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Received: 9 May 2022
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Accepted: 8 April 2023
DOI: 10.1002/jclp.23528
RESEARCH ARTICLE
Clinical determinants of involuntary psychiatric
hospitalization: A clinical profile approach
Eugenie Georgaca
1
|Sofia Machaira
1
|Dimitrios Stamovlasis
2
|
Lily Evangelia Peppou
3
|Christina Papachristou
1
|
Aikaterini Arvaniti
4
|Maria Samakouri
4
|Stelios Stylianidis
3
|
Vasileios Panteleimon Bozikas
5
|Ioannis Diakogiannis
6
|
Konstantinos Fokas
6
|Georgios Garyfallos
5
|Ioanna Gkolia
7
|
Vassiliki Karpouza
8
|Ioannis Nimatoudis
9
|
Georgios Patsinakidis
10
|Dimitrios Sevris
10
|Aikaterini Vlachaki
11
1
School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
2
School of Philosophy and Education, Aristotle University of Thessaloniki, Thessaloniki, Greece
3
Department of Psychology, Panteion University of Social Sciences, Athens, Greece
4
Department of Psychiatry, Democritus University of Thrace, Alexandroupoli, Greece
5
Second Department of Psychiatry, School of Medicine, Psychiatric Hospital of Thessaloniki, Aristotle University of Thessaloniki,
Thessaloniki, Greece
6
First Department of Psychiatry, School of Medicine, General Hospital “Papageorgiou”, Aristotle University of Thessaloniki,
Thessaloniki, Greece
7
Psychiatric Hospital of Thessaloniki, C Acute Ward, Thessaloniki, Greece
8
Psychiatric Hospital of Thessaloniki, D Acute Ward, Thessaloniki, Greece
9
Third Department of Psychiatry, School of Medicine, AHEPA University General Hospital‐Department of Mental Health, Aristotle
University of Thessaloniki, Thessaloniki, Greece
10
Psychiatric Hospital of Thessaloniki, B Acute Ward, Thessaloniki, Greece
11
Psychiatric Department, G. Papanikolaou General Hospital, Thessaloniki, Greece
Correspondence
Eugenie Georgaca, School of Psychology,
Aristotle University of Thessaloniki, Main
University Campus, Thessaloniki 54124, Greece.
Email: georgaca@psy.auth.gr
Abstract
Objectives: The study examines the clinical determinants of
involuntary psychiatric hospitalization. Specifically, it investi-
gates whether distinct clinical profiles of hospitalized patients
can be discerned, what other characteristics they are linked
with, and which profiles predict involuntary admission.
J Clin Psychol. 2023;1–20. wileyonlinelibrary.com/journal/jclp
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1
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
© 2023 The Authors. Journal of Clinical Psychology published by Wiley Periodicals LLC.
Methods: In this cross‐sectional multicentre population
study, data were collected for 1067 consecutive admis-
sions in all public psychiatric clinics of Thessaloniki,
Greece, during 12 months. Through Latent Class Analysis
distinct patient clinical profiles were established based on
Health of the Nation Outcome Scales ratings. The
profiles were then correlated with sociodemographic,
other clinical, and treatment‐related factors as covariates
and admission status as a distal outcome.
Results: Three profiles emerged. The “Disorganized Psy-
chotic Symptoms”profile, combining positive psychotic
symptomatology and disorganization, included mainly men,
with previous involuntary hospitalizations and poor contact
with mental health services and adherence to medication,
indicating a deteriorating condition and chronic course. Τhe
“Active Psychotic Symptoms”profile included younger
persons with positive psychotic symptomatology in the
context of normal functioning. The “Depressive Symptoms”
profile, characterized by depressed mood coupled with
nonaccidental self‐injury, included mainly older women in
regular contact with mental health professionals and
treatment. The first two profiles were associated with
involuntary admission and the third with voluntary
admission.
Conclusions: Identifying patient profiles allows the exam-
ination of the combined effect of clinical, sociodemographic,
and treatment‐related characteristics as risk factors for
involuntary hospitalization, moving beyond the variable‐
centered approach mainly adopted to date. The identifica-
tion of two profiles associated with involuntary admission
necessitates the development of interventions tailored to
chronic patients and younger persons suffering from
psychosis respectively.
KEYWORDS
clinical characteristics, HoNOS, involuntary hospitalization,
Latent Class Analysis, psychiatric admission
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1|INTRODUCTION
Involuntary psychiatric admission of individuals with mental health problems is a complex and controversial
practice, as it raises important ethical, clinical, and legal issues (Gooding et al., 2020; Thomsen et al., 2017).
Governed by the need to keep a balance between patients' human rights, the need for treatment, and public safety
(Stylianidis et al., 2016), involuntary admission imposes restrictions to the admitted person's autonomy, potentially
jeopardizing the therapeutic relationship (Saya et al., 2019; Sheehan & Burns, 2011) as well as relationships with
family members (Sugiura et al., 2020). Involuntary admission (IA) is practiced throughout the world with
considerable variation in rates between and within countries (Salize & Dressing, 2004; Sheridan Rains et al., 2019)
and overall rising rates have been detected in recent years (Weich et al., 2017; Wierdsma & Mulder, 2009). In
Greece, the percentage of IAs seems to be high, estimated by the few studies conducted to 50%–60% of total
admissions in public psychiatric units (Bozikas et al., 2003; Skokou et al., 2017; Stylianidis et al., 2017), at least
quadruple of the European average. This has been attributed mainly to the incomplete implementation of
community mental health care, resulting in a fragmented and unstable mental health service system
(Giannakopoulos & Anagnostopoulos, 2016; Pallis et al., 2007). Moreover, there are reports of frequent use of
coercive measures during IA (Kalisova et al., 2014) and problems in its implementation, indicating ineffective
protection of the admitted persons' rights (CPT, 2019; Greek‐Ombudsman, 2007).
