po 0.001
32.71%
81.05%
37.93%
75.94%
48.28%
81.47%
χ 2 ¼175.38, d.f. ¼ 5, Po 0.001
52.04% 0.79 0.16
45.26% 3.29 1.12
62.07% 0.03 0.14
52.63% 2.08 0.78
43.10% 2.19 0.34
39.38% 5.43 1.33
χ 2 ¼13.57, d.f. ¼5, P ¼0.02 F(5,837) ¼641.76, Po 0.001 F(5,837) ¼42.90, P o 0.01
1.89% 5.58% 44.24% 23.79% 10.04% 2.6% 11.9%
14.74% 32.63% 0.00% 22.11% 13.68% 1.05% 15.79%
24.14% 3.45% 34.48% 34.48% 0.00% 0.00% 3.45%
21.05% 19.55% 0.00% 41.35% 6.02% 1.5% 10.53%
10.34% 8.62% 37.93% 32.76% 8.62% 0.00% 1.72%
0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00%
11.15%
26.32%
34.48%
24.81%
17.24%
8.11%
1.49% 58.96 26.78
1.05% 30.53 28.44
0.00% 57.93 35.45
3.01% 33.08 50.41
1.72% 56.55 60.59
3.86% 31.74 57.69
χ 2 ¼ 5:14, d.f. ¼ 5, P¼ 0.40 F(5,837) ¼29,266.90, P o 0.001 F(5,837) ¼34,728.37, P o 0.001
22.3% 12.95
11.58% 12.68
17.24% 4.69
11.28% 12.38
20.69% 17.43
19.69% 15.12
χ 2 ¼10.73, d.f. ¼ 5, P ¼0.06 F(5,837) ¼827.90, P o0.001
5 (n ¼122)
6 (n¼ 154)
χ 2 ¼92.13, d.f. ¼ 5, P o0.001 χ 2 ¼110.56, d.f. ¼5, Po 0.001 χ 2 ¼496.49, d.f. ¼5, P o 0.001 χ 2 ¼114.83, d.f. ¼5, Po 0.001 χ 2 ¼35.12, d.f. ¼5, P o 0.001 χ 2 ¼8.88, d.f. ¼ 5, P¼ 0.11 χ 2 ¼43.27, d.f. ¼ 5, Po 0.001 χ 2 ¼40.49, d.f. ¼ 5, P o0.001
Table 6 Characteristics of identified subgroups for patients in general care wards (Sample 1, n ¼840). Subgroups
1 (n¼ 321)
2 (n ¼99)
3 (n ¼112)
4 (n¼ 32)
Age (years) Gender (Female) Arrival by ambulance (Yes) Admission type (Involuntary) Referral from other institutions (Yes) Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD10 F0þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission (Yes) Comorbidity during hospital stay (Yes) GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender (Female) Length of psychiatric practice (years)
34.09
66.94
36.07
63.44
66.74
52.33
F(5,837) ¼226.71, Po 0.001
59.81%
51.52%
60.71%
75.00%
61.48%
58.44%
χ 2 ¼ 6.16, d.f. ¼ 5, P¼ 0.29
5.30%
7.07%
10.71%
0.00%
11.48%
25.97%
χ 2 ¼ 52.63, d.f. ¼ 5, Po 0.001
6.23%
17.17%
27.68%
12.50%
51.64%
72.73%
χ 2 ¼ 263.04, d.f. ¼ 5, P o0.001
53.89% 0.22 0.10
34.34% 0.36 0.25
35.71% 0.93 0.10
71.88% 0.19 0.22
64.75% 0.79 1.16
42.86% 6.60 3.32
χ 2 ¼ 40.57, d.f. ¼5, Po 0.001 F(5,837) ¼10.68, P o0.001 F(5,837) ¼8.55, P o 0.001
0.00% 3.74% 0.00% 37.38% 12.77% 30.53% 15.58%
10.10% 8.08% 23.23% 0.00% 5.05% 25.25% 28.28%
0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00%
24.59% 4.10% 33.61% 24.59% 6.56% 1.64% 4.92%
19.48% 7.14% 44.16% 20.77% 4.54% 0.65% 3.25%
χ 2 ¼ 109.94, d.f. ¼5, Po 0.001 χ 2 ¼ 13.23, d.f. ¼5, P¼ 0.02 χ 2 ¼ 438.00, d.f. ¼5, Po 0.001 χ 2 ¼ 191.55, d.f. ¼ 5, P o0.001 χ 2 ¼ 28.25, d.f. ¼ 5, Po 0.001 χ 2 ¼ 136.25, d.f. ¼5, P¼ 0.11 χ 2 ¼ 71.10, d.f. ¼ 5, Po 0.001
8.10%
14.14%
15.18%
9.38%
31.97%
27.27%
χ 2 ¼ 51.56, d.f. ¼ 5, Po 0.001
0.62% 60.34 24.82
1.01% 67.37 29.88
0.89% 52.95 61.15
3.13% 63.44 71.44
5.74% 44.10 85.41
4.55% 27.08 90.23
χ 2 ¼ 15.96, d.f. ¼5, Po 0.01 F(5,837) ¼365.42, P o 0.001 F(5,837) ¼35.16, P o0.001
12.77% 16.88
8.08% 19.94
26.79% 12.83
25.00% 11.88
18.85% 13.89
11.04% 15.01
χ 2 ¼ 22.84, d.f. ¼ 5, Po 0.001 F(5,837) ¼10.55, Po 0.001
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4.1. Limitations The present study has several limitations. First, the Japanese Mental Health Act is unique to the country, and caution must be exercised in extrapolating the findings because they may not be externally valid. Second, caution must be used in generalizing these findings to other medical facilities in Japan because the present study used data from a single psychiatric institution. However, this is the epidemiological study, conducted at an institute with a distinct catchment area. Thus we believe the sample would be representative for other areas of Japan. Third, GAF was measured subjectively by a trained psychiatrist, and no outcome variables were evaluated by the patients themselves. However, the validity and reliability of the GAF score as an outcome measure in psychiatry have been well established (Söderberg et al., 2005). Fifth, the diagnoses used were made by experienced psychiatrists using ICD-10, but were not a research diagnosis. This might raise a concern regarding validity and reliability of the diagnosis. 4.2. Conclusions In summary, we applied SDA to DPC data and showed how patient and physician factors mutually interacted to determine LOS at a psychiatric hospital according to the type of ward. In acute or emergency wards, factors related to LOS were GAF at hospital admission, diagnosis of F2, patient age, arrival by ambulance, and duration of the physician's psychiatric practice. All subjects were categorized by these five factors into six subgroups, and the proportions of patients whose LOS≦30 days ranged between 30.1% and 71.0% among these subgroups. In general wards, factors related to LOS were patients with GAF score of more than 40 or 60 at admission, patient age, and patients with diagnoses of F3 and F2. All subjects were categorized by these five factors into six subgroups, and the proportions of patients with LOS≦90 days ranged between 61.3% and 95.6% among these subgroups. The association between longer durations of practice by psychiatrists and shorter LOS of patients may be explained by the possibility that knowledge of newer treatment advances might be less sufficient among experienced psychiatrists than among younger psychiatrists. In addition, a patient with severe conditions receives longer inpatient care. A future development of effective community care for them is important. Our results based upon SDA seem to suggest effective ways of decreasing the LOS in a psychiatric hospital. Further studies in multiple institutes using a follow-up design are necessary to verify these findings. References Abe, T., Ikeda, K., Kuroda, K., Hagihara, A., 2011. Assessment of psychiatric outcomes in Japan based on diagnostic procedure combination information. Psychiatry Quarterly 82, 163–175. Abe, T., Nagata, T., Hasegawa, M., Hagihara, A., 2012. Life support techniques related to survival after out-of-hospital cardiac arrest in infants. Resuscitation 83, 612–618. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders: Text Revision, 4th ed., American Psychiatric Association. Andreescu, C., Mulsant, B.H., Houck, R.R., Whyte, E.M., Mazumdar, S., Dombrovski, A.Y., Pollock, B.G., Reynolds III, C.F., 2008. Empirically derived decision trees for the treatment of late-life depression. American Journal of Psychiatry 165, 855–862.
