PDF - Archives of Clinical Psychiatry

Transcrição

Archives of
Online version: www.hcnet.usp.br/ipq/revista
iPad edition: APPSTORE/categoria MEDICINA/Psiquiatria Clinica
VOLUME 42 • NUMBER 1 • 2015
RS
CTO
IMPACT FA
Revista de Psiquiatria Clínica
)
rs
son Reute
m
o
h
(T
I
IS
0.89
ago
1.23 SCIm
SATISFAÇÃO DE VIVER SEM A DOR DA DEPRESSÃO. 1
EFICAZ E SEGURO
PACIENTES COM DOR ASSOCIADA À DEPRESSÃO
PODEM SE BENEFICIAR DE DULOXETINA. 1
RECONHECIDA EFICÁCIA ANTIDEPRESSIVA EM TRANSTORNO
DEPRESSIVO MAIOR, MESMO SEM DOR ASSOCIADA. 2
EFICAZ NOS QUADROS DE TRANSTORNO DA ANSIEDADE
GENERALIZADA (TAG), COM OU SEM DOR CRÔNICA ASSOCIADA.3,4
SEM CORANTE
MENOS RISCO DE
REAÇÃO ALÉRGICA6
APRESENTAÇÕES:
caixas com 30 mg x 15 cápsulas
e 60 mg x 30 cápsulas.6
POSOLOGIA: iniciar com 30 mg
1 vez ao dia por uma semana.
Manter com 60 mg 1 vez ao dia.
Dose máxima diária 120 mg.6
CONTRAINDICAÇÕES: pacientes com hipersensibilidade conhecida à duloxetina ou a qualquer um dos seus excipientes. INTERAÇÕES MEDICAMENTOSAS: houve relatos
de reações graves, às vezes fatais, em pacientes recebendo um inibidor da recaptação de serotonina em combinação com um IMAO tais como hipertermia, rigidez, mioclonia,
instabilidade autonômica com possíveis flutuações rápidas dos sinais vitais e alterações do estado mental, incluindo agitação extrema, progredindo para delírio e coma.
NEULOX (cloridrato de duloxetina). APRESENTAÇÕES: embalagens contendo 15 cápsulas de 30mg ou 30 cápsulas de 60mg. Uso oral. Uso Adulto acima de 18 anos. INDICAÇÕES: NEULOX é indicado para o tratamento da depressão. NEULOX é eficaz na manutenção da melhora clínica durante o tratamento contínuo, por até seis meses, em
pacientes que apresentaram resposta ao tratamento inicial. NEULOX é indicado para o tratamento de: transtorno depressivo maior; dor neuropática periférica diabética; fibromialgia (fm) em pacientes com ou sem transtorno depressivo maior (tdm); estados de dor crônica associados à dor lombar crônica; estados de dor crônica associados à dor
devido à osteoartrite de joelho em pacientes com idade superior a 40 anos e transtorno de ansiedade generalizada. CONTRAINDICAÇÕES: NEULOX é contraindicado em pacientes com hipersensibilidade conhecida à duloxetina ou a qualquer um dos seus excipientes. NEULOX não deve ser administrado concomitantemente com inibidores da
monoaminoxidase (IMAO) e deve ser administrado, no mínimo, 14 dias após a interrupção do tratamento com um IMAO. Com base na meia-vida de cloridrato de duloxetina, deve-se aguardar, no mínimo, 5 dias após a interrupção do tratamento com NEULOX, antes de se iniciar o tratamento com um IMAO. PRECAUÇÕES E ADVERTÊNCIAS:
a possibilidade de uma tentativa de suicídio é inerente ao transtorno depressivo maior e a outros transtornos psiquiátricos e pode persistir até que ocorra uma remissão significativa dos sintomas depressivos. Observou-se um aumento no risco de pensamentos e/ou comportamentos suicidas em pacientes pediátricos e adultos jovens (< 25 anos
de idade), em comparação com o grupo placebo. Os médicos devem incentivar seus pacientes a relatarem, a qualquer momento, quaisquer tipos de pensamentos ou sentimentos aflitivos. NEULOX deve ser usado com cuidado em pacientes com histórico de mania e em pacientes com aumento da pressão intraocular ou para aqueles com risco de
glaucoma de ângulo fechado. Em pacientes com hipertensão conhecida e/ou outra doença cardíaca, recomenda-se o monitoramento da pressão arterial. NEULOX não deve ser prescrito para pacientes que façam uso considerável de álcool ou que tenham evidência de doença hepática preexistente. NEULOX pode aumentar o risco de sangramentos.
Por isso, deve ser ter cuidado ao se administrar NEULOX à pacientes que façam uso de anticoagulantes e/ou substâncias que afetem a coagulação. NEULOX deve ser usado em gestantes somente se o benefício potencial justificar o risco para o feto. Não é recomendável amamentar durante o tratamento com NEULOX. Deve-se observar que cloridrato
de duloxetina não está aprovado para o tratamento de depressão bipolar. O desenvolvimento de uma síndrome serotoninérgica com potencial risco de vida ao paciente pode ocorrer com o uso de NEULOX, em particular com o uso concomitante de drogas serotoninérgicas (incluindo triptanos) e com drogas que prejudicam o metabolismo da
serotonina (incluindo IMAOs). INTERAÇÕES MEDICAMENTOSAS: houve relatos de reações graves, às vezes fatais, em pacientes recebendo um inibidor da recaptação de serotonina em combinação com um IMAO tais como hipertermia, rigidez, mioclonia, instabilidade autonômica com possíveis flutuações rápidas dos sinais vitais e alterações
do estado mental, incluindo agitação extrema, progredindo para delírio e coma. Deve-se ter cuidado com a administração simultânea de antidepressivos tricíclicos (ATC) e duloxetina, pois esta pode inibir o metabolismo dos ATC. Aconselha-se cautela ao se administrar NEULOX com inibidores da CYP1A2 (por ex.: alguns antibióticos à base de
quinolona) e, nesse caso, uma dose mais baixa de NEULOX deve ser usada. NEULOX é um inibidor moderado da CYP2D6. O uso concomitante com inibidores potentes da CYP2D6 pode resultar em concentrações mais altas de duloxetina, portanto, deve-se ter cuidado quando se administrar NEULOX com medicamentos predominantemente
metabolizados pela CYP2D6 e com índice terapêutico estreito. A paroxetina (20mg, uma vez ao dia) diminuiu em cerca de 37% o clearance plasmático aparente de cloridrato de duloxetina. Elevações graves das enzimas hepáticas (acima de dez vezes o limite superior do normal) ou dano hepático com um padrão colestático ou misto foram relatadas
em alguns casos associadas com uso excessivo de álcool ou doença hepática preexistente. Em condições extremamente ácidas, cloridrato de duloxetina, pode sofrer uma hidrólise, formando naftol. É aconselhável cuidado ao administrar cloridrato de duloxetina para pacientes que possam apresentar retardo no esvaziamento gástrico (por ex.: alguns
pacientes diabéticos). Medicamentos que aumentam o pH gastrointestinal podem promover uma liberação precoce de cloridrato de duloxetina. Podem ocorrer eventos indesejáveis com o uso concomitante de NEULOX e preparações fitoterápicas que contenham a Erva de São João (hypericum perforatum). Devido aos efeitos primários de cloridrato
de duloxetina serem sobre o SNC, deve-se tomar cuidado quando o mesmo for usado em combinação com outras drogas que agem no SNC. Cloridrato de duloxetina encontra-se altamente ligado a proteínas plasmáticas (> 90%). Portanto, sua administração a pacientes tomando outra droga que esteja altamente ligada a proteínas plasmáticas
pode causar aumentos das concentrações livres da outra droga. REAÇÕES ADVERSAS: boca seca, náusea, dor de cabeça, palpitações, visão borrada, diarreia, constipação, vômito, dispepsia, flatulência, dor abdominal, fadiga, diminuição de peso, diminuição do apetite, dor musculoesquelética, tontura, letargia, sonolência, tremor, disgeusia,
parestesia, insônia, alteração do orgasmo, retardo na ejaculação, distúrbio de ejaculação, diminuição da libido, disfunção erétil, ansiedade, distúrbio do sono, agitação, bocejo, hiperidrose, alteração da frequência urinária, rubor, dor orofaríngea, prurido, espasmo muscular, aumento da pressão sanguínea e sonhos anormais. Além destas reações
adversas, para o tratamento do Transtorno Depressivo Maior os eventos adversos comuns foram: queda, rigidez muscular, suores noturnos e zumbido no ouvido. Para o tratamento da Fibromialgia: Quedas, rigidez muscular, distúrbio de atenção, sede, aumento de peso, calafrios, bruxismo e suores noturnos. Para o tratamento dos Estados de Dor
Crônica3 Associados à Dor Lombar Crônica e a Dor devido à Osteoartrite de Joelho: vertigem e achados laboratoriais relacionados a alterações de enzimas hepáticas. Para o tratamento do Transtorno de Ansiedade Generalizada: midríase, achados laboratoriais relacionados a alterações de enzimas hepáticas, diminuição de apetite, bruxismo, hesitação
urinária, disúria e zumbido no ouvido. POSOLOGIA: não administrar mais que a quantidade total de NEULOX recomendada para períodos de 24 horas. Transtorno Depressivo Maior, Fibromialgia, Estados de Dor Crônica Associados à Dor Lombar Crônica e a Dor devido à Osteoartrite de Joelho, Transtorno de Ansiedade Generalizada: início com uma
dose de 60mg, uma vez ao dia. Para alguns pacientes pode ser conveniente iniciar o tratamento com a dose de 30mg, uma vez ao dia, durante uma semana, antes de aumentar a dose para 60mg, uma vez ao dia. Dor Neuropática Periférica Diabética: 60mg, uma vez ao dia. Para pacientes cuja tolerabilidade seja uma preocupação, uma dose inicial
mais baixa pode ser considerada. Tratamento Prolongado / Manutenção / Continuação: os pacientes devem ser periodicamente reavaliados para determinar a necessidade da manutenção do tratamento com NEULOX e a dosagem apropriada para tal. Interrupção do Tratamento: é recomendável que se faça uma redução gradual de sua dose (devendo
ser reduzida pela metade ou administrada em dias alternados) por um período, de no mínimo, 2 semanas antes da interrupção completa do tratamento. Populações Especiais: Pacientes com Insuficiência Renal e Pacientes com Insuficiência Hepática: não é recomendado para pacientes com doença renal em fase terminal (necessitando de diálise)
ou com disfunção renal grave (clearance de creatinina < 30mL/min) e em pacientes com insuficiência hepática clinicamente significativa (principalmente com relação a pacientes com cirrose). Se os benefícios do tratamento justificarem os potenciais riscos para estes pacientes, recomenda-se uma dose inicial de 30mg de cloridrato de duloxetina,
uma vez ao dia no caso de insuficiência renal e para insuficiência hepática, considerar uma dose mais baixa do que a normalmente recomendada e menos frequente. REGISTRO MS nº 1.2675.0171. DETENTORA: NOVA QUÍMICA FARMACÊUTICA LTDA. “SE PERSISTIREM OS SINTOMAS, O MÉDICO DEVERÁ SER CONSULTADO”.
VENDA SOB PRESCRIÇÃO MÉDICA SÓ PODE SER VENDIDO COM RETENÇÃO DA RECEITA. Referências: 1) Trivedi, MH. The Link Between Depression and Physical Symptoms. Prim Care Companion J Clin Psychiatry 2004;6 (suppl 1). 2) Dell’Osso B, Camuri G, Dobrea C et al. Duloxetine in Affective Disorders:a Naturalistic Study on
Psychiatric and Medical Comorbidity, Use in Association and Tolerability Across Different Age Groups. Clinical Practice & Epidemiology in Mental Health, 2012, 8, 120-125. 3) Able SL, Cui Z and Shen W. Duloxetine treatment adherence across mental health and chronic pain conditions. ClinicoEconomics and Outcomes Research 2014:6 75–81. 4) Oakes
TM et al. Safety and tolerability of duloxetine in elderly patients with major depressive disorder: a pooled analysis of two placebo-controlled studies. International Clinical Psychopharmacology 2013, 28:1–11. 5) Revista ABC Farma/Dez/2014. 6) Bula do produto.
www.nqfarma.com.br
www.novaquimicafarma.com.br
Rua Dr. Sampaio Ferraz, 453 | CAMPINAS/SP | CEP 13024-431 | Tel.Fax +55 19 3323 4000 | SAC 0800.026 22 74
Inovando para o seu bem-estar.
Editors
Editor-in-Chief: Assistant Editor:
Wagner F. Gattaz (São Paulo, Brazil)
Rodrigo Machado Vieira (Bethesda, USA)
Wulf Rössler (Zürich, Switzerland)
Regional Editor USA:
Regional Editor Europe:
Human Sciences Editor:
Psychology and Humanities
Assistant Editors:
Psychotherapy Transcultural Psychiatry
Editor:
Neurosciences Neurobiology
Geriatric Psychiatry
Basic Research
Neuropsychology
Assistant Editors:
Ines Hungerbühler (São Paulo, Brazil)
Francisco Lotufo Neto (São Paulo, Brazil)
Paulo Clemente Sallet (São Paulo, Brazil)
Felipe D’Alessandro F. Corchs (São Paulo, Brazil)
Orestes Forlenza (São Paulo, Brazil)
Breno Satler de Oliveira Diniz (Belo Horizonte, Brazil)
Clinical Psychiatry
Editor: Epidemiology Assistant Editors:
Psychopathology Neuroimaging
Biological Therapy
Geraldo Busatto (São Paulo, Brazil)
Marcus V. Zanetti (São Paulo, Brazil)
Tânia Correa de Toledo Ferraz Alves (São Paulo, Brazil)
Editor:
Instruments and Scales Assistant Editors :
Clarice Gorenstein (São Paulo, Brazil)
Elaine Henna (São Paulo, Brazil)
Juliana Teixeira Fiquer (São Paulo, Brazil)
Child and Adolescent Psychiatry Editor:
Assistant Editors:
Former Editors
Antonio Carlos Pacheco e Silva (1972-1985)
Fernando de Oliveira Bastos (1972-1985)
João Carvalhal Ribas (1980-1985)
José Roberto de Albuquerque Fortes (1985-1996)
Valentim Gentil Filho (1996-2010)
Guilherme Vanoni Polanczyk (São Paulo, Brazil)
Ana Soledade Graeff-Martins (São Paulo, Brazil)
Tais Moriyama (São Paulo, Brazil)
Editorial Board
Alexander Moreira-Almeida
( Juiz de Fora, Brazil)
Almir Ribeiro Tavares Jr.
(Belo Horizonte, Brazil)
André Malbergier
(São Paulo, Brazil)
Andrea Schmitt
(Göttingen, Germany)
Andréa H. Marques
Benedicto Crepo-Facorro
(Santander, Spain)
Carmita Helena Najjar Abdo
Christian Costa Kieling
(Porto Alegre, Brazil)
Daniel Martins de Souza
Doris Hupfeld Moreno
Eduardo Iacoponi
(London, UK)
Elida Paula Benquique Ojopi
Emmanuel Dias Neto
Ênio Roberto de Andrade
Ester Nakamura Palacios
(Vitória, Brazil)
Frederico Navas Demetrio
Fulvio Alexandre Scorza
Gunter Eckert
(Frankfurt, Germany)
Helena Paula Brentani Samaia
Helena Maria Calil
Hélio Elkis
Homero Pinto Vallada Filho
Irismar Reis de Oliveira
(Salvador, Brazil)
Jair Constante Soares
(Texas, USA)
Jerson Laks
(Rio de Janeiro, Brazil)
João Quevedo
(Criciúma, Brazil)
Jorge Ospina Duque
(Medellín, Colombia)
José Alexandre de Souza Crippa
(Ribeirão Preto, Brazil)
Ligia Ito
Liliana Rendón
(Assunção, Paraguai)
Marco Aurélio Romano Silva
(Belo Horizonte, Brazil)
Mônica Yassuda
Osvaldo Pereira de Almeida
(Crawley, Australia)
Paulo Belmonte Abreu
(Porto Alegre, Brazil)
Paulo Canineu
Paulo Rossi Menezes
Paulo Mattos
(Rio de Janeiro, Brazil)
Renato Teodoro Ramos
Renério Fraguás Junior
Ronaldo Ramos Laranjeira
Sandra Scivoletto
Táki Athanassios Cordás
Teng Chei Tung
Zacaria Borge Ali Ramadam
INSTRUCTIONS FOR AUTHORS
Available on the journals website (www.hcnet.usp.br/ipq/revista) and published in the last issue every year (number 6).
We would like to thank the artist Laila Gattaz, who
gently allowed, for exclusive use on the covers of the
Archives of Clinical Psychiatry, the series of art works
named “Imagens de São Paulo”.
This journal is printed on acid-free paper.
Cataloguing in Publication (CIP) Data
Archives of Clinical Psychiatry / University of São Paulo Medical School. Institute of Psychiatry - vol. 42, n. 1 (2015). – São Paulo: /
IPq-USP, 2011
From volume 29 (2001), the articles of this journal are available in electronic form in the SciELO (Scientific Electronic Library Online)
database.
1.1. Clinical Psychiatry. University of São Paulo Medical School. Institute of Psychiatry.
ISSN : 0101-6083 printed version
ISSN : 1806-938X online version
CDD 616.89
Indexing Sources
• ISI (Institute for Scientific Information)
•
•
- Science Citation Index Expanded (SciSearch®)
- Journal Citation Reports/Science Edition
EMBASE - Excerpta Medica Database
LILACS - Literatura Latino-Americana e do Caribe de Informação em Ciências
da Saúde
• PERIODICA - Índice de Revistas Latino-Americanas em Ciências
•
•
•
•
•
•
SciELO - Scientific Eletronic Library Online
SIIC - Sociedad Iberamericana de Información Científica
Scopus (www.scopus.com)
Gale Cengage Learning
DOAJ - Directory of Open Access Journals
HINARI - World Health Organization
Advertisers bear full responsibility for the content of their advertisements.
There is no commercial involvement by advertisers in the development of the content or in the editorial
decision-making process for the Archives of Clinical Psychiatry.
INDEX
Original articles
Stress and coping in a sample of medical students in Brazil..................................................................................... 1
Ana Margareth Siqueira Bassols, Bruna Brasil Carneiro, Guilherme Correa Guimarães, Lucas Mestre Seiki Okabayashi, Felipe Gutierrez Carvalho,
Anais Back da Silva, Gabriela Neubarth Cortes, Luis Augusto Paim Rohde, Claudio Laks Eizirik
Factors related to positive and negative outcomes in psychiatric inpatients in a General Hospital
Psychiatric Unit: a proposal for an outcomes index.................................................................................................... 6
Hugo Karling Moreschi, Gabriela Pavan, Julia Almeida Godoy, Rafael Mondrzak, Mariana Ribeiro de Almeida, Marco Antônio Pacheco,
Eduardo Lopes Nogueira, Lucas Spanemberg
Trust and expectation on psychiatrist and its correlation with satisfaction and adherence in
patients with mental illness............................................................................................................................................ 13
Dushad Ram, Basavana Gowdappa
Review articles
Post stroke depression: clinics, etiopathogenesis and therapeutics..................................................................... 18
Vinicius Sousa Pietra Pedroso, Leonardo Cruz de Souza, Andre R. Brunoni, Antônio Lúcio Teixeira
Associations between chronic pelvic pain and psychiatric disorders and symptoms....................................... 25
Ana Carolina Franco de Carvalho, Omero Benedito Poli-Neto, José Alexandre de Souza Crippa, Jaime Eduardo Cecílio Hallak, Flávia de Lima Osório
Letter to the editor
The relationship between mental disorder and violence......................................................................................... 31
Barbara Lay
Rua Anseriz, 27, Campo Belo – 04618-050 – São Paulo, SP. Fone: 11 3093-3300 • www.segmentofarma.com.br • [email protected]
Diretor-geral: Idelcio D. Patricio Diretor executivo: Jorge Rangel Gerente financeira: Andréa Rangel Comunicações médicas: Cristiana Bravo Coordenadora comercial: Izabela Teodoro Gerente editorial:
Cristiane Mezzari Coordenadora editorial: Sandra Regina Santana Imagem da Capa: Laila Gattaz Revisora: Glair Picolo Coimbra Produtor gráfico: Fabio Rangel Periodicidade: Bimestral Tiragem: 2.000
exemplares Cód. da publicação: 16378.3.15
Original article
Stress and coping in a sample of medical students in Brazil
Ana Margareth Siqueira Bassols1,2, Bruna Brasil Carneiro3, Guilherme Correa Guimarães3,
Lucas Mestre Seiki Okabayashi3, Felipe Gutierrez Carvalho3, Anais Back da Silva3,
Gabriela Neubarth Cortes3, Luis Augusto Paim Rohde1,2, Claudio Laks Eizirik1,4
Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
Division of Child and Adolescent Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), UFRGS, Porto Alegre, RS, Brazil.
UFRGS, Porto Alegre, RS, Brazil.
4 Division of Psychiatry, HCPA, UFRGS, Porto Alegre, RS, Brazil.
1
2
3
Received: 6/14/2014 – Accepted: 1/9/2015
DOI: 10.1590/0101-60830000000038
Abstract
Background: Medical training is a stressing situation, making medical students vulnerable to psychiatric disorders, such as depression and anxiety. Objective:
The study aimed to assess the prevalence of stress and coping in students of a public medical school in Brazil, comparing the groups from the first and sixth
years of training. Methods: Through a cross-sectional, observational study, a sample of 232 first and sixth-year regularly registered medical students has been
evaluated. Students filled a socio-demographic questionnaire, the Lipp Inventory of Stress Symptoms (ISSL), and the Coping Strategies Inventory (CSI). Results:
From the total sample of 232 students, 110 were first-year students and 122 sixth-year students. Stress symptoms were significantly higher in first-year students
(49.1%) than in the sixth-year group (33.6%; p = 0.018). Variables significantly associated with stress were: year of the training (1st year > 6th year), income (lower
> higher income), satisfaction with the training (dissatisfied > satisfied) and the use of escape/avoidance copying strategy (positive association). Discussion:
Considering the higher stress symptoms among first-year medical students and the positive association of the escape/avoidance copying strategy with stress,
strategies must be developed to enable students starting medical school to be better at coping with this stressful situations.
Bassols AMS et al. / Arch Clin Psychiatry. 2015;42(1):1-5
Keywords: Students, medical, psychological stress, coping behavior, adaptation, psychological.
Introduction
Medical school is recognized as a stress factor that has a great impact
on the life and health of undergraduates1,2. In the literature, depressive symptoms, like slowness of thought, difficulty in concentrating
and indecisiveness, are also frequent in samples of university student
and have been known to jeopardize academic development3-7; their
presence is associated with perfectionism and with the constant stress
this group is subjected3,8.
Since the XVIIth Century, the term stress began to be used in
English, meaning “affliction”, “adversity”9. Nowadays, the term stress
designates a process in which the body reacts through physical and/or
psychological components, which can result in physical and mental
problems. Stress is also understood as a state of tension causing a
disruption in the homeostasis of the individual, since, even before
an external stressor, the subject may respond in different ways, according to their individual characteristics10. It is known that severe
exposure to stress can lead to reduced self-esteem and low academic
performance, due to decreased attention, concentration and loss of
decision taking ability11,12. In addition, it can lead to dehumanization
in contact with the patient13,14.
Stress is very common in conflictive or major demanding situations, such as training to become a medical doctor. Among the socioeducational factors that contribute to intensifying stress, is the great
amount of information to be assimilated during the medical course;
the pressure resulting from constant evaluations; the developmental
period that the majority of students are in during the Medical School
(the transition from adolescence to adulthood); a higher degree of
curricular requirements; interaction with patients and changes in
lifestyle among others15,16.
Although the research focused on university students has gained
more space in recent decades17, Brazilian studies about symptoms
of stress and burnout within this group are still scarce, as referred
by Baldassin18.
The term coping refers to a complex process that is being used
in the sense of “ways of dealing with”, “confrontation strategies” or
“mechanisms commonly used by individuals to cope with stress”19.
The different ways in which the individual adapts to adverse circumstances, as well as the efforts made by the people in order to cope with
chronic or acute stressful situations, have been the object of study
during several decades20. Coping, under the situational perspective,
is a cognitive and behavioral process that is modified as a function
of time and the stress situation in which the individual is involved.
Therefore, the effectiveness and adaptability of coping strategies are
not determined a priori, but according to a person, the type of situation, the time and the results of their use21. In contrast, according
to the dispositional approach, individual differences can influence
coping responses based on the assumption of certain stability in
its manifestations, determining the use of preferential strategies22.
In this study, we understand coping as a response to stress, a
model defined by Folkman and Lazarus23, who identify two main
types of coping, centered on the problem and centered on the emotion. Although both are linked to the perception of control, they differ
in that the former is related to the effort to act on the situation that
gave rise to stress, trying to change it, while the second consists in
the regulation of the emotional state that is associated with stress or
is the result of a stressful event. According to this theory, most people
employ about eight coping strategies in all stressful events: confrontation, withdrawal, self-control, social support, escape/avoidance,
accepting responsibility, problem solving and positive reappraisal.
For these authors, coping is a process or interaction that occurs
between the individual and the environment; its function is managing the stressful situation processes. It presupposes the notion of
evaluation, i.e., how the phenomenon is perceived, interpreted and
cognitively represented in the mind of the individual, constituting a
mobilization of cognitive and behavioral efforts to manage (reduce,
minimize or tolerate) the internal or external demands that arise
from its interaction with the environment24.
Address correspondence to: Ana Margareth Siqueira Bassols. Serviço de Psiquiatria da Infância e Adolescência, Hospital de Clínicas de Porto Alegre. Rua Ramiro Barcelos, 2350 – 90035-903 –
Porto Alegre, RS, Brazil. Tel.: +55 (51) 3359-8294. E-mail: [email protected]
2
This research aimed to evaluate prevalence and levels of stress
symptoms, and coping strategies used to deal with stress in students
at the entry and exit of the Medical School in a public University,
in southern Brazil.
Methods
Sample
This is a cross-sectional study that evaluated students of both sexes
at the first and sixth-year of the medical course at one of the Public
University (Federal University of Rio Grande do Sul – UFRGS) in the
capital of the southernmost state of Brazil. The study was approved
by the Ethics Committee of the Hospital de Clinicas de Porto Alegre
(Protocol No. 09-444).
when comparing first and sixth-year students or in Simple Poisson
bivariate regression analyses were included in the final model. The
IBM SPSS, version18 was used for the analysis.
Results
Overall, 149 students were enrolled in the first year of the UFRGS
School of Medicine in 2010, and 195 students were enrolled in the
sixth year, in 2010 and 2011, according to a list provided by the
Graduation Committee of the Medical School (COMGRAD). Twohundred and thirty two questionnaires were collected, 110 from
the first year course (73.8% of those enrolled) and 122 among the
sixth year students (62.6% of the students enrolled). The following
variables were significantly different between groups: family income,
performing a paid activity, use of alcohol/drugs and use of medication (Table 1).
Procedures
Trained research assistants performed the data collection. The first
year students filled the forms in their classrooms in between two
disciplines (May 2010, half-way through the semester); the 6th year
data was collected at the hospital at different moments, since no discipline congregating all students exists at this stage of the course. As
expected, data collection was more difficult for sixth-year students,
making the period longer for this group. The subjects responded
anonymously and voluntarily to the survey instruments after signing
the Informed Consent form.