The process of IA in Greece is initiated by relatives, who request psychiatric assessment of the person from the
public prosecutor. The public prosecutor orders in turn transportation to a designated public psychiatric unit for
psychiatric assessment. Following assessment, a recommendation for involuntary hospitalization may be made
based on the following criteria: (a) the patient is suffering from mental illness, (b) because of their mental state they
are incapable of making decisions according to their best interests, and (c) if left untreated, the patient's health will
be gravely exacerbated, and they will pose a danger to themselves or others. The public prosecutor then refers the
case to the courts which make the final decision. The process may also be instigated “ex officio”by the public
prosecutor, usually after being alerted by the police or a member of the public, for example, neighbors
(Chatzisimeonidis et al., 2021; Stylianidis et al., 2017).
There has been significant research on the associations of sociodemographic, clinical‐and treatment‐related
factors with admission status. Regarding sociodemographic characteristics, lower education level (Hustoft et al.,
2013; Luo et al., 2019; Wynn, 2018), being unemployed (Chang et al., 2013; Lorenzo et al., 2018; Umama‐Agada
et al., 2018) or retired (Schmitz‐Buhl et al., 2019), being unmarried or separated/widowed (Canova Mosele et al.,
2018; Feeney et al., 2019; Gilhooley et al., 2017) and living alone (Drakonakis et al., 2021; Lorenzo et al., 2018; Van
Der Post et al., 2012) have been associated with IA. The findings regarding age (Gilhooley et al., 2017; Maina et al.,
2021; Silva et al., 2018) and gender (Curley et al., 2016; Eytan et al., 2013; Umama‐Agada et al., 2018) are
inconsistent.
Clinical factors associated with IA include specific diagnostic categories, such as schizophrenia and other
psychotic disorders (Feeney et al., 2019; Lorenzo et al., 2018; Myklebust et al., 2012; Wheeler et al., 2005), organic
mental disorders, mental retardation (Hoffmann et al., 2017; Silva et al., 2018; Thomsen et al., 2017) and substance
abuse (Hustoft et al., 2013; Schuepbach et al., 2006). The main treatment‐related factors associated with IA are
poor compliance with medication (Maria Balducci et al., 2017; Silva et al., 2018; Verdoux et al., 2000), less contact
with psychiatric services and/or absence of outpatient treatment before admission (Hustoft et al., 2013; Schmitz‐
Buhl et al., 2019) and previous involuntary hospitalization (Indu et al., 2018; Lebenbaum et al., 2018; Montemagni
et al., 2012; Silva et al., 2018). Previous involuntary hospitalizations were found to be among the strongest
predictors of compulsory readmission in recent studies (Lay et al., 2019; Tulloch et al., 2016), raising concerns about
the establishment of the revolving‐door phenomenon of compulsory admissions.
Regarding clinical symptoms, studies have shown that persons more likely to be involuntarily admitted were
those with higher levels of positive psychotic symptoms and aggressive behavior (Canova Mosele et al., 2018;
Ritsner et al., 2018; Silva et al., 2018; Zhou et al., 2015), while depressive and/or anxiety symptoms were found to
GEORGACA ET AL.
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be associated with voluntary admission (VA) (Hustoft et al., 2013; Opjordsmoen et al., 2010; Preti et al., 2009; Silva
et al., 2018). Severity of symptoms and problems in functioning were found significantly higher in involuntarily
admitted patients compared to those with voluntary status (Benedikt et al., 2018; Mulder et al., 2005; Opjordsmoen
et al., 2010). Among clinical symptoms, the strongest predictors of IA were found to be aggressive behavior,
hallucinations and delusions, low global functioning scores (Hustoft et al., 2013; Silva et al., 2018), and danger to
self or others (Mulder et al., 2005).
In this study, we focus on clinical characteristics because psychiatric hospitalization in general and involuntary
hospitalization in particular are justified on the basis of the emergency of the patient's clinical condition. Amongst
clinical characteristics, the most frequently examined factor in international research is diagnosis, despite evidence‐
based arguments that diagnosis does not exert an independent influence on the risk of IA after controlling for
clinical status (Montemagni et al., 2012). The studies that examine distinct clinical characteristics associated with IA
have mainly adopted a variable‐centered approach, seeking to find the main drivers of admission status among
various types of factors.
The present study adopts a methodological shift towards a person‐centered approach and explores the
existence of distinct patient profiles based on clinical characteristics that could be associated with admission status.
Highlighting clinical characteristics profiles associated with IA is expected to contribute to designing targeted
interventions and practices in mental health policy that would provide appropriate treatment in a timely manner, to
avoid the necessity of IA and to reduce its frequency. This is particularly important in countries, such as Greece,
characterized by high IA rates, frequent use of coercive measures during hospitalization and problems in
safeguarding the rights of involuntarily hospitalized patients.
The current study endeavors to investigate the existence of distinct profiles among patients based on clinical
characteristics, which could be associated with admission status. In addition, it is hypothesized that these profile
memberships encompassing specific symptom patterns could be associated with clinical, sociodemographic,
treatment‐related factors and IA. Designing the above exploratory inquiry, the following research questions were
posited: (1) Can distinct profiles among hospitalized patients be discerned based on clinical characteristics? (2)
Which sociodemographic, clinical, and treatment‐related characteristics are associated with the ensuing profiles? (3)
Which specific patients' clinical profiles, if any, can predict admission status?