Atienza, A.A., Yaroch, A.L., Mãsse, L.C., Moser, R.P., Hesse, B.W., King, A.C., 2006. Identifying sedentary subgroups: the National Cancer Institute's Health Information National Trends Survey. American Journal of Preventive Medicine 31, 383–390. Bowman, D., Scogin, F., Floyd, M., Mckendree-Smith, N., 2001. Psychotherapy length of stay and outcome: a meta-analysis of the effect of therapist sex. Psychotherapy 38, 142–148. Choudhry, N.K., Fletcher, R.H., Soumerai, S.B., 2005. Systematic review: the relationship between clinical experience and quality of health care. Annals of Internal Medicine 142, 260–273. Chung, W.J., Cho, W.H., Yoon, C.W., 2009. The influence of institutional characteristics on length of stay for psychiatric patients: a national database study in South Korea. Social Science & Medicine 68, 1137–1144. Epstein, S.A., Gonzares, J.J., Weinfurt, K., Boekeloo, B., Yuan, N., Chase, G., 2001. Are psychiatrists' characteristics related to how they care for depression in the medically ill? Psychosomatics 42, 482–489. Fujita, T., Sato, T., 2004. [Seishinbyoin deno chokitaizai ni kanrensuru youin] Factors associated with long length of stay in psychiatric institutes. [Kōsei no shihyō] Public Health and Welfare Index 52, 12–19. Hair Jr., J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2010. Multivariate Data Analysis, 7th ed. Prentice-Hall, Upper Saddle River, NJ. Imai, H., Hosomi, J., Nakao, H., Tsukino, H., Katoh, T., Itoh, T., Yoshida, T., 2005. Characteristics of psychiatric hospitals associated with length of stay in Japan. Health Policy 74, 115–121. James, K.E., White, R.F., Kraemer, H.C., 2005. Repeated split sample validation to assess logistic regression and recursive partitioning: an application to the prediction of cognitive impairment. Statistics in Medicine 24, 3019–3035. Kiernan, M., Kraemer, H.C., Winkleby, M.A., King, A.C., Taylor, C.B., 2001. Do logistic regression and signal detection identify different subgroups at risk?: implications for the design of tailored interventions. Psychological Methods 6, 35–48. Kraemer, H.C., 1992. Evaluating Medical Tests: Objective and Quantitative Guidelines. Sage, Newbury Park, CA. Kraemer, H.C., 2004. Reconsidering the odds ratio as a measure of 2by2 association in a population. Statistics in Medicine 23, 257–270. Lee, H.C., Lin, H.C., 2009. Are psychiatrist associated with postdischarge suicide of schizophrenia patients? Schizophrenia Bulletin 35, 760–765. Levine, S.Z., Rbinowitz, J., 2009. A population-based examination of the role of years of education, age of onset, and sex on the course of schizophrenia. Psychiatry Research 168, 11–17. Levine, S.Z., Lurie, I., Khon, R., Levav, I., 2011. Trajectories of the course of schizophrenia: From progressive deterioration to amelioration over three decades. Schizophrenia Research 126, 184–191. Levine, S.Z., 2008. Population-based examination of the relationship between type of first admission for schizophrenia and outcomes. Psychiatric Services 59, 1470–1473. Ministry of Health, Labour and Welfare, 2008a. Central Social Insurance Medical Council. Medical Fee Committee. 〈http:/www.mhlw.go.jp/shingi/2008/07/ s0716-4.html〉 (accessed 1.05.2010) (in Japanese). Ministry of Health, Labour and Welfare, 2008b. Summary of patients survey by the Ministry of Health, Labour and Welfare in 2008. 〈http:/www.mhlwh.go.jp/ toukei/saikin/hw/kanja/08/index.html〉 (accessed on 19.11.2011). Ministry of Health, Labour and Welfare, 2009. Medical costs for inpatient care [Nyuin kihon ryou]. 〈http:/www.mhlw.go.jp/shingi/2009/04/dl/s0415-9h.pdf〉 (accessed on 1.03.2012). Oshima, I., Nino, Y., Inomata, Y., 2003. Institutionalisation and schizophrenia in Japan: social environments and negative symptoms. British Journal of Psychiatry 183, 50–56. Rodriguez, M.A., Winkleby, M.A., Ahn, D., Sundquist, J., Kraemer, H.C., 2002. Identification of population subgroups of children and adolescents with high asthma prevalence. Archives of Pediatrics & Adolescent Medicine 156, 269–275. Söderberg, P., Tungström, S., Armelius, B.A., 2005. Reliability of global assessment of functioning ratings made by clinical psychiatric staff. Psychiatric Services 56, 434–438. The Aging Clinical Research Center, 2008. ROC5 software. Stanford University. West, J.C., Leaf, P.J., Zarin, D.A., 2000. Health plan characterisitcs and conformance with key practice guideline psychopharmacologic treatment recommendations for major depression. Mental Health Services Research 2, 223–237. Westert, G.P., Neiboer, A.P., Groenewegen, P,P., 1993. Variation in duration of hospital stay between hospitals and between doctors within hospitals. Social Science & Medicine 37, 833–839. World Health Organization. International Classification of Diseases (ICD)-10 Online, Current Version. 〈http:/apps.who.int/classifications/apps/icd/icd10online/〉 (accessed 1.05.2010).