Instruments
a) The socio-demographic and health questionnaire was constructed
for this study included data on gender, family and personal income,
where the student lived (with his/her family or in off-campus communal housing, participation in leisure activities, use of alcohol and
drugs, medication and the presence of some physical illness.
b) Inventory of Stress Symptoms for Adults, Lipp (ISSL) to rate
the degree of stress25. The ISSL was developed and validated for use
in Brazil, presenting adequate internal consistency (α = 0.91)25. It is
composed by four dimensions (Q) that divide temporally symptoms
into the last 24 hours (Q1 – 15 symptoms; the alert phase), last week
(Q2 – until 9 symptoms; the resistance) Q3 (near-exhaustion phase
above 10 symptoms) and last month (Q4 – 23 symptoms; exhaustion
phase or Burnout).
The symptoms are divided into physical and psychological
dimensions, corresponding to the most frequent manifestations of
stress9,28. In ISSL, a positive diagnosis of stress is based on the sum
of the symptoms of each frame of the inventory (Q1 > 6; Q2 > 3 or
Q3 > 9; Q4 > 8), allowing data to indicate not only that the person
has stress, but also the phase where symptoms are predominant25.
c) Coping Strategies Inventory adapted to the Brazilian Portuguese language by Savóia et al. to assess the strategies used to deal
with stressful situations26, like being a medical student. The Cronbach’s Alphas for the eight sub-scales were between 0.65 and 0.80.
The eight types of coping identified by the inventory also seem to
be appropriate.
Data analyses
The sample size calculation assuming differences in the order of
20% in the total scores of the instruments between the two groups,
considering the power of 80% and a significance level of 0.5% indicated a sample of approximately 103 students in each group. The first
objective of the analysis was to evaluate if the groups were statistically
comparable. These analyses were conducted through the chi-square
test for categorical variables and the T test for continuous variables.
Simple Poisson bivariate regression analyses – with adjustment
for robust variances – were performed, considering stress as an
outcome. Variables with “p” below 0.2 in bivariate analyses either
Table 1. Socio-demographic and health data
1º year
6º year
Total
p
Frequency (%) Frequency (%) Frequency (%)
Number of students
110 (47.4)
122 (52,6)
232
Age*
20.7 (SD 2.6) 25.34 (SD 2.7) 23.1 (SD 3.2)
Male students**
62 (56.4%)
55 (45.1%)
117 (50.4%)
0.113
Leisure time**
94 (87%)
109 (90.1%)
203 (88.6%)
0.605
Live with their family** 65 (59.6%)
56 (45.9%)
121 (52.4%)
0.051
Living in communal
8 (7.3%)
8 (6.6%)
16 (6.9%)
> 0.999
off-campus housing**
43 (40.2%)
32 (26.7%)
75 (33.0%)
0.043
Family income up to
U$ 1.749,27†**
Perform paid activity** 11 (10.0%)
43 (35.5%)
54 (23.4%) < 0.001
Coffee intake**
80 (74.1%)
101 (82.8%)
181 (78.8%)
0.147
Drug and alcohol use** 38 (35.2%)
66 (54.5%)
104 (45.4%)
0.005
Cigarette smoking**
5 (4.5%)
5 (4.2%)
10 (4.4%)
> 0.999
Presence of some
28 (25.9%)
42 (35.0%)
70 (30.7%)
0.180
illness**
Use of medication**
36 (33.6%)
59 (51.3%)
95 (42.8%)
0.012
Low satisfaction
27 (24.5%)
27 (22.1%)
54 (23.3%)
0.780
Variables
* Averages and Standard Deviation (SD) calculated through the Student’s t test.
** Variables compared through the Pearson Chi-square with continuity correction.
† Amount equivalent to R$ 5.000,00 at the time of data collection.
In the sample, 40.95% of the subjects (95 students) presented
stress symptoms. Among them 2 (2.1%) were in the alert phase, 89
(93.7%) in the resistance phase and 4 (4.2%) at the almost exhaustion
phase. The prevalence of stressed students in the first year was 49.1%
[(n = 54); 2 where in the alert phase (3.7%), resistance/endurance 51
(94.4%) and almost exhaustion 1 (1.9%)] and 33.6% in the sixth-year
students [within respective phases: n = 41; 0 (0,0%), 38 (92.7%) and 3
(7.3%)]. Thus, stress symptoms were significantly higher in first-year
students (49.1%) than in the sixth-year group (33.6%), p = 0.018. No
student of the sample scored in the burnout level.
In bivariate analyses, the following factors were significantly associated with stress: age, gender, year of the course, family income,
bear a disease, using some sort of medication and satisfaction with
the course (Table 2). The variables that remain statistically significant
after the control through multivariate analysis were year of the course,
income, satisfaction with the course (Table 2). Age was not included
in the multivariate model, since it has, as expected, a huge collinearity
with year of course. The bivariate analysis of coping strategies having
stress as an outcome variable is shown in table 2. In the multivariate
model, only escape/avoidance strategy was significantly higher in
stressed students.
3
Table 2. Table of bivariate analysis and Poisson’s Regression with stress as outcome
Gender
Female
Male
Age
Lower than median (less than 23 years)
Higher than median (more than 23 years)
Course year
First year
Sixth year
Leisure activities
Practice leisure activities
Do not practice leisure activities
Living with their family
Live with their family
Do not live with their family
Living in communal off-campus housing*
Live in a republic
Do not live in a republic
Family income
Up to 2,915.45 dollars†
More than 2,915.45 dollars†
Performing a paid activity
Perform paid activity
Do not perform a paid activity
Coffee intake
Drink coffee
Do not drink coffee
Alcohol/Drug use
Use alcohol/drug
Do not use alcohol/drug
Cigarette smoking (tobacco)
Smokers
Non-smokers
Illness bearer
Bear an illness
Do not bear any illness
Medication use
Use medication
Do not use medication
Satisfaction with the course
Dissatisfied
Satisfied
Coping
Escape/avoidance
Social support
Confrontation
Self-control
Withdrawal/Detachment
Responsibility acceptance
Problem resolving
Positive reevaluation
†
% of stress
Bivariate analysis
Relative risk brut
p
(I.C. 95%)
Poisson’s regression
Relative risk
p (adjus.)
adjusted (I.C. 95%)
60 (52.2%)
35 (29.9%)
1.74 (1.26 – 2.42)
1.00
0.001
1.30 (0.925 – 1.82)
1.00
0.131
59 (48.4%)
36 (32.7%)
1.48 (1.07 – 2.04)
1.00
0.018
54 (49.1%)
41 (33.6%)
1.46 (1.07 – 2.00)
1.00
0.018
1.48 (1.07 – 2.05)
1.00
0.017
84 (41.4%)
10 (38.5%)
1.08 (0.64 – 1.80)
1.00
0.780
46 (38.0%)
49 (44.5%)
0.85 (0.63 – 1.16)
1.00
0.314
0.93 (0.69 – 1.27)
1.00
0.668
8 (50.0%)
87 (40.3%)
1.24 (0.74 – 2.08)
1.00
0.412
38 (50.7%)
53 (34.9%)
1.45 (1.06 – 1.98)
1.00
0.019
1.40 (1.04 – 1.89)
1.00
0.027
18 (33.3%)
77 (43.5%)
0.77 (0.51 – 1.16)
1.00
0.206
0.97 (0.61 – 1.54)
1.00
0.885
74 (40.9%)
20 (40.8%)
1.00 (0.68 – 1.46)
1.00
0.993
0.95 (0.66 – 1.37)
1.00
0.780
38 (36.5%)
56 (44.8%)
0.82 (0.59 – 1.12)
1.00
0.211
0.94 (0.69 – 1.28)
1.00
0.698
4 (40.0%)
91 (41.7%)
0.96 (0.44 – 2.08)
1.00
0.914
38 (54.3%)
56 (35.4%)
1.53 (1.3 – 2.07)
1.00
0.005
1.27 (0.92 – 1.76)
1.00
0.147
49 (51.6%)
46 (36.2%)
1.42 (1.05 – 1.93)
1.00
0.022
1.37 (0.96 – 1.96)
1.00
0.084
29 (53.7%)
66 (37.1%)
1.45 (1.06 – 1.98)
1.00
0.020
1.52 (1.09 – 2.13)
1.00
0.014
1.21 ( 1.12 – 1.31)
1.03 (0.99 – 1.07)
1.07 ( 1.02 – 1.13)
0.99 ( 0.93 – 1.04)
1.02 ( 0.98 – 1.07)
1.04 ( 1.00 – 1.08)
0.97 (0.91 – 1.04 )
0.99 ( 0.97 – 1.03)
0.00
0.056
0.002
0.616
0.317
0.036
0.396
0.881
1.14 ( 1.04 – 1.25)
1.00 ( 0.97 – 1.04)
1.04 ( 0.99 – 1.01)
0.005
0.819
0.122
1.00 ( 0.96 – 1.04)
0.918
Amount equivalent to R$ 5,000.00 at the time of data collection.
Discussion
We found that the entrance in the Medical School was associated with
higher stress than the final part of the course. In addition, the following variables were associated with stress in this sample of Medical
students: year of the course, low income, satisfaction with the course
and the use of escape/avoidance copying strategy.
The way students cope with stress may influence their adjustment
to medical school and whether or not stress will detrimentally affect
4
their quality of life. According Tempski, despite students reported
that their quality of life in medical school is worse than in other
contexts of their life, they evaluated it positively due they perceived
sacrifices and difficulties of the program as necessary to become a
doctor. Such dissatisfaction is related to the learning environment
and the curriculum27.
The prevalence of stress symptoms found in the present study
was not as high as the one found in previous studies17,28. No student
of the sample scored in the burnout level, however, over 90% of the
students independently of the year at Medical School scored their
stress in the range compatible with the resistance phase. This level
is sufficient for producing physical fatigue, attention and memory
problems, insomnia, hypersensitivity and increased susceptibility
to infectious diseases in addition to impairment of productivity29.
Students at this level of stress might have an impaired efficiency,
yielding less than expected, according to their capacity.
When tracing the profile of the groups, income was higher and
performing a paid activity was more prevalent in the group of sixth
graders, as expected. This scenario can be a result of carrying out
activities such as research/extension and obtaining grants or even
working “on duty shifts”. Furthermore, the first-year group included
students admitted under affirmative action policies at the time of
data collection (20% of the total number of students), which might
have contributed to a reduction in mean household income. According to research from Psychosomatic Medicine30, it’s those in lower
socioeconomic levels who experience greater levels of stress and
experience more stress-related health problems, as well. Workers
who have higher levels of job stress experience a greater incidence
of the common cold, and call in sick more often. There has also
been a documented link between high job stress and lower levels
of mental health.
Our finding differs from previous results in other Brazilian
studies15,16 in which stress was higher in the sixth-year group or
increased in the middle of the course, when occurs the transition to
the medical clinic with greater contact with patients. It is possible for
students currently enrolled in the sixth grade has had a peak stress
in the middle of the course, but we cannot confirm this hypothesis.
Another aspect to consider is that their psychological development
and greater adaptation to routines could have enabled a reduction
of stress in these students.
The literature suggests a correlation between the degree of
satisfaction of medical students and the level of involvement and
engagement in curricular activities31,32. To this end, there has to be
a good adequacy of the student to the university environment and
acceptance of aspects as proposed by the course curriculum that will
contribute to greater satisfaction and would possibly favor the reduction of stress factors. The report of greater satisfaction with the course
being significantly higher in first year students raises the hypothesis
that, in spite of anxiety being higher at the beginning of the course33,
it is likely that the new condition of “freshman” and the interest in
learning, favor satisfaction. In fact, after enrolment will be the quality of teaching and learning that contribute most to the student (in)
satisfaction34,35. According to the literature, the dissatisfaction with
the traditional academic curriculum in medical schools has increased
dramatically, requiring evaluations of medical education and their
impact on student’s health and welfare processes10.
Regarding coping strategies, it is extremely relevant that stressed
students from the Medical School use more escape-avoidance tactics
than non-stressed students, even more considering that this is one
of the most maladaptive coping styles. Moreover, this was the only
copying mechanism significantly different between stressed and nonstressed students. Escape-avoidance coping involves disengaging or
staying away from a stressful situation and its behavioral and cognitive/emotional consequences. Typical strategies of this copying stile in
response to a stressful situation might encompass cognitive avoidance
(“Refused to believe”). Avoidance coping causes anxiety to snowball
because when people use avoidance coping they typically end up
experiencing more of the very thing they were trying to escape36.
Some limitations of the present study should be taken into account. Because it is a cross sectional investigation, it is impossible
to establish a temporal relationship between stress and year of the
course, income, satisfaction with the course and the use of escape/
avoidance copying strategy. Besides, we used only self-report measures of stress. Our enrollment rate for sixth-year students was lower
than the one for the first-year students, possibly due to facts such
as higher work-load, the on-duty shift regime and having to study
harder for the residency exams. The data collection for the first and
sixth year groups was performed in different environments due to the
reality of the students’ activities during the course’s program. While
disciplines of the core curriculum take place in classrooms, in initial
semesters, as the course advances, disciplines are mostly practical,
making the students move around in different areas – even in different
hospitals. Finally, the results refer to the sample of Medical Students at
the Universidade Federal do Rio Grande do Sul (UFRGS). Generalizability to other Medical School students should be done cautiously.
There are many coping inventories available in the literature, and
the present study employed only one of them. Findings might be
specific to this instrument. Furthermore, due to logistical issues, we
could neither collect data simultaneously nor obtain data from the
students who did not complete the study instruments.
Conclusion
Medical education exposes students to hospital reality since the
first year of the course, causing insecurity among freshmen who
must deal not only with illness situations but also face life and death
dilemmas prematurely. It is possible that the knowledge acquired
throughout the course; the maturity gained across time, the experience obtained through clinical practice and the coping methods used
allow for better psychological conditions in the sixth year students
to face similar problems to those they were confronted when entering medical school. In addition, they might develop some degree of
resistance to stress by the end of the course. Possible interventions
in order to avoid manifestations of stress and even burnout must be
implemented early in the Medical Course. Our findings suggest that
vulnerable students should be identified early in their first year and
provided with additional support. In addition, information about
effective coping strategies (i.e. active coping efforts) and ineffective
means of dealing with stress (escape-avoidance efforts) and training
in implementing more adaptive copying mechanisms to face stress
might be helpful in preventing distress37. To conclude, we need
interventions that help students cope with stress in a more mature
way, to make a smooth transition from school to medical school, and
also to adjust to different learning environments during the different
phases of medical education.
Acknowledgements
This study was funded by Fundo de Incentivo à Pesquisa do Hospital
de Clínicas de Porto Alegre (FIPE-HCPA).
The authors wish to thank Statistician Vania Naomi Hirakata for
performing of analyses and database management and the students
for their cooperation.
Disclosure
LAR has been on the speakers’ bureau/advisory board and/or acted
as consultant for Eli-Lilly, Janssen-Cilag, Novartis and Shire in the
last three years. The ADHD and Juvenile Bipolar Disorder Outpatient
Programs chaired by him received unrestricted educational and
research support from the following pharmaceutical companies in
the last three years: Eli-Lilly, Janssen-Cilag, Novartis, and Shire. The
other authors report no conflicts of interest.
References
1. Prinz P, Hertrich K, Hirschfelder U, Zwaan M. Burnout, depression and depersonalization – Psychological factors and coping
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
strategies in dental and medical students. GMS Z Med Ausbild.
2012;29(1):Doc 10.
Dahlin M, Joneborg N, Runeson B. Stress and depression among medical
students: a cross-sectional study. Medical Education. 2005;39:594-604.
Amaral GF, Gomide LMP, Batista MP, Piccolo PP, Teles TBG, Oliveira
PM, et al. Sintomas depressivos em acadêmicos de medicina da Universidade Federal de Goiás: um estudo de prevalência. Rev Psiquiatr Rio
Gd Sul. 2008;30:124-30.
Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet.
1997;349:1498-504.
Baldassin S. Ansiedade e depressão no estudante de medicina: revisão
de estudos brasileiros. Cadernos ABEM. 2010;6:19-26.
Yusoff MS, Abdul Rahim AF, Baba AA, Ismail SB, Mat Pa MN, Esa AR.
Prevalence and associated factor of stress, anxiety and depression among
prospective medical students. Asian JPsychiatr. 2012;6:128-33.
Barria ACR, Queiroz S, Nicastri S, Andrade AG. Comportamento do
universitário da área de biológicas da Universidade de São Paulo, em
relação ao uso de drogas. Rev Psiquiatr Clin (Sao Paulo). 2000;27:215-24.
Enns MW, Cox BJ, Sareen J, Freeman P. Adaptive and maladaptive
perfectionism in medical students: a longitudinal investigation. Med
Educ. 2001;35:1034-42.
Lipp MEN. Stress: conceitos básicos. In: Lipp, MEN (Org). Pesquisas
sobre o stress no Brasil: saúde, ocupações, grupos de risco. 2 ed. Campinas: Papirus, 2001a, p. 17-31.
West CP, Shanafelt TD. The influence of personal and environmental
factors on professionalism in medical education. BMC Medical Education. 2007;7:29.
Lipp MEN. O que eu tenho é stress? De onde ele vem? In: Lipp MEN (org.)
O stress está dentro de você. 4.ed. São Paulo: Contexto, 2001b, p. 9-18.
Niemi PM, Vainiomaki PT. Medical students’ academic distress, coping
and achievement strategies during the pre-clinical years. Teach Learn
Med. 1999;11:125-34.
Guthrie EA, Black D, Shaw CM, Hamilton J, Creed FH, Tomenson B.
Embarking upon a medical career: psychological morbidity in first year
medical students. Med Educ. 1995;29:337-4.
Dyrbye LN, Stanford Massie Jr F, Eacker A, Harper W, Power D, Durning
SJ, et al. Relationship between burnout and professional conduct and
attitudes among US medical students. JAMA. 2010;304:1173-80.
Aguiar SM, Vieira APGF, Vieira KMF, Aguiar SM, Nóbrega JO. Prevalência de sintomas de estresse nos estudantes de medicina. J Bras
Psiquiatr. 2009;58:34-8.
Guimarães KBS. Estresse e a formação médica: implicações na saúde
mental dos estudantes. Assis; 2005. Mestrado [Dissertação] — Universidade Estadual Paulista Júlio de Mesquita Filho, Unesp.
Dyrbye LN, Thomas MR, Shanafelt TD. Systematic review of depression,
anxiety, and other indicators of psychological distress among U.S. and
Canadian medical students. Acad Med. 2006;81(4):354-73.
Baldassin S. Anxiety and depression in medical students: a review of
Brazilian studies. Cadernos ABEM. 2010;6:19-26.
5
19. Ramos SIV, Carvalho AJR. Nível de stress e estratégias de coping dos
estudantes de 1º ano do Ensino Universitário de Coimbra. Rev Interacções. 2007.
20. Suls J, David JP, Harvey JH. Personality and coping: three generations of
research. J Pers. 1996;64:711-35.
21. Beresford BA. Resources and strategies: how parents cope with the care
of disabled child. J Child Psychol Psychiatry. 1994;35:171-209.
22. Costa ES, Leal IP. Estratégias de coping em estudantes do Ensino Superior.
Ana Psicol. 2006;24:189-99.
23. Folkman S, Lazarus RS. If it changes, it must be a process: a study of
emotion and coping during three stages of a college examination. J Pers
Soc Psychol. 1985;48:150-70.
24. Folkman S, Lazarus RS. An analysis of coping in a middle-aged community sample. J Health Soc Behav. 1980;21(3):219-39.
25. Lipp MEN. Inventário de sintomas de stress para adultos de lipp. 3.ed.
São Paulo: Casa do Psicólogo; 2005.
26. Savóia MG, Santana PR, Meijas NP. Adaptação do inventário de estratégias de coping de Folkman e Lazarus para o português. Psicologia USP.
1996;7:183-201.
27. Tempski P, Bellodi PL, Paro HBMS, Enns SC, Martins MA, Schraiber LB.
What do medical students think about their quality of life? A qualitative
study. BMC Medical Education. 2012;12:106.
28. Aktekin M, Karaman T, Senol YY, Erdem S, Erengin H, Akaydin M.
Anxiety, depression and stressful life events among medical students: a
prospective study in Antalya, Turkey. Med Educ. 2001;35:12-7.
29. Santos AF, Alves Júnior A. Estresse e estratégias de enfrentamento em
mestrandos de ciência de Saúde. Psicol Reflex Crit. 2007;20:104-13.
30. Cohen S, Doyle WJ, Baum A. Socioeconomic status is associated with
stress hormones. Psychosomatic Medicine. 2006;68:414-20.
31. Bardagi MP, Paradiso AC. Trajetória acadêmica e satisfação com a escolha
profissional de universitários em meio de curso. Rev Bras Orientac Prof
(São Paulo). 2003;4:153-66.
32. Abrão CB, Coelho EP, Passos LBS. Prevalência de sintomas depressivos
entre estudantes de medicina da Universidade Federal de Uberlândia.
Rev Bras Educ Med. (Rio de Janeiro). 2008;32:315-23.
33. Bassols AM, Okabayashi LS, Silva AB, Carneiro BB, Feijó F, Guimarães
GC, et al. First- and last-year medical students: is there a difference in
the prevalence and intensity of anxiety and depressive symptoms? Rev
Bras Psiquiatr. 2014;36(3):233-40.
34. Douglas J, Douglas A, Barnes B. Measuring student satisfaction at UK
university. Quality Assurance in Education. 2006;14:251-67.
35. Clark DC, Zeldow PB. Vicissitudes of depressed mood during four years
of medical school. JAMA. 1988;260:2521-7.
36. 36 . Boyes A. In: In Practice. Why Avoidance Coping is the Most Important Factor in Anxiety Avoidance coping causes anxiety to snowball.
March 5, 2013.
37. Stewart SM, Betson C, Lam TH, Marshall IB, Lee PW, Wong CM. Predicting stress in first year medical students: a longitudinal study. Hong
Kong. Med Educ. 1997;31(3):163-8.
Original article
Factors related to positive and negative outcomes in psychiatric inpatients in a
General Hospital Psychiatric Unit: a proposal for an outcomes index
Hugo Karling Moreschi1, Gabriela Pavan1, Julia Almeida Godoy1, Rafael Mondrzak1, Mariana Ribeiro de Almeida2,
Marco Antônio Pacheco1, Eduardo Lopes Nogueira1, Lucas Spanemberg1,2
1
2
Department of Psychiatry, Hospital São Lucas, Pontifícia Universidade Católica do Rio Grande do Sul (PUC-RS), Porto Alegre, RS, Brazil.
Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul (HCPA/UFRGS), Porto Alegre, RS, Brazil.
DOI: 10.1590/0101-60830000000039
Abstract
Background: General Hospital Psychiatric Units have a fundamental importance in the mental health care systems. However, there is a lack of studies regarding
the level of improvement of patients in this type of facility. Objective: To assess factors related to good and poor outcomes in psychiatric inpatients using an
index composed by clinical parameters easily measured. Methods: Length of stay (LOS), Global Assessment of Functioning (variation and at discharge) and
Clinical Global Impression (severity and improvement) were used to build a ten-point improvement index (I-Index). Records of psychiatric inpatients of a
general hospital during an 18-month period were analyzed. Three groups (poor, intermediate and good outcomes) were compared by univariate and multivariate models according to clinical and sociodemographic variables. Results: Two hundred and fifty patients were included, with a percentage in the groups
with poor, regular and good outcomes of 16.4%, 59,6% and 24.0% respectively. Poor outcome at the discharge was associated mainly with lower education,
transient disability, antipsychotics use, chief complaint “behavioral change/aggressiveness” and psychotic features. Multivariate analysis found a higher OR for
diagnoses of “psychotic disorders” and “personality disorders” and others variables in relation to protective categories in the poor outcome group compared
to the good outcome group. Discussion: Our I-Index proved to be an indicator of that allows an easy and more comprehensive evaluation to assess outcomes
of inpatients than just LOS. Different interventions addressed to conditions such as psychotic disorders and disruptive chief complaints are necessary.
Moreschi HK et al. / Arch Clin Psychiatry. 2015;42(1):6-12
Keywords: Psychiatry unit, inpatients, length of stay, clinical impression, global functioning, psychiatric diagnosis, outcomes.
Introduction
Since the late 1970s, a new proposal for the composition of mental
health care system was progressively implemented in some European
countries, based on deinstitutionalization and replacement of asylums for community-based psychiatric services and beds in general
hospitals1-3. Influenced by the European movement, the process of
reformation of the psychiatric care in Brazil has led to a significant
decrease in psychiatric beds in the past twenty years, even though
replacement services have not expanded at the same pace4,5. In this
context, psychiatric wards within general hospitals became the main
facilities for treatment of acute cases, increasing the importance of
General Hospital Psychiatric Units (GHPU)6.
Although GHPU have a fundamental importance in this new
model of care, there is a lack of studies regarding the level of improvement of patients in this type of facility. Therefore, it is also not
well established what are the best general parameters for evaluating
outcomes in patients admitted to general hospitals. Shorter psychiatric length of stay (LOS) has been considered a strong indicator of
good outcomes both in specialty and general hospitals7-9. Although
the LOS of psychiatric inpatients has decreased in recent decades
(from months to days), it is still longer than for patients with physical illnesses, increasing expenditures on health, generating stigma
and delaying social reintegration of the patient7. Functional ratings
as the Global Assessment of Functioning (GAF)10-12 and the Brief
Psychiatric Rating Scale (BPRS)13 also have been used to measure
outcomes in psychiatric inpatients acutely ill, as well as the Clinical
Global Impression (CGI)14, a measure of disease severity.
In Brazil, only one study was conducted to assess outcomes of
psychiatric inpatients in general hospitals. Dalgalarrondo et al.15
created a variable called “outcome of admission” on the basis of a
combination of two other variables: LOS and condition at discharge,
a non-standardized clinical assessment. These authors found three
variables (poor social functioning before admission, advanced age
and organic mental disorder) associated with the “worst outcomes”.
Despite the merits of this study, the measure constructed to assess
these “worst outcomes” used an unusual and subjective criterion
for evaluating the condition at discharge, making it difficult to be
reproduced.
The use and development of assessment outcomes parameters as
routine outcome measurements (ROM) is particularly important in
mental health. In addition to recent changes in model of care mentioned above, the evaluation of outcomes has a dual role: evaluating
clinical results and generating data for the construction of a care
policy and financing model in mental health. While countries like
England already possess broader and pragmatically built outcome
measures as the Health of the Nation Outcome Scales (HoNOS)16,
the care reality in low- and middle-income countries is much more
precarious. In Brazil, for example, the only variables available for
assessing results in mental health public system are the psychiatric
diagnosis and the LOS. The lack of funding and consequently the
lack of professionals and technologies for the assessment of outcome
parameters make it difficult to evaluate true reality of assistance. Thus,
the proposal of measures of minimal clinical parameters of evaluation
of outcomes in mental health is an urgent demand.
The present study aims: 1) to propose and test an index of
evaluation outcomes for psychiatric inpatients, using usual and
easily measured clinical variables to compose an outcome score;
2) to investigate clinical and sociodemographic factors related to
positive and negative outcomes in psychiatric inpatients in a GHPU
classified by this index.