2|METHODS
2.1 |Design and participants
This cross‐sectional study is part of the multicenter research program “Study of Involuntary Hospitalizations in
Greece (MANE)”that examined the process of and risk factors for IA in the public mental health care system in
Greece. The present study used data collected from the Thessaloniki site of the MANE project, which was
conducted under the auspices of the Schools of Psychology and Medicine of the Aristotle University of
Thessaloniki. The participation of all regional acute psychiatric clinics in the Thessaloniki site of the study offered a
unique opportunity for a population study, which would have been lost had the overall multisite sample been
included. The region of Thessaloniki includes a large metropolitan area of the city and its suburbs as well as
numerous villages in the broader area, with an estimated population of one million.
The study sample included all adult individuals who were hospitalized, voluntarily and involuntarily, in the
public psychiatric clinics of Thessaloniki from March 2018 to February 2019. Specifically, data were collected
regarding all consecutive hospitalizations at all eight acute public psychiatric clinics of the metropolitan area of
Thessaloniki. Five of them are part of the Psychiatric Hospital of Thessaloniki and the rest are psychiatric units of
general hospitals. For patients who had multiple hospitalizations during the data collection period, only data for
their first hospitalization were registered, to avoid duplication. Refusal to participate in the study was the only
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GEORGACA ET AL.
exclusion criterion. A total of 1125 patients were registered for the study. As a result of incomplete or missing data,
58 cases were excluded. Thus, the final data set for analysis consisted of 1067 cases. There were no significant
differences in terms of admission status, age, gender, and family status between participants and patients who did
not provide consent and were, thus, not included in the study.
2.2 |Procedures and measures
Data were collected from administrative and medical records and through a brief interview with admitted patients,
conducted within the first 10 days of admission. Data collection was performed by trained researchers, under the
supervision of the clinic staff and the project research team.
The following types of collected data were used for the present study:
a. Sociodemographic characteristics: gender (male, female); age; nationality (Greek, non‐Greek); family status
(unmarried, married, divorced/separated, widowed); living conditions (alone, with family of origin, in supported
accommodation, with others/nonrelatives, homeless); education level (none, primary, compulsory (until age 16),
secondary, higher, postgraduate); employment status (full time, part‐time, occasional work, supported
employment, unemployed, retired, stay‐at‐home, student, disabled status). Collected through administrative
records.
b. Clinical characteristics: primary diagnosis (F20‐F29 [schizophrenia, schizotypal and delusional disorders], F30‐
F31 [bipolar disorder], F32‐F33 [depressive disorder], F00‐F09 [organic mental disorders], F40‐F48 [anxiety
disorders], other); disorder duration (number of years from the onset of disorder); comorbidity with mental
retardation, personality disorders, and substance abuse. The diagnoses were given by treating psychiatrists in
each clinic according to ICD‐10 criteria. Collected through medical records.
c. Treatment‐related characteristics: type of clinic (psychiatric hospital, general hospital); number of previous
admissions; number of previous IAs; type of contact with mental health services 1 month and 1 year before
current admission (hospitalization, outpatient clinic, community services, private practitioner, none, other), type
of treatment 1 month and 1 year before current admission (medication, psychotherapy, rehabilitation, family
therapy, none, other), medication adherence before current admission (very bad, bad, moderate, good, very
good, no medication). Self‐reports, collected through interview with patient.
d. Admission status (involuntary, voluntary). Collected through administrative records.
The instruments used in the present study were the Health of the Nation Outcome Scales (HoNOS) and Global
Assessment of Functioning (GAF) scales. HoNOS is a brief instrument developed to measure problems in mental
health and psychosocial functioning of users of mental health services (Wing et al., 1998) and is routinely used as an
outcome measure in clinical practice and research in many countries (James et al., 2018; Pirkis et al., 2005),
including Greece (Drakonakis et al., 2021). The instrument consists of 12 items covering the following domains: (1)
overactive, aggressive disruptive or agitated behavior, (2) nonaccidental self‐injury, (3) problem drinking or drug‐
taking, (4) cognitive problems, (5) physical illness or disability problems, (6) problems associated with hallucinations
and/or delusions, (7) problems with depressed mood, (8) other mental and behavioral problems, (9) problems with
relationships, (10) problems with activities of daily living, (11) problems with housing and living conditions and (12)
problems with occupation and activities. Each item is scored on a 5‐point scale from 0 (no problem) to 4 (severe to
very severe problem). For the purpose of the MANE project, items 11 and 12 were not included because there are
concerns regarding their applicability in acute in‐patient settings (James et al., 2018; Trauer, 1998).
Functionality was assessed with the GAF scale. GAF axis V in DSM‐IV
TM
is generally used to rate general
functioning across three domains (psychological, social, occupational) on a hypothetical continuum of mental
GEORGACA ET AL.
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health‐illness (American Psychiatric Association, 1994). GAF scores range between 0 (inadequate information) and
1 (most impaired functioning) to 100 (superior functioning).
HoNOS and GAF ratings were based on clinical vignettes prepared by the data collection researchers based on
medical records and information by the treating clinician, and the vignettes were rated blindly by external trained
raters. Indicative reliability account for the assessment procedure was based on Inter‐Rater Reliability (IRR)
measures in an initial portion (∼10%) of the collected data, which was found satisfactory for HoNOS (IRR = 0.76)
and GAF (IRR = 0.81). Also, the transformation of the HoNOS scale to a dichotomous (0/1) categorical variable was
expected to result in enhanced reliability.
