|Year : 2017 | Volume
| Issue : 2 | Page : 112-119
Clinical variables and factors affecting duration of hospitalization in a sample of patients with affective and nonaffective psychoses
Nashaat A.M. Abdel-Fadeel, Mohamed A Abdelhameed, Mohamed Taha
Department of Psychiatry, Faculty of Medicine, Minia University, Minia, Egypt
|Date of Submission||01-Nov-2016|
|Date of Acceptance||28-Nov-2016|
|Date of Web Publication||5-Jul-2017|
Nashaat A.M. Abdel-Fadeel
Department of Psychiatry, Faculty of Medicine, Minia University, Minia, 61111
Source of Support: None, Conflict of Interest: None
Duration of hospitalization (DOH) and length of stay in psychiatric hospitals are important factors affecting psychiatric patients and their families, in addition to their impact on the healthcare system policy and budget.
The aims of this study were to identify the sociodemographic and clinical factors implicated in determining DOH and to study the effect of global functioning and insight on length of stay in psychiatric hospitals.
Patients and methods
A total of 129 inpatients diagnosed with affective and nonaffective psychoses were recruited from Minia Psychiatry Hospital. Data regarding their demographic and clinical manifestations were collected, including previous and current admissions. Their severity of illness was assessed with Positive and Negative Syndrome Scale, the level of initial functioning was evaluated by Clinical Global Impression Scale, and the details of their insight were measured by Scale to assess Unawareness of Mental Disorder.
Female participants tended to have longer DOH than males, whereas males tended to have higher number of previous hospitalizations than females. Divorced and single patients had significantly longer DOH than married patients. DOH of current admission was found to increase with increasing severity of mental illness, decreased awareness of mental disorders, deceased awareness of achieved effects of medications, and decreased awareness of social consequences of mental illness. Number of hospitalizations was found to significantly increase with younger age at onset, longer duration of illness, higher number of previous episodes, whereas it was found to decrease with higher total number of received electroconvulsive therapies.
Sex and marital status have an effect on DOH. Increased severity of mental illness and disturbance of insight increase DOH and so does the initial decrease in global functioning.
Keywords: duration of hospitalization, global functioning, insight, psychosis
|How to cite this article:|
Abdel-Fadeel NA, Abdelhameed MA, Taha M. Clinical variables and factors affecting duration of hospitalization in a sample of patients with affective and nonaffective psychoses. Egypt J Psychiatr 2017;38:112-9
|How to cite this URL:|
Abdel-Fadeel NA, Abdelhameed MA, Taha M. Clinical variables and factors affecting duration of hospitalization in a sample of patients with affective and nonaffective psychoses. Egypt J Psychiatr [serial online] 2017 [cited 2021 Sep 17];38:112-9. Available from: http://new.ejpsy.eg.net/text.asp?2017/38/2/112/209682
| Introduction|| |
Duration of hospitalization (DOH) and length of stay in psychiatric hospitals have a strong positive relationship with the cost of hospitalization. Therefore, duration of hospitalization in psychiatric hospitals has become an important factor, not only for patients but also for hospital administrators and healthcare providers (Ithman et al., 2014).
The number of days spent by patients in psychiatric hospitals may be used as an indicator of efficiency of inpatient management, quality of care, and as an important factor in hospital resources for future administration (Iezzoni, 2004). Unnecessarily long hospitalization may be unacceptable, and psychiatrists should make treatment efficient to the utmost possible and shorten patients’ length of stay (Adelufosi et al., 2014).
Factors that determine DOH for psychiatric patients have become a subject of intense concern and have been considerably discussed by many hospitals and bill payers (McLay et al., 2005). Factors that may be involved in DOH include some of the demographic variables such as age, sex, marital and employment status, type of admission, and place of residence (Herr et al., 1991).
Treatment variables include number of previous hospitalizations and received electroconvulsive therapy (ECT) sessions, psychotic features, and violent behavior necessitating restraints (Compton et al., 2006). ECT has been adopted as an effective acute treatment for psychiatric illnesses that fail to respond to conventional treatment and it tended to shorten the duration of stay of patients in hospitals (American Psychiatric Association, 2001).