Methods
Study design, data source and sampling design
All records of admission to a Psychiatric Unit of a General Hospital
(Hospital São Lucas da PUCRS – HSL/PUCRS) in Porto Alegre, Brazil, were selected during 18 months (from February, 2013, to August,
2014). This unit has 18 psychiatric beds for public (six beds) and
Address correspondence to: Lucas Spanemberg. Psychiatric Unit, 6º andar sul, Hospital São Lucas, Pontifícia Universidade Católica do Rio Grande do Sul – 90610-000 – Porto Alegre, RS, Brazil.
Phone: +55 (51) 3320-3041. Email: [email protected]
7
private (twelve beds) patients. We assessed data in two moments: 1)
admission and; 2) discharge (last day of hospitalization). All patients
admitted to the unit are evaluated on a routine protocol in the early
hours of hospitalization. This protocol is part of routine care of the
psychiatric unit and includes sociodemographic and clinical data,
as well as tools to assess global functioning and severity of illness
(described below). Some variables as “chief complaint” were categorized according to their distribution in the emergency room, in accordance with a classification already described in previous studies17.
All patients who received medical discharge during these 18 months
were included in the study. When the routine protocol is completed
at discharge, some measures of improvement are collected to assess
treatment response (as we describe below). Our initial sample consisted of 287 patients. We excluded from the analysis patients who
did not have any data of the five outcome variables (CGI-I, CGI-S
at discharge, GAF at discharge, Δ-GAF and LOS) and patients who
discontinued treatment before medical discharge (n = 37). The final
sample was composed of 250 patients.
Instruments
Clinical Global Impression – Severity (CGI-S): this is one of the most
widely used assessment tools in psychiatry, easy to apply and interpret, besides being in the public domain. The CGI is rated on a 7-point
scale, with the severity of illness scale using a range of responses from
1 (normal) to 7 (amongst the most severely ill patients)18.
Clinical Global Impression – Improvement (CGI-I): as the instrument described above, the CGI-I is also in the public domain and
assesses the degree of patient improvement or response to treatment.
Scores range from 1 (very much improved) to 7 (very much worse)18.
Global Assessment of Functioning (GAF): This tool composes the
so-called Axis V in the DSM-IV Multiaxial System19. It is used to report the clinician’s judgment of the overall level of functioning of the
patient, rating subjectively the social, occupational, and psychological
functioning of adults. The scale ranges from 0 (inadequate information) to 100 (higher functioning), with ten categories of functioning. Within each category, there is a range of 10 points, describing
and exemplifying patterns of functioning in various environments.
A number should be chosen as the most descriptive of the overall
functioning of the patient.
I-Index were calculated according to the distribution of each variable
as illustrated in the box 1. Differences between groups in sociodemographic and clinical continuous variables were analyzed with analysis
of variance (ANOVA) test, with Tukey’s multiple comparison test as
post hoc analysis. Categorical variables were analyzed with Pearson
chi-square tests and the analyses post hoc of the adjusted residuals
were also performed to reveal the differences among the categories of
each variable. In order to evaluate the correlations among variables
used in I-Index, Pearson correlation was calculated, with the following parameters: very weak (from 0.00 to 0.19), weak (from 0.20 to
0.39), moderate (from 0.40 to 0.59), strong (0.60 to 0.79) and very
strong (0.80 to 1.00) correlations20. To identify admission factors
independently associated with discharge measurements, the polytomous multivariable logistic regression was used. The I-Index “good
outcome” was chosen as a reference to estimate odds-ratios (OR) of
“regular outcome” and “poor outcome”. The variables included at the
multivariate analysis were those with p < 0.20 at uncontrolled analysis.
Since the variables “diagnosis” and “chief complaint” show covariance
two independent final models were calculated. The final models were
established excluding variables with less interference one-by-one.
The p value for significance was set at 0.05. The statistical analyses
were performed using the software SPSS 18.0 (IBM SPSS, Inc., 2009,
Chicago, IL, www.spss.com).
Ethics considerations
This study was approved by the Research Ethics Committee of Pontifical Catholic University of Rio Grande do Sul (protocol number:
565.190).
Box 1. Score Index-I according to the score of each variable
Poor outcome
(score = 0)
CGI-I
CGI-S at
discharge
Index to assess outcomes
In order to construct a measure that could consider several parameters
of improvement commonly used in the literature, inexpensive, and
easily collected in Brazilian care reality, we propose an Improvement
Index (I-Index) with a score ranging from 0 to 10 points. This index
takes into account five variables: length of stay, CGI-S at discharge,
CGI-I, GAF at discharge and GAF variation (GAF at discharge – GAF
at admission or Δ-GAF). These instruments were chosen due to four
pragmatic criteria: 1) they are readily applicable and information be
easily collected; 2) their application does not burden the assistant
psychiatrist or the patient, that is, the clinical care is not modified or
interfered with; 3) measures are usually assessed in clinical outcome
studies of psychiatric inpatients; and 4) the psychiatrists of our institution are acquainted with the measures. Each variable might score from
0 to 2 points, according to the guidelines in box 1. The final Index-I
score can achieve 10 points, generating three groups with the following cutoffs: from 0 to 4 points = poor outcome; from 5 to 7 points =
regular outcome; and from 8 to 10 points = good outcome. The scores
of GAF, Δ-GAF and LOS were data-driven defined, according to their
mean and standard deviations in our sample.
Data analyses
Descriptive analyses were presented by means and standard deviations
(SD) for continuous variables, and by numbers and percentages (%)
for categorical variables. The initial scores for each variable of the
GAF at
discharge
Δ-GAF
LOS
Final Index-I
score
Intermediate
outcome
(score = 1)
5 (minimally
improved)
Good outcome
(score = 2)
1 (very much worse),
6 (much improved)
2 (much worse), 3
or 7 (very much
(minimally worse) or
improved)
4 (no change)
5 (markedly ill),
3 (mildly ill) or 4
1 (normal, not at all
6 (severely ill)
(moderately ill)
ill) or 2 (borderline
or 7 (among the
mentally ill)
most extremely ill
patients)
The patient
The patient
The patient
that scored, at
that scored, at
that scored, at
discharge, more
discharge, less than discharge, between
the average of the
the average and
than one SD above
GAF for all patients one SD above the average of the GAF
for all patients
average of the GAF
for all patients
The Δ-GAF changed The Δ-GAF changed The Δ-GAF changed
less than one SD
between one SD
more than the
below the average below the average
average of the
Δ-GAF for all
of the Δ-GAF for all and the average of
the Δ-GAF for all
patients
patients
patients
Patient remained
Patient remained
Patient remained
hospitalized longer
hospitalized
hospitalized for
than one SD above
between the
fewer days than the
the average of the
average and 1 SD
average of LOS for
LOS for all patients above the average
all patients
of LOS for all
patients
0-4 = Poor
5-7 = Regular
> 7 = Good
CGI-S: Clinical Global Impression – Severity at discharge; CGI-I: Clinical Global Impression –
Improvement; GAF-D: Global Assessment Functioning at discharge; Δ-GAF: GAF variation (GAF
at discharge – GAF at admission); LOS: length of stay; SD: standard deviation.
8
Results
Table 1 lists the sociodemographic and clinical data of the total
sample (n = 250). Most of the patients were female (65.2%), with
an average age of 41 (SD = 17.6). The majority of them was either
single or separated (58.7%) and nonsmokers (70.2%); 38.4% were
employed or active. Most of the patients had previous psychiatric
hospitalizations (50.6%) and 48.4% had clinical comorbidities. The
most frequent specific chief complaint of evaluation was change
in behavior/aggressiveness (21.5%), followed by suicidal ideation
(19.4%), suicide attempt (19%), psychotic symptoms (14%) and
substance abuse (7.9%).
Table 2 compares clinical variables on admission and at discharge. The length of stay had a mean of 27.12 (±15.04) days. During
hospitalization, there was an increase in the use of antipsychotics
(+23.2%), with discrete changes in other classes of medication,
such as a decrease in the use of benzodiazepines (-4.8%). Patients
had an average increase in GAF of 31.92 points from admission to
discharge. The CGI-S decreased 2.06 points in mean and the average
of CGI-I was 5.8 points.
Table 1. Sociodemographic and clinical data of the total sample and univariate differences among groups according to type of outcome
Clinical variables
Age1 – Mean (SD)
Sex2 – Female – n (%)
Marital status – n (%)
Married
Single/separated
Widowed
Health insurance2 – n (%)
SUS (public health system)
Private health insurance
No insurance (Private costs)
Years of education2 – n (%)
0-8 years
8-12 years
> 12 years
Occupational status2 – n (%)
Employed/active
Unemployed
Retired
Transient disability (Government benefit)
Other
Chief complaint2 – n (%)
Suicidal ideation
Suicide attempt
Psychotic symptoms
Behavior change/aggressiveness
Substance abuse
Other
Clinical comorbidities2 – n (%)
Psychiatric/psychological treatment2
Yes, on treatment
No, never
Yes, but stopped
Main diagnostic hypothesis [admission]2 – n (%)
F10-F19
F20-F29
F30-F31
F32-F39
F60-F69
Other
Use of Benzodiazepines2 – n (%)
Use of Antidepressants2 – n (%)
Use of Antipsychotics2 – n (%)
Use of Lithium2 – n (%)
Use of Anticonvulsants2 – n (%)
n
250
250
247
Total sample
40.87 (17.65)
163 (65.2%)
Poor outcome
39.0 (17.38)
26 (63.4%)
Regular outcome
40.9 (17.73)
95 (63.8%)
Good outcome
42.0 (17.82)
42 (70%)
86 (34.8%)
145 (58.7%)
16 (6.5%)
8 (20%)
29 (72.5%)
3 (7.5%)
52 (34.7%)
88 (59.9%)
8 (3.2%)
27 (45%)
28 (46.7%)
5 (2.0%)
55 (22.3%)
179 (72.4%)
13 (5.3%)
15 (36.6%)
24 (58.5%)
2 (4.9%)
32 (21.8%)
107 (72.8%)
8 (5.4%)
8 (13.6%)
48 (81.4%)
3 (5.1%)
65 (26.4%)
96 (39%)
85 (34.6%)
18 (45%)2.9
9 (22.5%)-2.3
13 (32.5%)
36 (24.7%)
57 (39%)
53 (36.3%)
11 (18.3%)
30 (50%)2.0
19 (31.7%)
98 (38.4%)
60 (24.5%)
50 (20.3%)
31 (12.6%)
10 (4.1%)
9 (22.5%)-2.3
11 (27.5%)
8 (20%)
9 (22.5%)2.0
3 (7.5%)
53 (36.3%)
38 (26.0%)
29 (19.9%)
19 (13%)
7 (2.9%)
32 (54.2%)2.9
11 (18.6%)
13 (22%)
3 (5.1%)-2.0
0 (0%)
47 (19.4%)
46 (19%)
34 (14%)
52 (21.5%)
19 (7.9%)
44 (18.2%)
121 (48.4%)
3 (7.5%)-2.1
3 (7.5%)-2.0
11 (27.5%)2.7
14 (35.0%)2.3
3 (7.5%)
6 (15%)
21 (51.2%)
24 (16.9%)
34 (23.6%)2.2
17 (11.8%)
33 (22.9%)
8 (5.6%)
28 (19.4%)
68 (45.6%)
20 (34.5%)3.3
9 (15.5%)
6 (10.3%)
5 (8.6%)-2.7
8 (13.8%)
10 (17.2%)
32 (53.3%)
131 (56.2%)
31 (13.3%)
71 (30.5%)
23 (60.5%)
3 (7.9%)
12 (31.6%)
81 (58.7%)
16 (11.6%)
41 (29.7%)
27 (47.4%)
12 (21.1%)
18 (31.6%)
27 (10.8%)
36 (14.4%)
54 (21.6%)
62 (24.8%)
31 (12.4%)
38 (15.2%)
86 (34.4%)
95 (38.0%)
116 (46.4%)
22 (8.8%)
70 (28.0%)
3 (7.3%)
15 (36.6%)4.4
8 (19.5%)
3 (7.3%)-2.9
5 (12.2%)
7 (17.1%)
13 (33.3%)
12 (30.8%)
25 (64.1%)2.2
2 (5.1%)
9 (23.1%)
16 (10.9%)
16 (10.9%)-2.0
35 (23.8%)
35 (23.8%)
21 (14.3%)
24 (16.3%)
45 (31.3%)
56 (38.9%)
70 (48.6%)
16 (11.1%)
51 (35.4%)2.7
8 (13.3%)
5 (8.3%)
11 (18.3%)
24 (40%)3.1
5 (8.3%)
7 (11.7%)
28 (46.7%)
27 (45%)
21 (35%)-2.3
4 (6.7%)
10 (16.7%)-2.4
247
0.109
0.018*
246
0.027*
245
< 0.001**
242
250
233
0.557
0.303
0.001**
248
243
243
234
243
243
p
0.696
0.669
0.101
Superscript values corresponds

to post hoc analysis of residuals in variables with positive and negative Residuals ≥ 1.96; 1 ANOVA test; 2 Person Chi-square test.
* p < 0.05; ** p < 0.01.
0.106
0.365
0.017*
0.390
0.018*
9
Table 2. Clinical variables on admission and at discharge (n = 250)
Clinical variables
Days of hospitalization –
mean (SD)
F10-F19
F20-F29
F30-F31
F32-F39
F60-F69
Other
GAF – mean (SD)
Psychiatric medication
Not recorded in the chart
No medication
Benzodiazepines
Antidepressants
Antipsychotics
Lithium
Anticonvulsants
Others/does not know
CGI-Severity – n (%)
Mean
0 – Not assessed
1 – Normal, not at all ill
2 – Borderline mentally ill
3 – Mildly ill
4 – Moderately ill
5 – Markedly ill
6 – Severely ill
7 – Among the most
extremely ill patients
CGI-Improvement – n (%)
Mean
0 – Not assessed
1 – Very much worse
2 – Much worse
3 – Minimally worse
4 – No change
5 – Minimally improved
6 – Much improved
7 – Very much improved
Box 2. Data-driven Index-I scores according to the score of each variable
Admission
Discharge
-
-
Variation (Δ)
admission/
discharge
27.12 (15.04)
34.18 (12.86)
66.10 (13.77)
31.86 (20.11)
30.23 (17.48)
31.43 (12.58)
25.86 (13.61)
20.86 (11.18)
25.38 (13.61)
+31.92 (13.35)
7 (2.8%)
51 (20.4%)
86 (34.4%)
95 (38.0%)
116 (46.4%)
22 (8.8%)
70 (28.0%)
20 (8%)
2 (0.8%)
5 (2%)
74 (29.6%)
99 (39.6%)
174 (69.6%)
22 (8.8%)
72 (28.8%)
30 (12.0%)
-5 (-2%)
-46 (-18.4%)
-12 (-4.8%)
+4 (+1.6%)
+58 (+23.2%)
0 (0%)
+2 (+0.8%)
+10 (+4%)
Box 3. Pearson correlations among the variables used in the I-Index
5.22 (0.84)
9 (3.6%)
0 (0%)
2 (0.8%)
6 (2.4%)
39 (15.6%)
106 (42.4%)
80 (32%)
8 (3.2%)
3.16 (1.04)
0 (0%)
8 (3.2%)
41 (16.4%)
100 (40%)
75 (30%)
19 (7.6%)
6 (2.4%)
1 (0.4%)
-2.06
-9 (-3.6%)
+8 (+3.2%)
+39 (+15.6%)
+94 (+37.6%)
+26 (+14.6%)
-87 (-34.8%)
-74 (-29.6%)
-7 (-2.8%)
LOS: length of stay; GAF-D: Global Assessment Functioning at discharge; Δ GAF: GAF variation
(GAF at discharge – GAF at admission); CGI-S: Clinical Global Impression – Severity at discharge;
CGI-I: Clinical Global Impression – Improvement.
* Significant correlation at p < 0.05; ** Significant correlation at p < 0.01.
-
5.80
0 (0%)
1 (0.4%)
2 (0.8%)
6 (2.4%)
7 (2.8%)
53 (20.8%)
188 (47.2%)
64 (25.6%)
-
GAF: Global Assessment of Functioning; CGI: Clinical Global Impression. Variables presented in
number (percentage) or mean (standard deviation).
Poor outcome
(score = 0)
CGI-I
CGI-S at discharge
GAF at discharge
Δ-GAF
LOS
Final Index-I score
1, 2, 3 or 4
5, 6 or 7
< 65
< 19
> 42
0-4 = Poor
Intermediate
outcome
(score = 1)
5
3 or 4
65-80
19-32
27-42
5-7 = Regular
Good outcome
(score = 2)
6 or 7
1 or 2
> 80
> 32
< 27
> 7 = Good
GAF-D: Global Assessment Functioning at discharge; Δ-GAF: GAF variation (GAF at discharge – GAF
at admission); LOS: length of stay; SD: standard deviation. The mean and SD values were rounded.
I-Index score
LOS
CGI-S
CGI-I
GAF-D
Δ-GAF
I-Index
LOS
CGI-S
CGI-I
score
1
-0.355**
1
-0.600** 0.147*
1
0.505** 0.101 -0.207**
1
0.710** -0.127* -0.440** 0.281**
0.657** 0.063 -0.288** 0.300**
GAF-D
Δ-GAF
1
0.624**
1
The poor outcome group frequently had low education, higher
percentage of transient disability and smaller percentage of active/
employed in occupational status, more psychotic symptoms and
change in behavior/aggressiveness and less suicidal ideations in
chief complaints. This group also had more psychotic disorders and
less depressive disorders as psychiatric diagnoses and more use of
antipsychotics than the good outcome group (regular outcome group
presented usually intermediate results).
The main results of the multivariate analyses (Table 3) show two
different clinical admission factors related with a poorer outcome.
Two regression models are present. With regards to diagnosis at
admission, psychotic disorders (OR: 16.77; CI: 3.16 – 89.10), personality disorders (OR: 9.76; CI: 1.51 – 63.05) and “others” (OR: 8.19;
CI: 1.52 – 44.15) were associated with poorer outcome at discharge
compared with the reference variable depressive disorder. The admission chief complaints of “psychotic symptoms” (OR: 12.42; CI:
2.28 – 67.75) and “change in behavior/aggressiveness” (OR: 25.19; CI:
4.48 – 141.72) were also associated with poor outcome at discharge
(in relation to suicide ideation). Transient disability was associated
with poor outcome in both models.
Discussion
The box 2 presents the values used to generate the points of each
variable according to the means and SD of the variables. Box 3 presents the results of correlations among variables of the Index-I. The
LOS presented no significant (with CGI-I x Δ-GAF) or very weak
(with CGI-S at discharge x GAF-D) correlations with others variables.
The higher correlation was between GAF-D x Δ-GAF (strong) and
GAF-D x CGI-S at discharge (moderate), while the other correlations were weak. In relation to the I-Index score, strong correlations
with CGI-S (negative), GAF-D and Δ-GAF were found; moderate
correlation with CGI-I; and weak correlation with LOS (negative).
In relation to the Index-I, 41 patients (16.4%) were classified in
the poor outcome group, 140 (59.6%) in the regular outcome group
and 60 (20%) in the good outcome group. The results of the univariate analyses comparing these groups are also presented in table 1.
This study proposed an index of evaluation of outcomes for psychiatric inpatients using variables easily measured and routinely
collected in psychiatric units. Our strategy was able to identify
clinical and sociodemographic risk factors associated with positive
and negative outcomes regarding improvement at discharge. The
results replicate and extend findings of the literature that used single
variables as outcome, proposing more comprehensive and clinically
useful criteria for evaluating results in psychiatric inpatients, what
is particularly important for the mental care reality of low- and
middle-income countries.
The first strategy of this study was composing an index of improvement that encompasses more than one dimension related to the
outcome of inpatients. The most part of the literature has predominantly used isolated variables in this assessment, such as LOS10,12,21,22,
10
Table 3. Polytomous logistic regression comparing different patterns of discharge outcomes
Variables Model 1 (n = 240)
Years of education
0-8 years
8-12 years
> 12 years
Occupational status
Employed/active
Unemployed
Retired
Transient disability
Other
Main diagnostic hypothesis [admission]
F10-F19
F20-F29
F30-31
F60-F69
Other
F32-F39
Variables Model 2 (n = 234)
Years of education
0-8 years
8-12 years
> 12 years
Occupational status
Employed/active
Unemployed
Retired
Transient disability
Other
Chief complaint
Other
Suicide attempt
Psychotic symptoms
Behavior change/aggressiveness
Substance abuse
Suicidal ideation
Intermediate
OR (95% CI) [P value]
Poor
1.03 (0.42-2.55) [0.951]
0.62 (0.30-1.30) [0.204]
1.63 (0.51-5.20) [0.411]
0.29 (0.09-0.92) [0.036]
†
†
†
†
2.29 (0.99-5.24) [0.051]
1.52 (0.53. 4.36) [0.440]
3.96 (1.06-14.84) [0.041]
X
3.21 (0.95-10.88) [0.061]
3.06 (0.61-15.43) [0.175]
9.43 (1.88-47.31) [0.006]
X
1.50 (0.52-4.30) [0.453]
1.88 (0.58-6.11) [0.297]
2.01 (0.82-4.93) [0.127]
3.16 (0.96-10.46) [0.059]
2.33 (0.83-6.54) [0.108]
3.78 (0.58-24.86) [0.166]
16.77 (3.16-89.10) [0.001]
4.71 (0.96-23.04) [0.056]
9.76 (1.51-63.05) [0.017]
8.19 (1.52-44.15) [0.014]
†
†
Intermediate
Poor
0.92 (0.40-2.75) [0.951]
0.54 (0.25-1.19) [0.129]
1.57 (0.46-5.30) [0.470]
0.27 (0.08-0.90) [0.032]
†
†
†
†
2.86 (1.17-6.99) [0.022]
1.40 (0.47-4.14) [0.547]
4.36 (1.13-16.81) [0.033]
X
4.14 (1.15-14.96) [0.030]
3.68 (0.69-19.50) [0.126]
15.01 (2.89-78.85) [0.001]
X
2.50 (0.86-7.11) [0.095]
3.84 (1.38-10.69) [0.010]
1.98 (0.60-6.51) [0.260]
6.90 (2.11-22.50) [0.001]
0.83 (0.25-2.80) [0.763]
4.61 (0.82-26.01) [0.084]
3.30 (0.50-31.83) [0.22]
12.42 (2.28-67.75) [0.004]
25.19 (4.48-141.72) [< 0.001]
2.80 (0.40-19.54) [0.300]
†
†
Results are controlled for age, sex, civil status, and income. : reference category. X: insufficient number for analysis. The I-Index “good outcome” was chosen as a reference to estimate odds-ratios
(OR) of “intermediate outcome” and “poor outcome”. Values in bold represent significant differences with p < 0.05.
†
as a single parameter of outcome. However, LOS might mistakenly
evaluate a hospitalization for a long period as a poor outcome, despite
a significant improvement in symptoms and functionality of a patient.
Although LOS is recognized as an important parameter, it is often
chosen because it relates directly to health expenditure7, underestimating clinical issues. Furthermore, LOS may even underestimate
the degree of improvement. Prince et al. found very short admissions
as predictors of readmission in patients with mood disorder in a US
population-based study23, suggesting the insufficiency of this parameter. We found only a weak correlation between the I-index and the
LOS. In addition, the weak correlation between LOS and other variables of the I-Index and the weak correlations of most variables with
each other reinforce the independence and complementarity of them
in our sample. Other authors have used subjective variables (such as
“good clinical condition at discharge”)15 or indices constructed from
more complex methodologies24, complicating replicability or limiting
its application. Thus, the variables and the method used to measure
the I-Index can be considered simple and practical for treatment
environments with limited resources.
The sociodemographic variables associated with poor outcomes
were low education and patients with transient disability. While
several other variables have been usually related with poor outcomes
(such as unemployment, being unmarried and public insurance)10,21,
no other studies have evaluated transient disability, a very common
condition in inpatients in Brazil; our results show increased OR
for transient disability in relation to employed/active status on the
groups with intermediate (OR vary from 3.96 to 4.36) and poor (OR
vary from 9.43 to 15.01) outcomes. Disability pension due to mental
disorders has been associated with increased suicide risk25 and heavy
use of psychiatric inpatients services26, while this outcomes have not
yet been evaluated in transient disability. On the other hand, being
employed/active in occupational status was associated with a good
outcome, which has been extensively replicated in the literature10,15.
Psychotic symptoms and change in behavior/aggressiveness were
the most prevalent chief complaints and were both independently
associated with the poor outcome group, while suicidal ideation was
the most prevalent one in the good outcome group. Concerning psychiatric diagnosis, psychotic disorders were related to poor outcome
and unipolar depressive disorders to good outcome. Psychotic disorders were the diagnostic group most associated with poor outcome
when compared with depressive disorders. These results also possibly
explain respectively the highest prevalence of psychotic symptoms
and change in behavior/aggressiveness in the poor outcome group
(related to psychotic disorders and more severe cases) and a higher
prevalence of suicidal ideation in the good outcome group (related
to depressive disorders). The relation between psychotic disorders
and bad outcomes, including readmissions (“revolving door phenomenon”)27,28, longer hospitalizations7,10, 21 and higher mortality29, is
well established. This association further explains the poor outcome
for patients taking antipsychotics at admission. Psychotic disorders
as schizophrenia are usually chronic disorders associated with longlasting symptoms resistant or refractory to treatment30. It is also well
established that patients with aggression issues are difficult to treat
and keep in compliance on an outpatient basis and readmissions
rates are high for them28; this corroborates that these patients have
a poor outcome. On the other hand, unipolar depression was highly
prevalent in those with good outcomes. While Masters et al.10 found
depressive disorders with shorter LOS than schizophrenia and bipolar
disorders, Green and Griffiths31 found a substantial decline in LOS
over the last decades for patients with depressive disorders, with no
changes in LOS for schizophrenia in England. Our results suggest
that beyond the LOS, improving symptoms and level of functioning
also differentiates these two diagnostic groups.
In the multivariate analysis, both models found high OR for
specific variables associated with an increased risk for poor outcome
in relation to those related to protective factors. Thus, in addition
to psychotic disorders, transient disability and chief complaints of
psychotic symptoms and change in behavior/aggressiveness, the
poor outcome group presents a high chance (OR = 9.76) of having
a diagnosis of personality disorder than the protective diagnosis of
depressive disorder. Thus, although personality disorder showed
shorter LOS, our more complex approach to examine outcomes
could relativize the weight of this variable, valuing other clinical
aspects. In this line, Leontieva and Gregory found shorter LOS in
inpatients with borderline personality disorder compared with other
diagnoses, but significantly more management problems, such as
incidents of self-harm, episodes of restraint, stat administrations
of medications and readmissions32. Thus, the chronic disruptive
behavior of such patients can make the hospitalization insufficient
to improve the functioning and the severity of their symptoms,
regardless of LOS.
Although our index is composed of measures of easy extraction
and availability, it is limited to assess other dimensions that are associated with hospitalization outcomes. Quality of care measures,
satisfaction with care, quality of life, perception of improvement by
the patients and their family and evaluation of specific symptoms of
each diagnosis cluster (among others) can compose a more complex
and dynamic evaluation of outcomes, but require the availability of
human resources to conduct the process of applying them. Others
more complex ROM approaches using broad standard instruments
(as HoNOS) and more robust methodological designs (based on both
anchor- or distribution-based approaches)33 must also be developed
in more structured services. Thus, although limited, the measures of
the I-Index can serve as an initial outcome indicator in mental health
systems with limited resources and personnel.