2.3 |Ethics
The research protocol was approved by the National Data Protection Agency, the 3rd Regional Health Authority,
the Research Ethics Committee of the School of Psychology of the Aristotle University of Thessaloniki and the
Scientific Hospital Boards of all participating psychiatric clinics. Prospective participants received written
information about the study's aims and procedures, and their rights as research subjects, in accordance with the
European Data Protection Regulation (GDPR) and research ethics. Obtaining written informed consent was a
prerequisite for participation in the study. In compliance with GDPR procedures and research ethics rules, all data
were collected, anonymized, and securely transferred to a central database with access restricted to the project
managers.
2.4 |Statistical analysis: Latent Class Analysis
The statistical analyses consisted of descriptive statistics and multivariate methods. Descriptive statistics including
calculations of frequencies, mean values, and standard deviations on all sociodemographic, clinical, and treatment‐
related variables included in this study were performed in the statistical package SPSS (version 25).
A person‐centered approach, as opposed to the traditional variable‐centered approaches, was considered most
appropriate and was sought for data analysis. Contemporary advances in data analysis offer many options, including
the use of machine‐learning, decision trees and ensemble learning algorithms, which are supported by suitable
software programs (e.g., Blankers et al., 2020; Hotzy et al., 2018; Karasch et al., 2020; Peters et al., 2022; Silva et al.,
2021). In the present work, Latent Class Analysis (LCA) was chosen because it is a psychometric measurement
model and as a classification procedure is suitable for the present data and the theory‐driven orientation pursued.
LCA assumes the latent variable to be categorical and it is a model‐based method, that is, the results can be
generalized to the population, given the statistical inference support (Magidson & Vermunt, 2001).
LCA is a psychometric method and a model‐based cluster analysis, which, based on a member of input
variables, achieves the division of a set of response patterns into clusters, named latent classes (LCs) (Dayton, 1998).
The classification in LCA is based on shared conditional probabilities patterns and via Bayes' theorem the LC
assignment is achieved by applying different criteria (e.g., modal assignment) (Bakk et al., 2013). The goodness‐of‐fit
of an LC model is assessed by several indicators, such as the number of parameters (Npar), likelihood ratio statistic
(L
2
), Bayesian information criterion (BIC), degrees of freedom (df), and bootstrapped p‐value (Vermunt &
Magidson, 2005).
The ensuing cluster memberships could be associated with external factors, as covariates or distal outcomes. It
has been suggested that, in this case, a stepwise procedure is preferred (Vermunt, 2010). In the present analysis, the
three‐step approach was followed: in the first step the number of clusters are identified, in the second the
individuals are assigned to LCs, and then the ensuing class memberships are associated with covariates using linear,
cumulative logistic, and multinomial logistic regression models (Stamovlasis et al., 2018; Vermunt, 2010). The
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GEORGACA ET AL.
assignment of an external variable as a covariate or distal outcome is theory‐driven, given that statistical modeling
does not provide clues about causality.
LCA was performed with Latent Gold5.1 software (Vermunt & Magidson, 2005).
In the present study, LCA was used to classify the participants based on their patterns of HoNOS coexisting
symptoms. The classification in LCA was based on an adjusted scale of the HoNOS items. The initial ordinal 5‐point scale
of HoNOS items (0 = no problem, 1 = minor problem requiring no action, 2 = mild problem but definitely present,
3 = moderately severe problem and 4 = severe to very severe problem) was reduced to categorical (0/1) as follows: zero (0)
is the value when no problems are recorded and one (1) is the value when problems are recorded at any strength. These
transformed binary scales of nine items of HoNOS were used as input for the LC Analysis. Note that the HoNOS‐8item
was not included in the LC model because in a preliminary testing procedure its presence did not facilitate the convergence
of LCA.
3|RESULTS
3.1 |Descriptive statistics
Due to a large number of variables, tables with relative frequencies of all variables for involuntarily admitted, voluntarily
admitted patients and the sample overall are presented separately, as supporting information. Of 1067 total admissions,
463 (43.4%) were voluntary and 604 (56.6%) were involuntary.58.9%ofthesampleweremenand41.1%women.Ages
ranged from 18 to 87 years old with a mean age of 45.3 years. Most of the sample were unmarried (55.8%), living with
family of origin (41.4%), secondary education graduates (37.9%), and unemployed (42.1%).
3.2 |The emergent clusters
As Table 1shows, from the classification processes, based on the lower BIC, the three‐cluster solution was the best
parsimonious LC model (BIC = 10115.13, Npar = 29, df = 487, p= 0.07, and classification error = 0.098). The resulted
three clusters (Cluster 1, Cluster 2, and Cluster 3) included 40.30%, 30.38%, and 29.33% of the sample respectively.
Figure 1depicts the conditional probabilities for each HoNOS item in 0/1 scale for the three Clusters. Cluster 1
is characterized by the presence of problems in HoNOS1 (overactive, aggressive, disruptive, or agitated behavior),
HoNOS4 (cognitive problems), HoNOS6 (problems associated with hallucinations and/or delusions), HoNOS9
(problems with relationships), and HoNOS10 (problems with activities of daily living), indicating a pattern that
combines disorganization and positive psychotic symptoms; Cluster 1 is thus seen as displaying a pattern of
TABLE 1 LCA results with HoNOS items as input.
LL BIC (LL) Npar L
2
df p‐Value Class. Err.