Implicated variables regarding diagnosis include a primary diagnosis of a psychotic or mood disorder, psychiatric symptom severity, co-morbid medical conditions, and level of functioning at admission (Blais et al., 2003).
Several studies have found an association between diagnosis of schizophrenia or nonaffective psychoses and long duration of hospitalization. The chronic nature of psychotic disorders in comparison with mood disorders was offered as an explanation for this association (Lerner and Zilber, 2010 and Oladeji et al., 2012).
However, substance abuse has been shown to be associated with shorter hospital admission but higher readmission rates (Blais et al., 2003; Compton et al., 2006).
Assessing predictors of DOH is the cornerstone of appropriate management for inpatient resources. Identifying factors associated with long hospitalization might help focus on psychiatric health budget, resulting in less frequency and shorter duration of psychiatric hospitalization (Iezzoni, 2004).
| Patients and methods|| |
The present study was carried out at Minia Psychiatry Hospital, the official Psychiatry Hospital in Minia Governorate. It provides services for psychiatric patients and patients of substance use with an inpatient capacity of 80 beds. The hospital includes outpatient clinics (Psychiatry, Neurology and Internal Medicine clinics), a hot-line clinic for addiction, and some special units – for example, pediatric psychiatry unit and electroencephalogram.
| Participants of the study|| |
All patients diagnosed with schizophrenia spectrum disorders (schizophrenia, schizoaffective, or schizophreniform disorders) or affective psychoses disorders (bipolar affective disorder type Ι or psychotic depression), who were admitted to Minia Psychiatry Hospital, and discharged with improvement within the duration of 6 months (from 1 September 2015 to end of February 2016) were recruited. They were officially diagnosed with Structural Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-V) Research Version. These patients were screened at admission and discharge by Positive and Negative Syndrome Scale (PANSS) to assess symptom severity. Patient awareness and insight of his or her mental illness and symptoms were assessed by the Scale to assess Unawareness of Mental Disorder, and the Clinical Global Impression Scale (CGI) was used to assess severity and improvement of the general condition and functioning.
- Patients diagnosed with schizophrenia spectrum disorder or affective psychosis, with or without co-morbid substance use.
- Age range: 18–50 years.
- Both sexes.
- First or multiple admissions.
- Patients admitted to the hospital for a reason other than treatment (e.g. forensic reasons).
- Patients discharged from the hospital against medical advice.
- Patients discharged for other medical or surgical co-morbidities.
- PANSS (Kayet al., 1987):
The PANSS ratings are based on all information pertaining to a specified period, usually the previous week. The information derives from both clinical interview and reports of primary-care staff or family members. The interview is semiformalized and facilitates direct observation of affective, motor, cognitive, perceptual, attention, and other mental functions (Kay et al., 1987).
Data elicited by this assessment procedure are applied to the PANSS, a 30-item, seven-point rating instrument that has adapted 18 items from the Brief Psychiatric Rating Scale (Overall and Gorham, 1962) and 14 items from the Psychopathology Rating Schedule (Singh and Kay, 1975). Each item on the PANSS is accompanied by a complete definition as well as detailed anchoring criteria for all seven rating points, which represent increasing levels of psychopathology.
- CGI (Guy, 1976).
The CGI was developed for use in National Institute of Mental Health-sponsored clinical trials to provide a brief, stand-alone assessment of the clinician’s view of the patient’s global functioning before and after initiating a study medication.
The CGI provides an overall clinician-determined summary measure that takes into account all available history and clinical information. The CGI can track clinical progress across time by assessing two parameters: CGI-severity (the so-called baseline visit) and CGI-improvement (after medication has been initiated in comparison with the patient’s baseline condition) (Guy, 1976).
- SUMD (Amadoret al., 1994).
This is the most comprehensive scale for the assessment of insight. It is used to evaluate awareness of illness, awareness of social consequences of illness, and attribution of illness symptoms.