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Psychiatry Research journal homepage: www.elsevier.com/locate/psychres
Predicting the length of hospital stay of psychiatry patients using signal detection analysis Fase Badriah a, Takeru Abe a,b, Yoshihiro Nabeshima a, Kouji Ikeda c, Kenji Kuroda c, Akihito Hagihara a,n a
Kyushu University, Graduate School of Medicine, Department of Health Services Management and Policy, Higashi-ku, Fukuoka 812-8582, Japan Waseda University, Faculty of Human Sciences, Department of Health Sciences and Social Welfare, 2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan c Hannan Hospital, 277 Handa-minamino-cho, Naka-ku, Sakai City, Osaka 599-8263, Japan b
art ic l e i nf o
a b s t r a c t
Article history: Received 11 July 2012 Received in revised form 9 September 2013 Accepted 15 September 2013
In Japan, the length of hospital stay (LOS) at psychiatric institutions often exceeds a year, and factors related to such stays have been identified. However, we do not know how multiple patient, hospital, and physician factors interact to determine LOS. Patient data were collected from a psychiatric hospital in Osaka, Japan. We developed subgroups, which were determined by interactions related to LOS using signal detection theory. In acute or emergency wards, five factors related to LOS were identified, and subjects were categorized into six subgroups. The indices obtained by the five factors ranged 2.49–3.47 for odds ratio, 0.47–0.84 for sensitivity, 0.40–0.76 for specificity, and 0.52–0.71 for positive predictive value. In general wards, five factors related to LOS were identified, and subjects were categorized into six subgroups. The indices obtained by the five factors ranged 3.02–5.36 for odds ratio, 0.58–0.86 for sensitivity, 0.37–0.68 for specificity, and 0.85–0.92 for positive predictive value. Psychiatrists who have been practicing longer in acute or emergency wards appear to have significantly longer stay of patients, and older or more severe patients tend to be in need of longer inpatient care. Our results provide findings that may be helpful in decreasing LOS at psychiatric hospitals. & 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Outcome Prediction Signal detection analysis
1. Introduction In recent years, medical costs have been increasing because of the aging society and the progress of medical technologies in Japan. Revenue has been decreasing due to the economic recession. Thus, it is crucial to decrease medical costs. As the first step toward this end, it is important to understand the characteristics of patient care. Evaluated in terms of widely accepted measures such as the length of stay (LOS) and medical costs (Söderberg et al., 2005), psychiatric care in Japan has the following characteristics. First, the number of patients diagnosed with “mental and behavioral disorders” was 301,400 in 2007, and this number was the largest among all medical diagnosis groups (Ministry of Health, Labour and Welfare, 2008a, 2008b). Second, the mean length of stay (LOS) of patients in psychiatric institutions was 290.6 days, and this was the longest among all medical diagnosis groups in Japan (Ministry of Health, Labour and Welfare, 2008a, 2008b).
n
Corresponding author. Tel.: þ 81 92 642 6950. E-mail addresses: [email protected] (F. Badriah), [email protected] (T. Abe), [email protected] (Y. Nabeshima), [email protected] (K. Ikeda), [email protected] (K. Kuroda), [email protected] (A. Hagihara). 0165-1781/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.psychres.2013.09.019
Additionally, the mean LOS of patients in psychiatric institutions in Japan is longer than that of psychiatric patients in other countries (Oshima et al., 2003), such as 3–4 months as the mean LOS in Israel (Levine and Rbinowitz, 2009; Levine et al., 2011) and Denmark based on national registries in both countries. Thus far, patient, psychiatrist, and institutional characteristics have been reported as factors related to LOS in psychiatric care. Patients who had higher Global Assessment of Functioning (GAF) scores or who did not require restraint had shorter lengths of institutional stay than those who had lower GAF scores or required restraint (Abe et al., 2011). Patients who used community care, such as services offered by daycare centers, were found to have a shorter LOS than patients who did not use community care (Imai et al., 2005). The type of psychiatric condition also played a role in LOS; vascular dementia, mental retardation, and schizophrenia were associated with longer LOS in Japan (Imai et al., 2005). Moreover, male and older patients (Fujita and Sato, 2004; Chung et al., 2009), or male and forensic patients (Levine, 2008), were at increased risk of longer stay at a psychiatric institution. Patients admitted involuntarily tended to stay longer than patients who were voluntarily admitted (Imai et al., 2005; Abe et al., 2011). As for psychiatrist characteristics, it has been reported that lower quality medical care was associated with older physicians
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in all specializations, including psychiatry (Choudhry et al., 2005). Older psychiatrists were less uniform than younger psychiatrists in prescribing medications for mood disorders (West et al., 2000). Male psychiatrists were more likely to prescribe antidepressants for depressive episodes than were female psychiatrists (Epstein et al., 2001). A higher risk of suicide has been reported among the patients of older male psychiatrists (Lee and Lin, 2009). However, the gender of psychiatric care workers, such as psychiatrists or social workers, was not found to be related to patient LOS (Bowman et al., 2001). Institutional characteristics also influence LOS (Westert et al., 1993). LOS tended to be longer in institutions with higher numbers of outpatients or greater inpatient capacity (Imai et al., 2005), whereas it tended to be shorter in institutions with a large number of psychiatrists and psychiatric nurses (Imai et al., 2005; Chung et al., 2009). Patients staying in private institutions were likely to have longer stays than patients admitted to public institutions, and LOS was longer at institutions with a shortage of personnel compared with well-staffed institutions (Fujita and Sato, 2004). The type of hospital to which a patient was admitted was related to LOS (Chung et al., 2009). When a patient is admitted to a psychiatric hospital, multiple factors related to characteristics of the patient, the hospital, and the psychiatrist providing care are likely to interact to determine LOS of the patient. However, we do not know how the interactions among three, four, or more variables relate to LOS because there are typically problems with multi-collinearity, and the interpretation of higher-order interactions is very complex within the framework of a linear model. To avoid these problems, we used signal detection analysis (SDA) and analyzed higher-order interactions among variables that were potentially related to LOS among psychiatric patients. Because SDA can analyze the joint and interactive effects of factors and identify subgroups with common characteristics, it is easy to interpret the findings, and intervention approaches can be tailored to each subgroup's characteristics (Abe et al., 2012). Furthermore, if we can categorize groups by expected length of stay at the time of hospital admission, it might be possible to take steps to decrease LOS in psychiatric care. The purpose of the study was twofold. The first was to use SDA to reveal how higher-order interactions among patient and physician factors at the time of hospital admission relate to LOS. The second purpose was to determine whether SDA can provide information that can be effective in decreasing LOS at a psychiatric hospital.