1‐Cluster −5227.4 10516.41 9 1116.202 502 0 0
2‐Cluster −5044.17 10218.42 19 749.7522 492 0.014 0.0891
3‐Cluster* −4958.3 10115.13 29 578.0079 482 0.069 0.098
4‐Cluster −4935.09 10137.18 39 531.5917 472 0.065 0.1531
5‐Cluster −4915.86 10167.16 49 493.122 462 0.063 0.1695
Abbreviations: BIC, Bayesian information criterion; df, degrees of freedom; HoNOS, Health of the Nation Outcome Scales;
LCA, Latent Class Analysis; Npar, number of parameters.
GEORGACA ET AL.
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“Disorganized Psychotic Symptomatology”(DPS). Cluster 2 shares with Cluster 1 problems in HoNOS1, HoNOS6,
and HoNOS9 but with less strength; however, there are no cognitive problems or problems of daily functioning. The
distinguishing feature of this cluster seems to be the presence of active psychotic symptoms that are in turn
associated with agitation and problems with relationships, indicating a pattern of “Active Psychotic Symptomatol-
ogy”(APS). Finally, participants in Cluster 3 are characterized by problems in HoNOS2 (nonaccidental self‐injury),
HoNOS7 (problems with depressed mood), and HoNOS9 (problems with relationships), portraying a pattern of
“Depressive Symptomatology”(DS).
3.3 |Association of the emergent clusters with external variables
Subsequently, the ensuing LCs were associated with several covariates, that is, additional individual differences,
such as sociodemographic and other clinical and treatment‐related variables. In addition, the encountered cluster
memberships were used as categorical independent variables predicting the principal dependent variable that
operationalizes admission status. For this analysis, the step‐wise LCA was implemented (Bakk et al., 2013;
Stamovlasis et al., 2018). A schematic representation of the model is illustrated in Figure 2.
The effects of the cluster memberships on admission status (IA and VA) are presented in Table 2. It is observed
that the DPS cluster is positively associated with IA status (b= 0.495, p< 0.001). The APS cluster is also positively
associated with IA status (b= 0.269, p< 0.001), however with less strength and less corresponding odds of choosing
IA. The DS cluster represents the LC that is positively associated with VA status (b= 0.765, p< 0.001).
Consequently, patients placed within the DPS and ΑPS clusters are most probably admitted involuntarily, while
patients in the DS cluster are most probably admitted voluntarily.
Moreover, the cluster memberships were associated with the sociodemographic variables of the study
(Table 3a). The DPS cluster is positively associated with male gender (b= 0.174, p< 0.001), unmarried status
(b= 0.350, p< 0.001), and living with family of origin (with marginal statistical significance, b= 0.289, p< 0.05 [one‐
tail]). The APS cluster is also positively associated with unmarried family status (b= 0.239, p< 0.05) but negatively
FIGURE 1 Conditional probabilities for Health of the Nation Outcome Scales (HoNOS) symptoms pattern for
each cluster/profile.
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GEORGACA ET AL.
associated with age (b= 0.009, p< 0.5). Finally, the DS cluster is positively associated with female gender (b= 0.243,
p< 0.001), age (b= 0.010, p< 0.01), divorced/separated status (b= 0.335, p< 0.001), being in full‐time employment
(b= 1.702, p< 0.01), but also unemployed (b= 1.755, p< 0.01), retired (b= 1.923, p< 0.001), and stay‐at‐home
(b= 2.467, p< 0.001), and negatively associated with living with family of origin (b=−0.536, p< 0.001).
The ensuing cluster memberships were also associated with the other clinical variables of the study. As
Table 3b shows, the DPS cluster is positively associated with F20‐F29 diagnoses (b= 0.372, p< 0.001) and
comorbidity with mental retardation (b= 0.324, p< 0.001) and negatively associated with GAF (b=−0.032,
p< 0.001). That is, the DPS cluster most likely includes patients diagnosed with schizophrenia and other psychotic
disorders, as well as mental retardation, with lower general functionality. The APS cluster has also a positive
association with F20‐F29 diagnoses (b= 0.563, p< 0.001), but additionally, it is positively associated with F30‐F31
diagnoses (b= 0.487, p< 0.001) and with GAF (b= 0.028, p< 0.001). This means that APS cluster most likely
includes patients diagnosed with schizophrenia and other psychotic disorders as well as with bipolar disorder and
with higher general functionality. Finally, the DS cluster was found to be positively associated with F32‐F33
diagnoses (b= 1.792, p< 0.001) and with comorbidity with a personality disorder (b= 0.339, p< 0.001), including,
thus, patients diagnosed with the depressive disorder as well as with a personality disorder.
Lastly, the ensuing cluster memberships were associated with the treatment‐related variables of the study. As
Table 3c shows, the APS cluster is positively associated with admission to psychiatric hospital clinics (b= 0.311,
p< 0.001) while the DS cluster is positively associated with general hospital admission (b= 0.238, p< 0.001). The
existence of previous IAs is positively associated (b= 0.267, p< 0.001) with the DPS cluster, but negatively
FIGURE 2 A schematic representation of the model. HoNOS, Health of the Nation Outcome Scales.
TABLE 2 Relationships between cluster membership and distal outcomes.
a. Relationships between cluster membership and admission status
Cluster 1 DPS Cluster 2 APS Cluster 3 DS
Admission
Involuntary 0.495*** 0.269*** −0.765***
Voluntary −0.495*** −0.269*** 0.765***
Note: Association coefficients and statistical significance.
Abbreviations: APS, active psychotic symptomatology; DPS, disorganized psychotic symptomatology; DS, depressive
symptomatology.
*p< 0.5; **p< 0.01; ***p< 0.001.