The scale is composed of 20 items (three items are general: (i) awareness of mental disorder, (ii) awareness of the achieved effects of medication, and (iii) awareness of the social consequences of mental disorder (Amador et al., 1994).
The data collected were recorded on a separate file for each patient and were given codes; data analysis was performed using the Statistical Package of Social Sciences (Released 2007. SPSS for Windows, Version 16.0. Chicago, SPSS Inc.).
| Results|| |
Our final sample included 129 patients who were eligible according to inclusion and exclusion criteria of the study.
As shown in [Table 1], schizophrenia is the most prevalent psychiatric disorder in admitted psychiatric patients; about half of admitted patients were diagnosed with schizophrenia (51.9%), followed by bipolar affective disorders (BAD) in manic episodes (23.3%), and then substance-induced schizophrenia (10.1%).
|Table 1: Individual diagnoses of patients according to structured clinical interview for DSM-V|
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As shown in [Table 2], there were no statistically significant differences between the group of schizophrenia and related disorders and the group of mood disorders regarding sociodemographic characteristics, except in the domains of marital status (P=0.007), where the schizophrenia and related disorders group had more single patients (64%) in comparison with 36% of mood disorders, and in the domain of number of years of completed education (P=0.04), where the mean number years of completed education in the schizophrenia and related disorders group was 9.6 years whereas in the mood disorders group it was 7.7 years.
|Table 2: Comparison between schizophrenia and related disorders and mood disorders regarding sociodemographic characteristics|
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As shown in [Table 3], all DOH variables are affected by marital state in which divorced patients had the longest durations in all variables followed by single patients and then married patients; the difference was statistically significant regarding DOH of current admission (P=0.05). In addition, males had a higher number of previous admissions but females had longer DOH of current admission (P=0.01).
As shown in [Table 4], DOH of current admission was significantly longer in patients with schizophrenia and related disorders than in patients with mood disorders (P=0.002). In addition, involuntary admission was related to longer DOH of current admission in comparison with voluntary admission (P=0.03).
|Table 4: Duration of hospitalization variables according to diagnosis and type of admission|
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As shown in [Table 5], primary psychotic disorders had the longest DOH of current admission, followed by primary mood disorders and then both substance-induced psychotic disorders and mood disorders (P=0.001).
|Table 5: Comparison between primary psychotic and mood disorders and substance-induced psychotic and mood disorders regarding admission variables|
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As shown in [Table 6], the number of hospitalizations significantly increased with lower age at onset (P=0.03), longer duration of illness (P=0.001), and higher number of previous episodes (P=0.001), whereas it decreased with higher total number of ECTs (P=0.001). Regarding DOH of current admission, it increased with higher age at onset and decreased with longer duration of illness, higher number of previous episodes, and higher number of current and total ECTs without statistical significance.
|Table 6: Correlation of duration of hospitalization variables with some illness and electroconvulsive therapy-related variables|
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As shown in [Table 7], number of hospitalizations including current admission increased with higher severity of mental illness, poor global improvement, decreased awareness of mental disorders, decreased awareness of achieved effects of medications, and decreased awareness of social consequences of mental disorders. There was no statistical significance for any of them.
|Table 7: Correlations of hospitalization variables with Clinical Global Impression Scale and Scale to assess Unawareness of Mental Disorder|
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DOH of current admission increased with increasing severity of mental illness, decreased awareness of mental disorders, deceased awareness of achieved effects of medications, and decreased awareness of social consequences of mental illness, as well as with higher global improvement scores. There was statistical significance for only global improvement score and achieved effects of medications (P=0.001 and 0.04, respectively).