This work has a number of limitations. First, we used data
selected retrospectively from records, not being possible to test the
inter-rater reliability of measuring instruments. Second, diagnoses
were made by clinical evaluation without the use of standardized
instruments and using only the primary diagnosis (comorbidities
were not considered). Third, although our I-Index comprises five
different parameters, we did not use any instrument evaluating different symptom dimensions. While we strongly suggest that LOS,
CGI and GAF should be used, the BPRS may be another additional
simple measure to be used in the evaluation of outcomes in clinical
settings with limited time and personnel for the use of more complex
tools. In addition, more complex parameters (such as evaluation of
11
results by patients and use of more specific instruments assessing
other dimensions of variable) can be useful, but require a more
complex logistical organization that the current reality of mental
health assistance in Brazil. The comparison of this index with other
measures using more complex instruments can bring validation
data for both. Forth, our sample size is limited and was selected in
only one institution, limiting a generalization of the results and the
interpretation of the regression analysis for some variables. Finally,
shorter LOS is not necessary associated with a good outcome, just
as the I-index indicates. However, as the LOS is easily measured
and classically used as a measure of outcome, we prefer to ponder
its weight and keep it in the index. The use of other parameters and
the assignment of weights according to the specific objectives of each
evaluation (measurement of clinical improvement or use of data to
support the allocation of health funding) should be better tested in
large clinical samples.
In conclusion, we suggest that an assessment composed of simple
parameters can be useful for measuring outcomes in psychiatric inpatients. The identification of factors associated with poor outcomes
may help build strategies to minimize or lessen the health, social and
financial burden of mental disorders. Social support and health care
programs directed to vulnerable groups can relieve the patients after
hospitalization and prevent readmissions. The use of composed parameters to evaluate outcomes as the I-Index can be easily incorporated
by managers of mental health policies in treatment environments
to support funding of mental health service and evaluate its quality.
Acknowledgments
We thank our team at the Psychiatric Unit of Hospital São Lucas for
their help in the development of this study.
Conflicts of interest
The authors have no conflicts of interest.
References
1. Piccinelli M, Politi P, Barale F. Focus on psychiatry in Italy. Br J Psychiatry.
2002;181:538-44.
2. Sayers J. The world health report 2001 – Mental health: new understanding, new hope. Bulletin of the World Health Organization. 2001;79:1085.
3. Botega NJ. Prática Psiquiátrica no Hospital Geral: Interconsulta e Emergência: Artmed Editora; 2012.
4. Duarte SL, Garcia MLT. Psychiatric reform: the path of psychiatric beds
reduction in Brazil. Emancipação. 2013;13(1):39-54.
5. Weber CAT. Direction of mental health in Brazil after 1980. Revista
Debates em Psiquiatria. 2013;3:14-22.
6. Botega NJ. Psychiatric units in Brazilian general hospitals: a growing
philanthropic field. Int J Soc Psychiatry. 2002;48(2):97-102.
7. Tulloch AD, Fearon P, David AS. Length of stay of general psychiatric
inpatients in the United States: systematic review. Administration and
policy in mental health. 2011;38(3):155-68.
8. Glick ID, Sharfstein SS, Schwartz HI. Inpatient psychiatric care in the
21st century: the need for reform. Psychiatr Serv. 2011;62(2):206-9.
9. Baruch Y, Kotler M, Lerner Y, Benatov J, Strous R. Psychiatric admissions
and hospitalization in Israel: an epidemiologic study of where we stand
today and where we are going. Isr Med Assoc J. 2005;7(12):803-7.
10. Masters GA, Baldessarini RJ, Ongur D, Centorrino F. Factors associated with
length of psychiatric hospitalization. Compr Psychiatry. 2014;55(3):681-7.
11. Affairs DoV. Patient treatment file (PTF) coding instructions. Washington,
DC: Department of Veterans Affairs, 2008 February 4, 2008. Report No.
12. Douzenis A, Seretis D, Nika S, Nikolaidou P, Papadopoulou A, Rizos
EN, et al. Factors affecting hospital stay in psychiatric patients: the role
of active comorbidity. BMC Health Serv Res. 2012;12:166.
13. Rocca P, Mingrone C, Mongini T, Montemagni C, Pulvirenti L, Rocca
G, et al. Outcome and length of stay in psychiatric hospitalization, the
experience of the University Clinic of Turin. Soc Psychiatry Psychiatr
Epidemiol. 2010;45(6):603-10.
14. Warnke I, Rössler W, Herwig U. Does psychopathology at admission predict the length of inpatient stay in psychiatry? Implications for financing
psychiatric services. BMC Psychiatry. 2011;11(120):1-10.
12
15. Dalgalarrondo P, Botega NJ, Banzato CE. [Patients who benefit from
psychiatric admission in the general hospital]. Rev Saúde Pública.
2003;37(5):629-34.
16. Macdonald AJ, Elphick M. Combining routine outcomes measurement
and “Payment by Results”: will it work and is it worth it? Br J Psychiatry.
2011;199(3):178-9.
17. Spanemberg L, Nogueira EL, da Silva CT, Dargel AA, Menezes FS, Cataldo
Neto A. High prevalence and prescription of benzodiazepines for elderly:
data from psychiatric consultation to patients from an emergency room
of a general hospital. Gen Hosp Psychiatry. 2011;33(1):45-50.
18. Guy W. Clinical Global Impression (CGI). In: Guy W, editor. ECDEU
Assessment Manual for Psychopharmacology. Rockville: US Dept. of
Health, Education and Welfare; 1976. p. 218-22.
19. American Psychiatric Association, American Psychiatric Association.
Task Force on DSM-IV. Diagnostic and statistical manual of mental
disorders: DSM-IV-TR. 4th ed. Washington, DC: American Psychiatric
Association; 2000. xxxvii, 943 p.
20. Evans JD. Straightforward statistics for the behavioral sciences. Pacific
Grove: Brooks/Cole Pub. Co.; 1996. xxii, 600 p.
21. Warnke I, Rossler W, Herwig U. Does psychopathology at admission predict the length of inpatient stay in psychiatry? Implications for financing
psychiatric services. BMC Psychiatry. 2011;11(1):120.
22. Zhang J, Harvey C, Andrew C. 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. 2011;45(7):578-85.
23. Prince JD, Akincigil A, Kalay E, Walkup JT, Hoover DR, Lucas J, et al.
Psychiatric rehospitalization among elderly persons in the United States.
Psychiatric services. 2008;59(9):1038-45.
24. Barbato A, Parabiaghi A, Panicali F, Battino N, D’Avanzo B, de Girolamo
G, et al. Do patients improve after short psychiatric admission?: a cohort
study in Italy. Nord J Psychiatry. 2011;65(4):251-8.
25. Rahman S, Alexanderson K, Jokinen J, Mittendorfer-Rutz E. Risk factors
for suicidal behaviour in individuals on disability pension due to common
mental disorders – a nationwide register-based prospective cohort study
in Sweden. PLoS One. 2014;9(5):e98497.
26. Morlino M, Calento A, Schiavone V, Santone G, Picardi A, de Girolamo
G, et al. Use of psychiatric inpatient services by heavy users: findings
from a national survey in Italy. Eur Psychiatry. 2011;26(4):252-9.
27. Gastal FL, Andreoli SB, Quintana MIS, Gameiro MA, Leite SO, McGrath J. Predicting the revolving door phenomenon among patients with
schizophrenic, affective disorders and non-organic psychoses. Rev Saude
Publica. 2000;34:280-5.
28. Nourse R, Reade C, Stoltzfus J, Mittal V. Demographics, clinical characteristics, and treatment of aggressive patients admitted to the acute
behavioral unit of a community general hospital: a prospective observational study. Prim Care Companion CNS Disord. 2014;16(3).
29. Hoang U, Stewart R, Goldacre MJ. Mortality after hospital discharge for people with schizophrenia or bipolar disorder: retrospective
study of linked English hospital episode statistics, 1999-2006. BMJ.
2011;343:d5422.
30. Knapp M, Mangalore R, Simon J. The global costs of schizophrenia.
Schizophr Bull. 2004;30(2):279-93.
31. Green BH, Griffiths EC. Hospital admission and community treatment
of mental disorders in England from 1998 to 2012. Gen Hosp Psychiatry.
2014;36(4):442-8.
32. Leontieva L, Gregory R. Characteristics of patients with borderline
personality disorder in a state psychiatric hospital. J Pers Disord.
2013;27(2):222-32.
33. Parabiaghi A, Kortrijk HE, Mulder CL. Defining multiple criteria for
meaningful outcome in routine outcome measurement using the Health of the Nation Outcome Scales. Soc Psychiatry Psychiatr Epidemiol.
2014;49(2):291-305.
Original article
Trust and expectation on psychiatrist and its correlation with satisfaction and
adherence in patients with mental illness
Dushad Ram1, Basavana Gowdappa1
1
Department of Psychiatry, JSS Medical College and Hospital, MG Road Agrahara, Mysore.
DOI: 10.1590/0101-60830000000040
Abstract
Background: Trust and expectation are important aspect of doctor patient relationship and its role in patient’s satisfaction and medication adherence is unclear.
Objective: To study the levels of trust and expectation on psychiatrist and its relationship with patient’s satisfaction and treatment adherence. Methods: One
hundred and twenty three consecutive outpatients were recruited on follow-up if they satisfied the selection criteria. They were assessed with socio-demographic
and clinical proforma designed for this study, Patient Trust Scale, Patient Satisfaction Survey, Patient Expectations Questionnaire and Medication Adherence
Rating Scale. Results: There was a high mean score on trust scale (Mean 38.9, SD 8.5) and expectation questionnaire (Mean 13.5, SD 3.3). On Kruskal-Wallis
H test significant group differences were observed in nuclear vs joint family type (c2 = 18.496, h2 = .151, df = 1, Sig. = .000) and knowledge of treatment
option (medication only vs medication + psychotherapy) treatment option (c2 = 18.100, h2 = .148, df = 2, Sig. = .000) and occupational status (employed vs
unemployed) (c2 = 3.165, h2 =.029, df = 1, Sig. = .056) on the score of PTS. Similar differences were also observed in method of treatment sought before (no
treatment vs allopathic) (c2 = .065, h2 = .065, df = 3, Sig. = .005), knowledge about treatment option (medication only vs medication + psychotherapy) (c2 =
.026, h2 = .161, df = 2, Sig. = .000) and occupation (employed vs unemployed) (c2 = .061, h2 = .061, df = 1, Sig. = .006) on the score of PEQ. On regression
analysis (R2 = .723, F = 156.46, p = .000) value of the score on patient satisfaction was statistically significant as predicted by score on measure of expectation
(beta = -0.095, t = -1.966, p = 0.052) and trust (beta = .842, t = 17.504, p = .000). Discussion: Levels of patients trust and expectation on physician varies with
knowledge about treatment option & occupational status, and significantly associated with levels of satisfaction.
Ram D, Gowdappa B / Arch Clin Psychiatry. 2015;42(1):13-7
Keywords: Patient trust, patient expectation, patient satisfaction, medication adherence, mental illness.
Introduction
Patient expectations of therapeutic benefit are crucial in treatment
outcomes & satisfaction, and are shaped by characteristics of patient
and disease1-4. Few decades ago patients generally had an expectation
of symptom reduction. It is changing over time due to advancements
in treatment, growing awareness of mental health, reduced stigma
and increasing consumerism5. Patient’s trust is crucial in doctor
patient relationship. It is often influenced by general perception of
the community about a physician’s reputation6. The trust that the
physician will perform a particular action important to the patient,
may mediate the effectiveness of medical care and satisfaction6,7.
Though there has been a fair level of trust in physicians in the past,
it is changing over time due to the reports of high profile physician
fraud, drug pushing, malfeasance and malpractice7,8.
Treatment satisfaction is not an all or none phenomena. It varies
with treatment response, demographic feature, cultural background,
previous treatment experience and quality of services/care9-11. Similarly, the degree of medication adherence varies with the demographic
characteristics of the patients, psychopathology, associated disability
and supervision12-14. Role of patient’s expectation and trust on physician in mediating treatment satisfaction and adherence is unclear.
This study was carried out with the hypothesis that: 1) The levels of
patients’ trust and expectation varies with demographic and clinical
variables; 2) The levels of patients’ trust and expectation varies with
the levels of the patients’ satisfaction and medication adherence.
Methods
This study was conducted among patients with mental illness in
remission, who were living in the community and attending outpatient department of psychiatry for follow-up at a tertiary care
centre in south India. Out of 140 consecutive patients screened
over a period of three months; 123 met study selection criteria and
were recruited in this study after obtaining an informed consent.
The inclusion criteria were outpatient males and females, an ICD
10 diagnosis of axis I psychiatric disorder currently in remission as
per treating psychiatrist, aged 14-65 years and ≥ 2 consultation visit.
Patients were excluded if they had a co-morbid chronic physical
illness, diagnosis of unexplained physical complaint, involvement
(self or any family member) in delivering faith or other type of
healing practices and an ICD 10 diagnosis of mental retardation or
dementia. They were assessed with socio-demographic and clinical
proforma designed for this study. Patient Trust Scale (PTS) was
used to assess the level of patients’ trust on the psychiatrist15. This
scale has excellent internal reliability (Cronbach alpha = 0.94) and
contains 10 items to assess the domains of privacy, information related to illness, investigation or optimal care and treatment cost. To
evaluate expectation, the Patient Expectations Questionnaire (PEQ)
was used. This scale scored alpha = 0.82 on measure of reliability
and assess patients upbringing, opinion about the cause, physical
illness, emotional problem, current relationship, confidentiality,
ongoing treatment, consulting family physician, medication to be
given and best treatment plans16.
To determine satisfaction, the Patient Satisfaction Survey (PSS)
was used. This nine items tool includes waiting time for appointment
and office visit, convenience of office location, contact over the phone,
time spent with the physician, physicians’ skill attitude & explanation,
and overall satisfaction17. All item has 5 point rating, ranging from
poor to excellent. This tool is patient friendly; and has been used to
examine patient satisfaction in studies. This instrument (also known as
visit-specific satisfaction questionnaire) has established psychometric
properties that demonstrate reliability and validity; internal consistency
reliability estimates ranged from 0.87 to 0.93 based on Cronback’s α.
Medication Adherence Rating Scale (MARS) was used to
estimate the level of medication adherence18. This tool assessed
medication intake behaviour in terms of sincerity, effect of continuing or stopping medication, perception of self control etc.
This scale is commonly used and has a good internal reliability
(Cronbach α = 0.73).
Address correspondence to: Dushad Ram. Department of Psychiatry, Room 1106, JSS Hospital, MG Road, Mysore – 570004 – Karnataka, India. Email: [email protected]
14
The data were analysed using SPSS Version 16. Descriptive
statistics were used to express demographic and clinical characteristic. The distribution of the sample was assessed with the
Kolmogorov-Smirnov and Shapiro-Wilk test and found to be significantly skewed. Since analysis required comparison of more than
two variables, Kruskal-Wallis H test was used to know the group
difference of demographic and clinical variables on the score of different scales and a post hoc analysis was done (for comparison of ≥
3 groups). A regression analysis was conducted to know if patients
score on measure of trust and expectation can predict the values
of scores on measure of satisfaction and medication adherence.
The level of statistical significance was kept at p < 0.05 for all tests.
Results
The majority of the patients were married, Hindu, from the nuclear
family, consulting a single psychiatrist regularly; ongoing treatment
was the first psychiatric intervention and felt to refer other patient
to the psychiatrist (Table 1). Table 2 reveals scores on the PTS (Mean
38.9, SD ± 8.53), PSS (Mean 32.5, SD ± 8.0), MARS (Mean 16.0, SD
± 2.2) and PEQ (Mean 13.5, SD ± 3.3).
Table 1. Sociodemographic and clinical characteristics
Variables
Gender
Male
Female
Occupation
Unemployed
Self-employed
Employed
Socioeconomic status
Low
Middle
High
Variables
Religion
Hindu
Muslim
Marital status
Single
Married
Other
Domicile
Rural
Urban
Family type
Nuclear
Joint
Other
Consulting single psychiatrist
Yes
No
First referral by
Self
Family
Society
Health professionals
Regularity of follow-up
Always
Mostly
Half the time
Some time
n
%
61
62
49.6
50.4
57
39
27
46.3
31.7
22.0
54
68
1
Mean
43.9
55.3
0.8
Std. deviation
110
13
89.4
10.6
29
93
1
23.6
75.6
0.8
67
56
54.5
45.5
75
47
1
61.0
38.2
0.8
110
13
89.4
10.6
47
58
5
13
38.2
47.2
4.1
10.6
80
31
10
2
65.0
25.2
8.1
1.6
Variables
Diagnosis
F 10
F20
F 30
F 40
Other
Treatment sought before
No treatment
Magico-religious
Allopathic
Ayurvedic
Option about treatment decision
Doctor should decide
I should decide
Both should decide
Any of these
Referring others to his physician
Yes
No
Knowledge about treatment option
Medication only
Psychotherapy only
Medication + psychotherapy
Mean
Std. deviation
11
9
85
16
2
8.9
7.3
69.1
13.0
1.6
100
3
17
3
81.3
2.4
13.8
2.4
75
5
39
4
61.0
4.1
31.7
3.3
118
5
95.9
4.1
68
4
51
55.3
3.3
41.5
Table 2. Sociodemographic and clinical characteristics
Variables
Mean
Std. deviation
Age
37.12
12.74
Education
5.69
5.23
Age at onset
32.20
12.58
Total duration of illness
4.59
5.14
Duration of consultation
2.19
2.34
Score on Patient Trust Scale
38.98
8.53
Score on Patient Satisfaction Survey
32.52
8.05
Score on Medication Adherence Rating Scale
16.04
2.26
Score on Patient Expectations Questionnaire
13.57
3.30
On Kruskal-Wallis H test significant group differences were
observed in nuclear vs joint family type (c2 = 18.496, h2 = .151, df
= 1, Sig. = .000), knowledge of treatment option (medication only
vs medication + psychotherapy) (c2 = 18.100, h2 =.148, df = 2, Sig.
= .000) and occupational status (employed vs unemployed) (c2 =
3.165, h2 = .029, df = 1, Sig. = .056) on the score of PTS. Similar differences were also observed in method of treatment sought before
(no treatment vs allopathic) (c2 = .065, h2 = .065, df = 3, Sig. = .005),
knowledge about treatment option (medication only vs medication
+ psychotherapy) (c2 = .026, h2 = .161, df = 2, Sig. = .000) and occupation (employed vs unemployed) (c2 = .061, h2 = .061, df = 1,
Sig. = .006) on the score of PEQ (Tables 3 and 4).
Regression analysis was done to know if the score on measure
of patient expectation and trust can predict the value of score on
patient satisfaction (R2 = .723, F = 156.46, p = .000). Score on patient
expectation (beta = -0.095, t = -1.966, p = 0.052) and trust (beta =
.842, t = 17.504, p = .000) significantly predicted the value of score
on patient satisfaction. A regression analysis was also done to know
if the score on measure of patient trust and expectation can predict
the value of the score on patient medication adherence (R2 = .26, F =
1.629, p = .200). Both did not predict significantly the value of score
on the medication adherence (Table 5).
15
Table 3. Kruskal Wallis H Test for group comparison of demographic variables on score of PTS & PEQ
Group
Unemployed
Employed
Nuclear
Joint
Medication only
Psychotherapy only
Psychotherapy only
Medication only
Unemployed
Employed
No treatment
Magicoreligious
Magicoreligious
Allopathic
Allopathic
Ayurvedic
No treatment
Allopathic
No treatment
Ayurvedic
Magicoreligious
Ayurvedic
Medication only
Psychotherapy only
Psychotherapy only
Medication only
PTS Total* Occupation
PTS Total* Family type
PTS Total* Knowledge of treatment
1
2
3
PEQ Total* Occupation
PEQ Total* Treatment sought before
1
2
3
4
5
6
PEQ Total* Knowledge of treatment
1
2
3
Table 4. Relationship of expectation & trust with satisfaction
Model
1
Unstandardized Standardized
Coefficients
Coefficients
Predictor
B
Std. Error
Beta
(Constant) 4.698
2.449
t
Sig.
1.919
.057
PEQ Total
-.230
.117
-.095
-1.966
.052
PTS Total
.794
.045
.842
17.504
.000
Dependent variable: PSS total.
R2 = .723, F = 156.46, p = .000.
Table 5. Relationship of expectation & trust with adherence
Unstandardized
Coefficients
B
Std. Error
(Constant) 17.186
1.291
PTS Total .009
.024
PEQ Total -.108
.062
Model Predictor
1
Standardized
Coefficients
Beta
.032
-.158
t
Sig.
13.316
.360
-1.756
.000
.719
.082
Dependent variable: MARS total.
R2 = .26, F = 1.629, p = .200.
N
57
66
76
47
68
4
4
51
68
51
57
66
100
3
3
17
17
3
100
17
100
3
3
3
68
4
4
51
68
51
Mean Rank
68.61
56.30
51.14
79.55
36.26
40.50
13.88
29.11
48.37
75.51
52.80
69.95
52.52
34.50
4.83
11.50
11.03
7.50
55.44
79.94
51.89
55.67
2.83
4.17
35.60
51.75
49.88
26.28
75.01
39.98
χ2
3.658
η2
.029
df
1
Asymp. Sig
.056
18.496
.151
1
.000
.155
.002
1
.694
3.380
.062
1
.066
18.100
.153
1
.000
7.474
.061
1
.006
1.118
.010
1
.290
3.516
.185
1
.061
.977
.051
1
.323
8.029
.069
1
.005
.049
.000
1
.825
.784
.156
1
.376
2.454
.034
1
.117
8.442
0.153
1
.004
31.779
.267
1
.000
faith in their model of illness that can influence the trust and expectation.
Believe model of physical illness (depending upon severity of symptoms,
duration, distress, disability etc.) may influence the expectation and an
ICD 10 diagnosis of mental retardation or dementia may give rise to the
reliability issue, hence they were excluded from this study.
Socio-demographic characteristics
More patients were Hindu who did not receive any treatment before,
currently consulting a single psychiatrist regularly, and felt to refer
the other patients to psychiatrists. Such observation was likely as this
study was conducted at a place where a majority of the population
were Hindus, with few available mental health professionals.
We also observed a high mean score on the PTS, PEQ, PSS, and
MARS. High trust level probably reflects the prevailed paternalistic
model of doctor patient relationships in India. In most part of India
the doctors are traditionally believed to be honest, harmless to their
patients, save human life, and can cure all types of illness. High level
of satisfaction and adherence observed in this study was possibly the
result of improvement and limited availability of alternative mental
health care service.
Discussion
Relationship of socio-demographic and clinical variables
with expectation and trust
In this study, patients with an ICD 10 diagnosis of axis I psychiatric
disorder in remission were included because psychopathology may
interfere with the perception of trust and expectation. Similarly patients
involved in faith healing were excluded as they are known to have more
On the score of PTS there was a statistically significant group difference in family types (joint vs nuclear) and patient’s knowledge about
treatment options (medication only vs medication + psychotherapy).
Patients with a joint family are more likely to have social or general
16
trust, while those with a nuclear family are more likely to have interpersonal trust19. Similarly, a trust may vary with knowledge about
treatment options (and health literacy and orientation of illness
model). The knowledge and appreciation of psychological treatment
may mediate the trust on a qualified mental health professional who
are more likely to address an illness on the basis of the biopsychosocial model. Those with knowledge of only pharmacotherapy may
be medical model oriented and rely more on role of medication20.
On the score of the expectation measure there was a statistically
significant group difference in employment status (employed vs
unemployed), knowledge of different treatment options (medication only vs medication + psychotherapy) and methods of treatment
sought before (no treatment vs allopathic). The unemployed patients
may expect less treatment cost, while employed status demands more
confidentiality & best treatment in order to meet work demands.
Patients background may influence their expectation. Expectation
of subject without previous treatment, may depends upon their
orientation of illness model. Those who are oriented to the medical
model may expect more probing on illness related issues or appropriate medication, while bio-psychosocial model oriented patient
may expect their physician to address the psychosocial aspects of
illness20,21. During the initial consultation, the patient may expect
remission of symptoms or reduction of distress, while those who
already received pharmacotherapy (allopathic) may expect issues that
were unsuccessfully addressed by the previous health care provider.
In the same manner the knowledge of treatment option may also
influence patient expectation.
Relationship of patient’s expectation and trust with
satisfaction and adherence
Score on the measure of patient’s expectation significantly predicted
the value of patient satisfaction. However, the negative beta value
indicates that, if the expectation is more than satisfaction will be
low. Many views have been put forward for such observation. A
few decades ago higher satisfaction was thought to be due to the
congruence of patient orientations and provision of health services,
positive personal beliefs and values of care, personal preferences and
good interpersonal process of care22-25. Over time, multiple factors
appeared to mediate the satisfaction such as patients’ demographic
characteristics, personal experience, past experience, media, information provided by treatment provider and experiences from friends
or relatives that have utilised similar health care service26,27. Inappropriately high expectations may cause dissatisfaction with optimal
healthcare while inappropriately low expectations may be satisfied
with deficient health care. Though in multi-culture society accurate
assessment of patient expectation is often difficult, the presence of
a realistic expectation is associated with adequate satisfaction28. An
expectation is realistic when they correspond to known evidence of
probabilities of outcome for a person’s health profile. This finding may
have implication, since most health care providers are now focusing
more on patient satisfaction. Adequate information about patient
health status, available treatment option and limitation in current
understanding & treatment may help the patients have a realistic
expectation toward the physician.
In this study score on the measure of patient’s trust significantly
predicted the value of patient satisfaction. Generally Indian patients
have a high level of trust in physician7. There is a prevalent Indian
traditional belief that physicians are morally superior and do the
best to save the patient’s life. In contrast, in western countries the
trust develops when patients perceive their physician to be sincere,
credible, honest, and benevolent29,30. The high level of trust can result
in a high response rate (placebo response) and vice versa31. When
trust is high, little response may induce more satisfaction. Since all
patients in this study were improved with the treatment, satisfaction
appeared to be obvious13,24.
Patient score on the measure of trust and expectation did
not predict the value of score on medication adherence. Trust is
more likely to mediate the treatment seeking behaviour, while a
subsequent continuation of adherence behaviour depends upon
patient evaluation of service and experience of improvement32.
Adherence requires self-determination and continuous effort that
develops after evaluation of needs, symptom severity and improvement with medication33,34. The common cause of non-adherence
among Indians includes the absence of symptoms, transportation
problems, drug side effects, culture myth, social factors, economic
factors, knowledge & insight and misconception about the treatment and illness35.
Though our hypothesis appeared to be partially true, finding of
this study should be interpreted in the background of the limitations
of this study. Patient were recruited by consecutive method at tertiary
care centre, result may not be applicable to psychiatric patients in
the general population. Though the sample characteristics were
similar to other studies conducted in India36,37, the sample size was
small, sample composed predominantly of mood disorders patients,
study design was cross sectional without control and no assessment
of knowledge about illness (especially biomedical model). Further
study is needed with addressing limitation of this study.
Conflict of interest
Nil.
Acknowledgement
Author thanks staff of department of psychiatry for their moral
support.
References
1. Crow R, Gage H, Hampson S, Hart J, Kimber A, Thomas H. The role of
expectancies in the placebo effect and their use in the delivery of health
care: a systematic review. Health Technol Assess. 1999;3:1-96.