GEORGACA ET AL.
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TABLE 3 Relationships between cluster membership and the covariates.
(a) Relationships between cluster membership and
sociodemographic variables
(b) Relationships between cluster membership and clinical
variables
(c) Relationships between cluster‐membership and
treatment‐related variables
Cluster
1 DPS
Cluster
2 APS
Cluster
3DS
Cluster
1 DPS
Cluster
2 APS Cluster 3 DS
Cluster
1 DPS
Cluster
2 APS Cluster 3 DS
Gender Primary diagnosis Clinic type
Male 0.174*** 0.069 ns −0.243*** F20‐F29:
Schizophrenia,
schizotypal and
delusional
disorders
0.372*** 0.563*** −0.935*** Psychiatric hospital −0.073 ns 0.311*** −0.238***
Female −0.174*** −0.069 ns 0.243*** F30‐F31: Bipolar
disorder
−0.086 ns 0.487*** −0.402*** General hospital 0.073 ns −0.311*** 0.238***
Age −0.001 ns −0.009*0.010** F32‐F33:
Depressive
disorder
−0.409 ns −1.38*** 1.79*** Previous involuntary
admissions
0.267*** 0.083 ns −0.350***
Family status F00‐F09: Organic
mental
disorders
0.123 ns 0.332 ns −0.455** Contact with private
practitioner 1
month before
admission
−0.251*** −0.038 ns 0.288***
Unmarried 0.350*** 0.239*−0.589*** No contact with
mental health
services 1 month
before admission
0.163*** 0.061 ns −0.224***
Married −0.307*** 0.250
#
0.056 ns Comorbidity with
mental
retardation
0.324*** −0.216 ns −0.108 ns Contact with private
practitioner 1
year before
admission
−0.173** −0.058 ns 0.230***
Divorced/
separated
−0.198 ns −0.137 ns 0.335*** No contact with
mental health
0.151*** 0.037 ns −0.188***
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GEORGACA ET AL.
TABLE 3 (Continued)
(a) Relationships between cluster membership and
sociodemographic variables
(b) Relationships between cluster membership and clinical
variables
(c) Relationships between cluster‐membership and
treatment‐related variables
Cluster
1 DPS
Cluster
2 APS
Cluster
3DS
Cluster
1 DPS
Cluster
2 APS Cluster 3 DS
Cluster
1 DPS
Cluster
2 APS Cluster 3 DS
services 1 year
before admission
Living conditions Comorbidity with
personality
disorders
0.031 ns −0.370*** 0.339*** Medication 1 month
before admission
−0.162*** −0.064 ns 0.225***
With family of
origin
0.289
#
0.247 ns −0.536*** No treatment 1
month before
admission
0.139** 0.032 ns −0.171**
With own
family
−0.508*** 0.302 ns 0.206 ns GAF −0.032*** 0.028*** 0.004 ns Medication 1 year
before admission
−0.167*** −0.008 ns 0.175**
Employment
status
Adherence to
medication
Full time −1.196*** −0.507 ns 1.702** Very bad 0.377*** 0.094 ns −0.471***
Unemployed −0.588 ns −1.167*** 1.755** Very good −0.426*** −0.274 ns 0.701***
Retired −0.653 ns −1.271** 1.923*** No medication 0.179 ns 0.153 ns −0.332***
Stay‐at‐home −1.481*** −0.986 ns 2.467***
Note: Association coefficients and statistical significance.
Abbreviations: APS, active psychotic symptomatology; DPS, disorganized psychotic symptomatology; DS, depressive symptomatology; GAF, Global Assessment of Functioning.
*p< 0.5; **p< 0.01; ***p< 0.001;
#
p< 0.05.
GEORGACA ET AL.
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11
associated (b=−0.350, p< 0.001) with DS cluster. Taking into consideration that the DPS cluster is associated with
IA, it may be deduced that DPS cluster members with previous IAs are more likely to be involuntarily hospitalized
again. Regarding the type of previous treatments, DPS cluster membership is positively associated with the absence
of contact with mental health services both in the previous month (b= 0.163, p< 0.001) and previous year
(b= 0.151, p< 0.001), absence of treatment in the previous month (b= 0.139, p< 0.01) and very bad adherence to
medication (b= 0.377, p< 0.001). DS cluster membership is positively associated with being in contact with a
private practitioner both in the previous month (b= 0.288, p< 0.001) and previous year (b= 0.230, p< 0.001),
receiving medication treatment both in the previous month (b= 0.225, p< 0.001) and previous year (b= 0.175,
p< 0.01) and having very good adherence to medication (b= 0.701, p< 0.001; Figure 3).
FIGURE 3 A schematic representation summarizing the findings from Latent Class Analysis.
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GEORGACA ET AL.
4|DISCUSSION
This study investigated the clinical risk factors for involuntary hospitalization through establishing patient profiles
based on clinical characteristics and associating them with sociodemographic, other clinical‐, and treatment‐related
variables as well as with type of psychiatric admission. From the application of LCA, it emerged that the hospitalized
patients included in the data can be clustered in three, roughly equivalent, groups with distinct clinical profiles.
Patients clustered in the DPS profile present positive psychotic symptoms, cognitive problems, agitated
behavior, and problems with relationships and daily living. They tend to be male, unmarried, and living with their
family of origin. The predominant diagnosis of schizophrenia is consistent with APS. The comorbidity with mental
retardation and the low functionality, in conjunction with cognitive problems and problems with relationships and
daily living, all concur to the sense of pervasive clinical picture of disorganization in this group. The existence of
previous involuntary hospitalizations indicates a chronic course of repeated psychiatric hospitalizations. This, in
combination with no contact with mental health services and poor compliance with medication in the period before
admission, portrays a typical revolving door situation. Overall, the prototypical picture of patients in this group is of
persons with severe mental health difficulties in a chronic course and repeated involuntary hospitalizations upon
deterioration of mental state.