2. Methods 2.1. Participants This was an epidemiological study, obtaining patient data from one psychiatric hospital in Osaka, Japan. This privately owned institute has 690 beds: 50 for emergency care, 60 for acute care, 460 for general psychiatric care, and 120 for long-term care. The hospital provides healthcare in neurology, psychosomatic internal medicine, internal medicine, dentistry, and psychiatry and serves a population of about 840,000 people within a distinct area having little or no overlap with the catchment areas of neighboring hospitals. An increasing number of medical institutions have participated in the Diagnostic Procedure Combination (DPC) program to standardize healthcare in Japan. As of 2008, 1728 hospitals were participating or were ready to participate in this program. Together, these hospitals contained 460,000 beds, equivalent to 50.2% of all the hospital beds in Japan (i.e., 911,000 beds). We collected data from the Form 1 file of the DPC program database at the psychiatric hospital for two periods: between 1.09.2007, and 31.08.2008, and between 1.04.2009, and 31.03.2010. The data for the first period (“Sample 1”) included inpatients in acute or emergency wards (n¼ 843) and inpatients in general wards (n ¼840). The data for the second period (“Sample 2”) included inpatients in acute or emergency wards (n¼ 769) and inpatients in general wards (n¼ 879). The Form 1 file contains patient background and healthcare information per one hospitalization (Ministry of Health, Labour and Welfare, 2008a, 2008b). In addition to the DPC data, the present study also
investigated the medical records of 3331 patients who were discharged between the two periods. Patient outcomes that were either “died during stay” or “unknown” were excluded from further analysis. Additionally, data from patients whose LOS exceeded 1 year were excluded. This was a small, heterogeneous group (F2 [schizophrenia, schizotypal, and delusional disorders]: 8%, F3 [mood (affective) disorders]: 3%), and the length of stay was influenced by social factors (i.e., no family or personal support) rather than by clinical or institutional factors. The Institutional Review Board of the hospital approved the present study. 2.2. Variables The outcome variable was LOS. As factors potentially related to the outcome, we used the following patient characteristics: age; gender; arrival by ambulance (yes, no); admission type according to the Mental Health Act (voluntary and involuntary, which included medical protection, compulsory, and emergency); referral from other institution (yes, no); seclusion (number of days); restraint (number of days); comorbidity at admission (yes, no); comorbidity during hospital stay (yes, no); primary ICD-10 diagnosis; and GAF score at hospital admission. The GAF scale measures psychological, social, and occupational functioning on a hypothetical continuum of mental health-illness (American Psychiatric Association, 1994). GAF scores range from 1 to 100, and higher scores denote better functioning. The primary ICD-10 diagnosis was categorized according to the International Classification of Diseases 10th revision (ICD-10) (World Health Organization, 2010) as follows: organic, including symptomatic, mental disorders (F0); Alzheimer's disease (G3); mental and behavioral disorders due to psychoactive substance use (F1); schizophrenia, schizotypal, and delusional disorders (F2); mood (affective) disorders (F3); neurotic, stress-related, and somatoform disorders (F4); and epilepsy and recurrent seizures (G40). “Others” include behavioral syndromes associated with physiological disturbances and physical factors (F5); disorders of adult personality and behavior (F6); mental retardation (F7); disorders of psychological development (F8); and other diseases. Voluntary admission involved situations in which the patient was admitted on a voluntary basis. A medical protection admission occurred, as legally mandated, when the patient refused hospital admission, but a family member consented to the admission or when a designated psychiatrist determined that the admission was necessary for medical and safety reasons. A compulsory admission occurred when two designated psychiatrists decided, and the governor of a prefecture (or the mayor of a large city) concurred, that admission was necessary to prevent a patient from harming him/herself or others despite the patient's refusal to be admitted voluntarily. An emergency admission involved admission to a designated emergency psychiatric hospital for less than 72 h when a designated psychiatrist judged that the mental health and safety of a patient was at risk but consent could not be obtained from the patient or a family member, and the possibility of involuntary admission was absent. The physician factors we investigated were physician's gender and number of years of psychiatric practice. These data were retrieved from the medical records of the 3331 patients who were discharged during the two periods. 2.3. Analysis The analyses were performed by ward type (“acute or emergency” and “general”). The data of patients discharged between 1.09.2007, and 31.08.2008, (sample 1) were used for a prediction analysis, and the data of patients discharged between 1.04.2009, and 31.03.2010, (sample 2) were used for a validation analysis. According to a tariff in Japan, medical cost for inpatient care changes before or after 1 or 3 months from hospital admission in an “acute or emergency” or “general” ward, respectively, and hospital stay longer than 1 month in an “acute or emergency” ward or 3 months in a “general” ward are not recommended since longer hospital stay could lead a patient to lower quality of life and less social involvement (Ministry of Health, Labour and Welfare, 2009). Thus, LOS≦30 days in acute or emergency wards and LOS≦90 days in general wards were studied. Signal detection analysis (SDA) (ROC 5.0 software [The Aging Clinical Research Center, 2008]) was performed on sample 1 to identify factors related to hospital discharge within 30 days from admission in acute or emergency wards or to hospital discharge within 90 days from admission in general wards. SDA is a recursive partitioning and nonparametric process that assesses combinations of independent variables that are divided into two subgroups according to a selected criterion, such as a dichotomous variable or a specified cutoff point for a continuous variable. Recursive partitioning has been used in psychiatry (Andreescu et al., 2008). The subgroups identified are mutually exclusive, allowing for maximal discrimination among subgroups in a dichotomous outcome. SDA is appropriately and ideally applied for an exploratory analysis, particularly when higher-order interactions among independent variables are anticipated. Thus, linearity and normality in residuals from a model's equation or multicollinearity among independent variables, as in multivariate analysis, need not be considered (Kraemer, 1992, 2004; Kiernan et al., 2001; Rodriguez et al., 2002; James et al., 2005). The SDA partitioning process identifies unknown combinations of certain independent variables to maximize both sensitivity and specificity in predicting LOS. This optimally efficient
F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218 variable or cutoff point would be determined by the maximum chi-square statistic (Kraemer, 1992, 2004; Kiernan et al., 2001; Rodriguez et al., 2002; James et al., 2005). After selecting the first variable, the program repeats partitioning for each subgroup using independent variables until the stopping rules are applied. The stopping rules for the partitioning processes are triggered when (1) no further predictors occur in a newly formed subgroup, (2) no additional significant variables are detected at p o0.05, or (3) the number of subjects in the newly divided group becomes too small (n≦10) (Kraemer, 1992, 2004; Kiernan et al., 2001; Rodriguez et al., 2002; James et al., 2005). The Cochran–Mantel–Haenszel test was conducted to compare proportions of patients with LOS≦30 days in newly divided subgroups in acute or emergency wards or of patients with LOS≦90 days in newly divided subgroups in general wards between the prediction and validation samples (i.e., samples 1 and 2) (Atienza et al., 2006; Hair et al., 2010). Specifically, the validation sample (sample 2) was categorized into mutually exclusive subgroups based on the information provided by SDA using the prediction sample (sample 1), and the proportion of patients with LOS≦30 days or with LOS≦90 days was calculated. The split-sample is validated if no significant difference in proportions of patients with LOS≦30 days in acute or emergency wards or of patients with LOS≦90 days in general wards is found between samples 1 and 2 (Atienza et al., 2006; Hair et al., 2010). Using sample 1, the subgroups were compared according to the study variables listed in Table 1. Analysis of variance (ANOVA) for continuous variables and tests of independence for dichotomous or discrete variables were used. Two-tailed p-values o0.05 were deemed to indicate statistical significance. Analyses were conducted using SPSS software (version 19; SPSS, Chicago, IL, USA).