2. Constantino MJ, Arnkoff DB, Glass CR, Ametrano RM, Smith JZ. Expectations. J Clin Psychol. 2011;67:184-92.
3. van Hartingsveld F, Ostelo RWJG, Cuijpers P, de Vos R, Riphagen II, de
Vet HCW. Treatment-related and patient-related expectations of patients
with musculoskeletal disorders: a systematic review of published measurement tools. Clin J Pain. 2010;26:470-88.
4. Noble PC, Conditt MA, Cook KF, Mathis KB. The John Insall Award:
Patient expectations affect satisfaction with total knee arthroplasty. Clin
Orthop Relat Res. 2006;452:35-43.
5. Bowling A, Rowe G, Lambert N, Waddington M, Mahtani KR, Kenten C,
et al. The measurement of patients’ expectations for health care: a review
and psychometric testing of a measure of patients’ expectations. Health
Technol Assess. 2012;16(30):1-509.
6. Hall MA, Dugan E, Zheng B, Mishra AK. Trust in physicians and medical institutions: what is it, can it be measured, and does it matter? The
Milbank Quarterly. 2001;79(4):613-39.
7. Baidya M, Gopichandran V, Kosalram K. Patient-physician trust among
adults of rural Tamil Nadu: a community-based survey. J Postgrad Med.
2014;60:21-6.
8. Mainous AG, Kerse N, Brock CD, Hughes K, Pruitt C. Doctors developing
patient trust: perspectives from the United States and New Zealand. N
Z Fam Physician. 2003;30(5):336-41.
9. Himmel W, Lippert-Urbanke E, Kochen MM. Are patients more satisfied
when they receive a prescription? The effect of patient expectations in
general practice. Scand J Prim Health Care. 1997;15:118-22.
10. Baker R, Streatfield J. What type of general practice do patients prefer?
Exploration of practice characteristics influencing patient satisfaction.
Br J Gen Pract. 1995;45:654-59.
11. Baker R. Characteristics of practices, general-practitioners and patients
related to levels of patients’ satisfaction with consultations. Br J Gen
Pract. 1996;46:601-5.
12. Sabaté E. WHO Adherence Meeting Report. Geneva, World Health
Organization; 2001.
13. Griffith S. A review of the factors associated with patient compliance and
the taking of prescribed medicines. Br J Gen Pract. 1990;40(332):114-6.
14. Morris LS, Schulz RM. Patient compliance: an overview. J Clin Pharm
Ther. 1992;17:183-95.
15. Kao AC, Green DC, Davis NA, Koplan JP, Cleary PD. Patient’s trust in
their physicians: effects of choice, continuity, and payment method. J
Gen Intern Med. 1998;13:681-6.
16. Douglas BC, Noble LM, Newman SP. Improving the accuracy of patients’ expectations of the psychiatric out-patient consultation. Psy Bull.
1999;23:425-7.
17. Rubin HR, Gandek B, Rogers WH, Kosinski M, McHorney CA, Ware
JE Jr. Patients’ ratings of outpatient visits in different practice settings.
Results from the Medical Outcomes Study. JAMA. 1993;270(7):835-40.
18. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care.
1986;24:67-74.
19. Vinayak N. From doctor to social doctor. In: Madhok R, editor. Promoting Professionalism and Ethical Practices in Medicine: Indian doctors
from across the globe working together. A publication of global association of physicians of Indian origin (GAPIO); 2014. p. 15-7.
20. Furnham A. Psychiatric and psychotherapeutic literacy: attitudes to,
and knowledge of, psychotherapy. Int J Soc Psychiatry. 2009;55(6):52537.
21. Aloud N, Rathur A. Mental health and psychological services among Arab
Muslim populations. J Muslim Ment Health. 2009;4:79-103.
22. Fox JG, Storms DM. A different approach to sociodemographic predictors of satisfaction with health care. Soc Sci Med A. 1981;15(5):557-64.
23. Linder-Pelz SU. Toward a theory of patient satisfaction. Soc Sci Med.
1982;16(5):577-82.
24. Ware JE Jr, Snyder MK, Wright WR, Davies AR. Defining and measuring
patient satisfaction with medical care. Eval Program Plann. 1983;6(34):247-63.
25. Donabedian A. Explorations in Quality Assessment and Monitoring.
Vol. 1. The Definition of Quality and Approaches to Its Assessment. Ann
Arbor, MI: Health Administration Press; 1980.
17
26. Mancuso CA, Graziano S, Briskie LM, Peterson MG, Pellicci PM,
Salvati EA, et al. Randomized trials to modify patients’ preoperative
expectations of hip and knee arthroplasties. Clin Orthop Relat Res.
2008;466(2):424-31.
27. Gandhi R, Davey JR, Mahomed N. Patient expectations predict greater
pain relief with joint arthroplasty. J Arthroplasty. 2009;24(5):716-21.
28. Junod Perron N, Secretan F, Vannotti M, Pecoud A, Favrat B. Patient
expectations at a multicultural out-patient clinic in Switzerland. Family
Practice. 2003;20:428-33.
29. Doney PM, Cannon JP. An examination of the nature of trust in buyer-seller relationships. J Marketing. 1997;61(2):35-51.
30. Fugelli P. Trust-in general practice. Br J Gen Pract. 2001;51(468):575-9.
31. Rascol O, Hauser R, Stocchi F, Ha X, Capece R, Wolski K, et al. Post-hoc
analyses of phase-3 data with preladenant, an adenosine 2a antagonist, in
patients with Parkinson’s disease. Neurology. 2014;82(10):7.
32. Toonstra JL. The Relationship between Patient Expectations, Functional
Outcome, Self efficacy, and Rehabilitation Adherence Following Cartilage
Repair of the Knee: A Sequential Explanatory Analysis. 2014. Theses and
Dissertations--Rehabilitation Sciences. Paper 20. Available at: http://
uknowledge.uky.edu/rehabsci_etds/20. Assessed on: Jun 20, 2014.
33. Deci EL. Why We Do What We Do: Understanding Self-Motivation.
London: Penguin; 1995.
34. Kassis IT, Ghuloum S, Mousa H, Bener A. Treatment non-compliance
of psychiatric patients and associated factors: are patients satisfied from
their psychiatrist? B J Med Res. 2013;4(2):16-31.
35. Pareek B, Kalia R. Factors affecting non-compliance to psychotropic
drugs of patients with psychosis as perceived by their family members
attending the psychiatric outpatient department at selected hospital,
Mangalore. Nurs Midwifery Res J. 2013;9(2):56-62.
36. Shah PS. Trend of psychiatric disorders among out-patients and in-patients of a tertiary care center of India. Int J Res Med Sci. 2014;2(2):439-44.
37. Ganguli HC. Epidemiological findings on prevalence of mental disorders
in India. Indian J Psychiatry. 2000;42(1):14-20.
Review article
Post stroke depression: clinics, etiopathogenesis and therapeutics
Vinicius Sousa Pietra Pedroso1,2, Leonardo Cruz de Souza1,2, Andre R. Brunoni3, Antônio Lúcio Teixeira1,2
1
2
3
Interdisciplinary Laboratory of Medical Investigation, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
Neuroscience Branch, Interdisciplinary Laboratory of Medical Investigation, School of Medicine, UFMG, Belo Horizonte, MG, Brazil.
Interdisciplinary Center for Applied Neuromodulation (CINA), University Hospital, University of São Paulo (USP), São Paulo, SP, Brazil.
DOI: 10.1590/0101-60830000000041
Abstract
Background: Stroke is a major cause of morbidity and mortality worldwide. Neuropsychiatric disorders are often associated with stroke and, among them,
depression is the most prevalent. Post-stroke Depression (PSD) is related to disability, failure in returning to work, impairment in interpersonal functioning
and mortality. Its etiopathogenesis is still uncertain, as well as its treatment. In Brazil, there are few data on the impact of PSD. Objective: This work is dedicated to conduct a comprehensive review of the concept of PSD, its pathophysiology, morbidity and treatment. Methods: PubMed, Medline and Lilacs searches
of relevant terms yielded 3,265 papers in the last 10 years. We selected original studies and reviews that addressed the aspects mentioned above. Results: We
present the history of the notion of PSD and describe its epidemiology, looking to highlight Brazilian studies. Diagnostic criteria and clinical presentation
were detailed, with emphasis on cognitive aspects. The four main pathophysiological theories proposed to PSD are presented and we discuss the various treatment strategies, involving psychopharmacologic options, brain stimulation techniques and psychotherapy. Discussion: This work provides comprehensive
information on PSD, of great utility for clinical practice and research in this topic.
Pedroso VSP et al. / Arch Clin Psychiatry. 2015;42(1):18-24
Keywords: Stroke, cerebrovascular diseases, depression, post-stroke depression, vascular depression.
Introduction
Stroke is a major cause of death and disability worldwide1. In the
United States (US), there are approximately 610,000 new cases each
year1. In Brazil, epidemiologic data are scarce. The available information allows stating that stroke is the main cause of death in the
country, accounting for approximately 100,000 deaths annually2.
Nevertheless, over the last decades there has been a global trend of
decrease in stroke mortality. This is probably due to the improvement
in acute stroke management and to preventive measures, such as
arterial hypertension treatment. In Brazil, there is also a decrease in
stroke mortality, but restricted to the South and Southeast regions2.
Currently, an estimated 5 million stroke survivors live in the US1.
In Brazil, where the highest stroke death rates in Latin America are
found, it is estimated that stroke survivors achieve at least half of
that sum2. As the management of acute stroke continues to improve,
the number of survivors will increase even more and, since it often
results in major changes in the patient’s life, factors associated with
morbidity have received increased attention.
Stroke is frequently associated with psychiatric symptoms such
as depressed mood, anxiety and apathy3. The psychiatric complications of stroke, although recognized for more than one century, have
never received the attention that has been devoted to other stroke
complications, such as motor impairment, language problems, or
cognitive deficits4.
Depression is the most common neuropsychiatric condition
experienced after stroke4. More than a hundred years ago, Adolf
Meyer postulated that depression should be the consequence of the
combined effects of brain injury, affecting mainly the left frontal
lobe as well as other lobar convexities, and psychosocial vulnerability, such as past psychiatric history. In the beginning of the XX
century, Eugen Bleuler noted that after stroke “melancholic moods
lasting for months and sometimes longer appear frequently”. Further
in this direction, in 1962, after assessing 100 elderly patients with
depression, Post remarked that the association of brain ischemia
with a first episode of depressive disorder was so common that the
causes of atherosclerotic disease and depression should be “etiologically linked”. However, although the association of depression with
stroke has been clinically recognized for several decades, only in the
past 25 years systematic studies have been conducted, with emerging
evidence that depression after a stroke is associated with increased
disability, increased cognitive impairment and, ultimately, worse
rehabilitation outcomes and increased mortality3,4.
This review aimed to gather information on available epidemiological, pathophysiological, clinical and therapeutic aspects of
post-stroke depression (PSD), their impacts in patient’s recovery
and, when possible, to contextualize them to the Brazilian scenario.
Methods
We conducted a narrative review of the literature through Lilacs,
Medline and PubMed electronic databases, using the keywords
“stroke”, “cerebrovascular diseases”, “depression” and “post-stroke
depression”. When indicated, other bibliographies were consulted
from the reference lists of these articles. The search was restricted
to articles published in English and Portuguese, in the last ten years.
After this step, the titles and abstracts of all articles found were read
in order to identify studies that addressed the theme and purpose
of this review.
Results
Historical perspective
Isolated studies with patients affected by PSD started to appear in
the 1960s4. Most of these works adopted a perspective of “empathic
understanding”. In other words, these researchers explained PSD as
a natural and understandable emotional reaction of the individual
to a decrease in self-esteem produced by the combination of a lifethreatening injury, the associated physical and intellectual disability,
and the resulting loss of independence. Adopting such psychological
perspective, Guido Gainotti’s group, from the Catholic University of
Rome, Italy, conducted the first systematic study of neuropsychiatric
symptoms in patients with stroke or other brain injuries4. His works
can be considered one of the main representatives of the idea that
the depressive syndromes associated with stroke may not be “true”
Address correspondence to: Vinicius Sousa Pietra Pedroso. Laboratório Interdisciplinar de Investigação Médica (LIIM), sala 281, Faculdade de Medicina, Universidade Federal de Minas Gerais.
Av. Alfredo Balena, 190. Santa Efigênia – 30130-100 – Belo Horizonte, MG, Brazil. Phone: +55 (31) 3409-8073. E-mail: [email protected]
19
depressions, but a completely different category from Major Depressive Disorder (MDD) that would be associated with the patients’
adjustment to changes in their living conditions14.
In 1977, however, Folstein et al. conducted a study comparing the
prevalence of mood disorders in patients with stroke or orthopedic
problems which all presented comparable functional disability5. They
observed that patients with stroke exhibited a far greater frequency
of depression than orthopedic patients, and concluded that mood
disorders would be a complication of stroke that was linked not
only to the degree of functional disability. This seminal study led to
the development of a biological explanation for PSD, whereby brain
changes would lead to depressive symptoms. This line of reasoning
was primarily developed in the 1980’s by Robert Robinson’s group,
from the University of Iowa, in opposition to the psychological
perspective3,4. These opposing perspectives have led to many of the
continuing controversies and uncertainties about emotional disorders
following stroke.
pressive episode; “with major depressive-like episode”, if full criteria
are met for a major depressive episode (except for criterion C); and
“with mixed features”, if symptoms of mania or hypomania are also
present but do not predominate in the clinical picture.
Table 1. Frequency of post stroke depression in Brazilian studies
Author (year) City (state)
n
Simis, Nitrini Sorocaba 93
(2006)12
(SP)
Carod-Artal
Brasília 260*
et al. (2009)11
(DF)
Terroni et al. São Paulo
(SP)
(2009)13
Fróes et al.
Fortaleza
(2011)9
(CE)
Setting
H
O
73
H/O
64
RP
19*
O
139*
RP
Epidemiology
Since the first systematic studies, depression (major and minor) has
been regarded as the most common neuropsychiatric disorder after
stroke, with an estimated prevalence ranging from 18 to 60%3. According to DSM-V, MDD corresponds to the presence of at least four
accessory symptoms, besides depressed mood or anhedonia. Minor
Depressive Episode is a DMS-IV research diagnosis characterized
by two to four depressive symptoms, including depressed mood or
anhedonia6. DSM-V incorporated this syndrome into the category
Other Specified Depressive Disorders: Depressive Episode with
insufficient symptoms.
The prevalence of PSD among hospitalized patients in the acute
phase is around 22% for major depression and 17% for minor depression. In outpatient samples (from 3 months to 10 years after stroke),
it is around 23% for major depression and 35% for minor depression,
while community samples exhibit mean prevalence rates of 13% and
10%, respectively4. A recent meta-analysis showed that the prevalence
of depression at any time after stroke was 29%7.
A cohort of 4,022 patients followed for 15 years showed the dynamic history of PSD8. The peak prevalence of MDD occurs 3 to 6
months after the stroke. Most subjects had MDD remission one year
after stroke, however showing persistent subsyndromal depressive
symptoms and/or short lasting depressive episodes. Also according
to Robinson, MDD often subside without complete remission one
year after the index event4.
In Brazil, there are few data on the epidemiology of PSD. A study
conducted in Fortaleza, which investigated the quality of life (QoL) of
individuals from two to six years post-stroke, reported a 40% prevalence of depressive symptoms (predominantly mild to moderate)9.
The presence of depressive symptoms was the most important factor
in reducing QoL. Similarly, de Souza et al. evaluated patients with
Chagas disease and stroke, and found that QoL was more influenced
by depressive symptoms than neurological disability10. Carod-Artal
et al. also demonstrated that depression, disability and motor deficits
were the main determinants of health-related QoL in patients with
stroke, and depression was the strongest predictor of reduced QoL,
especially among women11. Table 1 presents Brazilian studies that
reported the frequency of PSD.
Diagnosis and clinical picture
The DSM-V criteria for the diagnosis of PSD match those for Depressive Disorder Due to a Medical Condition6. Stroke is one of the few
conditions listed in the former DSM-IV and the current DSM-V as
“directly” causing depression; therefore PSD is diagnosed differently
from depression following, for instance, a myocardial infarction or a
hip fracture, and can be named as Depressive Disorder Due to Stroke.
One of the following specifiers should be added to the diagnosis:
“with depressive features”, if full criteria are not met for a major de-
Scheffer et
al. (2011)14
Rangel et al.
(2013)15
Porto
Alegre
(RS)
Maceió
(AL)
Time after
Instrument
stroke
2 weeks
HAM-D
20.7
months
(mean)
1 week – 4
months
< 2 years:
9.4%
2-6 years:
50%
> 7 years:
40.7%
9 – 27
months
3 – 316
months
HADS
Frequency
(%)
59,1†
HAM-D
20,0†
(F: 25,0;
M: 15,4)
28,8‡
BDI
40,0†
BDI
33,3†
BDI
49,7†
BDI: Beck Depression Inventory; F: Female gender; H: Hospital sample; HADS: Hospital Anxiety
and Depression Scale; HAM-D: Hamilton Depression Scale; M: Male gender; O: Outpatient
sample; RP: Rehabilitation Program sample.
* The study included patients with hemorrhagic stroke (Ischemic stroke: Carod-Artal et al.: 87.7%,
Scheffer et al. 84.2%; Rangel et al.: 83.5%).
† Prevalence; ‡ Incidence in 4 months.
PSD should be distinguished from post-stroke demoralization,
which can be understood as a type of adjustment disorder. Several
authors have attempted to differentiate depression and demoralization16,17. This distinction may seem even more complicated when
considering the concept of PSD adopted by the psychological
perspective described above. Overall, demoralization is related
to feelings of incompetence and loss of self-control after repeated
failures, whereas depression is marked by anhedonia and decreased
motivation. Shader observed that individuals with demoralization
may respond favorably to positive stimuli and relief of stressors,
while patients with depression cannot get rid of their negative mood
state, regardless of environmental changes17. Once the presence of
demoralization is recognized, the clinician should work with the
patient in order to promote a sense of ability, mastery and return of
hope. Encouragement, support and education are essential.
According to Spalletta and Robinson18, although it seems likely
that some forms of PSD may be, in part, sustained by reaction to
disability, the attempt to differentiate between “reactive” (i.e. demoralization) and “endogenous” forms of depression ceased many
years ago because no clear etiopathogenetic or phenomenological
distinction has ever been shown to distinguish between them and
a mixture of these two forms is present in almost all patients with a
diagnosis of depression.
Fedoroff et al. assessed the suitability of the diagnostic criteria for
MDD in the diagnosis of PSD19. They observed that, except for earlymorning awakening, all symptoms of depression were more frequent
in the stroke patients with depressed mood than in the euthymic
ones. Cumming et al. also conducted an investigation to determine
whether the phenomenology of depression after stroke was different from the phenomenology of depression with no known medical
cause20. They noted that there were no major differences between the
symptom profiles of both groups, except that stroke patients were less
likely to report anhedonia than controls. Interestingly stroke patients
20
were no more likely than controls to report somatic complaints over
psychological symptoms. However, some authors point to clinical differences between patients with PSD and those with MDD. Compared
to these, stroke patients would have more cognitive impairment,
mood fluctuations, psychomotor retardation, anxiety and vegetative
and somatic symptoms21. Gainotti et al. identified depressed mood,
anhedonia and suicidal thoughts as more prevalent in non-stroke
depressive patients than in patients with PSD21. In Brazil, Terroni
et al. point out the relevance of fatigue symptoms and reduction of
general interests in the diagnosis of PSD13.
Despite these controversies, there is no evidence that the diagnosis of depression after stroke is less valid than the diagnosis of
depression in non-stroke populations18,20. Since there are no specific
biological markers, the diagnosis is based on clinical findings, and
this task can become very difficult in the presence of severe cognitive
deficits, especially language disorders.
Cognitive impairment in PSD
Cognitive deficits are commonly observed in depressed patients. Executive functions, including concept formation, planning, cognitive
control, initiation and psychomotor speed, have been regularly shown
to be impaired in depression22. Short term and working memory are
disturbed in depression, as assessed either by the digit span test or by
the digit ordering test. Objective memory deficit is regularly demonstrated in depressed patients, characterized by lower immediate
and delayed recall performance in both verbal and visual memory
tests, but with a normal cued recall and recognition22. This pattern
is typically described as a retrieval memory disorder, rather than a
storage dysfunction. Language and visuospatial abilities are generally
preserved in depression.
A series of papers has specifically investigated cognitive disorders in PSD. Post-stroke depression affects problem solving, verbal
and visual memory, language, visuospatial processes, attention and
psychomotor speed22,23. Moreover, the degree of cognitive impairment is associated with the severity of depressive symptoms23.
In a cohort of 143 patients who were followed up to 10 months
after a stroke, Nys et al. found that cognitive impairment at baseline
independently predicted long-term depressive symptoms24. More-
over, they found that cognitive deficits were related to worse quality
of life. Among all cognitive deficits, the QoL was mostly affected by
visuospatial and visuo-constructive disorders, while unilateral neglect at baseline assessment was the greatest risk factor for depressive
symptoms after 6 months.
Taken together, these data suggest that cognitive deficits may
account for PSD and, on the other hand, the degree of depression
impacts on cognitive performance. Patients with PSD should undergo
a formal neuropsychological evaluation, and therapeutic rehabilitation program adapted according to the cognitive profile of the patient.
Pathophysiology
The polarity between the biological and the psychological perspectives may have hampered the development of a comprehensive approach to PSD prevention and treatment. Table 2 presents the main
arguments of each school.
Among the major biological theories on the pathophysiology of
PSD, four main hypotheses can be listed: lesion location, biogenic
amines, inflammatory cytokines and gene polymorphism hypotheses.
The lesion location hypothesis was formulated by Robinson
based on the observation that depression severity was associated
with lesions in the left frontal lobe, and that this association was
stronger in the first 6 months after stroke4. However, this finding
has not been consistently replicated by other authors. Carson et
al., in a meta-analysis of 35 studies, observed that the risk for
developing depression was not associated with lesion location25.
Other authors proposed that strategic or specific location of the
ischemic damage might play a role in the development of PSD26.
Neuroimaging studies found the hippocampus, basal ganglia and
frontal areas to be associated with PSD26. Although the debate is
still open, the correlations between affected areas and depressive
symptoms seem to be weak.
The biogenic amines theory can be understood as a pathophysiological sophistication of the lesion location hypothesis. It was first
proposed by Robinson and Bloom27. They postulated that ischemic
lesions might interrupt the biogenic amine-containing axons
ascending from the brainstem nuclei to the cerebral cortex, thus
decreasing the release of serotonin (5-HT) and norepinephrine (NE)
Table 2. Key features of the biological and psychological hypothesis of post stroke depression (PSD) etiopathogenesis
Biological causation
Evidence
For
Higher frequency of depression in stroke
versus other similarly disabling medical
illness
Temporal relationship between stroke
and onset of depression
Specific lesions associated with PSD
PSD may occur in the context of silent
infarcts
Against
Finding not consistently replicated
Temporal relationship between
psychological stressors (e.g.
bereavement) and depression
Finding not consistently replicated
-
Explanatory theories
Lesion location theory: PSD may be related to the location of the lesions, disturbing
specific areas of the brain (e.g. left frontal lobes, hippocampus, basal ganglia)
Biogenic amines theory: PSD may be related to disruption of monoamines circuitry,
through direct or indirect mechanisms
Inflammatory cytokines theory: PSD may be related to the production of
“depressogenic” cytokines by the inflammatory response to ischemia
Genetic polymorphisms theory: PSD may be related to genetic predisposition,
especially in the serotoninergic system
Psychological causation
Evidence
For
Against
PSD symptom profile is not specific
“Functional” depression may have
biological underpinnings
and may be a form of “functional”
Temporal relationship between stroke
depression
Temporal relationship between
and onset of depression
psychological stressors (e.g.
These risk factors may reflect biological
bereavement) and depression
predisposition (e.g. genetic causes)
Risk factors unrelated with stroke
predicts occurrence of depression (e.g.
family history of depression)
Disability severity is the most
consistent risk factor for PSD
and psychosocial factors become
increasingly important in later onset
PSD
Explanatory theories
Patients with stroke experience a traumatic event that undermines their physical
and mental integrity, their autonomy and self-esteem as well as their social
lives. Psychological coping mechanisms, as well as premorbid personality, are
responsible for the development of PSD
in the limbic structures of the frontal and temporal lobes as well as in
the basal ganglia. According to this hypothesis, lesions located in the
anterior portions of the frontal lobes could interrupt the ascending
monoaminergic axonal bundles leading to depression. Indeed, the
studies of Robinson suggested that anterior lesions, located close
to the left frontal pole, would be associated with the development
of depressive symptoms4. Later it was suggested that dysfunction
of cortico-striato-pallido-thalamic-cortical circuits predisposes to
PSD, and that these loops could even be disrupted indirectly by
secondary degeneration when not included in the primary ischemic lesion by means of anterograde or retrograde degeneration
and vasogenic edema. This could explain, at least partly, the great
variability described by anatomo-clinic correlational studies. In
addition, it was observed that in acute brain lesion, there is decreased monoamine synthesis because of enzyme inhibition during
ischemia. Accordingly, significantly lower cerebrospinal fluid (CSF)
concentrations of the 5-HT metabolite 5-hydroxy-indoleacetic
acid were measured in PSD patients compared to non-depressed
stroke survivors28. Positron Emission Tomography (PET) findings
on 5-HT1a receptor availability after stroke suggest that changes in
5-HT neurotransmission may occur in the early phase of stroke and
can be modulated by treatment with Selective Serotonin Reuptake
Inhibitors (SSRIs)29.
Based on the strong association of proinflammatory cytokines,
such as interkeukin-1β (IL-1β), tumor necrosis factor-α (TNF-α),
IL-6, IL-8 and IL-18, with ischemic brain injury and the evidence
of interleukins playing an important role in certain subtypes of
depression, Spalletta et al. proposed the inflammatory cytokines
hypothesis for PSD30. In the last two decades, increased blood and
CSF concentrations of pro-inflammatory cytokines, including IL-1β,
IL-6, Interferon-γ (IFNγ) and TNFα, acute phase proteins, such as
C-reactive protein (CRP), and their receptors, chemokines, adhesion
molecules and other inflammatory mediators, have been demonstrated in MDD subjects31. The long-term exposure to cytokines may
be associated with the onset of depression. The best studied example
comprises patients receiving IFN-α to treat melanoma and hepatitis
C virus infection32. In experimental animals, the administration of
cytokines, such as IL-6, or cytokine inducers, such as lipopolysaccharide (LPS), have been found to induce depressive-like behaviors33. The
inflammatory mediators seem to activate the widespread tryptophane
catabolizing enzyme indoleamine 2,3-deoxygenase (IDO) leading
to decreased synthesis of 5-HT. The importance of the activation of
IDO in the pathophysiology of depression is also supported by the
evidence that, in mice, the blockade of IDO inhibits the onset of the
LPS-related “sickness behavior”33. Concerning PSD, experimental
studies showed that, in mice, IL-1β and TNF-α can induce post-stroke
depressive-like behavior resembling the somatic syndrome of depression in humans34. Regarding clinical studies, Jiménez et al. reported
that serum leptin levels in patients with a first episode of ischemic
stroke were associated with PSD at discharge from hospital and at one
month after stroke35. Yang et al. observed that serum IL-18 measured
7 days after hospital admission for stroke was associated with PSD
in the acute stage and 6 months after stroke36. Recently, Spalletta et
al. found that that serum IL-6 was increased in patients with depressive disorders after stroke, and that its levels were associated with
the severity of apathetic-amotivational and somatic symptoms of
depression and of neurological deficits 72 hours after stroke37. There
are many problems with this theory: several cytokines are involved
in post-stroke inflammatory process, and they play different roles in
distinct stages after stroke. Furthermore, many studies of cytokines
are based on animals, and in patients, only the serum and CSF levels
can be tested, while it is impossible to measure cytokines in specific
areas of the living human brain. Besides that, molecular cascades
after stroke also include a marked induction of anti-inflammatory
cytokines, which may counterbalance the “depressiogenic” effect
of proinflammatory cytokines. To examine the hypothesis, further
researches are needed.