The APS profile is characterized by the predominance of positive psychotic symptoms, combined with agitated
behavior and problems with relationships, although milder than the previous profile. This profile contains mainly
younger persons, who are likely to be unmarried. Apart from schizophrenia, a likely diagnosis given is bipolar
disorder, indicating that active psychotic symptoms may be coupled with manic mood before and during
hospitalization. The high functionality and lack of problems in everyday living indicate a well‐functioning group, who
are possibly only a few years from the onset of psychotic problems and have not suffered disorganization and
deterioration of mental state.
Patients in the DS profile are characterized by depressive mood coupled by nonaccidental self‐injury and
problems with relationships. They are most likely women, older, divorced/separated, in various employment
situations. The most likely diagnosis of depression concurs with the pervasive depressed mood in this group. The
likely presence of a personality disorder diagnosis, as well as prior contact with mental health professionals,
indicates a chronic course of the problems faced. Members of the DS profile tend to have been in regular contact
with a private mental health practitioner and to have been regularly taking medication before admission.
In terms of admission status, the DPS and APS profiles are associated with IA, while the DS profile with VA. This
is not surprising, given that in the literature there is an established association of positive psychotic symptoms and
diagnosis of schizophrenia and other psychotic disorders with IA (Feeney et al., 2019; Lorenzo et al., 2018;
Myklebust et al., 2012) while depressive symptoms and/or depressive disorder are more frequently found in
voluntarily hospitalized patients (Drakonakis et al., 2021; Hustoft et al., 2013). In contrast to findings that the
severity of symptoms drives IA (Benedikt et al., 2018; Mulder et al., 2005), this study indicates that it is not the
severity of symptoms per se but the types of symptoms that influence type of admission; namely, the presence of
positive psychotic symptoms drive IA while the presence of depressive symptoms drive VA. The link of IA with low
levels of functioning (Opjordsmoen et al., 2010) holds true in this study only for persons with chronic psychotic
symptoms in the DPS profile but not for younger persons with APS, who have high levels of functioning yet are
more likely to be admitted involuntarily; this possibly indicates that the type of symptomatology, specifically level of
disorganization, mediates between the level of functioning and admission type.
The link between agitated and disruptive behavior and IA, found in both psychotic symptoms profiles, is in
accordance with the literature linking aggressive behavior with IA (Canova Mosele et al., 2018; Ritsner et al., 2018)
and might be explained by the danger to self or others being a prerequisite to IA in many countries, including
Greece (Chatzisimeonidis et al., 2021). The association of previous involuntary hospitalizations with IA (Lebenbaum
et al., 2018; Montemagni et al., 2012) seems to also hold true for the DPS cluster members, presumably mediated
by mental deterioration and disorganization, that characterizes this group. The repeated cycles of involuntary
GEORGACA ET AL.
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13
hospitalization for this group are linked with, and possibly exacerbated by, the lack of contact with mental health
services and the poor adherence to medication before hospitalizations, that is documented in other studies (Hustoft
et al., 2013; Silva et al., 2018; Verdoux et al., 2000). The reverse relation between regular community‐based contact
with mental health professionals (Nestrigue et al., 2017) is also confirmed for the DS cluster members.
The finding that APS cluster members tend to be admitted in psychiatric hospitals while DS cluster members in
psychiatric units of general hospitals might be related to IA and VA status respectively, possibly reflecting a
preference of voluntarily admitted patients for general hospital admission due to the stigma surrounding psychiatric
hospitals or a systemic trend of differential admission practices between types of clinics (Abualenain et al., 2013;
Pines et al., 2017).
In terms of sociodemographic characteristics, this study confirms findings that being unmarried is linked to IA
(Hustoft et al., 2013; Kelly et al., 2018), while being separated/divorced is linked to VA (Hatling et al., 2002). Being
unemployed, contrary to the literature (Lorenzo et al., 2018; Umama‐Agada et al., 2018), is also linked to VA in our
study. The link between living alone and IA in the literature (Lorenzo et al., 2018; Van Der Post et al., 2012) was not
confirmed in this study; on the contrary, involuntarily admitted members of the DPS cluster tend to live with their
family of origin. What this study adds, however, to the literature linking sociodemographic, clinical, and treatment‐
related factors with risk for IA is examining how this relation is affected by clinical factors involving
symptomatology and levels of functioning.
This study indicates the existence of two distinct pathways to IA, linked to two patterns of psychotic
symptoms, respectively. One pattern is of unmarried men, living with their family of origin, with chronic recurring
psychotic symptoms and disorganization, poor interpersonal and everyday functioning, poor contact with mental
health services and treatment adherence, who tend to follow a course of repeated IAs. The second pattern is of
younger persons with active psychotic symptomatology that disrupts their mental state, behavior, and relationships
and leads them, despite retaining high levels of functioning, to hospitalization, most likely involuntary.