3. Results Descriptive characteristics of the study population in acute or emergency wards are shown in Table 1, and characteristics of the study population in general wards are shown in Table 2. In acute or emergency wards, the numbers of patients in samples 1 and
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2 were 843 and 769, respectively. The mean LOS was 42.82 7 42.78 (days) in Sample 1 and 41.54 7 30.65 (days) in Sample 2, and there was no significant difference between Samples 1 and 2 (t¼ 0.69, d.f. ¼1526, P ¼0.75). There was a significant difference between Samples 1 and 2 with respect to prevalence of admission type (χ 2 ¼16.69, d.f. ¼1, P o0.001), prevalence of diagnosis (t ¼12.88, d. f.¼ 6, P ¼0.05), and mean length of psychiatric practice (t¼ 2.30, d.f. ¼45, P o0.05) (Table 1). In general wards, the numbers of patients in samples 1 and 2 were 840 and 879, respectively. The mean LOS was 52.83 767.61 (days) in Sample 1 and 48.53 760.33 (days) in Sample 2; the difference was not statistically significant (t¼ 1.39, d.f. ¼1717, P¼ 0.16). There was a significant difference between Samples 1 and 2 with respect to mean age (t¼ 2.03, d.f. ¼1717, P ¼0.04), prevalence of diagnosis (χ 2 ¼ 25.93, d.f. ¼6, Po 0.001), and prevalence of comorbidity during hospital stay (χ 2 ¼4.66, d.f. ¼1, P¼ 0.03) (Table 2). As shown in Fig. 1, in acute or emergency wards in the prediction sample (Sample 1), the proportion of LOS≦30 days was 50.2%. The SDA identified five factors and six distinct subgroups. Factors related to LOS≦30 days were (1) patients with a GAF score of more than 50 at admission; (2) not diagnosed with F2; (3) patients age younger than 50; (4) arrival by ambulance; and (5) psychiatrists with less than 12 years of practice. The proportions of patients with LOS≦30 days ranged between 30.1% and 71.0% among the subgroups. The subgroups were numbered in descending order of the proportion with LOS≦30 days (Table 3 and Fig. 1). As shown in Fig. 2, in general wards in the prediction sample (Sample 1), the proportion of LOS≦90 days was 81.1%.
Table 1 Demographic and medical characteristics of patients discharged from acute or emergency wards. Sample 1 (n¼ 843)
Age (years) Gender Male Female Arrival by ambulance No Yes Admission type Voluntary Involuntary Referral from other institutions No Yes Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD-10 F0þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission No Yes Comorbidity during hospital stay No Yes GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender Male Female Length of psychiatric practice (years)
Sample 2 (n¼ 769)
Mean/frequency
S.D. (%)
Range
Mean/frequency
S.D. (%)
Range
43.06
16.41
(13–90)
43.79
15.75
(14–90)
371 472
44.01 55.99
369 400
47.98 52.02
χ 2 ¼2.56, d.f. ¼ 1, P ¼0.11
585 258
69.40 30.60
563 206
73.21 26.79
χ 2 ¼2.86, d.f. ¼ 1, P ¼0.10
327 516
38.79 61.21
224 545
29.13 70.87
χ 2 ¼16.69, d.f. ¼ 1, Po 0.001
445 398 2.77 0.74
52.79 47.21 7.75 3.34
390 379 5.41 1.18
50.72 49.28 11.59 4.21
χ 2 ¼0.69, d.f. ¼ 1, P ¼0.41
60 78 410 169 53 10 63
7.12 9.25 48.64 20.05 6.29 1.19 7.47
35 92 361 152 53 3 73
4.56 11.96 46.94 19.77 6.89 0.39 9.49
χ 2 ¼12.88, d.f. ¼ 6, P ¼0.05
714 129
84.70 15.30
643 126
83.62 16.38
χ 2 ¼0.35, d.f. ¼ 1, P¼ 0.55
823 20 43.11 42.82 (n¼ 25)
97.74 2.26 15.51 42.78
755 14 40.45 41.54 (n¼ 22)
98.18 1.82 13.78 30.65
χ 2 ¼0.59, d.f. ¼ 1, P ¼0.44
20 5 11.56
80.00 20.00 9.49
16 6 14.05
72.73 27.27 10.07
(0–78) (0–44)
(10–100) (1–352)
(1–38)
(0–117) (0–74)
(0–80) (1–231)
t¼ 0.91, d.f. ¼1610, P¼ 0.37
t¼ 5.32, d.f. ¼ 1322, P o 0.001 t¼ 2.35, d.f. ¼1462, P ¼0.02
t¼ 3.65, d.f. ¼ 1609, Po 0.001 t¼ 0.69, d.f. ¼ 1526, P ¼0.49
χ 2 ¼3.18, d.f. ¼ 1, P ¼0.75 (3–40)
t¼ 2.30, d.f. ¼ 45, P¼ 0.02
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Table 2 Demographic and medical characteristics of patients discharged from general care wards. Sample 1 (n¼840)
Age (years) Gender Male Female Arrival by ambulance No Yes Admission type Voluntary Involuntary Referral from other institutions No Yes Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD-10 F0 þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission No Yes Comorbidity during hospital stay No Yes GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender Male Female Length of psychiatric practice (years)
Sample 2 (n¼ 879)
Mean/frequency
S.D. (%)
Mean/frequency
S.D. (%)
Range
47.43
18.57
49.27
19.16
(8–92)
340 500
40.55 59.45
375 504
42.66 57.34
χ 2 ¼ 0.85, d.f. ¼ 1, P¼ 0.36
750 90
89.30 10.70
791 88
89.99 10.01
χ 2 ¼ 0.23, d.f. ¼ 1, P¼ 0.36
593 247
70.51 29.49
612 267
69.62 31.38
χ 2 ¼ 0.19, d.f. ¼ 1, P ¼0.66
425 415 1.58 0.87
50.54 49.46 9.76 5.51
496 383 2.07 1.46
56.43 43.57 10.49 8.28
χ 2 ¼ 5.88, d.f. ¼ 1, P¼ 0.02
70 36 244 214 61 126 89
8.32 4.28 29.01 25.45 7.25 14.98 10.70
93 37 197 178 75 172 127
10.58 4.21 22.41 20.25 8.53 19.57 14.45
χ 2 ¼ 25.93, d.f. ¼ 6, Po 0.001
699 141
83.23 16.77
755 124
85.89 14.11
χ 2 ¼ 2.36, d.f. ¼ 1, P¼ 0.12
821 19 51.85 52.83 (n ¼25)
97.63 2.37 16.31 67.61
843 36 52.19 48.53 (n¼ 22)
95.50 4.50 15.17 60.33
χ 2 ¼ 4.66, d.f. ¼ 1, P ¼0.03
19 6 11.92
76.00 24.00 9.32
16 6 14.05
72.73 27.27 10.07
(15–96)
(0–215) (0–107)
(0–80) (1–364)
(1–38)
The SDA identified five factors and six distinct subgroups. Factors related to LOS≦90 days were (1) patients with a GAF score of more than 40 at admission; (2) patients age younger than 52; (3) patients with a GAF score of more than 60 at admission; (4) not diagnosed as F3; and (5) not diagnosed as F2. The proportions of patients whose LOS was≦90 days ranged between 61.3% and 95.6% among the subgroups. The subgroups were numbered in descending order of the proportion with LOS≦90 days (Table 3 and Fig. 2). To verify the results of SDA, we used Sample 2 data and performed prediction analyses. As Table 4 shows, there were no significant differences in proportions of patients with LOS≦30 days in acute or emergency wards or in proportions of patients with LOS≦90 days in general wards between samples 1 and 2 (Table 4). The subgroups created by SDA using sample 1 were validated. Tables 5 and 6 summarize the six subgroups in acute or emergency and general wards in terms of the initial explanatory variables. In acute or emergency wards, there was no significant difference among the six subgroups with respect to explanatory variables except for patient's gender, diagnosis of G40, and physician's gender (Table 5). In general wards, there was no significant difference among the six subgroups with respect to explanatory variables except for patient's gender (Table 6).