It has been proposed that MDD results from the interaction of
predisposing genes and the environment. Nowadays, this relation-
21
ship emerges as the gene polymorphism hypothesis of PSD. Based
on the theory of biogenic amines, the serotonergic system appears as
a canor genetic susceptibility to PSD. Few studies have investigated
the role of serotonin genes polymorphisms in PSD. For instance,
Ramasubbu et al. reported that the Serotonin Transporter GeneLinked Promoter Region (5-HTTLPR) s allele was associated with
PSD in a sample of 26 stroke patients with major depression and
25 non-depressed stroke patients, the first genetic study of PSD38.
Later, Kohen et al. replicated this finding with a larger sample of 75
depressive and 75 non-depressive stroke patients categorized by the
Geriatric Depression Scale39. Kim et al. found that 5-HTTLPR s/s
genotype was associated with PSD40. Besides, they observed that Brain
Derived Neurotrophic Factor (BDNF) met/met and 5-HT2a receptor
(5-HTR2a) 1438 A/A genotypes were associated with PSD. On the
other hand, Zhou et al. reported that serum BDNF concentrations
were decreased in PSD patients 3 to 6 months after stroke, but this
was not associated with BDNF gene Val66Met polymorphisms41.
Tang et al. suggested a possible role for genetic variation in 5-HT2c
receptors (HTR2C receptors) in the pathogenesis of PSD42. Based on
the theory of inflammatory cytokines, Kim et al. reported that the
IL-4 + 33C/C and the IL-10 –1082A/A genotypes were associated
with PSD43. All these studies rely on very small samples and need to
be replicated consistently in larger populations.
It should be noted that, traditionally, most studies on the pathophysiology of PSD has focused on large vessel disease, without making an explicit mention of lacunes26. However, paralleling this debate,
the literature on “vascular depression” hypothesis is increasingly
emphasizing the role of small vessel and microvascular chronic burden in triggering depressive episodes26. According to this hypothesis,
cerebrovascular disease could predispose, precipitate or perpetuate
some geriatric depressive syndromes. Nevertheless, longitudinal
studies in large community-based series of patients with PSD are
needed to test the validity of this interesting proposal.
As the evidence supporting the different lines of biological explanations has not pointed to definite conclusions, the psychological
school still maintains proposals of psychosocial mechanisms for the
pathogenesis of PSD. For instance, Gainotti et al. found that the symptom profiles and anatomical–clinical correlates of major PSD were not
different in the acute and more chronic stages21. He argued that this
finding was more consistent with a psychological than a neurobiological model of PSD. Lieberman et al. studied 516 hospitalized elderly
patients, 221 after stroke and 295 after hip fracture44. There were no
differences in the symptoms of depression score between the two
groups, contradicting the influential work of Folstein et al.5. Bozikas
et al. performed a clinicopathological analysis of 95 consecutively
autopsied elderly individuals who survived an initial stroke and were
followed to record the occurrence of PSD45. They observed that the
severity of brain vessel arteriosclerosis and the frequency of brain
vascular lesions were not significantly different between 21 cases with
PSD and 74 cases without depression. No lesion pattern characterized
the depression group. Thus, they suggested that psychological rather
than neuropathological factors were the main determinants of PSD.
In fact, patients who had a stroke experience a traumatic event that
undermines their physical and mental integrity, their autonomy and
self-esteem as well as their marital and professional lives21. Psychological coping mechanisms, as well as premorbid personality, certainly
play an important role in the development of PSD. However, these
arguments are not sufficient to explain the emergence of depression
after silent infarctions or in patients with anosognosia.
Ultimately, this polarity of thought appears unreasonable given
the current understanding on the inseparable nature of somatic and
psychiatric illness. PSD does not appear to be the result of “pure”
biological versus psychological causes, but instead is multifactorial
in origin and consistent with the biopsychosocial model of mental
illness. At this time, more investigations are needed to clarify the relative contributions of both biological and psychosocial risk factors and
their interactions to the development of poststroke psychopathology.
22
Many factors have been roughly associated with PSD46,47, as can
be seen in figure 1, such as previous history of psychiatric disorders,
female gender, family history of depression, and cerebrovascular risk
factors, among others. Of those, physical disability, stroke severity
and cognitive impairment have been more consistently associated
with PSD48. Further, it has been suggested that patients who develop
PSD just after a stroke have different risk factors than those who
present a first episode later on49. Accordingly, early PSD would be
closely related to biological mechanisms, whereas PSD developed six
months after a stroke would be related to psychosocial mechanisms.
A better understanding of the influence of these risk factors on the
course of PSD and treatment response will lead to better treatment
and, possibly, primary prevention interventions.
Treatment
There is consensus that, if PSD is left untreated, it may exert negative impact on functional recovery4. Longitudinal studies show that
major and minor depressions are determinants of disability, failure
in returning to work, impairment in interpersonal functioning and
mortality4.
The relation between PSD and functional impairment is complex. Patients with PSD have significantly higher disability in ADLs
than euthymic patients with comparable neurological deficits3. PSD
negatively impacts on the involvement in rehabilitation programs and
is associated with more institutional care and increase in using of
health services3. These findings suggest a phenomenon of reciprocity,
in which depression influences the recovery of ADLs and the impairment of ADLs influences the severity and duration of depression.
Increased mortality is perhaps the ultimate validation of the
importance of depression in the prognosis following stroke. PSD
appears to be a significant risk factor for increased death as early as 1
year and as late as 7 years following stroke4. The mechanism underlying increased death rates is an important issue, which has not been
examined in depth. One study showed that PSD is associated with
decreased heart rate variability (HRV)50. In this line, Tokgozoglu et al.
reported that patients with decreased HRV, as a result of stroke lesions
of the insular cortex, are at risk for sudden death51, and Makikallio et
al. found that decreased long-term HRV was the only multivariate
predictor of death after adjusting for age52.
There are no guidelines for PSD treatment and the effectiveness of interventions is not well established. In a systematic review,
Hackett et al. concluded that the use of antidepressants is associated
with a small but significant beneficial effect53. According to the meta-
Lesion location
Biogenic amines
Cytokines
Gene polymorphisms
Older age
Female gender
Living alone
Poor social/family support
Social
analysis conducted by Price et al., the use of antidepressants may be
indicated on both major and minor depressive disorders, but there are
no specific guidelines for the selection of drugs54. The most studied
drugs were tricyclic antidepressants, especially nortriptyline, and
SSRIs, particularly fluoxetine, sertraline and citalopram. There were
also studies evaluating the use of trazodone, venlafaxine, reboxetine,
mirtazapine, milnacipran and methylphenidate.
Some authors recommend the use of Nortriptyline as the first
line drug, based on the evidence that it has a better efficacy than any
other antidepressant available53. However, Nortriptyline may determine undesirable side effects and drug interactions, which can be
problematic in a population at higher risk of cardiovascular disease.
In this scenario, SSRIs are an interesting alternative. The use of antidepressants in the prevention of PSD is even more controversial55.
Isolated studies have shown benefits with nortriptyline, fluoxetine,
sertraline, mirtazapine and methylphenidate. Nevertheless, a Cochrane systematic review found no significant effect of antidepressant
use in the prevention of PSD55.
One very interesting point is that antidepressants, especially
SSRIs, have been associated with improvement of motor recovery
and dependence after stroke even in patients without depression.
Experimental studies reporting neurogenic and neuroprotective effects of SSRIs provide a plausible mechanism of action. The largest
study conducted to date has been the “Fluoxetine in motor recovery
of patients with acute ischaemic stroke” (FLAME) trial56. It was a
double-blind, placebo-controlled trial, in which 118 ischemic stroke
patients with moderate to severe motor deficits, without PSD, were
randomly assigned to a 3-month treatment with Fluoxetine or placebo. The authors reported that, after 90 days, the early prescription
of Fluoxetine with physiotherapy enhanced motor recovery. Besides,
they noted that the early use of Fluoxetine prevented PSD. In a recent
meta-analysis, 52 trials randomizing 4,059 patients to SSRI or control
were assessed (28 used fluoxetine, 7 sertraline, 10 paroxetine, 5 citalopram, 1 escitalopram, and 1 either sertraline or fluoxetine)57. The
authors concluded that the favorable effects of SSRIs on disability,
depressive symptoms, and neurological deficit scores were greater in
participants who were depressed at randomization, but this may be
due to quality bias. Besides, the authors report evidence of benefits
of SSRIs in patients without depression, especially fluoxetine, the
most studied drug. SSRIs appeared to improve dependence, disability,
neurological impairment, anxiety and depression after stroke, despite
the heterogeneity of the trials and several methodological limitations.
Large, well-designed trials are needed to determine whether SSRIs
should be routinely given to patients with stroke.
Biological
Family history of major depression
Previous cerebrovascular events
Physical disability
Social isolation
Few social contacts
Sleep disturbances
Aphasia
Cognitive impairment
Stroke severity
Previous history of depression
or psychiatric treatment
Dependence for ADLs
Coping skills
Personality traits (neuroticism)
Recent adverse life
events (e.g. bereavement)
Psychological
Figure 1. An integrative model of factors involved in post stroke depression pathogenesis. ADLs: activities of daily living.
Although it has not been used in a randomized controlled trial,
electroconvulsive therapy has also been reported to be effective in
treating PSD4. Another emerging technique for the treatment of
PSD is non invasive brain stimulation, which encompasses two
main techniques: repetitive transcranial magnetic stimulation
(rTMS) and transcranial direct current stimulation (tDCS). TMS
depolarizes neurons using potent, focal electromagnetic fields that
are generated beneath the coil positioned over the patient’s head.
The electric depolarization, thereafter, induces action potentials.
When applied repetitively and for several days, rTMS is able to
induce clinical effects in several psychiatric disorders and is already
used as a clinical (non-experimental) treatment in several countries, including Brazil. tDCS, in turn, is based on the application
of a weak (0.5-2mA), direct electric current in the brain using
electrodes placed over the head. Clinical effects are observed in
several psychiatric disorders when applied daily for several days.
These techniques might be particularly suitable for PSD as their side
effects are limited to discomfort over the local of the application of
the electrode/coil – i.e., they can induce depression improvement
without systemic side effects.
Despite the emerging evidence of these techniques, they are
relatively poorly investigated in the context of major depression
secondary to clinical disorders, including PSD. For instance, there
are only two randomized, double-blinded clinical trials evaluating the efficacy of rTMS in PSD, studying 18 and 20 patients58,59.
Both studies applied high-frequency (stimulatory) rTMS over
the left dorsolateral prefrontal cortex, observing improvement
of depressive symptoms. For tDCS, there is only one case report
describing a patient with PSD, who showed marked improvement
of symptoms after a 10-day course of tDCS60. In this context, there
is one ongoing large double-blinded, randomized clinical trial
enrolling 48 patients with PSD in the University of São Paulo,
Brazil, which will provide more evidence regarding the use of
tDCS for PSD treatment. This trial is registered at clinicaltrials.
gov (NCT01525524).
Finally, psychological treatment in isolation has been found to
be no more effective than placebo. Psychotherapeutic approach associated with antidepressant use appears to be of some benefit. In
the meta-analysis of Hackett et al., a small but significant effect was
found for psychotherapy in preventing PSD55.
Conclusion
A narrative review of the literature was conducted to present a
comprehensive panorama on PSD. It should be noted that, due the
extent of the addressed theme, we opted to perform a non-systematic
search. This method, however, imposes limitations associated with
its non-quantitative nature. On the other hand, it was possible to
describe in a detailed manner several aspects associated with this
complication of stroke.
As discussed above, depression is the most frequent psychiatric
complication of cerebrovascular disorder. It is clear that it influences
prognosis and functional recovery and its approach must be guaranteed for every stroke patient. Despite its great clinical relevance,
there is little insight into its underlying etiological mechanisms and
treament. For this reason, research addressed to elucidate the pathophysiological mechanisms of PSD are important. In Brazil, despite the
great impact of cerebrovascular diseases, data are scarce even for the
implications of PSD, indicating the need for epidemiological surveys
to characterize the population affected by poststroke neuropsychiatric
syndromes in the country.
A multidimensional approach taking into account biological,
psychological and social perspectives is currently the most reasonable
to the understanding of depressive symptoms following stroke, and
to foster the development of evidence-based therapeutic strategies.
Acknowledgments
This work was funded by CNPq and Fapemig.
23
Disclosure
The authors report no conflicts of interest.
References
1. American Heart Association Statistics Committee and Stroke Statistics
Subcommittee. Heart disease and stroke statistics – 2013 update: a report
from the American Heart Association. Circulation. 2013;127(1):e6-e245.
2. de Carvalho JJ, Alves MB, Viana GA, Machado CB, dos Santos BF, Kanamura AH, et al. Stroke epidemiology, patterns of management, and
outcomes in Fortaleza, Brazil: a hospital-based multicenter prospective
study. Stroke. 2011;42(12):3341-6.
3. Angelelli P, Paolucci S, Bivona U, Piccardi L, Ciurli P, Cantagallo A, et
al. Development of neuropsychiatric symptoms in poststroke patients: a
cross-sectional study. Acta Psychiatr Scand. 2004;110(1):55-63.
4. Robinson RG. The clinical neuropsychiatry of stroke. New York: Cambridge University Press; 2006.
5. Folstein MF, Maiberger R, McHugh PR. Mood disorder as a specific complication of stroke. J Neurol Neurosurg Psychiatry. 1977;40(10):1018-20.
6. American Psychiatric Association. Diagnostic and statistical manual of
mental disorders. 5th ed. Arlington: American Psychiatric Publishing; 2013.
7. Ayerbe L, Ayis S, Wolfe CD, Rudd AG. Natural history, predictors and
outcomes of depression after stroke: systematic review and meta-analysis.
Br J Psychiatry. 2013;202(1):14-21.
8. Ayerbe L, Ayis S, Crichton S, Wolfe CD, Rudd AG. The natural history of
depression up to 15 years after stroke: the South London Stroke Register.
Stroke. 2013;44(4):1105-10.
9. Fróes KS, Valdés MT, Lopes Dde P, Silva CE. Factors associated with
health-related quality of life for adults with stroke sequelae. Arq Neuropsiquiatr. 2011;69(2B):371-6.
10. Souza AC, Rocha MO, Teixeira AL, Dias Júnior JO, Sousa LA, Nunes MC.
Depressive symptoms and disability in chagasic stroke patients: impact
on functionality and quality of life. J Neurol Sci. 2013;324(1-2):34-7.
11. Carod-Artal FJ, Trizotto DS, Coral LF, Moreira CM. Determinants of quality of life in Brazilian stroke survivors. J Neurol Sci. 2009;284(1-2):63-8.
12. Simis S, Nitrini R. Cognitive improvement after treatment of depressive symptoms in the acute phase of stroke. Arq Neuropsiquiatr.
2006;64(2B):412-7.
13. Terroni LM, Fráguas R, Lucia M, Tinone G, Mattos P, Iosifescu DV, et al.
Importance of retardation and fatigue/interest domains for the diagnosis
of major depressive episode after stroke: a four months prospective study.
Rev Bras Psiquiatr. 2009;31(3):202-7.
14. Scheffer M, Monteiro JK, de Almeida RMM. Frontal stroke: problem
solving, decision making, impulsiveness, and depressive symptoms in
men and women. Psychol Neurosci. 2011;4(2):267-78.
15. Rangel ESS, Belasco AGS, Diccini S. Qualidade de vida de pacientes
com acidente vascular cerebral em reabilitação. Acta Paul Enferm.
2013;26(2):205-12.
16. Clarke DM, Kissane DW. Demoralization: its phenomenology and importance. Aust NZJ Psychiatry. 2002;36(6):733-42.
17. Shader RI. Demoralization revisited. J Clin Psychopharmacology.
2005;25(4):291-2.
18. Spalletta G, Robinson RG. How should depression be diagnosed in
patients with stroke? Acta Psychiatr Scand. 2010;121(6):401-3.
19. Fedoroff JP, Starkstein SE, Parikh RM, Price TR, Robinson RG. Are
depressive symptoms nonspecific in patients with acute stroke? Am J
Psychiatry. 1991;148(9):1172-6.
20. Cumming TB, Churilov L, Skoog I, Blomstrand C, Linden T. Little
evidence for different phenomenology in poststroke depression. Acta
Psychiatr Scand. 2010;121(6):424-30.
21. Gainotti G, Azzoni A, Marra C. Frequency, phenomenology and anatomical-clinical correlates of major poststroke depression. Br J Psychiatry.
1999;175:163-7.
22. Kauhanen M, Korpelainen JT, Hiltunen P, Brusin E, Mononen H, Määttä
R, et al. Poststroke depression correlates with cognitive impairment and
neurological deficits. Stroke. 1999;30(9):1875-80.
23. Nys GM, van Zandvoort MJ, van der Worp HB, de Haan EH, de Kort PL,
Kappelle LJ. Early depressive symptoms after stroke: neuropsychological
correlates and lesion characteristics. J Neurol Sci. 2005;228(1):27-33.
24. Nys GM, van Zandvoort MJ, van der Worp HB, de Haan EH, de Kort
PL, Jansen BP, et al. Early cognitive impairment predicts long-term
24
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
depressive symptoms and quality of life after stroke. J Neurol Sci.
2006;247(2):149-56.
Carson AJ, MacHale S, Allen K, Lawrie SM, Dennis M, House A, et al.
Depression after stroke and lesion location: a systematic review. Lancet.
2000;356(9224):122-6.
Alexopoulos GS, Meyers BS, Young RC, Campbell S, Silbersweig D,
Charlson M. Vascular depression hypothesis. Arch Gen Psychiatry.
1997;54(10):915-22.
Robinson RG, Bloom FE. Pharmacological treatment following experimental cerebral infarction: implications for understanding psychological
symptoms of human stroke. Biol Psychiatry. 1977;12(5):669-80.
Bryer JB, Starkstein SE, Votypka V, Parikh RM, Price TR, Robinson RG.
Reduction of CSF monoamine metabolites in poststroke depression: a
preliminary report. J Neuropsychiatry Clin Neurosci. 1992;4(4):440-2.
Moller M, Andersen G, Gjedde A. Serotonin 5-HT1a receptor availability
and pathological crying after stroke. Acta Neurol Scand. 2007;116(2):83-90.
Spalletta G, Bossu P, Ciaramella A, Bria P, Caltagirone C, Robinson RG.
The etiology of poststroke depression: a review of the literature and
a new hypothesis involving inflammatory cytokines. Mol Psychiatry.
2006;11(11):984-91.
Mossner R, Mikova O, Koutsilieri E, Saoud M, Ehlis AC, Müller N, et
al. Consensus paper of the WFSBP Task Force on Biological Markers:
Biological markers in depression. W J Biol Psych. 2007;8(3):141-74.
Fábregas BC, Vitorino FD, Rocha DM, Moura AS, Carmo RA, Teixeira
AL. Screening inventories to detect depression in chronic hepatitis C
patients. Gen Hosp Psychiatry. 2012;34(1):40-5.
O’Connor JC, Lawson MA, Andre C, Moreau M, Lestage J, Castanon N, et al.
Lipopolysaccharide-induced depressive-like behavior is mediated by indoleamine 2,3-dioxygenase activation in mice. Mol Psychiatry. 2009;14(5):511-22.
Craft TK, DeVries AC. Role of IL-1 in poststroke depressive-like behavior
in mice. Biol Psychiatry. 2006;15;60(8):812-8.
Jiménez I, Sobrino T, Rodríguez-Yáñez M, Pouso M, Cristobo I, Sabucedo M, et al. High serum levels of leptin are associated with post-stroke
depression. Psychol Med. 2009;39(7):1201-9.
Yang L, Zhang Z, Sun D, Xu Z, Zhang X, Li L. The serum interleukin-18
is a potential marker for development of post-stroke depression. Neurol
Res. 2010;32(4):340-6.
Spalletta G, Cravello L, Imperiale F, Salani F, Bossù P, Picchetto L, et
al. Neuropsychiatric symptoms and interleukin-6 serum levels in acute
stroke. J Neuropsychiatry Clin Neurosci. 2013;25(4):255-63.
Ramasubbu R, Tobias R, Buchan AM, Bech-Hansen NT. Serotonin transporter gene promoter region polymorphism associated with poststroke
major depression. J Neuropsychiatry Clin Neurosci. 2006;18(1):96-9.
Kohen R, Cain KC, Buzaitis A, Johnson V, Becker KJ, Teri L, et al. Response to psychosocial treatment in poststroke depression is associated
with serotonin transporter polymorphisms. Stroke. 2011;42(7):2068-70.
Kim JM, Stewart R, Bae KY, Kim SW, Kang HJ, Shin IS, et al. Serotonergic
and BDNF genes and risk of depression after stroke. J Affect Disord.
2012;136(3):833-40.
Zhou Z, Lu T, Xu G, Yue X, Zhu W, Ma M, et al. Decreased serum brain-derived neurotrophic factor (BDNF) is associated with post-stroke
depression but not with BDNF gene Val66Met polymorphism. Clin
Chem Lab Med. 2011;49(2):185-9.
Tang WK, Tang N, Liao CD, Liang HJ, Mok VC, Ungvari GS, et al.
Serotonin receptor 2C gene polymorphism associated with post-stroke
depression in Chinese patients. Genet Mol Res. 2013;12(2):1546-53.
43. Kim JM, Stewart R, Kim SW, Shin IS, Kim JT, Park MS, et al. Associations
of cytokine gene polymorphisms with post-stroke depression. World J
Biol Psychiatry. 2012;13(8):579-87.
44. Lieberman D, Friger M, Fried V, Grinshpun Y, Mytlis N, Tylis R, et al.
Characterization of elderly patients in rehabilitation: stroke versus hip
fracture. Disabil Rehabil. 1999;21(12):542-7.
45. Bozikas VP, Gold G, Kovari E, Herrmann F, Karavatos A, Giannakopoulos
P, et al. Pathological correlates of poststroke depression in elderly patients.
Am J Geriatr Psychiatry. 2005;13(2):166-9.
46. Oldehinkel AJ, Ormel J, Brilman EI, van den Berg MD. Psychosocial and vascular risk factors depression in later life. J Affect Disord.
2003;74(3):237-46.
47. Paolucci S, Gandolfo C, Provinciali L, Torta R, Toso V; DESTRO Study
Group. The Italian multicenter observational study on post-stroke depression (DESTRO). J Neurol. 2006;253(5):556-62.
48. Hackett ML, Yapa C, Parag V, Anderson CS. Frequency of depression
after stroke: a systematic review of observational studies. Stroke.
2005;36(6):1330-40.
49. Gainotti G, Azzoni A, Marra C. Frequency, phenomenology and
anatomical-clinical correlates of major post-stroke depression. Br J
Psychiatry. 1999;175:163-7.
50. Robinson RG, Spalletta G, Jorge RE, Bassi A, Colivicchi F, Ripa A, et al.
Decreased heart rate variability is associated with poststroke depression.
Am J Geriatr Psychiatry. 2008;16(11):867-73.
51. Tokgozoglu SL, Batur MK, Topcuoglu MA, Saribas O, Kes S, Oto A.
Effects of stroke localization on cardiac autonomic balance and sudden
death. Stroke. 1999;30(7):1307-11.
52. Makikallio AM, Makikallio TH, Korpelainen JT, Sotaniemi KA, Huikuri
HV, Myllylä VV. Heart rate dynamics predict poststroke mortality. Neurology. 2004;62(10):1822-6.
53. Hackett ML, Anderson CS, House AO, Xia J. Interventions for treating
depression after stroke. Stroke. 2009;40(7):e487-8.
54. Price A, Rayner L, Okon-Rocha E, Evans A, Valsraj K, Higginson IJ, et al.
Antidepressants for the treatment of depression in neurological disorders:
a systematic review and meta-analysis of randomised controlled trials. J
Neurol Neurosurg Psychiatry. 2011;82(8):914-23.
55. Hackett ML, Anderson CS, House A, Halteh C. Interventions for
preventing depression after stroke. Cochrane Database Syst Rev.
2008;(3):CD003689.
56. Chollet F, Tardy J, Albucher JF, Thalamas C, Berard E, Lamy C, et al.
Fluoxetine for motor recovery after acute ischaemic stroke (FLAME):
a randomised placebo-controlled trial. Lancet Neurol. 2011;10(2):12330.
57. Mead GE, Hsieh CF, Lee R, Kutlubaev M, Claxton A, Hankey GJ, et al.
Selective serotonin reuptake inhibitors for stroke recovery: a systematic
review and meta-analysis. Stroke. 2013;44(3):844-50.
58. Kim BR, Kim DY, Chun MH, Yi JH, Kwon JS. Effect of repetitive
transcranial magnetic stimulation on cognition and mood in stroke
patients: a double-blind, sham-controlled trial. Am J Phys Med Rehabil.
2010;89(5):362-8.
59. Jorge RE, Moser DJ, Acion L, Robinson RG. Treatment of vascular depression using repetitive transcranial magnetic stimulation. Arch Gen
Psychiatry. 2008;65(3):268-76.
60. Bueno VF, Brunoni AR, Boggio PS, Bensenor IM, Fregni F. Mood and
cognitive effects of transcranial direct current stimulation in post-stroke
depression. Neurocase. 2011;17(4):318-22.
Review article
Associations between chronic pelvic pain and psychiatric disorders and symptoms
Ana Carolina Franco de Carvalho1, Omero Benedito Poli-Neto1,
José Alexandre de Souza Crippa1,2, Jaime Eduardo Cecílio Hallak1,2, Flávia de Lima Osório1,2
1
2
Ribeirão Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil.
INCT Translational Medicine, National Council for Scientific and Technological Development (CNPq), Brasília, DF, Brazil.
DOI: 10.1590/0101-60830000000042
Abstract
Background: Chronic pelvic pain (CPP) is a complex condition wich is associated with emotional factors, specially depression and anxiety. Objectives: To
make a systematic review to provide a detailed summary of relevant literature on the association between CPP and different psychiatric disorders/symptoms.
Methods: A systematic review of articles in the international literature published between 2003 and 2014 was performed in the electronic databases PubMed,
PsycINFO, LILACS, and SciELO using the terms (chronic pelvic pain) AND (psychiatry OR psychiatric OR depression OR anxiety OR posttraumatic stress OR
somatoform). The searches returned a total of 529 matches that were filtered according to predefined inclusion and exclusion criteria. A total of 18 articles
were selected. Results: The investigations focused mainly on the assessment of depression and anxiety disorders/symptoms, with rather high rates (17-38.6%).
Depression and anxiety symptoms were more prevalent among women with CPP compared to healthy groups. Comparisons between groups with CPP and
with specific pathologies that also have pain as a symptom showed that depression indicators are more frequent in CPP. Depressive symptoms tend to be more
common in CPP and have no particular association with pain itself, the core feature of CPP. Discussion: Other aspects of CPP seem to play a specific role in
this association. Anxiety and other psychiatric disorders require further investigation so that their impact on CPP can be better understood.