As shown in [Table 8], it was found that the independent variable affecting number of hospitalizations was the number of ECTs (β=0.67, P=0.001) followed by age, age of onset, and duration of illness.
|Table 8: Regression analysis for variables affecting number of hospitalizations|
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As shown in [Table 9], the independent variables affecting DOH of current admission were blunted affect on admission (n1a) (β=0.40, P=0.01*), followed by emotional withdrawal on admission and preoccupation on admission.
|Table 9: Regression analysis for variables affecting duration of hospitalization of current admission|
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| Discussion|| |
Duration of hospitalization has attracted much clinical and research attention during recent years (Ithman et al., 2014). The current study aimed at assessing this important issue in an Egyptian sample of inpatients with affective and nonaffective psychoses. This might be reflected on improving management programs (Niehaus et al., 2008; Yussuf et al., 2008).
Schizophrenia was the most common diagnosis in our sample (51%), followed by bipolar affective disorder in manic episodes (23.3%); this was in agreement with a previous Egyptian study, which found schizophrenia to be the most common chronic psychosis in Egypt and accounts for the majority of in-patients in mental hospitals (Okasha, 2004).
These figures may be explained by the aggressive, suicidal, or homicidal behavior, delusions, or poor insight found in schizophrenia and BAD patients during manic episodes, necessitating hospitalization. In addition, schizophrenia is still the diagnosis that has the greatest influence on hospitalization decisions (Slagg, 1993; Schnyder et al., 1999; Way and Banks, 2001; Wingerson et al., 2001; Giampieri et al., 2002).
Our sample showed male predominance in both groups as males represented 78.8% of schizophrenia and related disorders and 72.7 of mood disorders.
The majority of schizophrenia and related disorders patients (64.5%) were single, whereas 45.5% of mood disorders patients were married. This was similar to the results of other studies (Pacheco et al., 2010). This could be explained by the tendency of patients with schizophrenia and related disorders to display a younger age at onset, higher severity of illness, more impairment in occupational and social functioning, and poor judgment and insight when compared with those with mood disorders.
It seems that affective and nonaffective psychoses affect occupational functioning to a comparable degree, as 52.9% of nonaffective psychotic patients and 47.7% of affective psychotic patients were currently not working and about 30% of both groups never had a job. This might be explained by psychological barriers imposed by severe mental illness to professional inclusion (Laberon, 2014). In addition, cognitive dysfunction in both affective and nonaffective psychoses may affect occupational functioning (Lewandowski et al., 2013) through difficulty getting or maintaining a job.
Females tended to have longer DOH than males, whereas males tended to have higher number of previous hospitalizations than females. This was in contrast to the results of Goldstein (1988) and Angermeyer et al. (1990) who found that men with schizophrenia tended to spend more time in the hospital than women with schizophrenia.
This could be explained by the fact that families of female patients in our culture are very reluctant to take the decision of admission of female patients ‘due to stigma’ unless symptoms are very severe. This leads to fewer admissions but longer DOH. In addition, the possibility of stigma to the family may lead to avoiding health facilities that lead to worsening the severity of illness and prolonged DOH (Fraser, 2003). Furthermore, Hodgson et al. (2001) and Heeren et al. (2002) found that the shorter the length of stay at hospital, the more is the readmission for psychiatric patients.
Divorced and single patients had significantly longer DOH than married patients. This could be explained by the better social support in married patients than single and divorced patients making their DOH shorter. This is in agreement with Ithman et al. (2014) who found that increased social support in the form of marriage might facilitate discharge and Ismail et al. (2012) who found that living alone may predict longer DOH.
DOH of current admission for schizophrenia and related disorders (41.9 days) was found to be significantly longer than for mood disorders (32.2 days) and was similar to the results of other studies (Thompson et al., 2004; Fekadu et al., 2007; Lerner and Zilber, 2010 and Oladeji et al., 2012). These studies generally found that a diagnosis of schizophrenia or nonaffective psychoses is a predictor of long hospital stays. The chronic nature of psychotic disorders compared with mood disorders might explain this finding (Addisu et al., 2015). However, the rapid response of BAD patients to available treatments that are proven to be effective makes their DOH shorter compared with nonaffective psychotic patients (Karamustafalıoğlu et al., 2014). Another explanation is that patients suffering from schizophrenia do not receive proper treatment in the community leading to increased hospital stays (Mitchell and Malone, 2006).