4. Discussion In the present study, we applied SDA to DPC data and demonstrated how factors related to patients and physicians mutually
(0–111) (0–139)
(10–80) (1–343)
t¼ 2.03, d.f. ¼1717, P ¼0.04
t¼ 1.00, d.f. ¼1716, P¼ 0.32 t¼ 1.75, d.f. ¼ 1536, P¼ 0.08
t¼ 0.45, d.f. ¼1717, P ¼0.65 t¼ 1.39, d.f. ¼ 1717, P¼ 0.16
χ 2 ¼ 1.15, d.f. ¼ 1, P¼ 0.28 (3–40)
t¼ 2.43, d.f. ¼45, P¼ 0.02
interacted to determine LOS at a psychiatric hospital; these effects varied with the type of ward. First, the time that the psychiatrist had been practicing was a significant predictor of LOS among psychiatric patients in acute or emergency wards but not among psychiatric patients in general wards (Figs. 1 and 2). A possible explanation for the association between longer durations of practice by psychiatrists and shorter LOS of patients is that experienced psychiatrists may rely on their experience and become less familiar with newer treatment advances, such as emergency psychiatry (which was only introduced in Japan in 1995), compared to younger psychiatrists. Second, SDA identifies subgroups via combinations of the different independent variables while predicting LOS between acute or emergency wards and general wards (Figs. 1 and 2). In acute or emergency wards, LOS was influenced by two different interactions: (1) the patient's GAF score at admission, the patient's age and the number of years the psychiatrist has been practicing and (2) the patient's GAF score at admission, their F2 diagnosis and whether they arrived by ambulance. This finding is a totally new one. Among the factors that were related to LOS, all have been previously reported (West et al., 2000; Epstein et al., 2001; Fujita and Sato, 2004; Choudhry et al., 2005; Chung et al., 2009; Abe et al., 2011). In general wards, conversely, LOS was influenced by two different interactions: (1) the patient's GAF score at admission, the patient's age and the patient's F2 diagnosis and (2) the patient's GAF score at admission, the patient's age and the patient's F3 diagnosis. This finding is also a new one. Among the factors that were related to LOS, all have been previously reported (West et al., 2000; Epstein
F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218
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Fig. 1. The results of signal detection analysis of factors related to LOS≦30 days in acute or emergency wards.
Table 3 Factors obtained from signal detection analysis and test-performance. In acute or emergency ward (n¼ 843) Outcome: LOS≦30days
GAF score at admission 50
Age 50 years old
F2 diagnosis
Years in practice 12 years
Arrival by ambulance
Odds ratio Sensitivity Specificity Positive predictive value
2.61 0.54 0.69 0.64
3.47 0.84 0.40 0.71
2.49 0.60 0.62 0.52
2.91 0.47 0.76 0.59
2.79 0.53 0.71 0.66
In general ward (n¼ 840) Outcome: LOS≦90days
GAF score at admission 40
Age 52 years old
F2 diagnosis
GAF score at admission 60
F3 diagnosis
Odds ratio Sensitivity Specificity Positive predictive value
3.68 0.86 0.37 0.86
3.66 0.68 0.64 0.92
5.36 0.77 0.61 0.96
3.02 0.58 0.68 0.85
4.05 0.80 0.50 0.90
et al., 2001; Fujita and Sato, 2004; Choudhry et al., 2005; Chung et al., 2009; Abe et al., 2011) with the exception of the diagnosis of mood disorders (F3), which may be explained by differences in financial incentives in Japan. Third, older patients who were involuntarily admitted with F2 or F3 diagnoses received inpatient care for longer durations (Tables 5 and 6). These patients may have required more resources for inpatient care, which consequently
led to longer LOS. In summary, our results based on SDA appear to indicate an effective means for decreasing LOS in psychiatric hospitals. These findings have important practical implications. First, in terms of hospital administration, we suggest that clinicians greater numbers of years of practice receive more training on discharge planning and facilitation of community based care to comply with
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F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218
Fig. 2. The results of signal detection analysis of factors related to LOS≦90 days in general wards.
Table 4 Subgroups and hospital discharge in prediction and validation samples. Group 1 (%)
Group 2 (%)
Group 3 (%)
Group 4 (%)
Group 5 (%)
Group 6 (%)
Cochran Mantel–Haenszel
d.f.
P
Proportions of patients discharged within 30 days from acute or emergency wards Sample 1 (n¼ 843) 71.00 66.32 58.62 41.35 Sample 2 (n¼ 769) 63.50 62.65 39.53 39.47
32.76 51.22
30.12 28.80
1.65
1
0.20
Proportions of patients discharged within 90 days from general care wards Sample 1 (n¼ 840) 95.64 89.90 80.36 Sample 2 (n¼ 879) 93.75 85.40 76.74
64.75 70.49
61.04 64.02
0.03
1
0.87
68.75 72.22
Note: Sample 1 was used for prediction, and Sample 2 was used for validation.
the shift from inpatient care to outpatient care, reduce medical costs and challenge the social stigmas associated with mental illness. Second, for patients in those groups with short estimated LOS, if they choose to use community care after discharge from the hospital, LOS can be shortened by applying for community care use at the time of hospital admission, which would avoid extending the hospital stay while the patients wait for available community care. Community care, such as services offered by daycare
centers, is frequently suggested for patients upon discharge from psychiatric hospitals (Imai et al., 2005). Third, patients with F2 or F3 diagnoses and severe conditions require more inpatient care. Community psychiatric care should be promoted in Japan. However, the promotion of this care requires careful consideration of the patients' clinical conditions. Further development of effective community care, especially care designed for those with severe conditions, is necessary.