Carvalho ACF et al. / Arch Clin Psychiatry. 2015;42(1):25-30
Keywords: Anxiety, comorbitidy, cronic pelvic pain, depression, psychiatric.
Introduction
Chronic pelvic pain (CPP) is a common and disabling condition
among women in reproductive age1,2 and is currently regarded as a
public health problem3. International and Brazilian studies report
prevalence rates of CPP ranging between 4% and 25.4%1,4-8. Although
there is no complete agreement about the definition of CPP, the
condition is characterized by the presence of continuous or intermittent pain in the lower abdomen (below the navel) and/or in the
pelvis, persistent for at least six months, not exclusively associated
with menstruation, sexual intercourse or neoplastic disease, severe
enough to cause disability or functional impairment, and requiring
clinical and/or surgical treatment9-13.
CPP is a complex condition of usually unknown etiology and
influenced by or resulting from the interaction of various systems,
including the gastrointestinal, urinary and genital tracts, and neurological and psychological aspects2. In up to 60% of cases, CPP is
associated with emotional factors14, most commonly represented by
depression and anxiety15. Researchers have been trying to identify
the role played by emotional factors in the onset and maintenance
of CPP, regarding such factors both as possible consequences of the
chronic condition and also as etiological agents.
In this direction, evidence exists about the causal relationship
between psychological factors and CPP, as women with the condition
present significantly higher rates of psychiatric disorders as compared
to control groups16-18. Other authors still report that negative mood
and emotion can cause or increase pain19 and that the experience of
pain, as a personal and subjective phenomenon, is probably affected
by emotional states and, therefore, by psychosocial factors8.
Other investigators suggest exactly the opposite. To them, it is
the context of chronic pain itself that favors feelings of frustration,
preoccupation, anxiety, and depression8. Women with CPP have to
deal with the loss of a healthy and active body and reach a state of
dependence and disability that may be responsible for changes in
affective, family, social, and sexual dynamics, thus having a negative
impact on quality of life20. A study by Roth et al.21 investigated this
hypothesis through the comparison of women with CPP and with
other conditions involving chronic pain, especially migraine. Their
findings showed that women with CPP were more dissatisfied with
their marriage and capacity for sexual intercourse; however, no differences were found in regard to indicators of depression and anxiety
or personality traits. These data suggest that, in general, when CPP
patients present with psychological disturbances, these are likely to
reflect the effects of chronic pain.
It should be noted, however, that many studies had inconclusive
results because of the methodology or design adopted, and that the
role played by emotional factors in the onset and maintenance of the
condition remains largely unknown8,20,22,23.
Despite the large number of studies investigating the association
between CPP and different psychological and psychiatric aspects,
no systematic reviews or meta-analyses have been published to date
dealing with this topic and contributing to expand the comprehension of this association.
Objectives
The objective of this study was to perform a systematic literature
review of articles published over the last 12 years in order to identify
possible associations between CPP and different psychiatric disorders
and/or symptoms.
Methods
This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses) Statement and the guidelines of the Cochrane Handbook
for Systematic Reviews of Interventions.
We made a systematic search for articles published between
January 2003 and July 2014 indexed in the online databases PubMed,
PsycINFO, LILACS, and SciELO using the search expression (chronic
pelvic pain) AND (psychiatry OR psychiatric OR depression OR anxiety
OR posttraumatic stress OR somatoform). The searches returned a total
of 529 matches that were filtered according to predefined inclusion
and exclusion criteria, as shown in figure 1.
Address correspondence to: Flávia L. Osório. Avenida dos Bandeirantes, 3900 – 14048-900 – Ribeirão Preto, SP, Brazil. Email: [email protected]
26
18 articles
selected
45 articles
included
27 repeated
articles
484
articles
excluded
CPP and other psychological
conditions = 24
Total of 529
articles
Inclusion
criteria:
CPP x psychiatric
disorders/symptoms
Databases: PubMed,
PsycINFO, LILACS
and SciELO
Languages:
Portuguese, English,
and Spanish
Period: 2003 – 2014
(last 12 years)
Studies with
women aged ≥ 18 years
CPP and experiences of sexual
and/or physical abuse = 18
CPP and
sexuality = 13
Association between CPP and
other diseases and clinical
conditions = 43
Treatment of
CPP = 54
Theoretical studies involving
general aspects of CPP = 7
Studies not involving
CPP = 188
Studies with children,
adolescents and men = 91
Book chapters, dissertations,
letters to the editor, errata,
summaries, and
guidelines = 40
Studies in other
languages = 5
Animal
studies = 1
Figure 1. Flowchart showing the procedure of article selection and inclusion/
exclusion criteria.
Results
A total of 18 articles were selected and independently examined in
respect to their adequacy for inclusion in the review by two psychologists and mental health researchers. From these, five studies24-28 used
an observational descriptive design and the remaining 138,20,21,29-38
used a case-control design. Most of the studies were conducted in the
United States (n = 6) and Brazil (n = 5). Table 1 provides data on the
studies’ design, country of origin, and main sample characteristics.
The clinical groups with CPP assessed in the studies included
samples ranging between 12 and 713 subjects, with mean of 94.4
and median of 44. Subjects had a mean age of 35.6 years and large
variations in education. Most of the studies established the presence
of CPP through clinical diagnosis (n = 14) and presence of pain for
at least six months (n = 12).
Control groups consisted of samples ranging between 20 and
1,131 subjects, with mean of 140.9 and median of 50. The mean age
of participants in control groups was 35.3 years, equivalent to that
of clinical groups, and similarly varied education. In general, control
groups consisted of healthy women (n = 7) or people with other
specific conditions involving pain (n = 8).
The most frequent inclusion criteria for the clinical groups were
duration of pain symptoms8,20,21,24-27,29,31-36, which ranged from three to
six months, and specificities related to the etiology of pain21,27,31-34,36,37,
for example, pain resulting from pelvic adhesions. It is noteworthy
that not all studies strictly followed the international criteria for the
diagnosis of CPP, which require a minimum duration of six months
for pain symptoms9-13.
Criteria for the inclusion in control groups consisted mainly of
the absence of CPP20,21,27,29-36,38 and presence of other specific clinical
conditions such as lower back pain and migraine27,29,30,34,36.
Exclusion criteria for the composition of CPP and control groups
consisted mainly of the presence of other general (e.g.: interstitial
cystitis, positive HIV, hypertension, diabetes) and/or gynecological
conditions (e.g.: uterine fibroids and cysts) and pregnancy. It should
be noted that nine studies24,26-28,29,31,33,34,36 described no exclusion
criteria.
The main outcome measures assessed in the studies included
(a) mood indicators (n = 17); (b) anxiety indicators (n = 9); and
(c) somatization/dissociation indicators (n = 3). Table 2 shows the
outcome measures and assessment instruments used in the studies
reviewed.
As seen in table 2, a number of different instruments were used
to assess outcome variables, most of which were self-rated. Mood
indicators were mainly evaluated through the Beck Depression
Inventory (BDI; n = 8; 44.4%), with only one study that reported
the use of the Structured Clinical Interview for DSM-IV (SCIDIV), regarded as the gold standard for psychiatric diagnoses. In the
assessment of anxiety indicators, only the Beck Anxiety Inventory
(BAI) and the Hospital Anxiety and Depression Scale (HADS) were
used in more than one study. For the assessment of somatization
and dissociation indicators, the SCID-IV was the main instrument
of choice (n = 2).
The main associations between CPP and psychiatric disorders/
symptoms are shown in table 3.
The groups with CPP included in the studies had a high frequency
of anxiety and depression symptoms, with respective prevalence
rates of 38.6% and 29.5%. Relative to control groups, CPP subjects
presented higher rates of depression (n = 7) and anxiety (n = 3),
regardless of the instruments used in the assessments. Comparisons
between groups with CPP and endometriosis concerning depression
indicators showed that subjects with CPP had a higher frequency of
mood symptoms.
When CPP groups were compared to control groups with chronic
lower back pain, authors reported divergent findings related to depression rates29,30, despite the fact that the two studies making these
comparisons had similar sampling procedures and used the same
assessment instrument. Another investigation found equivalent rates
of depression and anxiety in subjects with CPP and migraine21, and
two others found no differences when comparing CPP groups with
different etiologies27,37.
There are reports of significant positive associations between
CPP and anxiety, depression and dissociation indicators, pain, and
physical or sexual abuse24,25,28,29,38. On the other hand, negative associations have been found between CPP and depression, anxiety,
sexual adjustment and quality of life31,36.
Finally, bipolar affective disorder (BAD) was more prevalent
in a group with endometriosis36 and post-traumatic stress disorder
(PTSD) was found in 31.3% of a group of subjects with CPP26.
Dissociative and somatoform symptoms were highly prevalent in
samples with CPP24,33.
Discussion
This study systematically assessed the existence of associations between CPP and different psychiatric indicators/disorders through
the exam of empirical articles published between 2003 and 2014.
Some remarks should be made concerning the methodological
aspects of the studies included in this review, as they may affect the
interpretation of the results presented. The first one refers to the study
design adopted in the investigations, as 27.7% were descriptive studies.
Also, there was great variability in the measures used, with a total of 18
different instruments, which hinders direct comparisons between their
results. Most of the studies reviewed used screening instruments (83.4%)
based on self-report. Only three studies used the SCID-IV, a structured,
clinician-rated diagnostic interview regarded as the gold standard for
the establishment of psychiatric diagnoses. We conclude, therefore, that
most studies assessed the presence of symptoms, and not disorders.
The fact that most studies focused on the assessment of symptoms
of depression and anxiety is also noteworthy. Other disorders as, for
27
Table 1. Main characteristics of the samples in the studies reviewed
Ref.
Authors
Country
29
Lampe
et al. (2003)
Austria
24
Nijenhuis
The
et al. (2003) Netherlands
Heinberg
United
et al. (2004)
States
30
25
31
32
26
20
33
34
35
36
8
27
21
37
38
28
43F
CPP groups
Mean
Marital
age
Education
status
(SD)
32.4(8.8)
-----
52F
37.9(9.7)
N
22F 37.8(13.7)
Control groups
CPP
diagnosis
Duration
of pain
---
≥6
months
≥6
months
---
---
---
---
14.7
years
---
---
Mean age
Marital
Education
(SD)
status
40F
22F
NA
40(9.1)
29.3(8.1)
NA
22M
----NA
Clinical
characteristics
----NA
Chronic LBP
Healthy
NA
40.9(14.4) 14.5 years
---
22F
28M
NA
51(12.5)
43.8(9.8)
NA
13.8 years
13.5 years
NA
----NA
Men w/urogenital
pain
Women w/LBP
Men w/LBP
NA
25F
30.6(7.3)
---
---
Healthy
50F
32.8(7.1)
---
Endometriosis
NA
NA
NA
22 S
28 M
NA
Poleshuck
et al. (2005)
Kaya et al.
(2006)
Lorençatto
et al. (2006)
MeltzerBrody et al.
(2007)
Romão
et al. (2009)
United
States
Turkey
63F 39.2(11.7)
---
---
Clinical
19F
34.1(9.3)
---
---
Clinical
Brazil
50F
35.3(6.4)
---
Clinical
United
States
713F 35.3(9.8)
14.9
years
8S
42 M
---
Brazil
52F
31.7(8.1)
---
Clinical
≥6
months
54F
30.3(6.2)
9BE; 19ES;
25HS;
1SE
---
Healthy
Wingenfeld
et al. (2009)
Kumar
et al. (2010)
Germany
18F
---
12BE;
18ES;
19HS;
3SE
---
---
Clinical
24F
---
---
---
Healthy
India
100F
---
---
---
Clinical
≥4
months
≥6
months
Barcelos
et al. (2010)
Kumar
et al. (2011)
Silva et al.
(2011)
Brazil
30F
35.2(7.5)
7.5 years
22 M
Clinical
100F
100F
20F
----36(9.3)
----8.5 years
----13 M
Canada
12F
35.6(---)
---
---
27F
30.3(---)
---
---
Brazil
147F
40.4(15)
54 ES
1,131F
43(15.6)
305 ES
57F
35.8(8.6)
---
NA
NA
NA
402 S
225 D
504 M
NA
Healthy
Brazil
15 S
8D
31 M
---
Clinical +
laparoscopy
Clinical
Endometriosis
Healthy
Other gynecological
conditions
Endometriosis
United
States
United
States
39F 33.5(10.5) 4.9 years
---
Clinical
38F
5.4 years
---
Migraine
30F
---
---
---
Clinical +
laparoscopy
Turkey
44F
35.6(9.9)
---
---
---
Myofascial pain
Pelvic adhesions
Healthy
273F 34.8(11.3)
97 S;
139
M; 23
D; 14
other
Clinical
---
NA
NA
----26 ES
5 HS
NA
-------
United
States
35 ES; 9
HS
11 BE;
126 HS;
136 SE
70F
38F
31F
36.3
(11.1)
----38.8(4.2)
NA
NA
Souza et al.
(2011)
Roth et al.
(2011a)
Roth et al.
(2011b)
Demir et al.
(2012)
As-Sanie
et al. (2014)
≥6
months
≥6
months
≥6
months
≥6
months
N
Clinical
Clinical
≥6
months
≥6
months
≥6
months
>6
months
>3
months
---
NA
NA
M: married; D: divorced; S: single; CPP: chronic pelvic pain; BE: basic education; ES: elementary school; HS: high school; SE: superior education; F: female; M: male; LBP: lower back pain; NA: not applicable.
example, dysthymia and personality disorders, among others, were
not contemplated, and attention to these conditions could be relevant
for a better comprehension of psychic conditions associated with CPP.
Specifically in respect to the results of the studies reviewed,
investigations using a case-control design enrolled healthy subjects
and/or subjects with other pathological conditions associated or not
with the presence of pain in their control groups. When assessing the
prevalence rates reported in descriptive studies, depression and anxiety rates (29.5% ad 38.6%, respectively) are much higher than those
found in the general population by a recent Brazilian epidemiological
survey (9.4% and 19.9%, respectively)39. These rates are also higher
than those reported by international epidemiological studies, as the
one by Kessler et al.40 in which the lifetime rates of depression and
anxiety were respectively 18.3% and 21.4% in an adult population.
This large difference should be regarded with caution since the studies
reviewed here assessed symptoms and not the presence of disorders
as in epidemiological surveys. Still, however, the rates are high and
should be considered as signs of increased difficulties.
Studies involving control groups formed by healthy subjects
found that depression was more prevalent among women with CPP
(n = 7), regardless of the instruments used to measure depression
indicators. The same result was found by studies comparing anxiety
in women with CPP and healthy women (n = 3), with the exception
of the study by Kaya et al.32, where no differences were found.
28
Table 2. Outcome variables and instruments used in the studies (N = 19; non-exclusive categories)
Outcome variables
Psychiatric symptoms
and disorders
(N = 18)
Mood disorders/symptoms
(N = 17)
Depression/depressive symptoms:
N = 168,20,21,24,25,27-35,37,38
BAD: N = 1
Anxiety disorders/symptoms
(N = 9)
Anxiety symptoms:
N = 88,20,21,24,25,27,31,38
PTSD: N = 126
Somatization/
dissociation
(N = 3)
N = 324,33,38
Instruments
BDI (N = 8)21,27,29-32,37,38
BSI (N = 1)25,37
HADS (N = 2)20,24
CESD (N = 1)28,30
SRDS (N = 1)33
DDS (N = 1)34
SCID-IV (N = 1)36
Self-report (N = 1)35
Semi/structured interview (N = 1)8
BAI (N = 2)31,38
HADS (N = 2)20,24
BSI (N = 1)25
SPAN (N = 1)26
STAI (N = 2)21,31
HARS (N = 1)27
Semi/structured interview (N = 1)8
STSD (N = 1)24
SCID IV (N = 2)24,33
SDQ (N = 1)24
DES (N = 1)24
IPPS (N = 1)38
BAI: Beck Anxiety Inventory; BDI: Beck Depression Inventory; BSI: Brief Symptom Inventory; CESD: Center for Epidemiological Studies – Depression scale; DDS: Deep Depression Scale; DES: Dissociative
Experiences Scale; HADS: Hospital Anxiety and Depression Scale; HARS: Hamilton Anxiety Rating Scale; IPPS: International Pelvic Pain Society questionnaire, SCID-IV: Structured Clinical Interview
for DSM-IV; SDQ: Somatoform Dissociation Questionnaire; SPAN: Startle, Physiological Arousal, Anger and Numbness scale; SRDS: Self-Rating Depression Scale; STAI: State-Trait Anxiety Inventory;
STSD: Screening Test for Somatization Disorder; BAD: Bipolar Affective Disorder; PTSD: Post-traumatic Stress Disorder.
Table 3. Main associations between CPP and psychiatric disorders and symptoms reported in the studies, with outcome variables as parameters
Psychiatric
disorders
and
symptoms
(N = 18)
Mood
disorders/
symptoms
(N = 17)
Depression/
depressive
symptoms:
N = 16
CPP = 29.5% depression (N = 1)38
CPP > healthy (N = 7)8,20,29,31,33-35
> CLBP/UGP (N = 1)30
> endometriosis (N = 3)32,34,36
CPP < CLBP (N = 1)29
CPP = migraine (N = 1)21
= different etiologies (N = 2)27,37
↑ depression: ↑ dissociation (N = 1)24
↑ HPA axis activity (N = 1)33
↑ pain (N = 1)38
↑ sexual/physical abuse (N = 2)25,28,29
↓ sexual adjustment (N = 1)31
↓ QOL (N = 1)36
BAD: N = 1
CPP < endometriosis (N = 1)36
Anxiety
Anxiety
CPP = 38.6% anxiety (N = 1)38
disorders/
symptoms:
CPP > healthy (N = 3)8,20,35
symptoms
N=8
CPP = healthy (N = 1)31
(N = 9)
= migraine (N = 1)21
= different etiologies (N = 1)27
↑ anxiety: ↑ dissociation (N = 1)24
↑ sexual/physical abuse (N = 1)25
↑ pain (N = 1)38
↓ sexual adjustment (N = 1)31
↓ QOL (N = 1)36
PTSD: N = 1 CPP = 31.3% PTSD (N = 1)26
Somatization/ CPP = 26.9% dissociative symptoms (N = 1)24
dissociation
CPP = 100% somatoform pain (N = 1)33
(N = 3)
= 6.8% somatization disorder (N = 1)38
↑ dissociation: ↑ somatization (N = 1)24
↑ traumatic experiences (N = 1)24
< : lower; > : higher; ↑ : increased; ↓ : decreased; CLBP: chronic lower back pain; CPP: chronic pelvic pain; UGP: urogenital pain; HPA: hypothalamic-pituitary-adrenal; QOL: quality of life; BAD: bipolar
affective disorder; PTSD: post-traumatic stress disorder.
Few studies included in the review (n = 5) compared groups with
CPP and groups of subjects with specific pathologies that included
pain as a symptom (namely, lower back and/or urogenital pain, migraine, and endometriosis) in an attempt to ascertain the influence
of this variable. These investigations used common instruments
and suggest that depression indicators tend to be more frequent in
CPP groups, which opens the perspective to think of the existence
of particular characteristics of CPP that associate with depression
besides pain alone. In relation to anxiety, this type of comparative
study is still scarce and do not allow for conclusions.
It should be noted that indicators of depression and anxiety va
ried between groups of CPP with different etiologies (endometriosis,
myofascial pain, pelvic adhesions etc.), with inconclusive results from
the different studies in the area.
Bipolar affective disorder was found in 44.4% of women with
CPP associated with endometriosis, while no women with CPP
without endometriosis presented this psychiatric condition. The
fact that the rate of BAD in the general Brazilian population was
estimated in 1.5%39 makes it difficult to explain the high frequency
of this condition in the CPP group with associated endometriosis.
However, if we consider CPP alone, the rate of BAD is close to that
of the general population, or even smaller.
In CPP groups, the results available show associations of the
condition with rates of depression and anxiety and increased presence of dissociation, pain, and physical and/or sexual abuse. On
the other hand, reduced sexual adjustment and quality of life were
negatively associated with these rates. These data raise the hypothesis
of a possible chain reaction in which trauma, such as sexual abuse
in childhood, could contribute to the etiology of CPP and also to
the increase in depressive and dissociative experiences and somatic
conditions25,28,29, in addition to having a negative impact on quality of
life and sexual adjustment. The experience of these impairments and
difficulties may feed back the chain, favoring experiences of anxiety
and depression, which may also act as risk factors for the increase
in experiences of pain, especially of somatic origin.
Along the same line, PTSD was observed in 31.3% of women
with CPP, once again a high rate as compared with national39 and
international40 data pointing to prevalence rates of 1.6% and 8%,
respectively, in the general population. By relating these findings to
the significant rates of dissociative symptoms and somatoform pain
in CPP groups, we can again hypothesize that trauma might be a
risk factor for the development of CPP. Only one study28, however,
included logistic regression in its analysis. Future studies with this
focus are therefore extremely necessary to provide evidence concerning the predictive role of trauma in the development of CPP.
In conclusion, depressive symptoms tend to be more present
in CPP and this relationship does not seem to be specifically connected to pain, a core feature of CPP. Other factors particular to CPP
seem to be implicated in this association. In this review, we found
that traumatic experiences in childhood or adult life are some of
the aspects that deserve attention. Also, anxiety and other specific
disorders assessed, such as BAD, PTSD, and somatization disorder,
require further investigation for the establishment of their role in CPP.
Directions for future research include: (a) greater methodological
refinement involving other study designs, such as logistic regression,
to investigate the impact of specific variables like early trauma; (b)
detailed assessment of variables related to the duration and intensity
of pain; and (c) use of diagnostic instruments with greater specificity
and reliability.
Acknowledgment
This study received funding from National Council for Scientific and
Technological Development (CNPq – Process No. 471441/2012-0).
References
1. Mathias SD, Kuppermann M, Liberman RF, Lipschutz RC, Steege JF.
Chronic pelvic pain: prevalence, health-related quality of life, and economic correlates. Obstet Gynecol. 1996; 87(3):321-7.
2. Howard FM. Chronic pelvic pain. Obstet Gynecol. 2003;101(3):594-611.
3. Nogueira AA, Reis FJC, Poli-Neto OB. Abordagem da dor pélvica crônica
em mulheres. Rev Bras Ginecol Obstet. 2006;28(12):733-40.
4. Zondervan KT, Yudkin PL, Vessey MP, Jenkinson CP, Dawes MG, Barlow
D. et al. The community prevalence of chronic pelvic pain in women
and associated illness behaviour. Br J Gen Pract. 2001;51(468):541-7.
5. Grace VM, Zondervan KT. Chronic pelvic pain in New Zealand: prevalence, pain severity, diagnoses and use of the health services. Aust N Z
J Public Health. 2004;28(4):369-75.
29
6. Latthe P, Latthe M, Say L, Gulmezoglu M, Khan KS. WHO systematic
review of prevalence of chronic pelvic pain: a neglected reproductive
health morbidity. BMC Public Health. 2006;6,177.
7. Pitts MK, Ferris JA, Smith AM, Shelley JM, Richters J. Prevalence and
correlates of three types of pelvic pain in a nationally representative
sample of Australian women. Med J Aust. 2008;189(3):138-43.
8. Silva GP, Nascimento AL, Michelazzo D, Alves Junior FF, Rocha MG,
Silva JC, et al. High prevalence of chronic pelvic pain in women in
Ribeirão Preto, Brazil and direct association with abdominal surgery.
Clinics. 2011;66(8):1307-12.
9. Howard FM. The role of laparoscopy in chronic pelvic pain: promise and
pitfalls. Obstet Gynecol Sur. 1993;48(6):357-87.
10. Campbell F, Collett BJ. Chronic pelvic pain. Br J Anaesth. 1994;73(5):
571-3.
11. Gelbaya TA, El-Halwagy HE. Focus on primary care: chronic pelvic pain
in women. Obstet Gynecol Sur. 2001;56(12):757-64.
12. American Congress of Obstetricians and Gynecologists. Practice Bulletin
No. 51. Chronic pelvic pain. Obstet Ginecol. 2004;103:589-605.
13. Williams RE, Hartmann KE, Steege JF. Documenting the current definitions of chronic pelvic pain: implications for research. Obstet Gynecol.
2004;103(4):686-91.
14. Romão APMS, Romão GS, Gorayeb R, Nogueira AA. O funcionamento
psicológico e sexual da mulher com dor pélvica crônica: atualização.
Femina. 2009;37(1):19-22.
15. Latthe P, Mignini L, Gray R, Hills R, Khan K. Factors predisposing women
to chronic pelvic pain: systematic review. Br Med J. 2006;332(7544):74955.
16. Harrop-Griffiths J, Katon W, Walker E, Holm L, Russo J, Hickok L. The
association between chronic pelvic pain, psychiatric diagnoses, and
childhood sexual abuse. Obstet Gynecol. 1988;71(4):589-94.
17. Hodgkiss AD, Sufraz R, Watson JP. Psychiatric morbidity and illness
behaviour in women with chronic pelvic pain. J Psychosom Res.
1994;38(1):3-9.
18. Walker EA, Katon WJ, Hansom J, Harrop-Griffiths J, Holm L, Jones ML,
et al. Psychiatric diagnoses and sexual victimization in women with
chronic pelvic pain. Psychosomatics. 1995;36(6):531-40.
19. Wiech K, Tracey I. The influence of negative emotions on pain: behavioral
effects and neural mechanisms. NeuroImage. 2009;47(3):987-94.
20. Romão AP, Gorayeb R, Romão GS, Poli-Neto OB, dos Reis FJ, Rosa-e-Silva JC, et al. High levels of anxiety and depression have a negative
effect on quality of life of women with chronic pelvic pain. Int J Clin
Pract. 2009;63(5):707-11.
21. Roth RS, Punch MR, Bachman JE. Psychological factors and chronic
pelvic pain in women: a comparative study with women with chronic
migraine headaches. Health Care for Women Int. 2011;32(8):746-61.
22. Fry RP, Crisp AH, Beard RW, McGuigan S. Psychosocial aspects of
chronic pelvic pain, with special reference to sexual abuse. A study of
164 women. Postgrad Med J. 1993;69(813):566-74.
23. Fishbain, DA, Cutler R, Rosomoff HL, Rosomoff RS. Chronic pain
associated depression: antecedent or consequence of chronic pain? A
review. Clin J Pain. 1997;13(2):116-37.
24. Nijenhuis ER, van Dyck R, ter Kuile MM, Mourits MJ, Spinhoven P, van
der Hart O. Evidence for associations among somatoform dissociation,
psychological dissociation and reported trauma in patients with chronic
pelvic pain. J Psychosom Obstet Gynecol. 2003;24(2):87-98.
25. Poleshuck EL, Dworkin RH, Howard FM, Foster DC, Shields CG,
Giles DE, et al. Contributions of physical and sexual abuse to women’s
experiences with chronic pelvic pain. J Reprod Med. 2005;50(2):91-100.
26. Meltzer-Brody S, Leserman J, Zolnoun D, Steege J, Green E, Teich A.
Trauma and posttraumatic stress disorder in women with chronic pelvic
pain. Obstet Gynecol. 2007;109(4):902-8.
27. Souza CA, Oliveira LM, Scheffel C, Genro VK, Rosa V, Chaves MF, et
al. Quality of life associated to chronic pelvic pain is independent of
endometriosis diagnosis--a cross-sectional survey. Health Qual Life
Outcomes. 2011;10(9):41-6.