Other studies have also revealed that schizophrenia is related to longer DOH (Addisu et al., 2015) with a mean of DOH for schizophrenia around 28 days and for BAD around 23–25 days. These figures are shorter than what were found in our study. These differences could be explained by methodological differences, severity of symptoms, and psychopathology in our sample or the nature of the hospital where we conducted our study in, because it is a governmental hospital, free of charge with little pressure for fast turnover by insurance companies as in western societies in an effort to reduce costs (Leigh, 2011).
Moreover, absence of accepted DOH guidelines for psychiatric in-patients may reflect the differences in psychiatrists’ practice styles (Figueroa et al., 2004).
In agreement with Blais et al. (2003), Adegunloye et al. (2009), and Ithman et al. (2014), involuntary admission in our study was associated with significantly longer DOH (39.3 days) than voluntary admission (24.5 days).
Regarding substance misuse, DOH in substance-related psychiatric disorders (affective or nonaffective psychoses) was found to be significantly shorter than that in primary psychiatric disorders; this is consistent with the results of other studies (Herr et al., 1991; Huntley et al., 1998; Blais et al., 2003; Compton et al., 2006; Zhang et al., 2011).
Number of hospitalizations was found to significantly increase with younger age at onset (P=0.03), longer duration of illness (P=0.001), and higher number of previous episodes (P=0.001), whereas it was found to decrease with higher total number of ECTs (P=0.001). Our results are consistent with the results of other studies (American Psychiatric Association, 2001; Suominen et al., 2007).
DOH of current admission was found to increase with higher age at onset and decrease with longer duration of illness, higher number of previous episodes, and higher number of current and total ECTs taken during the whole course of illness. The American Psychiatric Association (2001) reported similarly regarding ECT, suggesting that that the prompt effectiveness of ECT shortened the DOH of patients.
Number of hospitalizations including current admission was found to increase with higher severity of mental illness, poor global improvement, decreased awareness of mental disorders, decreased awareness of achieved effects of medications, and decreased awareness of social consequences of mental disorders.
In agreement with Martin-Carrasco et al. (2012) and Moreschi et al. (2015), DOH of current admission was found to increase with increasing severity of mental illness, decreased awareness of mental disorders, deceased awareness of achieved effects of medications, and decreased awareness of social consequences of mental illness; thus, suggesting that patients with more severity of illness and impairment of insight are expected to spend more time hospitalized.
Regression analysis for variables affecting number of hospitalizations revealed that the independent variables affecting number of hospitalizations were number of ECTs followed by age, age of onset, and duration of illness. This was consistent with other studies (American Psychiatric Association, 2001).
Regression analysis for variables affecting DOH of current admission revealed that the independent variables affecting it were blunted affect followed by emotional withdrawal and preoccupation on admission. These findings are in agreement with Patel et al. (2015) who found that negative symptoms were associated with longer DOH and more frequent admissions. This might be explained by persistence and poor response of negative symptoms to available treatments. 
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Addisu F, Wondafrash M, Chemali Z, Dejene T, Tesfaye M (2015). Length of stay of psychiatric admissions in a general hospital in Ethiopia: a retrospective study. Int J Ment Health Syst 9:13.
Adegunloye OA, Yussuf AD, Ajiboye PO, Issa BA, Buhari OIN (2009). Correlates of length of stay among psychiatric in-patients in a tertiary health institution in Nigeria. Res J Med Sci 3:56–61.
Adelufosi AO, Fakorede K, Obasan A, Opeewe IO, Ojo TM (2014). Patient and treatment related variables as predictors of length of stay in a Nigerian Neuropsychiatric hospital. J Behav Health 3:117–121.
Amador XF, Flaum M, Andreasen NC, Strauss DH, Yale SA, Clark SC, Gorman JM (1994). Awareness of illness in schizophrenia and schizoaffective and mood disorders. Arch Gen Psychiatry 51:826–836.
American Psychiatric Association Task Force on Electroconvulsive Therapy (2001). The Practice of Electroconvulsive Therapy: Recommendations for Treatment, Training and Privileging, Task Force Report on ECT, 2nd edition. Washington, DC:American Psychiatric Association.