F. Badriah et al. / Psychiatry Research 210 (2013) 1211–1218
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Table 5 Characteristics of identified subgroups of patients in acute or emergency wards (Sample 1, n ¼ 843). Subgroups
1 (n¼ 269)
Age (years) Gender (Female) Arrival by ambulance (Yes) Admission type (Involuntary) Referral from other institutions (Yes) Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD-10 F0þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission (Yes) Comorbidity during hospital stay (Yes) GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender (Female) Length of psychiatric practice (years)
33.75
2 (n¼ 95)
3 (n¼ 29)
4 (n ¼133)
5 (n¼ 58)
6 (n ¼259)
42.77
65.17
49.76
64.05
42.23
F(5,837) ¼73.53, Po 0.001
56.13%
47.37%
48.28%
57.14%
53.45%
59.85%
χ 2 ¼5.36, d.f. ¼ 5, P¼ 0.37
22.30%
100.00%
6.90%
0.00%
31.03%
32.05%
χ 2 ¼290.72, d.f. ¼5, Po 0.001
32.71%
81.05%
37.93%
75.94%
48.28%
81.47%
χ 2 ¼175.38, d.f. ¼ 5, Po 0.001
52.04% 0.79 0.16
45.26% 3.29 1.12
62.07% 0.03 0.14
52.63% 2.08 0.78
43.10% 2.19 0.34
39.38% 5.43 1.33
χ 2 ¼13.57, d.f. ¼5, P ¼0.02 F(5,837) ¼641.76, Po 0.001 F(5,837) ¼42.90, P o 0.01
1.89% 5.58% 44.24% 23.79% 10.04% 2.6% 11.9%
14.74% 32.63% 0.00% 22.11% 13.68% 1.05% 15.79%
24.14% 3.45% 34.48% 34.48% 0.00% 0.00% 3.45%
21.05% 19.55% 0.00% 41.35% 6.02% 1.5% 10.53%
10.34% 8.62% 37.93% 32.76% 8.62% 0.00% 1.72%
0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00%
11.15%
26.32%
34.48%
24.81%
17.24%
8.11%
1.49% 58.96 26.78
1.05% 30.53 28.44
0.00% 57.93 35.45
3.01% 33.08 50.41
1.72% 56.55 60.59
3.86% 31.74 57.69
χ 2 ¼ 5:14, d.f. ¼ 5, P¼ 0.40 F(5,837) ¼29,266.90, P o 0.001 F(5,837) ¼34,728.37, P o 0.001
22.3% 12.95
11.58% 12.68
17.24% 4.69
11.28% 12.38
20.69% 17.43
19.69% 15.12
χ 2 ¼10.73, d.f. ¼ 5, P ¼0.06 F(5,837) ¼827.90, P o0.001
5 (n ¼122)
6 (n¼ 154)
χ 2 ¼92.13, d.f. ¼ 5, P o0.001 χ 2 ¼110.56, d.f. ¼5, Po 0.001 χ 2 ¼496.49, d.f. ¼5, P o 0.001 χ 2 ¼114.83, d.f. ¼5, Po 0.001 χ 2 ¼35.12, d.f. ¼5, P o 0.001 χ 2 ¼8.88, d.f. ¼ 5, P¼ 0.11 χ 2 ¼43.27, d.f. ¼ 5, Po 0.001 χ 2 ¼40.49, d.f. ¼ 5, P o0.001
Table 6 Characteristics of identified subgroups for patients in general care wards (Sample 1, n ¼840). Subgroups
1 (n¼ 321)
2 (n ¼99)
3 (n ¼112)
4 (n¼ 32)
Age (years) Gender (Female) Arrival by ambulance (Yes) Admission type (Involuntary) Referral from other institutions (Yes) Length of seclusion (days) Length of restraint (days) Diagnosis based on ICD10 F0þ G3 F1 F2 F3 F4 G40 Others Comorbidity at admission (Yes) Comorbidity during hospital stay (Yes) GAF score at admission Length of stay (LOS) (days) Psychiatrist characteristics Gender (Female) Length of psychiatric practice (years)
34.09
66.94
36.07
63.44
66.74
52.33
F(5,837) ¼226.71, Po 0.001
59.81%
51.52%
60.71%
75.00%
61.48%
58.44%
χ 2 ¼ 6.16, d.f. ¼ 5, P¼ 0.29
5.30%
7.07%
10.71%
0.00%
11.48%
25.97%
χ 2 ¼ 52.63, d.f. ¼ 5, Po 0.001
6.23%
17.17%
27.68%
12.50%
51.64%
72.73%
χ 2 ¼ 263.04, d.f. ¼ 5, P o0.001
53.89% 0.22 0.10
34.34% 0.36 0.25
35.71% 0.93 0.10
71.88% 0.19 0.22
64.75% 0.79 1.16
42.86% 6.60 3.32
χ 2 ¼ 40.57, d.f. ¼5, Po 0.001 F(5,837) ¼10.68, P o0.001 F(5,837) ¼8.55, P o 0.001
0.00% 3.74% 0.00% 37.38% 12.77% 30.53% 15.58%
10.10% 8.08% 23.23% 0.00% 5.05% 25.25% 28.28%
0.00% 0.00% 100.00% 0.00% 0.00% 0.00% 0.00%
0.00% 0.00% 0.00% 100.00% 0.00% 0.00% 0.00%
24.59% 4.10% 33.61% 24.59% 6.56% 1.64% 4.92%
19.48% 7.14% 44.16% 20.77% 4.54% 0.65% 3.25%
χ 2 ¼ 109.94, d.f. ¼5, Po 0.001 χ 2 ¼ 13.23, d.f. ¼5, P¼ 0.02 χ 2 ¼ 438.00, d.f. ¼5, Po 0.001 χ 2 ¼ 191.55, d.f. ¼ 5, P o0.001 χ 2 ¼ 28.25, d.f. ¼ 5, Po 0.001 χ 2 ¼ 136.25, d.f. ¼5, P¼ 0.11 χ 2 ¼ 71.10, d.f. ¼ 5, Po 0.001
8.10%
14.14%
15.18%
9.38%
31.97%
27.27%
χ 2 ¼ 51.56, d.f. ¼ 5, Po 0.001
0.62% 60.34 24.82
1.01% 67.37 29.88
0.89% 52.95 61.15
3.13% 63.44 71.44
5.74% 44.10 85.41
4.55% 27.08 90.23
χ 2 ¼ 15.96, d.f. ¼5, Po 0.01 F(5,837) ¼365.42, P o 0.001 F(5,837) ¼35.16, P o0.001
12.77% 16.88
8.08% 19.94
26.79% 12.83
25.00% 11.88
18.85% 13.89
11.04% 15.01
χ 2 ¼ 22.84, d.f. ¼ 5, Po 0.001 F(5,837) ¼10.55, Po 0.001
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4.1. Limitations The present study has several limitations. First, the Japanese Mental Health Act is unique to the country, and caution must be exercised in extrapolating the findings because they may not be externally valid. Second, caution must be used in generalizing these findings to other medical facilities in Japan because the present study used data from a single psychiatric institution. However, this is the epidemiological study, conducted at an institute with a distinct catchment area. Thus we believe the sample would be representative for other areas of Japan. Third, GAF was measured subjectively by a trained psychiatrist, and no outcome variables were evaluated by the patients themselves. However, the validity and reliability of the GAF score as an outcome measure in psychiatry have been well established (Söderberg et al., 2005). Fifth, the diagnoses used were made by experienced psychiatrists using ICD-10, but were not a research diagnosis. This might raise a concern regarding validity and reliability of the diagnosis. 4.2. Conclusions In summary, we applied SDA to DPC data and showed how patient and physician factors mutually interacted to determine LOS at a psychiatric hospital according to the type of ward. In acute or emergency wards, factors related to LOS were GAF at hospital admission, diagnosis of F2, patient age, arrival by ambulance, and duration of the physician's psychiatric practice. All subjects were categorized by these five factors into six subgroups, and the proportions of patients whose LOS≦30 days ranged between 30.1% and 71.0% among these subgroups. In general wards, factors related to LOS were patients with GAF score of more than 40 or 60 at admission, patient age, and patients with diagnoses of F3 and F2. All subjects were categorized by these five factors into six subgroups, and the proportions of patients with LOS≦90 days ranged between 61.3% and 95.6% among these subgroups. The association between longer durations of practice by psychiatrists and shorter LOS of patients may be explained by the possibility that knowledge of newer treatment advances might be less sufficient among experienced psychiatrists than among younger psychiatrists. In addition, a patient with severe conditions receives longer inpatient care. A future development of effective community care for them is important. Our results based upon SDA seem to suggest effective ways of decreasing the LOS in a psychiatric hospital. Further studies in multiple institutes using a follow-up design are necessary to verify these findings. References Abe, T., Ikeda, K., Kuroda, K., Hagihara, A., 2011. Assessment of psychiatric outcomes in Japan based on diagnostic procedure combination information. Psychiatry Quarterly 82, 163–175. Abe, T., Nagata, T., Hasegawa, M., Hagihara, A., 2012. Life support techniques related to survival after out-of-hospital cardiac arrest in infants. Resuscitation 83, 612–618. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders: Text Revision, 4th ed., American Psychiatric Association. Andreescu, C., Mulsant, B.H., Houck, R.R., Whyte, E.M., Mazumdar, S., Dombrovski, A.Y., Pollock, B.G., Reynolds III, C.F., 2008. Empirically derived decision trees for the treatment of late-life depression. American Journal of Psychiatry 165, 855–862.