28. As-Sain, Clevenger LA, Geisser ME, Williams DA, Roth RS. History of
abuse and its relationship to pain experience and depression in women
with chronic pelvic pain. Am J Obstet Gynecol. 2014;210(4):317.e1-8.
29. Lampe A, Doering S, Rumpold G, Sölder E, Krismer M, Kantner-Rumplmair W, et al. Chronic pain syndromes and their relation to childhood
abuse and stressful life events. J Psychosom Res. 2003;54(4):361-7.
30
30. Heinberg LJ, Fisher BJ, Wesselmann U, Reed J, Haythornthwaite JA.
Psychological factors in pelvic/urogenital pain: the influence of site of
pain versus sex. Pain. 2004;108(1-2):88-94.
31. Kaya B, Unal S, Ozenli Y, Gursoy N, Tekiner S, Kafkasli A. Anxiety,
depression and sexual dysfunction in women with chronic pelvic pain.
Sex Relationship Ther. 2006;21(2):187-96.
32. Lorençatto C, Petta CA, Navarro MJ, Bahamondes L, Matos A. Depression
in women with endometriosis with and without chronic pelvic pain. Acta
Obstet Gynecol Scand. 2006;85(1):88-92.
33. Wingenfeld K, Hellhammer DH, Schmidt I, Wagner D, Meinlschmidt
G, Heim C. HPA axis reactivity in chronic pelvic pain: association with
depression. J Psychosom Obstet Gynecol. 2009;30(4):282-6.
34. Kumar A, Gupta V, Maurya A. Mental health and quality of life of chronic
pelvic pain and endometriosis patients. J Project Psychol Mental Health.
2010;17(2):153-7.
35. Barcelos PR, Conde DM, Deus JM, Martinez EZ. Qualidade de vida
de mulheres com dor pélvica crônica: um estudo de corte transversal
analítico. Rev Bras Ginecol Obstet. 2010;32(5):247-53.
36. Kumar V, Khan M, Vilos GA, Sharma V. Revisiting the association
between endometriosis and bipolar disorder. J Obstet Gynecol Can.
2011;33(11):1141-5.
37. Roth RS, Punch M, Bachman JE. Psychological factors in chronic pelvic
pain due to endometriosis: a comparative study. Gynecol Obstet Invest.
2011;72(1):15-9.
38. Demir F, Ozcimen EE, Oral HB. The role of gynecological, urological,
and psychiatric factors in chronic pelvic pain. Arch Gynecol Obstet.
2012;286(5):1215-20.
39. Andrade LH, Wang Y, Andreoni S, Silveira CM, Alexandrino-Silva C,
Siu ER, et al. Mental disorders in megacities: findings from the São
Paulo Megacity Mental Health Survey, Brazil. Plos One. 2012;7:132837.
40. Kessler RC, Petukhova M, Sampson NA, Zaslavsky AM, Wittchen HU.
Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr
Res. 2012;21:169-84.
Letter to the editor
The relationship between mental disorder and violence
Barbara Lay1
1
Department for Psychiatry, Psychotherapy and Psychosomatics University, Hospital of Psychiatry Zurich, Switzerland.
DOI: 10.1590/0101-60830000000043
Lay B / Arch Clin Psychiatry. 2015;42(1):31-2
Dear Editor,
The letter to the editor “Patricide and schizophrenia – A case
report”1, published in the last issue of the Archives of Clinical
Psychiatry, addresses an issue that has always concerned forensic
psychiatry, namely the relationship between mental disorder and
violent crimes.
Besides this long-standing professional debate, such cases attract
much attention in the mass media, which exposes us daily to movie
depictions of crazed killers – linking violence to mental illness in the
public perception by showing mental disorder in particular in the
light of unpredictability and dangerousness2.
However, one shouldn’t overlook the fact that most mentally
ill people never commit violent crimes and schizophrenia is not a
sufficient cause for violence. Besides, schizophrenia as a nosological construct encompasses most heterogeneous psychopathological
conditions that vary considerably with respect to core signs and
symptoms, psychiatric comorbidity and social adjustment.
It should be further kept in mind, that in contrast to the disturbing frequency of brutal crimes in the TV programmes, homicides in
general are rare events in real life (the rates per 100,000 inhabitants
as reported by UNODC3 are 3.0 for Europe, 4.7 for the USA, 25.2 for
Brazil) and parricide in particular (usually not reported separately
in official crime statistics) only comprises a small fraction (in the
single digits) of these.
Against this background, the question arises of how an anecdotal
case report like the one brought to our attention by Moscatello is to be
seen in the context of existing scientific research. Four questions, in
particular, are of public concern: (A) what is the causal role of severe
mental illness in the occurrence of violence, (B) how much of the
violence in the community can be attributed to mental illness, (C)
what are the factors that mediate between severe mental illness and
behaving violently and (D) what could be done to reduce the violence.
There is converging evidence from numerous international studies that (A) the risk of antisocial behaviour and violent offence is
increased, and that of homicides even markedly increased, in people
with schizophrenia, compared to the general population4-6. This evidence, based on unselected birth cohorts, representative population
studies and retrospective cohorts (schizophrenia patients; prison
inmates), is quite robust and does not leave much room for controversial interpretations. Findings suggest e.g. that the risk of an individual
with psychosis committing a violent offence is 2 to 6 times higher for
men and 2 to 8 times higher for women of similar age, compared to
the general population7. Five percent to 28% of those charged with
murder in prisons in Western countries have been diagnosed with a
schizophrenia spectrum disorder8,9. As for the population-attributable
risk (B) research estimates the overall contribution of people with
severe mental illness to violent crime to be between 2% and 10%, with
coexisting substance abuse substantially increasing the risk4.
Therefore, cases such as this, although extremely rare, are of
high impact on the societal level. Moreover, despite considerable
advances in care provision including antipsychotic medication in
recent decades, the elevated risk of violent acts by severely mentally
ill patients (with schizophrenia as well as personality disorders) has
not been reduced. It has been hypothesized that still too few people
with severe mental illness enter the mental health care system betimes, and that those who do remain there for a too short period8,10.
(C) In this particular case, there are several dispositional, historical and contextual factors which are well known to increase the risk
of violence: the family history of severe mental illness and violence,
and his long-standing aggressive behaviour and repeated violent assaults in the past, as mentioned by Moscatello, should have deserved
attention. Such factors operating before and during periods of active illness have been identified as being pivotal to the prediction of
violent acts and hence form an integral part of current forensic risk
assessment tools11. (D) The crime reported therefore emphasizes the
need, once again, to look beyond psychotic symptoms and to consider
a patient’s historical and current life situation more closely in order
to prevent such serious assaults.
Moreover, it is well known that the vast majority of the victims
of schizophrenic offenders are found among the closest relatives12.
Family members are also the ones on whom most of the burden
of care for those with serious mental diseases is placed. Efforts to
address the risk of violence therefore have to increase the focus on
these potential victims who should receive the necessary support
and counsel from mental health professionals.
As for the further clinical implications, one has to agree with
Moscatello conclusions, and even more so as the incidence of aggressive violence in schizophrenia can be reduced by intense mental
health care and close supervision5,8. Treatment with antipsychotic
drugs is indicated but in itself cannot guarantee non-violence. Special programmes of comprehensive psychiatric aftercare following
discharge from general psychiatric or forensic hospitals which take
into account illness history, psychosocial functioning and substance
use are mandatory for patients with schizophrenia who engage in
aggressive behaviour towards others – which is what experts have
long been calling for. Data from large-scale studies, however, suggest
that the care currently provided by general psychiatric services for
these “difficult” severely mentally ill persons is mostly inadequate,
if not – as in the present case – non-existent, and fails to reduce
antisocial and criminal behaviours13.
The gap between scientific research and psychiatric practice thus
could not be demonstrated more clearly than by this case. To prevent
such tragedies we will have to integrate the current knowledge into
mental health care – both, with regard to a careful and early risk
assessment and the provision of evidence-based treatments that address the complex of problems of people with serious mental illness
who are violent.
Address correspondence to: Barbara Lay. Zentrum für Psychiatrische Forschung, Militärstrasse 8, 8021 Zurich, Switzerland. Phone: +41 (0)44 296 7372. E-mail: [email protected]
32
Lay B / Arch Clin Psychiatry. 2015;42(1):31-2
References
1. Moscatello R. Patricide and schizophrenia – A case report. Arch Clin
Psychiatry. 2014;41(6):159.
2. Stuart H. Violence and mental illness: an overview. World Psychiatry.
2003;2(2):121-4.
3. UNODC Homicide statistics 2013. Available at: http://www.unodc.org/
gsh/en/data.html. Retrieved: 2014-11-07.
4. Walsh E, Buchanan A, Fahy T. Violence and schizophrenia: examining
the evidence. BJP. 2002;180:490-5.
5. Stadtland C, Nedopil N. Psychiatrische Erkrankungen und die Prognose
krimineller Rückfälligkeit [Psychiatric disorders and the prognosis for
criminal recidivism]. Nervenarzt. 2005;76(11):1402-11.
6. Hodgins S. Violent behaviour among people with schizophrenia: a
framework for investigations of causes, and effective treatment, and
prevention. Philos Trans R Soc Lond B Biol Sci. 2008;363(1503):2505-18.
7. Fazel S, Grann M. The population impact of severe mental illness on
violent crime. Am J Psychiatry. 2006;163:1397-403.
8. Hodgins S. Gewalt und Kriminalität bei psychisch Kranken. Neuropsychiatrie. 2006;20(1):7-14.
9. Mullen PE. Schizophrenia and violence: from correlations to preventive
strategies. Adv Psychiatr Treat. 2006;12:239-48.
10. Kroeber HL. Forensische Psychiatrie – Ihre Beziehungen zur klinischen
Psychiatrie und zur Kriminologie. Nervenarzt. 2005;76:1376-81.
11. Elbogen EB, Johnson SC. The intricate link between violence and mental
disorder. Arch Gen Psychiatry. 2009;66(2):152-61.
12. Steadman HJ, Mulvey EP, Monahan J, Clark Robbins P, Appelbaum PS,
Grisso T, et al. Violence by people discharged from acute psychiatric
inpatient facilities and by others in the same neighborhoods. Arch Gen
Psychiatry. 1998;55(5):393-401.
13. Hodgins S. The interface between general and forensic psychiatric services. Eur Psychiatry. 2009;24:354-5.
O sono na dose ideal
Único zolpidem sublingual1:
induz ao sono
5
rapidamente3.
metade da dose:
sem efeitos residuais no dia seguinte3.
mg,
INDUZ AO SONO RAPIDAMENTE 1- Staner L, Eriksson M, Cornette F, Santoro F, Muscat N, Luthinger R, et al. Sublingual zolpidem is more effective
than oral zolpidem in initiating early onset of sleep in the post-nap modelo transient insomnia: Polysomnographic study. Sleep Med. 2009; 10:61620.2- Staner C, Joly F, Jacquot N, Vlasova ID, Nehlin M, Lundqvist T, et al. Sublingual zolpidem in early onset of sleep compared to oral zolpidem:
polysomnographic study in patients with primary insomnia. Current Medical Research & Opinion. 2010;26:1423-31. 5mg, METADE DA DOSE. SEM
EFEITOS RESIDUAIS NO DIA SEGUINTE 3- Valente KD, Hasan R, Tavares SM and Gattaz WF. Lower doses of sublingual Zolpidem are more effective
than oral Zolpidem to anticipate sleep onset in healthy volunteers. Sleep Med. 2013 Jan; 14(1):20-3.
Patz SL (hemitartarato de zolpidem): APRESENTAÇÕES: Comprimidos sublinguais de 5mg. Embalagens contendo 30 comprimidos sublinguais. Uso oral. Uso adulto. INDICAÇÕES: Patz
SL está indicado no tratamento da insônia ocasional, transitória ou crônica. CONTRAINDICAÇÕES: Patz SL está contraindicado nos seguintes casos de hipersensibilidade ao zolpidem
ou a qualquer um dos componentes da fórmula; insuficiência respiratória severa ou aguda; insuficiência hepática severa. Este medicamento é contraindicado para menores de 12 anos.
PRECAUÇÕES E ADVERTÊNCIAS: O zolpidem deve ser usado com cautela em pacientes com apneia noturna e miastenia gravis. No caso de sedativos / hipnóticos com curta duração de
ação, pode ocorrer o fenômeno de retirada durante intervalo de dose. Em pacientes com insuficiência respiratória, deve-se levar em consideração que hipnóticos e similares podem causar
depressão respiratória. Pacientes que dirigem veículos ou operam máquinas devem ser alertados para a possibilidade de sonolência na manhã seguinte à administração de zolpidem.
Este medicamento não deve ser utilizado por mulheres grávidas sem orientação médica e embora a concentração de zolpidem no leite materno seja baixa, ele não deve ser utilizado
por mulheres durante o período de amamentação. Pacientes idosos ou debilitados podem apresentar uma sensibilidade maior aos efeitos do zolpidem. Hipnóticos como o zolpidem,
não devem ser a medicação principal para o tratamento de pacientes psicóticos. Sedativos e hipnóticos como o zolpidem podem causar amnésia anterógrada, que em geral ocorre
algumas horas após administração. O zolpidem deve ser administrado com cautela em pacientes que apresentam sintomas de depressão e que podem apresentar tendências suicidas.
Depressão pré-existente pode ser desmascarada durante o uso de zolpidem. Alguns sedativos/hipnóticos como o zolpidem podem apresentar perda de eficácia dos efeitos hipnóticos
após uso prolongado por algumas semanas. Deve-se tomar extremo cuidado com pacientes com história de alcoolismo ou dependência a drogas. Deve-se ter cuidado com pacientes
com insuficiência hepática, pois o clearance e o metabolismo do zolpidem estão reduzidos. Pacientes idosos devem ter atenção especial. INTERAÇÕES MEDICAMENTOSAS: O álcool
promove uma intensificação do efeito de sedativos e hipnóticos ou de substâncias relacionadas, portanto a ingestão de Patz SL juntamente com bebidas alcoólicas ou de medicamentos
contendo álcool não é recomendada. O aumento da depressão do Sistema Nervoso Central pode ocorrer no caso de uso concomitante com antipsicóticos (neurolépticos), hipnóticos,
ansiolíticos/sedativos, agentes antidepressivos, analgésicos narcóticos, drogas antiepiléticas, anestésicos e anti-histamínicos. No caso de analgésicos narcóticos, pode ocorrer aumento
da sensação de euforia levando a ocorrência de dependência psicológica. Compostos que inibem o citocromo P450 podem aumentar a atividade de alguns hipnóticos como o zolpidem.
O hemitartarato de zolpidem é metabolizado por várias enzimas hepáticas do citocromo P450: sendo as principais CYP3A4 com a contribuição da CYP1A2. O efeito farmacodinâmico do
hemitartarato de zolpidem é menor quando é administrado com rifampicina (um indutor de CYP3A4). Entretanto, quando o hemitartarato de zolpidem foi administrado com itraconazol
(um inibidor do CYP3A4), a farmacocinética e a farmacodinâmica, não foram significativamente modificadas. A relevância destes resultados não é conhecida. A co-administração de
zolpidem com cetoconazol (200mg, duas vezes ao dia), um potente inibidor CYP3A4, prolonga a meia-vida de eliminação do zolpidem, aumenta o AUC total e diminui o clearance quando
comparado com zolpidem mais placebo.Quando co-administrado com cetoconazol, o AUC total aumenta modestamente (fator 1,83 quando comparado com zolpidem sozinho). Um ajuste
de dosagem de zolpidem não é necessário, mas, os pacientes devem ser advertidos que a co-administração de zolpidem com cetoconazol pode aumentar os efeitos sedativos. REAÇÕES
ADVERSAS: As reações comuns são: sonolência, dor de cabeça, tontura, insônia exacerbada, amnésia anterógrada (os efeitos da amnésia podem estar associados a um comportamento
inapropriado). Alucinações, agitação e pesadelos. Fadiga. Diarreia, náusea, vômito e dor abdominal. POSOLOGIA: A duração do tratamento deve ser a mais curta possível, não devendo
ultrapassar a quatro semanas: Insônia ocasional: de 2 a 5 dias. Insônia transitória: de 2 a 3 semanas. Em alguns casos pode ser necessário ultrapassar o período de quatro semanas.
Isso só deverá ser feito após uma reavaliação do estado clínico do paciente. Adultos abaixo de 65 anos: um comprimido sublingual de 5mg uma vez ao dia imediatamente antes de se
deitar. Adultos com idade acima de 65 anos ou com insuficiência hepática: 1 comprimido sublingual de 5mg uma vez ao dia imediatamente antes de se deitar. A dose somente deve ser
aumentada sob orientação médica. Registro MS nº 1.3569.0643. Detentor: EMS SIGMA PHARMA LTDA. “SE PERSISTIREM OS SINTOMAS, O MÉDICO DEVERÁ SER CONSULTADO”. VENDA
SOB PRESCRIÇÃO MÉDICA. SÓ PODE SER VENDIDO COM RETENÇÃO DA RECEITA.
SE PERSISTIREM OS SINTOMAS, O MÉDICO DEVERÁ SER CONSULTADO.
Transformando vidas*.
Benefícios:
Eficácia Superior** ao citalopram
e paroxetina na depressão1,2
Scitalax® é bioequivalente ao referência3,4
Menor risco de interação medicamentosa
vs paroxetina e fluoxetina5
Apresentação7:
Preço6
Acessibilidade
ao tratamento
10
28
mg
em embalagem de
comprimidos
revestidos
*Scitalax é indicado para tratamento e prevenção da recaída ou recorrência da depressão; tratamento do transtorno do pânico, com ou sem agorafobia, do transtorno de ansiedade
generalizada (TAG), do transtorno de ansiedade social (TAS) e transtorno obsessivo compulsivo (TOC). **Redução nos escores de tratamento.
Referências Bibliográficas: 1) Lepolla U et al. Do equivalent doses of escitalopram and citalopram have similar efficacy? A pooled analysis of two positive placebo-controlled studies in major depressive disorder. Int Clin Psychopharmacol. 2004;19(3):149–155. 2) Boulenger JP et al. A comparative study of the efficacy of long-term treatment with escitalopram and paroxetine in severely depressed patients. Curr Med
Res Opin. 2006;22(7):1331-41. 3) DATA ON FILE Scitalax (escitalopram 10 mg). 4) Lista de medicamentos intercambiáveis da ANVISA, disponível em: http://portal.anvisa.gov.br/wps/wcm/connect/8b10e60046ef5207aa1abb41cdd33a01/lista+site+intercambiaveis+05-01-15.pdf?MOD=AJPERES. Acesso em: 27 jan 2015. 5) Miguel C, Albuquerque E. Drug interaction in psycho-oncology: antidepressants and
antineoplastics. Pharmacology. 2011;88:333-9. 6) Lista Preço CMED 18% PMC - Scitalax 10 mg em embalagem de 28 comprimidos revestidos (R$ 59,99- R$ 2,14 por comprimido) e Lexapro 10 mg em embalagem de 28 comprimidos revestidos (R$ 197,30 - R$ 7,04 por comprimido). Lista de preços ANVISA. Disponível em: <http://goo.gl/x755Dz > (short link). Acesso em: 04/12/2014. 7) Bula do produto.
SCITALAX (OXALATO DE ESCITALOPRAM). REG M.S.: 1.2352.0218 INDICAÇÕES: INDICADO PARA TRATAMENTO E PREVENÇÃO DA RECAÍDA OU RECORRÊNCIA DA DEPRESSÃO, TRATAMENTO DO TRANSTORNO DO PÂNICO, COM OU SEM AGORAFOBIA,
TRATAMENTO DO TRANSTORNO DE ANSIEDADE GENERALIZADA (TAG), TRATAMENTO DO TRANSTORNO DE ANSIEDADE SOCIAL (FOBIA SOCIAL) E TRATAMENTO DO TRANSTORNO OBSESSIVO COMPULSIVO (TOC). CONTRAINDICAÇÕES: O OXALATO DE
ESCITALOPRAM NÃO DEVE SER ADMINISTRADO EM PACIENTES ALÉRGICOS A QUALQUER UM DOS COMPONENTES DA FÓRMULA OU SE O PACIENTE ESTIVER EM USO DE PIMOZIDA OU MEDICAMENTOS INIBIDORES DA MONOAMINOXIDASE (IMAO). PRECAUÇÕES E ADVERTÊNCIAS: O TRATAMENTO COM OXALATO DE ESCITALOPRAM DEVE SER DESCONTINUADO SE OCORRER CONVULSÕES OU UM AUMENTO DA FREQUÊNCIA DAS CRISES CONVULSIVA. EM PACIENTES COM COMPROMETIMENTO DO
FUNCIONAMENTO DOS RINS E/OU DO FÍGADO PODE SER NECESSÁRIO O AJUSTE DA DOSE. O CONTROLE GLICÊMICO PODE SER ALTERADO DURANTE O TRATAMENTO COM O OXALATO DE ESCITALOPRAM E PODE SER NECESSÁRIO UM AJUSTE DA DOSE
DO HIPOGLICEMIANTE ORAL OU DA INSULINA. O MÉDICO DEVERÁ SER AVISADO EM CASOS DE NÍVEIS DE SÓDIO DIMINUÍDOS NO SANGUE, TENDÊNCIA A SANGRAMENTOS OU MANCHAS ROXAS, SE ESTÁ EM TERAPIA ELETROCONVULSIVA OU SE POSSUI
DOENÇA CARDÍACA CORONARIANA. COMO OCORRE COM OUTROS MEDICAMENTOS USADOS NO TRATAMENTO DA DEPRESSÃO E DOENÇAS RELACIONADAS, A MELHORA PODE NÃO SER OBTIDA IMEDIATAMENTE, SERÃO NECESSÁRIAS ALGUMAS SEMANAS APÓS O INÍCIO DO TRATAMENTO PARA QUE O PACIENTE SE SINTA MELHOR. O OXALATO DE ESCITALOPRAM NÃO DEVE SER UTILIZADO EM CRIANÇAS. NORMALMENTE O OXALATO DE ESCITALOPRAM NÃO DEVE SER UTILIZADO NO TRATAMENTO
DE ADOLESCENTES COM MENOS DE 18 ANOS DE IDADE, ESTES APRESENTAM UM RISCO MAIOR PARA ALGUNS EFEITOS ADVERSOS QUANDO FAZEM USO DESTA CLASSE DE MEDICAMENTOS, PORÉM O MÉDICO PODE DECIDIR PRESCREVER ESTE MEDICAMENTO PARA MENORES DE 18 ANOS, POIS DECIDIU SER A MELHOR CONDUTA MÉDICA PARA AQUELE PACIENTE. ESTE MEDICAMENTO NÃO DEVE SER UTILIZADO POR MULHERES GRÁVIDAS SEM ORIENTAÇÃO MÉDICA E NÃO DEVE SER UTILIZADO
DURANTE A AMAMENTAÇÃO, EXCETO SE O MÉDICO JÁ TENHA INFORMADO SOBRE OS RISCOS E BENEFÍCIOS RELACIONADOS. DURANTE O TRATAMENTO, O PACIENTE NÃO DEVE DIRIGIR VEÍCULOS OU OPERAR MÁQUINAS, POIS A HABILIDADE E ATENÇÃO
PODEM ESTAR PREJUDICADAS. INTERAÇÕES MEDICAMENTOSAS: A EFICÁCIA DOS ANTICOAGULANTES ORAIS PODE SER ALTERADA EM ASSOCIAÇÃO COM OXALATO DE ESCITALOPRAM E O TEMPO DE COAGULAÇÃO DEVE SER AVALIADO PARA VERIFICAR A ADEQUAÇÃO DA DOSE DO ANTICOAGULANTE. O USO DE CIMETIDINA E OMEPRAZOL EM CONJUNTO COM O OXALATO DE ESCITALOPRAM PODE CAUSAR AUMENTO DA QUANTIDADE DO OXALATO DE ESCITALOPRAM NO ORGANISMO. O USO
ASSOCIADO DA ERVA DE SÃO JOÃO PODE AUMENTAR O RISCO DE EFEITOS ADVERSOS, ASSIM COMO O USO CONCOMITANTE COM INIBIDORES DA MAO-B QUE CONTENHAM SELEGILINA. A ASSOCIAÇÃO COM NEUROLÉPTICOS POSSIBILITA A DIMINUIÇÃO
DO LIMIAR PARA CONVULSÕES. NÃO OCORRE INTERAÇÃO COM ALIMENTOS OU BEBIDAS E O OXALATO DE ESCITALOPRAM NÃO POTENCIALIZA OS EFEITOS DO ÁLCOOL, MAS A INGESTÃO DE ÁLCOOL DURANTE O TRATAMENTO COM OXALATO DE ESCITALOPRAM NÃO É RECOMENDADO. REAÇÕES ADVERSAS: OS EFEITOS ADVERSOS SÃO GERALMENTE AMENOS E DESAPARECEM ESPONTANEAMENTE APÓS ALGUNS DIAS DE TRATAMENTO. AS REAÇÕES MAIS COMUNS SÃO NÁUSEA, SINUSITE, AUMENTO OU DIMINUIÇÃO DO APETITE, ANSIEDADE, INQUIETUDE, SONHOS ANORMAIS, DIFICULDADE PARA DORMIR, SONOLÊNCIA DIURNA, TONTURAS, BOCEJOS, TREMORES, SENSAÇÃO DE AGULHADAS NA PELE, DIARREIA, CONSTIPAÇÃO, VÔMITOS,
BOCA SECA, AUMENTO DO SUOR, MIALGIAS E ATRALGIAS, DISTÚRBIOS SEXUAIS, CANSAÇO, FEBRE E AUMENTO DE PESO. POSOLOGIA: A ADMINISTRAÇÃO DEVE SER FEITA POR VIA ORAL, UMA ÚNICA VEZ AO DIA, COM OU SEM ALIMENTOS. PREFERENCIALMENTE DEVE SER TOMADO NO MESMO HORÁRIO. OS COMPRIMIDOS DEVEM SER ENGOLIDOS COM ÁGUA E NÃO DEVEM SER MASTIGADOS. A DOSE USUAL É DE 10 MG/DIA. VENDA SOB PRESCRIÇÃO MÉDICA. SÓ PODE SER VENDIDO COM
RETENÇÃO DA RECEITA. IMPORTADO E REGISTRADO POR: RANBAXY FARMACÊUTICA LTDA. SERVIÇO DE ATENDIMENTO AO CONSUMIDOR: 0800-7047222.COMERCIALIZADO POR: DAIICHI SANKYO BRASIL FARMACÊUTICA LTDA. MB02.
CONTRAINDICAÇÃO: PACIENTES ALÉRGICOS A QUALQUER UM DOS COMPONENTES DA FÓRMULA. INTERAÇÕES MEDICAMENTOSAS:
ANTICOAGULANTES ORAIS, CIMETIDINA, OMEPRAZOL, ERVA DE SÃO JOÃO E INIBIDORES MAO-B QUE CONTENHAM SELEGILINA.
SCITALAX É UM MEDICAMENTO. DURANTE SEU USO, NÃO DIRIJA VEÍCULOS OU OPERE
MÁQUINAS, POIS SUA AGILIDADE E ATENÇÃO PODEM ESTAR PREJUDICADAS.
SNC(25) Março/2015 – 550569
Material destinado exclusivamente à classe médica.