Angermeyer MC, Kuhn L, Goldstein JM (1990). Gender and The course of schizophrenia: differences in treated outcome. Schizophr Bull 16:293–307.
Blais MA, Matthews J, Orlando R, Lechner E, Jacobo M (2003). Predicting length of stay on an acute care medical psychiatric inpatient service. Adm Policy Ment Health 31:15–29.
Compton MT, Craw J, Rudisch BE (2006). Determinants of inpatient psychiatric length of stay in an Urban County Hospital. Psychiatr Q 77:173–188.
Fekadu A, Desta M, Alem A, Prince M (2007). A descriptive analysis of admissions to Amanuel Psychiatric Hospital in Ethiopia. Ethiop J Health Dev 21:173–178.
Figueroa R, Harman J, Engberg J (2004). Use of claims data to examine the impact of length of inpatients psychiatric stay on readmission rate. Psychiatr Serv 55:560–565.
Fraser S (2003). Early Psychosis Intervention (EPI) Program, Fraser Area, BC. Copyright, Fraser South EPI Program.
Giampieri E, Ratti A, Beretta A, Mattavelli C, Ferrarini E, Pruneri C, Carta I (2002). Determinations of hospitalization from psychiatric ER from S. Gerardo hospital in Monza: epidemiological cross-sectional study. Epidemiol Psichiatr Soc 11:266–276.
Goldstein JM (1988). Gender differences in the course of schizophrenia. Am J Psychiatry 145:684–689.
Guy W (1976). ECDEU assessment manual for psychopharmacology. Rockville, MD: Department of Health, Education, and Welfare.
Heeren O, Dixon L, Gavirneni S, Regenold W (2002). The association between decreasing length of stay and readmission rate on a psychogeriatric unit. Psychiatr Serv 53:76–79.
Herr BE, Abraham HD, Anderson W (1991). Length of stay in a general hospital psychiatric unit. Gen Hosp Psychiatry 13:68–70.
Hodgson R, Lewis M, Boardman A (2001). Prediction of readmission to acute psychiatric units. Soc Psychiatry Psychiatr Epidemiol 36:304–309.
Huntley DA, Cho DW, Christman J, Csernasky JG (1998). Predicting length of stay in acute psychiatric hospital. Psychiatr Serv 49:1049–1053.
Iezzoni LI (2004). Risk adjustment for measuring health care outcomes. Int J Qual Health Care 16:181–182.
Ismail Z, Arenovich T, Grieve C, Willett P, Sajeev G, Mamo DC (2012). Predicting hospital length of stay for geriatric patients with mood disorders. Can J Psychiatry 57:696–703.
Ithman MH, Goplarkrishna G, Beck NC, Das J, Petroski G (2014). Predictors of length of stay in an acute psychiatric hospital. J Biosafety Health Educ 2:119.
Karamustafalıoğlu O, Reif A, Atmaca M, Gonzalez D, Moreno-Manzanaro M, Gonzalez MA et al.
(2014). Hospital stay in patients admitted for acute bipolar manic episodes prescribed quetiapine immediate or extended release: a retrospective non-interventional cohort study (HOME). BMC Psychiatry 14:246.
Kay SR, Fiszbein A, Opler LA (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 13:261–276.
Laberon S (2014). Psychological barriers to professional inclusion of people with mental disabilities. Encephale 40:103–114.
Leigh JP (2011). Economic burden of occupational injury and illness in the United States. Milbank Q 89:728–772.
Lerner Y, Zilber N (2010). Predictors of cumulative length of psychiatric inpatient stay over one year: a National Case Register Study. Isr J Psychiatry Relat Sci 47:304–307.
Lewandowski KE, Cohen BM, Keshavan MS, Sperry SH, Ongür D (2013). Neuropsychological functioning predicts community outcomes in affective and non-affective psychoses: a 6-month follow-up. Schizophr Res 148:34–37.