Atienza, A.A., Yaroch, A.L., Mãsse, L.C., Moser, R.P., Hesse, B.W., King, A.C., 2006. Identifying sedentary subgroups: the National Cancer Institute's Health Information National Trends Survey. American Journal of Preventive Medicine 31, 383–390. Bowman, D., Scogin, F., Floyd, M., Mckendree-Smith, N., 2001. Psychotherapy length of stay and outcome: a meta-analysis of the effect of therapist sex. Psychotherapy 38, 142–148. Choudhry, N.K., Fletcher, R.H., Soumerai, S.B., 2005. Systematic review: the relationship between clinical experience and quality of health care. Annals of Internal Medicine 142, 260–273. Chung, W.J., Cho, W.H., Yoon, C.W., 2009. The influence of institutional characteristics on length of stay for psychiatric patients: a national database study in South Korea. Social Science & Medicine 68, 1137–1144. Epstein, S.A., Gonzares, J.J., Weinfurt, K., Boekeloo, B., Yuan, N., Chase, G., 2001. Are psychiatrists' characteristics related to how they care for depression in the medically ill? Psychosomatics 42, 482–489. Fujita, T., Sato, T., 2004. [Seishinbyoin deno chokitaizai ni kanrensuru youin] Factors associated with long length of stay in psychiatric institutes. [Kōsei no shihyō] Public Health and Welfare Index 52, 12–19. Hair Jr., J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2010. Multivariate Data Analysis, 7th ed. Prentice-Hall, Upper Saddle River, NJ. Imai, H., Hosomi, J., Nakao, H., Tsukino, H., Katoh, T., Itoh, T., Yoshida, T., 2005. Characteristics of psychiatric hospitals associated with length of stay in Japan. Health Policy 74, 115–121. James, K.E., White, R.F., Kraemer, H.C., 2005. Repeated split sample validation to assess logistic regression and recursive partitioning: an application to the prediction of cognitive impairment. Statistics in Medicine 24, 3019–3035. Kiernan, M., Kraemer, H.C., Winkleby, M.A., King, A.C., Taylor, C.B., 2001. Do logistic regression and signal detection identify different subgroups at risk?: implications for the design of tailored interventions. Psychological Methods 6, 35–48. Kraemer, H.C., 1992. Evaluating Medical Tests: Objective and Quantitative Guidelines. Sage, Newbury Park, CA. Kraemer, H.C., 2004. Reconsidering the odds ratio as a measure of 2by2 association in a population. Statistics in Medicine 23, 257–270. Lee, H.C., Lin, H.C., 2009. Are psychiatrist associated with postdischarge suicide of schizophrenia patients? Schizophrenia Bulletin 35, 760–765. Levine, S.Z., Rbinowitz, J., 2009. A population-based examination of the role of years of education, age of onset, and sex on the course of schizophrenia. Psychiatry Research 168, 11–17. Levine, S.Z., Lurie, I., Khon, R., Levav, I., 2011. Trajectories of the course of schizophrenia: From progressive deterioration to amelioration over three decades. Schizophrenia Research 126, 184–191. Levine, S.Z., 2008. Population-based examination of the relationship between type of first admission for schizophrenia and outcomes. Psychiatric Services 59, 1470–1473. Ministry of Health, Labour and Welfare, 2008a. Central Social Insurance Medical Council. Medical Fee Committee. 〈http:/www.mhlw.go.jp/shingi/2008/07/ s0716-4.html〉 (accessed 1.05.2010) (in Japanese). Ministry of Health, Labour and Welfare, 2008b. Summary of patients survey by the Ministry of Health, Labour and Welfare in 2008. 〈http:/www.mhlwh.go.jp/ toukei/saikin/hw/kanja/08/index.html〉 (accessed on 19.11.2011). Ministry of Health, Labour and Welfare, 2009. Medical costs for inpatient care [Nyuin kihon ryou]. 〈http:/www.mhlw.go.jp/shingi/2009/04/dl/s0415-9h.pdf〉 (accessed on 1.03.2012). Oshima, I., Nino, Y., Inomata, Y., 2003. Institutionalisation and schizophrenia in Japan: social environments and negative symptoms. British Journal of Psychiatry 183, 50–56. Rodriguez, M.A., Winkleby, M.A., Ahn, D., Sundquist, J., Kraemer, H.C., 2002. Identification of population subgroups of children and adolescents with high asthma prevalence. Archives of Pediatrics & Adolescent Medicine 156, 269–275. Söderberg, P., Tungström, S., Armelius, B.A., 2005. Reliability of global assessment of functioning ratings made by clinical psychiatric staff. Psychiatric Services 56, 434–438. The Aging Clinical Research Center, 2008. ROC5 software. Stanford University. West, J.C., Leaf, P.J., Zarin, D.A., 2000. Health plan characterisitcs and conformance with key practice guideline psychopharmacologic treatment recommendations for major depression. Mental Health Services Research 2, 223–237. Westert, G.P., Neiboer, A.P., Groenewegen, P,P., 1993. Variation in duration of hospital stay between hospitals and between doctors within hospitals. Social Science & Medicine 37, 833–839. World Health Organization. International Classification of Diseases (ICD)-10 Online, Current Version. 〈http:/apps.who.int/classifications/apps/icd/icd10online/〉 (accessed 1.05.2010).