The identification of these two distinct profiles of persons admitted involuntarily has significant clinical
implications for the development of strategies to prevent IA. Given that for the group of patients in a chronic course
the main driver of IA is the deterioration of clinical condition and mental state in the context of the absence of
contact with mental health services, establishing continuity of care through providing outreach and community‐
based monitoring of mental state (Krokidas et al., 2016; Stylianidis, 2021b) as well as establishing therapeutic
relationships with care workers (Farrelly et al., 2014; Roche et al., 2014) would be essential. Availability of
community‐care networks, that would foster social and community engagement while providing a framework for
continuity of engagement with mental health services would also be important (Wierdsma et al., 2007). Finally,
given that persons in this group tend to live with their family of origin, that is the main source of support and also
the main initiator of IA, working with families to build their resilience and managing capacity could deter IA for their
members (Claxton et al., 2017).
For the group of young persons, the driver for IA seems to be the intensity of active psychotic symptoms in the
absence of community‐based mental health services that would allow them to manage these experiences
effectively. The unexpectedly large percentage of persons located in this cluster might be related to the
unavailability of early intervention services for psychosis in Greece (Bargiota et al., 2018). This calls for the
development of early intervention services that would assist persons upon onset of psychosis with managing their
experiences and would provide systematic community‐based monitoring and support (Agius et al., 2007; Breitborde
et al., 2015; Stylianidis, 2021a).
4.1 |Strengths and limitations
This study has several strengths. This is the largest study examining involuntary psychiatric admissions in Greece. It
is a population study for the metropolitan area of Thessaloniki, covering all the regional public psychiatric clinics.
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GEORGACA ET AL.
Another strength is the uniqueness of its methodological design. Through adopting a person‐centered rather than
the usual variable‐centered approach, it established hospitalized patient profiles based on clinical characteristics,
and then associated these profiles with sociodemographic, other clinical, and treatment‐related variables as well as
with type of psychiatric admission.
A central limitation concerns managing the HoNOS items. Grouping HoNOS items is not a standard procedure,
requiring a level of judgment according to the study aims and rationale. After careful consideration of groupings
adopted in previous studies (e.g., Golay et al., 2016; Johansen et al., 2012; Tulloch et al., 2016), we decided that for
the purposes of this study, that focuses on the presence or not‐presence of symptomatology, we would group the
scores into 0 = “no problems”and 1–4=“problems at any strength.”This meant that participants with low levels of
symptomatology were grouped together with those with severe symptomatology and that the presence of
problems in the clusters indicates all persons presenting the specific problem at any strength. If mild
symptomatology had been grouped together with lack of symptomatology, a different depiction of the clusters
might have emerged.
In terms of other limitations, this study was conducted in a region of northern Greece. Therefore, it is unclear to
what extent the findings are generalizable to other regions or countries; this warrants further research. The blind
rating of HoNOS and GAF by external raters based on clinical vignettes and not by the treating clinicians may have
increased objectivity of ratings but poses limitations in terms of ecological validity and clinical relevance. It would be
interesting if future research re‐examined these findings performing LCA in a confirmatory mode. Moreover, only
patients who agreed to participate in the study were included in the sample; results might have been different, were
data collected for all hospitalized patients. Finally, the cross‐sectional nature of the study does not allow for causal
conclusions regarding the observed associations.
5|CONCLUSION
This study applied a novel approach using a person‐centered method of analysis. The two distinct clinical profiles
found among involuntarily hospitalized patients indicated that there is not just one pathway or specific factors per
se that lead individuals with severe symptomatology to an involuntary hospitalization. This implication is expected
to contribute both to the future design of research on IA and to a more coherent understanding by mental health
practitioners of the combination of clinical features when deciding on the need for compulsory hospitalization for
people with mental health problems. In addition, the resulting correlations between these profiles and other clinical
and treatment‐related features provided evidence necessary for the development of targeted mental health policy
interventions that could meet the treatment needs of these patients sufficiently early in community services to
reduce the incidence of involuntary hospitalization.
AUTHOR CONTRIBUTIONS
Eugenie Georgaca, Sofia Machaira, Lily Evangelia Peppou, Stelios Stylianidis: Conceptualization. Eugenie
Georgaca, Sofia Machaira, Lily Evangelia Peppou: Methodology. Dimitrios Stamovlasis, Sofia Machaira: Formal
analysis and investigation. Eugenie Georgaca, Sofia Machaira, Dimitrios Stamovlasis: Writing—original draft
preparation. Christina Papachristou, Lily Evangelia Peppou, Aikaterini Arvaniti, Maria Samakouri, Stelios
Stylianidis, Thessaloniki MANE Group: Writing—review and editing. Thessaloniki MANE Group: Resources.
Eugenie Georgaca, Maria Samakouri, Stelios Stylianidis: Supervision.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
GEORGACA ET AL.
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15
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data
are not publicly available due to privacy or ethical restrictions.
ETHICS STATEMENT
The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki
and its later amendments or comparable ethical standards. Approval was granted by the Hospital Scientific Boards
of all participating clinics, the National Data Protection Agency, the 3rd Regional Health Authority and the Research
Ethics Committee of the School of Psychology of Aristotle University of Thessaloniki. Informed consent was
obtained from all individual participants included in the study.
ORCID
Eugenie Georgaca http://orcid.org/0000-0002-5904-2534
PEER REVIEW
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-
review/10.1002/jclp.23528.
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How to cite this article: Georgaca, E., Machaira, S., Stamovlasis, D., Peppou, L. E., Papachristou, C., Arvaniti,
A., Samakouri, M., Stylianidis, S., Bozikas, V. P., Diakogiannis, I., Fokas, K., Garyfallos, G., Gkolia, I., Karpouza,
V., Nimatoudis, I., Patsinakidis, G., Sevris, D., & Vlachaki, A. (2023). Clinical determinants of involuntary
psychiatric hospitalization: A clinical profile approach. Journal of Clinical Psychology,1–20.
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