Martin-Carrasco M, Gonzalez-Pinto A, Galan JL, Ballesteros J, Maurino J, Vieta E (2012). Number of prior episodes and the presence of depressive symptoms are associated with longer length of stay for patients with acute manic episodes. Ann Gen Psychiatry 11:7.
McLay RN, Daylo A, Hammer PS (2005). Predictors of length of stay in a psychiatric ward serving active duty military and civilian patients. Mil Med 170:219–222.
Mitchell AJ, Malone D (2006). Physical health and schizophrenia. Curr Opin Psychiatry 19:432–437.
Moreschi HK, Pavan G, Godoy JA, Mondrzak R, Pacheco MA, Nogueira EL et al.
(2015). Factors related to positive and negative outcomes in psychiatric inpatients in a General Hospital Psychiatric Unit: a proposal for an outcomes index. Arch Clin Psychiatry 42:6–12.
Niehaus DJ, Koen L, Galal U, Dhansay K, Oosthuizen PP, Emsley RA, Jordaan E (2008). Crisis discharges and readmission risk in acute psychiatric male inpatients. BMC Psychiatry 8:44.
Okasha A (2004). Focus on psychiatry in Egypt. Br J Psychiatry 185:266–272.
Oladeji BD, Ogundele AT, Dairo M (2012). Determinants of length of stay in the psychiatric wards of the University College Hospital, Ibadan, Nigeria. Afr J Med Med Sci 41:147–152.
Overall JE, Gorham DR (1962). The Brief Psychiatric Rating Scale. Psychol Rep 10: 799–812.
Pacheco A, Barguil M, Contreras J, Montero P, Dassori A, Escamilla MA, Raventós H (2010). Social and clinical comparison between schizophrenia and bipolar disorder type I with psychosis in Costa Rica. Soc Psychiatry Psychiatr Epidemiol 45:675–680.
Patel R, Jayatilleke N, Broadbent M, Chang C, Foskett N, Gorrell G et al.
(2015). Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method. BMJ Open 5:e007619.
Schnyder U, Klaghofer R, Leuthold A, Buddeberg C (1999). Characteristics of psychiatric emergencies and the choice of intervention strategies. Acta Psychiatr Scand 99:179–187.
Singh MM, Kay SR (1975). A comparative study of haloperidol and chlorpromazine in terms of clinical effects and therapeutic reversal with benztropine in schizophrenia. Theoretical implications for potency differences among neuroleptics. Psychopharmacologia 43:103–113.
Slagg NB (1993). Characteristics of emergency room patients that predict hospitalization or disposition to alternative treatments. Hosp Community Psychiatry 44:252–256.
Suominen K, Mantere O, Valtonen H, Arvilommi P, Leppämäki S, Paunio T, Isometsä E (2007). Early age at onset of bipolar disorder is associated with more severe clinical features but delayed treatment seeking. Bipolar Disord 9:698–705.
Thompson A, Shaw AM, Harrison G, Verne J, Davidson H (2004). Patterns of hospital admission for adult psychiatric illness in England: analysis of Hospital Episode Statistics data. Br J Psychiatry 185:334–341.
Way BB, Banks S (2001). Clinical factors related to admission and release decisions in psychiatric emergency services. Psychiatr Serv 52:214–218.
Wingerson D, Russo J, Ries R, Dagadakis C, Roy-Byrne P (2001). Use of psychiatric emergency services and enrollment status in a public managed mental health care plan. Psychiatr Serv 52:1494–1501.
Yussuf AD, Kuranga SA, Balogun OR, Ajiboye PO, Issa BA, Adegunloye O, Parakoyi MT (2008). Predictors of psychiatric readmissions to the psychiatric unit of a tertiary health facility in a Nigerian city − a 5-year study. Afr J Psychiatry (Johannesbg) 11:187–190.
Zhang J, Harvey C, Andrew C (2011). Factors associated with length of stay and the risk of readmission in an acute psychiatric inpatient facility: a retrospective study. Aust N Z J Psychiatry 45:578–585.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9]